技术领域Technical Field
本发明涉及仓储库存管理领域，特别涉及为一种电商仓储库存管理的监控方法及系统。The present invention relates to the field of warehouse inventory management, and in particular to a monitoring method and system for e-commerce warehouse inventory management.
背景技术Background technique
目前，当电商企业的管理规模涉及到多地点库存管理时，电商企业需要考虑如何在不同地点的仓库之间有效地管理和调配库存，以满足不同地区的需求，最大程度地减少库存成本和运营风险。Currently, when the management scale of e-commerce enterprises involves multi-location inventory management, e-commerce enterprises need to consider how to effectively manage and allocate inventory between warehouses in different locations to meet the needs of different regions and minimize inventory costs and operational risks.
发明内容Summary of the invention
本发明旨在解决如何在不同地点的仓库之间有效地管理和调配库存的问题，提供一种电商仓储库存管理的监控方法及系统。The present invention aims to solve the problem of how to effectively manage and allocate inventory between warehouses in different locations, and provides a monitoring method and system for e-commerce warehouse inventory management.
本发明为解决技术问题采用如下技术手段：The present invention adopts the following technical means to solve the technical problem:
本发明提供一种电商仓储库存管理的监控方法，包括：The present invention provides a monitoring method for e-commerce warehouse inventory management, comprising:
基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数，其中，所述仓储参数具体包括仓储规模和仓储定位；Based on the pre-recorded storage area data, obtaining storage parameters corresponding to the storage area data, wherein the storage parameters specifically include storage scale and storage location;
判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件；Determining whether the storage parameters match the allocation conditions preset in the storage area data;
若是，则将所述仓储参数对应的仓库信息录入至预设的库存管理数据库中，在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享，生成对所述仓库信息和所述其他仓库信息的库存分布内容，根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配；If so, the warehouse information corresponding to the warehousing parameters is entered into a preset inventory management database, cloud inventory content of the warehouse information is constructed in the inventory management database, the cloud inventory content is synchronized and shared in real time from the inventory management database based on other pre-recorded warehouse information, inventory distribution content of the warehouse information and other warehouse information is generated, a distribution strategy for the storage area data is established according to the inventory distribution content, and the distribution strategy is applied to coordinate the upstream and downstream supply chains preset for the storage area data for allocation and distribution;
判断所述调拨分配能否达到预设的配送效率；Determine whether the allocation can achieve the preset distribution efficiency;
若否，则获取所述仓储地区数据对应的订单分流信息，基于所述订单分流信息应用预设的库存分配算法对所述调拨分配进行需求预测，根据所述需求预测实时优化所述仓库信息和所述其他仓库信息的物流协同，依据所述物流协同调整所述调拨分配，其中，所述订单分流信息具体包括地域分析、订单分配和物流成本。If not, then obtain the order diversion information corresponding to the storage area data, apply a preset inventory allocation algorithm based on the order diversion information to perform demand forecasting on the allocation and distribution, optimize the logistics coordination of the warehouse information and the other warehouse information in real time according to the demand forecast, and adjust the allocation and distribution according to the logistics coordination, wherein the order diversion information specifically includes regional analysis, order allocation and logistics costs.
进一步地，所述在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享的步骤中，还包括：Furthermore, the step of constructing the cloud inventory content of the warehouse information in the inventory management database and performing real-time synchronization and sharing of the cloud inventory content from the inventory management database based on other pre-recorded warehouse information further includes:
基于预定义的库存事件，应用预设的监听器对所述库存管理数据库的消息队列进行订阅，其中，所述库存事件具体包括库存变更、入库操作和出库操作；Based on predefined inventory events, a preset listener is applied to subscribe to the message queue of the inventory management database, wherein the inventory events specifically include inventory changes, warehousing operations, and outbound operations;
判断所述库存事件能否被所述监听器单独进行抽取；Determine whether the inventory event can be extracted by the listener alone;
若能，则触发所述监听器预设的执行流程，解析所述库存事件对应的事件信息，根据所述事件信息生成数据同步操作的日志内容，其中，所述事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库。If so, the execution process preset by the listener is triggered, the event information corresponding to the inventory event is parsed, and the log content of the data synchronization operation is generated according to the event information, wherein the event information specifically includes reading inventory information, generating inventory notifications, and updating the cloud inventory database.
进一步地，所述根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配的步骤前，还包括：Furthermore, before the step of establishing a distribution strategy for the storage area data according to the inventory distribution content and applying the distribution strategy to coordinate the upstream and downstream supply chains preset by the storage area data to perform allocation and distribution, the method further includes:
从所述仓储地区数据中获取预收录的历史配送数据，基于所述历史配送数据生成当前季节的库存需求预测信息，其中，所述历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据；Acquire pre-recorded historical distribution data from the storage area data, and generate inventory demand forecast information for the current season based on the historical distribution data, wherein the historical distribution data specifically includes seasonal data, promotion activity data, and market trend data;
判断所述库存需求预测信息是否符合当前季节对应的配送阶段，其中，所述配送阶段具体包括产品上市阶段、成熟阶段和衰退阶段；Determining whether the inventory demand forecast information meets the distribution stage corresponding to the current season, wherein the distribution stage specifically includes the product launch stage, the mature stage, and the decline stage;
若是，则应用预训练的时间序列模型对所述库存需求预测信息进行构建，根据所述配送阶段持续生成所述库存需求预测信息的预测结果，依据所述预测结果制定对所述仓储地区数据的配送策略。If so, the pre-trained time series model is used to construct the inventory demand forecast information, the forecast results of the inventory demand forecast information are continuously generated according to the distribution stage, and the distribution strategy for the storage area data is formulated based on the forecast results.
进一步地，所述则应用预训练的时间序列模型对所述库存需求预测信息进行构建的步骤中，包括：Furthermore, the step of applying the pre-trained time series model to construct the inventory demand forecast information includes:
应用所述历史配送数据对预选取的训练模型进行探索性数据分析，基于所述探索性数据分析生成对应的数据集，将所述数据集根据预设比例划分为训练集和测试集，其中，所述探索性数据分析具体包括绘制时序图、绘制自相关图和绘制偏自相关图；Applying the historical delivery data to perform exploratory data analysis on a pre-selected training model, generating a corresponding data set based on the exploratory data analysis, and dividing the data set into a training set and a test set according to a preset ratio, wherein the exploratory data analysis specifically includes drawing a time series diagram, drawing an autocorrelation diagram, and drawing a partial autocorrelation diagram;
判断所述训练模型能否通过所述训练集进行拟合；Determining whether the training model can be fitted by the training set;
若能，则从拟合过程中获取所述训练模型的参数估计值，采用所述测试集对所述训练模型进行指标评估，依据评估结果调整所述训练模型的超参数，得到训练完毕的时间序列模型，使用所述时间序列模型对预设时段的库存需求进行预测，以构建所述库存需求预测信息，其中，所述指标评估具体包括均方根误差、平均绝对误差和均方误差，所述超参数具体包括滞后阶数和季节性周期。If possible, the parameter estimation values of the training model are obtained from the fitting process, the test set is used to perform index evaluation on the training model, the hyperparameters of the training model are adjusted according to the evaluation results, and a trained time series model is obtained. The time series model is used to predict the inventory demand for a preset time period to construct the inventory demand forecast information, wherein the index evaluation specifically includes the root mean square error, the mean absolute error and the mean square error, and the hyperparameters specifically include the lag order and the seasonal cycle.
进一步地，所述判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件的步骤中，还包括：Furthermore, the step of determining whether the storage parameters match the allocation conditions preset in the storage area data further includes:
获取所述仓储地区数据预设的调拨优先级；Obtaining the preset transfer priority of the storage area data;
判断所述调拨优先级是否匹配预设的调拨效益需求；Determining whether the allocation priority matches the preset allocation benefit requirement;
若否，则从所述仓储地区数据识别预收录的紧急调拨需求，采集所述仓储参数的库存周转率，基于所述紧急调拨需求将所述库存周转率作为所述调拨条件的最高优先级，其中，所述紧急调拨需求具体包括缺货客户订单、生产线停机和原料短缺。If not, identify the pre-recorded emergency transfer needs from the storage area data, collect the inventory turnover rate of the storage parameters, and take the inventory turnover rate as the highest priority of the transfer conditions based on the emergency transfer needs, wherein the emergency transfer needs specifically include out-of-stock customer orders, production line shutdowns and raw material shortages.
进一步地，所述判断所述调拨分配能否达到预设的配送效率的步骤中，还包括：Furthermore, the step of determining whether the allocation and distribution can achieve a preset distribution efficiency further includes:
获取所述仓储地区数据预设的配送时长；Obtain the delivery time preset in the storage area data;
判断所述配送时长是否超出所述供应链上下游预设的配送时段；Determine whether the delivery time exceeds the preset delivery time period of the upstream and downstream of the supply chain;
若是，则从所述供应链上下游接收当次配送附带的运输成本数据，基于所述运输成本数据修正所述配送效率，将所述配送效率反馈至所述仓储地区数据，在所述仓储地区数据根据所述配送效率调整所述配送时长。If so, the transportation cost data associated with the current delivery is received from the upstream and downstream of the supply chain, the delivery efficiency is corrected based on the transportation cost data, the delivery efficiency is fed back to the storage area data, and the delivery time is adjusted according to the delivery efficiency in the storage area data.
进一步地，所述基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数的步骤中，还包括：Furthermore, the step of obtaining storage parameters corresponding to the storage area data based on the pre-recorded storage area data further includes:
识别所述仓储参数预设的库存数据格式；Identifying the inventory data format preset by the storage parameters;
判断所述库存数据格式能否适配于所述仓储地区数据；Determining whether the inventory data format is compatible with the storage area data;
若否，则对所述库存数据格式进行预设的数据清洗，基于所述仓储地区数据对所述库存数据格式的字段统一规范化，其中，所述数据清洗具体包括去除符号、缺失填补和类型转换。If not, the inventory data format is subjected to a preset data cleaning, and the fields of the inventory data format are standardized based on the storage area data, wherein the data cleaning specifically includes removing symbols, filling in missing parts, and type conversion.
本发明还提供一种电商仓储库存管理的监控系统，包括：The present invention also provides a monitoring system for e-commerce warehouse inventory management, comprising:
获取模块，用于基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数，其中，所述仓储参数具体包括仓储规模和仓储定位；An acquisition module, used to acquire storage parameters corresponding to the storage area data based on the pre-recorded storage area data, wherein the storage parameters specifically include storage scale and storage location;
判断模块，用于判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件；A judgment module, used to judge whether the storage parameters match the transfer conditions preset in the storage area data;
执行模块，用于若是，则将所述仓储参数对应的仓库信息录入至预设的库存管理数据库中，在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享，生成对所述仓库信息和所述其他仓库信息的库存分布内容，根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配；an execution module, for, if yes, entering the warehouse information corresponding to the warehousing parameters into a preset inventory management database, constructing cloud inventory content of the warehouse information in the inventory management database, performing real-time synchronization and sharing of the cloud inventory content from the inventory management database based on other pre-recorded warehouse information, generating inventory distribution content for the warehouse information and the other warehouse information, establishing a distribution strategy for the storage area data according to the inventory distribution content, and applying the distribution strategy to coordinate the upstream and downstream supply chains preset for the storage area data for allocation and distribution;
第二判断模块，用于判断所述调拨分配能否达到预设的配送效率；The second judgment module is used to judge whether the allocation can achieve the preset distribution efficiency;
第二执行模块，用于若否，则获取所述仓储地区数据对应的订单分流信息，基于所述订单分流信息应用预设的库存分配算法对所述调拨分配进行需求预测，根据所述需求预测实时优化所述仓库信息和所述其他仓库信息的物流协同，依据所述物流协同调整所述调拨分配，其中，所述订单分流信息具体包括地域分析、订单分配和物流成本。The second execution module is used for, if not, obtaining the order diversion information corresponding to the storage area data, applying a preset inventory allocation algorithm based on the order diversion information to perform demand forecasting on the allocation and distribution, optimizing the logistics coordination of the warehouse information and the other warehouse information in real time according to the demand forecast, and adjusting the allocation and distribution according to the logistics coordination, wherein the order diversion information specifically includes regional analysis, order allocation and logistics cost.
进一步地，所述执行模块还包括：Furthermore, the execution module also includes:
应用单元，用于基于预定义的库存事件，应用预设的监听器对所述库存管理数据库的消息队列进行订阅，其中，所述库存事件具体包括库存变更、入库操作和出库操作；An application unit, configured to apply a preset listener to subscribe to a message queue of the inventory management database based on predefined inventory events, wherein the inventory events specifically include inventory changes, warehousing operations, and outbound operations;
判断单元，用于判断所述库存事件能否被所述监听器单独进行抽取；A judging unit, used for judging whether the inventory event can be extracted by the listener alone;
执行单元，用于若能，则触发所述监听器预设的执行流程，解析所述库存事件对应的事件信息，根据所述事件信息生成数据同步操作的日志内容，其中，所述事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库。The execution unit is used to trigger the execution process preset by the listener if possible, parse the event information corresponding to the inventory event, and generate log content of the data synchronization operation according to the event information, wherein the event information specifically includes reading inventory information, generating inventory notifications, and updating the cloud inventory database.
进一步地，还包括：Furthermore, it also includes:
生成模块，用于从所述仓储地区数据中获取预收录的历史配送数据，基于所述历史配送数据生成当前季节的库存需求预测信息，其中，所述历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据；A generating module, used to obtain pre-recorded historical distribution data from the storage area data, and generate inventory demand forecast information for the current season based on the historical distribution data, wherein the historical distribution data specifically includes seasonal data, promotion activity data and market trend data;
第三判断模块，用于判断所述库存需求预测信息是否符合当前季节对应的配送阶段，其中，所述配送阶段具体包括产品上市阶段、成熟阶段和衰退阶段；A third judgment module is used to judge whether the inventory demand forecast information meets the distribution stage corresponding to the current season, wherein the distribution stage specifically includes the product launch stage, the mature stage and the decline stage;
第三执行模块，用于若是，则应用预训练的时间序列模型对所述库存需求预测信息进行构建，根据所述配送阶段持续生成所述库存需求预测信息的预测结果，依据所述预测结果制定对所述仓储地区数据的配送策略。The third execution module is used to apply the pre-trained time series model to construct the inventory demand forecast information, continuously generate the forecast results of the inventory demand forecast information according to the distribution stage, and formulate the distribution strategy for the storage area data based on the forecast results.
本发明提供了电商仓储库存管理的监控方法及系统，具有以下有益效果：The present invention provides a monitoring method and system for e-commerce warehouse inventory management, which has the following beneficial effects:
本发明通过实时监控和调配库存，可以更好地掌握库存情况，避免库存积压或库存不足的情况发生，实现库存的优化管理，同时通过建立配送策略、实施物流协同和实时调拨分配，可以提高配送效率，降低配送成本，缩短配送时间，提升客户满意度，并且利用库存管理数据库和实时同步共享的云端库存内容，可以为管理层提供实时的数据支持和决策参考，增强数据驱动型管理。The present invention can better grasp the inventory situation by real-time monitoring and allocation of inventory, avoid inventory backlogs or insufficient inventory, and achieve optimized inventory management. At the same time, by establishing distribution strategies, implementing logistics collaboration and real-time allocation and distribution, it can improve distribution efficiency, reduce distribution costs, shorten distribution time, and improve customer satisfaction. In addition, by utilizing the inventory management database and real-time synchronized and shared cloud inventory content, it can provide real-time data support and decision-making reference for management, thereby enhancing data-driven management.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明电商仓储库存管理的监控方法一个实施例的流程示意图；FIG1 is a flow chart of an embodiment of a monitoring method for e-commerce warehouse inventory management according to the present invention;
图2为本发明电商仓储库存管理的监控系统一个实施例的结构框图。FIG. 2 is a structural block diagram of an embodiment of a monitoring system for e-commerce warehouse inventory management according to the present invention.
具体实施方式Detailed ways
应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明，本发明为目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.
下面将结合本发明的实施例中的附图，对本发明的实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明的一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
参考附图1，为本发明一实施例中的电商仓储库存管理的监控方法，包括：Referring to FIG. 1 , a monitoring method for e-commerce warehouse inventory management in one embodiment of the present invention includes:
S1：基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数，其中，所述仓储参数具体包括仓储规模和仓储定位；S1: Based on the pre-recorded storage area data, obtain storage parameters corresponding to the storage area data, wherein the storage parameters specifically include storage scale and storage location;
S2：判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件；S2: Determine whether the storage parameters match the transfer conditions preset in the storage area data;
S3：若是，则将所述仓储参数对应的仓库信息录入至预设的库存管理数据库中，在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享，生成对所述仓库信息和所述其他仓库信息的库存分布内容，根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配；S3: If yes, then the warehouse information corresponding to the warehousing parameters is entered into a preset inventory management database, cloud inventory content of the warehouse information is constructed in the inventory management database, the cloud inventory content is synchronized and shared in real time from the inventory management database based on other pre-recorded warehouse information, inventory distribution content of the warehouse information and other warehouse information is generated, a distribution strategy for the storage area data is established according to the inventory distribution content, and the distribution strategy is applied to coordinate the upstream and downstream of the preset supply chain of the storage area data for allocation and distribution;
S4：判断所述调拨分配能否达到预设的配送效率；S4: Determine whether the allocation and distribution can achieve the preset distribution efficiency;
S5：若否，则获取所述仓储地区数据对应的订单分流信息，基于所述订单分流信息应用预设的库存分配算法对所述调拨分配进行需求预测，根据所述需求预测实时优化所述仓库信息和所述其他仓库信息的物流协同，依据所述物流协同调整所述调拨分配，其中，所述订单分流信息具体包括地域分析、订单分配和物流成本。S5: If not, then obtain the order diversion information corresponding to the storage area data, apply a preset inventory allocation algorithm based on the order diversion information to perform demand forecasting on the allocation and distribution, optimize the logistics coordination of the warehouse information and the other warehouse information in real time according to the demand forecast, and adjust the allocation and distribution according to the logistics coordination, wherein the order diversion information specifically includes regional analysis, order allocation and logistics cost.
在本实施例中，系统基于预先收录有的仓储地区数据，获取仓储地区数据中对应的仓储参数，包括某地区的仓储规模和某地区的仓储定位，而后系统判断这些仓储参数是否匹配仓储地区数据预先设有的调拨条件，以执行对应的步骤；例如，当系统判定到仓储参数无法匹配仓储地区数据预先设有的调拨条件时，则系统会认为仓储参数设置与实际情况不符，或者预先设定的调拨条件无法满足当前的仓储需求，系统会对数据进行重新录入或者修正，以保证仓储参数与实际情况的一致性，对仓储地区数据进行核查和修正，确保数据的准确性和完整性，同时引入自动化调整和人工干预的机制，对调拨分配流程进行改进和优化，增加异常处理机制和容错机制，提高系统的稳定性和鲁棒性，并且建立持续监控和改进机制，对系统运行情况进行定期检查和评估，及时发现和解决问题，不断优化系统性能和效率；例如，当系统判定到仓储参数能够匹配仓储地区数据预先设有的调拨条件时，此时系统会认为仓储参数设置与地区实际情况相符，系统会将仓储参数对应的当前仓库信息录入至预先设有的库存管理数据库中，在库存管理数据库中构建当前仓库信息的云端库存内容，从库存管理数据库中对云端库存内容基于预先收录的其他仓库信息进行实时同步共享，生成当前仓库信息和其他仓库信息的库存分布内容，根据库存分布内容建立对仓储地区数据的配送策略，应用该配送策略协同仓储地区数据预先设有的供应链上下游进行调拨分配，系统通过实时同步共享 将当前仓库信息录入库存管理数据库并构建云端库存内容，实现了对库存的实时管理和共享，提高了库存管理的效率和准确性，同时通过生成当前仓库信息和其他仓库信息的库存分布内容，可以清晰了解各个仓库的库存情况和分布情况，有助于优化调拨策略，合理分配库存资源，提高库存利用率，并且应用配送策略协同仓储地区数据的供应链上下游进行调拨分配，实现了供应链的协同调配，通过有效的供应链协同，可以实现库存和订单的快速响应，提高供应链的灵活性和响应能力；而后系统判断调拨分配时能否达到预先设有的配送效率，以执行对应的步骤；例如，当系统判定到进行调拨分配时能够达到预先设有的配送效率时，则系统会认为调拨分配的操作能够满足预期的配送要求，保证了物流运输的高效性和准时性，系统会根据实际情况和需求量，合理安排调拨频率和调拨数量，保证物流运输的高效性和准时性，确保调拨策略的制定合理有效，同时提前规划调拨分配的时间和路径，避免临时的情况发生，通过人工建立或提前构建好合理的计划和预测，避免物流运输的延误和拥堵，并且合理优化仓储布局，减少物流运输的距离和时间，可以通过合理配置仓库位置和库存量，减少调拨过程中的运输时间和成本；例如，当系统判定到进行调拨分配时无法达到预先设有的配送效率时，此时系统会认为调拨分配的操作无法满足预期的配送要求，系统会获取仓储地区数据对应的订单分流信息，基于订单分流信息应用预先设有的库存分配算法对调拨分配进行需求预测，根据需求预测实时优化当前仓库信息和其他仓库信息的物流协同，依据物流协同调整调拨分配；系统基于订单分流信息进行地域分析和订单分配，可以更准确地了解不同地区的需求情况和订单分布情况，通过预设的库存分配算法进行需求预测，可以预测未来一段时间内各地区的库存需求，为调拨分配提供准确的数据支持，同时根据需求预测实时优化仓库信息和其他仓库信息的物流协同，可以根据实际情况和需求变化调整物流流程和配送计划，通过实时优化物流协同，可以提高物流效率和配送准时性，降低物流成本，并且通过准确的需求预测和实时优化物流协同，可以更好地满足客户的订单需求，提高订单交付的及时性和准确性，从而提高客户满意度，增强客户忠诚度。In this embodiment, the system obtains the corresponding storage parameters in the storage area data based on the pre-recorded storage area data, including the storage scale of a certain area and the storage location of a certain area, and then the system determines whether these storage parameters match the pre-set allocation conditions of the storage area data to execute the corresponding steps; for example, when the system determines that the storage parameters cannot match the pre-set allocation conditions of the storage area data, the system will consider that the storage parameter settings are inconsistent with the actual situation, or the pre-set allocation conditions cannot meet the current storage needs. The system will re-enter or correct the data to ensure the consistency of the storage parameters with the actual situation, check and correct the storage area data to ensure the accuracy and completeness of the data, and at the same time introduce automatic adjustment and manual intervention mechanisms to improve and optimize the allocation and distribution process, increase exception handling mechanisms and fault tolerance mechanisms, and improve the stability and robustness of the system. It is sticky and establishes a continuous monitoring and improvement mechanism to conduct regular inspections and evaluations on the operation of the system, timely discover and solve problems, and continuously optimize system performance and efficiency; for example, when the system determines that the storage parameters can match the pre-set allocation conditions of the storage area data, the system will consider that the storage parameter settings are consistent with the actual situation in the region, and the system will enter the current warehouse information corresponding to the storage parameters into the pre-set inventory management database, build the cloud inventory content of the current warehouse information in the inventory management database, synchronize and share the cloud inventory content from the inventory management database based on other pre-collected warehouse information in real time, generate inventory distribution content of the current warehouse information and other warehouse information, establish a distribution strategy for the storage area data based on the inventory distribution content, apply the distribution strategy to coordinate the pre-set upstream and downstream supply chain of the storage area data for allocation and distribution, and the system synchronizes and shares the inventory in real time. Entering the current warehouse information into the inventory management database and building cloud inventory content can realize real-time management and sharing of inventory, improve the efficiency and accuracy of inventory management, and generate inventory distribution content of current warehouse information and other warehouse information to clearly understand the inventory status and distribution of each warehouse, which is helpful to optimize the allocation strategy, reasonably allocate inventory resources, improve inventory utilization, and apply the distribution strategy to coordinate the allocation and distribution of the upstream and downstream supply chains of the storage area data, so as to realize the coordinated allocation of the supply chain. Through effective supply chain collaboration, rapid response of inventory and orders can be achieved, and the flexibility and responsiveness of the supply chain can be improved; then the system can judge the allocation and distribution. The system will determine whether the preset distribution efficiency can be achieved to execute the corresponding steps; for example, when the system determines that the preset distribution efficiency can be achieved during allocation and distribution, the system will believe that the allocation and distribution operation can meet the expected distribution requirements and ensure the high efficiency and punctuality of logistics transportation. The system will reasonably arrange the allocation frequency and allocation quantity according to the actual situation and demand, ensure the high efficiency and punctuality of logistics transportation, ensure that the allocation strategy is reasonable and effective, and plan the time and route of allocation and distribution in advance to avoid temporary situations. By manually establishing or building reasonable plans and forecasts in advance, delays and congestion in logistics transportation can be avoided, and the warehouse layout can be reasonably optimized to reduce logistics. The distance and time of logistics transportation can be reduced by reasonably configuring warehouse locations and inventory levels to reduce transportation time and costs during the allocation process; for example, when the system determines that the pre-set distribution efficiency cannot be achieved during allocation, the system will consider that the allocation operation cannot meet the expected distribution requirements. The system will obtain the order diversion information corresponding to the storage area data, and use the pre-set inventory allocation algorithm based on the order diversion information to make demand forecasts for the allocation. According to the demand forecast, the logistics coordination of the current warehouse information and other warehouse information is optimized in real time, and the allocation is adjusted based on the logistics coordination. The system performs regional analysis and order allocation based on the order diversion information, which can more accurately understand The demand situation and order distribution in different regions can be used to predict demand through the preset inventory allocation algorithm, which can predict the inventory demand in various regions in the future and provide accurate data support for allocation and distribution. At the same time, the logistics coordination of warehouse information and other warehouse information can be optimized in real time according to demand forecasts. The logistics process and distribution plan can be adjusted according to the actual situation and demand changes. Through real-time optimization of logistics coordination, the logistics efficiency and delivery punctuality can be improved, and the logistics cost can be reduced. Through accurate demand forecasting and real-time optimization of logistics coordination, customer order needs can be better met, and the timeliness and accuracy of order delivery can be improved, thereby improving customer satisfaction and enhancing customer loyalty.
需要说明的是，应用库存分配算法对调拨分配进行需求预测的具体示例如下：It should be noted that the specific example of applying the inventory allocation algorithm to forecast demand for transfer allocation is as follows:
假设某电商公司在不同地区有多个仓库，需要预测未来一周某商品的销售量，以便进行调拨分配，我们使用ARIMA模型来进行需求预测；Suppose an e-commerce company has multiple warehouses in different regions and needs to predict the sales volume of a certain product in the next week in order to allocate it. We use the ARIMA model to predict demand;
公司收集了过去12周的该商品销售量数据，每周的销售数据如下：The company collected sales data for the product over the past 12 weeks, and the weekly sales data is as follows:
选择ARIMA模型进行需求预测，首先需要确定模型的参数，包括AR阶数、差分阶数、MA阶数，可以通过观察自相关图（ACF）和偏自相关图（PACF）来选择参数，在这个例子中，选择ARIMA(1,1,1)模型，然后使用历史销售数据对ARIMA模型进行训练；To select the ARIMA model for demand forecasting, you first need to determine the model parameters, including AR order, difference order, and MA order. You can select the parameters by observing the autocorrelation plot (ACF) and partial autocorrelation plot (PACF). In this example, select the ARIMA (1,1,1) model, and then use historical sales data to train the ARIMA model.
在训练好模型后，通过对模型进行评估，可以使用残差分析等方法来评估模型的拟合程度，如果模型拟合不佳，可以尝试调整参数或尝试其他模型，在这个例子中，假设模型的拟合程度良好；After training the model, you can evaluate the model by using methods such as residual analysis to assess the fit of the model. If the model does not fit well, you can try to adjust the parameters or try other models. In this example, assume that the model fits well.
利用训练好的ARIMA模型对未来一周的销售量进行预测，假设要预测第13周的销售量，通过模型预测，得到了第13周的销售量预测值为165；Use the trained ARIMA model to predict the sales volume for the next week. Suppose you want to predict the sales volume for the 13th week. Through model prediction, the sales volume forecast value for the 13th week is 165.
根据预测的销售量，系统可以调整各个仓库的库存水平，如果预测的销售量较高，系统可以增加该地区仓库的库存量；如果销售量较低，系统也可以减少库存量，以避免库存积压；Based on the forecasted sales volume, the system can adjust the inventory level of each warehouse. If the forecasted sales volume is high, the system can increase the inventory level of the warehouse in the area; if the sales volume is low, the system can also reduce the inventory level to avoid inventory backlogs;
综上所述，通过ARIMA模型的需求预测，系统可以更准确地估计未来销售量，从而更有效地进行库存调拨分配，降低库存成本，提高供应链效率。In summary, through the demand forecasting of the ARIMA model, the system can estimate future sales more accurately, thereby more effectively allocating inventory, reducing inventory costs, and improving supply chain efficiency.
在本实施例中，在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享的步骤S3中，还包括：In this embodiment, in the step S3 of constructing the cloud inventory content of the warehouse information in the inventory management database and performing real-time synchronization and sharing of the cloud inventory content from the inventory management database based on other pre-recorded warehouse information, it also includes:
S31：基于预定义的库存事件，应用预设的监听器对所述库存管理数据库的消息队列进行订阅，其中，所述库存事件具体包括库存变更、入库操作和出库操作；S31: Based on predefined inventory events, a preset listener is applied to subscribe to a message queue of the inventory management database, wherein the inventory events specifically include inventory changes, warehousing operations, and outbound operations;
S32：判断所述库存事件能否被所述监听器单独进行抽取；S32: Determine whether the inventory event can be extracted by the listener alone;
S33：若能，则触发所述监听器预设的执行流程，解析所述库存事件对应的事件信息，根据所述事件信息生成数据同步操作的日志内容，其中，所述事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库。S33: If yes, trigger the execution process preset by the listener, parse the event information corresponding to the inventory event, and generate log content of the data synchronization operation according to the event information, wherein the event information specifically includes reading inventory information, generating inventory notification and updating the cloud inventory database.
在本实施例中，系统基于预先定义有的库存事件，应用预先设有的监听器对库存管理数据库的库存消息队列进行订阅，库存事件具体包括库存变更、入库操作和出库操作，而后系统判断这些库存事件能否被监听器单独进行抽取，以执行对应的步骤；例如，当系统判定到某库存事件无法被监听器单独进行抽取时，则系统会认为该库存事件与其他事件存在复杂的关联性，无法简单地通过监听器单独进行抽取和处理，系统会建议管理人员使用更复杂的监听机制，能够同时监控多个条件或者组合条件的事件触发，这样可以确保系统能够及时捕捉到所有相关的事件，同时如果某些事件需要多个条件的组合才能触发，建议管理人员将这些条件进行合并，形成一个新的综合事件，然后监听器就可以监听这个综合事件，一旦触发就进行相应的处理；例如，当系统判定到某库存事件能够被监听器单独进行抽取，此时系统会认为该库存事件可以简单通过监听器进行处理，系统会触发监听器预先设有的执行流程，解析该库存事件对应的事件信息，事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库，根据事件信息生成数据同步操作的日志内容，在云端库存内容中生成；系统通过设置监听器来处理库存事件，系统能够自动地响应和处理各种库存变化，这种自动化库存管理可以大大减少人工干预的需要，提高库存管理的效率和准确性，同时当系统触发监听器预先设定的执行流程时，会实时解析库存事件并更新云端库存数据库，可以确保库存信息的及时更新和同步，使库存信息始终保持最新状态，并且在云端库存内容中生成更新后的库存信息，使所有系统或部门都能够访问到最新的库存信息，促进跨部门协同和业务流程的顺畅进行。In this embodiment, the system subscribes to the inventory message queue of the inventory management database based on pre-defined inventory events using a pre-set listener. The inventory events specifically include inventory changes, warehousing operations, and outbound operations. The system then determines whether these inventory events can be extracted by the listener alone to execute the corresponding steps. For example, when the system determines that a certain inventory event cannot be extracted by the listener alone, the system will believe that the inventory event has a complex correlation with other events and cannot be simply extracted and processed by the listener alone. The system will suggest that the manager use a more complex monitoring mechanism that can monitor the event triggering of multiple conditions or combined conditions at the same time. This ensures that the system can capture all related events in a timely manner. At the same time, if some events require a combination of multiple conditions to trigger, it is recommended that the manager merge these conditions to form a new comprehensive event. The listener can then monitor the comprehensive event and perform corresponding processing once it is triggered. For example, when the system determines that a certain inventory event It can be extracted separately by the listener. At this time, the system will think that the inventory event can be simply processed by the listener. The system will trigger the execution process pre-set by the listener to parse the event information corresponding to the inventory event. The event information specifically includes reading inventory information, generating inventory notifications and updating the cloud inventory database. The log content of the data synchronization operation is generated according to the event information and generated in the cloud inventory content; the system processes inventory events by setting up listeners. The system can automatically respond to and process various inventory changes. This automated inventory management can greatly reduce the need for manual intervention and improve the efficiency and accuracy of inventory management. At the same time, when the system triggers the execution process pre-set by the listener, it will parse the inventory event in real time and update the cloud inventory database, which can ensure the timely update and synchronization of inventory information, so that the inventory information is always kept up to date, and the updated inventory information is generated in the cloud inventory content, so that all systems or departments can access the latest inventory information, promote cross-departmental collaboration and smooth business processes.
在本实施例中，根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配的步骤S3前，还包括：In this embodiment, before the step S3 of establishing a distribution strategy for the storage area data according to the inventory distribution content and applying the distribution strategy to coordinate the upstream and downstream supply chains preset by the storage area data to perform allocation and distribution, the following is also included:
S301：从所述仓储地区数据中获取预收录的历史配送数据，基于所述历史配送数据生成当前季节的库存需求预测信息，其中，所述历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据；S301: Acquire pre-recorded historical distribution data from the storage area data, and generate inventory demand forecast information for the current season based on the historical distribution data, wherein the historical distribution data specifically includes seasonal data, promotion activity data, and market trend data;
S302：判断所述库存需求预测信息是否符合当前季节对应的配送阶段，其中，所述配送阶段具体包括产品上市阶段、成熟阶段和衰退阶段；S302: Determine whether the inventory demand forecast information meets the distribution stage corresponding to the current season, wherein the distribution stage specifically includes the product launch stage, the mature stage and the decline stage;
S303：若是，则应用预训练的时间序列模型对所述库存需求预测信息进行构建，根据所述配送阶段持续生成所述库存需求预测信息的预测结果，依据所述预测结果制定对所述仓储地区数据的配送策略。S303: If so, the pre-trained time series model is used to construct the inventory demand forecast information, the forecast results of the inventory demand forecast information are continuously generated according to the distribution stage, and the distribution strategy for the storage area data is formulated based on the forecast results.
在本实施例中，系统从仓储地区数据中获取预先收录有的历史配送数据，历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据，基于这些历史配送数据生成当前季节的库存需求预测信息，而后系统判断该库存需求预测信息是否符合当前季节对应的配送阶段，以执行对应的步骤；例如，当系统判定到库存需求预测信息无法符合当前季节对应的配送阶段，则系统会认为预测的库存需求与当前季节或市场情况不一致，需要进行相应的调整和处理，系统需要重新评估季节性因素对库存需求的影响，可能是由于季节性因素的变化导致了需求预测的不准确，需要重新考虑季节性因素的影响程度，并相应调整预测模型或参数，同时根据实际情况和预测的库存需求，动态调整配送计划，即需要加大或减少配送量，调整配送频率，以适应当前季节的需求变化，并且根据实际需求和市场情况，调整库存水平和库存结构，需要增加某些季节性产品的库存，减少其他产品的库存，以适应当前季节的需求变化；例如，当系统判定到库存需求预测信息能够符合当前季节对应的配送阶段时，此时系统会认为预测的库存需求与当前季节或市场情况一致，系统会应用预先训练好的时间序列模型对库存需求预测信息进行构建，根据配送阶段持续生成库存需求预测信息的预测结果，依据各个预测结果制定对仓储地区数据的配送策略；系统通过应用预先训练好的时间序列模型对库存需求预测信息进行构建，可以实现对当前季节或市场情况的准确预测，这样可以更准确地了解未来的库存需求情况，为制定合理的配送策略提供可靠的数据支持，同时根据持续生成的库存需求预测信息的预测结果，系统可以及时制定配送策略，根据预测的库存需求量和地区分布情况，合理安排仓储地区数据的配送计划，确保库存能够及时满足市场需求，并且通过根据预测的库存需求量制定配送策略，可以优化库存管理，合理分配库存资源，避免库存积压或库存不足的情况发生，提高库存周转率和资金利用效率。In this embodiment, the system obtains pre-recorded historical distribution data from the storage area data. The historical distribution data specifically includes seasonal data, promotional activity data and market trend data. The inventory demand forecast information of the current season is generated based on these historical distribution data. Then the system determines whether the inventory demand forecast information meets the distribution stage corresponding to the current season to execute the corresponding steps; for example, when the system determines that the inventory demand forecast information cannot meet the distribution stage corresponding to the current season, the system will consider that the predicted inventory demand is inconsistent with the current season or market conditions, and corresponding adjustments and processing are required. The system needs to re-evaluate the impact of seasonal factors on inventory demand. It may be due to changes in seasonal factors that lead to inaccurate demand forecasts. It is necessary to reconsider the impact of seasonal factors and adjust the forecast model or parameters accordingly. At the same time, according to the actual situation and predicted inventory demand, the distribution plan is dynamically adjusted, that is, the distribution volume needs to be increased or reduced, and the distribution frequency needs to be adjusted to adapt to the demand changes in the current season. In addition, the inventory level and inventory structure need to be adjusted according to actual demand and market conditions. It is necessary to increase the inventory of certain seasonal products and reduce the inventory of other products to adapt to the current season. For example, when the system determines that the inventory demand forecast information can meet the distribution stage corresponding to the current season, the system will consider that the predicted inventory demand is consistent with the current season or market conditions, and the system will apply the pre-trained time series model to construct the inventory demand forecast information, and continuously generate the forecast results of the inventory demand forecast information according to the distribution stage, and formulate the distribution strategy for the storage area data according to each forecast result; the system can accurately predict the current season or market conditions by applying the pre-trained time series model to construct the inventory demand forecast information, so as to more accurately understand the future inventory demand situation and provide reliable data support for the formulation of reasonable distribution strategies. At the same time, according to the forecast results of the continuously generated inventory demand forecast information, the system can formulate distribution strategies in time, and reasonably arrange the distribution plan of the storage area data according to the predicted inventory demand and regional distribution, so as to ensure that the inventory can meet the market demand in time. In addition, by formulating distribution strategies according to the predicted inventory demand, it is possible to optimize inventory management, reasonably allocate inventory resources, avoid inventory backlogs or insufficient inventory, and improve inventory turnover and capital utilization efficiency.
在本实施例中，则应用预训练的时间序列模型对所述库存需求预测信息进行构建的步骤S301中，包括：In this embodiment, the step S301 of applying the pre-trained time series model to construct the inventory demand forecast information includes:
S3031：应用所述历史配送数据对预选取的训练模型进行探索性数据分析，基于所述探索性数据分析生成对应的数据集，将所述数据集根据预设比例划分为训练集和测试集，其中，所述探索性数据分析具体包括绘制时序图、绘制自相关图和绘制偏自相关图；S3031: Apply the historical delivery data to perform exploratory data analysis on the pre-selected training model, generate a corresponding data set based on the exploratory data analysis, and divide the data set into a training set and a test set according to a preset ratio, wherein the exploratory data analysis specifically includes drawing a time series diagram, drawing an autocorrelation diagram, and drawing a partial autocorrelation diagram;
S3032：判断所述训练模型能否通过所述训练集进行拟合；S3032: Determine whether the training model can be fitted by the training set;
S3033：若能，则从拟合过程中获取所述训练模型的参数估计值，采用所述测试集对所述训练模型进行指标评估，依据评估结果调整所述训练模型的超参数，得到训练完毕的时间序列模型，使用所述时间序列模型对预设时段的库存需求进行预测，以构建所述库存需求预测信息，其中，所述指标评估具体包括均方根误差、平均绝对误差和均方误差，所述超参数具体包括滞后阶数和季节性周期。S3033: If possible, obtain the parameter estimation value of the training model from the fitting process, use the test set to perform index evaluation on the training model, adjust the hyperparameters of the training model according to the evaluation results, and obtain a trained time series model. Use the time series model to predict the inventory demand for a preset time period to construct the inventory demand forecast information, wherein the index evaluation specifically includes the root mean square error, the mean absolute error and the mean square error, and the hyperparameters specifically include the lag order and the seasonal cycle.
在本实施例中，系统应用历史配送数据对预先选取的训练模型进行探索性数据分析，探索性数据分析具体包括绘制时序图、绘制自相关图和绘制偏自相关图，基于探索性数据分析后生成对应的数据集，将这些数据集根据预先设定的比例划分为训练集和测试集，而后系统判断训练模型能否通过训练集进行拟合，以执行对应的步骤；例如，当系统判定到训练模型无法通过训练集进行拟合时，则系统会认为数据之间的关系过于复杂，使得模型无法很好地捕捉到数据的规律，系统会进行特征工程，对数据进行处理和转换，提取更有意义的特征，以便更好地拟合模型，包括进行特征缩放、特征选择、特征组合等操作，以增强模型对数据的拟合能力，同时使用集成学习的方法，如随机森林、梯度提升树，集成学习通过结合多个弱学习器的预测结果，可以获得更强大的预测能力，对复杂关系的数据拟合效果更好，并且增加训练数据量，可能会有助于提高模型的拟合能力，更多的数据可以使模型更好地捕捉数据的分布和规律；例如，当系统判定到训练模型能够通过训练集进行拟合时，此时系统会认为模型能够捕捉到数据的规律，系统会从拟合过程中获取训练模型的参数估计值，采用测试集对训练模型进行指标评估，指标评估具体包括均方根误差、平均绝对误差和均方误差，依据评估结果调整训练模型的超参数，超参数具体包括滞后阶数和季节性周期，以此得到训练完毕的时间序列模型，使用该时间序列模型对预先设有时段的库存需求进行预测，构建库存需求预测信息；系统通过从拟合过程中获取训练模型的参数估计值，可以确保模型能够捕捉到数据的规律，对训练数据集有较好的拟合效果，这样可以提高模型的预测准确性，同时基于训练完毕的时间序列模型，系统可以对预先设定的时段的库存需求进行准确预测，构建库存需求预测信息，这样可以为电商企业提供及时、准确的库存需求预测，有助于制定合理的库存管理策略和供应链计划，并且通过指标评估结果，系统可以对训练模型的超参数进行调整，例如滞后阶数和季节性周期，有助于优化模型的性能，使其更好地适应数据的特征和变化规律，进而提高预测准确性；综上所述，通过训练完毕的时间序列模型进行库存需求预测，能够提高预测准确性，降低成本，提高效率，并最终提升客户满意度，对企业的运营和发展具有积极的促进作用，通过准确的库存需求预测有助于避免库存过剩或不足的情况发生，从而降低了库存成本和运营风险，合理的库存管理能够提高库存周转率和资金利用效率，进而提高企业的运营效率和竞争力。In this embodiment, the system applies historical delivery data to perform exploratory data analysis on a pre-selected training model. The exploratory data analysis specifically includes drawing a time series diagram, drawing an autocorrelation diagram, and drawing a partial autocorrelation diagram. Based on the exploratory data analysis, a corresponding data set is generated, and these data sets are divided into a training set and a test set according to a preset ratio. Then the system determines whether the training model can be fitted through the training set to execute the corresponding steps; for example, when the system determines that the training model cannot be fitted through the training set, the system will believe that the relationship between the data is too complex, so that the model cannot capture the laws of the data well. The system will perform feature engineering, process and transform the data, and extract more meaningful features to better fit the model, including performing feature engineering. Operations such as feature scaling, feature selection, and feature combination are used to enhance the model's ability to fit the data. At the same time, ensemble learning methods such as random forest and gradient boosting tree are used. Ensemble learning can obtain more powerful prediction capabilities by combining the prediction results of multiple weak learners, and has better fitting effects on data with complex relationships. Increasing the amount of training data may help improve the model's fitting ability. More data can enable the model to better capture the distribution and laws of the data. For example, when the system determines that the training model can be fitted through the training set, the system will believe that the model can capture the laws of the data. The system will obtain the parameter estimates of the training model from the fitting process, and use the test set to evaluate the training model. The indicator evaluation specifically includes root mean square error, average The mean absolute error and mean square error are calculated, and the hyperparameters of the training model are adjusted according to the evaluation results. The hyperparameters specifically include the lag order and the seasonal cycle, so as to obtain a trained time series model. The time series model is used to predict the inventory demand in a preset time period and construct inventory demand forecast information. The system obtains the parameter estimation value of the training model from the fitting process to ensure that the model can capture the regularity of the data and has a good fitting effect on the training data set, which can improve the prediction accuracy of the model. At the same time, based on the trained time series model, the system can accurately predict the inventory demand in a preset time period and construct inventory demand forecast information, which can provide e-commerce companies with timely and accurate inventory demand forecasts and help to formulate reasonable inventory demand forecasts. Inventory management strategies and supply chain plans, and through the indicator evaluation results, the system can adjust the hyperparameters of the training model, such as the lag order and seasonal cycle, which helps to optimize the performance of the model and make it better adapt to the characteristics and changing laws of the data, thereby improving the prediction accuracy; In summary, inventory demand forecasting through the trained time series model can improve prediction accuracy, reduce costs, improve efficiency, and ultimately improve customer satisfaction, which has a positive effect on the operation and development of the enterprise. Accurate inventory demand forecasting helps to avoid excess or insufficient inventory, thereby reducing inventory costs and operational risks. Reasonable inventory management can improve inventory turnover and capital utilization efficiency, thereby improving the operational efficiency and competitiveness of the enterprise.
在本实施例中，判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件的步骤S2中，还包括：In this embodiment, the step S2 of determining whether the storage parameters match the allocation conditions preset in the storage area data further includes:
S21：获取所述仓储地区数据预设的调拨优先级；S21: Obtaining the preset allocation priority of the storage area data;
S22：判断所述调拨优先级是否匹配预设的调拨效益需求；S22: Determine whether the allocation priority matches the preset allocation benefit requirement;
S23：若否，则从所述仓储地区数据识别预收录的紧急调拨需求，采集所述仓储参数的库存周转率，基于所述紧急调拨需求将所述库存周转率作为所述调拨条件的最高优先级，其中，所述紧急调拨需求具体包括缺货客户订单、生产线停机和原料短缺。S23: If not, identify the pre-recorded emergency transfer needs from the storage area data, collect the inventory turnover rate of the storage parameters, and take the inventory turnover rate as the highest priority of the transfer conditions based on the emergency transfer needs, wherein the emergency transfer needs specifically include out-of-stock customer orders, production line shutdowns and raw material shortages.
在本实施例中，系统通过获取仓储地区数据预先设有的调拨优先级，而后系统判断该调拨优先级是否匹配预先设有的调拨效益需求，以执行对应的步骤；例如，当系统判定到仓储地区数据预先设有的调拨优先级能够匹配预先设有的调拨效益需求时，则系统会认为在调拨过程中，满足了供应链上下游的条件目标，包括成本最小化、库存平衡和服务水平提高，系统会根据预先设定的调拨优先级和效益需求，执行相应的调拨计划，确保按照优先级顺序对仓储地区数据进行调拨，以最大程度地满足调拨的效益要求，同时对执行的调拨计划进行监控和跟踪，确保调拨过程按照预期进行，及时发现和解决可能出现的问题，保证调拨的顺利进行，并且在调拨完成后，对调拨效果进行评估和分析。比较实际效果与预期效果，检验调拨是否达到了预期的效益要求，如果有必要，建议管理人员对调拨策略进行调整和优化；例如，当系统判定到仓储地区数据预先设有的调拨优先级无法匹配预先设有的调拨效益需求时，此时系统会认为在调拨过程中无法满足供应链上下游的条件目标，系统会从仓储地区数据识别预先收录的紧急调拨需求，紧急调拨需求具体包括缺货客户订单、生产线停机和原料短缺，采集仓储参数的库存周转率，基于紧急调拨需求将库存周转率作为调拨条件的最高优先级；系统通过将紧急调拨需求的识别能够有效地应对突发情况，如缺货客户订单、生产线停机和原料短缺等，将这些紧急需求作为调拨条件的最高优先级，系统可以及时响应，并确保关键业务运作不受影响，同时将库存周转率作为调拨条件的最高优先级，可以帮助优化库存管理，通过调拨高周转率的库存，可以提高库存周转率，降低库存积压和资金占用成本，提高资金利用效率，并且通过优先满足紧急调拨需求，可以保证供应链上下游的条件目标得到满足，有助于提高供应链的响应速度和效率，减少因供应链中断而导致的损失和影响。In this embodiment, the system obtains the pre-set allocation priority of the storage area data, and then the system determines whether the allocation priority matches the pre-set allocation benefit requirement to execute the corresponding steps; for example, when the system determines that the pre-set allocation priority of the storage area data can match the pre-set allocation benefit requirement, the system will believe that in the allocation process, the conditional goals of the upstream and downstream of the supply chain are met, including cost minimization, inventory balance and service level improvement. The system will execute the corresponding allocation plan according to the pre-set allocation priority and benefit requirement to ensure that the storage area data is allocated in order of priority to maximize the benefit requirements of the allocation. At the same time, the executed allocation plan is monitored and tracked to ensure that the allocation process proceeds as expected, and possible problems are discovered and resolved in a timely manner to ensure the smooth progress of the allocation. After the allocation is completed, the allocation effect is evaluated and analyzed. Compare the actual effect with the expected effect, check whether the allocation has achieved the expected benefit requirements, and if necessary, suggest that managers adjust and optimize the allocation strategy; for example, when the system determines that the allocation priority set in the storage area data cannot match the pre-set allocation benefit requirements, the system will believe that the upstream and downstream conditions of the supply chain cannot be met during the allocation process. The system will identify the pre-recorded emergency allocation needs from the storage area data. The emergency allocation needs specifically include out-of-stock customer orders, production line shutdowns and raw material shortages, collect the inventory turnover rate of the storage parameters, and use the inventory turnover rate as the highest priority for the allocation conditions based on the emergency allocation needs; the system will Identification of demand can effectively respond to emergencies, such as out-of-stock customer orders, production line downtime, and raw material shortages. By giving these urgent demands the highest priority in allocation conditions, the system can respond in a timely manner and ensure that key business operations are not affected. At the same time, giving inventory turnover rate the highest priority in allocation conditions can help optimize inventory management. By allocating high-turnover inventory, inventory turnover rate can be improved, inventory backlog and capital occupation costs can be reduced, and capital utilization efficiency can be improved. By giving priority to meeting urgent allocation needs, it can ensure that the conditional goals of the upstream and downstream of the supply chain are met, which helps to improve the response speed and efficiency of the supply chain and reduce the losses and impacts caused by supply chain disruptions.
在本实施例中，判断所述调拨分配能否达到预设的配送效率的步骤S4中，还包括：In this embodiment, the step S4 of judging whether the allocation and distribution can achieve the preset distribution efficiency further includes:
S41：获取所述仓储地区数据预设的配送时长；S41: Obtaining the delivery time preset in the storage area data;
S42：判断所述配送时长是否超出所述供应链上下游预设的配送时段；S42: Determine whether the delivery time exceeds the preset delivery time period of the upstream and downstream of the supply chain;
S43：若是，则从所述供应链上下游接收当次配送附带的运输成本数据，基于所述运输成本数据修正所述配送效率，将所述配送效率反馈至所述仓储地区数据，在所述仓储地区数据根据所述配送效率调整所述配送时长。S43: If so, the transportation cost data associated with the current delivery is received from the upstream and downstream of the supply chain, the delivery efficiency is corrected based on the transportation cost data, the delivery efficiency is fed back to the storage area data, and the delivery time is adjusted according to the delivery efficiency in the storage area data.
在本实施例中，系统获取仓储地区数据预先设有的配送时长，而后系统判断该配送时长是否超出供应链上下游预先设有的配送时段，以执行对应的步骤；例如，当系统判定到仓储地区数据预先设有的配送时长并未超出供应链上下游预设的配送时段，则系统会认为当前的配送时长在预设的范围内，符合供应链的预期，表明供应链的运作相对顺利，能够按时满足配送需求，系统会持续监控和优化配送流程。确保配送的效率和准时性，及时发现和解决可能出现的问题，反馈给相关管理人员，以提高供应链的运作效率，同时当配送时长在预设范围内时，可以评估供应链的稳定性和可靠性，通过分析配送数据和指标，评估供应链的运作情况，发现潜在的风险和瓶颈，并采取措施加以解决，确保供应链的稳定运行；例如，当系统判定到仓储地区数据预先设有的配送时长超出了供应链上下游预设的配送时段，此时系统会认为当前的配送时长不在预设的范围内，不符合供应链的预期，系统会从供应链上下游接收当次进行配送时附带的运输成本数据，基于这些运输成本数据修正配送效率，将配送效率反馈至仓储地区数据，在仓储地区数据根据配送效率对应重新调整原有配送时长，形成新的配送时长；系统通过接收当次配送时附带的运输成本数据，可以及时了解配送效率的实际情况，基于这些数据可以对配送效率进行修正，将修正后的结果反馈给仓储地区数据，这样可以实现配送时长的实时调整，以适应当前的运营状况和需求变化，同时修正配送效率有助于提高运营效率，通过及时调整配送时长，可以优化配送流程，减少不必要的等待和延误时间，提高配送效率和准时性，降低运输成本，从而提高整体运营效率，并且及时调整配送时长有助于优化客户服务，确保配送时长符合客户期望和要求，提高配送的准时性和可靠性，增强客户对服务的信任和满意度，提升客户忠诚度，促进业务的持续发展。In this embodiment, the system obtains the delivery time preset in the storage area data, and then the system determines whether the delivery time exceeds the delivery time period preset in the upstream and downstream of the supply chain to execute the corresponding steps; for example, when the system determines that the delivery time preset in the storage area data does not exceed the delivery time period preset in the upstream and downstream of the supply chain, the system will consider that the current delivery time is within the preset range and meets the expectations of the supply chain, indicating that the operation of the supply chain is relatively smooth and can meet the delivery needs on time, and the system will continue to monitor and optimize the delivery process. Ensure the efficiency and punctuality of distribution, discover and solve possible problems in time, and feedback to relevant managers to improve the operating efficiency of the supply chain. At the same time, when the delivery time is within the preset range, the stability and reliability of the supply chain can be evaluated. By analyzing the distribution data and indicators, the operation of the supply chain can be evaluated, potential risks and bottlenecks can be discovered, and measures can be taken to solve them to ensure the stable operation of the supply chain. For example, when the system determines that the delivery time preset in the warehousing area data exceeds the preset delivery time period of the upstream and downstream of the supply chain, the system will consider that the current delivery time is not within the preset range and does not meet the expectations of the supply chain. The system will receive the transportation cost data attached to the distribution from the upstream and downstream of the supply chain, and correct the distribution efficiency based on these transportation cost data, and feed the distribution efficiency back to the warehousing area data. The warehousing area data will be re-adjusted according to the distribution efficiency. The original delivery time is newly adjusted to form a new delivery time; the system can timely understand the actual situation of delivery efficiency by receiving the transportation cost data attached to the current delivery, and can correct the delivery efficiency based on these data, and feed the corrected results back to the warehousing area data, so that real-time adjustment of delivery time can be achieved to adapt to the current operating conditions and demand changes. At the same time, correcting the delivery efficiency helps to improve operational efficiency. By adjusting the delivery time in time, the delivery process can be optimized, unnecessary waiting and delay time can be reduced, delivery efficiency and punctuality can be improved, and transportation costs can be reduced, thereby improving overall operational efficiency. In addition, timely adjustment of delivery time helps to optimize customer service, ensure that the delivery time meets customer expectations and requirements, improve the punctuality and reliability of delivery, enhance customer trust and satisfaction with the service, improve customer loyalty, and promote the sustainable development of the business.
在本实施例中，基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数的步骤S1中，还包括：In this embodiment, based on the pre-recorded storage area data, the step S1 of acquiring the storage parameters corresponding to the storage area data further includes:
S11：识别所述仓储参数预设的库存数据格式；S11: Identify the inventory data format preset by the storage parameters;
S12：判断所述库存数据格式能否适配于所述仓储地区数据；S12: Determine whether the inventory data format is compatible with the storage area data;
S13：若否，则对所述库存数据格式进行预设的数据清洗，基于所述仓储地区数据对所述库存数据格式的字段统一规范化，其中，所述数据清洗具体包括去除符号、缺失填补和类型转换。S13: If not, the inventory data format is subjected to a preset data cleaning, and the fields of the inventory data format are standardized based on the storage area data, wherein the data cleaning specifically includes removing symbols, filling in missing parts, and converting types.
在本实施例中，系统通过识别仓储参数预先设有的库存数据格式，而后判断库存数据格式是否适配于仓储地区数据，以执行对应的步骤；例如，当系统判定到库存数据格式能够适配于仓储地区数据时，则系统会认为库存数据的格式与仓储地区数据的格式相匹配，能够无缝地整合和处理，系统会将符合要求的库存数据导入到仓储地区数据中，包括将库存数据存储在统一的数据库或数据仓库中，或者将库存数据直接与仓储地区数据进行集成，同时将库存数据与仓储地区数据进行集成，以便进行进一步的分析和处理，包括数据表的关联和字段的映射操作，确保数据的一致性和完整性，并且根据集成后的数据进行相关的分析、报告或决策，利用仓储地区数据和库存数据的集成，可以为企业提供更全面、准确的信息支持，帮助企业更好地理解和管理库存和仓储活动；例如，当系统判定到库存数据格式无法适配于仓储地区数据时，此时系统会认为库存数据的格式与仓储地区数据的格式不匹配，系统会对库存数据格式进行预先设有的数据清洗操作，数据清洗具体包括去除符号、缺失填补和类型转换，基于仓储地区数据对库存数据格式的字段统一规范化；系统通过对库存数据格式进行预先设定的数据清洗操作，可以使库存数据的格式与仓储地区数据的格式保持一致，确保数据的一致性，有助于简化数据处理和分析过程，避免因数据格式不一致而导致的错误或混乱，同时统一规范化库存数据格式的字段，使其与仓储地区数据的格式保持一致，有助于提高数据的可用性，这样可以更轻松地进行数据整合和分析，提高数据的可用性和可操作性，为企业的决策和业务提供更可靠的支持，并且通过清洗和规范化库存数据格式，可以降低数据处理和分析的复杂度，提高数据分析的效率，清洗后的数据更易于理解和操作，可以更快地进行数据分析和挖掘，加快决策的速度和精度。In this embodiment, the system identifies the inventory data format pre-set by the warehousing parameters, and then determines whether the inventory data format is compatible with the storage area data to execute the corresponding steps; for example, when the system determines that the inventory data format is compatible with the storage area data, the system will consider that the format of the inventory data matches the format of the storage area data and can be seamlessly integrated and processed. The system will import the inventory data that meets the requirements into the storage area data, including storing the inventory data in a unified database or data warehouse, or directly integrating the inventory data with the storage area data, and integrating the inventory data with the storage area data at the same time for further analysis and processing, including data table association and field mapping operations to ensure data consistency and integrity, and perform relevant analysis, reporting or decision-making based on the integrated data. The integration of storage area data and inventory data can provide enterprises with more comprehensive and accurate information support, helping enterprises to better understand and manage inventory and warehousing activities; for example, when the system determines that the inventory data format is not compatible with the storage area data, the system The system will consider that the format of the inventory data does not match the format of the storage area data, and will perform pre-set data cleaning operations on the inventory data format. Data cleaning specifically includes removing symbols, filling in missing data, and type conversion, and standardizing the fields of the inventory data format based on the storage area data. By performing pre-set data cleaning operations on the inventory data format, the system can make the format of the inventory data consistent with the format of the storage area data, ensure data consistency, and help simplify the data processing and analysis process, avoid errors or confusion caused by inconsistent data formats, and at the same time, standardize the fields of the inventory data format to make it consistent with the format of the storage area data, which helps to improve data availability, so that data integration and analysis can be carried out more easily, improve data availability and operability, and provide more reliable support for corporate decision-making and business. In addition, by cleaning and standardizing the inventory data format, the complexity of data processing and analysis can be reduced, and the efficiency of data analysis can be improved. The cleaned data is easier to understand and operate, and data analysis and mining can be carried out faster, which speeds up the speed and accuracy of decision-making.
参考附图2，为本发明一实施例中电商仓储库存管理的监控系统，包括：Referring to FIG. 2 , a monitoring system for e-commerce warehouse inventory management in one embodiment of the present invention is shown, including:
获取模块10，用于基于预收录的仓储地区数据，获取所述仓储地区数据对应的仓储参数，其中，所述仓储参数具体包括仓储规模和仓储定位；The acquisition module 10 is used to acquire storage parameters corresponding to the storage area data based on the pre-recorded storage area data, wherein the storage parameters specifically include storage scale and storage location;
判断模块20，用于判断所述仓储参数是否匹配所述仓储地区数据预设的调拨条件；A judgment module 20, used to judge whether the storage parameters match the transfer conditions preset in the storage area data;
执行模块30，用于若是，则将所述仓储参数对应的仓库信息录入至预设的库存管理数据库中，在所述库存管理数据库中构建所述仓库信息的云端库存内容，从所述库存管理数据库中对所述云端库存内容基于预收录的其他仓库信息进行实时同步共享，生成对所述仓库信息和所述其他仓库信息的库存分布内容，根据所述库存分布内容建立对所述仓储地区数据的配送策略，应用所述配送策略协同所述仓储地区数据预设的供应链上下游进行调拨分配；Execution module 30, for: if yes, entering the warehouse information corresponding to the warehousing parameters into a preset inventory management database, constructing cloud inventory content of the warehouse information in the inventory management database, performing real-time synchronization and sharing of the cloud inventory content from the inventory management database based on other pre-recorded warehouse information, generating inventory distribution content for the warehouse information and the other warehouse information, establishing a distribution strategy for the storage area data according to the inventory distribution content, and applying the distribution strategy to coordinate the upstream and downstream supply chains preset for the storage area data for allocation and distribution;
第二判断模块40，用于判断所述调拨分配能否达到预设的配送效率；The second judgment module 40 is used to judge whether the allocation can achieve the preset distribution efficiency;
第二执行模块50，用于若否，则获取所述仓储地区数据对应的订单分流信息，基于所述订单分流信息应用预设的库存分配算法对所述调拨分配进行需求预测，根据所述需求预测实时优化所述仓库信息和所述其他仓库信息的物流协同，依据所述物流协同调整所述调拨分配，其中，所述订单分流信息具体包括地域分析、订单分配和物流成本。The second execution module 50 is used to, if not, obtain the order diversion information corresponding to the storage area data, apply a preset inventory allocation algorithm based on the order diversion information to perform demand forecasting on the allocation and distribution, optimize the logistics coordination of the warehouse information and the other warehouse information in real time according to the demand forecast, and adjust the allocation and distribution according to the logistics coordination, wherein the order diversion information specifically includes regional analysis, order allocation and logistics cost.
在本实施例中，获取模块10基于预先收录有的仓储地区数据，获取仓储地区数据中对应的仓储参数，包括某地区的仓储规模和某地区的仓储定位，而后判断模块20判断这些仓储参数是否匹配仓储地区数据预先设有的调拨条件，以执行对应的步骤；例如，当系统判定到仓储参数无法匹配仓储地区数据预先设有的调拨条件时，则系统会认为仓储参数设置与实际情况不符，或者预先设定的调拨条件无法满足当前的仓储需求，系统会对数据进行重新录入或者修正，以保证仓储参数与实际情况的一致性，对仓储地区数据进行核查和修正，确保数据的准确性和完整性，同时引入自动化调整和人工干预的机制，对调拨分配流程进行改进和优化，增加异常处理机制和容错机制，提高系统的稳定性和鲁棒性，并且建立持续监控和改进机制，对系统运行情况进行定期检查和评估，及时发现和解决问题，不断优化系统性能和效率；例如，当系统判定到仓储参数能够匹配仓储地区数据预先设有的调拨条件时，此时执行模块30会认为仓储参数设置与地区实际情况相符，系统会将仓储参数对应的当前仓库信息录入至预先设有的库存管理数据库中，在库存管理数据库中构建当前仓库信息的云端库存内容，从库存管理数据库中对云端库存内容基于预先收录的其他仓库信息进行实时同步共享，生成当前仓库信息和其他仓库信息的库存分布内容，根据库存分布内容建立对仓储地区数据的配送策略，应用该配送策略协同仓储地区数据预先设有的供应链上下游进行调拨分配，系统通过实时同步共享 将当前仓库信息录入库存管理数据库并构建云端库存内容，实现了对库存的实时管理和共享，提高了库存管理的效率和准确性，同时通过生成当前仓库信息和其他仓库信息的库存分布内容，可以清晰了解各个仓库的库存情况和分布情况，有助于优化调拨策略，合理分配库存资源，提高库存利用率，并且应用配送策略协同仓储地区数据的供应链上下游进行调拨分配，实现了供应链的协同调配，通过有效的供应链协同，可以实现库存和订单的快速响应，提高供应链的灵活性和响应能力；而后第二判断模块40判断调拨分配时能否达到预先设有的配送效率，以执行对应的步骤；例如，当系统判定到进行调拨分配时能够达到预先设有的配送效率时，则系统会认为调拨分配的操作能够满足预期的配送要求，保证了物流运输的高效性和准时性，系统会根据实际情况和需求量，合理安排调拨频率和调拨数量，保证物流运输的高效性和准时性，确保调拨策略的制定合理有效，同时提前规划调拨分配的时间和路径，避免临时的情况发生，通过人工建立或提前构建好合理的计划和预测，避免物流运输的延误和拥堵，并且合理优化仓储布局，减少物流运输的距离和时间，可以通过合理配置仓库位置和库存量，减少调拨过程中的运输时间和成本；例如，当系统判定到进行调拨分配时无法达到预先设有的配送效率时，此时第二执行模块50会认为调拨分配的操作无法满足预期的配送要求，系统会获取仓储地区数据对应的订单分流信息，基于订单分流信息应用预先设有的库存分配算法对调拨分配进行需求预测，根据需求预测实时优化当前仓库信息和其他仓库信息的物流协同，依据物流协同调整调拨分配；系统基于订单分流信息进行地域分析和订单分配，可以更准确地了解不同地区的需求情况和订单分布情况，通过预设的库存分配算法进行需求预测，可以预测未来一段时间内各地区的库存需求，为调拨分配提供准确的数据支持，同时根据需求预测实时优化仓库信息和其他仓库信息的物流协同，可以根据实际情况和需求变化调整物流流程和配送计划，通过实时优化物流协同，可以提高物流效率和配送准时性，降低物流成本，并且通过准确的需求预测和实时优化物流协同，可以更好地满足客户的订单需求，提高订单交付的及时性和准确性，从而提高客户满意度，增强客户忠诚度。In this embodiment, the acquisition module 10 acquires the corresponding storage parameters in the storage area data based on the pre-recorded storage area data, including the storage scale of a certain area and the storage location of a certain area, and then the judgment module 20 judges whether these storage parameters match the pre-set allocation conditions of the storage area data to execute the corresponding steps; for example, when the system determines that the storage parameters cannot match the pre-set allocation conditions of the storage area data, the system will consider that the storage parameter settings are inconsistent with the actual situation, or the pre-set allocation conditions cannot meet the current storage needs. The system will re-enter or correct the data to ensure the consistency of the storage parameters with the actual situation, check and correct the storage area data to ensure the accuracy and completeness of the data, and at the same time introduce automatic adjustment and manual intervention mechanisms to improve and optimize the allocation and distribution process, increase exception handling mechanisms and fault-tolerant mechanisms, and improve the stability of the system. and robustness, and establish a continuous monitoring and improvement mechanism, conduct regular inspections and assessments on system operation, promptly discover and solve problems, and continuously optimize system performance and efficiency; for example, when the system determines that the storage parameters can match the pre-set allocation conditions of the storage area data, the execution module 30 will consider that the storage parameter settings are consistent with the actual regional conditions, and the system will enter the current warehouse information corresponding to the storage parameters into a pre-set inventory management database, build cloud inventory content of the current warehouse information in the inventory management database, synchronize and share the cloud inventory content from the inventory management database in real time based on other pre-collected warehouse information, generate inventory distribution content of the current warehouse information and other warehouse information, establish a distribution strategy for the storage area data based on the inventory distribution content, apply the distribution strategy to coordinate the pre-set upstream and downstream supply chain of the storage area data for allocation and distribution, and the system synchronizes and shares the inventory information in real time. Entering the current warehouse information into the inventory management database and building cloud inventory content realizes real-time management and sharing of inventory, improves the efficiency and accuracy of inventory management, and at the same time, by generating the inventory distribution content of the current warehouse information and other warehouse information, the inventory status and distribution of each warehouse can be clearly understood, which is helpful to optimize the allocation strategy, reasonably allocate inventory resources, improve inventory utilization, and apply the distribution strategy to coordinate the upstream and downstream of the supply chain of the storage area data to allocate and distribute, realize the coordinated allocation of the supply chain, through effective supply chain collaboration, it can achieve rapid response of inventory and orders, and improve the flexibility and responsiveness of the supply chain; then the second judgment module 40 judges the allocation distribution Whether the allocation can achieve the preset distribution efficiency, the corresponding steps can be executed; for example, when the system determines that the preset distribution efficiency can be achieved during allocation and distribution, the system will believe that the allocation and distribution operation can meet the expected distribution requirements, ensuring the high efficiency and punctuality of logistics transportation. The system will reasonably arrange the allocation frequency and allocation quantity according to the actual situation and demand, ensure the high efficiency and punctuality of logistics transportation, ensure that the allocation strategy is reasonable and effective, and plan the time and route of allocation and distribution in advance to avoid temporary situations. By manually establishing or building reasonable plans and forecasts in advance, delays and congestion in logistics transportation can be avoided, and the warehouse layout can be reasonably optimized to reduce logistics. The distance and time of logistics transportation can be reduced by reasonably configuring the warehouse location and inventory level, thereby reducing the transportation time and cost during the allocation process; for example, when the system determines that the pre-set distribution efficiency cannot be achieved during allocation, the second execution module 50 will consider that the allocation operation cannot meet the expected distribution requirements, and the system will obtain the order diversion information corresponding to the storage area data, and use the pre-set inventory allocation algorithm based on the order diversion information to perform demand forecasting on the allocation, optimize the logistics coordination of the current warehouse information and other warehouse information in real time according to the demand forecast, and adjust the allocation according to the logistics coordination; the system performs regional analysis and order allocation based on the order diversion information, which can be more accurate By accurately understanding the demand situation and order distribution in different regions, and performing demand forecasting through the preset inventory allocation algorithm, it is possible to predict the inventory demand in various regions in the future, and provide accurate data support for allocation and distribution. At the same time, based on the demand forecast, the logistics coordination of warehouse information and other warehouse information is optimized in real time, and the logistics process and distribution plan can be adjusted according to the actual situation and demand changes. Through real-time optimization of logistics coordination, the logistics efficiency and delivery punctuality can be improved, and the logistics cost can be reduced. Moreover, through accurate demand forecasting and real-time optimization of logistics coordination, the customer's order needs can be better met, and the timeliness and accuracy of order delivery can be improved, thereby improving customer satisfaction and enhancing customer loyalty.
在本实施例中，执行模块还包括：In this embodiment, the execution module further includes:
应用单元，用于基于预定义的库存事件，应用预设的监听器对所述库存管理数据库的消息队列进行订阅，其中，所述库存事件具体包括库存变更、入库操作和出库操作；An application unit, configured to apply a preset listener to subscribe to a message queue of the inventory management database based on predefined inventory events, wherein the inventory events specifically include inventory changes, warehousing operations, and outbound operations;
判断单元，用于判断所述库存事件能否被所述监听器单独进行抽取；A judging unit, used for judging whether the inventory event can be extracted by the listener alone;
执行单元，用于若能，则触发所述监听器预设的执行流程，解析所述库存事件对应的事件信息，根据所述事件信息生成数据同步操作的日志内容，其中，所述事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库。The execution unit is used to trigger the execution process preset by the listener if possible, parse the event information corresponding to the inventory event, and generate log content of the data synchronization operation according to the event information, wherein the event information specifically includes reading inventory information, generating inventory notifications, and updating the cloud inventory database.
在本实施例中，系统基于预先定义有的库存事件，应用预先设有的监听器对库存管理数据库的库存消息队列进行订阅，库存事件具体包括库存变更、入库操作和出库操作，而后系统判断这些库存事件能否被监听器单独进行抽取，以执行对应的步骤；例如，当系统判定到某库存事件无法被监听器单独进行抽取时，则系统会认为该库存事件与其他事件存在复杂的关联性，无法简单地通过监听器单独进行抽取和处理，系统会建议管理人员使用更复杂的监听机制，能够同时监控多个条件或者组合条件的事件触发，这样可以确保系统能够及时捕捉到所有相关的事件，同时如果某些事件需要多个条件的组合才能触发，建议管理人员将这些条件进行合并，形成一个新的综合事件，然后监听器就可以监听这个综合事件，一旦触发就进行相应的处理；例如，当系统判定到某库存事件能够被监听器单独进行抽取，此时系统会认为该库存事件可以简单通过监听器进行处理，系统会触发监听器预先设有的执行流程，解析该库存事件对应的事件信息，事件信息具体包括读取库存信息、生成库存通知和更新云端库存数据库，根据事件信息生成数据同步操作的日志内容，在云端库存内容中生成；系统通过设置监听器来处理库存事件，系统能够自动地响应和处理各种库存变化，这种自动化库存管理可以大大减少人工干预的需要，提高库存管理的效率和准确性，同时当系统触发监听器预先设定的执行流程时，会实时解析库存事件并更新云端库存数据库，可以确保库存信息的及时更新和同步，使库存信息始终保持最新状态，并且在云端库存内容中生成更新后的库存信息，使所有系统或部门都能够访问到最新的库存信息，促进跨部门协同和业务流程的顺畅进行。In this embodiment, the system subscribes to the inventory message queue of the inventory management database based on pre-defined inventory events using a pre-set listener. The inventory events specifically include inventory changes, warehousing operations, and outbound operations. The system then determines whether these inventory events can be extracted by the listener alone to execute the corresponding steps. For example, when the system determines that a certain inventory event cannot be extracted by the listener alone, the system will believe that the inventory event has a complex correlation with other events and cannot be simply extracted and processed by the listener alone. The system will suggest that the manager use a more complex monitoring mechanism that can monitor the event triggering of multiple conditions or combined conditions at the same time. This ensures that the system can capture all related events in a timely manner. At the same time, if some events require a combination of multiple conditions to trigger, it is recommended that the manager merge these conditions to form a new comprehensive event. The listener can then monitor the comprehensive event and perform corresponding processing once it is triggered. For example, when the system determines that a certain inventory event It can be extracted separately by the listener. At this time, the system will think that the inventory event can be simply processed by the listener. The system will trigger the execution process pre-set by the listener to parse the event information corresponding to the inventory event. The event information specifically includes reading inventory information, generating inventory notifications and updating the cloud inventory database. The log content of the data synchronization operation is generated according to the event information and generated in the cloud inventory content; the system processes inventory events by setting up listeners. The system can automatically respond to and process various inventory changes. This automated inventory management can greatly reduce the need for manual intervention and improve the efficiency and accuracy of inventory management. At the same time, when the system triggers the execution process pre-set by the listener, it will parse the inventory event in real time and update the cloud inventory database, which can ensure the timely update and synchronization of inventory information, so that the inventory information is always kept up to date, and the updated inventory information is generated in the cloud inventory content, so that all systems or departments can access the latest inventory information, promote cross-departmental collaboration and smooth business processes.
在本实施例中，还包括：In this embodiment, it also includes:
生成模块，用于从所述仓储地区数据中获取预收录的历史配送数据，基于所述历史配送数据生成当前季节的库存需求预测信息，其中，所述历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据；A generating module, used to obtain pre-recorded historical distribution data from the storage area data, and generate inventory demand forecast information for the current season based on the historical distribution data, wherein the historical distribution data specifically includes seasonal data, promotion activity data and market trend data;
第三判断模块，用于判断所述库存需求预测信息是否符合当前季节对应的配送阶段，其中，所述配送阶段具体包括产品上市阶段、成熟阶段和衰退阶段；A third judgment module is used to judge whether the inventory demand forecast information meets the distribution stage corresponding to the current season, wherein the distribution stage specifically includes the product launch stage, the mature stage and the decline stage;
第三执行模块，用于若是，则应用预训练的时间序列模型对所述库存需求预测信息进行构建，根据所述配送阶段持续生成所述库存需求预测信息的预测结果，依据所述预测结果制定对所述仓储地区数据的配送策略。The third execution module is used to apply the pre-trained time series model to construct the inventory demand forecast information, continuously generate the forecast results of the inventory demand forecast information according to the distribution stage, and formulate the distribution strategy for the storage area data based on the forecast results.
在本实施例中，系统从仓储地区数据中获取预先收录有的历史配送数据，历史配送数据具体包括季节性数据、促销活动数据和市场趋势数据，基于这些历史配送数据生成当前季节的库存需求预测信息，而后系统判断该库存需求预测信息是否符合当前季节对应的配送阶段，以执行对应的步骤；例如，当系统判定到库存需求预测信息无法符合当前季节对应的配送阶段，则系统会认为预测的库存需求与当前季节或市场情况不一致，需要进行相应的调整和处理，系统需要重新评估季节性因素对库存需求的影响，可能是由于季节性因素的变化导致了需求预测的不准确，需要重新考虑季节性因素的影响程度，并相应调整预测模型或参数，同时根据实际情况和预测的库存需求，动态调整配送计划，即需要加大或减少配送量，调整配送频率，以适应当前季节的需求变化，并且根据实际需求和市场情况，调整库存水平和库存结构，需要增加某些季节性产品的库存，减少其他产品的库存，以适应当前季节的需求变化；例如，当系统判定到库存需求预测信息能够符合当前季节对应的配送阶段时，此时系统会认为预测的库存需求与当前季节或市场情况一致，系统会应用预先训练好的时间序列模型对库存需求预测信息进行构建，根据配送阶段持续生成库存需求预测信息的预测结果，依据各个预测结果制定对仓储地区数据的配送策略；系统通过应用预先训练好的时间序列模型对库存需求预测信息进行构建，可以实现对当前季节或市场情况的准确预测，这样可以更准确地了解未来的库存需求情况，为制定合理的配送策略提供可靠的数据支持，同时根据持续生成的库存需求预测信息的预测结果，系统可以及时制定配送策略，根据预测的库存需求量和地区分布情况，合理安排仓储地区数据的配送计划，确保库存能够及时满足市场需求，并且通过根据预测的库存需求量制定配送策略，可以优化库存管理，合理分配库存资源，避免库存积压或库存不足的情况发生，提高库存周转率和资金利用效率。In this embodiment, the system obtains pre-recorded historical distribution data from the storage area data. The historical distribution data specifically includes seasonal data, promotional activity data and market trend data. The inventory demand forecast information of the current season is generated based on these historical distribution data. Then the system determines whether the inventory demand forecast information meets the distribution stage corresponding to the current season to execute the corresponding steps; for example, when the system determines that the inventory demand forecast information cannot meet the distribution stage corresponding to the current season, the system will consider that the predicted inventory demand is inconsistent with the current season or market conditions, and corresponding adjustments and processing are required. The system needs to re-evaluate the impact of seasonal factors on inventory demand. It may be due to changes in seasonal factors that lead to inaccurate demand forecasts. It is necessary to reconsider the impact of seasonal factors and adjust the forecast model or parameters accordingly. At the same time, according to the actual situation and predicted inventory demand, the distribution plan is dynamically adjusted, that is, the distribution volume needs to be increased or reduced, and the distribution frequency needs to be adjusted to adapt to the demand changes in the current season. In addition, the inventory level and inventory structure need to be adjusted according to actual demand and market conditions. It is necessary to increase the inventory of certain seasonal products and reduce the inventory of other products to adapt to the current season. For example, when the system determines that the inventory demand forecast information can meet the distribution stage corresponding to the current season, the system will consider that the predicted inventory demand is consistent with the current season or market conditions, and the system will apply the pre-trained time series model to construct the inventory demand forecast information, and continuously generate the forecast results of the inventory demand forecast information according to the distribution stage, and formulate the distribution strategy for the storage area data according to each forecast result; the system can accurately predict the current season or market conditions by applying the pre-trained time series model to construct the inventory demand forecast information, so as to more accurately understand the future inventory demand situation and provide reliable data support for the formulation of reasonable distribution strategies. At the same time, according to the forecast results of the continuously generated inventory demand forecast information, the system can formulate distribution strategies in time, and reasonably arrange the distribution plan of the storage area data according to the predicted inventory demand and regional distribution, so as to ensure that the inventory can meet the market demand in time. In addition, by formulating distribution strategies according to the predicted inventory demand, it is possible to optimize inventory management, reasonably allocate inventory resources, avoid inventory backlogs or insufficient inventory, and improve inventory turnover and capital utilization efficiency.
在本实施例中，第三执行模块还包括：In this embodiment, the third execution module further includes:
划分单元，用于应用所述历史配送数据对预选取的训练模型进行探索性数据分析，基于所述探索性数据分析生成对应的数据集，将所述数据集根据预设比例划分为训练集和测试集，其中，所述探索性数据分析具体包括绘制时序图、绘制自相关图和绘制偏自相关图；A partitioning unit, used for applying the historical delivery data to perform exploratory data analysis on a pre-selected training model, generating a corresponding data set based on the exploratory data analysis, and partitioning the data set into a training set and a test set according to a preset ratio, wherein the exploratory data analysis specifically includes drawing a time series diagram, drawing an autocorrelation diagram, and drawing a partial autocorrelation diagram;
第二判断单元，用于判断所述训练模型能否通过所述训练集进行拟合；A second judgment unit, used to judge whether the training model can be fitted by the training set;
第二执行单元，用于若能，则从拟合过程中获取所述训练模型的参数估计值，采用所述测试集对所述训练模型进行指标评估，依据评估结果调整所述训练模型的超参数，得到训练完毕的时间序列模型，使用所述时间序列模型对预设时段的库存需求进行预测，以构建所述库存需求预测信息，其中，所述指标评估具体包括均方根误差、平均绝对误差和均方误差，所述超参数具体包括滞后阶数和季节性周期。The second execution unit is used to obtain the parameter estimation value of the training model from the fitting process if possible, use the test set to perform index evaluation on the training model, adjust the hyperparameters of the training model according to the evaluation results, obtain the trained time series model, and use the time series model to predict the inventory demand for a preset time period to construct the inventory demand forecast information, wherein the index evaluation specifically includes root mean square error, mean absolute error and mean square error, and the hyperparameters specifically include lag order and seasonal cycle.
在本实施例中，系统应用历史配送数据对预先选取的训练模型进行探索性数据分析，探索性数据分析具体包括绘制时序图、绘制自相关图和绘制偏自相关图，基于探索性数据分析后生成对应的数据集，将这些数据集根据预先设定的比例划分为训练集和测试集，而后系统判断训练模型能否通过训练集进行拟合，以执行对应的步骤；例如，当系统判定到训练模型无法通过训练集进行拟合时，则系统会认为数据之间的关系过于复杂，使得模型无法很好地捕捉到数据的规律，系统会进行特征工程，对数据进行处理和转换，提取更有意义的特征，以便更好地拟合模型，包括进行特征缩放、特征选择、特征组合等操作，以增强模型对数据的拟合能力，同时使用集成学习的方法，如随机森林、梯度提升树，集成学习通过结合多个弱学习器的预测结果，可以获得更强大的预测能力，对复杂关系的数据拟合效果更好，并且增加训练数据量，可能会有助于提高模型的拟合能力，更多的数据可以使模型更好地捕捉数据的分布和规律；例如，当系统判定到训练模型能够通过训练集进行拟合时，此时系统会认为模型能够捕捉到数据的规律，系统会从拟合过程中获取训练模型的参数估计值，采用测试集对训练模型进行指标评估，指标评估具体包括均方根误差、平均绝对误差和均方误差，依据评估结果调整训练模型的超参数，超参数具体包括滞后阶数和季节性周期，以此得到训练完毕的时间序列模型，使用该时间序列模型对预先设有时段的库存需求进行预测，构建库存需求预测信息；系统通过从拟合过程中获取训练模型的参数估计值，可以确保模型能够捕捉到数据的规律，对训练数据集有较好的拟合效果，这样可以提高模型的预测准确性，同时基于训练完毕的时间序列模型，系统可以对预先设定的时段的库存需求进行准确预测，构建库存需求预测信息，这样可以为电商企业提供及时、准确的库存需求预测，有助于制定合理的库存管理策略和供应链计划，并且通过指标评估结果，系统可以对训练模型的超参数进行调整，例如滞后阶数和季节性周期，有助于优化模型的性能，使其更好地适应数据的特征和变化规律，进而提高预测准确性；综上所述，通过训练完毕的时间序列模型进行库存需求预测，能够提高预测准确性，降低成本，提高效率，并最终提升客户满意度，对企业的运营和发展具有积极的促进作用，通过准确的库存需求预测有助于避免库存过剩或不足的情况发生，从而降低了库存成本和运营风险，合理的库存管理能够提高库存周转率和资金利用效率，进而提高企业的运营效率和竞争力。In this embodiment, the system applies historical delivery data to perform exploratory data analysis on a pre-selected training model. The exploratory data analysis specifically includes drawing a time series diagram, drawing an autocorrelation diagram, and drawing a partial autocorrelation diagram. Based on the exploratory data analysis, a corresponding data set is generated, and these data sets are divided into a training set and a test set according to a preset ratio. Then the system determines whether the training model can be fitted through the training set to execute the corresponding steps; for example, when the system determines that the training model cannot be fitted through the training set, the system will believe that the relationship between the data is too complex, so that the model cannot capture the laws of the data well. The system will perform feature engineering, process and transform the data, and extract more meaningful features to better fit the model, including performing feature engineering. Operations such as feature scaling, feature selection, and feature combination are used to enhance the model's ability to fit the data. At the same time, ensemble learning methods such as random forest and gradient boosting tree are used. Ensemble learning can obtain more powerful prediction capabilities by combining the prediction results of multiple weak learners, and has better fitting effects on data with complex relationships. Increasing the amount of training data may help improve the model's fitting ability. More data can enable the model to better capture the distribution and laws of the data. For example, when the system determines that the training model can be fitted through the training set, the system will believe that the model can capture the laws of the data. The system will obtain the parameter estimates of the training model from the fitting process, and use the test set to evaluate the training model. The indicator evaluation specifically includes root mean square error, average The mean absolute error and mean square error are calculated, and the hyperparameters of the training model are adjusted according to the evaluation results. The hyperparameters specifically include the lag order and the seasonal cycle, so as to obtain a trained time series model. The time series model is used to predict the inventory demand in a preset time period and construct inventory demand forecast information. The system obtains the parameter estimation value of the training model from the fitting process to ensure that the model can capture the regularity of the data and has a good fitting effect on the training data set, which can improve the prediction accuracy of the model. At the same time, based on the trained time series model, the system can accurately predict the inventory demand in a preset time period and construct inventory demand forecast information, which can provide e-commerce companies with timely and accurate inventory demand forecasts and help to formulate reasonable inventory demand forecasts. Inventory management strategies and supply chain plans, and through the indicator evaluation results, the system can adjust the hyperparameters of the training model, such as the lag order and seasonal cycle, which helps to optimize the performance of the model and make it better adapt to the characteristics and changing laws of the data, thereby improving the prediction accuracy; In summary, inventory demand forecasting through the trained time series model can improve prediction accuracy, reduce costs, improve efficiency, and ultimately improve customer satisfaction, which has a positive effect on the operation and development of the enterprise. Accurate inventory demand forecasting helps to avoid excess or insufficient inventory, thereby reducing inventory costs and operational risks. Reasonable inventory management can improve inventory turnover and capital utilization efficiency, thereby improving the operational efficiency and competitiveness of the enterprise.
在本实施例中，判断模块还包括：In this embodiment, the judging module further includes:
获取单元，用于获取所述仓储地区数据预设的调拨优先级；An acquisition unit, used for acquiring the preset allocation priority of the storage area data;
第三判断单元，用于判断所述调拨优先级是否匹配预设的调拨效益需求；A third judgment unit is used to judge whether the allocation priority matches the preset allocation benefit requirement;
第三执行单元，用于若否，则从所述仓储地区数据识别预收录的紧急调拨需求，采集所述仓储参数的库存周转率，基于所述紧急调拨需求将所述库存周转率作为所述调拨条件的最高优先级，其中，所述紧急调拨需求具体包括缺货客户订单、生产线停机和原料短缺。The third execution unit is used for, if not, identifying the pre-recorded emergency transfer needs from the storage area data, collecting the inventory turnover rate of the storage parameters, and taking the inventory turnover rate as the highest priority of the transfer conditions based on the emergency transfer needs, wherein the emergency transfer needs specifically include out-of-stock customer orders, production line shutdowns and raw material shortages.
在本实施例中，系统通过获取仓储地区数据预先设有的调拨优先级，而后系统判断该调拨优先级是否匹配预先设有的调拨效益需求，以执行对应的步骤；例如，当系统判定到仓储地区数据预先设有的调拨优先级能够匹配预先设有的调拨效益需求时，则系统会认为在调拨过程中，满足了供应链上下游的条件目标，包括成本最小化、库存平衡和服务水平提高，系统会根据预先设定的调拨优先级和效益需求，执行相应的调拨计划，确保按照优先级顺序对仓储地区数据进行调拨，以最大程度地满足调拨的效益要求，同时对执行的调拨计划进行监控和跟踪，确保调拨过程按照预期进行，及时发现和解决可能出现的问题，保证调拨的顺利进行，并且在调拨完成后，对调拨效果进行评估和分析。比较实际效果与预期效果，检验调拨是否达到了预期的效益要求，如果有必要，建议管理人员对调拨策略进行调整和优化；例如，当系统判定到仓储地区数据预先设有的调拨优先级无法匹配预先设有的调拨效益需求时，此时系统会认为在调拨过程中无法满足供应链上下游的条件目标，系统会从仓储地区数据识别预先收录的紧急调拨需求，紧急调拨需求具体包括缺货客户订单、生产线停机和原料短缺，采集仓储参数的库存周转率，基于紧急调拨需求将库存周转率作为调拨条件的最高优先级；系统通过将紧急调拨需求的识别能够有效地应对突发情况，如缺货客户订单、生产线停机和原料短缺等，将这些紧急需求作为调拨条件的最高优先级，系统可以及时响应，并确保关键业务运作不受影响，同时将库存周转率作为调拨条件的最高优先级，可以帮助优化库存管理，通过调拨高周转率的库存，可以提高库存周转率，降低库存积压和资金占用成本，提高资金利用效率，并且通过优先满足紧急调拨需求，可以保证供应链上下游的条件目标得到满足，有助于提高供应链的响应速度和效率，减少因供应链中断而导致的损失和影响。In this embodiment, the system obtains the pre-set allocation priority of the storage area data, and then the system determines whether the allocation priority matches the pre-set allocation benefit requirement to execute the corresponding steps; for example, when the system determines that the pre-set allocation priority of the storage area data can match the pre-set allocation benefit requirement, the system will believe that in the allocation process, the conditional goals of the upstream and downstream of the supply chain are met, including cost minimization, inventory balance and service level improvement. The system will execute the corresponding allocation plan according to the pre-set allocation priority and benefit requirement to ensure that the storage area data is allocated in order of priority to maximize the benefit requirements of the allocation. At the same time, the executed allocation plan is monitored and tracked to ensure that the allocation process proceeds as expected, and possible problems are discovered and resolved in a timely manner to ensure the smooth progress of the allocation. After the allocation is completed, the allocation effect is evaluated and analyzed. Compare the actual effect with the expected effect, check whether the allocation has achieved the expected benefit requirements, and if necessary, suggest that managers adjust and optimize the allocation strategy; for example, when the system determines that the allocation priority set in the storage area data cannot match the pre-set allocation benefit requirements, the system will believe that the upstream and downstream conditions of the supply chain cannot be met during the allocation process. The system will identify the pre-recorded emergency allocation needs from the storage area data. The emergency allocation needs specifically include out-of-stock customer orders, production line shutdowns and raw material shortages, collect the inventory turnover rate of the storage parameters, and use the inventory turnover rate as the highest priority for the allocation conditions based on the emergency allocation needs; the system will Identification of demand can effectively respond to emergencies, such as out-of-stock customer orders, production line downtime, and raw material shortages. By giving these urgent demands the highest priority in allocation conditions, the system can respond in a timely manner and ensure that key business operations are not affected. At the same time, giving inventory turnover rate the highest priority in allocation conditions can help optimize inventory management. By allocating high-turnover inventory, inventory turnover rate can be improved, inventory backlogs and capital occupation costs can be reduced, and capital utilization efficiency can be improved. By giving priority to meeting urgent allocation needs, it can ensure that the conditional targets of the upstream and downstream of the supply chain are met, which helps to improve the response speed and efficiency of the supply chain and reduce the losses and impacts caused by supply chain disruptions.
在本实施例中，第二判断模块还包括：In this embodiment, the second determination module further includes:
第二获取单元，用于获取所述仓储地区数据预设的配送时长；A second acquisition unit is used to acquire the delivery time preset in the storage area data;
第四判断单元，用于判断所述配送时长是否超出所述供应链上下游预设的配送时段；A fourth judgment unit, used to judge whether the delivery time exceeds the delivery time period preset by the upstream and downstream of the supply chain;
第四执行单元，用于若是，则从所述供应链上下游接收当次配送附带的运输成本数据，基于所述运输成本数据修正所述配送效率，将所述配送效率反馈至所述仓储地区数据，在所述仓储地区数据根据所述配送效率调整所述配送时长。The fourth execution unit is used to receive the transportation cost data associated with the current delivery from the upstream and downstream of the supply chain, correct the delivery efficiency based on the transportation cost data, feed back the delivery efficiency to the storage area data, and adjust the delivery time according to the delivery efficiency in the storage area data.
在本实施例中，系统获取仓储地区数据预先设有的配送时长，而后系统判断该配送时长是否超出供应链上下游预先设有的配送时段，以执行对应的步骤；例如，当系统判定到仓储地区数据预先设有的配送时长并未超出供应链上下游预设的配送时段，则系统会认为当前的配送时长在预设的范围内，符合供应链的预期，表明供应链的运作相对顺利，能够按时满足配送需求，系统会持续监控和优化配送流程。确保配送的效率和准时性，及时发现和解决可能出现的问题，反馈给相关管理人员，以提高供应链的运作效率，同时当配送时长在预设范围内时，可以评估供应链的稳定性和可靠性，通过分析配送数据和指标，评估供应链的运作情况，发现潜在的风险和瓶颈，并采取措施加以解决，确保供应链的稳定运行；例如，当系统判定到仓储地区数据预先设有的配送时长超出了供应链上下游预设的配送时段，此时系统会认为当前的配送时长不在预设的范围内，不符合供应链的预期，系统会从供应链上下游接收当次进行配送时附带的运输成本数据，基于这些运输成本数据修正配送效率，将配送效率反馈至仓储地区数据，在仓储地区数据根据配送效率对应重新调整原有配送时长，形成新的配送时长；系统通过接收当次配送时附带的运输成本数据，可以及时了解配送效率的实际情况，基于这些数据可以对配送效率进行修正，将修正后的结果反馈给仓储地区数据，这样可以实现配送时长的实时调整，以适应当前的运营状况和需求变化，同时修正配送效率有助于提高运营效率，通过及时调整配送时长，可以优化配送流程，减少不必要的等待和延误时间，提高配送效率和准时性，降低运输成本，从而提高整体运营效率，并且及时调整配送时长有助于优化客户服务，确保配送时长符合客户期望和要求，提高配送的准时性和可靠性，增强客户对服务的信任和满意度，提升客户忠诚度，促进业务的持续发展。In this embodiment, the system obtains the delivery time preset in the storage area data, and then the system determines whether the delivery time exceeds the delivery time period preset in the upstream and downstream of the supply chain to execute the corresponding steps; for example, when the system determines that the delivery time preset in the storage area data does not exceed the delivery time period preset in the upstream and downstream of the supply chain, the system will consider that the current delivery time is within the preset range and meets the expectations of the supply chain, indicating that the operation of the supply chain is relatively smooth and can meet the delivery needs on time, and the system will continue to monitor and optimize the delivery process. Ensure the efficiency and punctuality of distribution, discover and solve possible problems in time, and feedback to relevant managers to improve the operating efficiency of the supply chain. At the same time, when the delivery time is within the preset range, the stability and reliability of the supply chain can be evaluated. By analyzing the distribution data and indicators, the operation of the supply chain can be evaluated, potential risks and bottlenecks can be discovered, and measures can be taken to solve them to ensure the stable operation of the supply chain. For example, when the system determines that the delivery time preset in the warehousing area data exceeds the preset delivery time period of the upstream and downstream of the supply chain, the system will consider that the current delivery time is not within the preset range and does not meet the expectations of the supply chain. The system will receive the transportation cost data attached to the distribution from the upstream and downstream of the supply chain, and correct the distribution efficiency based on these transportation cost data, and feed the distribution efficiency back to the warehousing area data. The warehousing area data will be re-adjusted according to the distribution efficiency. The original delivery time is newly adjusted to form a new delivery time; the system can timely understand the actual situation of delivery efficiency by receiving the transportation cost data attached to the current delivery, and can correct the delivery efficiency based on these data, and feed the corrected results back to the warehousing area data, so that real-time adjustment of delivery time can be achieved to adapt to the current operating conditions and demand changes. At the same time, correcting the delivery efficiency helps to improve operational efficiency. By adjusting the delivery time in time, the delivery process can be optimized, unnecessary waiting and delay time can be reduced, delivery efficiency and punctuality can be improved, and transportation costs can be reduced, thereby improving overall operational efficiency. In addition, timely adjustment of delivery time helps to optimize customer service, ensure that the delivery time meets customer expectations and requirements, improve the punctuality and reliability of delivery, enhance customer trust and satisfaction with the service, improve customer loyalty, and promote the sustainable development of the business.
在本实施例中，获取模块还包括：In this embodiment, the acquisition module further includes:
识别单元，用于识别所述仓储参数预设的库存数据格式；An identification unit, used to identify the inventory data format preset by the storage parameters;
第五判断单元，用于判断所述库存数据格式能否适配于所述仓储地区数据；A fifth judgment unit, used for judging whether the inventory data format is adaptable to the storage area data;
第五执行单元，用于若否，则对所述库存数据格式进行预设的数据清洗，基于所述仓储地区数据对所述库存数据格式的字段统一规范化，其中，所述数据清洗具体包括去除符号、缺失填补和类型转换。The fifth execution unit is used for, if not, performing preset data cleaning on the inventory data format, and standardizing the fields of the inventory data format based on the storage area data, wherein the data cleaning specifically includes removing symbols, filling in missing items, and converting types.
在本实施例中，系统通过识别仓储参数预先设有的库存数据格式，而后判断库存数据格式是否适配于仓储地区数据，以执行对应的步骤；例如，当系统判定到库存数据格式能够适配于仓储地区数据时，则系统会认为库存数据的格式与仓储地区数据的格式相匹配，能够无缝地整合和处理，系统会将符合要求的库存数据导入到仓储地区数据中，包括将库存数据存储在统一的数据库或数据仓库中，或者将库存数据直接与仓储地区数据进行集成，同时将库存数据与仓储地区数据进行集成，以便进行进一步的分析和处理，包括数据表的关联和字段的映射操作，确保数据的一致性和完整性，并且根据集成后的数据进行相关的分析、报告或决策，利用仓储地区数据和库存数据的集成，可以为企业提供更全面、准确的信息支持，帮助企业更好地理解和管理库存和仓储活动；例如，当系统判定到库存数据格式无法适配于仓储地区数据时，此时系统会认为库存数据的格式与仓储地区数据的格式不匹配，系统会对库存数据格式进行预先设有的数据清洗操作，数据清洗具体包括去除符号、缺失填补和类型转换，基于仓储地区数据对库存数据格式的字段统一规范化；系统通过对库存数据格式进行预先设定的数据清洗操作，可以使库存数据的格式与仓储地区数据的格式保持一致，确保数据的一致性，有助于简化数据处理和分析过程，避免因数据格式不一致而导致的错误或混乱，同时统一规范化库存数据格式的字段，使其与仓储地区数据的格式保持一致，有助于提高数据的可用性，这样可以更轻松地进行数据整合和分析，提高数据的可用性和可操作性，为企业的决策和业务提供更可靠的支持，并且通过清洗和规范化库存数据格式，可以降低数据处理和分析的复杂度，提高数据分析的效率，清洗后的数据更易于理解和操作，可以更快地进行数据分析和挖掘，加快决策的速度和精度。In this embodiment, the system identifies the inventory data format pre-set by the warehousing parameters, and then determines whether the inventory data format is compatible with the storage area data to execute the corresponding steps; for example, when the system determines that the inventory data format is compatible with the storage area data, the system will consider that the format of the inventory data matches the format of the storage area data and can be seamlessly integrated and processed. The system will import the inventory data that meets the requirements into the storage area data, including storing the inventory data in a unified database or data warehouse, or directly integrating the inventory data with the storage area data, and integrating the inventory data with the storage area data at the same time for further analysis and processing, including data table association and field mapping operations to ensure data consistency and integrity, and perform relevant analysis, reporting or decision-making based on the integrated data. The integration of storage area data and inventory data can provide enterprises with more comprehensive and accurate information support, helping enterprises to better understand and manage inventory and warehousing activities; for example, when the system determines that the inventory data format is not compatible with the storage area data, the system The system will consider that the format of the inventory data does not match the format of the storage area data, and will perform pre-set data cleaning operations on the inventory data format. Data cleaning specifically includes removing symbols, filling in missing data, and type conversion, and standardizing the fields of the inventory data format based on the storage area data. By performing pre-set data cleaning operations on the inventory data format, the system can make the format of the inventory data consistent with the format of the storage area data, ensure data consistency, and help simplify the data processing and analysis process, avoid errors or confusion caused by inconsistent data formats, and at the same time, standardize the fields of the inventory data format to make it consistent with the format of the storage area data, which helps to improve data availability, so that data integration and analysis can be carried out more easily, improve data availability and operability, and provide more reliable support for corporate decision-making and business. In addition, by cleaning and standardizing the inventory data format, the complexity of data processing and analysis can be reduced, and the efficiency of data analysis can be improved. The cleaned data is easier to understand and operate, and data analysis and mining can be carried out faster, which speeds up the speed and accuracy of decision-making.
尽管已经示出和描述了本发明的实施例，对于本领域的普通技术人员而言，可以理解在不脱离本发明的原理和精神的情况下可以对这些实施例进行多种变化、修改、替换和变型，本发明的范围由所附权利要求及其等同物限定。Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410383553.3ACN117974013A (en) | 2024-04-01 | 2024-04-01 | A monitoring method and system for e-commerce warehouse inventory management |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410383553.3ACN117974013A (en) | 2024-04-01 | 2024-04-01 | A monitoring method and system for e-commerce warehouse inventory management |
| Publication Number | Publication Date |
|---|---|
| CN117974013Atrue CN117974013A (en) | 2024-05-03 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410383553.3APendingCN117974013A (en) | 2024-04-01 | 2024-04-01 | A monitoring method and system for e-commerce warehouse inventory management |
| Country | Link |
|---|---|
| CN (1) | CN117974013A (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118966970A (en)* | 2024-08-13 | 2024-11-15 | 上海斐驼网络科技有限公司 | AI-driven self-service warehouse and distribution dynamic management method and platform |
| CN119168536A (en)* | 2024-09-02 | 2024-12-20 | 无锡商业职业技术学院 | Intelligent food inventory management method and system |
| CN119250706A (en)* | 2024-12-05 | 2025-01-03 | 湖南富丽真金家纺有限公司 | Intelligent management method and system for textile warehouse based on popular element information monitoring |
| CN119539920A (en)* | 2025-01-21 | 2025-02-28 | 君世立信科技集团有限公司 | E-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001082135A1 (en)* | 2000-04-21 | 2001-11-01 | Science Applications International Corporation | System and method of supply chain management |
| CN112488612A (en)* | 2020-11-26 | 2021-03-12 | 国网河北省电力有限公司物资分公司 | Visualization-based full inventory resource monitoring and displaying method |
| CN114022085A (en)* | 2022-01-05 | 2022-02-08 | 深圳市思迅网络科技有限公司 | Allocation method, system, equipment and storage medium based on inventory data |
| CN114266395A (en)* | 2021-12-22 | 2022-04-01 | 四川省烟草公司成都市公司 | Cigarette logistics distribution center information system based on combined prediction method |
| CN116993275A (en)* | 2023-07-31 | 2023-11-03 | 诚天国际供应链(深圳)有限公司 | Visual management control system of warehouse |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001082135A1 (en)* | 2000-04-21 | 2001-11-01 | Science Applications International Corporation | System and method of supply chain management |
| CN112488612A (en)* | 2020-11-26 | 2021-03-12 | 国网河北省电力有限公司物资分公司 | Visualization-based full inventory resource monitoring and displaying method |
| CN114266395A (en)* | 2021-12-22 | 2022-04-01 | 四川省烟草公司成都市公司 | Cigarette logistics distribution center information system based on combined prediction method |
| CN114022085A (en)* | 2022-01-05 | 2022-02-08 | 深圳市思迅网络科技有限公司 | Allocation method, system, equipment and storage medium based on inventory data |
| CN116993275A (en)* | 2023-07-31 | 2023-11-03 | 诚天国际供应链(深圳)有限公司 | Visual management control system of warehouse |
| Title |
|---|
| 张程著: "分布式系统架构", 31 May 2020, 机械工业出版社, pages: 234 - 235* |
| 陈栋著: "物流与供应链管理智慧化发展探索", 30 June 2021, 吉林科学技术出版社, pages: 183 - 185* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118966970A (en)* | 2024-08-13 | 2024-11-15 | 上海斐驼网络科技有限公司 | AI-driven self-service warehouse and distribution dynamic management method and platform |
| CN119168536A (en)* | 2024-09-02 | 2024-12-20 | 无锡商业职业技术学院 | Intelligent food inventory management method and system |
| CN119250706A (en)* | 2024-12-05 | 2025-01-03 | 湖南富丽真金家纺有限公司 | Intelligent management method and system for textile warehouse based on popular element information monitoring |
| CN119250706B (en)* | 2024-12-05 | 2025-04-04 | 湖南富丽真金家纺有限公司 | Intelligent management method and system for textile warehouse based on popular element information monitoring |
| CN119539920A (en)* | 2025-01-21 | 2025-02-28 | 君世立信科技集团有限公司 | E-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis |
| CN119539920B (en)* | 2025-01-21 | 2025-04-04 | 君世立信科技集团有限公司 | E-commerce intelligent operation monitoring and collaborative decision-making method based on big data analysis |
| Publication | Publication Date | Title |
|---|---|---|
| CN117974013A (en) | A monitoring method and system for e-commerce warehouse inventory management | |
| CN117114583B (en) | Supply chain management system based on cloud service platform | |
| US10896203B2 (en) | Digital analytics system | |
| US9269062B2 (en) | Methods for optimizing energy consumption and devices thereof | |
| US12223401B2 (en) | Integrating machine-learning models impacting different factor groups for dynamic recommendations to optimize a parameter | |
| CN119094335B (en) | A method for automatically configuring data center resources | |
| CN116258337A (en) | Industry chain collaborative management system based on enterprise manufacturing operation | |
| CN119476871B (en) | Intelligent scheduling plan scheduling collaborative optimization system | |
| CN118822227B (en) | Intelligent production management system for factory production | |
| CN117876059A (en) | Method, device, equipment and medium for online order management | |
| CN119323333A (en) | Supply chain multiparty sharing management system based on digital transaction platform | |
| CN120258481A (en) | A method and system for enterprise digital management based on data mining | |
| CN119250740A (en) | Hierarchical collaborative management and early warning method, device, equipment and medium based on supply chain integration | |
| US20130041712A1 (en) | Emerging risk identification process and tool | |
| CN118691309A (en) | Logistics electronic waybill price monitoring management method, device, equipment and storage medium | |
| CN116167543B (en) | Measurement asset supply command platform system | |
| CN118863979A (en) | Intelligent forecasting inventory analysis method and system | |
| US20140297831A1 (en) | Continuous improvement of global service delivery augmented with social network analysis | |
| US20240177020A1 (en) | Method for dynamically recommending forecast adjustments that collectively optimize objective factor using automated ml systems | |
| KR20110026339A (en) | Logistics Operation Planning System and Method | |
| US20130060588A1 (en) | Modeling and monitoring a relationship with a client and assessing the quality of the relationship | |
| Budde et al. | Smart Factory Framework | |
| Chu et al. | Enhancing the customer service experience in call centers using preemptive solutions and queuing theory | |
| Dong et al. | Unraveling When and Why Couriers Wait: Empirical Analysis from Structural Breakpoint Detection | |
| CN119338158A (en) | Intelligent carton production management system and method |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication | Application publication date:20240503 |