技术领域Technical Field
本发明涉及数据可视化领域，尤其涉及一种用于企业平台的业务数据可视化方法及系统。The present invention relates to the field of data visualization, and in particular to a business data visualization method and system for an enterprise platform.
背景技术Background Art
在当今瞬息万变的商业环境中，企业需要及时获取和分析自身的业务数据，以支撑战略决策和提升运营效率，然而，企业通常会积累大量的业务数据，包括销售、采购、财务、生产等多个领域，这些数据往往分散在不同的信息系统中，数据格式和结构各异，给企业数据整合和分析带来了挑战，传统的数据分析方法依赖于Excel等工具进行手动处理和制作报表，存在着分析效率低、可视化效果欠佳的问题，随着大数据和人工智能技术的发展，市面上出现了许多业务数据可视化的解决方案，通过将复杂的数据通过图表、仪表盘等直观的方式展示出来，帮助企业管理者更好地洞察业务现状，发现潜在问题，然而，现有的通用型数据可视化工具往往需要企业自行进行二次开发和定制，才能满足特定行业和业务场景的需求，这不仅增加了企业的IT投入，还要求具有一定数据分析和可视化开发经验的人员参与，对中小企业而言存在较高的使用门槛，因此，急需一种针对企业需求的业务数据可视化方法，能够无缝对接企业内部的各类业务系统，自动完成数据抽取、整合和可视化呈现，降低企业IT投入和人力成本，同时具备灵活的自定义分析功能，以满足不同企业在战略规划、运营管控等方面的实际需求。In today's ever-changing business environment, enterprises need to obtain and analyze their own business data in a timely manner to support strategic decision-making and improve operational efficiency. However, enterprises usually accumulate a large amount of business data, including sales, procurement, finance, production and other fields. These data are often scattered in different information systems, with different data formats and structures, which brings challenges to enterprise data integration and analysis. Traditional data analysis methods rely on tools such as Excel for manual processing and report making, which has problems of low analysis efficiency and poor visualization effect. With the development of big data and artificial intelligence technology, many business data visualization solutions have appeared on the market, which can display complex data in intuitive ways such as charts and dashboards. Help enterprise managers better understand the current business situation and discover potential problems. However, existing general-purpose data visualization tools often require enterprises to conduct secondary development and customization to meet the needs of specific industries and business scenarios. This not only increases the company's IT investment, but also requires the participation of personnel with certain data analysis and visualization development experience. There is a high threshold for use for small and medium-sized enterprises. Therefore, there is an urgent need for a business data visualization method tailored to enterprise needs, which can seamlessly connect to various business systems within the enterprise, automatically complete data extraction, integration and visualization, reduce enterprise IT investment and labor costs, and at the same time have flexible custom analysis functions to meet the actual needs of different enterprises in strategic planning, operation control and other aspects.
发明内容Summary of the invention
本发明为解决上述技术问题，提出了一种用于企业平台的业务数据可视化方法及系统，以解决至少一个上述技术问题In order to solve the above technical problems, the present invention proposes a business data visualization method and system for an enterprise platform to solve at least one of the above technical problems.
为实现上述目的，本发明提供一种用于企业平台的业务数据可视化方法，包括以下步骤：To achieve the above object, the present invention provides a business data visualization method for an enterprise platform, comprising the following steps:
步骤S1：基于企业平台获取历史业务日志；对历史业务日志进行全局语义图谱构建处理，得到语义特征知识图谱；Step S1: Obtain historical business logs based on the enterprise platform; construct and process the global semantic graph of the historical business logs to obtain a semantic feature knowledge graph;
步骤S2：基于语义特征知识图谱对历史业务日志进行多维度异常检测，以生成离群点差异值；基于离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志；Step S2: Perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to generate outlier difference values; perform outlier correction processing on the historical business logs based on the outlier difference values to obtain outlier corrected business logs;
步骤S3：对离群修正业务日志进行业务交互挖掘，得到业务交互线；对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络；Step S3: Perform business interaction mining on the outlier-corrected business logs to obtain business interaction lines; perform topological association fitting on the business interaction lines to construct a business topological association network;
步骤S4：基于离群修正业务日志以得到各个交互线的业务属性；利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络；Step S4: correcting the service log based on the outliers to obtain the service attributes of each interaction line; using the service attributes of each interaction line to perform attribute space mapping on the service topology association network to obtain an attribute mapping topology network;
步骤S5：对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；对动态拓扑演变网络进行路径演变关联强弱变化分析，从而得到拓扑关联强弱变化数据；Step S5: simulate the dynamic topology evolution of the attribute mapping topology network to obtain the dynamic topology evolution network; analyze the path evolution correlation strength change of the dynamic topology evolution network to obtain the topology correlation strength change data;
步骤S6：基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型。Step S6: Perform three-dimensional visualization processing on the dynamic topology evolution network based on the topology association strength change data, and construct a three-dimensional visualization business evolution model.
本发明通过基于企业平台获取历史业务日志，获得丰富的业务数据，对历史业务日志进行全局语义图谱构建处理，将业务数据中的关键信息提取出来，形成语义特征知识图谱，语义特征知识图谱包含了业务数据的语义关联和特征信息，更好地理解和分析业务数据，基于语义特征知识图谱对历史业务日志进行多维度异常检测，识别出与正常业务行为不符的异常点，生成离群点差异值，这些值表示了每个数据点与正常业务行为的偏差程度，用于量化异常程度，对历史业务日志进行离群点修正处理，通过修正离群点提高数据的准确性和可靠性，对离群修正业务日志进行业务交互挖掘，发现业务数据中的交互行为，得到业务交互线，这些线表示了业务数据中不同实体之间的交互关系，对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络，以描述业务数据中实体之间的拓扑结构和关联关系，基于离群修正业务日志以得到各个交互线的业务属性，从业务数据中提取出与交互线相关的特征和属性信息，利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，将业务属性映射到拓扑网络中的节点和边上，得到属性映射拓扑网络，这个网络将业务数据的属性信息与拓扑结构相结合，提供了更丰富的业务数据表示形式，对属性映射拓扑网络进行动态拓扑演变模拟，模拟业务数据中的拓扑结构随时间的演变过程，得到动态拓扑演变网络，这个网络反映了业务数据中拓扑关系的动态变化情况，对动态拓扑演变网络进行路径演变关联强弱变化分析，量化拓扑关联强度和变化程度，得到拓扑关联强弱变化数据，基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，将业务数据以可视化的形式展示出来，构建三维可视化业务演变模型，通过可视化技术将业务数据的演变过程呈现给用户，三维可视化业务演变模型帮助用户更直观地理解和分析业务数据的演变趋势和关联关系。The present invention obtains rich business data by acquiring historical business logs based on an enterprise platform, constructs a global semantic graph for the historical business logs, extracts key information from the business data, and forms a semantic feature knowledge graph. The semantic feature knowledge graph contains the semantic association and feature information of the business data, so as to better understand and analyze the business data. The present invention performs multi-dimensional anomaly detection on the historical business logs based on the semantic feature knowledge graph, identifies anomalies that are inconsistent with normal business behaviors, generates outlier difference values, which represent the degree of deviation of each data point from normal business behaviors and are used to quantify the degree of anomalies. The present invention performs outlier correction processing on the historical business logs, improves the accuracy and reliability of the data by correcting the outliers, performs business interaction mining on the outlier-corrected business logs, discovers the interactive behaviors in the business data, and obtains business interaction lines, which represent the interactive relationships between different entities in the business data. The business interaction lines are topologically associated and fitted, and a business topological association network is constructed to describe the topological structure and association relationships between entities in the business data. The business logs are corrected based on the outliers to obtain the business attributes of each interaction line. The characteristics and attribute information related to the interaction lines are extracted from the business data, and the business attributes of each interaction line are used to perform attribute space mapping on the business topology association network. The business attributes are mapped to the nodes and edges in the topology network to obtain the attribute mapping topology network. This network combines the attribute information of the business data with the topological structure, providing a richer form of business data representation. The dynamic topological evolution simulation is performed on the attribute mapping topological network to simulate the evolution of the topological structure in the business data over time to obtain the dynamic topological evolution network. This network reflects the dynamic changes in the topological relationship in the business data. The path evolution association strength change analysis is performed on the dynamic topological evolution network to quantify the topological association strength and degree of change to obtain the topological association strength change data. Based on the topological association strength change data, the dynamic topological evolution network is visualized in three dimensions to display the business data in a visualized form, and a three-dimensional visualized business evolution model is constructed. The evolution process of the business data is presented to the user through visualization technology. The three-dimensional visualized business evolution model helps users to more intuitively understand and analyze the evolution trend and association relationship of business data.
优选地，步骤S1包括以下步骤：Preferably, step S1 comprises the following steps:
步骤S11：基于企业平台获取历史业务日志；Step S11: Obtaining historical business logs based on the enterprise platform;
步骤S12：对历史业务日志进行日志数据结构化处理，得到结构化字段；Step S12: Performing log data structuring processing on the historical business log to obtain structured fields;
步骤S13：对结构化字段进行全局上下文语义分析，从而生成全局上下文语义特征；Step S13: performing global context semantic analysis on the structured field to generate global context semantic features;
步骤S14：对全局上下文语义特征进行深度学习建模，构建语义特征知识图谱。Step S14: Perform deep learning modeling on the global context semantic features and construct a semantic feature knowledge graph.
本发明通过基于企业平台获取历史业务日志，获取到大量的原始业务数据，历史业务日志记录了过去的业务活动和事件，包含了丰富的信息和细节，对历史业务日志进行日志数据结构化处理，将原始的文本日志转换为结构化字段，结构化字段包含了从日志中提取出来的关键信息，例如时间戳、用户ID、操作类型、关联实体等，结构化字段的生成使得业务数据更易于处理和分析，为后续步骤提供了基础数据，对结构化字段进行全局上下文语义分析，通过处理字段之间的关联和上下文信息，提取出全局上下文语义特征，全局上下文语义特征包含了字段之间的语义关联和上下文信息，能够更好地捕捉业务数据的语义意义和关系，对全局上下文语义特征进行深度学习建模，利用深度学习算法构建语义特征知识图谱，语义特征知识图谱将业务数据的语义特征和关联关系进行建模和表示，形成一个结构化的图谱，语义特征知识图谱提供了更高层次的语义理解和表达能力，能够更全面地描述和分析业务数据的语义特征。The present invention obtains a large amount of original business data by obtaining historical business logs based on an enterprise platform. The historical business logs record past business activities and events and contain rich information and details. The log data is structured and processed to convert the original text logs into structured fields. The structured fields contain key information extracted from the logs, such as timestamps, user IDs, operation types, associated entities, etc. The generation of structured fields makes business data easier to process and analyze, and provides basic data for subsequent steps. Global context semantic analysis is performed on the structured fields, and global context semantic features are extracted by processing the associations and context information between the fields. The global context semantic features contain the semantic associations and context information between the fields, and can better capture the semantic meanings and relationships of the business data. Deep learning modeling is performed on the global context semantic features, and a semantic feature knowledge graph is constructed using a deep learning algorithm. The semantic feature knowledge graph models and represents the semantic features and associations of the business data to form a structured graph. The semantic feature knowledge graph provides a higher level of semantic understanding and expression capabilities, and can more comprehensively describe and analyze the semantic features of the business data.
优选地，步骤S13的具体步骤为：Preferably, the specific steps of step S13 are:
对历史业务日志进行时序解析，生成业务日志序列；Perform time series analysis on historical business logs to generate business log sequences;
对结构化字段进行空值率计算，得到字段空值率；Calculate the null value rate of the structured field to obtain the field null value rate;
基于字段空值率对业务日志序列进行空值字段频率分布分析，生成空值字段频率分布数据；Perform frequency distribution analysis of null value fields on business log sequences based on field null value rates to generate frequency distribution data of null value fields;
基于空值字段频率分布数据对业务日志序列进行语义特征解析，以得到日志语义特征；Based on the frequency distribution data of null value fields, semantic feature analysis is performed on the business log sequence to obtain log semantic features;
对日志语义特征进行相邻语义特征条件概率转移计算，生成条件概率转移数据矩阵；Calculate the conditional probability transfer of adjacent semantic features on the log semantic features to generate a conditional probability transfer data matrix;
对条件概率转移数据矩阵进行语义特征关联分析，得到邻间语义特征依赖关系；Perform semantic feature association analysis on the conditional probability transfer data matrix to obtain the dependency relationship between neighboring semantic features;
根据邻间语义特征依赖关系对业务日志序列进行全局上下文语义挖掘，从而生成全局上下文语义特征。The global context semantics mining is performed on the business log sequence according to the dependency relationship between the neighboring semantic features to generate the global context semantic features.
本发明通过对历史业务日志进行时序解析，生成业务日志序列，按照时间顺序排列的日志记录，业务日志序列反映了业务活动的时间流程和演变，提供了对业务过程的时序分析能力，对结构化字段进行空值率计算，确定每个字段中的数据缺失情况，字段空值率表示字段中空值（缺失值）的比例，帮助了解数据的完整性和可用性，基于字段空值率，对业务日志序列进行空值字段频率分布分析，获得不同字段空值的分布情况，空值字段频率分布数据展示了每个字段空值的频率和分布情况，帮助发现数据缺失的模式和趋势，基于空值字段频率分布数据，对业务日志序列进行语义特征解析，以获取日志的语义特征，日志语义特征反映了业务数据中的潜在语义信息，帮助理解业务数据的内在含义和关联关系，对日志语义特征进行相邻语义特征条件概率转移计算，生成条件概率转移数据矩阵，条件概率转移数据矩阵展示了相邻语义特征之间的条件概率关系，揭示了语义特征之间的转移模式和依赖关系，对条件概率转移数据矩阵进行语义特征关联分析，发现邻间语义特征之间的依赖关系，邻间语义特征依赖关系揭示了业务数据中语义特征之间的相互作用和关联程度，帮助理解业务数据的语义结构，根据邻间语义特征依赖关系，对业务日志序列进行全局上下文语义挖掘，从而生成全局上下文语义特征，全局上下文语义特征融合了业务数据中不同语义特征之间的依赖关系，提供了更全面和综合的语义理解能力。The present invention generates a business log sequence by performing time series analysis on historical business logs. The log records are arranged in chronological order. The business log sequence reflects the time flow and evolution of business activities, provides time series analysis capabilities for business processes, calculates the null value rate of structured fields, and determines the data missing situation in each field. The field null value rate represents the proportion of null values (missing values) in the field, which helps to understand the integrity and availability of the data. Based on the field null value rate, the business log sequence is subjected to a frequency distribution analysis of null value fields to obtain the distribution of null values in different fields. The frequency distribution data of null value fields shows the frequency and distribution of null values in each field, which helps to discover patterns and trends of data missing. Based on the frequency distribution data of null value fields, the business log sequence is subjected to semantic feature analysis to obtain the semantic features of the logs. The log semantic features reflect the potential semantics in the business data. Information helps understand the intrinsic meaning and correlation of business data, calculates the conditional probability transfer of adjacent semantic features of log semantic features, and generates a conditional probability transfer data matrix. The conditional probability transfer data matrix shows the conditional probability relationship between adjacent semantic features, and reveals the transfer mode and dependency relationship between semantic features. Semantic feature association analysis is performed on the conditional probability transfer data matrix to find the dependency relationship between adjacent semantic features. The dependency relationship between adjacent semantic features reveals the interaction and correlation degree between semantic features in business data, which helps understand the semantic structure of business data. Based on the dependency relationship between adjacent semantic features, global context semantic mining is performed on business log sequences to generate global context semantic features. Global context semantic features integrate the dependency relationship between different semantic features in business data, and provide a more comprehensive and integrated semantic understanding capability.
优选地，步骤S2的具体步骤为：Preferably, the specific steps of step S2 are:
步骤S21：基于语义特征知识图谱对历史业务日志进行多维度异常检测，得到异常离群点；Step S21: Perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to obtain abnormal outliers;
步骤S22：对异常离群点进行主体差异量化，以生成离群点差异值；Step S22: quantifying the main difference of the abnormal outliers to generate outlier difference values;
步骤S23：对历史业务日志进行主体数据分布特征分析，得到主体数据分布特征；Step S23: Analyze the main data distribution characteristics of the historical business logs to obtain the main data distribution characteristics;
步骤S24：基于主体数据分布特征对离群点差异值进行异常校正阈值计算，得到异常校正范围；Step S24: Calculate the abnormality correction threshold value for the outlier difference value based on the main data distribution characteristics to obtain the abnormality correction range;
步骤S25：基于异常校正数据对历史业务日志进行离群点修正处理，得到离群修正业务日志。Step S25: Perform outlier correction processing on the historical business log based on the abnormal correction data to obtain an outlier corrected business log.
本发明通过基于语义特征知识图谱对历史业务日志进行多维度异常检测，检测出在多个语义特征维度上存在异常的离群点，异常离群点表示与正常业务行为明显不同的数据记录，包含错误、异常活动或潜在的问题，对异常离群点进行主体差异量化，生成离群点差异值，离群点差异值反映了异常离群点与正常数据之间的差异程度，帮助衡量异常程度的大小，对历史业务日志进行主体数据分布特征分析，获取主体数据在各个维度上的分布情况，主体数据分布特征描述了业务数据在不同维度上的数据分布模式，提供了对正常数据行为的基准理解，基于主体数据分布特征，对离群点差异值进行异常校正阈值计算，确定异常校正的范围，异常校正阈值指定了在特定的主体数据分布下，离群点差异值被认为是异常的范围，用于判断哪些离群点需要进行修正，基于异常校正数据，对历史业务日志进行离群点修正处理，得到离群修正后的业务日志，离群修正业务日志是对原始离群点数据进行修正，使其更接近正常数据分布，提高数据的准确性和可靠性。The present invention performs multi-dimensional anomaly detection on historical business logs based on semantic feature knowledge graphs, detects abnormal outliers in multiple semantic feature dimensions, and detects abnormal outliers. Abnormal outliers represent data records that are significantly different from normal business behaviors, including errors, abnormal activities or potential problems. The main differences of abnormal outliers are quantified to generate outlier difference values. The outlier difference values reflect the degree of difference between abnormal outliers and normal data, and help measure the degree of abnormality. The main data distribution characteristics of historical business logs are analyzed to obtain the distribution of main data in various dimensions. The main data distribution characteristics describe the data distribution patterns of business data in different dimensions, and provide a benchmark understanding of normal data behavior. Based on the main data distribution characteristics, an abnormal correction threshold is calculated for the outlier difference value to determine the range of abnormal correction. The abnormal correction threshold specifies the range in which the outlier difference value is considered to be abnormal under a specific main data distribution, and is used to determine which outliers need to be corrected. Based on the abnormal correction data, outlier correction processing is performed on the historical business log to obtain an outlier-corrected business log. The outlier-corrected business log corrects the original outlier data to make it closer to the normal data distribution, thereby improving the accuracy and reliability of the data.
优选地，步骤S21的具体步骤为：Preferably, the specific steps of step S21 are:
对历史业务日志进行实体识别，生成业务日志实体；Perform entity recognition on historical business logs to generate business log entities;
基于语义特征知识图谱对业务日志实体进行实体关系分析，得到日志实体关联数据；Perform entity relationship analysis on business log entities based on semantic feature knowledge graph to obtain log entity association data;
对业务日志实体进行交互频次统计，生成日志实体交互频次值；Perform interaction frequency statistics on business log entities and generate log entity interaction frequency values;
基于日志实体交互频次值对日志实体关联数据进行实体关联偏差分析，生成实体关联偏差数据；Perform entity association deviation analysis on log entity association data based on the log entity interaction frequency value to generate entity association deviation data;
对业务日志实体进行多维特征提取，得到多维业务特征数据；所述多维业务特征数据包括时间、位置及数量；Extracting multi-dimensional features from the business log entity to obtain multi-dimensional business feature data; the multi-dimensional business feature data includes time, location and quantity;
基于实体关联偏差数据对多维业务特征的数据进行异常分布识别，得到业务特征异常分布数据；Based on the entity association deviation data, abnormal distribution of multi-dimensional business feature data is identified to obtain business feature abnormal distribution data;
对业务特征异常分布数据进行异常程度置信度计算，得到维度业务特征的置信度得分；Calculate the confidence level of abnormality for abnormal distribution data of business features to obtain the confidence score of dimensional business features;
基于预设的日志异常数据阈值对每个维度业务特征的置信度得分进行比较，当预设的日志异常数据阈值小于或等于维度业务特征的置信度得分，则判定为异常业务特征；The confidence score of each dimensional business feature is compared based on the preset log abnormal data threshold. When the preset log abnormal data threshold is less than or equal to the confidence score of the dimensional business feature, it is determined to be an abnormal business feature;
根据异常业务特征对历史业务日志进行离群点定位，得到异常离群点。According to the abnormal business characteristics, outliers are located in the historical business logs to obtain abnormal outliers.
本发明通过对历史业务日志进行实体识别，从日志中提取出具有实际意义的业务日志实体，业务日志实体是人、地点、物品或其他与业务相关的实体，提供了对业务数据的具体描述和标识，基于语义特征知识图谱对业务日志实体进行实体关系分析，识别出实体之间的关联关系，日志实体关联数据反映了业务日志实体之间的关系网络，帮助理解实体之间的交互和依赖关系，对业务日志实体进行交互频次统计，计算每个实体之间的交互频次值，日志实体交互频次值提供了实体之间交互程度的度量，帮助识别出频繁交互或稀有交互的实体关系，基于日志实体交互频次值，对日志实体关联数据进行实体关联偏差分析，计算实体关联的偏差程度，实体关联偏差数据描述了实体关联情况与预期关联的差异程度，发现异常的实体关联模式，对业务日志实体进行多维特征提取，包括时间、位置和数量等多个维度，多维业务特征数据提供了对业务日志实体在不同维度上的描述，帮助理解业务数据的多方面特征，基于实体关联偏差数据，对多维业务特征数据进行异常分布识别，发现业务特征数据在不同维度上的异常分布模式，业务特征异常分布数据揭示了业务数据在不同维度上的异常情况，帮助检测出与正常模式不符的数据分布，对业务特征异常分布数据进行异常程度置信度计算，量化每个维度业务特征的异常程度，维度业务特征的置信度得分反映了业务特征异常程度的大小，用于评估异常的严重程度，基于预设的日志异常数据阈值，对每个维度业务特征的置信度得分进行比较，判定是否为异常业务特征，当预设的日志异常数据阈值小于或等于维度业务特征的置信度得分时，认定该维度的业务特征为异常，根据异常业务特征，对历史业务日志进行离群点定位，确定异常离群点，异常离群点表示与正常业务行为明显不同的数据记录，识别潜在的问题和异常活动。The present invention performs entity recognition on historical business logs to extract business log entities with practical significance from the logs. Business log entities are people, places, objects or other entities related to the business, and provide a specific description and identification of business data. Based on the semantic feature knowledge graph, entity relationship analysis is performed on the business log entities to identify the association relationship between entities. The log entity association data reflects the relationship network between business log entities, which helps to understand the interaction and dependency relationship between entities. The interaction frequency statistics are performed on the business log entities to calculate the interaction frequency value between each entity. The log entity interaction frequency value provides a measure of the degree of interaction between entities, which helps to identify entity relationships with frequent or rare interactions. Based on the log entity interaction frequency value, entity association deviation analysis is performed on the log entity association data to calculate the deviation degree of entity association. The entity association deviation data describes the degree of difference between the entity association situation and the expected association, and abnormal entity association patterns are discovered. Multi-dimensional feature extraction is performed on the business log entities, including multiple dimensions such as time, location and quantity. The multi-dimensional business feature data provides The description of business log entities in different dimensions helps to understand the multi-faceted characteristics of business data. Based on entity association deviation data, the abnormal distribution of multi-dimensional business feature data is identified to discover the abnormal distribution patterns of business feature data in different dimensions. The abnormal distribution data of business features reveals the abnormal situation of business data in different dimensions and helps to detect data distribution that does not conform to the normal pattern. The confidence score of the abnormal degree of business feature distribution data is calculated to quantify the abnormal degree of business features in each dimension. The confidence score of the dimensional business feature reflects the magnitude of the abnormal degree of business features and is used to evaluate the severity of the abnormality. Based on the preset log abnormal data threshold, the confidence score of each dimensional business feature is compared to determine whether it is an abnormal business feature. When the preset log abnormal data threshold is less than or equal to the confidence score of the dimensional business feature, the business feature of the dimension is considered to be abnormal. According to the abnormal business features, the outliers of the historical business logs are located to determine the abnormal outliers. The abnormal outliers represent data records that are significantly different from normal business behaviors, and identify potential problems and abnormal activities.
优选地，步骤S3的具体步骤为：Preferably, the specific steps of step S3 are:
步骤S31：对离群修正业务日志进行业务交互挖掘，得到业务交互线；Step S31: performing business interaction mining on the outlier correction business log to obtain business interaction lines;
步骤S32：对业务交互线进行业务活动频率及流量定量计算，以生成业务线直接关联强度数据；Step S32: Quantitatively calculate the business activity frequency and flow of the business interaction line to generate business line direct correlation strength data;
步骤S33：对业务交互线进行潜在嵌入关联分析，以得到业务线间接关联矩阵；Step S33: performing potential embedding association analysis on the business interaction lines to obtain a business line indirect association matrix;
步骤S34：基于业务线直接关联强度数据及业务线间接关联矩阵对离群修正业务日志进行拓扑关联拟合，构建业务拓扑关联网络。Step S34: Perform topological correlation fitting on the outlier corrected business logs based on the business line direct correlation strength data and the business line indirect correlation matrix to construct a business topological correlation network.
本发明通过对离群修正业务日志进行业务交互挖掘，发现业务日志中的业务交互行为，业务交互线描述了业务日志中不同实体之间的直接交互关系，提供了业务数据中交互模式的可视化表示，对业务交互线进行业务活动频率及流量定量计算，计算业务活动的频率和流量大小，业务线直接关联强度数据反映了业务活动之间的直接关联程度，帮助理解业务数据中不同活动之间的重要性和关注度，对业务交互线进行潜在嵌入关联分析，揭示业务交互线之间的潜在关联模式，业务线间接关联矩阵提供了业务交互线之间的间接关联程度，帮助理解业务数据中不同交互线之间的潜在关联性，基于业务线直接关联强度数据及业务线间接关联矩阵，对离群修正业务日志进行拓扑关联拟合，构建业务拓扑关联网络，业务拓扑关联网络呈现了业务数据中不同业务线之间的关联关系，以及它们之间的直接和间接关联程度。The present invention discovers business interaction behaviors in business logs by performing business interaction mining on outlier-corrected business logs. Business interaction lines describe the direct interaction relationships between different entities in business logs, provide a visual representation of interaction patterns in business data, perform quantitative calculations on business activity frequencies and flows on business interaction lines, calculate the frequencies and flows of business activities, business line direct correlation strength data reflects the degree of direct correlation between business activities, and helps understand the importance and attention between different activities in business data. Business interaction lines are subjected to potential embedded correlation analysis to reveal potential correlation patterns between business interaction lines. A business line indirect correlation matrix provides the degree of indirect correlation between business interaction lines, and helps understand the potential correlation between different interaction lines in business data. Based on the business line direct correlation strength data and the business line indirect correlation matrix, topological correlation fitting is performed on outlier-corrected business logs to construct a business topological correlation network. The business topological correlation network presents the correlation relationships between different business lines in business data, as well as the degrees of direct and indirect correlation between them.
优选地，步骤S4的具体步骤为：Preferably, the specific steps of step S4 are:
步骤S41：对离群修正业务日志进行关键业务指标计算，以得到业务指标值；Step S41: Calculate key business indicators for the outlier correction business log to obtain business indicator values;
步骤S42：基于业务指标值对业务交互线进行业务属性分析，以得到各个交互线的业务属性；Step S42: performing business attribute analysis on the business interaction lines based on the business indicator values to obtain business attributes of each interaction line;
步骤S43：利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络。Step S43: Utilize the service attributes of each interaction line to perform attribute space mapping on the service topology association network to obtain an attribute mapping topology network.
本发明通过对离群修正业务日志进行关键业务指标计算，从业务数据中提取出重要的业务指标，业务指标值反映了业务数据中的关键性能、效率或其他重要度量，为后续分析和可视化提供了基础数据，基于业务指标值，对业务交互线进行业务属性分析，了解每个交互线所具有的特定业务属性，业务属性描述了业务交互线在业务数据中的特征和特性，例如交互线的类型、作用、重要性等，利用各个交互线的业务属性，对业务拓扑关联网络进行属性空间映射，将业务属性映射到拓扑关联网络上，属性映射拓扑网络展示了业务数据中不同业务线之间的关联关系，并在拓扑网络上呈现了业务属性的空间分布。The present invention calculates key business indicators for outlier corrected business logs and extracts important business indicators from business data. The business indicator values reflect the key performance, efficiency or other important metrics in the business data, and provide basic data for subsequent analysis and visualization. Based on the business indicator values, business attribute analysis is performed on business interaction lines to understand the specific business attributes of each interaction line. The business attributes describe the characteristics and properties of the business interaction lines in the business data, such as the type, function, and importance of the interaction lines. The business attributes of each interaction line are used to perform attribute space mapping on the business topology association network, and the business attributes are mapped to the topology association network. The attribute mapping topology network shows the association relationship between different business lines in the business data, and presents the spatial distribution of business attributes on the topology network.
优选地，步骤S5的具体步骤为：Preferably, the specific steps of step S5 are:
步骤S51：对业务交互线进行时序交互演化分析，以得到业务线时序演化规律；Step S51: performing a time-series interaction evolution analysis on the service interaction line to obtain a time-series evolution rule of the service line;
步骤S52：基于业务线时序演化规律对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；Step S52: based on the time-series evolution law of the service line, a dynamic topology evolution simulation is performed on the attribute mapping topology network, thereby obtaining a dynamic topology evolution network;
步骤S53：对动态拓扑演变网络进行业务传导轨迹识别，得到动态业务传导轨迹；Step S53: Identify the service transmission trace of the dynamic topology evolving network to obtain the dynamic service transmission trace;
步骤S54：对动态业务传导轨迹进行路径演变趋势分析，生成业务轨迹演变趋势数据；Step S54: performing path evolution trend analysis on the dynamic service transmission trajectory to generate service trajectory evolution trend data;
步骤S55：对业务轨迹演变趋势数据进行关联强弱变化分析，从而得到拓扑关联强弱变化数据。Step S55: analyzing the correlation strength change of the business trajectory evolution trend data, thereby obtaining topological correlation strength change data.
本发明通过对业务交互线进行时序交互演化分析，观察业务线随时间的变化，揭示业务线的时序演化规律，时序交互演化分析能够帮助用户了解业务线在不同时间段内的活动强度、变化趋势以及存在的周期性或趋势性规律，基于业务线时序演化规律，对属性映射拓扑网络进行动态拓扑演变模拟，生成动态拓扑演变网络，动态拓扑演变模拟将业务线的时序演化规律应用于属性映射拓扑网络，模拟业务线在时间上的动态变化，从而生成动态拓扑演变网络，对动态拓扑演变网络进行业务传导轨迹识别，识别出业务在拓扑网络中的传导轨迹，业务传导轨迹识别揭示了业务在拓扑网络中的传播路径和传导方式，帮助用户理解业务在网络中的传递过程，对动态业务传导轨迹进行路径演变趋势分析，生成业务轨迹演变趋势数据，业务轨迹演变趋势数据描述了业务传导路径随时间的变化趋势，帮助用户理解业务路径的演变和变化模式，对业务轨迹演变趋势数据进行关联强弱变化分析，得到拓扑关联强弱变化数据，关联强弱变化数据反映了业务路径之间关联程度的变化和演化，帮助用户理解业务关联关系的动态变化。The present invention performs a time-series interaction evolution analysis on the business interaction line, observes the changes of the business line over time, and reveals the time-series evolution law of the business line. The time-series interaction evolution analysis can help users understand the activity intensity, change trend, and existing periodic or trend laws of the business line in different time periods. Based on the time-series evolution law of the business line, the attribute mapping topology network is dynamically simulated to generate a dynamic topology evolution network. The dynamic topology evolution simulation applies the time-series evolution law of the business line to the attribute mapping topology network, simulates the dynamic changes of the business line over time, and thus generates a dynamic topology evolution network. The business conduction trajectory of the dynamic topology evolution network is identified. The transmission trajectory of the business in the topological network. The business transmission trajectory identification reveals the propagation path and transmission mode of the business in the topological network, helping users understand the transmission process of the business in the network, and performing path evolution trend analysis on the dynamic business transmission trajectory to generate business trajectory evolution trend data. The business trajectory evolution trend data describes the changing trend of the business transmission path over time, helping users understand the evolution and change pattern of the business path. The business trajectory evolution trend data is analyzed for correlation strength changes to obtain topological correlation strength change data. The correlation strength change data reflects the change and evolution of the correlation degree between business paths, helping users understand the dynamic changes of business correlation relationships.
优选地，步骤S6的具体步骤为：Preferably, the specific steps of step S6 are:
步骤S61：基于拓扑关联强弱变化数据对动态拓扑演变网络进行业务交互演变预测，构建业务演变预测网络；Step S61: predicting the evolution of business interactions in a dynamic topology evolution network based on the topology association strength change data, and building a business evolution prediction network;
步骤S62：对业务演变预测网络进行多维空间投影，生成业务演变预测空间模型；Step S62: Perform multi-dimensional spatial projection on the business evolution prediction network to generate a business evolution prediction space model;
步骤S63：对业务演变预测空间模型进行三维可视化处理，构建三维可视化业务演变模型。Step S63: Perform three-dimensional visualization processing on the business evolution prediction space model to construct a three-dimensional visualized business evolution model.
本发明通过基于拓扑关联强弱变化数据，对动态拓扑演变网络进行业务交互演变预测，构建业务演变预测网络，通过分析拓扑关联强弱的变化，预测业务在拓扑网络中的演变趋势和交互模式，从而构建业务演变预测网络，对业务演变预测网络进行多维空间投影，生成业务演变预测空间模型，多维空间投影将业务演变预测网络映射到一个更低维度的空间中，以便更好地理解和分析业务演变的关系和模式，对业务演变预测空间模型进行三维可视化处理，构建三维可视化业务演变模型，通过将业务演变预测空间模型可视化为三维模型，用户直观地观察和探索业务演变的结构、趋势和关联关系。The present invention predicts the evolution of business interactions in a dynamic topology evolution network based on data on changes in the strength of topology associations, constructs a business evolution prediction network, predicts the evolution trend and interaction pattern of the business in the topology network by analyzing changes in the strength of topology associations, and thus constructs a business evolution prediction network. The business evolution prediction network is projected into a multidimensional space to generate a business evolution prediction space model. The multidimensional space projection maps the business evolution prediction network into a space of a lower dimension to better understand and analyze the relationships and patterns of business evolution. The business evolution prediction space model is visualized in three dimensions to construct a three-dimensional visualized business evolution model. By visualizing the business evolution prediction space model as a three-dimensional model, a user can intuitively observe and explore the structure, trend and correlation of business evolution.
在本说明书中，提供一种用于企业平台的业务数据可视化系统，用于执行如上所述的用于企业平台的业务数据可视化方法，包括：In this specification, a business data visualization system for an enterprise platform is provided, which is used to execute the business data visualization method for an enterprise platform as described above, including:
语义图谱模块，用于基于企业平台获取历史业务日志；对历史业务日志进行全局语义图谱构建处理，得到语义特征知识图谱；The semantic graph module is used to obtain historical business logs based on the enterprise platform; the global semantic graph is constructed and processed for the historical business logs to obtain a semantic feature knowledge graph;
离群修正模块，用于基于语义特征知识图谱对历史业务日志进行多维度异常检测，以生成离群点差异值；基于离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志；An outlier correction module is used to perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to generate outlier difference values; perform outlier correction processing on historical business logs based on the outlier difference values to obtain outlier-corrected business logs;
拓扑关联模块，用于对离群修正业务日志进行业务交互挖掘，得到业务交互线；对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络；The topological association module is used to perform business interaction mining on the outlier correction business logs to obtain business interaction lines; perform topological association fitting on the business interaction lines to construct a business topological association network;
属性空间映射模块，用于基于离群修正业务日志以得到各个交互线的业务属性；利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络；An attribute space mapping module is used to correct the business log based on the outliers to obtain the business attributes of each interaction line; the business attributes of each interaction line are used to perform attribute space mapping on the business topology association network to obtain an attribute mapping topology network;
拓扑演变模块，用于对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；对动态拓扑演变网络进行路径演变关联强弱变化分析，从而得到拓扑关联强弱变化数据；The topology evolution module is used to simulate the dynamic topology evolution of the attribute mapping topology network, thereby obtaining the dynamic topology evolution network; analyze the change of path evolution correlation strength of the dynamic topology evolution network, thereby obtaining the topology correlation strength change data;
三维可视化模块，用于基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型。The three-dimensional visualization module is used to perform three-dimensional visualization processing on the dynamic topology evolution network based on the data of the strength change of topology associations, and to build a three-dimensional visualization business evolution model.
本发明通过基于企业平台获取历史业务日志，通过全局语义图谱构建处理，得到语义特征知识图谱，语义特征知识图谱包含了历史业务日志中的语义关系和特征信息，能够更全面地描述业务数据的内在结构和语义关联，基于语义特征知识图谱，对历史业务日志进行多维度异常检测，生成离群点差异值，通过离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志，离群点修正模块能够发现和修正历史业务日志中的异常数据，提高数据的准确性和可靠性，对离群修正业务日志进行业务交互挖掘，得到业务交互线，通过拓扑关联拟合，构建业务拓扑关联网络，拓扑关联模块能够发现业务数据之间的交互关系，并构建起业务拓扑关联网络，帮助理解业务数据的整体结构和关联性，对离群修正业务日志进行属性提取，得到各个交互线的业务属性，利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络，属性空间映射模块能够将业务数据的属性信息融入拓扑关联网络中，提供更丰富的数据表达和分析能力，对属性映射拓扑网络进行动态拓扑演变模拟，得到动态拓扑演变网络，通过路径演变关联强弱变化分析，得到拓扑关联强弱变化数据，拓扑演变模块能够模拟业务数据的动态演变过程，并通过分析拓扑关联强弱变化数据，揭示业务数据的演变趋势和关联性的变化，基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型，使用户直观地观察和理解业务数据的演变过程和拓扑结构，三维可视化模块能够提供直观的可视化界面，帮助用户更好地理解和分析业务数据的演变情况。The present invention obtains historical business logs based on an enterprise platform, and obtains a semantic feature knowledge graph through global semantic graph construction and processing. The semantic feature knowledge graph contains semantic relations and feature information in the historical business logs, and can more comprehensively describe the intrinsic structure and semantic association of business data. Based on the semantic feature knowledge graph, multi-dimensional anomaly detection is performed on the historical business logs to generate outlier difference values. Outlier correction processing is performed on the historical business logs through the outlier difference values to obtain outlier-corrected business logs. The outlier correction module can discover and correct abnormal data in the historical business logs to improve the accuracy and reliability of the data. Business interaction mining is performed on the outlier-corrected business logs to obtain business interaction lines. A business topology association network is constructed through topological association fitting. The topology association module can discover the interaction relationship between business data and construct a business topology association network to help understand the overall structure and association of business data. Attributes are extracted from the outlier-corrected business logs to obtain The business attributes of each interactive line are used to perform attribute space mapping of the business topology association network to obtain the attribute mapping topology network. The attribute space mapping module can integrate the attribute information of the business data into the topology association network to provide richer data expression and analysis capabilities. The dynamic topology evolution simulation of the attribute mapping topology network is performed to obtain a dynamic topology evolution network. The topology association strength change data is obtained through the analysis of the path evolution association strength change. The topology evolution module can simulate the dynamic evolution process of business data and reveal the evolution trend and correlation change of business data by analyzing the topology association strength change data. Based on the topology association strength change data, the dynamic topology evolution network is visualized in three dimensions to construct a three-dimensional visualization business evolution model, allowing users to intuitively observe and understand the evolution process and topological structure of business data. The three-dimensional visualization module can provide an intuitive visualization interface to help users better understand and analyze the evolution of business data.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明一种用于企业平台的业务数据可视化方法的步骤流程示意图；FIG1 is a schematic diagram of a process flow of a business data visualization method for an enterprise platform according to the present invention;
图2为步骤S1的详细实施步骤流程示意图；FIG2 is a schematic diagram of a detailed implementation process of step S1;
图3为步骤S2的详细实施步骤流程示意图；FIG3 is a schematic diagram of a detailed implementation process of step S2;
图4为步骤S3的详细实施步骤流程示意图。FIG. 4 is a schematic flow chart of the detailed implementation steps of step S3.
具体实施方式DETAILED DESCRIPTION
应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.
本申请实例提供一种方用于企业平台的业务数据可视化法及系统。所述用于企业平台的业务数据可视化方法及系统的执行主体包括但不限于搭载该系统的：机械设备、数据处理平台、云服务器节点、网络上传设备等可看作本申请的通用计算节点，所述数据处理平台包括但不限于：音频图像管理系统、信息管理系统、云端数据管理系统至少一种。The example of this application provides a business data visualization method and system for an enterprise platform. The execution subject of the business data visualization method and system for an enterprise platform includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of an audio image management system, an information management system, and a cloud data management system.
请参阅图1至图4，本发明提供了用于企业平台的业务数据可视化方法，所述用于企业平台的业务数据可视化方法包括以下步骤：Referring to FIG. 1 to FIG. 4 , the present invention provides a business data visualization method for an enterprise platform, and the business data visualization method for an enterprise platform includes the following steps:
步骤S1：基于企业平台获取历史业务日志；对历史业务日志进行全局语义图谱构建处理，得到语义特征知识图谱；Step S1: Obtain historical business logs based on the enterprise platform; construct and process the global semantic graph of the historical business logs to obtain a semantic feature knowledge graph;
步骤S2：基于语义特征知识图谱对历史业务日志进行多维度异常检测，以生成离群点差异值；基于离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志；Step S2: Perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to generate outlier difference values; perform outlier correction processing on the historical business logs based on the outlier difference values to obtain outlier corrected business logs;
步骤S3：对离群修正业务日志进行业务交互挖掘，得到业务交互线；对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络；Step S3: Perform business interaction mining on the outlier-corrected business logs to obtain business interaction lines; perform topological association fitting on the business interaction lines to construct a business topological association network;
步骤S4：基于离群修正业务日志以得到各个交互线的业务属性；利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络；Step S4: correcting the service log based on the outliers to obtain the service attributes of each interaction line; using the service attributes of each interaction line to perform attribute space mapping on the service topology association network to obtain an attribute mapping topology network;
步骤S5：对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；对动态拓扑演变网络进行路径演变关联强弱变化分析，从而得到拓扑关联强弱变化数据；Step S5: simulate the dynamic topology evolution of the attribute mapping topology network to obtain the dynamic topology evolution network; analyze the path evolution correlation strength change of the dynamic topology evolution network to obtain the topology correlation strength change data;
步骤S6：基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型。Step S6: Perform three-dimensional visualization processing on the dynamic topology evolution network based on the topology association strength change data, and construct a three-dimensional visualization business evolution model.
本发明通过基于企业平台获取历史业务日志，获得丰富的业务数据，对历史业务日志进行全局语义图谱构建处理，将业务数据中的关键信息提取出来，形成语义特征知识图谱，语义特征知识图谱包含了业务数据的语义关联和特征信息，更好地理解和分析业务数据，基于语义特征知识图谱对历史业务日志进行多维度异常检测，识别出与正常业务行为不符的异常点，生成离群点差异值，这些值表示了每个数据点与正常业务行为的偏差程度，用于量化异常程度，对历史业务日志进行离群点修正处理，通过修正离群点提高数据的准确性和可靠性，对离群修正业务日志进行业务交互挖掘，发现业务数据中的交互行为，得到业务交互线，这些线表示了业务数据中不同实体之间的交互关系，对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络，以描述业务数据中实体之间的拓扑结构和关联关系，基于离群修正业务日志以得到各个交互线的业务属性，从业务数据中提取出与交互线相关的特征和属性信息，利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，将业务属性映射到拓扑网络中的节点和边上，得到属性映射拓扑网络，这个网络将业务数据的属性信息与拓扑结构相结合，提供了更丰富的业务数据表示形式，对属性映射拓扑网络进行动态拓扑演变模拟，模拟业务数据中的拓扑结构随时间的演变过程，得到动态拓扑演变网络，这个网络反映了业务数据中拓扑关系的动态变化情况，对动态拓扑演变网络进行路径演变关联强弱变化分析，量化拓扑关联强度和变化程度，得到拓扑关联强弱变化数据，基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，将业务数据以可视化的形式展示出来，构建三维可视化业务演变模型，通过可视化技术将业务数据的演变过程呈现给用户，三维可视化业务演变模型帮助用户更直观地理解和分析业务数据的演变趋势和关联关系。The present invention obtains rich business data by acquiring historical business logs based on an enterprise platform, constructs a global semantic graph for the historical business logs, extracts key information from the business data, and forms a semantic feature knowledge graph. The semantic feature knowledge graph contains the semantic association and feature information of the business data, so as to better understand and analyze the business data. The present invention performs multi-dimensional anomaly detection on the historical business logs based on the semantic feature knowledge graph, identifies anomalies that are inconsistent with normal business behaviors, generates outlier difference values, which represent the degree of deviation of each data point from normal business behaviors and are used to quantify the degree of anomalies. The present invention performs outlier correction processing on the historical business logs, improves the accuracy and reliability of the data by correcting the outliers, performs business interaction mining on the outlier-corrected business logs, discovers the interactive behaviors in the business data, and obtains business interaction lines, which represent the interactive relationships between different entities in the business data. The business interaction lines are topologically associated and fitted, and a business topological association network is constructed to describe the topological structure and association relationships between entities in the business data. The business logs are corrected based on the outliers to obtain the business attributes of each interaction line. The characteristics and attribute information related to the interaction lines are extracted from the business data, and the business attributes of each interaction line are used to perform attribute space mapping on the business topology association network. The business attributes are mapped to the nodes and edges in the topology network to obtain the attribute mapping topology network. This network combines the attribute information of the business data with the topological structure, providing a richer form of business data representation. The dynamic topological evolution simulation is performed on the attribute mapping topological network to simulate the evolution of the topological structure in the business data over time to obtain the dynamic topological evolution network. This network reflects the dynamic changes in the topological relationship in the business data. The path evolution association strength change analysis is performed on the dynamic topological evolution network to quantify the topological association strength and degree of change to obtain the topological association strength change data. Based on the topological association strength change data, the dynamic topological evolution network is visualized in three dimensions to display the business data in a visualized form, and a three-dimensional visualized business evolution model is constructed. The evolution process of the business data is presented to the user through visualization technology. The three-dimensional visualized business evolution model helps users to more intuitively understand and analyze the evolution trend and association relationship of business data.
本发明实施例中，参阅图1，为本发明一种用于企业平台的业务数据可视化方法的步骤流程示意图，在本实例中，所述用于企业平台的业务数据可视化方法的步骤包括：In an embodiment of the present invention, referring to FIG. 1 , it is a schematic flow chart of a method for visualizing business data for an enterprise platform of the present invention. In this example, the steps of the method for visualizing business data for an enterprise platform include:
步骤S1：基于企业平台获取历史业务日志；对历史业务日志进行全局语义图谱构建处理，得到语义特征知识图谱；Step S1: Obtain historical business logs based on the enterprise platform; construct and process the global semantic graph of the historical business logs to obtain a semantic feature knowledge graph;
本实施例中，在获得企业平台授权之后，从企业平台中获取历史业务日志数据，这些日志数据包括企业内部系统的操作日志、用户交互记录、业务流程记录等，对历史业务日志进行预处理，包括数据清洗、去重和格式转换等，使用自然语言处理（NLP）技术对日志文本进行语义分析和实体识别，提取关键词、主题和实体信息，基于提取的语义信息，构建语义图谱，语义图谱是一种图结构，其中节点表示实体或关键词，边表示实体之间的关系或关键词之间的语义关联，在构建语义图谱时，使用图数据库或图分析工具来存储和处理图结构数据，根据构建的语义图谱，提取语义特征知识，包括实体属性、关系信息和关键词的重要程度等。In this embodiment, after obtaining authorization from the enterprise platform, historical business log data is obtained from the enterprise platform. These log data include operation logs of the enterprise's internal system, user interaction records, business process records, etc. The historical business logs are preprocessed, including data cleaning, deduplication and format conversion, etc. Natural language processing (NLP) technology is used to perform semantic analysis and entity recognition on the log text, and keywords, topics and entity information are extracted. Based on the extracted semantic information, a semantic graph is constructed. The semantic graph is a graph structure in which nodes represent entities or keywords, and edges represent relationships between entities or semantic associations between keywords. When constructing the semantic graph, a graph database or graph analysis tool is used to store and process graph structure data. According to the constructed semantic graph, semantic feature knowledge is extracted, including entity attributes, relationship information and the importance of keywords.
步骤S2：基于语义特征知识图谱对历史业务日志进行多维度异常检测，以生成离群点差异值；基于离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志；Step S2: Perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to generate outlier difference values; perform outlier correction processing on the historical business logs based on the outlier difference values to obtain outlier corrected business logs;
本实施例中，对历史业务日志进行多维度的异常检测，多维度包括时间维度、业务维度、用户维度等，通过比较当前日志数据与历史数据的差异，计算离群点差异值，离群点差异值表示当前数据与历史数据之间的偏离程度，根据生成的离群点差异值，对历史业务日志进行离群点修正处理，针对离群点，采取不同的修正策略，一种常见的策略是将离群点视为异常值，进行删除或标记处理，另一种策略是根据离群点的差异值进行数据调整或插值，使其更接近正常范围内的数据，修正后的业务日志称为离群修正业务日志，其中离群点已被处理，数据更加准确和可靠。In this embodiment, multi-dimensional anomaly detection is performed on historical business logs, and the multiple dimensions include time dimension, business dimension, user dimension, etc. The outlier difference value is calculated by comparing the difference between the current log data and the historical data. The outlier difference value indicates the degree of deviation between the current data and the historical data. According to the generated outlier difference value, the historical business log is corrected for outliers. Different correction strategies are adopted for outliers. A common strategy is to regard outliers as outliers and delete or mark them. Another strategy is to adjust or interpolate data according to the difference value of the outliers to make it closer to the data within the normal range. The corrected business log is called an outlier-corrected business log, in which the outliers have been processed and the data is more accurate and reliable.
步骤S3：对离群修正业务日志进行业务交互挖掘，得到业务交互线；对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络；Step S3: Perform business interaction mining on the outlier-corrected business logs to obtain business interaction lines; perform topological association fitting on the business interaction lines to construct a business topological association network;
本实施例中，针对离群修正业务日志，进行业务交互的挖掘和分析，业务交互包括系统操作、用户行为、业务流程等，根据业务日志中的时间戳和操作记录，识别和提取业务交互线，业务交互线是一系列相关操作或事件的序列，表示业务流程的执行路径，基于提取的业务交互线，对交互线进行拓扑关联拟合，拓扑关联表示业务交互线之间的关系，如先后顺序、依赖关系等，根据业务交互线的执行顺序和关联规则，构建业务拓扑关联网络，业务拓扑关联网络是一个图结构，其中节点表示业务交互线，边表示交互线之间的关系，利用构建的业务拓扑关联网络，进行业务分析和决策支持，通过图分析算法，如社区发现、图中心性分析等，对业务拓扑关联网络进行分析，发现关键业务流程、瓶颈点或异常情况，基于分析结果，为业务决策提供支持，如优化业务流程、改进用户体验或调整系统配置等。In this embodiment, business logs are corrected for outliers to mine and analyze business interactions. Business interactions include system operations, user behaviors, business processes, etc. Business interaction lines are identified and extracted based on timestamps and operation records in business logs. Business interaction lines are sequences of related operations or events, representing the execution path of business processes. Based on the extracted business interaction lines, topological association fitting is performed on the interaction lines. Topological association represents the relationship between business interaction lines, such as sequence, dependency, etc. A business topological association network is constructed based on the execution sequence and association rules of the business interaction lines. The business topological association network is a graph structure, in which nodes represent business interaction lines and edges represent the relationship between interaction lines. Business analysis and decision support are performed using the constructed business topological association network. The business topological association network is analyzed through graph analysis algorithms, such as community discovery and graph centrality analysis, to discover key business processes, bottlenecks or abnormal situations. Based on the analysis results, support is provided for business decisions, such as optimizing business processes, improving user experience, or adjusting system configuration.
步骤S4：基于离群修正业务日志以得到各个交互线的业务属性；利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络；Step S4: correcting the service log based on the outliers to obtain the service attributes of each interaction line; using the service attributes of each interaction line to perform attribute space mapping on the service topology association network to obtain an attribute mapping topology network;
本实施例中，对离群修正业务日志进行分析，提取每个交互线的业务属性。业务属性包括交互线的类型、执行时间、参与者角色、操作类型等。In this embodiment, the outlier correction service log is analyzed to extract the service attributes of each interaction line, including the type of interaction line, execution time, participant role, operation type, etc.
根据业务日志中记录的信息和领域知识，识别和提取每个交互线的业务属性。将每个交互线的业务属性映射到属性空间中，构建属性映射拓扑网络。属性映射拓扑网络是一个基于属性空间的图结构，其中节点仍表示交互线，边表示交互线之间的关系。Based on the information and domain knowledge recorded in the business log, the business attributes of each interaction line are identified and extracted. The business attributes of each interaction line are mapped into the attribute space to construct an attribute mapping topology network. The attribute mapping topology network is a graph structure based on the attribute space, in which nodes still represent interaction lines and edges represent the relationship between interaction lines.
属性空间是多维空间，其中每个维度表示一个业务属性。将交互线的业务属性映射到属性空间中的相应维度上。The attribute space is a multidimensional space, in which each dimension represents a business attribute. The business attributes of the interaction line are mapped to the corresponding dimensions in the attribute space.
步骤S5：对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；对动态拓扑演变网络进行路径演变关联强弱变化分析，从而得到拓扑关联强弱变化数据；Step S5: simulate the dynamic topology evolution of the attribute mapping topology network to obtain the dynamic topology evolution network; analyze the path evolution correlation strength change of the dynamic topology evolution network to obtain the topology correlation strength change data;
本实施例中，基于属性映射拓扑网络，进行动态拓扑演变模拟，动态拓扑演变模拟是模拟业务交互线的演化过程，根据交互线的关系和属性变化，构建动态的拓扑演变网络，在模拟过程中，考虑交互线的新加入、删除、属性变化等情况，使得拓扑网络能够反映业务的动态变化，基于动态拓扑演变网络，进行路径演变关联强弱变化分析，路径演变关联强弱变化分析是分析拓扑网络中路径的变化情况，包括路径的形成、消失、强度变化等，通过计算路径的权重、度中心性等指标，评估路径的关联强度，同时，还分析路径的变化趋势，识别关键路径和关联强弱变化的模式。In this embodiment, a dynamic topology evolution simulation is performed based on the attribute mapping topology network. The dynamic topology evolution simulation simulates the evolution process of the business interaction line. According to the relationship and attribute changes of the interaction line, a dynamic topology evolution network is constructed. In the simulation process, the new addition, deletion, attribute changes of the interaction line, etc. are considered, so that the topology network can reflect the dynamic changes of the business. Based on the dynamic topology evolution network, the path evolution correlation strength change analysis is performed. The path evolution correlation strength change analysis is to analyze the changes of the path in the topology network, including the formation, disappearance, and strength changes of the path. By calculating the weight of the path, degree centrality and other indicators, the correlation strength of the path is evaluated. At the same time, the change trend of the path is analyzed to identify the key path and the pattern of correlation strength change.
步骤S6：基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型。Step S6: Perform three-dimensional visualization processing on the dynamic topology evolution network based on the topology association strength change data, and construct a three-dimensional visualization business evolution model.
本实施例中，选择合适的节点表示方式，如球体、立方体等，用于表示交互线或路径，使用连线或管道等方式表示交互线或路径之间的关联关系，根据关联强度的变化，调整连线的粗细或颜色，设计动画或交互功能，使得业务拓扑的演变过程以时间序列的方式呈现，根据关联强度的大小设定节点的大小或高度，关联强度较高的节点表示为较大或较高的形状，而关联强度较低的节点表示为较小或较低的形状，对于连接表示，根据关联强度的变化调整连线的粗细或颜色，关联强度较高的连接表示为较粗或较鲜明的线条，而关联强度较低的连接表示为较细或较淡的线条，构建三维可视化业务演变模型，根据设计和数据映射的结果，利用相应的三维可视化工具或库，构建三维可视化业务演变模型，将动态拓扑演变网络及其关联强弱变化数据导入工具中，并设置相应的参数和交互方式，进行三维可视化演示和分析，通过三维可视化业务演变模型，展示业务拓扑的动态演变过程，进行交互操作，如旋转、缩放等，以观察和分析业务的拓扑变化，通过动画、时间轴或交互控件等方式呈现演变过程。In this embodiment, a suitable node representation method is selected, such as a sphere, a cube, etc., to represent an interaction line or path, and a connection line or a pipe is used to represent the association relationship between the interaction lines or paths. According to the change of the association strength, the thickness or color of the connection line is adjusted, and an animation or interactive function is designed so that the evolution process of the business topology is presented in a time series manner. The size or height of the node is set according to the magnitude of the association strength. Nodes with higher association strength are represented as larger or higher shapes, while nodes with lower association strength are represented as smaller or lower shapes. For connection representation, the thickness or color of the connection line is adjusted according to the change of the association strength. Connections with higher association strength are represented as Thicker or brighter lines, while connections with lower association strength are represented as thinner or lighter lines. A three-dimensional visual business evolution model is constructed. According to the results of design and data mapping, the corresponding three-dimensional visualization tools or libraries are used to construct a three-dimensional visual business evolution model. The dynamic topology evolution network and its association strength change data are imported into the tool, and the corresponding parameters and interaction methods are set for three-dimensional visualization demonstration and analysis. The dynamic evolution process of the business topology is displayed through the three-dimensional visual business evolution model. Interactive operations such as rotation and zooming are performed to observe and analyze the topological changes of the business, and the evolution process is presented through animation, timeline or interactive controls.
本实施例中，参阅图2，为步骤S1的详细实施步骤流程示意图，本实施例中，所述步骤S1的详细实施步骤包括：In this embodiment, referring to FIG. 2 , which is a schematic flow chart of detailed implementation steps of step S1, in this embodiment, the detailed implementation steps of step S1 include:
步骤S11：基于企业平台获取历史业务日志；Step S11: Obtaining historical business logs based on the enterprise platform;
步骤S12：对历史业务日志进行日志数据结构化处理，得到结构化字段；Step S12: Performing log data structuring processing on the historical business log to obtain structured fields;
步骤S13：对结构化字段进行全局上下文语义分析，从而生成全局上下文语义特征；Step S13: performing global context semantic analysis on the structured field to generate global context semantic features;
步骤S14：对全局上下文语义特征进行深度学习建模，构建语义特征知识图谱。Step S14: Perform deep learning modeling on the global context semantic features and construct a semantic feature knowledge graph.
本实施例中，确定需要获取历史业务日志的企业平台，例如企业的信息系统、应用程序、网络设备等，针对所选平台，与相关部门或负责人沟通，获取历史业务日志的存储位置、格式和获取方式，根据获取方式，使用适当的工具或方法，从企业平台中获取历史业务日志数据，需要进行身份验证或权限控制，以确保获取到所需的历史日志数据，对获取到的历史业务日志进行预处理和清洗，以去除无效或冗余的数据，确保数据的准确性和一致性，根据业务需求和日志数据的特点，设计合适的日志数据结构，将日志数据转化为结构化的字段，使用相应的日志处理工具或编程语言，通过解析和提取日志数据中的关键信息，将其映射到预定义的结构化字段中，使用正则表达式、分词技术等方法进行信息提取和字段匹配，对结构化字段进行全局上下文语义分析，旨在理解字段之间的关联和上下文语义，使用自然语言处理（NLP）技术，如词向量模型（如Word2Vec、GloVe）、语义角色标注、实体识别等，对结构化字段中的文本信息进行处理和分析，基于NLP技术和语义分析方法，将结构化字段转化为全局上下文语义特征，这些特征包括词义相似度、语义角色、实体关系等，用于表达字段之间的语义关联，基于全局上下文语义特征，设计深度学习模型进行建模，使用图神经网络、知识图谱表示学习等技术，将全局上下文语义特征转化为可计算和分析的表示形式，根据具体的建模需求，选择合适的深度学习算法和模型架构，如图卷积网络（GCN）、注意力机制（Attention）、图注意力网络（GAT）等，用于构建语义特征知识图谱，基于所选择的模型，进行训练和优化，以捕捉全局上下文语义特征之间的关系，并生成具有语义信息的知识图谱，知识图谱表示为节点和边的形式，其中节点代表语义特征，边代表特征之间的关联关系。In this embodiment, the enterprise platform from which historical business logs need to be obtained is determined, such as the enterprise's information system, application, network equipment, etc. For the selected platform, the relevant department or person in charge is communicated to obtain the storage location, format and acquisition method of the historical business log. According to the acquisition method, appropriate tools or methods are used to obtain historical business log data from the enterprise platform. Identity authentication or permission control is required to ensure that the required historical log data is obtained. The obtained historical business logs are preprocessed and cleaned to remove invalid or redundant data to ensure data accuracy and consistency. According to business requirements and the characteristics of log data, a suitable log data structure is designed to convert log data into structured fields. The corresponding log processing tools or programming languages are used to parse and extract key information from the log data and map it to predefined structured fields. Regular expressions, word segmentation technology and other methods are used to extract information and match fields. Global context semantic analysis is performed on structured fields to understand the association and context semantics between fields. Natural language processing (NLP) technology is used. Such as word vector models (such as Word2Vec, GloVe), semantic role labeling, entity recognition, etc., to process and analyze the text information in the structured fields. Based on NLP technology and semantic analysis methods, the structured fields are converted into global context semantic features. These features include word meaning similarity, semantic roles, entity relationships, etc., which are used to express the semantic associations between fields. Based on the global context semantic features, a deep learning model is designed for modeling. Using graph neural networks, knowledge graph representation learning and other technologies, the global context semantic features are converted into a representation that can be calculated and analyzed. According to specific modeling needs, appropriate deep learning algorithms and model architectures are selected, such as graph convolutional networks (GCN), attention mechanisms (Attention), graph attention networks (GAT), etc., to construct semantic feature knowledge graphs. Based on the selected model, training and optimization are performed to capture the relationship between global context semantic features and generate a knowledge graph with semantic information. The knowledge graph is represented in the form of nodes and edges, where nodes represent semantic features and edges represent the association between features.
本实施例中，步骤S13的具体步骤为：In this embodiment, the specific steps of step S13 are:
对历史业务日志进行时序解析，生成业务日志序列；Perform time series analysis on historical business logs to generate business log sequences;
对结构化字段进行空值率计算，得到字段空值率；Calculate the null value rate of the structured field to obtain the field null value rate;
基于字段空值率对业务日志序列进行空值字段频率分布分析，生成空值字段频率分布数据；Perform frequency distribution analysis of null value fields on business log sequences based on field null value rates to generate frequency distribution data of null value fields;
基于空值字段频率分布数据对业务日志序列进行语义特征解析，以得到日志语义特征；Based on the frequency distribution data of null value fields, semantic feature analysis is performed on the business log sequence to obtain log semantic features;
对日志语义特征进行相邻语义特征条件概率转移计算，生成条件概率转移数据矩阵；Calculate the conditional probability transfer of adjacent semantic features on the log semantic features to generate a conditional probability transfer data matrix;
对条件概率转移数据矩阵进行语义特征关联分析，得到邻间语义特征依赖关系；Perform semantic feature association analysis on the conditional probability transfer data matrix to obtain the dependency relationship between neighboring semantic features;
根据邻间语义特征依赖关系对业务日志序列进行全局上下文语义挖掘，从而生成全局上下文语义特征。The global context semantics mining is performed on the business log sequence according to the dependency relationship between the neighboring semantic features to generate the global context semantic features.
本实施例中，载入历史业务日志数据，并根据时间戳或其他时间信息对日志进行排序，确保按照时间顺序组织，根据业务需求和日志数据的结构，确定需要提取的关键字段，如时间戳、事件类型、用户ID等，逐行读取日志数据，提取关键字段，并按照时间顺序将其组织成业务日志序列，针对结构化字段，统计每个字段的空值数量，即字段中缺失值的数量，计算每个字段的空值率，即字段的空值数量除以总样本数或有效样本数，得到每个字段的空值率，作为后续分析的参考指标，对于每个字段，统计在业务日志序列中的空值频率分布，即每个字段出现空值的次数与总样本数或有效样本数的比例，根据空值频率分布数据，绘制直方图或其他可视化图表，以展示每个字段的空值情况和分布，利用空值字段频率分布数据，对业务日志序列进行语义特征解析，根据字段的空值率高低、空值的分布模式等进行解析，将字段分为高空值率字段、低空值率字段或其他类别，并为每个字段赋予相应的语义特征标签，根据解析结果，为业务日志序列中的每条日志赋予相应的语义特征，形成日志语义特征序列，基于日志语义特征序列，计算相邻语义特征之间的条件概率转移，即计算在已知前一个特征的情况下，出现下一个特征的概率，统计每个语义特征与其后继特征之间的转移频率，并将其归一化为概率值，将转移概率存储为数据矩阵，其中行表示当前特征，列表示后继特征，矩阵元素表示从当前特征到后继特征的转移概率，基于条件概率转移数据矩阵，进行语义特征关联分析，以揭示邻间语义特征之间的依赖关系，采用聚类分析、关联规则挖掘、网络分析等方法，发现特征之间的关联模式和依赖关系，根据分析结果，得到邻间语义特征的依赖关系，如特征A和特征B之间的相关性、条件概率等，基于邻间语义特征依赖关系，对业务日志序列进行全局上下文语义挖掘，旨在发现特征之间更广泛的语义关联和上下文信息，利用邻间特征的依赖关系，进行特征的扩展或补充，例如，根据依赖关系将相关特征组合成新的特征，或者通过填充缺失的特征值以获得更完整的上下文信息，基于全局上下文语义挖掘的结果，生成全局上下文语义特征，用于后续分析和应用。In this embodiment, historical business log data is loaded, and the logs are sorted according to timestamps or other time information to ensure that they are organized in chronological order. According to business needs and the structure of log data, key fields that need to be extracted are determined, such as timestamps, event types, user IDs, etc. The log data is read line by line, key fields are extracted, and they are organized into business log sequences in chronological order. For structured fields, the number of null values in each field is counted, that is, the number of missing values in the field, and the null value rate of each field is calculated, that is, the number of null values in the field is divided by the total number of samples or the number of valid samples, to obtain the null value rate of each field as a reference indicator for subsequent analysis. For each Field, count the frequency distribution of null values in the business log sequence, that is, the ratio of the number of null values in each field to the total number of samples or the number of valid samples. Draw a histogram or other visual chart based on the null value frequency distribution data to show the null value situation and distribution of each field. Use the null value field frequency distribution data to perform semantic feature analysis on the business log sequence. According to the null value rate of the field and the distribution pattern of the null value, the fields are divided into high null value rate fields, low null value rate fields or other categories, and each field is assigned a corresponding semantic feature label. According to the analysis results, each log in the business log sequence is assigned a corresponding semantic feature to form a log. Log semantic feature sequence, based on the log semantic feature sequence, calculate the conditional probability transfer between adjacent semantic features, that is, calculate the probability of the next feature appearing when the previous feature is known, count the transfer frequency between each semantic feature and its successor feature, and normalize it to a probability value, store the transfer probability as a data matrix, where the row represents the current feature, the column represents the successor feature, and the matrix element represents the transfer probability from the current feature to the successor feature. Based on the conditional probability transfer data matrix, perform semantic feature association analysis to reveal the dependency relationship between adjacent semantic features, and use clustering analysis, association rule mining, network analysis and other methods to find features. The correlation patterns and dependencies between them are obtained according to the analysis results, such as the correlation and conditional probability between feature A and feature B. Based on the dependencies between neighboring semantic features, global context semantic mining is performed on the business log sequence to discover a wider range of semantic associations and context information between features. The dependencies between neighboring features are used to expand or supplement features. For example, related features are combined into new features according to dependencies, or missing feature values are filled to obtain more complete context information. Based on the results of global context semantic mining, global context semantic features are generated for subsequent analysis and application.
本实施例中，参阅图3，为步骤S2的详细实施步骤流程示意图，本实施例中，所述步骤S2的详细实施步骤包括：In this embodiment, referring to FIG. 3 , which is a schematic flow chart of detailed implementation steps of step S2, in this embodiment, the detailed implementation steps of step S2 include:
步骤S21：基于语义特征知识图谱对历史业务日志进行多维度异常检测，得到异常离群点；Step S21: Perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to obtain abnormal outliers;
步骤S22：对异常离群点进行主体差异量化，以生成离群点差异值；Step S22: quantifying the main difference of the abnormal outliers to generate outlier difference values;
步骤S23：对历史业务日志进行主体数据分布特征分析，得到主体数据分布特征；Step S23: Analyze the main data distribution characteristics of the historical business logs to obtain the main data distribution characteristics;
步骤S24：基于主体数据分布特征对离群点差异值进行异常校正阈值计算，得到异常校正范围；Step S24: Calculate the abnormality correction threshold value for the outlier difference value based on the main data distribution characteristics to obtain the abnormality correction range;
步骤S25：基于异常校正数据对历史业务日志进行离群点修正处理，得到离群修正业务日志。Step S25: Perform outlier correction processing on the historical business log based on the abnormal correction data to obtain an outlier corrected business log.
本实施例中，建立语义特征知识图谱，其中节点表示语义特征，边表示特征之间的关系，利用领域知识或数据分析方法构建知识图谱，使用知识图谱进行多维度异常检测，采用图结构的异常检测算法，如基于图的离群点检测算法或基于图的异常子图发现算法，分析历史业务日志数据在知识图谱上的表现，识别出异常离群点，即与其他节点关系异常或特征值明显不同的数据点，针对异常离群点，将其与正常数据进行对比，量化离群点与正常数据主体之间的差异，计算离群点与正常数据在各个维度上的差异值，例如使用欧氏距离、曼哈顿距离或其他距离度量方法，将差异值作为离群点的差异度量，表示离群点与正常数据的差异程度，对历史业务日志中的正常数据进行主体数据分布特征分析，考虑各个特征的分布情况、均值、方差、偏度、峰度等统计特征，采用直方图、概率密度图、箱线图等可视化方法，探索数据的分布特征，并进行数值计算和统计分析，利用主体数据分布特征，计算异常离群点差异值的异常校正阈值，根据正态分布假设或其他分布模型，计算离群点差异值的阈值范围，例如基于均值和标准差的方法，确定异常校正范围，将超出该范围的离群点标记为需要修正的异常数据，使用异常校正范围，对历史业务日志中的离群点进行修正处理，将超出异常校正范围的离群点修正为合理的值，使用插值、替换为均值或中位数等方法进行修正，修正后的业务日志数据即为离群修正业务日志，这些数据点在维持日志整体分布特征的基础上进行了异常值的修正。In this embodiment, a semantic feature knowledge graph is established, in which nodes represent semantic features and edges represent relationships between features. Domain knowledge or data analysis methods are used to construct a knowledge graph, and the knowledge graph is used for multi-dimensional anomaly detection. A graph-structured anomaly detection algorithm, such as a graph-based outlier detection algorithm or a graph-based abnormal subgraph discovery algorithm, is used to analyze the performance of historical business log data on the knowledge graph, identify abnormal outliers, i.e., data points with abnormal relationships with other nodes or significantly different feature values. For abnormal outliers, they are compared with normal data, the difference between the outliers and the main body of normal data is quantified, and the difference values between the outliers and normal data in various dimensions are calculated. For example, Euclidean distance, Manhattan distance or other distance measurement methods are used, and the difference value is used as the difference measurement of the outliers to represent the degree of difference between the outliers and normal data. The main data distribution characteristics of the normal data in the historical business logs are analyzed. Analysis, considering the distribution of each feature, mean, variance, skewness, kurtosis and other statistical characteristics, using histograms, probability density plots, box plots and other visualization methods to explore the distribution characteristics of the data, and perform numerical calculations and statistical analysis, using the main data distribution characteristics to calculate the anomaly correction threshold of the abnormal outlier difference value, according to the normal distribution assumption or other distribution models, calculate the threshold range of the outlier difference value, such as based on the mean and standard deviation method, determine the anomaly correction range, mark the outliers beyond the range as abnormal data that need to be corrected, use the anomaly correction range to correct the outliers in the historical business logs, correct the outliers beyond the anomaly correction range to a reasonable value, use interpolation, replace with the mean or median and other methods to correct, the corrected business log data is the outlier-corrected business log, these data points have been corrected for outliers while maintaining the overall distribution characteristics of the log.
本实施例中，步骤S21的具体步骤为：In this embodiment, the specific steps of step S21 are:
对历史业务日志进行实体识别，生成业务日志实体；Perform entity recognition on historical business logs to generate business log entities;
基于语义特征知识图谱对业务日志实体进行实体关系分析，得到日志实体关联数据；Perform entity relationship analysis on business log entities based on semantic feature knowledge graph to obtain log entity association data;
对业务日志实体进行交互频次统计，生成日志实体交互频次值；Perform interaction frequency statistics on business log entities and generate log entity interaction frequency values;
基于日志实体交互频次值对日志实体关联数据进行实体关联偏差分析，生成实体关联偏差数据；Perform entity association deviation analysis on log entity association data based on the log entity interaction frequency value to generate entity association deviation data;
对业务日志实体进行多维特征提取，得到多维业务特征数据；所述多维业务特征数据包括时间、位置及数量；Extracting multi-dimensional features from the business log entity to obtain multi-dimensional business feature data; the multi-dimensional business feature data includes time, location and quantity;
基于实体关联偏差数据对多维业务特征的数据进行异常分布识别，得到业务特征异常分布数据；Based on the entity association deviation data, abnormal distribution of multi-dimensional business feature data is identified to obtain business feature abnormal distribution data;
对业务特征异常分布数据进行异常程度置信度计算，得到维度业务特征的置信度得分；Calculate the confidence level of abnormality for abnormal distribution data of business features to obtain the confidence score of dimensional business features;
基于预设的日志异常数据阈值对每个维度业务特征的置信度得分进行比较，当预设的日志异常数据阈值小于或等于维度业务特征的置信度得分，则判定为异常业务特征；The confidence score of each dimensional business feature is compared based on the preset log abnormal data threshold. When the preset log abnormal data threshold is less than or equal to the confidence score of the dimensional business feature, it is determined to be an abnormal business feature;
根据异常业务特征对历史业务日志进行离群点定位，得到异常离群点。According to the abnormal business characteristics, outliers are located in the historical business logs to obtain abnormal outliers.
本实施例中，使用自然语言处理（NLP）技术对历史业务日志进行文本解析和实体识别，应用实体识别算法，例如命名实体识别（NER）算法，来提取业务日志中的实体，如人名、地点、组织机构等，识别和抽取的实体将作为后续步骤的基础数据，利用语义特征知识图谱，将业务日志实体及其属性与图谱中的节点和边进行关联，分析业务日志实体之间的关联关系，如上下文关系、依赖关系、相似性等，利用图算法进行实体关系的推理和分析，生成日志实体关联数据，记录业务日志实体之间的关联信息，以便后续分析，统计业务日志实体之间的交互频次，即它们在历史日志中出现在同一上下文中的次数，对于每对实体，计算它们的交互频次值，表示它们的交互紧密程度，分析日志实体的交互频次值，比较不同实体之间的交互频次是否存在偏差，计算实体之间的交互频次差异、相对频次偏差等指标，衡量实体关联的偏差程度，生成实体关联偏差数据，记录实体关联的偏差信息，用于后续分析和异常检测，从业务日志实体中提取多维特征，包括时间、位置和数量等，时间特征包括时间戳、日期、时间段等，用于分析业务活动的时间分布和趋势，位置特征包括地理坐标、区域、位置关系等，用于分析业务活动的空间分布和相关性，数量特征包括业务数量、频次等，用于分析业务活动的数量分布和规律，利用实体关联偏差数据，分析多维业务特征数据的分布情况，采用统计方法、数据挖掘算法或机器学习模型，识别多维业务特征数据中的异常分布模式，生成业务特征异常分布数据，记录异常分布模式和异常程度，用于后续分析和异常检测，对业务特征异常分布数据进行异常程度的计算，使用统计方法、离群点检测算法等，根据异常程度计算每个维度业务特征的置信度得分，表示该维度业务特征的异常程度，设定预先定义的日志异常数据阈值，该阈值用于判断维度业务特征的异常程度是否超过阈值，将每个维度业务特征的置信度得分与预设的阈值进行比较，如果维度业务特征的置信度得分大于阈值，表示该维度业务特征异常，需要进行后续处理，根据判定为异常的业务特征，对历史业务日志进行筛选和过滤，只保留异常业务特征相关的日志数据，应用离群点检测算法，如基于聚类分析、统计方法或机器学习模型等，对保留的日志数据进行离群点定位，最终得到异常离群点，即在历史业务日志中与异常业务特征相关的异常数据点或异常事件。In this embodiment, natural language processing (NLP) technology is used to perform text parsing and entity recognition on historical business logs, and entity recognition algorithms, such as named entity recognition (NER) algorithms, are applied to extract entities in business logs, such as names, places, organizations, etc. The recognized and extracted entities will be used as basic data for subsequent steps. The semantic feature knowledge graph is used to associate business log entities and their attributes with nodes and edges in the graph, and the association relationships between business log entities, such as contextual relationships, dependencies, similarities, etc., are analyzed. Graph algorithms are used to reason and analyze entity relationships, generate log entity association data, and record association information between business log entities for subsequent analysis and statistics of business log entities. The interaction frequency between entities is the number of times they appear in the same context in historical logs. For each pair of entities, their interaction frequency values are calculated to indicate the closeness of their interaction. The interaction frequency values of log entities are analyzed to compare whether there is a deviation in the interaction frequency between different entities. Indicators such as the interaction frequency difference and relative frequency deviation between entities are calculated to measure the degree of deviation of entity associations. Entity association deviation data is generated and entity association deviation information is recorded for subsequent analysis and anomaly detection. Multidimensional features are extracted from business log entities, including time, location, and quantity. Time features include timestamps, dates, and time periods, which are used to analyze the time distribution and trends of business activities. Location features include geographic coordinates, region, and time zone. Domain, location relationship, etc. are used to analyze the spatial distribution and correlation of business activities. Quantitative characteristics include business quantity, frequency, etc., which are used to analyze the quantitative distribution and regularity of business activities. Entity association deviation data is used to analyze the distribution of multi-dimensional business feature data. Statistical methods, data mining algorithms or machine learning models are used to identify abnormal distribution patterns in multi-dimensional business feature data, generate business feature abnormal distribution data, record abnormal distribution patterns and abnormality levels for subsequent analysis and anomaly detection, calculate the abnormality level of business feature abnormal distribution data, use statistical methods, outlier detection algorithms, etc., calculate the confidence score of each dimensional business feature according to the abnormality level, and indicate the abnormality level of the business feature of that dimension. A predefined log abnormal data threshold is set, which is used to determine whether the abnormal degree of the dimensional business feature exceeds the threshold. The confidence score of each dimensional business feature is compared with the preset threshold. If the confidence score of the dimensional business feature is greater than the threshold, it means that the dimensional business feature is abnormal and needs to be processed later. According to the business features determined to be abnormal, the historical business logs are screened and filtered, and only the log data related to the abnormal business features are retained. The outlier detection algorithm is applied, such as based on cluster analysis, statistical methods or machine learning models, to locate the outliers of the retained log data, and finally the abnormal outliers are obtained, that is, the abnormal data points or abnormal events related to the abnormal business features in the historical business logs.
本实施例中，参阅图4，为步骤S3的详细实施步骤流程示意图，本实施例中，所述步骤S3的详细实施步骤包括：In this embodiment, referring to FIG. 4 , which is a schematic flow chart of detailed implementation steps of step S3, in this embodiment, the detailed implementation steps of step S3 include:
步骤S31：对离群修正业务日志进行业务交互挖掘，得到业务交互线；Step S31: performing business interaction mining on the outlier correction business log to obtain business interaction lines;
步骤S32：对业务交互线进行业务活动频率及流量定量计算，以生成业务线直接关联强度数据；Step S32: Quantitatively calculate the business activity frequency and flow of the business interaction line to generate business line direct correlation strength data;
步骤S33：对业务交互线进行潜在嵌入关联分析，以得到业务线间接关联矩阵；Step S33: performing potential embedding association analysis on the business interaction lines to obtain a business line indirect association matrix;
步骤S34：基于业务线直接关联强度数据及业务线间接关联矩阵对离群修正业务日志进行拓扑关联拟合，构建业务拓扑关联网络。Step S34: Perform topological correlation fitting on the outlier corrected business logs based on the business line direct correlation strength data and the business line indirect correlation matrix to construct a business topological correlation network.
本实施例中，针对修正后的离群业务日志数据，进行业务交互挖掘，通过分析业务日志中的交互行为，例如请求-响应、调用关系等，识别业务之间的交互关系，根据业务日志中的交互行为和关联信息，构建业务交互线，表示业务之间的直接交互关系，对业务交互线进行分析，统计业务活动的频率和流量，计算每条业务交互线的频率，表示业务之间的交互频繁程度，计算每条业务交互线的流量，表示业务之间交互的数据量或资源消耗，生成业务线直接关联强度数据，记录业务之间的直接关联强度，用于后续分析和拓扑关联拟合，利用潜在嵌入模型（如节点嵌入、图嵌入等），对业务交互线进行关联分析，将业务交互线转化为向量表示，并通过嵌入模型学习业务线之间的潜在关联，基于学习到的嵌入向量，构建业务线间接关联矩阵，表示业务线之间的间接关联程度，综合利用业务线直接关联强度数据和业务线间接关联矩阵，进行拓扑关联拟合，基于业务线的直接关联强度，构建业务线之间的直接连接边，基于业务线的间接关联矩阵，构建业务线之间的间接连接边，结合直接连接边和间接连接边，构建业务拓扑关联网络，表示业务线之间的关联关系。In this embodiment, business interaction mining is performed on the corrected outlier business log data. By analyzing the interaction behaviors in the business logs, such as request-response, call relationships, etc., the interaction relationships between businesses are identified. According to the interaction behaviors and associated information in the business logs, business interaction lines are constructed to represent the direct interaction relationships between businesses. The business interaction lines are analyzed, the frequency and flow of business activities are counted, the frequency of each business interaction line is calculated to represent the frequency of interaction between businesses, the flow of each business interaction line is calculated to represent the amount of data or resource consumption of interaction between businesses, the business line direct association strength data is generated, and the direct association strength between businesses is recorded for subsequent analysis and topological association fitting. , use potential embedding models (such as node embedding, graph embedding, etc.) to perform association analysis on business interaction lines, convert business interaction lines into vector representations, and learn the potential associations between business lines through the embedding model. Based on the learned embedding vectors, construct a business line indirect association matrix to represent the degree of indirect association between business lines. Comprehensively utilize the business line direct association intensity data and the business line indirect association matrix to perform topological association fitting. Based on the direct association intensity of the business lines, construct direct connection edges between business lines. Based on the indirect association matrix of the business lines, construct indirect connection edges between business lines. Combine direct connection edges and indirect connection edges to construct a business topological association network to represent the association relationship between business lines.
本实施例中，步骤S4包括以下步骤：In this embodiment, step S4 includes the following steps:
步骤S41：对离群修正业务日志进行关键业务指标计算，以得到业务指标值；Step S41: Calculate key business indicators for the outlier correction business log to obtain business indicator values;
步骤S42：基于业务指标值对业务交互线进行业务属性分析，以得到各个交互线的业务属性；Step S42: performing business attribute analysis on the business interaction lines based on the business indicator values to obtain business attributes of each interaction line;
步骤S43：利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络。Step S43: Utilize the service attributes of each interaction line to perform attribute space mapping on the service topology association network to obtain an attribute mapping topology network.
本实施例中，针对修正后的离群业务日志数据，进行关键业务指标的计算，根据业务需求和目标，确定需要计算的关键业务指标，例如响应时间、吞吐量、错误率等，针对每个业务指标，对业务日志数据进行相应的计算和统计，得到相应的业务指标值，对业务交互线进行业务属性分析，根据业务指标值，对业务交互线进行分类或标记，例如将交互线分为高负载、低响应等不同的业务属性，分析业务交互线的特征和行为，识别出每个交互线的具体业务属性，对业务拓扑关联网络进行属性空间映射，将每个交互线的业务属性映射到拓扑网络的节点上，是通过节点的颜色、大小、形状等方式表示，根据业务属性的相似性或相关性，调整拓扑网络中节点之间的布局和连接方式，使具有相似属性的节点更加靠近或连接。In this embodiment, key business indicators are calculated for the corrected outlier business log data, and the key business indicators that need to be calculated are determined according to business needs and goals, such as response time, throughput, error rate, etc. For each business indicator, the business log data is calculated and counted accordingly to obtain the corresponding business indicator value, and business attribute analysis is performed on the business interaction lines. According to the business indicator values, the business interaction lines are classified or marked, for example, the interaction lines are divided into different business attributes such as high load and low response, the characteristics and behaviors of the business interaction lines are analyzed, and the specific business attributes of each interaction line are identified. The business topology association network is mapped in attribute space, and the business attributes of each interaction line are mapped to the nodes of the topological network, which are represented by the color, size, shape, etc. of the nodes. According to the similarity or correlation of the business attributes, the layout and connection method between the nodes in the topological network are adjusted to make the nodes with similar attributes closer or connected.
本实施例中，步骤S5的具体步骤为：In this embodiment, the specific steps of step S5 are:
步骤S51：对业务交互线进行时序交互演化分析，以得到业务线时序演化规律；Step S51: performing a time-series interaction evolution analysis on the service interaction line to obtain a time-series evolution rule of the service line;
步骤S52：基于业务线时序演化规律对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；Step S52: based on the time-series evolution law of the service line, a dynamic topology evolution simulation is performed on the attribute mapping topology network, thereby obtaining a dynamic topology evolution network;
步骤S53：对动态拓扑演变网络进行业务传导轨迹识别，得到动态业务传导轨迹；Step S53: Identify the service transmission trace of the dynamic topology evolving network to obtain the dynamic service transmission trace;
步骤S54：对动态业务传导轨迹进行路径演变趋势分析，生成业务轨迹演变趋势数据；Step S54: performing path evolution trend analysis on the dynamic service transmission trajectory to generate service trajectory evolution trend data;
步骤S55：对业务轨迹演变趋势数据进行关联强弱变化分析，从而得到拓扑关联强弱变化数据。Step S55: analyzing the correlation strength change of the business trajectory evolution trend data, thereby obtaining topological correlation strength change data.
本实施例中，对业务交互线进行时序分析，观察业务线的演化过程和模式，根据时间序列的数据，分析业务交互线的变化趋势、周期性和趋势性等，通过统计和数据挖掘方法，得到业务线的时序演化规律，例如高峰期、低谷期等，对属性映射拓扑网络进行动态演变模拟，根据时序演化规律，调整拓扑网络中节点的属性映射和连接关系，模拟业务线在不同时间段的拓扑变化，迭代模拟过程，生成一系列的拓扑网络快照，表示业务线的动态拓扑演变过程，根据动态拓扑演变网络的快照序列，识别业务传导轨迹，分析拓扑网络中节点之间的变化和连接关系，识别出业务传导的路径和传递方向，根据节点的属性映射信息，确定传导轨迹所对应的业务属性，分析动态业务传导轨迹的路径演变趋势，观察业务传导路径的变化，识别出路径的增长、缩短、分支等演变趋势，生成业务轨迹演变趋势数据，记录路径演变趋势的变化情况，基于业务轨迹演变趋势数据，分析业务轨迹之间的关联强弱变化，比较不同业务轨迹的演变趋势，识别出关联强度的增强、减弱或变化的模式，生成拓扑关联强弱变化数据，记录业务轨迹之间关联强度的变化情况。In this embodiment, a time series analysis is performed on the business interaction line to observe the evolution process and mode of the business line. Based on the time series data, the change trend, periodicity and trend of the business interaction line are analyzed. Through statistical and data mining methods, the time series evolution law of the business line, such as peak period, trough period, etc., is obtained. The attribute mapping topology network is dynamically evolved. According to the time series evolution law, the attribute mapping and connection relationship of the nodes in the topology network are adjusted to simulate the topological changes of the business line in different time periods. The simulation process is iterated to generate a series of topological network snapshots to represent the dynamic topological evolution process of the business line. According to the snapshot sequence of the dynamic topological evolution network, the business transmission trajectory is identified and the topology is analyzed. Changes and connection relationships between nodes in the network, identify the path and direction of business transmission, determine the business attributes corresponding to the transmission trajectory based on the node attribute mapping information, analyze the path evolution trend of the dynamic business transmission trajectory, observe changes in the business transmission path, identify the path growth, shortening, branching and other evolution trends, generate business trajectory evolution trend data, record changes in path evolution trends, analyze changes in the strength of the association between business trajectories based on the business trajectory evolution trend data, compare the evolution trends of different business trajectories, identify the pattern of enhancement, weakening or change of association strength, generate topological association strength change data, and record changes in the strength of the association between business trajectories.
本实施例中，步骤S6包括以下步骤：In this embodiment, step S6 includes the following steps:
步骤S61：基于拓扑关联强弱变化数据对动态拓扑演变网络进行业务交互演变预测，构建业务演变预测网络；Step S61: predicting the evolution of business interactions in a dynamic topology evolution network based on the topology association strength change data, and building a business evolution prediction network;
步骤S62：对业务演变预测网络进行多维空间投影，生成业务演变预测空间模型；Step S62: Perform multi-dimensional spatial projection on the business evolution prediction network to generate a business evolution prediction space model;
步骤S63：对业务演变预测空间模型进行三维可视化处理，构建三维可视化业务演变模型。Step S63: Perform three-dimensional visualization processing on the business evolution prediction space model to construct a three-dimensional visualized business evolution model.
本实施例中，对动态拓扑演变网络进行业务交互演变预测，分析拓扑关联强弱变化的模式和趋势，预测未来业务交互线之间的关联强度和变化情况，基于预测结果，构建业务演变预测网络，表示未来业务线之间的关联和变化，针对业务演变预测网络，进行多维空间投影，根据业务演变预测网络中节点之间的关联强弱变化，将节点映射到多维空间中的坐标点，通过多维空间投影，生成业务演变预测空间模型，表示节点之间的空间关系和演化趋势，将业务演变预测空间模型进行三维可视化处理，将多维空间投影得到的坐标点映射到三维空间中，表示节点在三维空间中的位置，根据节点之间的关联强弱变化和空间位置，构建三维可视化业务演变模型，以可视化方式展示业务线的演变趋势和关联关系。In this embodiment, business interaction evolution is predicted for a dynamic topology evolution network, patterns and trends of changes in topology association strength are analyzed, and the strength and changes of associations between future business interaction lines are predicted. Based on the prediction results, a business evolution prediction network is constructed to represent the associations and changes between future business lines. Multidimensional space projection is performed on the business evolution prediction network. According to the changes in the strength of associations between nodes in the business evolution prediction network, the nodes are mapped to coordinate points in the multidimensional space. Through the multidimensional space projection, a business evolution prediction space model is generated to represent the spatial relationship and evolution trend between the nodes. The business evolution prediction space model is three-dimensionally visualized, and the coordinate points obtained by the multidimensional space projection are mapped to three-dimensional space to represent the positions of the nodes in the three-dimensional space. According to the changes in the strength of associations between the nodes and the spatial positions, a three-dimensional visualized business evolution model is constructed to display the evolution trends and associations of the business lines in a visualized manner.
在本实施例中，提供一种用于企业平台的业务数据可视化系统，用于执行如上所述的用于企业平台的业务数据可视化方法，包括：In this embodiment, a business data visualization system for an enterprise platform is provided, which is used to execute the business data visualization method for an enterprise platform as described above, including:
语义图谱模块，用于基于企业平台获取历史业务日志；对历史业务日志进行全局语义图谱构建处理，得到语义特征知识图谱；The semantic graph module is used to obtain historical business logs based on the enterprise platform; the global semantic graph is constructed and processed for the historical business logs to obtain a semantic feature knowledge graph;
离群修正模块，用于基于语义特征知识图谱对历史业务日志进行多维度异常检测，以生成离群点差异值；基于离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志；An outlier correction module is used to perform multi-dimensional anomaly detection on historical business logs based on the semantic feature knowledge graph to generate outlier difference values; perform outlier correction processing on the historical business logs based on the outlier difference values to obtain outlier-corrected business logs;
拓扑关联模块，用于对离群修正业务日志进行业务交互挖掘，得到业务交互线；对业务交互线进行拓扑关联拟合，构建业务拓扑关联网络；The topological association module is used to perform business interaction mining on the outlier correction business logs to obtain business interaction lines; perform topological association fitting on the business interaction lines to construct a business topological association network;
属性空间映射模块，用于基于离群修正业务日志以得到各个交互线的业务属性；利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络；An attribute space mapping module is used to correct the business log based on the outliers to obtain the business attributes of each interaction line; the business attributes of each interaction line are used to perform attribute space mapping on the business topology association network to obtain an attribute mapping topology network;
拓扑演变模块，用于对属性映射拓扑网络进行动态拓扑演变模拟，从而得到动态拓扑演变网络；对动态拓扑演变网络进行路径演变关联强弱变化分析，从而得到拓扑关联强弱变化数据；The topology evolution module is used to simulate the dynamic topology evolution of the attribute mapping topology network, thereby obtaining the dynamic topology evolution network; analyze the change of path evolution correlation strength of the dynamic topology evolution network, thereby obtaining the topology correlation strength change data;
三维可视化模块，用于基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型。The three-dimensional visualization module is used to perform three-dimensional visualization processing on the dynamic topology evolution network based on the data of the strength change of topology associations, and to build a three-dimensional visualization business evolution model.
本发明通过基于企业平台获取历史业务日志，通过全局语义图谱构建处理，得到语义特征知识图谱，语义特征知识图谱包含了历史业务日志中的语义关系和特征信息，能够更全面地描述业务数据的内在结构和语义关联，基于语义特征知识图谱，对历史业务日志进行多维度异常检测，生成离群点差异值，通过离群点差异值对历史业务日志进行离群点修正处理，得到离群修正业务日志，离群点修正模块能够发现和修正历史业务日志中的异常数据，提高数据的准确性和可靠性，对离群修正业务日志进行业务交互挖掘，得到业务交互线，通过拓扑关联拟合，构建业务拓扑关联网络，拓扑关联模块能够发现业务数据之间的交互关系，并构建起业务拓扑关联网络，帮助理解业务数据的整体结构和关联性，对离群修正业务日志进行属性提取，得到各个交互线的业务属性，利用各个交互线的业务属性对业务拓扑关联网络进行属性空间映射，得到属性映射拓扑网络，属性空间映射模块能够将业务数据的属性信息融入拓扑关联网络中，提供更丰富的数据表达和分析能力，对属性映射拓扑网络进行动态拓扑演变模拟，得到动态拓扑演变网络，通过路径演变关联强弱变化分析，得到拓扑关联强弱变化数据，拓扑演变模块能够模拟业务数据的动态演变过程，并通过分析拓扑关联强弱变化数据，揭示业务数据的演变趋势和关联性的变化，基于拓扑关联强弱变化数据对动态拓扑演变网络进行三维可视化处理，构建三维可视化业务演变模型，使用户直观地观察和理解业务数据的演变过程和拓扑结构，三维可视化模块能够提供直观的可视化界面，帮助用户更好地理解和分析业务数据的演变情况。The present invention obtains historical business logs based on an enterprise platform, and obtains a semantic feature knowledge graph through global semantic graph construction and processing. The semantic feature knowledge graph contains semantic relations and feature information in the historical business logs, and can more comprehensively describe the intrinsic structure and semantic association of business data. Based on the semantic feature knowledge graph, multi-dimensional anomaly detection is performed on the historical business logs to generate outlier difference values. Outlier correction processing is performed on the historical business logs through the outlier difference values to obtain outlier-corrected business logs. The outlier correction module can discover and correct abnormal data in the historical business logs to improve the accuracy and reliability of the data. Business interaction mining is performed on the outlier-corrected business logs to obtain business interaction lines. A business topology association network is constructed through topological association fitting. The topology association module can discover the interaction relationship between business data and construct a business topology association network to help understand the overall structure and association of business data. Attributes are extracted from the outlier-corrected business logs to obtain The business attributes of each interactive line are used to perform attribute space mapping of the business topology association network to obtain the attribute mapping topology network. The attribute space mapping module can integrate the attribute information of the business data into the topology association network to provide richer data expression and analysis capabilities. The dynamic topology evolution simulation of the attribute mapping topology network is performed to obtain a dynamic topology evolution network. The topology association strength change data is obtained through the analysis of the path evolution association strength change. The topology evolution module can simulate the dynamic evolution process of business data and reveal the evolution trend and correlation change of business data by analyzing the topology association strength change data. Based on the topology association strength change data, the dynamic topology evolution network is visualized in three dimensions to construct a three-dimensional visualization business evolution model, allowing users to intuitively observe and understand the evolution process and topological structure of business data. The three-dimensional visualization module can provide an intuitive visualization interface to help users better understand and analyze the evolution of business data.
因此，无论从哪一点来看，均应将实施例看作是示范性的，而且是非限制性的，本发明的范围由所附权利要求而不是上述说明限定，因此旨在将落在申请文件的等同要件的含义和范围内的所有变化涵括在本发明内。Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.
如上所述仅是本发明的具体实施方式，使本领域技术人员能够理解或实现本发明。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所发明的原理和新颖特点相一致的最宽的范围。The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.
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| CN202411026991.0ACN118551040B (en) | 2024-07-30 | 2024-07-30 | A business data visualization method and system for enterprise platform |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118710224A (en)* | 2024-08-28 | 2024-09-27 | 深圳市拜特科技股份有限公司 | Enterprise platform security management method and system based on artificial intelligence |
| CN119294241A (en)* | 2024-09-26 | 2025-01-10 | 临沂市启成电子科技有限公司 | A data modeling method, system and storage medium |
| CN119445016A (en)* | 2024-11-05 | 2025-02-14 | 中国人民解放军陆军装甲兵学院 | Large language model driven geographic scene parameterized three-dimensional modeling method, system, storage medium and program product |
| CN119474041A (en)* | 2025-01-16 | 2025-02-18 | 创域智能(常熟)网联科技有限公司 | Business log information processing method and server based on artificial intelligence |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040090472A1 (en)* | 2002-10-21 | 2004-05-13 | Risch John S. | Multidimensional structured data visualization method and apparatus, text visualization method and apparatus, method and apparatus for visualizing and graphically navigating the world wide web, method and apparatus for visualizing hierarchies |
| CN114168747A (en)* | 2021-12-03 | 2022-03-11 | 上海德衡数据科技有限公司 | Knowledge base construction method and system based on cloud service |
| CN117575011A (en)* | 2023-12-06 | 2024-02-20 | 深圳市万恒科技有限公司 | Customer data management method and system based on big data |
| CN118195533A (en)* | 2024-03-27 | 2024-06-14 | 汉林汇融(深圳)科技服务有限公司 | Project application and enterprise information interaction method and system based on artificial intelligence |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040090472A1 (en)* | 2002-10-21 | 2004-05-13 | Risch John S. | Multidimensional structured data visualization method and apparatus, text visualization method and apparatus, method and apparatus for visualizing and graphically navigating the world wide web, method and apparatus for visualizing hierarchies |
| CN114168747A (en)* | 2021-12-03 | 2022-03-11 | 上海德衡数据科技有限公司 | Knowledge base construction method and system based on cloud service |
| CN117575011A (en)* | 2023-12-06 | 2024-02-20 | 深圳市万恒科技有限公司 | Customer data management method and system based on big data |
| CN118195533A (en)* | 2024-03-27 | 2024-06-14 | 汉林汇融(深圳)科技服务有限公司 | Project application and enterprise information interaction method and system based on artificial intelligence |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118710224A (en)* | 2024-08-28 | 2024-09-27 | 深圳市拜特科技股份有限公司 | Enterprise platform security management method and system based on artificial intelligence |
| CN118710224B (en)* | 2024-08-28 | 2025-01-17 | 深圳市拜特科技股份有限公司 | Enterprise platform safety management method and system based on artificial intelligence |
| CN119294241A (en)* | 2024-09-26 | 2025-01-10 | 临沂市启成电子科技有限公司 | A data modeling method, system and storage medium |
| CN119445016A (en)* | 2024-11-05 | 2025-02-14 | 中国人民解放军陆军装甲兵学院 | Large language model driven geographic scene parameterized three-dimensional modeling method, system, storage medium and program product |
| CN119474041A (en)* | 2025-01-16 | 2025-02-18 | 创域智能(常熟)网联科技有限公司 | Business log information processing method and server based on artificial intelligence |
| Publication number | Publication date |
|---|---|
| CN118551040B (en) | 2024-11-12 |
| Publication | Publication Date | Title |
|---|---|---|
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