技术领域Technical Field
本发明涉及金融领域，具体为一种基于数据仓库模型的数据处理方法及系统。The present invention relates to the financial field, and in particular to a data processing method and system based on a data warehouse model.
背景技术Background Art
在当今的金融行业中，数据的采集、管理和分析变得至关重要。随着交易量的增加和数据类型的多样化，传统的数据处理方法已无法满足高效、准确地处理大规模复杂数据的需求。因此，基于数据仓库的数据处理方法应运而生，旨在提高数据处理效率，支持复杂的数据分析，并为决策提供科学依据。In today's financial industry, data collection, management, and analysis have become crucial. With the increase in transaction volume and the diversification of data types, traditional data processing methods can no longer meet the needs of efficiently and accurately processing large-scale complex data. Therefore, data processing methods based on data warehouses have emerged to improve data processing efficiency, support complex data analysis, and provide a scientific basis for decision-making.
数据仓库技术提供了一种有效的数据整合解决方案，使得企业能够将来自不同源的数据进行集中管理和分析。通过建立一个中心化的数据存储环境，企业可以更好地进行数据挖掘，预测分析，以及数据可视化。此外，星型模型作为数据仓库中常见的数据模型，通过将数据组织在事实表和多个维度表中，使得查询效率得到显著提升，从而加快了决策过程。Data warehouse technology provides an effective data integration solution, enabling enterprises to centrally manage and analyze data from different sources. By establishing a centralized data storage environment, enterprises can better conduct data mining, predictive analysis, and data visualization. In addition, the star model, as a common data model in data warehouses, significantly improves query efficiency by organizing data in fact tables and multiple dimension tables, thereby speeding up the decision-making process.
鉴于此，本发明提出一种基于数据仓库模型的数据处理方法及系统。In view of this, the present invention proposes a data processing method and system based on a data warehouse model.
发明内容Summary of the invention
本部分的目的在于概述本发明的实施例的一些方面以及简要介绍一些较佳实施例。在本部分以及本申请的说明书摘要和发明名称中可能会做些简化或省略以避免使本部分、说明书摘要和发明名称的目的模糊，而这种简化或省略不能用于限制本发明的范围。The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.
鉴于上述存在的问题，提出了本发明。In view of the above-mentioned problems, the present invention is proposed.
为解决上述技术问题，本发明提供如下技术方案：一种基于数据仓库模型的数据处理方法，其特征在于，包括：从金融交易源获取交易数据，对交易数据进行预处理；设计数据仓库，将交易数据以星型表进行关联，并对交易数据进行数据聚合；对交易数据进行挖掘和分析，运用统计分析和机器学习技术对数据进行分析；构建可视化报告，根据交易数据分析结果制作报告，为金融决策提供支持。To solve the above technical problems, the present invention provides the following technical solutions: a data processing method based on a data warehouse model, characterized in that it includes: acquiring transaction data from a financial transaction source and preprocessing the transaction data; designing a data warehouse, associating the transaction data with a star table, and aggregating the transaction data; mining and analyzing the transaction data, and analyzing the data using statistical analysis and machine learning techniques; constructing a visual report, and preparing a report based on the transaction data analysis results to provide support for financial decision-making.
从多种金融交易源获取实时和历史金融数据，采用API s和Web爬虫技术获取实时性数据，所述金融交易源包括股市交易系统、银行交易记录以及金融聚合媒体；Acquire real-time and historical financial data from multiple financial transaction sources, using APIs and web crawler technology to obtain real-time data. The financial transaction sources include stock market trading systems, bank transaction records, and financial aggregation media;
对获取到的数据进行预处理，去除异常值和合格不合规的数据项，对数据进行标准化，统一数据格式；对获取的数据添加标签，为数据创建行业分类标签和风险标签，并与主数据合并，作为完整的交易数据进行存储。Preprocess the acquired data to remove outliers and qualified but non-compliant data items, standardize the data, and unify the data format; add labels to the acquired data, create industry classification labels and risk labels for the data, and merge them with the master data to store them as complete transaction data.
设计数据仓库，用于存储交易数据；Design a data warehouse to store transaction data;
将交易数据中的数据内容以星型表进行关联，存储于数据仓库中；在星型表中，以事实表和维度表的形式表示数据项之间的关系；所述事实表包括交易金额、交易时间以及交易类型；所述维度表包括客户维表、时间维表以及地理维表；所述客户维表用于记录客户的信息和行为类型；所述时间维表用于记录交易的时间信息，并以时间序列的形式进行存储；所述地理维表包含交易地点信息，用于地区分析；The data contents in the transaction data are associated in a star table and stored in the data warehouse; in the star table, the relationship between the data items is represented in the form of a fact table and a dimension table; the fact table includes the transaction amount, transaction time and transaction type; the dimension table includes a customer dimension table, a time dimension table and a geographic dimension table; the customer dimension table is used to record the customer's information and behavior type; the time dimension table is used to record the time information of the transaction and is stored in the form of a time series; the geographic dimension table contains transaction location information for regional analysis;
以星型表的形式对数据进行聚合，以数据项之间的关联性作为数据之间的拓扑关系。The data is aggregated in the form of a star table, and the association between data items is used as the topological relationship between the data.
选择数据库作为数据仓库模型的载体，所述数据库包括Orcle、SQLSever以及MySOL；将数据仓库中的交易数据按照星型模型的拓扑逻辑，转换为数据库中的表、列、数据类型及其约束；在数据库中，对事实表和维度表创建索引优化查询性能；按照星型表的中的拓扑逻辑，对数据库中的表，按照交易金额、交易时间以及交易类型进行表分区；Select a database as the carrier of the data warehouse model, including Oracle, SQL Server and MySOL; convert the transaction data in the data warehouse into tables, columns, data types and constraints in the database according to the topological logic of the star model; create indexes for fact tables and dimension tables in the database to optimize query performance; partition the tables in the database according to transaction amount, transaction time and transaction type according to the topological logic in the star table;
为数据库建立配置认证机制，确保授权用户访问数据仓库，定义不同类型用户和权限，以控制不同用户和用户组对数据仓库的访问，对存储在数据仓库中的数据进行加密，所述加密包括静态数据加密和传输数据加密；使用TLS/SSL协议保护数据传输。Establish and configure an authentication mechanism for the database to ensure that authorized users access the data warehouse, define different types of users and permissions to control access to the data warehouse by different users and user groups, encrypt the data stored in the data warehouse, including static data encryption and transmission data encryption; use TLS/SSL protocol to protect data transmission.
对不同交易数据之间进行相关性分析，计算不同类型交易数据之间的相关性，采用偏相关系数对不同类型交易数据的相关性进行分析。Conduct correlation analysis between different transaction data, calculate the correlation between different types of transaction data, and use partial correlation coefficients to analyze the correlation between different types of transaction data.
对不同的交易数据X和交易数据Y进行相关性分析，构建交易数据X和Y的趋势：Perform correlation analysis on different transaction data X and transaction data Y to construct the trend of transaction data X and Y:
X＝β0A+β1AT+β2AD+∈x；X＝β0A +β1A T+β2A D+∈x;
Y＝β0B+β1BT+β2BD+∈y；Y＝β0B +β1B T+β2B D+∈y;
其中，β0A、β0B、β1A、β1B、β2A和β2B是回归系数，∈x和∈y回归残差，代表去除交易类型和交易时间影响后，交易金额A和B的变化；∈x和∈y是从实际交易金额中减去由交易类型和时间预测的部分得到的；Where β0A , β0B , β1A , β1B , β2A and β2B are regression coefficients, ∈x and ∈y are regression residuals, representing the changes in transaction amounts A and B after removing the effects of transaction type and transaction time; ∈x and ∈y are obtained by subtracting the portion predicted by transaction type and time from the actual transaction amount;
使用∈x和∈y的值表示交易数据X和Y之间的相关性：Use the values of ∈x and ∈y to represent the correlation between transaction data X and Y:
其中，和是残差的平均值；γ(XY,M)表示交易数据X和交易数据Y之间的相关性，若γ(XY,M)显著不为零，则交易数据X和交易数据存在显著线性关联。in, and is the mean value of the residuals; γ(XY,M) represents the correlation between transaction data X and transaction data Y. If γ(XY,M) is significantly different from zero, there is a significant linear correlation between transaction data X and transaction data.
采用机器学习技术，在机器学习模型学习不同类型交易数据的风险等级后，对新创建的交易进行风险等级划分；Using machine learning technology, after the machine learning model learns the risk levels of different types of transaction data, newly created transactions are classified into risk levels;
根据交易行为特征，通过人工对交易数据进行风险等级划分，将风险等级作为交易数据的标签，将交易数据和对应的风险标签作为数据集，训练机器学习模型学习交易风险划分；所述数据集包括训练集、验证集和测试集；According to the transaction behavior characteristics, the transaction data is manually classified into risk levels, the risk levels are used as labels for the transaction data, and the transaction data and the corresponding risk labels are used as data sets to train the machine learning model to learn transaction risk classification; the data sets include training sets, validation sets, and test sets;
机器学习模型学习不同类型交易数据和对应风险等级，以训练集数据中的不同风险等级所占的概率为输出，当达到机器学习模型的训练次数时停止训练。The machine learning model learns different types of transaction data and corresponding risk levels, and uses the probabilities of different risk levels in the training set data as output. The training stops when the number of training times of the machine learning model is reached.
将训练完成的机器学习模型部署到数据仓库中，在数据仓库新存入交易数据时，通过机器学习模型分析交易数据的风险等级；所述机器学习模型为决策树模型。The trained machine learning model is deployed to the data warehouse. When new transaction data is stored in the data warehouse, the risk level of the transaction data is analyzed by the machine learning model; the machine learning model is a decision tree model.
对数据仓库中存储的数据生成可视化报告，使用数据分析工具，创建仪表板和图表，显示直观分析结果；Generate visual reports on data stored in the data warehouse, use data analysis tools, create dashboards and charts to display intuitive analysis results;
通过网络平台以及内部系统分享分析结果，确保用户访问结果信息。Share analysis results through online platforms and internal systems to ensure user access to result information.
一种基于数据仓库模型的数据处理方法的系统，包括：数据获取和处理模块、数据仓库模块、数据分析模块以及可视化模块；A system for a data processing method based on a data warehouse model, comprising: a data acquisition and processing module, a data warehouse module, a data analysis module and a visualization module;
所述数据获取和处理模块，用于从金融交易源获取交易数据，对交易数据进行预处理；The data acquisition and processing module is used to acquire transaction data from a financial transaction source and pre-process the transaction data;
所述数据仓库模块，用于设计数据仓库，将交易数据以星型表进行关联，并对交易数据进行数据聚合；The data warehouse module is used to design a data warehouse, associate transaction data with a star table, and perform data aggregation on the transaction data;
所述数据分析模块，用于对交易数据进行挖掘和分析，运用统计分析和机器学习技术对数据进行分析；The data analysis module is used to mine and analyze transaction data, and analyze the data using statistical analysis and machine learning techniques;
所述可视化模块，构建可视化报告，根据交易数据分析结果制作报告，为金融决策提供支持。The visualization module constructs visualization reports and produces reports based on the transaction data analysis results to provide support for financial decision-making.
本发明的有益效果：提高数据处理效率，通过从多个金融交易源实时获取数据，并采用预处理技术如去除异常值、标准化和添加标签，本发明能有效提高数据的准确性和处理速度。The beneficial effects of the present invention are as follows: improving data processing efficiency, by acquiring data from multiple financial transaction sources in real time and adopting preprocessing techniques such as removing outliers, standardizing and adding labels, the present invention can effectively improve data accuracy and processing speed.
支持复杂的数据分析，采用星型模型使得数据的存储与查询更加高效，同时通过统计分析和机器学习技术，能够对数据进行深入挖掘，发现数据间的隐含关系和趋势，为金融市场分析提供强有力的技术支持。It supports complex data analysis and uses a star model to make data storage and query more efficient. At the same time, through statistical analysis and machine learning technology, it can conduct in-depth data mining and discover implicit relationships and trends between data, providing strong technical support for financial market analysis.
增强数据安全和隐私保护，在数据仓库中实施加密措施和配置认证机制，确保只有授权用户能访问敏感数据，从而保护企业数据安全和客户隐私。Enhance data security and privacy protection, implement encryption measures and configure authentication mechanisms in the data warehouse to ensure that only authorized users can access sensitive data, thereby protecting enterprise data security and customer privacy.
支持决策制定，通过机器学习模型对交易数据的风险等级进行分析，结合生成的可视化报告，决策者可以直观了解市场动态和潜在风险，更快做出基于数据的决策。Support decision-making by analyzing the risk level of transaction data through machine learning models and generating visual reports. Decision makers can intuitively understand market dynamics and potential risks and make data-based decisions faster.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其它的附图。其中：In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:
图1为本发明一种基于数据仓库模型的数据处理方法流程图；FIG1 is a flow chart of a data processing method based on a data warehouse model according to the present invention;
图2为本发明一种基于数据仓库模型的数据处理系统结构图。FIG. 2 is a structural diagram of a data processing system based on a data warehouse model according to the present invention.
具体实施方式DETAILED DESCRIPTION
为使本发明的上述目的、特征和优点能够更加浅显易懂，下面结合说明书附图对本发明的具体实施方式做详细的说明，显然所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明的保护的范围。In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.
在下面的描述中阐述了很多具体细节以便于充分理解本发明，但是本发明还可以采用其他不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本发明内涵的情况下做类似推广，因此本发明不受下面公开的具体实施例的限制。In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.
其次，此处所称的“一个实施例”或“实施例”是指可包含于本发明至少一个实现方式中的特定特征、结构或特性。在本说明书中不同地方出现的“在一个实施例中”并非均指同一个实施例，也不是单独的或选择性的与其他实施例互相排斥的实施例。Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.
本发明结合示意图进行详细描述，在详述本发明实施例时，为便于说明，表示器件结构的剖面图会不依一般比例作局部放大，而且所述示意图只是示例，其在此不应限制本发明保护的范围。此外，在实际制作中应包含长度、宽度及深度的三维空间尺寸。The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.
同时在本发明的描述中，需要说明的是，术语中的“上、下、内和外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。此外，术语“第一、第二或第三”仅用于描述目的，而不能理解为指示或暗示相对重要性。At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.
本发明中除非另有明确的规定和限定，术语“安装、相连、连接”应做广义理解，例如：可以是固定连接、可拆卸连接或一体式连接；同样可以是机械连接、电连接或直接连接，也可以通过中间媒介间接相连，也可以是两个元件内部的连通。对于本领域的普通技术人员而言，可以具体情况理解上述术语在本发明中的具体含义。In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
实施例1Example 1
参照图1，为本发明的第一个实施例，提供了一种基于数据仓库模型的数据处理方法。1 , which is a first embodiment of the present invention, provides a data processing method based on a data warehouse model.
从多种金融交易源获取实时和历史金融数据，采用API s和Web爬虫技术获取实时性数据，所述金融交易源包括股市交易系统、银行交易记录以及金融聚合媒体。Acquire real-time and historical financial data from multiple financial transaction sources, using APIs and web crawler technology to obtain real-time data. The financial transaction sources include stock market trading systems, bank transaction records, and financial aggregation media.
对获取到的数据进行预处理，去除异常值和合格不合规的数据项，对数据进行标准化，统一数据格式；对获取的数据添加标签，为数据创建行业分类标签和风险标签，并与主数据合并，作为完整的交易数据进行存储。Preprocess the acquired data to remove outliers and qualified but non-compliant data items, standardize the data, and unify the data format; add labels to the acquired data, create industry classification labels and risk labels for the data, and merge them with the master data to store them as complete transaction data.
设计数据仓库，用于存储交易数据。Design a data warehouse to store transaction data.
将交易数据中的数据内容以星型表进行关联，存储于数据仓库中；在星型表中，以事实表和维度表的形式表示数据项之间的关系；所述事实表包括交易金额、交易时间以及交易类型；所述维度表包括客户维表、时间维表以及地理维表；所述客户维表用于记录客户的信息和行为类型；所述时间维表用于记录交易的时间信息，并以时间序列的形式进行存储；所述地理维表包含交易地点信息，用于地区分析。The data content in the transaction data is associated in a star table and stored in the data warehouse; in the star table, the relationship between data items is represented in the form of fact table and dimension table; the fact table includes transaction amount, transaction time and transaction type; the dimension table includes customer dimension table, time dimension table and geographic dimension table; the customer dimension table is used to record customer information and behavior type; the time dimension table is used to record transaction time information and store it in the form of time series; the geographic dimension table contains transaction location information for regional analysis.
以星型表的形式对数据进行聚合，以数据项之间的关联性作为数据之间的拓扑关系。The data is aggregated in the form of a star table, and the association between data items is used as the topological relationship between the data.
选择数据库作为数据仓库模型的载体，所述数据库包括Orcle、SQLSever以及MySOL；将数据仓库中的交易数据按照星型模型的拓扑逻辑，转换为数据库中的表、列、数据类型及其约束；在数据库中，对事实表和维度表创建索引优化查询性能；按照星型表的中的拓扑逻辑，对数据库中的表，按照交易金额、交易时间以及交易类型进行表分区。A database is selected as the carrier of the data warehouse model, and the database includes Oracle, SQLSever and MySOL; the transaction data in the data warehouse is converted into tables, columns, data types and their constraints in the database according to the topological logic of the star model; in the database, indexes are created for fact tables and dimension tables to optimize query performance; according to the topological logic in the star table, the tables in the database are partitioned according to transaction amount, transaction time and transaction type.
为数据库建立配置认证机制，确保授权用户访问数据仓库，定义不同类型用户和权限，以控制不同用户和用户组对数据仓库的访问，对存储在数据仓库中的数据进行加密，所述加密包括静态数据加密和传输数据加密；使用TLS/SSL协议保护数据传输。Establish and configure an authentication mechanism for the database to ensure that authorized users access the data warehouse, define different types of users and permissions to control access to the data warehouse by different users and user groups, encrypt the data stored in the data warehouse, including static data encryption and transmission data encryption; use TLS/SSL protocol to protect data transmission.
对不同交易数据之间进行相关性分析，计算不同类型交易数据之间的相关性，采用偏相关系数对不同类型交易数据的相关性进行分析。Conduct correlation analysis between different transaction data, calculate the correlation between different types of transaction data, and use partial correlation coefficients to analyze the correlation between different types of transaction data.
对不同的交易数据X和交易数据Y进行相关性分析，构建交易数据X和Y的趋势：Perform correlation analysis on different transaction data X and transaction data Y to construct the trend of transaction data X and Y:
X＝β0A+β1AT+β2AD+∈x；X＝β0A +β1A T+β2A D+∈x;
Y＝β0B+β1BT+β2BD+∈y；Y＝β0B +β1B T+β2B D+∈y;
其中，β0A、β0B、β1A、β1B、β2A和β2B是回归系数，∈x和∈y回归残差，代表去除交易类型和交易时间影响后，交易金额A和B的变化；∈x和∈y是从实际交易金额中减去由交易类型和时间预测的部分得到的。Whereβ0A ,β0B ,β1A ,β1B ,β2A andβ2B are regression coefficients, ∈x and ∈y are regression residuals, representing the changes in transaction amounts A and B after removing the effects of transaction type and transaction time; ∈x and ∈y are obtained by subtracting the part predicted by transaction type and time from the actual transaction amount.
使用∈x和∈y的值表示交易数据X和Y之间的相关性：Use the values of ∈x and ∈y to represent the correlation between transaction data X and Y:
其中，和是残差的平均值；γ(XY,M)表示交易数据X和交易数据Y之间的相关性，若γ(XY,M)显著不为零，则交易数据X和交易数据存在显著线性关联。in, and is the mean value of the residuals; γ(XY,M) represents the correlation between transaction data X and transaction data Y. If γ(XY,M) is significantly different from zero, there is a significant linear correlation between transaction data X and transaction data.
采用机器学习技术，在机器学习模型学习不同类型交易数据的风险等级后，对新创建的交易进行风险等级划分。Using machine learning technology, after the machine learning model learns the risk levels of different types of transaction data, newly created transactions are classified into risk levels.
根据交易行为特征，通过人工对交易数据进行风险等级划分，将风险等级作为交易数据的标签，将交易数据和对应的风险标签作为数据集，训练机器学习模型学习交易风险划分；所述数据集包括训练集、验证集和测试集。According to the transaction behavior characteristics, the transaction data is manually divided into risk levels, the risk levels are used as labels for the transaction data, and the transaction data and the corresponding risk labels are used as data sets to train a machine learning model to learn transaction risk classification; the data set includes a training set, a validation set, and a test set.
机器学习模型学习不同类型交易数据和对应风险等级，以训练集数据中的不同风险等级所占的概率为输出，当达到机器学习模型的训练次数时停止训练。The machine learning model learns different types of transaction data and corresponding risk levels, and uses the probabilities of different risk levels in the training set data as output. Training stops when the number of training times of the machine learning model is reached.
将训练完成的机器学习模型部署到数据仓库中，在数据仓库新存入交易数据时，通过机器学习模型分析交易数据的风险等级；所述机器学习模型为决策树模型。The trained machine learning model is deployed to the data warehouse. When new transaction data is stored in the data warehouse, the risk level of the transaction data is analyzed by the machine learning model; the machine learning model is a decision tree model.
对数据仓库中存储的数据生成可视化报告，使用数据分析工具，创建仪表板和图表，显示直观分析结果。Generate visual reports on the data stored in the data warehouse, use data analysis tools, create dashboards and charts to display intuitive analysis results.
通过网络平台以及内部系统分享分析结果，确保用户访问结果信息。Share analysis results through online platforms and internal systems to ensure user access to result information.
实施例2Example 2
本发明的第二个实施例，提供了一种基于数据仓库模型的数据处理系统。The second embodiment of the present invention provides a data processing system based on the data warehouse model.
所述系统包括：数据获取和处理模块、数据仓库模块、数据分析模块以及可视化模块。The system includes: a data acquisition and processing module, a data warehouse module, a data analysis module and a visualization module.
所述数据获取和处理模块，用于从金融交易源获取交易数据，对交易数据进行预处理。The data acquisition and processing module is used to acquire transaction data from a financial transaction source and pre-process the transaction data.
所述数据仓库模块，用于设计数据仓库，将交易数据以星型表进行关联，并对交易数据进行数据聚合。The data warehouse module is used to design a data warehouse, associate transaction data with a star table, and perform data aggregation on the transaction data.
所述数据分析模块，用于对交易数据进行挖掘和分析，运用统计分析和机器学习技术对数据进行分析。The data analysis module is used to mine and analyze transaction data, and analyze the data using statistical analysis and machine learning techniques.
所述可视化模块，构建可视化报告，根据交易数据分析结果制作报告，为金融决策提供支持。The visualization module constructs visualization reports and produces reports based on the transaction data analysis results to provide support for financial decision-making.
实施例3Example 3
本发明第三个实施例，其不同于前一个实施例的是：The third embodiment of the present invention is different from the previous embodiment in that:
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-OnlyMemory)、随机存取存储器(RAM，RandomAccessMemory)、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.
在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行系统、装置或设备(如基于计算机的系统、包括处理器的系统或其他可以从指令执行系统、装置或设备取指令并执行指令的系统)使用，或结合这些指令执行系统、装置或设备而使用。就本说明书而言，“计算机可读介质”可以是任何可以包含、存储、通信、传播或传输程序以供指令执行系统、装置或设备或结合这些指令执行系统、装置或设备而使用的装置。The logic and/or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.
计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)、便携式计算机盘盒(磁装置)、随机存取存储器(RAM)、只读存储器(ROM)、可擦除可编辑只读存储器(EPROM或闪速存储器)、光纤装置以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.
应当理解，本发明的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行系统执行的软件或固件来实现。例如，如果用硬件来实现，和在另一实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
此外，为了提供示例性实施方案的简练描述，可以不描述实际实施方案的所有特征(即，与当前考虑的执行本发明的最佳模式不相关的那些特征，或于实现本发明不相关的那些特征)。Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.
应理解的是，在任何实际实施方式的开发过程中，如在任何工程或设计项目中，可做出大量的具体实施方式决定。这样的开发努力可能是复杂的且耗时的，但对于那些得益于此公开内容的普通技术人员来说，不需要过多实验，所述开发努力将是一个设计、制造和生产的常规工作。It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.
应说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本发明技术方案的精神和范围，其均应涵盖在本发明的权利要求范围当中。It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.
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