Method and system for realizing business process data processing based on trend analysis

Abstract

The invention provides a method and a system for realizing business process data processing based on trend analysis. The method comprises the following steps: constructing a business hierarchical tree structure through linear regression analysis; according to a linear regression formula, determining a relationship between the dependent variable and the independent variable so as to establish a unique code of each node; collecting and processing business process related data, carrying out trend identification and prediction, and constructing a business main database step by step; forming a global unique code of each data object according to the depth and hierarchical structure mapping of the tree nodes, and automatically updating the corresponding codes and mapping when a data hierarchical rule changes; based on the incidence relation between the business main data object and the task grading model file, task guides are set, and each task guide is consistent with the corresponding component code; and when the task is loaded, de-weighting verification is carried out, and a visual scene is automatically constructed. The system comprises an information configuration module, a database establishment module and a content setting module. According to the invention, the efficiency and accuracy of data management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for implementing business process data processing based on trend analysis. Background Art

[0002] As enterprises face increasingly fierce market competition and diversified and personalized customer needs, enterprises have an increasingly urgent need to standardize and optimize business processes. In terms of business process management, traditional business processes often have problems such as non-standardization, inefficiency, and complexity. These problems not only affect the operational efficiency of enterprises, but also restrict their innovation and development. Therefore, enterprises need a scientific and systematic method to standardize business processes and improve the efficiency and quality of processes. At the same time, with the rapid development of big data and artificial intelligence technologies, data analysis has become an important basis for corporate decision-making, and has also provided new ideas and methods for the standardization of business processes; data analysis technology, especially trend analysis technology, provides new solutions for the standardization of business processes. Trend analysis is a method of analyzing historical data to find out the laws and trends of data changes, thereby predicting what may happen in the future.

[0003] When enterprises are promoting information process management, they often find that the existing information system is far from achieving the expected results, with slow system response, complicated processes and insufficient data accuracy as the main factors. These problems directly affect the efficiency and effectiveness of the implementation of information business processes. At the same time, data integration between systems is difficult, and data between subsystems cannot be effectively integrated, forming "data islands", making it difficult for enterprises to fully understand and utilize data resources.

[0004] At present, when domestic enterprises and institutions are designing business process informatization, due to insufficient in-depth understanding of the business or insufficient understanding of industry development trends, the process design is unreasonable and cannot meet actual business needs. The process is not standardized. Due to changes in business needs and personnel mobility, the process is often not standardized, resulting in the ineffective functioning of the informatization system. With the advancement of enterprise process management informatization, the storage and transmission of large amounts of data and information have become the norm, leading to increased security risks; the emergence of security issues such as data leakage and system attacks will have a significant impact on the business operation and reputation of the enterprise. Enterprises usually have multiple informatization systems, such as ERP, OA, CRM, etc. These systems often operate independently, making it difficult to achieve information sharing and process collaboration, which brings many inconveniences to enterprise operations.

[0005] Prior art 1, application number: CN 202210717624.X discloses a real estate transaction system, including a basic map database, a housing thematic database, a place name and address database, historical data and other supporting databases, the basic map database includes a basic topographic map and an image database map, the housing thematic database includes housing information, housing surrounding information, housing location information and community information, the place name and address database includes place name and address data, the historical database includes historical housing data and historical map data, and other supporting databases include a metadata database and a user database. Although the standardized management of the entire process information of the main business such as housing management, real estate transactions, financial management, and administrative management is realized, thereby integrating upstream and downstream systems and running through the entire value chain, so that the business process of the production and operation center is standardized and the operation is standardized. The real estate transaction system. However, the lack of processing of the relevant task decomposition model leads to more task data processing, which reduces the efficiency of business process management.

[0006] Prior art 2, application number: CN 202011337116.6 discloses a business process management system, including a front-end module and a back-end module; the front-end module is used to visualize the back-end module; the back-end module includes: a basic data module, which is used to provide basic data for the process design module to design the process; the process design module is used to design the business process through the process designer, and convert the collected business requirements into a defined process; the process deployment module is used to deploy and publish the defined process after the process design module completes the process design; the process execution module is used to execute the defined process according to the pre-defined process processing line. Although the business process is optimized through process design, process deployment, and process execution to ensure the standardized operation of the business process and improve work efficiency. However, its intelligence level is low, and there is a lack of detailed processing of multi-task data, resulting in low efficiency in the implementation of business process scenarios.

[0007] Prior art three, application number: CN 202011341400.0 discloses a transshipment management method, system and storage medium, the method comprising the following steps: receiving a first entrustment information sent by a first client, generating a second entrustment information based on the first entrustment information, and sending the second entrustment information to a second client; after receiving a confirmation instruction from the second client for the second entrustment information, allocating the first transportation information corresponding to the second entrustment information. Through the above method, the first entrustment information received by different communication forms can be effectively integrated and then the second entrustment information can be automatically generated according to the first entrustment information, so that the transshipment business process is more standardized. Although at the same time, since the second entrustment information is automatically generated, compared with the method of manually entering the second entrustment information, the business processing efficiency is effectively improved. However, the automatic association between the business master data and the model cannot be realized, resulting in low efficiency in business process data processing and poor transshipment management effect.

[0008] At present, the existing technologies 1, 2 and 3 have the problems that hidden dangers of the on-site safety state machine cannot be uploaded in time and discovered early, the automatic association between the business master data and the model cannot be achieved, and the efficiency of the implementation of the business process scenario is low. Therefore, the present invention provides a method and system for processing business process data based on trend analysis, which uses trend analysis to build a task decomposition model to achieve automatic classification and decomposition of tasks; based on task classification and subdivision, a task guide is provided; based on the task guide, unfinished tasks are reminded to achieve standardized management of business processes. Summary of the invention

[0009] In order to solve the above technical problems, the present invention provides a method for processing business process data based on trend analysis, comprising the following steps:

[0010] Build a business hierarchy tree structure through linear regression analysis, set corresponding attributes and coding rules for each hierarchy; establish a unique code for each node;

[0011] Collect and process business process-related data, identify and predict trends, and build a business master database level by level;

[0012] Based on the association between the business master data object and the task classification model file, set the task guide, and each task guide is consistent with the corresponding component code; deduplication and verification are performed when loading tasks, and a visualization scene is automatically built.

[0013] Optionally, the process of establishing a unique code for each node includes the following steps:

[0014] Collect and process business process data. The resulting data set contains information on independent variables and dependent variables. The data set is cleaned and normalized.

[0015] Determine the levels and nodes of the tree structure. For each level, configure the type attribute, the number of bits of encoding, and the encoding type.

[0016] Using the results of linear regression analysis, corresponding coding rules are established for each node.

[0017] Optionally, the process of constructing a business master database using descriptive statistics to perform a preliminary analysis of the business process data in the database includes the following steps:

[0018] Select the variables to be analyzed and calculate basic statistics for the variables, including median and quartiles;

[0019] Receive the calculation results of basic statistics, perform feature extraction, and obtain feature data; segment the variables to obtain partitions; and establish the corresponding relationship between feature data and partitions;

[0020] Construct a box plot and insert the abnormal data traversed from the feature data into the box plot; create a box plot of the variables that need to be analyzed.

[0021] Optionally, the feature data is sorted, a lower quartile, an upper quartile, and a median in the feature data are determined, and an upper limit and a lower limit of the feature data are calculated.

[0022] Optionally, in a box plot, the middle line represents the median, indicating the central location of sales; the quartiles arrange the edges of the box, and the points outside the whiskers of the box plot represent abnormal data, identifying extreme cases of sales.

[0023] Optionally, the process of building a business master database to integrate all data objects, descriptions and their codes into a business process database includes the following steps:

[0024] Adopting a hierarchical structure design based on semantic networks, using a graph database to dynamically represent each data object and its relationship; intelligently encoding the data objects and attributes of each level;

[0025] Equipped with intelligent coding generation tools, it automatically generates unique codes and dynamically updated inheritance models.

[0026] Optionally, before the data flows to the target database, the integration of all data objects is handled through a programmable data conversion model; a distributed database is used through batch insertion to ensure that all data objects, descriptions and codes are in the business process database.

[0027] Optionally, the process of setting up a task guide includes the following steps:

[0028] Determine the model to be disassembled and its logical hierarchical structure, including the roles and responsibilities of each level; according to the definition of each level, disassemble the entire model into multiple independent model files; each file should contain the data coding specific to that level and the corresponding task guide;

[0029] In each model file, the corresponding task guide is set based on the specific hierarchical data coding; in each task guide, the component coding is configured to ensure that each coding is consistent with the data object coding in the system's model component hierarchy;

[0030] Create an import program to batch import all model files into the task guide model in the system; confirm that the program supports various model file formats, and during the import process, verify whether the task guide and component coding defined in each model file meet the preset standards; record possible errors during the import process; develop a lightweight program to process the imported task guide models one by one.

[0031] Optionally, the task instructions should clearly state the purpose, process, execution steps and expected results of the task.

[0032] The present invention provides a business process data processing system based on trend analysis, comprising:

[0033] The information configuration module is responsible for constructing the business hierarchy tree structure through linear regression analysis, setting corresponding attributes and coding rules for each hierarchy, and establishing unique codes for each node;

[0034] The database establishment module is responsible for collecting and processing business process-related data, identifying and predicting trends, and building the business master database level by level;

[0035] The content setting module is responsible for setting the task guide based on the association between the business master data object and the task classification model file. Each task guide is consistent with the corresponding component code; it performs deduplication verification when loading tasks and automatically builds a visualization scene.

[0036] The business hierarchical tree structure of the present invention is constructed by the result of linear regression analysis to establish a clear business hierarchical tree structure, in which each level has clear attributes and coding rules; the relationship between the dependent variable and the independent variable is determined, and the unique code of each node is generated using the linear regression formula to ensure the consistency and uniqueness of the data object in the global scope. Significance: It provides a structured framework that enables enterprises to better organize and manage business processes and facilitate subsequent data processing and analysis; through clear coding rules, it reduces the possibility of data confusion and misuse, and lays a solid foundation for subsequent operations. Data collection and master database construction, by collecting and processing business process-related data, effective trend identification and prediction are carried out to generate a business master database; when the hierarchical structure changes, the coding and mapping can be automatically updated to ensure the real-time and accuracy of the data. Significance: It optimizes the storage and management of business information, enabling enterprises to effectively adjust strategies in a rapidly changing market environment; it provides a dynamically updated database to support data-driven decision-making processes and improve the flexibility and responsiveness of the overall business. Task guide setting and visual scene construction, based on the relationship between business master data objects and task classification model files, set up clear task guides, improve the clarity and traceability of task execution; through deduplication verification and automatic construction of visual scenes, provide an intuitive operation interface to facilitate users' rapid management and execution; support voice recognition and keyword query functions, greatly improving the convenience and efficiency of user interaction. Significance: In the process of business execution, through clear task guides and visual tools, the team's collaboration efficiency is enhanced and the communication cost is reduced; the intelligence level of task management is improved, making the business process more automated and efficient, and achieving accurate business operations and resource allocation.

[0037] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0038] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0040] Figure 1 This is a flow chart of a method for processing business process data based on trend analysis in Example 1 of the present invention;

[0041] Figure 2 A process diagram for establishing a unique code for each node in Embodiment 2 of the present invention;

[0042] Figure 3 This is a process diagram for constructing a business master database in Example 3 of the present invention;

[0043] Figure 4 This is a process diagram of using descriptive statistics to perform preliminary analysis on business process data in a database in Example 4 of the present invention;

[0044] Figure 5 A flowchart of using time series analysis to identify potential trends and seasonal patterns in data in Example 5 of the present invention;

[0045] Figure 6 A process diagram for integrating all data objects, descriptions and their encodings into a business process database in Embodiment 6 of the present invention;

[0046] Figure 7 A process diagram of the association relationship between the business master data object and the task classification model file in Example 7 of the present invention;

[0047] Figure 8 A process diagram for setting a task guide in Embodiment 8 of the present invention;

[0048] Fig. 9 A process diagram of automatically constructing a visualization scene in Embodiment 9 of the present invention;

[0049] Fig.10 This is a block diagram of a business process data processing system based on trend analysis in Example 10 of the present invention. DETAILED DESCRIPTION

[0050] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0051] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0052] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application described in detail. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0053] Example 1: Figure 1 As shown, an embodiment of the present invention provides a method for processing business process data based on trend analysis, comprising the following steps:

[0054] S100: construct a business hierarchy tree structure through linear regression analysis, set corresponding attributes and coding rules for each hierarchy; determine the relationship between the dependent variable and the independent variable according to the linear regression formula to establish a unique code for each node;

[0055] S200: Collect and process business process-related data, identify and predict trends, and build a business master database level by level; form a globally unique code for each data object based on the depth of the tree node and the hierarchical structure mapping, and automatically update the corresponding code and mapping when the data hierarchy rules change;

[0056] S300: Based on the association between the business master data object and the task classification model file, set the task guide, and each task guide is consistent with the corresponding component code; deduplicate verification is performed when loading tasks, and visual scenes are automatically built; voice recognition and keyword query are supported at the same time to optimize task management and execution process.

[0057] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first constructs a business hierarchical tree structure through linear regression analysis, and sets corresponding attributes and coding rules for each level; according to the linear regression formula, determines the relationship between the dependent variable and the independent variable to establish the unique code of each node; secondly, collects and processes business process related data, performs trend identification and prediction, and constructs the business master database step by step; according to the depth of the tree node and the hierarchical structure mapping, forms a global unique code for each data object, and automatically updates the corresponding code and mapping when the data hierarchical rules change; finally, based on the association between the business master data object and the task classification model file, sets the task guide, and each task guide is consistent with the corresponding component code; removes duplicate verification when loading tasks, and automatically builds a visual scene; supports voice recognition and keyword query at the same time, and optimizes task management and execution process. In the step S100 of the above scheme, a clear business hierarchical tree structure is established through the results of linear regression analysis, and each level has clear attributes and coding rules; determines the relationship between the dependent variable and the independent variable, and uses the linear regression formula to generate the unique code of each node to ensure the consistency and uniqueness of the data object in the global scope. Significance: It provides a structured framework that enables enterprises to better organize and manage business processes and facilitates subsequent data processing and analysis; through clear coding rules, it reduces the possibility of data confusion and misuse, laying a solid foundation for subsequent operations. Step S200 Data collection and master database construction, by collecting and processing business process-related data, effective trend identification and prediction are carried out to generate a business master database; when the hierarchical structure changes, the coding and mapping can be automatically updated to ensure the real-time and accuracy of the data. Significance: It optimizes the storage and management of business information, enabling enterprises to effectively adjust their strategies in a rapidly changing market environment; it provides a dynamically updated database to support data-driven decision-making processes and improve the flexibility and responsiveness of the overall business. Step S300 Task guide setting and visual scene construction, based on the association between business master data objects and task hierarchical model files, a clear task guide is set to improve the clarity and traceability of task execution; through deduplication verification and automatic construction of visual scenes, an intuitive operation interface is provided to facilitate users' rapid management and execution; it supports voice recognition and keyword query functions, greatly improving the convenience and efficiency of user interaction. Significance: In the process of business execution, through clear task guidelines and visualization tools, the team's collaboration efficiency is enhanced and communication costs are reduced; the intelligence level of task management is improved, making business processes more automated and efficient, and achieving accurate business operations and resource allocation.

[0058] In summary, the business process data processing method based on trend analysis in this embodiment not only improves the efficiency and accuracy of data management, but also brings higher business flexibility and adaptability to enterprises. The implementation of this methodology can help enterprises respond quickly and optimize resource allocation in a complex and changing environment, thereby enhancing their competitive advantage. At the same time, the application of intelligent and visualization technologies also provides important support for the digital transformation of enterprises.

[0059] This embodiment solves the problem that hidden dangers of the on-site safety state machine cannot be uploaded in time and discovered early through a data collection method and system; based on the trend analysis method, the business data hierarchy depth and coding are configured through task guidelines, and the model's disassembly and coding table are simultaneously specified to achieve automatic association between business master data and models; the secondary development work of the model is greatly reduced through personalized configuration of business master data objects displayed by trend analysis; the implementation efficiency of business process scenarios is improved, and it can be flexibly applied to a variety of scenarios in multiple industries.

[0060] Example 2: Figure 2 As shown, based on Example 1, the process of establishing a unique code for each node provided in the embodiment of the present invention includes the following steps:

[0061] S101: Collect and process business process data. The resulting data set contains information on independent variables and dependent variables, which comes from the company's operation reports, customer feedback, and market research. Clean and normalize the data set.

[0062] S102: Determine the levels and nodes of the tree structure, and for each level, configure the type attribute, the number of encoding bits, and the encoding type;

[0063] Hierarchical definition: Define each level in the tree structure according to business needs; for example, a typical tree structure can be defined as: business line > product category > product item;

[0064] Number of nodes: Determine the number of nodes N in the entire tree structure. The number of nodes may depend on the actual business level complexity and data characteristics;

[0065] Type attribute: defines the type of the hierarchy, such as "business department", "product category", etc., so as to facilitate subsequent business analysis at different levels;

[0066] Coding digits: Determine the coding digits for each level to achieve uniqueness of coding between levels and scalability of the system;

[0067] Coding type: Select the type of coding, which may be digital coding, alphabetic coding, or a combination of the two, to identify each node;

[0068] S103: Using the result of linear regression analysis, establish a corresponding coding rule for each node;

[0069] The coding principles include:

[0070] Dependent variables and independent variables: Use the relationship generated by linear regression to determine the relationship between independent variables and dependent variables, so as to generate corresponding codes for nodes at each level; possible independent variables include time, market demand, and equipment utilization; dependent variables are the performance indicators to be optimized in the end, such as sales and customer satisfaction;

[0071] The calculation expression of linear regression is:

[0072] y=a+bx

[0073] In the formula, y is the dependent variable (or response variable), x is the independent variable (or predictor variable), a and b are regression coefficients, representing the intercept and slope of the line respectively;

[0074] Best fit line: By calculating the regression coefficients a and b, the best fit line is obtained, which provides a basis for the calculation of nodes at different levels; for example, if there may be a direct relationship between a certain level and sales volume, the coding and attributes of the level can be dynamically adjusted according to the linear regression results;

[0075] Code generation: Each node’s code can be generated by combining the features of the hierarchy with the result of linear regression to form a unique identifier; for example, a code like “TYPE001-001” can be generated based on the node’s type, order, and regression output.

[0076] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the business process data is first collected and processed, and the data set formed contains information on independent variables and dependent variables, which comes from the enterprise's operation report, customer feedback and market research, etc.; the data set is cleaned and normalized; the level and node of the tree structure are determined, and for each level, the type attribute, the number of coding bits and the coding type are configured; finally, the result of linear regression analysis is used to establish corresponding coding rules for each node. Step S101 of the above scheme collects and processes business process data, collects relevant data from multiple channels such as the enterprise's operation report, customer feedback and market research, and forms a complete data set containing independent variables (such as market demand, time, etc.) and dependent variables (such as sales, customer satisfaction, etc.); the collected data is cleaned, missing or outliers are eliminated, and normalized to ensure that the data is in the same dimension, which is convenient for subsequent analysis. Significance: Ensuring data quality is the basis for subsequent analysis. A clean, accurate and unified data set can improve the reliability of the analysis results and provide a basis for establishing effective models and decisions; the structuring and standardization of data makes subsequent linear regression analysis more intuitive and convenient. Step S102 determines the levels and nodes of the tree structure, and clarifies the hierarchical relationship of the tree structure according to business needs, such as business line>product category>product item; configure type attributes, coding digits and coding types: set type attributes (such as "business department", "product category", etc.), specific coding digits (to ensure uniqueness and scalability), and appropriate coding types (numbers, letters, or a combination of both) for each level. Significance: A clear hierarchical structure helps to systematically manage complex business environments and facilitate data flow and information sharing between levels; by setting attributes and coding rules, each node is given a clear meaning and position, making the system more maintainable and flexible when expanding. Step S103 uses linear regression analysis to connect independent variables and dependent variables, and generates corresponding codes for nodes at each level, which helps to understand how independent variables affect business performance; uses a linear regression model to calculate regression coefficients (a) and (b), and determines the best fit line (y=a+bx) based on the relationship expression, providing a quantitative analysis basis for the performance of each node; combines hierarchical features, linear regression analysis results, and other node information to generate a unique code such as "TYPE001-001" to ensure efficient distinction and management between nodes. Significance: Combining business data with linear regression analysis results not only improves the scientific nature of the code, but also effectively reflects business status and trends, facilitating subsequent monitoring and decision-making; through personalized code generation strategies, data confusion can be effectively prevented, and the uniqueness of the code can be ensured, which is conducive to the scalability and maintenance of the system.

[0077] In summary, through the implementation of the above steps, the entire workflow of this embodiment forms a scientific and systematic data coding management solution. Specifically: the step-by-step workflow simplifies the complexity of traditional data processing and improves the efficiency of data processing and analysis; coding management based on scientific analysis can support enterprises to make accurate decisions quickly in a dynamic environment; the scalable coding system and hierarchical structure design can adapt to the growing operational needs of enterprises and promote the digital transformation of enterprises. Ultimately, this systematic process enables enterprises to maintain competitiveness in a dynamic market and provides strong data support for long-term business development and decision-making.

[0078] Example 3: Figure 3 As shown, based on Example 1, the process of building a business master database provided by the embodiment of the present invention includes the following steps:

[0079] S201: Use API or crawler to obtain business process data through data sources, including enterprise operation reports, customer feedback, market research and financial statements; pre-process the obtained business process data, including data cleaning, standardizing data format and data normalization, and store the pre-processed business process data in the database;

[0080] S202: Use descriptive statistics to conduct a preliminary analysis of the business process data in the database, and use time series analysis to identify potential trends and seasonal patterns in the business process data; determine the dependent variable (the indicator to be predicted, such as sales) and the independent variable (the factor that affects the dependent variable), use the linear regression model to calculate the parameters, and obtain the prediction equation; apply the established regression model, input new independent variable data, and calculate the predicted dependent variable value; interpret the model output results and analyze the impact on the business;

[0081] S203: Set the levels of the tree structure, define the data objects and their attributes at each level; organize the business data by level, and establish objects level by level; determine the number of bits and types of codes at each level, set the code inheritance rules, and generate a unique code for each data object according to the above rules; integrate all data objects, descriptions and their codes into a business process database, the relationship between tables in the business process database can reflect the hierarchical relationship, and insert the final data objects and codes into the business process database level by level;

[0082] S204: Establish a configuration management system to record changes in the hierarchical structure and coding rules in a timely manner; identify the changed data hierarchy, remap and encode; apply the linear regression model to re-predict related dependent variables based on the new hierarchical structure; update the corresponding data objects and codes in the business process database.

[0083] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the business process data is first obtained through data sources using API or crawlers, and the data sources include enterprise operation reports, customer feedback, market research and financial statements, etc.; the obtained business process data is preprocessed, and the preprocessing includes data cleaning, standardizing data formats and data normalization, and the preprocessed business process data is stored in a database; secondly, descriptive statistics are used to perform a preliminary analysis of the business process data in the database, and time series analysis is used to identify potential trends and seasonal patterns in the data; the dependent variable (the indicator to be predicted, such as sales) and the independent variable (the factor affecting the dependent variable) are determined, and the parameters are calculated using a linear regression model to obtain a prediction equation; the established regression model is applied, new independent variable data is input, and the predicted dependent variable value is calculated; the model output is explained The results are analyzed to determine the impact on the business; then the tree structure hierarchy is set to define the data objects and their attributes at each hierarchy; the business data is organized by hierarchy and objects are established level by level; the number of coding digits and types at each hierarchy are determined, and the coding inheritance rules are set to generate a unique code for each data object based on the above rules; all data objects, descriptions and their codes are integrated into a business process database, where the relationship between tables can reflect the hierarchical relationship, and the final data objects and codes are inserted into the business process database level by level; finally, a configuration management system is established to record changes in the hierarchical structure and coding rules in a timely manner; the changed data hierarchy is identified, and remapped and encoded; based on the new hierarchical structure, a linear regression model is applied to re-predict the relevant dependent variables; and the corresponding data objects and codes in the business process database are updated. Step S201 of the above scheme is data acquisition and preprocessing. It extracts data from multiple data sources (corporate operation reports, customer feedback, market research, financial statements, etc.) through API or crawler technology; data cleaning removes noise and incomplete data to ensure data accuracy; standardizes data formats to unify data from different data sources for subsequent processing; and normalizes numerical data to eliminate dimensional effects and improve model effects. Significance: Integrate multiple data sources to ensure the comprehensiveness and diversity of data and obtain a more comprehensive business view; improve the accuracy and consistency of data to ensure that subsequent analysis and prediction are based on high-quality data, thereby improving the effectiveness of decision-making. Step S202 is preliminary analysis and predictive modeling. It summarizes and describes the data to discover the basic characteristics and distribution of the data; identifies trends and seasonal patterns in the data to help gain insight into business cycles; clarifies the relationship between dependent and independent variables and builds a predictive model; applies the established regression model to predict future dependent variables; and interprets the model results for business analysis and strategy adjustment. Significance: Through statistical analysis, enterprises can identify key business drivers and help formulate more effective operational strategies. Through model prediction, enterprises can identify potential market changes in advance and adjust strategies in a timely manner, thereby reducing risks and improving competitiveness.Step S203: Business data structuring and standardization, defining the hierarchical relationship and data objects of business data to form a clear data organization structure; formulating the number of coding bits, types and inheritance rules to ensure the uniqueness of all data objects; integrating data objects, descriptions and coding to create a relational database, in which the relationship between tables can effectively reflect the data hierarchy. Significance: Through structuring and standardization, enterprises can manage and retrieve data more efficiently and avoid data redundancy and ambiguity; adopting unified coding rules and hierarchical structures to provide a standardized basis for future data expansion and maintenance, and facilitate subsequent updates and optimizations. Step S204: Configuration management and dynamic updates, establishing a system to record and manage changes in hierarchical structures and coding rules to ensure flexibility in data management; Significance: In response to changes in the business environment, the database structure or prediction model can be adjusted in a timely manner to maintain business responsiveness; through automatic updates, manual intervention can be reduced, management efficiency can be improved, and the error rate can be reduced.

[0084] In summary, the entire process of this embodiment complements each other. The ultimate goal of building a business master database is to enhance the data management, analysis and prediction capabilities of the enterprise. Through systematic steps, the enterprise can not only effectively integrate and analyze existing data, but also adjust strategies in real time, optimize business processes, and improve the scientificity and accuracy of decision-making. It will provide strong data support for enterprises in the fierce market competition, thereby achieving sustainable long-term development.

[0085] Example 4: Figure 4 As shown, based on Example 3, the process of using descriptive statistics to perform preliminary analysis on business process data in a database provided by the embodiment of the present invention includes the following steps:

[0086] S2021: Select the variables to be analyzed and calculate basic statistics for the variables, including median and quartiles;

[0087] S2022: receiving the calculation results of basic statistics, and performing feature extraction to obtain feature data; segmenting the variables to obtain partitions; and establishing a corresponding relationship between the feature data and the partitions; sorting the feature data, determining the lower quartile, upper quartile and median in the feature data, and calculating the upper and lower limits of the feature data;

[0088] S2023: Construct a box plot and insert the abnormal data traversed from the feature data into the box plot; create a box plot of the variables to be analyzed; in the box plot, the middle line represents the median, indicating the central position of sales; the quartiles are used to lay out the edges of the box, and the points outside the whiskers of the box plot represent abnormal data, identifying extreme sales.

[0089] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first selects the variables to be analyzed, calculates the basic statistics for the variables, including the median and quartiles; secondly, receives the calculation results of the basic statistics, performs feature extraction, and obtains feature data; segments the variables to obtain partitions; and establishes a corresponding relationship between the feature data and the partitions; sorts the feature data, determines the lower quartile, upper quartile and median in the feature data, and calculates the upper and lower limits of the feature data; finally, constructs a box plot, and inserts the abnormal data traversed in the feature data into the box plot; establishes a box plot of the variables to be analyzed; in the box plot, the middle line represents the median, indicating the central position of the sales; the quartiles arrange the edges of the box, and the points outside the whiskers of the box plot represent abnormal data, identifying extreme sales. Step S2021 of the above scheme selects variables and calculates basic statistics. Selecting appropriate variables is the first step in data analysis, ensuring that the analysis focuses on key factors, which can affect business decisions and strategic directions; calculating basic statistics (such as median and quartiles) can provide a preliminary understanding of data distribution and a basis for analysis, including: median: identifies the central trend of data; quartile: describes the degree of dispersion and distribution range of data. Significance achieved: Through basic data analysis, decision makers can better understand data behavior and patterns and identify potential problems; lay the foundation for subsequent analysis steps such as feature extraction and data cutting, ensuring that these analyses are based on reliable statistical basis. Step S2022 Feature extraction and data segmentation, extract key feature data from basic statistics, which helps to simplify the data structure and convert complex data into a format that is easier to analyze; by partitioning variables, connections between different segments can be established. That is, different categories or frequency bands are divided to facilitate subsequent comparison and analysis; sorting feature data, calculating the lower quartile, upper quartile, and median, and defining upper and lower limits, helps to understand the distribution range of the data set. Significance achieved: Feature extraction and data partitioning enable analysts to more easily identify patterns and trends in data; through partitioning and feature establishment, a clear analysis framework is generated, which can be used for subsequent data visualization and anomaly detection; knowing the upper and lower limits of the data can help us identify which data points are abnormal in subsequent steps. Step S2023 constructs a box plot and identifies anomalies, visualizes the distribution of feature data, clearly displays the median and quartiles, and summarizes the characteristics of the entire data set; in the box plot, points outside the whiskers represent abnormal data, which can identify extreme cases at a glance; the middle line of the box plot represents the median, indicating the center position of the data and providing a quick overview of the data.Significance achieved: The box plot can intuitively display the distribution of data and help non-technical personnel understand the data structure. Identifying outliers provides important information for subsequent decision-making, which can help business personnel focus on potential risks and opportunities and optimize business strategies. Through clear visualization, data analysis results can be more effectively communicated to team members or stakeholders, promoting data-based decision-making.

[0090] In summary, the steps of this embodiment complement each other and constitute a comprehensive data analysis process. Each step technically lays the foundation for subsequent analysis and promotes data-driven decision-making in a strategic sense, helping to identify trends, anomalies and potential problems, so that enterprises can more effectively optimize their operations and strategic directions.

[0091] Example 5: Figure 5 As shown, based on Example 3, the process of using time series analysis to identify potential trends and seasonal patterns in data provided by the embodiment of the present invention specifically includes the steps of:

[0092] S2024: Arrange the initially analyzed business process data in chronological order, use line graphs to show the trend of variables over time, and identify trends and seasonal patterns;

[0093] S2025: Calculate moving averages to remove short-term fluctuations and highlight long-term trends by smoothing data; use time as an independent variable to build a linear regression model and fit time series data to identify long-term trends; use additive or multiplicative models to seasonally decompose time series to separate trend, seasonality, and random components; identify the presence of any seasonal patterns by plotting autocorrelation function graphs and partial autocorrelation functions to help determine seasonal cycles;

[0094] Additive decomposition model: y(t) = T(t) + S(t) + R(t);

[0095] Multiplication decomposition model: y(t) = T(t)*S(t)*R(t);

[0096] S2026: Based on the results and analysis of the business process of the preliminary data analysis, select a time series model, divide the data of the business process of the preliminary analysis into a training set and a test set, and obtain the time series model through the training set; use the trained time series model to predict the dependent variables of the future time period to generate future sales or other target indicators.

[0097] The working principle and beneficial effects of the above technical solution are as follows: First, the business process data of the preliminary analysis are arranged in chronological order, and a line chart is used to show the trend of the variables changing over time, and the trend and seasonal pattern are identified; secondly, the moving average is calculated, and the short-term fluctuations are eliminated by smoothing the data to highlight the long-term trend; time is used as an independent variable, and a linear regression model is used to fit the time series data to identify the long-term trend; the time series is seasonally decomposed using an additive or multiplicative model to separate the trend, seasonality and random components; the existence of any seasonal pattern is identified by drawing an autocorrelation function graph and a partial autocorrelation function to help determine the seasonal cycle; finally, according to the results and analysis of the business process of the preliminary analysis of the data, a time series model is selected, and the data of the business process of the preliminary analysis is divided into a training set and a test set, and a time series model is obtained through the training set; the trained time series model is used to predict the dependent variable of the future time period, and the future sales or other target indicators are generated. Step S2024 of the above scheme arranges and visualizes the time series data to ensure that the time series data is orderly, which is convenient for identifying the behavior and pattern of the data changing over time; a line chart is used to intuitively show the trend of the variable changing over time, so as to identify the overall trend and seasonal pattern. Significance achieved: Through visualization, analysts can quickly understand whether there is a long-term trend of rising, falling or stable in the data, which is of great reference value for decision making; line charts help analysts observe whether there are recurring patterns in the data in different time periods, such as annual seasonal fluctuations, so that the business can adjust its strategy to cope with these fluctuations; this step is a preliminary exploration of data analysis, which helps to identify the need for data cleaning and processing and improve the effectiveness of subsequent modeling. Step S2025 smooths data and models, smoothes data through moving average, eliminates short-term fluctuations, and makes long-term trends more obvious; uses time as an independent variable to fit time series data and identify long-term trends; uses additive or multiplicative models to decompose time series, and divides time series results into trend (T), seasonality (S) and random components (R), which helps to analyze each component independently; autocorrelation and partial autocorrelation function graphs: draw autocorrelation function (ACF) and partial autocorrelation function (PACF) to identify seasonal patterns and their periodicity in the data. Significance achieved: Moving average helps analysts better identify long-term trends, eliminate noise, and thus enhance the ability to predict future data changes; identifying long-term trends and seasonal components during the modeling process enables companies to foresee future changes and plan resources and business strategies in a targeted manner; through accurate time series models, a reliable basis can be provided for future forecasts, reducing decision-making risks; Confirmation of seasonal patterns: Autocorrelation and partial autocorrelation analysis help confirm seasonal patterns in time series, allowing analysts to adjust models and forecasting strategies more accurately.Step S2026: Model selection and prediction. According to the preliminary analysis and business needs, select the appropriate time series model to ensure the applicability and effectiveness of the model; divide the data into training set and test set, so that the model can be trained on one data set and verified on another data set to improve the generalization ability of the model; use the trained model to predict the dependent variables in the future time period to generate future sales or other key indicators. Significance achieved: Accurate prediction based on time series models can help enterprises make more scientific decisions in budget preparation, inventory management and marketing; good prediction ability helps enterprises identify market changes and their potential risks in advance, and take corresponding preventive measures to reduce economic losses; through the prediction of future indicators, enterprises can formulate longer-term strategic plans, optimize resource allocation, and improve operational efficiency; data collection methods ensure that the model passes the real data test, and the results help to further optimize the model and improve the accuracy of the overall prediction.

[0098] In summary, the steps of this embodiment complement each other to form a comprehensive time series analysis and forecasting process. Each step not only lays the foundation for subsequent analysis in terms of technology, but also greatly enhances the efficiency of data-driven decision-making in terms of strategy, helps to identify changing trends and seasonal patterns, optimize resource allocation, and formulate long-term business strategies.

[0099] Example 6: Figure 6 As shown, based on Example 3, the process of integrating all data objects, descriptions and their codes into a business process database provided by the embodiment of the present invention includes the following steps:

[0100] S2031: Adopt a hierarchical structure design based on semantic networks, and use a graph database to dynamically represent each data object and its relationship; intelligently encode the data objects and attributes of each level, and use a scalable hash algorithm for encoding;

[0101] S2032: Provide intelligent generation tools for coding, automatically generate unique codes and dynamically update inheritance models;

[0102] S2033: Before data flows to the target database, the integration of all data objects is handled through a programmable data conversion model; a distributed database is used through batch insertion to ensure that all data objects, descriptions and codes are in the business process database.

[0103] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment firstly adopts a hierarchical structure design based on a semantic network, and uses a graph database to dynamically represent each data object and its relationship; the data objects and attributes of each level are intelligently encoded, and the encoding method adopts an extensible hash algorithm; secondly, an intelligent generation tool for encoding is given to automatically generate a unique encoding and a dynamically updated inheritance model; finally, before the data flows to the target database, a programmable data conversion model is used to process the integration of all data objects; a distributed database is used to ensure that all data objects, descriptions and encodings are in the business process database through batch insertion. Step S2031 of the above solution is based on the hierarchical structure design and intelligent encoding of the semantic network, and uses a graph database (such as Neo4j, ArangoDB, etc.) to realize the modeling of dynamic data objects and their relationships, which can intuitively express the complex relationships between multiple data objects in a graphical way and realize flexible query capabilities; an extensible hash algorithm is used to generate unique encodings for data objects and attributes. This design not only ensures the uniqueness of the encoding, but also reduces the risk of encoding conflicts, and supports the dynamic expansion of subsequent encodings. Significance: Through the advantages of graph databases, hierarchical relationships and interconnections between data can be understood more clearly, which is helpful for subsequent in-depth data analysis and decision support; the combination of intelligent coding and hashing algorithms makes it easy to expand data objects and hierarchies in the future without drastically modifying the entire database structure, thereby improving the flexibility and adaptability of the overall system. Step S2032 Intelligent generation tools and dynamic update models of unique codes. With the help of machine learning and other technologies, intelligent generation tools can automatically identify new data objects and their attributes, and generate unique codes to ensure that newly added data follows the existing coding rules and systems; the model can automatically update coding rules and relationships according to business changes (such as adjustments to the data hierarchy structure) to ensure data coherence and consistency. Significance: Through intelligent generation tools, maximum automation is achieved to reduce human errors, which is particularly important in environments with large amounts of data and frequent changes; dynamic updates ensure that when business needs change, the system can quickly adjust the relevant data structure, enhancing the agility and coverage of the system, thereby improving the efficiency of business operations. Step S2033 programmable data conversion models and distributed database processing for batch insertion. These models allow complex data conversion and integration logic to be supported before the data flows to the target database, ensuring that the data is stored in a format that meets the requirements of the target database; using distributed databases (such as Google BigQuery, Amazon Redshift, etc.) for batch insertion operations can significantly improve data writing efficiency and concurrent processing capabilities.Significance: The programmable transformation model greatly improves the integration efficiency of multi-source data, ensures the flexibility and accuracy of data processing, and reduces the time required for data preprocessing and loading. The use of distributed databases enables horizontal expansion of data storage, which can cope with massive data storage needs while maintaining high-performance query and data processing capabilities, providing a stable foundation for subsequent data analysis and applications.

[0104] In summary, this embodiment integrates all data objects, descriptions and their codes into a business process database by gradually implementing the above steps. This process realizes flexible, intelligent and automated data management at the technical level. At the business level, it ensures the accuracy, consistency and reliability of data, greatly improves the data processing speed and real-time performance, and provides strong data guarantee for decision-making support of enterprises in a dynamic market environment, thereby improving overall business efficiency and competitiveness. These technical means are combined to realize the modernization of data management and meet the needs of today's data-driven decision-making.

[0105] Example 7: Figure 7 As shown, based on Example 1, the process of associating the business master data object with the task classification model file provided by the embodiment of the present invention includes the following steps:

[0106] S301: Determine the list of business master data objects, including the attributes, functions and task guide related information of each object; according to the logic of the information, establish the relationship tree structure between the model disassembly level and the model component level, and clarify the parent-child relationship of each level and the nodes it contains;

[0107] S302: Organize the existing model files, ensure that each file is associated with the corresponding data set, validation set, test set and evaluation index, and record the associated information; each model file should have a unified format; traverse the hierarchical structure of the model disassembly, extract the data objects and task guides of each hierarchical node; establish a traceable mapping between the data objects and the corresponding model files, and record the file path, file name and related parameters associated with each hierarchical node;

[0108] S303: In the business master data object, for each level node, retrieve the component corresponding to the model file related to it; use the component code as a matching keyword to traverse and retrieve the associated model files; for the retrieved components, compare their component codes with the component codes in the current model level to see if they are consistent; if the component codes are the same, establish a component-level association relationship between the model component and the data object, and record this association, including the relevant input and output parameters and their functional descriptions;

[0109] S304: The established association relationship is formed into a data structure, and the relationship structure is stored in a graph database. A dynamic update mechanism is adopted to ensure that the association relationship is updated in real time when the model file, data object or component is changed.

[0110] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first determines the list of business master data objects, including the attributes, functions and task guide information associated with each object; according to the logic of the information, establishes a tree structure of the relationship between the model disassembly level and the model component level, and clarifies the parent-child relationship of each level and the nodes it contains; secondly, organizes the existing model files to ensure that each file is associated with the corresponding data set, verification set, test set and evaluation index, and records the associated information; each model file should have a unified format; traverses the hierarchical structure of the model disassembly, extracts the data objects and task guides of each level node; establishes a traceable mapping between the data objects and the corresponding model files, and records the association of each level node. File path, file name and related parameters; then in the business master data object, for each level node, retrieve the corresponding component of the model file related to it; use the component code as the matching keyword to traverse and retrieve the associated model files; for the components that have been retrieved, compare their component codes with the component codes in the current model level to see if they are consistent; if the component codes are the same, establish a component-level association relationship between the model component and the data object, and record this association, including the relevant input and output parameters and their functional descriptions; finally, form the established association relationship into a data structure, use a graph database to store the relationship structure, and adopt a dynamic update mechanism to ensure that when the model file, data object or component changes, the association relationship is updated in real time. Step S301 of the above scheme determines the business master data object and the model level construction, creates a detailed list of business master data objects, clarifies the attributes, functions and their relationship with the task guide of each object; establishes a tree structure of the model disassembly level and the model component level through logical analysis, clearly defines the parent-child relationship of each level, and shows the logical meaning between the levels. Significance achieved: It helps to systematize and standardize the management of data objects, making it easier for the business team to understand the data and its functions; the hierarchical structure can quickly retrieve and understand the dependencies between data objects, improving the transparency of information; it provides a logical basis and framework support for the subsequent model file organization and the establishment of association relationships, so that the whole process has a clear direction. Step S302: Model file organization and mapping relationship establishment, integrating all model files with corresponding data sets, verification sets, test sets and evaluation indicators, ensuring that each file has a clear standard format; by traversing the hierarchical structure, a traceable mapping is established between the relationship between data objects and model files, and the associated file path, file name and related parameters are recorded. Significance achieved: Ensure the consistency of all model files and data sets in structure and format, thereby improving the efficiency and accuracy of data processing; establishing a traceable relationship helps manage and track data sources, ensures that a clear connection is established between model components and data objects, and enhances the availability and coordination of data; a clear mapping relationship will provide support for subsequent audits and analysis, making it easier to identify the source and change process of data generation.Step S303: Component retrieval and component-level association establishment. Component codes are used as keywords to quickly retrieve components related to the model file to ensure an efficient matching process. Component codes are compared. If they are the same, the association between the model component and the data object is established, and relevant information, including input and output parameters and functional descriptions, is recorded. Significance achieved: Through component-level association, existing components can be quickly identified and reused to reduce development costs and improve flexibility. Recording component inputs, outputs and functional descriptions helps to deepen the team's understanding of model components and enhance the effectiveness of teamwork. Ensure that the corresponding model components can be flexibly adjusted and maintained in the face of business changes, and improve the company's ability to respond to changes. Step S304: Storage and dynamic update mechanism implementation. The established association relationship is stored in the graph database, and its good query efficiency and relationship processing capabilities are used to provide a basis for data management. An automated dynamic update mechanism is designed to ensure that once the model file, data object or component is changed, the association relationship can be updated in real time. Significance achieved: The efficient data retrieval capability brought by the graph database helps to analyze business data in real time and support quick decision-making; the dynamic update mechanism ensures the continuity and consistency of data and business, reducing errors or delays caused by data inconsistency; improves the system's adaptability and robustness to changes, and can maintain performance and responsiveness in a complex business environment.

[0111] Associated model files: Model files contain designed model parameters and structures. These files are associated with input datasets, validation sets, test sets, and evaluation metrics. In this business design, one or more model files are associated with other files, data, or components. This association is to achieve business visualization analysis.

[0112] Model components: Describe the software architecture by describing the interfaces and implementations of components and the associations between components. In the software architecture, the structure and behavior of the system are described, which promotes reuse and interaction. It supports multiple aspects such as facility management, design, environmental analysis, and intelligent manufacturing. By understanding and applying model components, complex systems can be built, managed, and optimized more effectively.

[0113] Component code: In software engineering, the code of a reusable software module used to implement a specific function or provide a specific service. These code modules can be object classes, class trees, functional modules, software frameworks, software architectures, documents, design patterns, etc. Component code is a reusable software component used to build other software and support the overall functional implementation of the software system by providing specific functions or services.

[0114] In summary, this embodiment, through the technical architecture constructed according to the above steps, not only realizes the effective association between the business master data object and the task classification model file, but also ensures the rigor and flexibility of data management. This whole process effectively improves the ability of enterprises in data-driven decision-making, business analysis and system development, and provides important support for achieving multiple business goals such as intelligent manufacturing, facility management and environmental analysis.

[0115] Example 8: Figure 8 As shown, based on Example 1, the process of setting a task guide provided in this embodiment of the present invention includes the following steps:

[0116] S305: Determine the model to be disassembled and its logical hierarchical structure, including the roles and responsibilities of each level; according to the definition of each level, disassemble the entire model into multiple independent model files; each file should contain the data code specific to that level and the corresponding task guide;

[0117] S306: In each model file, set the corresponding task guide based on the specific hierarchical data coding; the task guide should clearly define the purpose, process, execution steps and expected results of the task; in each task guide, configure the component coding to ensure that each coding is consistent with the data object coding in the model component hierarchy of the system;

[0118] S307: Create an import program to batch import all model files into the task guide model in the system; confirm that the program supports various model file formats, and during the import process, verify whether the task guide and component coding defined in each model file meet the preset standards; record possible errors during the import process; develop a lightweight program to process the imported task guide models one by one.

[0119] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first determines the model to be disassembled and its logical hierarchical structure, including the roles and responsibilities of each level; according to the definition of each level, the entire model is disassembled into multiple independent model files; each file should contain the data code specific to the level and the corresponding task guide; secondly, in each model file, the corresponding task guide is set based on the specific hierarchical data code; the task guide should clearly define the purpose, process, execution steps and expected results of the task; in each task guide, the component code is configured to ensure that each code is consistent with the data object code in the model component level of the system; finally, an import program is created to batch import all model files into the task guide model of the system; confirm that the program supports various model file formats, and in the import process, verify whether the task guide and component code defined in each model file meet the preset standards; record possible errors during the import process; and develop a lightweight program to process the imported task guide models one by one. Step S305 of the above scheme is model disassembly and file generation. By determining the logical hierarchy and structure of the model, the roles and responsibilities of each level are clearly defined, providing a framework for subsequent disassembly; the entire model is disassembled into multiple independent model files, so that each file can be processed, maintained and expanded independently; each model file is guaranteed to contain specific data coding and corresponding task guidelines, so that the file content has a consistent logical relationship. The significance achieved: It is helpful for the systematic management of complex business models, reducing information overlap and contradictions, and improving work efficiency; independent model files enable the system to quickly adapt to changes when needed without affecting the overall structure, improving the flexibility of maintenance; a clear hierarchical structure can enhance the sense of responsibility and collaboration efficiency in the team, ensuring that tasks at each level can be effectively executed. Step S306 is task guide setting. Each model file contains a detailed task guide, which clarifies the purpose, process, execution steps and expected results of the task, and improves the effectiveness of the execution guide; by configuring component coding, it is ensured that these codes are consistent with the data object coding in the system model component level, providing a basis for component reuse and management. Significance achieved: Detailed task guides provide personnel with clear directions and references when performing tasks, reducing errors caused by misunderstandings; coding consistency ensures the standardization of component management in the system, allowing components to be easily reused in multiple tasks, improving development efficiency; clear task guides and component coding provide a basis for cross-departmental cooperation, facilitating information sharing and collaboration. Step S307 Batch import and lightweight processing program, create an import program that can support multiple model file formats, simplify the model data integration process, and improve import efficiency and accuracy; during the import process, observe and record potential errors to ensure that problems can be discovered and solved in a timely manner, improving system reliability; the developed lightweight program can process the imported task guide models one by one, thereby reducing resource waste and making the system run more efficiently.Significance achieved: The batch import program makes data integration and management faster, significantly reduces the workload of manual operations, and improves resource utilization efficiency; by recording errors during the import process, it is possible to promptly discover unreasonable aspects of the system, provide a basis for subsequent optimization, and enhance system stability; the lightweight processing program ensures efficient operation of the system under high load conditions, can better cope with complex business needs and rapidly changing environments, and improves the system's adaptability.

[0120] In summary, this embodiment enhances the management efficiency of the business model as a whole, provides clear execution guidance, improves the interoperability between modules, and optimizes the speed and accuracy of data processing through the process of setting task guidelines from steps S305 to S307. The combined effect of each step not only improves the implementation effect of the system, but also ensures the collaborative efficiency of the team in daily operations, and ultimately promotes the realization of the overall business goals.

[0121] Example 9: Fig. 9 As shown, based on Example 1, the process of automatically constructing a visualization scene provided by the embodiment of the present invention includes the following steps:

[0122] S308: Access the stored model file database, load the associated model files one by one, and extract the business master data objects and their associated information defined in the model files during the loading process; each business master data object and component should have a unique identifier (such as UUID or globally unique code);

[0123] S309: When loading each new model, use the existing business object code, name and level information for comparison; if the same business object code and component already exist in the system, mark it as a duplicate; set a deduplication standard, and perform verification based on the pinyin syllable and processed string similarity algorithm; for duplicate model loading, record its details and provide feedback for the user to confirm whether to continue using the existing data or overwrite and update;

[0124] S310: Using the visualization engine, a visualization scene is dynamically generated based on the loaded business objects and components to display the relationship between objects and their functions. On the user interface, the query results after deduplication verification are highlighted and arranged in the center according to the hierarchy and importance of the components, so as to quickly focus on key tasks and information.

[0125] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first accesses the stored model file database, loads the associated model files one by one, and during the loading process, extracts the business master data objects and their associated information defined in the model files; each business master data object and component should have a unique identifier (such as UUID or globally unique code); secondly, when loading each new model, use the existing business object code, name and hierarchy information for comparison; if the same business object code and component already exist in the system, mark them as duplicates; set deduplication standards, and verify based on pinyin syllables and processed string similarity algorithms; for duplicate model loading, record their details and provide feedback for the user to confirm whether to continue using the existing data or overwrite and update; finally, use the visualization engine to dynamically generate visualization scenes based on the loaded business objects and components to display the relationship between objects and their functions; on the user interface, the query results after deduplication verification are highlighted and arranged in the center according to the hierarchy and importance of the components to quickly focus on key tasks and information. Step S308 of the above scheme loads the associated model file. The system can dynamically access the database storing the model files and load the model files related to the business master data objects one by one; extract the defined business master data objects and their associated information from each loaded model file to prepare for subsequent processing; assign a unique identifier (such as UUID) to each business master data object and component to provide an accurate identification and reference mechanism so that different objects can be effectively distinguished and managed in subsequent operations. Significance achieved: By accessing the model file database, a complete business data set is built to provide a basis for subsequent data analysis and visualization; the use of unique identifiers can eliminate errors caused by duplicate or ambiguous information, and improve the accuracy and efficiency of information retrieval and update; the extracted objects and associated information provide the necessary basic data for subsequent deduplication verification and dynamic generation of visualization. Step S309 is deduplication verification. When loading each new model, the system automatically compares it with the existing business object code, name and hierarchical information to determine whether there is duplication; deduplication logic is implemented based on pinyin syllables and processed string similarity algorithms (such as Levenshtein distance) to ensure that effective comparison can be performed even if the names are not completely consistent; for duplicate model loadings found, the system will record the characteristics of these duplicate objects in detail and feedback to the user, providing confirmation options (continue to use or overwrite and update). Significance achieved: Deduplication verification ensures the uniqueness of business objects in the system, thereby avoiding confusion and management difficulties caused by duplicate information and maintaining data consistency; users can better understand the status of the data through the feedback mechanism, which helps to make more informed operational decisions; the deduplication mechanism helps to improve data quality and fundamentally reduce redundant information, thereby improving the accuracy of subsequent data analysis and reporting.Step S310 generates a visualization scene. Using visualization engine technology, the system can dynamically generate a visualization scene based on the loaded business objects and components, showing the multi-dimensional relationship and interactive functions between objects; after deduplication, the query results will be highlighted and centered on the user interface, allowing users to quickly identify key tasks and information. Significance achieved: Dynamically generated visualization scenes can make complex data relationships more intuitive, help users quickly understand the connections and functions between business objects, and greatly enhance the user's operating experience; interface design optimization (such as highlighting and centering) reduces the user's cognitive burden, allowing users to focus on key tasks more efficiently and make decisions quickly; by displaying business objects and components in an organized manner, users can more easily manage task progress and enhance the overall collaborative efficiency of the workflow.

[0126] In summary, through the implementation of these three steps, this embodiment can effectively integrate and manage model file data, achieve efficient deduplication verification, and finally generate a clear and visual scene display. In this process, each step improves the efficiency and accuracy of the system technically, enhances the user experience in business, and optimizes decision-making and workflow management. Overall, this process design provides strong support for automated task management and visual scene construction.

[0127] Example 10: Fig.10 As shown, based on Embodiment 1 to Embodiment 9, the business process data processing system based on trend analysis provided by the embodiment of the present invention includes:

[0128] The information configuration module is responsible for configuring the business hierarchy and coding rules, building a business hierarchy tree structure through linear regression analysis, and setting corresponding attributes and coding rules for each hierarchy; according to the linear regression formula, the relationship between the dependent variable and the independent variable is determined to establish a unique code for each node and form a clear hierarchical structure;

[0129] The database establishment module is responsible for establishing the business master database, collecting and processing relevant data, identifying and predicting trends, and building the business master database step by step; according to the depth of the tree node and the hierarchical structure mapping, a global unique code for each data object is formed, and when the data hierarchy rules change, the corresponding code and mapping are automatically updated;

[0130] The content setting module is responsible for setting task guides and visualization scenarios. It sets task guides based on the relationship between business master data objects and model files to ensure that each task guide is consistent with the corresponding component code. When loading tasks, it avoids repeated loading through deduplication verification, automatically builds visualization scenarios, supports voice recognition and keyword queries, and optimizes task management and execution processes.

[0131] The working principle and beneficial effects of the above technical solution are as follows: the information configuration module of this embodiment configures the business hierarchy and coding rules, constructs the business hierarchy tree structure through linear regression analysis, and sets corresponding attributes and coding rules for each hierarchy; according to the linear regression formula, the relationship between the dependent variable and the independent variable is determined to establish the unique code of each node to form a clear hierarchical structure; the database establishment module establishes the business master database, collects and processes relevant data, performs trend identification and prediction, and constructs the business master database step by step; according to the depth of the tree node and the hierarchical structure mapping, a global unique code for each data object is formed, and it is ensured that the corresponding code and mapping are automatically updated when the data hierarchy rules change; the content setting module sets the task guide and the visualization scene, and sets the task guide based on the association between the business master data object and the model file to ensure that each task guide is consistent with the corresponding component code; when loading tasks, duplicate loading is avoided through deduplication verification, and a visualization scene is automatically constructed, and voice recognition and keyword query are supported to optimize task management and execution process. The information configuration module of the above scheme allows users to configure business hierarchies and coding rules, and flexibly set the attributes of different hierarchies; through linear regression analysis, the business hierarchical tree structure is established, and the causal relationship between the levels is clarified to establish a meaningful hierarchical relationship; the linear regression formula is used to determine the relationship between the dependent variable and the independent variable, and a unique code is generated for each hierarchical node to form a clear hierarchical structure. The significance achieved: The construction of the business hierarchical tree makes the internal process of the enterprise clearer, and the relationship between different business modules can be intuitively understood, which promotes management efficiency; through dynamic coding rules, it can adapt to business changes, avoid confusion caused by organizational structure adjustments, and ensure the consistency and accuracy of coding; the setting of unique coding lays a solid foundation for subsequent data management and analysis, and effectively supports information tracking and tracing. The database establishment module system is responsible for establishing a centralized business master database to collect and process various types of relevant data; using machine learning algorithms and analysis models, the database can identify and predict trends to provide support for business decisions; according to the depth of the tree node and the hierarchical structure mapping, a global unique code for each data object is formed, and the code is automatically updated when the hierarchical rules change. Significance achieved: The centralized database promotes data integration and management, helps eliminate information islands, and improves data utilization efficiency; the trend analysis function helps management understand market dynamics and provides data support for the formulation of forward-looking strategies to enhance the competitiveness of enterprises; the automatic update mechanism ensures that the system can quickly respond to business changes and improve the flexibility and sustainability of the system. The content setting module is responsible for setting task guidelines based on the relationship between business master data objects and model files to ensure information consistency; it automatically performs deduplication verification when loading tasks to avoid repeated loading, and automatically builds visual scenes for easy classification and management; it provides voice recognition and keyword query functions to enhance user interaction experience and improve the efficiency of task management and execution.Significance achieved: By automatically generating task guides and visual scenarios, users can manage and execute tasks more conveniently, reducing human operational errors and information bias; interactive functions such as voice recognition and keyword search improve the friendliness of the system, allowing users to retrieve information and execute tasks more naturally, reducing learning costs; flexibility and automation enable the system to continuously optimize the task management process, gradually forming a learning and feedback mechanism for user behavior, thereby improving the overall performance of the system.

[0132] In the content setting module, a high-performance speech recognition API is integrated to support the extraction of keywords from the user's voice input; when the user issues a voice command or enters a keyword, the system converts this information into a query request and performs a matching query with the business object code, name, hierarchical attributes, and other related attributes; the query result will return the business objects and components associated with the keyword, and the results after deduplication will be automatically highlighted in the visualization scene;

[0133] Optimize the task management and execution process. The system dynamically adjusts the displayed tasks and components based on real-time data and user queries, allowing users to quickly identify the most important tasks at the moment; use machine learning algorithms to intelligently recommend related tasks and components based on user history records, search and operating habits, and improve user work efficiency in the form of clue push or reminders; optimize the system to support access from multiple devices (PC, tablet, mobile terminal), so that users can obtain information anytime and anywhere, improving flexibility of use; add multi-user collaboration functions, allowing task progress to be shared in a visual scene and real-time feedback to improve team collaboration efficiency.

[0134] In summary, the three modules of this embodiment each play a key role and together form an efficient and flexible business process data processing system. The information configuration module establishes the business structure and coding rules, providing a basis for data management; the database establishment module integrates and analyzes data to provide support for decision-making; and the content setting module optimizes user interaction and task management. Overall, the system's architectural design helps to improve business operation efficiency, the quality and accuracy of data management, and ultimately supports enterprises to better adapt to market changes and achieve strategic goals.

[0135] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of equivalent technologies of the present invention, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing business process data based on trend analysis, characterized in that: The following steps are involved: Build a business hierarchy tree structure through linear regression analysis, set corresponding attributes and coding rules for each hierarchy; establish a unique code for each node; Collect and process business process-related data, identify and predict trends, and build a business master database level by level; Based on the association between the business master data object and the task classification model file, set the task guide, and each task guide is consistent with the corresponding component code; deduplication and verification are performed when loading tasks, and a visualization scene is automatically built.

2. The method for processing business process data based on trend analysis according to claim 1, characterized in that: The process of establishing a unique code for each node includes the following steps: Collect and process business process data. The resulting data set contains information on independent variables and dependent variables. The data set is cleaned and normalized. Determine the levels and nodes of the tree structure. For each level, configure the type attribute, the number of bits of encoding, and the encoding type. Using the results of linear regression analysis, corresponding coding rules are established for each node.

3. The method for processing business process data based on trend analysis according to claim 1, characterized in that: The process of using descriptive statistics to conduct preliminary analysis of business process data in the database in the business master database includes the following steps: Select the variables to be analyzed and calculate basic statistics for the variables, including median and quartiles; Receive the calculation results of basic statistics, perform feature extraction, and obtain feature data; Split the variables to obtain partitions; and establish the corresponding relationship between feature data and partitions; Construct a box plot and insert the abnormal data traversed from the feature data into the box plot; create a box plot of the variables that need to be analyzed.

4. The method for processing business process data based on trend analysis according to claim 3, characterized in that: The feature data are sorted, the lower quartile, upper quartile and median of the feature data are determined, and the upper and lower limits of the feature data are calculated.

5. The method for processing business process data based on trend analysis as claimed in claim 3, characterized in that: In a box plot, the middle line represents the median, indicating the central location of sales; the quartiles arrange the edges of the box, and the points outside the whiskers of the box plot represent abnormal data, identifying extreme cases of sales.

6. The method for processing business process data based on trend analysis according to claim 1, characterized in that: The process of building a business master database that integrates all data objects, descriptions, and their codes into a business process database includes the following steps: Adopting a hierarchical structure design based on semantic networks, using a graph database to dynamically represent each data object and its relationship; intelligently encoding the data objects and attributes of each level; Equipped with intelligent coding generation tools, it automatically generates unique codes and dynamically updated inheritance models.

7. The method for processing business process data based on trend analysis according to claim 6, characterized in that: Before the data flows to the target database, the integration of all data objects is handled through a programmable data conversion model; a distributed database is used through batch insertion to ensure that all data objects, descriptions and codes are in the business process database.

8. The method for processing business process data based on trend analysis according to claim 1, characterized in that: The process of setting up a task guide includes the following steps: Determine the model to be disassembled and its logical hierarchical structure, including the roles and responsibilities of each level; according to the definition of each level, disassemble the entire model into multiple independent model files; each file should contain the data coding specific to that level and the corresponding task guide; In each model file, the corresponding task guide is set based on the specific hierarchical data coding; in each task guide, the component coding is configured to ensure that each coding is consistent with the data object coding in the system's model component hierarchy; Create an import program to batch import all model files into the task guide model in the system; confirm that the program supports various model file formats, and during the import process, verify whether the task guide and component coding defined in each model file meet the preset standards; record possible errors during the import process; Develop a lightweight program to process the imported task guide models one by one.

9. The method for processing business process data based on trend analysis according to claim 8, characterized in that: The task guide should clearly state the purpose, process, execution steps and expected results of the task.

10. A business process data processing system based on trend analysis, characterized in that: Include: The information configuration module is responsible for constructing the business hierarchy tree structure through linear regression analysis, setting corresponding attributes and coding rules for each hierarchy, and establishing unique codes for each node; The database establishment module is responsible for collecting and processing business process-related data, identifying and predicting trends, and building the business master database level by level; The content setting module is responsible for setting the task guide based on the association between the business master data object and the task classification model file. Each task guide is consistent with the corresponding component code; it performs deduplication verification when loading tasks and automatically builds a visualization scene.

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