Performance accounting method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of artificial intelligence and financial science and technology, and relates to a performance accounting method, which comprises the steps of obtaining to-be-processed evaluation data, inputting the to-be-processed evaluation data into a preset evaluation analysis model, capturing the to-be-processed evaluation data through an index processing layer to obtain an atomic index, and calculating the performance of the to-be-processed evaluation data according to the atomic index. And performing data processing on the atomic indexes through the performance calculation layer to obtain performance accounting data. The invention further provides a performance accounting device, computer equipment and a storage medium. According to the invention, through integrating the data screening module, the index processing module, the performance calculation module, the data blood relationship tracking module and the like, an automatic, efficient and traceable performance accounting process is realized, the accuracy and efficiency of performance accounting are remarkably improved, and meanwhile, through the data blood relationship tracking function, the transparency and auditing performance of data processing are enhanced, and the data processing efficiency is improved. And powerful data support is provided for enterprise management and decision making.

Description

Technical Field

[0001] The present application relates to the fields of artificial intelligence and financial technology, and in particular to a performance accounting method, apparatus, computer equipment and storage medium. Background Art

[0002] With the development of the times, the traditional manual bonus performance accounting in the financial industry has been gradually liberated, and this function has been undertaken by the IT big data R&D team. However, the market is changeable. In the financial field, due to frequent changes in bank team management and business models, the supporting business departments of its performance accounting system will also be continuously added and adjusted.

[0003] To address this situation, the current common approach is to create a new performance accounting system based on the business line or department. However, this solution lacks a unified data model and data standard, and does not accumulate analytical dimensions and models, which is not conducive to or cannot build upper-level business analysis. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a performance accounting method, apparatus, computer equipment and storage medium to solve the problems of inefficiency and lack of unified standards and models caused by the repeated construction of performance accounting systems in multiple business situations.

[0005] In order to solve the above technical problems, the present application embodiment provides a performance accounting method, which adopts the following technical solution:

[0006] Obtaining evaluation data to be processed;

[0007] Inputting the to-be-processed evaluation data into a preset evaluation analysis model, wherein the evaluation analysis model includes an indicator processing layer and a performance calculation layer;

[0008] The evaluation data to be processed is captured and processed by the indicator processing layer to obtain atomic indicators;

[0009] The atomic indicators are processed through the performance calculation layer to obtain performance accounting data.

[0010] Furthermore, the step of obtaining the evaluation data to be processed specifically includes:

[0011] Get the target data field;

[0012] Screening the data mart based on the target data field to obtain a target performance table;

[0013] The contents of all fields in the target performance table are parsed to obtain the evaluation data to be processed.

[0014] Furthermore, the indicator processing layer is configured with preset data source configuration information, and the step of capturing and processing the evaluation data to be processed through the indicator processing layer to obtain the atomic indicator specifically includes:

[0015] Matching the to-be-processed evaluation data based on the preset data source configuration information to generate a data capture strategy;

[0016] Determine a query statement based on the data crawling strategy to obtain a query request, wherein the query request includes a plurality of the query statements;

[0017] The to-be-processed evaluation data is queried according to all query statements in the query request to obtain the atomic index.

[0018] Furthermore, the performance calculation layer has a built-in visual script editor, and the step of processing the atomic indicators through the performance calculation layer to obtain performance accounting data specifically includes:

[0019] Inputting the atomic indicator into the visual script editor to generate an atomic indicator node, wherein the visual script editor includes a mathematical operation symbol node, an indicator parameter, and a function node;

[0020] Perform node fusion on the indicator parameter and the atomic indicator node to obtain a derived indicator node;

[0021] The derived indicator node, the function node and the mathematical operation symbol node are dragged and dropped to generate a performance expression through the visual script editor;

[0022] The performance accounting data is obtained by calculating based on the performance expression.

[0023] Furthermore, before the step of inputting the to-be-processed evaluation data into a preset evaluation analysis model, the method further includes:

[0024] The preset data lineage tracking module is embedded into the preset evaluation and analysis model, wherein the preset data lineage tracking module includes a first lineage module and a second lineage module, wherein:

[0025] Embedding the first bloodline module into the output end of the indicator processing layer;

[0026] The second bloodline module is embedded at the output end of the performance calculation layer.

[0027] Furthermore, after the step of processing the atomic indicators through the performance calculation layer to obtain performance accounting data, the step further includes:

[0028] Summarize the performance accounting data according to user levels and generate a performance data report;

[0029] Collect target bloodline tracing data through the preset data bloodline tracing module;

[0030] Constructing a data blood relationship graph based on the target blood relationship tracking data;

[0031] The performance data report and the data lineage relationship diagram are distributed to the user's bound mailbox.

[0032] Furthermore, the step of collecting target lineage tracing data through a preset data lineage tracing module specifically includes:

[0033] Setting an event listener in the first bloodline module and the second bloodline module;

[0034] When the event listener detects that the indicator processing layer outputs the atomic indicator, the first lineage module is triggered to collect the first lineage tracking data;

[0035] When the event listener detects that the performance calculation layer outputs the performance accounting data, it triggers the second lineage module to collect the second lineage tracking data;

[0036] The first bloodline tracing data and the second bloodline tracing data are verified to obtain the target bloodline tracing data.

[0037] In order to solve the above technical problems, the embodiment of the present application also provides a performance accounting device, which adopts the following technical solution:

[0038] An acquisition module, used to acquire evaluation data to be processed;

[0039] An input module, used for inputting the evaluation data to be processed into a preset evaluation analysis model, wherein the evaluation analysis model includes an indicator processing layer and a performance calculation layer;

[0040] A capture module, used for capturing and processing the evaluation data to be processed through the indicator processing layer to obtain atomic indicators;

[0041] The processing module is used to process the atomic indicators through the performance calculation layer to obtain performance accounting data.

[0042] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0043] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the performance accounting method when executing the computer-readable instructions.

[0044] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0045] A computer-readable storage medium, characterized in that computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the steps of the performance accounting method are implemented.

[0046] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0047] The embodiment of the present application obtains the evaluation data to be processed, inputs the evaluation data to be processed into a preset evaluation analysis model, captures and processes the evaluation data to be processed through the indicator processing layer to obtain atomic indicators, and processes the atomic indicators through the performance calculation layer to obtain performance accounting data. By integrating modules such as data screening, indicator processing, and performance calculation, the whole chain processing from raw data to performance accounting data can be completed automatically and efficiently, thereby ensuring the accuracy and timeliness of performance accounting, providing enterprises with accurate and timely performance evaluation basis, and facilitating scientific and refined management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0050] Figure 2 A flow chart of an embodiment of a performance accounting method according to the present application;

[0051] Figure 3 is a schematic diagram of the structure of an embodiment of a performance accounting device according to the present application;

[0052] Figure 4 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0054] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0056] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0057] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0058] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0059] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0060] It should be noted that the performance accounting method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the performance accounting device is generally set in the server / terminal device.

[0061] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0062] Continue to refer Figure 2 , shows a flow chart of an embodiment of a performance accounting method according to the present application. The performance accounting method comprises the following steps:

[0063] Step S201, obtaining evaluation data to be processed.

[0064] In this embodiment, the performance accounting method can be deployed in a performance accounting platform. The performance accounting platform can be constructed by a server or a server cluster. The server or server cluster can be any electronic device with functions such as text recognition, text processing, data transmission, and data storage. The performance accounting method runs on the electronic device (for example, Figure 1 The server / terminal device shown in the figure can receive the evaluation data to be processed through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0065] In this embodiment, the evaluation data to be processed may be a raw data set that has not been specifically processed and analyzed in the performance accounting platform. Specifically, the evaluation data to be processed may include employee performance data, sales, customer feedback and other content information, and the evaluation data to be processed may be stored in the form of a data table in the business operating system of the insurance and financial enterprise, such as a customer relationship management (CRM) system, a human resources management system (HRMS), or a financial management system.

[0066] Step S202: input the evaluation data to be processed into a preset evaluation analysis model.

[0067] In this embodiment, the above-mentioned preset evaluation and analysis model can be an end-to-end data processing model built based on a machine learning model framework such as TensorFlow, PyTorch or Microsoft Azure Machine Learning.

[0068] Specifically, the above-mentioned evaluation and analysis model includes an indicator processing layer and a performance calculation layer, wherein the above-mentioned indicator processing layer can be a data processing module built based on data processing and analysis tools such as Apache Spark, Pandas and SQL Databases, and the above-mentioned performance calculation layer can be a data calculation module built based on deep learning models such as Scikit-learn, Microsoft Power BI or Tableau.

[0069] In this embodiment, the input port of the indicator processing layer is used to receive the evaluation data to be processed, the output port of the indicator processing layer is connected to the input port of the performance calculation layer, and the result output by the output port of the performance calculation layer is the output result of the preset evaluation analysis model.

[0070] Step S203, the evaluation data to be processed is captured and processed through the indicator processing layer to obtain atomic indicators.

[0071] In this embodiment, the atomic indicator may be a most basic data point extracted from the evaluation data to be processed that cannot be further decomposed. The atomic indicator may include field information such as loan number, contract number, personnel type, customer number, customer type, organization code, and personnel code number.

[0072] Specifically, regarding the specific implementation process of capturing and processing the evaluation data to be processed through the indicator processing layer to obtain the atomic indicators, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here.

[0073] Step S204, processing the atomic indicators through the performance calculation layer to obtain performance accounting data.

[0074] In this embodiment, the above-mentioned performance accounting data may include data such as performance score reward amounts and risk warnings. The above-mentioned performance accounting data is used to evaluate the performance, salary, rewards, career development and other influencing factors of individuals or teams within a certain period of time. The performance accounting data can support the company's human resource management and strategic decision-making, ensure that the incentives issued by the company match the actual performance of individuals or teams, thereby optimizing resource allocation and improving overall organizational efficiency.

[0075] Specifically, regarding the specific implementation process of processing the atomic indicators through the performance calculation layer to obtain performance accounting data, this application will provide further details in subsequent specific embodiments and will not be elaborated here.

[0076] The present application obtains the evaluation data to be processed, inputs the evaluation data to be processed into a preset evaluation analysis model, captures and processes the evaluation data to be processed through the indicator processing layer to obtain atomic indicators, and processes the atomic indicators through the performance calculation layer to obtain performance accounting data. By integrating modules such as data screening, indicator processing, and performance calculation, the entire chain of processing from raw data to performance accounting data can be automatically and efficiently completed, thereby ensuring the accuracy and timeliness of performance accounting, providing enterprises with accurate and timely performance evaluation basis, and facilitating scientific and refined management decisions.

[0077] In some optional implementations, step S201 includes the following steps:

[0078] Get the target data field;

[0079] Filter the data in the data mart based on the target data field to obtain the target performance table;

[0080] Parse the contents of all fields in the target performance table to obtain the evaluation data to be processed.

[0081] In this embodiment, the target data fields may include query fields such as database name, data table name, and data node name.

[0082] The above-mentioned data mart is a data storage system designed for performance processing fields, departments or user groups. It can be part of the data warehouse architecture and is used to provide the above-mentioned evaluation data to be processed, thereby supporting specific analysis needs and business decisions. Specifically, the above-mentioned data mart can integrate performance data from multiple databases.

[0083] In a possible embodiment, query fields such as database name, data table name and data node name are first obtained as target data fields, and the above target data fields are used as query conditions. ETL (Extract, Transform, Load) tools such as Talend, Apache NiFi or Informatica are used to pull corresponding data from the data mart and import them into a new data table to obtain the above target performance table. Finally, an automated pipeline is set up through the SSIS (SQL Server Integration Services) tool to regularly extract and transform data from the above target performance table to obtain the above evaluation data to be processed.

[0084] This application obtains the evaluation data to be processed through the data mart, which can respond to business changes and growth more flexibly. When data requirements of new business departments or business lines arise, the data mart can also be expanded by adding new databases or data sources without having to reconstruct the entire system.

[0085] In some optional implementations, step S203 includes the following steps:

[0086] Match the evaluation data to be processed based on the preset data source configuration information and generate a data capture strategy;

[0087] Determine the query statement based on the data crawling strategy and obtain the query request;

[0088] The evaluation data to be processed is queried according to all query statements in the query request to obtain atomic indicators.

[0089] In this embodiment, the indicator processing layer is configured with preset data source configuration information. Specifically, the preset data source configuration information can be used to specify the capture source and format of the evaluation data to be processed. For example, the preset data source configuration information can include data access rights and credential parameters to ensure that the capture behavior of the evaluation data to be processed meets security and authority requirements; data update frequency parameters are used to identify the capture frequency of the evaluation data to be processed, and the capture frequency can be real-time, daily or weekly; data quality parameters are used to ensure that all necessary fields of the evaluation data to be processed are not empty, as well as data type checks, etc.

[0090] In this embodiment, the data capture strategy can be a set of systematic methods formulated according to preset data source configuration information, which can be used to effectively extract data from a data mart or data source. Specifically, the data capture strategy can be a strategy that uses data quality as a screening condition, uses capture time as a screening condition, and uses data permissions as a screening condition.

[0091] In this embodiment, the query request includes multiple query statements, and the query statements may be SQL query statements.

[0092] In a possible embodiment, the preset data source configuration information configures the time frequency of data capture, the preset time range and the data quality parameters, such as capturing the data of the past month and ensuring that the data is non-empty data, so as to facilitate trend analysis and performance evaluation. Based on the preset data source configuration information and the contents of fields such as employee ID, sales, and working time records in the evaluation data to be processed, a data capture strategy with capture time and data quality as screening conditions is formed, and a query statement set is determined based on the data capture strategy. The query statement set can be as follows:

[0093] SELECT employee_id,total_sales FROM sales_records WHERE sale_date>='2022-01-01';

[0094] SELECT project_id,hours_spent FROM project_timesheet WHERE project_end_date IS NULL;

[0095] SELECT staff_id,client_feedback_score FROM client_feedback WHEREfeedback_date BETWEEN'2022-01-01'AND'2022-06-30';

[0096] Import the above query statement set into the SQL script for query to obtain atomic indicators.

[0097] This application matches the evaluation data to be processed based on preset data source configuration information and generates data crawling strategies, further determines specific query statements based on these strategies, and executes these queries to obtain data. It can efficiently and accurately extract key information from diverse data sources, generate atomic indicators, optimize data access efficiency, reduce system resource usage, and improve the degree of automation of data processing, ensuring the real-time and accuracy of data, thereby supporting complex data analysis and decision-making needs.

[0098] In some optional implementations, step S204 includes the following steps:

[0099] Input the atomic indicator into the visual script editor to generate the atomic indicator node;

[0100] Perform node fusion on the indicator parameters and the atomic indicator nodes to obtain the derived indicator nodes;

[0101] Use the visual script editor to drag and drop derived indicator nodes, function nodes, and mathematical operation symbol nodes to generate performance expressions;

[0102] The performance accounting data is obtained by calculating based on the performance expression.

[0103] In this embodiment, the performance calculation layer has a built-in visual script editor, which includes mathematical operation symbol nodes, indicator parameters and function nodes. Specifically, the visual script editor can be a visual script editor, which is a tool that allows users to create, edit and manage scripts or program codes through a graphical interface without directly writing traditional codes. The mathematical operation symbol nodes can include basic mathematical operators such as addition (+), subtraction (-), multiplication (*), division ( / ), modulus (%), etc., which are used to perform basic mathematical calculations. The indicator parameters can include but are not limited to time periods (such as months, quarters, years), departments, project types, etc. The indicator parameters are used to define and limit the screening range or specific characteristics of the data. The function nodes can include statistical functions such as average (AVG), sum (SUM), maximum (MAX), minimum (MIN), etc., which are used to perform aggregate calculations of data.

[0104] In an embodiment of the present invention, the above-mentioned atomic indicator node can be a draggable node represented in the form of an icon or a small box obtained after the above-mentioned atomic indicator is processed by data encapsulation through a visual script editor, and the above-mentioned derived indicator node can be a more complex indicator obtained by performing mathematical operations and logical processing on the atomic indicator node, such as employee efficiency (the ratio of working hours to sales), customer satisfaction improvement rate, etc.

[0105] In one possible embodiment, the atomic indicators are input into a visual script editor, and the data of the atomic indicators such as sales, customer satisfaction scores, or employee attendance records are encapsulated into separate nodes through the visual script editor. Each node is represented in the visual script editor as an icon or a small box, and the icon or box is marked with the name or a brief description of the indicator so that the user can easily identify it. In the visual script editor, the required atomic indicator nodes can be selected from a library and then dragged to the workspace as part of the data processing flow. Derived indicator nodes are created by combining multiple atomic indicator nodes and operator or function nodes. The derived indicator nodes, function nodes, and mathematical operator symbol nodes are dragged and dropped through visual connecting lines to generate performance expressions. For example, the "sales" node is connected to the "calculate total" function node to calculate the total sales.

[0106] This application combines derived indicator nodes with function nodes and mathematical operation symbol nodes through drag-and-drop operations to form performance expressions, and finally calculates performance accounting data based on these expressions, so that non-technical users can intuitively participate in complex data processing and analysis processes without having to have an in-depth understanding of programming or database query languages, thereby accelerating the decision-making process, improving data utilization efficiency, and making performance management more precise and personalized.

[0107] In some optional implementations, before step S202, the following steps are also included:

[0108] The preset data lineage tracking module is embedded in the preset evaluation and analysis model, wherein the preset data lineage tracking module includes a first lineage module and a second lineage module, wherein:

[0109] Embed the first bloodline module at the output end of the indicator processing layer;

[0110] The second bloodline module is embedded at the output of the performance calculation layer.

[0111] In this embodiment, the above-mentioned preset data lineage tracking module can be a data management tool for recording and visualizing the flow and changes of data in the entire processing process. The above-mentioned preset data lineage tracking module can be implemented through data lineage tools such as Apache Atlas, Collibra or Informatica.

[0112] The first lineage module mentioned above can be responsible for tracking the conversion process of the evaluation data to be processed into atomic indicators, recording each step of the data from input to output after processing (such as aggregation, filtering, and conversion), including the data source used, the conversion logic applied, and the intermediate data generated.

[0113] The second lineage module can be responsible for tracking the transformation process from atomic indicators to final performance accounting data. This covers the generation of derived indicators, the application of performance expressions, and the calculation of final accounting data.

[0114] By embedding a preset data lineage tracking module, this application can accurately track each conversion step of data from original input to final performance accounting results, thereby identifying and resolving data quality issues, optimizing data processing flows, and enhancing trust in data processing logic and results, thereby achieving transparency and traceability of the entire data processing process.

[0115] In some optional implementations, after step S204, the following steps are further included:

[0116] Summarize performance accounting data by user level and generate performance data reports;

[0117] Collect target bloodline tracking data through a preset data bloodline tracking module;

[0118] Construct a data blood relationship graph based on target blood relationship tracking data;

[0119] Distribute performance data reports and data lineage diagrams to the user's bound mailbox.

[0120] In this implementation, the above-mentioned user levels may include customer level, important customer level, and institutional level, and the above-mentioned performance data reports may be dashboard reports, trend analysis reports, and comparative analysis reports, etc., which are used to provide detailed performance analysis and help management and relevant stakeholders understand and evaluate the performance of individuals, teams, or entire institutions.

[0121] In this embodiment, the target bloodline tracking data may include all processing steps, data sources, and data flow information related to generating performance data.

[0122] In this embodiment, the data lineage diagram may be a data path view for displaying the entire flow and conversion path of data from a source to a target.

[0123] Specifically, regarding the specific implementation process of constructing the data bloodline relationship diagram based on the target bloodline tracking data, this application will provide further detailed descriptions in subsequent specific embodiments, and will not be elaborated on here.

[0124] This application aggregates performance data by user level and generates reports, so that organizations can provide customized performance views for managers at different levels, which helps to make targeted decisions and management. Secondly, the data lineage tracking module is used to collect and display the complete path of data flow and processing, which enhances the transparency of the data processing process and makes the source and change process of the data traceable and auditable to users. This is crucial for meeting compliance requirements, improving data credibility, and optimizing data process management. Finally, by automatically distributing performance reports and data lineage diagrams to the user's bound mailbox, timely delivery and high accessibility of information are ensured, thereby improving the response speed of the entire organization and the efficiency of data-driven decision-making.

[0125] In some optional implementations, the above-mentioned “collecting target lineage tracing data through a preset data lineage tracing module” further includes the following steps:

[0126] Setting an event listener in the first bloodline module and the second bloodline module;

[0127] When the event listener detects that the indicator processing layer outputs the atomic indicator, the first lineage module is triggered to collect the first lineage tracking data;

[0128] When the event listener detects that the performance calculation layer outputs performance accounting data, it will trigger the second lineage module to collect the second lineage tracking data;

[0129] The first bloodline tracing data and the second bloodline tracing data are verified to obtain target bloodline tracing data.

[0130] In this embodiment, the event listener may be a listener specific to monitoring and responding to database or application level transaction events, and the event listener may be implemented through a database trigger.

[0131] In this embodiment, the first bloodline tracing data may include the following data:

[0132] Data source information of the evaluation data to be processed: including specific database tables, files or external data interfaces; conversion logic: including specific conversion rules, formulas or algorithms for generating atomic indicators; timestamp of data conversion: including the specific time when the evaluation data to be processed is processed or converted; data quality indicators: quality inspection results during the data conversion process, such as completeness and accuracy checks, etc.

[0133] The second bloodline tracing data may include the following data:

[0134] Atomic indicator list: includes all atomic indicators involved in generating performance accounting data, as well as their sources and generation time; derived indicator generation rules: includes calculation formulas or logic used for node fusion of indicator parameters and atomic indicator nodes; timestamp of performance calculation: includes the time when performance accounting data is calculated and recorded; data dependency: represents the dependency between atomic indicator nodes and derived indicator nodes, showing the direction of data flow and dependency structure.

[0135] In a possible embodiment, after collecting the first lineage tracking data and the second lineage tracking data, the lineage paths of the first lineage module and the second lineage module are traced back through the data quality inspection and data governance framework provided by Informatica, Talend and Collibra, and an inspection log is generated. If no abnormality occurs in the inspection log, the verification passes. If an abnormal log occurs in the inspection log, the abnormal log is sent to the system administrator through the IBMMQ message queue and backed up in the abnormal log library.

[0136] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned performance accounting data information and target bloodline tracking data, the above-mentioned performance accounting data information and target bloodline tracking data can also be stored in a node of a blockchain.

[0137] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0138] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0139] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0140] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0141] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0142] Further references Figure 3 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a performance accounting device, and the embodiment of the device is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0143] like Figure 3As shown, the performance accounting device 300 described in this embodiment includes: an acquisition module 301, an input module 302, a capture module 303 and a processing module 304. Among them:

[0144] An acquisition module 301 is used to acquire evaluation data to be processed;

[0145] An input module 302 is used to input the evaluation data to be processed into a preset evaluation analysis model, wherein the evaluation analysis model includes an indicator processing layer and a performance calculation layer;

[0146] A capture module 303 is used to capture and process the evaluation data to be processed through the indicator processing layer to obtain atomic indicators;

[0147] The processing module 304 is used to process the atomic indicators through the performance calculation layer to obtain performance accounting data.

[0148] In this embodiment, by acquiring the evaluation data to be processed, the evaluation data to be processed is input into a preset evaluation analysis model, the evaluation data to be processed is captured and processed by the indicator processing layer to obtain atomic indicators, and the atomic indicators are processed by the performance calculation layer to obtain performance accounting data. By integrating modules such as data screening, indicator processing, and performance calculation, the whole chain processing from raw data to performance accounting data can be completed automatically and efficiently, ensuring the accuracy and timeliness of performance accounting, providing enterprises with accurate and timely performance evaluation basis, and facilitating scientific and refined management decisions.

[0149] Wherein, the acquisition module 301 includes:

[0150] The acquisition submodule is used to obtain the target data field;

[0151] A data screening submodule, used to screen the data mart based on the target data field to obtain a target performance table;

[0152] The parsing submodule is used to parse the contents of all fields in the target performance table to obtain the evaluation data to be processed.

[0153] The capture module 303 includes

[0154] A matching submodule, used to match the evaluation data to be processed based on the preset data source configuration information and generate a data capture strategy;

[0155] A determination submodule, configured to determine a query statement based on the data crawling strategy to obtain a query request, wherein the query request includes a plurality of the query statements;

[0156] The query submodule is used to query the evaluation data to be processed according to all query statements in the query request to obtain the atomic indicator.

[0157] The processing module 304 includes:

[0158] An input submodule, used for inputting the atomic indicator into the visual script editor to generate an atomic indicator node, wherein the visual script editor includes a mathematical operation symbol node, an indicator parameter and a function node;

[0159] A node fusion submodule, used for performing node fusion on the indicator parameter and the atomic indicator node to obtain a derived indicator node;

[0160] A node combination submodule, used for performing drag-and-drop node combination of the derived indicator node, the function node and the mathematical operation symbol node through the visual script editor to generate a performance expression;

[0161] A calculation submodule is used to calculate the performance accounting data based on the performance expression.

[0162] Before the input module 302, it also includes:

[0163] The embedding module is used to embed a preset data lineage tracking module into a preset evaluation and analysis model, wherein the preset data lineage tracking module includes a first lineage module and a second lineage module, wherein:

[0164] A first embedding submodule, used for embedding the first bloodline module into an output end of the indicator processing layer;

[0165] The second embedding submodule is used to embed the second bloodline module into the output end of the performance calculation layer.

[0166] The processing module 304 further includes:

[0167] A dimension aggregation module is used to aggregate the performance accounting data according to user levels and generate a performance data report;

[0168] A collection module, used for collecting target bloodline tracing data through the preset data bloodline tracing module;

[0169] A construction module, used to construct a data blood relationship graph based on the target blood relationship tracking data;

[0170] The distribution module is used to distribute the performance data report and the data lineage relationship diagram to the user's bound mailbox.

[0171] The building blocks include:

[0172] Setting a submodule, for setting an event listener in the first bloodline module and the second bloodline module;

[0173] A first monitoring submodule, configured to trigger the first lineage module to collect first lineage tracking data when the event listener detects that the indicator processing layer outputs the atomic indicator;

[0174] A second monitoring submodule is configured to trigger the second lineage module to collect second lineage tracking data when the event listener detects that the performance calculation layer outputs the performance accounting data;

[0175] The verification submodule is used to verify the first bloodline tracing data and the second bloodline tracing data to obtain the target bloodline tracing data.

[0176] In this embodiment, the operations performed by the above-mentioned units or modules respectively correspond one-to-one to the steps of the performance accounting method of the above-mentioned implementation mode, and will not be repeated here.

[0177] To solve the above technical problems, the present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0178] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0179] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0180] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the performance accounting method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0181] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions stored in the memory 41 or process data, such as computer-readable instructions for running the performance accounting method.

[0182] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0183] The present embodiment provides a computer device, which obtains evaluation data to be processed, inputs the evaluation data to be processed into a preset evaluation analysis model, captures and processes the evaluation data to be processed through the indicator processing layer to obtain atomic indicators, and processes the atomic indicators through the performance calculation layer to obtain performance accounting data. By integrating modules such as data screening, indicator processing, and performance calculation, the whole chain processing from raw data to performance accounting data can be completed automatically and efficiently, thereby ensuring the accuracy and timeliness of performance accounting, providing enterprises with accurate and timely performance evaluation basis, and facilitating scientific and refined management decisions.

[0184] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the performance accounting method as described above.

[0185] The present embodiment provides a computer-readable storage medium, which obtains evaluation data to be processed, inputs the evaluation data to be processed into a preset evaluation analysis model, captures and processes the evaluation data to be processed through the indicator processing layer to obtain atomic indicators, and processes the atomic indicators through the performance calculation layer to obtain performance accounting data. By integrating modules such as data screening, indicator processing, and performance calculation, the entire chain of processing from raw data to performance accounting data can be completed automatically and efficiently, ensuring the accuracy and timeliness of performance accounting, providing enterprises with accurate and timely performance evaluation basis, and facilitating scientific and refined management decisions.

[0186] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0187] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A performance accounting method, characterized in that: The steps include: Obtaining evaluation data to be processed; Inputting the to-be-processed evaluation data into a preset evaluation analysis model, wherein the evaluation analysis model includes an indicator processing layer and a performance calculation layer; The evaluation data to be processed is captured and processed by the indicator processing layer to obtain atomic indicators; The atomic indicators are processed through the performance calculation layer to obtain performance accounting data.

2. The performance accounting method according to claim 1, characterized in that: The step of obtaining the evaluation data to be processed specifically includes: Get the target data field; Screening the data mart based on the target data field to obtain a target performance table; The contents of all fields in the target performance table are parsed to obtain the evaluation data to be processed.

3. The performance accounting method according to claim 1, characterized in that: The indicator processing layer is configured with preset data source configuration information. The step of capturing and processing the evaluation data to be processed through the indicator processing layer to obtain the atomic indicator specifically includes: Matching the to-be-processed evaluation data based on the preset data source configuration information to generate a data capture strategy; Determine a query statement based on the data crawling strategy to obtain a query request, wherein the query request includes a plurality of the query statements; The to-be-processed evaluation data is queried according to all query statements in the query request to obtain the atomic index.

4. The performance accounting method according to claim 1, characterized in that: The performance calculation layer has a built-in visual script editor, and the step of processing the atomic indicators through the performance calculation layer to obtain performance accounting data specifically includes: Inputting the atomic indicator into the visual script editor to generate an atomic indicator node, wherein the visual script editor includes a mathematical operation symbol node, an indicator parameter, and a function node; Perform node fusion on the indicator parameter and the atomic indicator node to obtain a derived indicator node; The derived indicator node, the function node and the mathematical operation symbol node are dragged and dropped to generate a performance expression through the visual script editor; The performance accounting data is obtained by calculating based on the performance expression.

5. The performance accounting method according to claim 1, characterized in that: Before the step of inputting the to-be-processed evaluation data into a preset evaluation analysis model, the method further includes: The preset data lineage tracking module is embedded into the preset evaluation and analysis model, wherein the preset data lineage tracking module includes a first lineage module and a second lineage module, wherein: Embedding the first bloodline module into the output end of the indicator processing layer; The second bloodline module is embedded at the output end of the performance calculation layer.

6. The performance accounting method according to claim 5, characterized in that: After the step of processing the atomic indicators through the performance calculation layer to obtain performance accounting data, the method further includes: Summarize the performance accounting data according to user levels and generate a performance data report; Collect target bloodline tracing data through the preset data bloodline tracing module; Constructing a data blood relationship graph based on the target blood relationship tracking data; The performance data report and the data lineage relationship diagram are distributed to the user's bound mailbox.

7. The performance accounting method according to claim 6, characterized in that: The step of collecting target lineage tracing data through the preset data lineage tracing module specifically includes: Setting an event listener in the first bloodline module and the second bloodline module; When the event listener detects that the indicator processing layer outputs the atomic indicator, the first lineage module is triggered to collect the first lineage tracking data; When the event listener detects that the performance calculation layer outputs the performance accounting data, it triggers the second lineage module to collect the second lineage tracking data; The first bloodline tracing data and the second bloodline tracing data are verified to obtain the target bloodline tracing data.

8. A performance accounting device, characterized in that: include: An acquisition module, used to acquire evaluation data to be processed; An input module, used for inputting the evaluation data to be processed into a preset evaluation analysis model, wherein the evaluation analysis model includes an indicator processing layer and a performance calculation layer; A capture module, used for capturing and processing the evaluation data to be processed through the indicator processing layer to obtain atomic indicators; The processing module is used to process the atomic indicators through the performance calculation layer to obtain performance accounting data.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the performance accounting method as claimed in any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the performance accounting method according to any one of claims 1 to 7.

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