A data processing method and a data processing platform

Abstract

This application provides a data processing method and platform, comprising: receiving assessment tasks issued by the assessment subjects; selecting target configuration items from a visual interface according to the assessment content, and creating a target configuration interface corresponding to the assessment content; selecting business data of the work corresponding to the target configuration items from a business database according to the target configuration items; for each assessment subject, scoring the business data of the assessment subject according to the scoring criteria corresponding to the assessment content, and obtaining an assessment score representing the assessment subject under the assessment content; obtaining the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content, thereby completing the assessment task, and displaying the assessment results to the assessment subjects. This application reduces the workload of the assessment subjects and improves the efficiency of the assessment process.

Description

Technical Field

[0001] This application relates to the field of data management technology, and more specifically, to a data processing method and a data processing platform. Background Technology

[0002] Currently, corporate performance evaluations generally adopt a combination of online and offline methods, including interviews, inspections, assessments, and discussions. The evaluations are implemented through various stages such as corporate self-assessment, superior review, on-site evaluation, data summarization, and results application.

[0003] The assessment process mostly takes place at the end of the year and the beginning of the new year, emphasizing year-end assessments over regular assessments, and focusing on static assessments over dynamic assessments. Each assessed entity needs to compile supporting materials, which consumes a lot of manpower, is highly subjective, and requires thousands of supporting materials to be uploaded online through a platform. Each functional department of the enterprise needs to review these thousands of supporting materials one by one. This superior review work is heavy, the qualitative and quantitative data verification of the supporting materials is difficult, and the review efficiency is low. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a data processing method and a data processing platform to overcome at least one of the above-mentioned defects.

[0005] In a first aspect, embodiments of this application provide a data processing method, the method comprising: receiving an assessment task issued by an assessment subject, the assessment task including at least one assessment content and a scoring standard corresponding to each assessment content; filtering target configuration items from a visualization interface according to the assessment content, and creating a target configuration interface corresponding to the assessment content; filtering business data of work corresponding to the target configuration items from a business database according to the target configuration items, the business database storing work records of each assessment subject in the enterprise when performing work; scoring the business data of each assessment subject according to the scoring standard corresponding to the assessment content, for each assessment subject, to obtain an assessment score representing the assessment subject under the assessment content; obtaining the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content, to complete the assessment task, and displaying the assessment results to the assessment subject.

[0006] In one optional embodiment of this application, the assessment content corresponds one-to-one with the assessment type, and the assessment type is determined according to the assessment cycle. The step of obtaining the assessment results for all assessed objects based on their business data and assessment scores under the assessment content includes: determining all business data of each assessed object under the assessment content as a first set, and determining all assessment scores of each assessed object under the assessment content as a second set; inputting the first set, the second set, the standard values ​​of the business data, and the standard values ​​of the assessment scores into the target assessment model to obtain the efficiency coefficient, input slack variable, and output slack variable corresponding to each assessed object. The efficiency coefficient characterizes the efficiency level of the assessment content for the assessed object, the input slack variable characterizes the reduction in input required to adjust the business data of the assessed object to match the standard values ​​of the business data, and the output slack variable characterizes the increase in output required to adjust the business data of the assessed object to match the standard values ​​of the business data; and determining the assessment result for each assessed object based on its efficiency coefficient, input slack variable, and output slack variable.

[0007] In one optional embodiment of this application, determining the assessment result of each assessed object based on its efficiency coefficient, input slack variable, and output slack variable includes: for each assessed object, determining assessment suggestions based on its input slack variable and output slack variable; for each assessed object, distinguishing the assessment type corresponding to its efficiency coefficient, calculating the product of the efficiency coefficient of the assessment type and a preset coefficient to obtain the assessment score for that assessed object, and determining the assessment suggestions and the assessment score as the assessment result for that assessed object.

[0008] In one optional embodiment of this application, the assessment score for each assessed object under the assessment content and assessment type is determined in the following manner: for each assessed object, the assessment type corresponding to the assessment content is determined, and business data of the work corresponding to the target configuration item is filtered from the business database according to the assessment content and the assessment type; for each assessed object, the business data of the assessed object is scored according to the scoring criteria corresponding to the assessment content, so as to obtain the assessment score of the assessed object under the assessment content and assessment type.

[0009] In one optional embodiment of this application, the step of filtering target configuration items from the visualization interface according to the assessment content and creating a target configuration interface corresponding to the assessment content includes: filtering target configuration items corresponding to the assessment content from the initial components in the visualization interface; and creating a target configuration interface corresponding to the assessment content by dragging and dropping components and configuration attributes from multiple target configuration items.

[0010] In one optional embodiment of this application, the efficiency coefficient, input slack variable, and output slack variable for each evaluated object are calculated using the following formula:

[0011]

[0012]

[0013] Among them, X j Y represents the first set of all business data for the j-th assessed object under the assessment content. j Let S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content, X0 represent the standard business data value, Y0 represent the standard assessment score value, and S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content. j - S represents the input slack variable for the j-th subject under the stated assessment content. j + Let θ represent the output slack variable of the j-th subject under the given assessment content. j λ represents the efficiency coefficient corresponding to the j-th evaluated object, n represents the total number of evaluated objects in the enterprise, n≤j≤1, and λ j This represents the coefficient corresponding to the j-th assessed object, 0 ≤ θ j ≤1, 0≤S j - ≤1, 0≤S j + ≤1.

[0014] In one optional embodiment of this application, the initial assessment model is trained in the following manner: A training sample set is obtained, comprising multiple training samples, each training sample including a first set, a second set, standard business data values, standard assessment scores, an efficiency coefficient, input slack variables, and output slack variables corresponding to the assessed object under the assessment content; the first set, the second set, the standard business data values, and the standard assessment scores corresponding to the assessed object under the assessment content are used as inputs to the initial assessment model; the efficiency coefficient, input slack variables, and output slack variables corresponding to the assessed object under the assessment content are used as outputs to train the initial assessment model.

[0015] Secondly, this application also provides a data processing platform, comprising: a receiving module for receiving assessment tasks issued by assessment subjects, the assessment tasks including at least one assessment content and a scoring standard corresponding to each assessment content; a creation module for filtering target configuration items from a visualization interface according to the assessment content and creating a target configuration interface corresponding to the assessment content; a filtering module for filtering business data of work corresponding to the target configuration items from a business database according to the target configuration items, the business database storing work records of each assessment subject in the enterprise when performing work; a scoring module for scoring the business data of each assessment subject according to the scoring standard corresponding to the assessment content, to obtain an assessment score representing the assessment subject under the assessment content; and an assessment result determination module for obtaining the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content, to complete the assessment task and display the assessment results to the assessment subjects.

[0016] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the method described above are performed.

[0017] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the method described above.

[0018] The data processing method and platform provided in this application receive assessment tasks issued by the assessment subjects; select target configuration items from a visual interface according to the assessment content, and create a target configuration interface corresponding to the assessment content; select business data of the work corresponding to the target configuration items from the business database according to the target configuration items; for each assessment subject, score the business data of the assessment subject according to the scoring criteria corresponding to the assessment content to obtain an assessment score representing the assessment subject under the assessment content; obtain the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content to complete the assessment task, and display the assessment results to the assessment subjects. This application reduces the workload of the assessment subjects, improves the efficiency of the assessment work, presents the assessment results in an intuitive form for the assessment subjects to refer to and make decisions, and realizes the automation, standardization, and real-time processing of the assessment process, thereby improving assessment efficiency and accuracy.

[0019] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the data processing method provided in the embodiments of this application;

[0022] Figure 2 This is a schematic diagram of the structure of the data processing platform provided in the embodiments of this application;

[0023] Figure 3 The present application provides a schematic diagram of the structure of an electronic device. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0025] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of data management.

[0026] Research has shown that with the development of information technology, performance evaluation is gradually undergoing digital transformation. However, traditional performance evaluation methods suffer from problems such as cumbersome processes, inconsistent standards, and inaccurate data. Furthermore, they are conducted within specific timeframes, neglecting real-time monitoring of daily work, resulting in evaluation results that fail to reflect actual work performance in a timely manner, leading to a heavy workload and low efficiency.

[0027] Based on this, embodiments of this application provide a data processing method and a data processing platform, which receive assessment tasks issued by assessment subjects; select target configuration items from a visual interface according to the assessment content, and create a target configuration interface corresponding to the assessment content; select business data of the work corresponding to the target configuration items from the business database according to the target configuration items; for each assessment subject, score the business data of the assessment subject according to the scoring criteria corresponding to the assessment content to obtain an assessment score representing the assessment subject under the assessment content; obtain the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content to complete the assessment task, and display the assessment results to the assessment subjects. This application reduces the workload of assessment subjects and improves the efficiency of the assessment process.

[0028] Please see Figure 1 , Figure 1 A flowchart illustrating the data processing method provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the data processing method includes:

[0029] S101. Receive assessment tasks from the assessment targets.

[0030] The assessment task includes at least one assessment item and a corresponding scoring standard for each assessment item.

[0031] The assessment tasks are issued by the assessment recipients, and the executor of this application is the assessment administrator. After logging into the low-code platform, the assessment administrator can see the assessment tasks displayed on the interface.

[0032] In one optional embodiment, this application provides a domestically compatible rule configuration interface, allowing users to visually set assessment indicators, scoring standards, etc. The system then calculates scores based on the scoring rules.

[0033] Preferably, the assessment content and scoring criteria for the assessment tasks are shown in Table 1 below:

[0034] Table 1:

[0035]

[0036] As shown in Table 1 above, the indicator names correspond to the assessment content. Each assessment task corresponds to at least one indicator name, and each indicator name corresponds to the indicator type and the scoring criteria for that indicator name.

[0037] Table 1 above clearly lists each assessment indicator in tabular form, including indicator name, indicator type, and scoring criteria, making the assessment work based on evidence. The scoring criteria section of the table quantifies the assessment indicators, avoids interference from subjective judgment, and improves the accuracy and fairness of the assessment. Using the table, assessment rules can be easily set and adjusted, reducing the complexity of rule configuration and improving the flexibility of the assessment work.

[0038] S102. Based on the assessment content, select target configuration items from the visualization interface and create a target configuration interface corresponding to the assessment content.

[0039] Specifically, the target configuration items corresponding to the assessment content are selected from the initial components in the visual interface;

[0040] Here, the visualization interface includes multiple initial configuration items. For example, these can be components, including basic components, advanced components, and layout components. From these components, the target component corresponding to the assessment content is selected as the target configuration item.

[0041] Multiple target configuration items can be created into a target configuration interface corresponding to the assessment content by dragging and dropping components and configuration properties.

[0042] In this step, a preset configuration template is called based on the target configuration items, and a target configuration interface corresponding to the assessment content is created based on multiple target configuration items and the preset configuration template.

[0043] In one optional embodiment, the preset configuration template can be a page template and business process template compatible with the domestic information technology innovation environment, covering various application scenarios required for the responsibility system assessment, and users can quickly reuse or customize the template.

[0044] For example, the page template for attending a meeting includes the meeting name, meeting topic, attendees, meeting resolutions, and meeting photos, while the business process template includes reviewers and approval processes.

[0045] In another optional embodiment, if the assessment content is employee information management, the target configuration interface includes the following configuration items, including but not limited to the target configuration items such as the organization, photo, type of entry into this information system, personnel category and job position, and the preset configuration template is the basic information template, including but not limited to components such as name, gender, ID number, date of birth, ethnicity, mobile phone number, employer, highest education level, highest degree, current residence, and registered residence.

[0046] This application uses domestic UI frameworks (such as Element UI, Ant Design Vue, etc.) to build an intuitive drag-and-drop design interface, which allows users to quickly build application interfaces and business processes by dragging and dropping components and configuring properties. This application also supports a WYSIWYG editing experience, allowing users to view the application effect in real time during the design process. It supports one-click preview and online debugging functions to ensure the accuracy of the target configuration interface construction corresponding to the assessment content.

[0047] This application allows users to quickly build the required application interface and business process by dragging and dropping components and configuring properties, without having to write a lot of code. This lowers the development threshold and enables more non-professional developers to participate in application building.

[0048] The intuitive drag-and-drop interface allows users to see the effects of their applications in real time, enhancing user engagement and satisfaction. Users can fine-tune the interface according to their actual needs to ensure that the final application interface matches the company's image and style. The drag-and-drop interface supports user-defined templates and components, making it convenient for companies to personalize their applications according to their own business performance requirements.

[0049] S103. Based on the target configuration item, filter the business data of the work corresponding to the target configuration item from the business database.

[0050] The business database stores the work records of each employee being evaluated within the company.

[0051] In one optional embodiment, the assessment content is identified, keywords are extracted, and business data of all assessed objects related to the keywords are filtered from the business database. For example, the assessment content is the number of meetings held in March, and business data can be filtered from the business database based on the meeting times.

[0052] Retrieve business data related to performance indicators from the business database of the digital platform, and perform preprocessing work such as cleaning, deduplication, and filling in missing values ​​to ensure data quality and prepare for subsequent analysis.

[0053] This section is responsible for collecting data related to performance indicators from the platform. The data types are based on the quantitative data of the annual performance indicators. It should have efficient data capture and processing capabilities to ensure the accuracy and completeness of the data.

[0054] S104. For each assessed object, score the business data of the assessed object according to the scoring criteria corresponding to the assessment content to obtain the assessment score used to characterize the assessed object under the assessment content.

[0055] In this step, the assessment content corresponds one-to-one with the assessment type, and the assessment type is determined according to the assessment cycle.

[0056] Preferably, the assessment type can be determined as a daily assessment or a centralized assessment based on the assessment cycle. Here, the assessment cycle can be determined according to the assessment needs of the assessment object. Here, we only take the daily assessment or centralized assessment as an example.

[0057] For each assessed individual, determine the assessment type corresponding to the assessment content, and based on the assessment content and assessment type, filter business data from the business database that corresponds to the target configuration item.

[0058] For example, the business database can use a domestic relational database (such as DM Database, Shentong Database, etc.) to store business data and assessment results, ensuring data security and controllability. A data warehouse can be built based on a domestic big data platform (such as StarRing TDH, Vastbase, etc.) to store historical data and support complex queries and analysis.

[0059] This section provides connectors for domestically sourced databases, data warehouses, file systems, and other data sources, enabling users to directly access various domestically sourced data sources within a low-code platform.

[0060] For each assessed entity, its business data is scored according to the scoring criteria corresponding to the assessment content, thus obtaining the assessment score for that entity under the assessment content and assessment type.

[0061] Here, based on the scoring criteria shown in Table 1 above, we can obtain the assessment score for each assessed object corresponding to each indicator name.

[0062] This application quantifies the assessment indicators one by one, forms a rule algorithm engine, and implements the various assessment indicators throughout the year into daily assessments (monthly, quarterly, or semi-annually). There is no need for each unit to upload supporting materials. The application analyzes business data in real time and automatically. Based on the quantitative indicator system, it formulates clear and executable scoring rules and calculates the assessment scores of each grassroots organization according to the preset rules.

[0063] The scoring rules are input into the rule engine, which can automatically calculate scores based on real-time updated business data. The rule engine can monitor data changes in real time and trigger score calculation immediately once the scoring conditions are met. The rule engine is driven by business data and updates the assessment scores of each grassroots organization in real time, ensuring that the assessment results keep up with business progress and achieve seamless assessment.

[0064] This application uses an automated scoring model based on a rules engine to analyze business data in real time and automatically. It calculates the assessment scores and rankings of each grassroots organization according to preset rules, and is responsible for in-depth analysis of the collected data. Through preset algorithms and models, it can quantitatively evaluate work effectiveness, such as whether organizational activities are carried out on time and at the required frequency, and employee education participation rate.

[0065] S105. Based on the business data of all assessed entities under the assessment content and the assessment scores of all assessed entities under the assessment content, obtain the assessment results of all assessed entities to complete the assessment task, and display the assessment results to the assessed entities.

[0066] The results of data analysis are presented to the assessed individuals in an intuitive way. The assessment results can be displayed in the form of charts, reports, etc., so that users can quickly understand the performance evaluation. In addition, this application also supports real-time updates to reflect the dynamic changes in the work in a timely manner. The relevant assessment results can be generated by dragging and dropping in the low-code platform.

[0067] Specifically, all business data of each assessed object under the assessment content is determined as the first set, and all assessment scores of each assessed object under the assessment content are determined as the second set.

[0068] Suppose a company has n entities to be evaluated, denoted as n Decision Units (DMUs). Each entity has m indicators corresponding to its evaluation criteria, and n×m evaluation scores. The first set of the j-th DMU is:

[0069] X j =(x 11 x 1m , ..., x n1 x nm ) T >0

[0070] For example, when j = 3 and m = 2, X3 = (x 11 x 12 x 21 x 22 x 31 x 32 ) T .

[0071] Among them, X j Let x represent the first set of all business data for the j-th assessed object under the assessment content. ab This represents the business data for the b-th performance indicator corresponding to the a-th evaluated object.

[0072] Similarly, the second set of the j-th DMU is:

[0073] Y j =(y 11 y 1m , ..., y n1 , ynm ) T >0

[0074] For example, when j = 3 and m = 2, Y3 = (y 11 y 12 y 21 y 22 y 31 y 32 ) T .

[0075] Among them, Y j Let y represent the second set of all assessment scores for the j-th subject under the stated assessment content. ab This represents the assessment score of the b-th assessment indicator corresponding to the a-th assessed object.

[0076] Input the first set, the second set, the standard values ​​of business data, and the standard values ​​of assessment scores into the target assessment model to obtain the efficiency coefficient, input slack variables, and output slack variables for each assessed object.

[0077] By inputting standard business data values ​​and performance evaluation criteria into the target performance evaluation model, the work performance of each evaluated individual can be quantitatively assessed. This quantitative evaluation method is more objective and accurate than traditional qualitative evaluation, more precisely reflecting the work efficiency and results of the evaluated individuals. Through the calculated efficiency coefficients, input slack variables, and output slack variables, the shortcomings and deficiencies in the work of the evaluated individuals can be identified. These variables provide clear directions for improvement, helping them to optimize work processes and enhance efficiency. For central enterprises, analyzing the efficiency coefficients of evaluated individuals allows for understanding the work efficiency of each unit, enabling more rational allocation of resources to support high-performing or high-potential units, further improving the overall work level.

[0078] Here, the efficiency coefficient is used to characterize the efficiency level of the assessment content of the assessed object, the input slack variable is used to characterize the amount of input reduction required to adjust the business data of the assessed object to match the standard value of the business data, and the output slack variable is used to characterize the amount of output increase required to adjust the business data of the assessed object to match the standard value of the business data.

[0079] Based on the specific needs and business characteristics of the assessment, select a suitable analytical model. This may include, but is not limited to, statistical analysis models (such as descriptive statistics, regression analysis, time series analysis, etc.), machine learning models (such as classification, clustering, regression, prediction models, etc.), and other quantitative evaluation models (such as analytic hierarchy process, fuzzy comprehensive evaluation method, etc.). Configure and train the parameters of the selected model, such as setting thresholds, weights, and learning rates, to ensure that the model can accurately reflect the actual situation of the work.

[0080] The model applies pre-defined rules and algorithms to calculate data based on quantified assessment indicators. For example, for the on-time completion rate of organized activities, the model may calculate the ratio of the number of activities actually completed on time to the number of planned activities within a certain period. For situations that require comprehensive evaluation by combining multiple indicators, such as the educational participation rate, composite models such as weighted average and index comprehensive methods may be used to comprehensively consider the scores of various relevant indicators.

[0081] The selected algorithms and models are used to conduct in-depth analysis of the preprocessed data, revealing patterns, trends, and anomalies in the work. For example, time series analysis can be used to identify seasonal variations in organizational activities, and cluster analysis can be used to distinguish the cluster distribution of work levels in different units. The model outputs are interpreted in conjunction with business knowledge and expert opinions to ensure that the evaluation results are consistent with actual work practices and are understandable and actionable.

[0082] This approach reveals patterns, trends, and anomalies in operations. In-depth analysis of business data can identify key information such as seasonal variations in organizational activities and clustered distributions of work levels across different units. This information helps central enterprises better understand the overall status and trends of their work, providing strong support for developing targeted improvement measures. By combining data analysis results with actual conditions, the accuracy and reliability of the model can be verified, ensuring that the assessment results are understandable and actionable. This helps enterprises better apply data analysis results in their actual work, driving continuous improvement and enhancement.

[0083] Traditional performance evaluation methods often suffer from high subjectivity and cumbersome data collection. However, using algorithms and models for data analysis can more objectively and accurately reflect the performance of each unit. This helps eliminate the influence of human factors on evaluation results, improves the fairness and objectivity of evaluations, and provides strong support for high-quality development of work.

[0084] In conclusion, by using selected algorithms and models to conduct in-depth analysis of preprocessed data, and by interpreting the model output results in conjunction with business knowledge and expert opinions, we can reveal patterns, trends, and anomalies in the work, thereby improving the accuracy and fairness of performance evaluations.

[0085] Preferably, in this application, the business data and performance evaluation scores corresponding to each unit are placed in a set, as shown below:

[0086]

[0087] Among them, T C 2 R λ is the set of business data and performance scores for all evaluated entities. j X represents the coefficient corresponding to the j-th assessed object. j Y represents the first set of all business data for the j-th assessed object under the assessment content. j Let X0 represent the second set of all assessment scores for the j-th assessed object under the assessment content, X0 represent the standard value of business data, Y0 represent the standard value of assessment score, and n represent the total number of assessed objects in the enterprise, where n≤j≤1.

[0088] Specifically, the efficiency coefficient, input slack variable, and output slack variable for each evaluated object are calculated using the following formulas:

[0089]

[0090]

[0091] Among them, X j Y represents the first set of all business data for the j-th assessed object under the assessment content. j Let S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content, X0 represent the standard business data value, Y0 represent the standard assessment score value, and S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content. j - S represents the input slack variable for the j-th subject under the stated assessment content. j + Let θ represent the output slack variable of the j-th subject under the given assessment content. j λ represents the efficiency coefficient corresponding to the j-th evaluated object, n represents the total number of evaluated objects in the enterprise, n≤j≤1, and λ j This represents the coefficient corresponding to the j-th assessed object, 0 ≤ θ j ≤1, 0≤S j - ≤1, 0≤S j + ≤1, 0≤λ j ≤1.

[0092] By calculating the efficiency coefficient, input slack variables, and output slack variables for each assessed entity, we can achieve many benefits and effects, such as precise quantitative assessment, optimized resource allocation, improved assessment fairness, promotion of continuous improvement, support for seamless assessment, and enhanced daily assessment. These benefits and effects together drive the high-quality development of enterprise operations.

[0093] This application allows users to view the target configuration interface for each created assessment object on a visual interface. It supports functions such as searching, classifying, sharing, and version management for assessment objects, making it convenient for users to manage and access their own target configuration interface. This application can also deploy the designed application to a server in a domestic IT innovation environment with one click, such as Kylin OS or UnionTech UOS, and access and run it through domestic browsers (such as Redcore and 360 Security Browser).

[0094] Specifically, the assessment result of each assessed individual can be determined based on their efficiency coefficient, input slack variables, and output slack variables.

[0095] Here, for each person being assessed, assessment recommendations are determined based on their input slack variables and output slack variables.

[0096] Based on the principle of relative efficiency of decision units, there exists an optimal solution to this linear programming problem: λ, S-, S + , θ.

[0097] Where θ≤1. If θ=1, the assessment content of the unit to which it belongs is said to be weakly DEA effective (C 2 R); If θ = 1, and each optimal solution S - = S + =0, then the assessment content of the unit to which it belongs is determined to be DEA valid (C 2 R), where the optimal solution for θ is the minimum value of θ under the condition that it is less than or equal to 1.

[0098] The economic meaning of the above formula is: while keeping output Y0 constant, minimize input X0.

[0099] In one optional embodiment, taking the number of meetings in March as an example, if S has a value, it means that the number of meetings can be reduced; if S... + A value indicates that the number of participants in the meeting can be increased. Here, S- and S + The corresponding meaning is not unique and can be determined based on the actual situation, which will not be elaborated here.

[0100] If it cannot be reduced, it means that the assessment content of the unit is an effective production activity; if it can be reduced, it means that the assessment content of the unit is not an effective production activity. This is used to evaluate the efficiency of the assessment content of the unit from the perspective of input.

[0101] Compare performance evaluation results with performance targets, historical data, or industry benchmarks to assess whether work performance has met expectations and whether there is room for improvement. For example, compare employee training participation rates with superiors' requirements or the average level of peer units to determine if they have met the standards. If any abnormalities or deviations from targets are found, promptly notify relevant personnel through an early warning mechanism to trigger appropriate intervention measures, such as adjusting work plans and strengthening weak areas.

[0102] For each assessed individual, the assessment type corresponding to the efficiency coefficient of that individual is identified. The product of the efficiency coefficient of the assessment type and the preset coefficient is calculated to obtain the assessment score for that individual. The assessment suggestions and assessment score are then determined as the assessment result for that individual.

[0103] Depending on the different assessment types and specific implementation processes, companies assign different weights to daily and centralized assessment types. Specifically, the assessment score for the j-th assessed individual is:

[0104] ω j =αθ 日常 +βθ 集中

[0105] Among them, w j Let θ be the assessment score of the j-th subject. 日常 Let θ be the efficiency coefficient of the assessment content for the j-th assessed individual under the daily assessment type. 集中 Let α be the efficiency coefficient of the assessment content of the j-th subject under the centralized assessment type, α be the proportion of the assessment content of the j-th subject under the daily assessment type, β be the proportion of the assessment content of the j-th subject under the centralized assessment type, 0≤α≤1, 0≤β≤1, α+β=1.

[0106] Here, based on domestically developed rule engines (such as Easy Rules, Drools, etc.), preset responsibility assessment rules are automatically executed, assessment results are calculated in real time, and dynamic loading and updating of rules are supported.

[0107] Specifically, the initial assessment model can be trained in the following ways:

[0108] Obtain the training sample set.

[0109] The training sample set includes multiple training samples. Each training sample includes the first set, the second set, the standard value of business data, the standard value of assessment score, the efficiency coefficient, the input slack variable, and the output slack variable corresponding to the assessed object under the assessment content.

[0110] The first set, the second set, the standard values ​​of business data, and the standard values ​​of assessment scores corresponding to the assessed objects under the assessment content are used as the inputs to the initial assessment model. The efficiency coefficient, input slack variables, and output slack variables corresponding to the assessed objects under the assessment content are used as the outputs to train the initial assessment model.

[0111] The data processing method and platform provided in this application receive assessment tasks issued by the assessment subjects; select target configuration items from a visual interface according to the assessment content, and create a target configuration interface corresponding to the assessment content; select business data of the work corresponding to the target configuration items from the business database according to the target configuration items; for each assessment subject, score the business data of the assessment subject according to the scoring criteria corresponding to the assessment content to obtain an assessment score representing the assessment subject under the assessment content; obtain the assessment results of all assessment subjects based on the business data and assessment scores of all assessment subjects under the assessment content to complete the assessment task, and display the assessment results to the assessment subjects. This application reduces the workload of the assessment subjects and improves the assessment efficiency of the enterprise's work.

[0112] This application fully considers the compatibility of domestic software and hardware environments, supports users in quickly building and configuring responsibility system assessment applications through low-code methods, realizes effective data management, analysis and integration, ensures the stable operation and data security of the platform in the domestic information technology innovation environment, and builds a digital platform with the help of low-code technology. It makes full use of digital and information technology, and the assessment targets and publicity-related departments quantify assessment indicators one by one around the central government's deployment and annual key tasks, form rule algorithms, and implement various assessment indicators throughout the year into daily assessments (monthly, quarterly or semi-annually), without requiring each unit to upload supporting materials.

[0113] All quantitative assessment materials are sourced from the digital platform. Daily assessment results and rankings are generated in real time based on the assessment timeline, strengthening the seamless daily assessment process. At the end of the year, various functional departments within the company can use on-site interviews and evaluations to examine whether each unit's work is "substantial" and "thorough," dynamically adjusting the assessment results. With the standardization and institutionalization of the responsibility system assessment, more than 90%, or even all, of the assessment indicators can be quantified and implemented on the digital platform, achieving seamless assessment.

[0114] This application utilizes low-code technology to build a digital platform. By quantifying assessment indicators and rule algorithms, it automates, standardizes, and enables real-time assessment processes, thereby improving assessment efficiency and accuracy and avoiding issues of accuracy and fairness caused by subjective factors in qualitative and quantitative supporting materials.

[0115] This application integrates domestically developed data visualization tools (such as SuperSet and FineBI) to enable users to quickly create data dashboards and reports, display data analysis results, and also supports users to build data analysis models based on domestically developed data analysis libraries (such as Python libraries Pandas and NumPy, ensuring compatibility in a domestic environment), such as cluster analysis and trend prediction, to deeply mine business data.

[0116] This application provides a domestic API gateway (such as Apache APISIX, Dubbo, etc.) to support users in creating, publishing, and managing API interfaces, ensuring the security, stability, and efficiency of the interfaces, providing data synchronization interfaces with business systems and other internal enterprise systems, and using domestic ETL tools (such as Kettle, DataX, etc.) for data extraction, transformation, and loading to ensure accurate data synchronization.

[0117] This application uses domestically developed message middleware (such as RocketMQ, Apache Pulsar, etc.) and Enterprise Service Bus (ESB) to achieve asynchronous message passing and data exchange with other systems, supports standard interface protocols (such as RESTful, SOAP, Dubbo, etc.) in the domestic IT innovation environment, and achieves seamless integration with external systems.

[0118] This application integrates domestically developed identity authentication services (such as unified identity authentication systems and domestically developed CAS) to achieve user authentication and access control. It employs domestically developed encryption algorithms (such as SM2, SM3, and SM4) to encrypt and store sensitive data during transmission, supports data anonymization, and ensures data security. Annual performance indicators are formulated based on central government directives and the group's key annual tasks. Business modules (organizational management, employee management, relationship transfer, and responsibility list data reporting) are built using low-code methods in conjunction with these performance indicators. Each unit conducts business on the platform according to relevant work plans, generating business data.

[0119] This application utilizes a low-code platform to automate the entire assessment process, reducing manual intervention. This means that assessments can be conducted without adding extra workload, achieving a "seamless" effect. Based on the real-time data update capability of the low-code platform, this invention also provides an early warning mechanism. When work anomalies occur or expected goals are not achieved, the early warning mechanism can promptly issue alerts, reminding relevant personnel to intervene and make adjustments. This helps to identify and resolve problems in a timely manner, further improving work efficiency.

[0120] This invention, through standardized data analysis methods and assessment processes, ensures a unified standard for performance evaluation across different departments and regions. This helps eliminate the influence of human factors on assessment results, making them more fair and objective. The low-code platform's characteristics give the assessment method of this invention a certain degree of scalability and customization capability. Enterprises can flexibly adjust the scope of data collection, analysis methods, and assessment standards according to their own business needs and development status. This allows the invention to better adapt to the actual needs of different enterprises.

[0121] The low-code platform enables intelligent and seamless performance evaluation, effectively improving work efficiency, reducing the workload of manual evaluation, and ensuring the objectivity and fairness of the evaluation, thus powerfully promoting the scientific and standardized process of evaluation work.

[0122] This application follows the principles of automation and seamlessness of the assessment process, real-time feedback and early warning mechanisms, standardization and uniformity, and scalability and customization to build a low-code platform. The platform includes a data acquisition module, a rule algorithm engine module, a data analysis module, and a data display module.

[0123] The data acquisition module collects relevant assessment data through API interfaces or manual input; the rule algorithm engine module configures relevant rules according to preset algorithms; the data analysis module analyzes the collected data in conjunction with the rule algorithm to evaluate the work completion status. The data analysis module can use machine learning algorithms, statistical methods, etc. to analyze the collected data and evaluate the work completion status; the assessment result display module displays the assessment results in the form of charts or reports.

[0124] Based on the same inventive concept, this application also provides a data processing platform corresponding to the data processing method. Since the principle of the platform in this application to solve the problem is similar to the data processing method described above in this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0125] Please see Figure 2 , Figure 2 This is a schematic diagram of the data processing platform provided in an embodiment of this application. Figure 2 As shown, the data processing platform 200 includes:

[0126] The receiving module 201 is used to receive the assessment task issued by the assessment object. The assessment task includes at least one assessment content and a scoring standard corresponding to each assessment content.

[0127] Module 202 is used to filter target configuration items from the visualization interface according to the assessment content and create a target configuration interface corresponding to the assessment content.

[0128] The filtering module 203 is used to filter business data of the work corresponding to the target configuration item from the business database according to the target configuration item. The business database stores the work records of each assessed object in the enterprise when performing work.

[0129] The scoring module 204 is used to score the business data of each assessed object according to the scoring criteria corresponding to the assessment content, and obtain an assessment score that represents the assessed object under the assessment content.

[0130] The assessment result determination module 205 is used to obtain the assessment results of all assessed objects based on the business data of all assessed objects under the assessment content and the assessment scores of all assessed objects under the assessment content, so as to complete the assessment task and display the assessment results to the assessed objects.

[0131] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0132] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1 The steps of the data processing method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0133] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The steps of the data processing method in the illustrated method embodiment can be found in the method embodiment for specific implementation, and will not be repeated here.

[0134] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0138] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0139] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method, characterized in that, include: Receive assessment tasks from the assessment subjects, wherein the assessment tasks include at least one assessment content and a corresponding scoring standard for each assessment content; Based on the assessment content, filter the target configuration items from the visualization interface and create a target configuration interface corresponding to the assessment content; Based on the target configuration item, the business data of the work corresponding to the target configuration item is filtered from the business database. The business database stores the work records of each assessed object in the enterprise when performing work. For each assessed object, the business data of the assessed object is scored according to the scoring criteria corresponding to the assessment content, so as to obtain the assessment score used to characterize the assessed object under the assessment content. Based on the business data and assessment scores of all assessed entities under the assessment content, the assessment results of all assessed entities are obtained to complete the assessment task, and the assessment results are then displayed to the assessed entities.

2. The method according to claim 1, characterized in that, The assessment content corresponds one-to-one with the assessment type, and the assessment type is determined according to the assessment cycle. The step of obtaining the assessment results for all assessed individuals based on their business data and assessment scores under the assessment content includes: All business data of each assessed object under the assessment content are determined as the first set, and all assessment scores of each assessed object under the assessment content are determined as the second set. The first set, the second set, the standard value of business data, and the standard value of assessment scores are input into the target assessment model to obtain the efficiency coefficient, input slack variable, and output slack variable for each assessed object. The efficiency coefficient is used to characterize the efficiency level of the assessment content of the assessed object. The input slack variable is used to characterize the amount of input that needs to be reduced to adjust the business data of the assessed object to match the standard value of business data. The output slack variable is used to characterize the amount of output that needs to be increased to adjust the business data of the assessed object to match the standard value of business data. The assessment result for each assessed individual is determined based on their efficiency coefficient, input slack variables, and output slack variables.

3. The method according to claim 2, characterized in that, The process of determining the assessment result for each assessed individual based on their efficiency coefficient, input slack variables, and output slack variables includes: For each person being assessed, assessment recommendations are determined based on their input slack variables and output slack variables. For each assessed individual, the assessment type corresponding to the efficiency coefficient of that individual is identified, and the product of the efficiency coefficient of the assessment type and the preset coefficient is calculated to obtain the assessment score for that individual. The assessment suggestions and the assessment score are then determined as the assessment result for that individual.

4. The method according to claim 2, characterized in that, The assessment score for each assessee, based on the assessment content and type, is determined using the following methods: For each assessed object, determine the assessment type corresponding to the assessment content, and filter business data of the work corresponding to the target configuration item from the business database according to the assessment content and the assessment type; For each assessed entity, the business data of the assessed entity is scored according to the scoring criteria corresponding to the assessment content, thereby obtaining the assessment score of the assessed entity under the assessment content and assessment type.

5. The method according to claim 1, characterized in that, The step of filtering target configuration items from the visualization interface based on the assessment content and creating a target configuration interface corresponding to the assessment content includes: Filter the target configuration items corresponding to the assessment content from the initial components in the visualization interface; Multiple target configuration items can be created into a single target configuration interface corresponding to the assessment content by dragging and dropping components and configuration properties.

6. The method according to claim 2, characterized in that, The efficiency coefficient, input slack variable, and output slack variable for each evaluated object are calculated using the following formulas: Among them, X j Y represents the first set of all business data for the j-th assessed object under the assessment content. j Let S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content, X0 represent the standard business data value, Y0 represent the standard assessment score value, and S represent the second set of all assessment scores for the j-th assessed object under the stated assessment content. j - S represents the input slack variable for the j-th subject under the stated assessment content. j + Let θ represent the output slack variable of the j-th subject under the given assessment content. j λ represents the efficiency coefficient corresponding to the j-th evaluated object, n represents the total number of evaluated objects in the enterprise, n≤j≤1, and λ j This represents the coefficient corresponding to the j-th assessed object, 0 ≤ θ j ≤1, 0≤S j - ≤1, 0≤S j + ≤1.

7. The method according to claim 1, characterized in that, The initial assessment model was trained using the following methods: Obtain a training sample set, which includes multiple training samples. Each training sample includes a first set, a second set, business data standard values, assessment scoring standard values, efficiency coefficients, input slack variables, and output slack variables corresponding to the assessed object under the assessment content. The first set, the second set, the standard value of business data, and the standard value of assessment scoring corresponding to the assessed object under the assessment content are used as inputs to the initial assessment model. The efficiency coefficient, input slack variable, and output slack variable corresponding to the assessed object under the assessment content are used as outputs to train the initial assessment model.

8. A data processing platform, characterized in that, include: The receiving module is used to receive assessment tasks issued by the assessment subject. The assessment task includes at least one assessment content and a scoring standard corresponding to each assessment content. A module is created to filter target configuration items from the visualization interface based on the assessment content and create a target configuration interface corresponding to the assessment content. The filtering module is used to filter business data of the work corresponding to the target configuration item from the business database according to the target configuration item. The business database stores the work records of each assessed object in the enterprise when performing work. The scoring module is used to score the business data of each assessed object according to the scoring criteria corresponding to the assessment content, and obtain an assessment score that represents the assessed object under the assessment content. The assessment result determination module is used to obtain the assessment results of all assessed entities based on their business data and assessment scores under the assessment content, in order to complete the assessment task and display the assessment results to the assessed entities.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method as described in any one of claims 1 to 7.

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