Human resource intelligent management system based on enterprise performance improvement

Abstract

The invention relates to the technical field of human resource management, in particular to an enterprise performance improvement-based human resource intelligent management system, which comprises a data acquisition module for acquiring medium and long-term strategic targets, operation data and employee data of an enterprise; the dynamic index management module is used for disassembling a medium-and-long-term strategic target of an enterprise into department and post-level performance indexes through a strategic decoding algorithm, constructing an index dynamic adjustment model and automatically adjusting index weights; the performance attribution analysis module is used for mining performance and association degree through an AI attribution analysis algorithm and generating a performance influence factor report; the personalized intervention module pushes a customized performance improvement scheme through an intelligent matching engine, and scheme parameters are automatically adjusted until the performance reaches the standard; and the generality problem identification and performance prediction module identifies enterprise generality problems through a clustering algorithm, constructs a performance prediction model, and predicts the overall performance growth value of the enterprise. Therefore, the problems of shallow attribution analysis, homogenization of intervention schemes, insufficient predictability of common problems and the like in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of human resource management, in particular to an intelligent human resource management system based on enterprise performance improvement. BACKGROUND

[0002] The prior art has formed a basic support capability in the field of human resource management, mainly in the aspects of data aggregation, index decomposition and routine analysis. The traditional HR system or early intelligent tools can realize the collection and storage of employee basic data (such as attendance, salary) and part of the operating data, providing original materials for enterprise performance statistics; some systems generate static performance indicators of departments or posts through preset templates, assisting managers to establish a basic evaluation framework; in addition, based on the report function of simple statistical model (such as same ratio, ratio analysis), the performance results of the team or individual can be presented, providing limited reference for experience-based decision-making. These technologies can meet the basic human resource management needs and provide preliminary data support for enterprise performance improvement in the stable stage of small-scale enterprises and stable business model.

[0003] In the face of the fine and dynamic needs of enterprise performance improvement, the prior art has significant shortcomings. The data integration capability is weak, and multi-source data is still scattered in different systems or departments, without forming a global data pool, which leads to performance analysis limited to a single dimension and unable to dig deep correlations between strategy, business and talent. The index management is rigid, and the performance indicators are mostly set by manual experience and have fixed weights, which makes it difficult to dynamically adapt to changes in strategy adjustment or business scenarios, causing a disconnection between goals and execution. The attribution analysis is shallow, and the existing technology mostly relies on descriptive statistics, lacking AI-driven causal mining capabilities, which cannot accurately identify key factors affecting performance, resulting in a lack of targeted intervention measures. The intervention scheme is homogeneous, and the performance improvement suggestions provided by traditional systems are mostly standardized templates, without intelligent matching of individual employee characteristics, and lacking a dynamic optimization mechanism for execution feedback, which easily leads to ineffective intervention. The common problem lacks predictability, and the existing technology is difficult to identify cross-department and cross-team common efficiency bottlenecks through clustering analysis, and lacks a performance prediction model, which cannot predict the enterprise's overall performance growth space based on current improvement results, making it difficult to provide forward-looking support for long-term strategy implementation. The above defects make it difficult for the existing technology to support the core demand of enterprises to accurately drive performance improvement through human resource management, and intelligent systems are needed to make targeted breakthroughs. SUMMARY

[0004] The present application provides a high-reliability low-attenuation energy storage battery management system based on intelligent control to solve the problems of shallow attribution analysis, homogeneous intervention scheme and lack of predictability of common problems in the prior art.

[0005] The first aspect of this application provides a human resource intelligent management system based on enterprise performance improvement, including: a data acquisition module, a dynamic indicator management module, a performance attribution analysis module, a personalized intervention module, and a common problem identification and performance prediction module; wherein, the data acquisition module is used to collect the enterprise's medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data; the dynamic indicator management module is used to decompose the enterprise's medium- and long-term strategic goals into departmental and job-level performance indicators through a strategic decoding algorithm, construct an indicator dynamic adjustment model, and automatically adjust the weights of performance indicators; the performance attribution analysis module is used to mine the correlation between performance and various data dimensions through an AI attribution analysis algorithm, identify key performance influencing factors, and generate a performance influencing factor report; the personalized intervention module is used to push customized performance improvement plans through an intelligent matching engine, and if the performance does not reach the expected threshold after the plan is implemented, the plan parameters are automatically adjusted until the performance reaches the target; the common problem identification and performance prediction module is used to identify common problems of the enterprise through a clustering algorithm, push targeted human resource efficiency optimization suggestions, and simultaneously construct a performance prediction model to predict the overall performance growth value of the enterprise based on individual improvement effects and the improvement of common problems of the enterprise.

[0006] Preferably, the data acquisition module includes an enterprise strategic business data acquisition unit and an employee comprehensive data acquisition unit. The enterprise strategic business data acquisition unit is used to simultaneously acquire the enterprise's medium- and long-term strategic goals and core business indicators such as revenue, cost, and market share, providing a top-level basis and business foundation for performance indicator decomposition and analysis. The employee comprehensive data acquisition unit is used to integrate and capture information on employee task progress, output quality, behavioral performance, skill level, and training results, supporting individual / team performance evaluation, attribution analysis, and the formulation of personalized improvement plans.

[0007] Preferably, the dynamic indicator management module includes a strategic goal analysis unit, a strategic decoding unit, a dynamic adjustment model unit, and an indicator calibration unit. The strategic goal analysis unit is responsible for structurally decomposing the company's medium- and long-term strategic goals, extracting key strategic elements, target values, and time nodes to form quantifiable strategic benchmarks. The strategic decoding unit uses a strategic decoding algorithm to transform high-level strategic goals layer by layer into departmental and job-level performance indicators, clarifying the definition, calculation logic, responsible parties, and horizontal collaborative relationships of each level of indicator. The dynamic adjustment model unit constructs a dynamic indicator adjustment model based on real-time operating data and changes in the external environment, automatically optimizing the weight allocation of different performance indicators. The indicator calibration unit is used to periodically link performance attribution analysis and common problem identification results to verify the alignment between the indicator system and strategic goals, and to propose correction suggestions for indicators that deviate from the strategic direction.

[0008] Preferably, the performance attribution analysis module includes a correlation calculation unit, a key influencing factor screening unit, and a report generation unit. The correlation calculation unit uses an AI attribution algorithm to quantify the correlation strength between performance results and employee capability data, behavioral data, and enterprise operational data, generating a correlation matrix. The key influencing factor screening unit combines the correlation matrix with business scenario weights, and uses threshold filtering and significance testing to select key factors that play a decisive role in performance from highly correlated factors, excluding secondary interfering factors. The report generation unit integrates key influencing factors, quantified correlation results, and attribution logic into a structured report, which is then presented visually through heatmaps and influence path diagrams.

[0009] Preferably, the personalized intervention module includes a needs diagnosis unit, a customized solution generation unit, a multi-channel intelligent push unit, and an execution process monitoring and dynamic optimization unit. The needs diagnosis unit accurately identifies individual improvement needs by analyzing employee performance gaps, skill deficiencies, and job characteristics. The customized solution generation unit, based on the needs analysis, combines an enterprise resource library with AI matching algorithms to generate a customized performance improvement plan that includes specific action items, resource support, and timelines. The multi-channel intelligent push unit selects the optimal channel to push the plan based on employee reach preferences and synchronizes key objectives. The execution process monitoring and dynamic optimization unit collects plan execution data in real time, tracks progress through a visual dashboard, sets performance achievement thresholds, and automatically analyzes hindering factors, adjusts plan parameters, and iteratively optimizes until performance targets are met if expectations are not met after execution.

[0010] Preferably, the common problem identification and performance prediction module includes a common problem mining unit, an optimization suggestion generation unit, and a performance prediction unit. The common problem mining unit uses clustering algorithms to identify performance convergence deviations across departments / positions, pinpointing common management pain points at the enterprise level. The optimization suggestion generation unit, based on the identified common problems, combines industry practices and the enterprise strategy library to automatically match training supplements, process optimization, and human resource efficiency improvement solutions, and pushes them to the responsible departments. The performance prediction unit integrates individual improvement trajectories and common problem improvement data, predicts the overall enterprise performance growth value through a performance prediction model, and outputs the growth range and key driving factors for future cycles to support strategic decision-making.

[0011] The second aspect of this application provides a method for an intelligent human resource management system based on enterprise performance improvement, comprising: collecting enterprise medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data; based on the enterprise medium- and long-term strategic goals and operational data, using a strategic decoding algorithm to decompose the enterprise medium- and long-term strategic goals into departmental and job-level performance indicators, constructing a dynamic adjustment model for the indicators, automatically adjusting the weights of the performance indicators, and simultaneously using an AI attribution analysis algorithm to mine the correlation between performance and each data dimension, identifying key performance influencing factors, and generating a performance influencing factor report; based on the performance indicator weights and the performance influencing factor report, pushing customized performance improvement plans through an intelligent matching engine, and automatically adjusting the plan parameters if the performance does not reach the expected threshold after the plan is implemented until the performance reaches the target, obtaining the performance data of all employees; using a clustering algorithm to identify common problems in the enterprise based on the performance data of all employees, pushing targeted human resource efficiency optimization suggestions, and simultaneously constructing a performance prediction model to predict the overall performance growth value of the enterprise based on the individual improvement effect and the improvement of common problems in the enterprise.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the method of the intelligent human resource management system based on enterprise performance improvement as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method of the intelligent human resource management system based on enterprise performance improvement as described in the above embodiments.

[0014] A fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing the method of a human resource intelligent management system based on enterprise performance improvement as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application's embodiments comprehensively collect multi-dimensional data on strategic goals, operations, and employees through a data acquisition module, laying a solid foundation for analysis. A dynamic indicator management module uses a strategic decoding algorithm to break down strategy into departmental / position-level indicators, and automatically calibrates weights using a dynamic adjustment model to ensure indicators align with the strategy. A performance attribution analysis module leverages AI algorithms to deeply explore the correlation between performance and data dimensions, accurately identifying key influencing factors and generating reports to solve the problem of inefficiency. A personalized intervention module uses an intelligent matching engine to push customized improvement plans, dynamically adjusting parameters until targets are met, addressing the pain point of how to improve efficiency. A common problem identification and performance prediction module uses clustering algorithms to locate common shortcomings within the enterprise and push optimization suggestions, simultaneously building a predictive model. Combining individual and common improvement results, it predicts overall performance growth, significantly enhancing the pertinence, scientific rigor, and foresight of performance improvement, facilitating efficient strategic implementation and continuous improvement in human resource efficiency. Thus, it solves the problems of superficial attribution analysis, homogenized intervention plans, and insufficient predictability of common problems in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the structure of an intelligent human resource management system based on enterprise performance improvement provided in the embodiments of this application; Figure 2 This is a schematic diagram of a data acquisition module provided according to an embodiment of this application; Figure 3 This is a schematic diagram of a dynamic indicator management module provided according to an embodiment of this application; Figure 4 This is a schematic diagram of a performance attribution analysis module provided according to an embodiment of this application; Figure 5 This is a schematic diagram of a personalized intervention module provided according to an embodiment of this application; Figure 6 This is a schematic diagram of a common problem identification and performance prediction module according to an embodiment of this application; Figure 7 A flowchart illustrating a method for providing an intelligent human resource management system based on enterprise performance improvement according to one embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] 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. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The following describes an embodiment of the intelligent human resource management system based on enterprise performance improvement, with reference to the accompanying drawings. Addressing the issue of insufficient predictability of common problems mentioned in the background art, this application provides an intelligent human resource management system based on enterprise performance improvement. In this system, a data acquisition module comprehensively collects multi-dimensional data on strategic goals, operations, and employees, laying a solid foundation for analysis; a dynamic indicator management module uses a strategic decoding algorithm to break down strategies into departmental / position-level indicators, and automatically calibrates weights using a dynamic adjustment model to ensure that indicators are aligned with the strategy; a performance attribution analysis module relies on AI algorithms to deeply explore the correlation between performance and data dimensions, accurately identify key influencing factors, and generate reports, solving the problem of why efficiency is low; a personalized intervention module uses an intelligent matching engine to push customized improvement plans, dynamically adjusting parameters until targets are met, addressing the pain point of how to improve efficiency; and a common problem identification and performance prediction module uses clustering algorithms to locate common shortcomings of the enterprise and push optimization suggestions, simultaneously building a prediction model, combining individual and common improvement results to predict overall performance growth, significantly enhancing the pertinence, scientific nature, and foresight of performance improvement, and helping to efficiently implement strategies and continuously improve human resource efficiency. This addresses the problems in existing technologies, such as superficial attribution analysis, homogenized intervention plans, and insufficient predictability of common problems.

[0020] Figure 1 This is a schematic diagram of the structure of an intelligent human resource management system based on enterprise performance improvement provided in an embodiment of this application.

[0021] This application provides an intelligent human resource management system based on enterprise performance improvement. The system 10 includes: The system includes a data acquisition module (100), a dynamic indicator management module (200), a performance attribution analysis module (300), a personalized intervention module (400), and a common problem identification and performance prediction module (500).

[0022] The system includes the following modules: Data Acquisition Module 100, which collects enterprise medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data; Dynamic Indicator Management Module 200, which uses a strategic decoding algorithm to break down the enterprise's medium- and long-term strategic goals into departmental and job-level performance indicators, constructs a dynamic adjustment model for indicators, and automatically adjusts the weights of performance indicators; Performance Attribution Analysis Module 300, which uses an AI attribution analysis algorithm to mine the correlation between performance and various data dimensions, identify key performance influencing factors, and generate a performance influencing factor report; Personalized Intervention Module 400, which uses an intelligent matching engine to push customized performance improvement plans. If the performance does not reach the expected threshold after the plan is implemented, the plan parameters are automatically adjusted until the performance meets the target; and Common Problem Identification and Performance Prediction Module 500, which uses a clustering algorithm to identify common problems in the enterprise, pushes targeted human resource efficiency optimization suggestions, and constructs a performance prediction model to predict the overall performance growth of the enterprise based on individual improvement effects and the improvement of common problems in the enterprise.

[0023] Understandably, this application embodiment comprehensively collects multi-dimensional data on strategic goals, operations, and employees through a data acquisition module, laying a solid foundation for analysis; the dynamic indicator management module uses a strategic decoding algorithm to break down the strategy into departmental / position-level indicators, and automatically calibrates weights using a dynamic adjustment model to ensure that indicators are in sync with the strategy; the performance attribution analysis module relies on AI algorithms to deeply explore the correlation between performance and data dimensions, accurately identify key influencing factors, and generate reports to solve the problem of why efficiency is low; the personalized intervention module pushes customized improvement plans through an intelligent matching engine, dynamically adjusting parameters until targets are met, addressing the pain point of how to improve efficiency; the common problem identification and performance prediction module uses clustering algorithms to locate common shortcomings of the enterprise and push optimization suggestions, simultaneously building a prediction model, and combining individual and common improvement results to predict overall performance growth, significantly enhancing the pertinence, scientific nature, and foresight of performance improvement, and helping to achieve efficient strategic implementation and continuous leaps in human resource efficiency. Thus, it solves the problems of superficial attribution analysis, homogenized intervention plans, and insufficient predictability of common problems in existing technologies.

[0024] In this embodiment of the application, the data acquisition module 100 includes: Figure 2 As shown, there are two data collection units: the enterprise strategic management data collection unit and the employee comprehensive data collection unit.

[0025] Among them, the enterprise strategic operation data collection unit is used to simultaneously acquire the enterprise's medium and long-term strategic goals and core operation indicators such as revenue, cost, and market share, providing a top-level basis and business foundation for the decomposition and analysis of performance indicators; the employee comprehensive data collection unit is used to integrate and capture information on employee task progress, output quality, behavioral performance, skill level, and training results, supporting individual / team performance evaluation, attribution analysis, and the formulation of personalized improvement plans.

[0026] It is understood that the embodiments of this application synchronize medium- and long-term strategic goals with core operating indicators such as revenue and cost through the enterprise strategic business data collection unit. This can anchor the business foundation and top-level direction of performance work, ensuring that the breakdown of performance indicators does not deviate from the core of enterprise development. By integrating multi-dimensional information such as task progress and skill level through the employee comprehensive data collection unit, it can not only provide a comprehensive and objective basis for individual and team performance evaluation, but also help to quickly attribute performance problems and formulate personalized improvement plans that meet the needs of employees. Ultimately, it can open up the key link between strategy implementation and employee execution, and promote the two-way improvement of enterprise performance and employee capabilities.

[0027] For example, after a listed pharmaceutical group introduced this intelligent human resources management system, its corporate strategic business data collection unit simultaneously captured the group's medium- and long-term goal of "increasing the revenue share of innovative drugs to 35% within three years," as well as the corresponding quarterly core business indicators such as innovative drug sales, R&D investment costs, and key market share. Through system algorithms, the top-level goal was broken down into departmental performance indicators such as "completing five Class I new drug clinical trials annually" for the R&D department and "innovative drug mass production qualification rate ≥ 99.5%" for the production department, providing clear business anchors for subsequent evaluations. Simultaneously, the employee comprehensive data collection unit integrated output data such as R&D personnel's experimental progress in the project management system and sample purity recorded in the quality inspection system. Combined with cross-team collaboration performance collected through 360-degree evaluation, and information such as formulation process level and compliance training pass rate in the skills matrix, it automatically generated individual and project team performance reports. When it was found that a formulation team's experimental cycle was extended due to substandard "formulation process simulation" skills, the system quickly attributed the cause and pushed a personalized improvement plan including virtual simulation practical courses, helping the team improve process optimization efficiency by 22% in the next quarter.

[0028] In this embodiment of the application, the dynamic indicator management module 200 includes: Figure 3 As shown, there are strategic goal analysis unit, strategic decoding unit, dynamic adjustment model unit, and indicator calibration unit.

[0029] The strategic goal analysis unit is responsible for structurally breaking down the company's medium- and long-term strategic goals, extracting key strategic elements, target values, and time nodes to form quantifiable strategic benchmarks. The strategic decoding unit uses strategic decoding algorithms to transform high-level strategic goals into departmental and job-level performance indicators, clarifying the definition, calculation logic, responsible parties, and horizontal collaborative relationships of each level of indicators. The dynamic adjustment model unit constructs a dynamic adjustment model for indicators based on real-time operating data and changes in the external environment, automatically optimizing the weight allocation of different performance indicators. The indicator calibration unit is used to periodically link the results of performance attribution analysis and common problem identification to verify the alignment between the indicator system and strategic goals, and to propose correction suggestions for indicators that deviate from the strategic direction.

[0030] It is understood that the embodiments of this application use a strategic goal analysis unit to structurally decompose the company's medium- and long-term strategic goals and extract key elements to form quantifiable strategic benchmarks, effectively improving the executability and implementation orientation of strategic goals and providing a clear top-level basis for subsequent indicator decomposition; the strategic decoding unit uses algorithms to transform high-level goals into departmental and job-level indicators layer by layer and clarify the relationship of rights and responsibilities, ensuring that strategic requirements are accurately transmitted between organizational levels, promoting cross-departmental collaboration and responsibility implementation, and strengthening the consistency from strategy to execution; the dynamic adjustment model unit builds an indicator weight optimization model based on real-time data and environmental changes, improving the sensitivity of the indicator system to external dynamics, ensuring that performance evaluation is synchronized with actual business operations, and enhancing the timeliness and adaptability of indicators; the indicator calibration unit links attribution analysis and common problem identification to verify the strategic fit and propose correction suggestions, effectively avoiding the risk of indicator deviation, continuously optimizing the matching between the indicator system and the strategy, and providing a dynamic guarantee mechanism for the stable achievement of strategic goals.

[0031] For example, when a manufacturing company uses this dynamic indicator management module to promote its "three-year digital transformation" strategy, the strategic goal analysis unit first breaks it down into quantifiable benchmarks such as "30% increase in production efficiency" and "20% reduction in customer response time," clearly defining the time nodes for completing the deployment of the intelligent production system in 2024, pilot production line operation in 2025, and factory-wide promotion in 2026. The strategic decoding unit uses algorithms to transform the overall goal into tiered indicators such as "system on-time launch rate ≥ 95%" for the IT department, "key equipment connectivity rate ≥ 80% and OEE improved to 25%" for the production department, and "supplier data interoperability rate ≥ 70% and order processing cycle ≤ 48 hours" for the supply chain department, and also marks the requirements for cross-departmental collaboration. Request: Due to sudden changes in market demand mid-year, the dynamic adjustment model unit, based on real-time order data (customized orders accounted for 45%) and industry competition analysis, automatically adjusted the weight of "timely delivery rate of customized orders" in the production department from 20% to 35%, while simultaneously reducing the weight of "standardized product output". At the end of the year, the indicator calibration unit, in conjunction with the performance attribution report (which found that the equipment networking rate was high but the OEE did not meet the target, mainly due to insufficient operation training), proposed a correction suggestion—adding "employee digital skills assessment pass rate ≥ 90%" to the production department indicators. After verification, it was confirmed that the alignment of the adjusted indicator with the strategic sub-goal of "improving flexible production capabilities" increased from 82% to 91%, ensuring that the indicator system continues to anchor the strategic direction.

[0032] In this embodiment of the application, the performance attribution analysis module 300 includes: Figure 4 As shown, there are three units: correlation calculation unit, key influencing factor screening unit, and report generation unit.

[0033] The correlation calculation unit uses AI attribution algorithms to quantify the correlation strength between performance results and employee capability data, behavioral data, and enterprise operation data, generating a correlation matrix. The key influencing factor screening unit combines the correlation matrix with business scenario weights, and uses threshold filtering and significance testing to screen out key factors that play a decisive role in performance from highly correlated factors, excluding secondary interfering factors. The report generation unit integrates key influencing factors, correlation quantification results, and attribution logic into a structured report, which is presented visually through heatmaps and influence path diagrams.

[0034] It is understood that the embodiments of this application use an AI attribution algorithm to quantify the correlation strength between performance results and employee capabilities, behaviors, and enterprise operating data through a correlation calculation unit, and generate a correlation matrix. This breaks the ambiguity and subjectivity of traditional performance attribution, transforming performance impact relationships from experience-based judgments to data-driven support. By using a key impact factor screening unit that combines business scenario weights, threshold filtering, and significance testing to screen key factors and eliminate interference, enterprises can accurately identify the core elements that play a decisive role in performance, avoiding resource misallocation to secondary factors. Furthermore, the report generation unit integrates key factors, quantitative results, and attribution logic into a structured report and presents them visually using heatmaps and impact path diagrams. This lowers the threshold for data interpretation, making it easier for managers to quickly grasp key performance drivers, and provides a clear basis for subsequent optimization of human resource strategies, targeted improvement of employee capabilities, and adjustment of business direction, ultimately helping enterprises efficiently improve overall performance.

[0035] For example, in the performance attribution analysis of a manufacturing company's sales team, the performance attribution analysis module operates as follows: The correlation calculation unit first retrieves quarterly sales performance data (such as average sales per employee and new customer conversion rate), and simultaneously extracts employee competency data (customer negotiation skills score, industry knowledge test score), behavioral data (average daily customer visits, frequency of online business opportunity follow-ups), and enterprise operating data (regional market promotion budget, product promotion discount intensity). It then outputs a correlation matrix using a random forest attribution algorithm, showing that the correlation between "customer negotiation skills score" and "new customer conversion rate" is 0.78, the correlation between "frequency of online business opportunity follow-ups" and "average sales per employee" is 0.65, while the correlation between "industry knowledge test score" is only 0.42. The key influencing factor screening unit combines current... The business focus (new customer acquisition weight 0.6, repeat purchase weight of existing customers 0.4) was analyzed. Factors with a correlation greater than 0.6 underwent a chi-square significance test (p < 0.05), and "frequency of online business opportunity follow-up" was removed (p = 0.07). Ultimately, "customer negotiation skills score" and "regional market promotion budget" were identified as the core factors. The report generation unit integrated the results, visually presenting the strong correlation between the two factors and performance using a heatmap (highlighted in red). An impact path diagram illustrated the transmission logic of "increased market promotion budget → improved target customer reach → increased customer negotiation conversion opportunities → sales growth," and the attribution conclusion that "insufficient negotiation skills led to the loss of 30% of high-potential business opportunities" was highlighted. This provides data support for enterprises to conduct targeted negotiation training and optimize market budget allocation.

[0036] In this embodiment, the personalized intervention module 400 includes, as follows: Figure 5 As shown, the unit comprises a requirement diagnosis unit, a customized solution generation unit, a multi-channel intelligent push unit, and an execution process monitoring and dynamic optimization unit.

[0037] The system comprises several modules: a needs diagnosis unit, a customized solution generation unit, and a multi-channel intelligent push unit. The latter analyzes employee performance gaps, skill deficiencies, and job characteristics to accurately identify individual improvement needs. The former combines the needs analysis with the company's resource library and AI matching algorithms to generate customized performance improvement plans that include specific action items, resource support, and timelines. The latter selects the optimal channels to push the plan based on employee preferences and synchronizes key objectives. The former collects plan execution data in real time, tracks progress through a visual dashboard, sets performance achievement thresholds, and automatically analyzes obstacles and adjusts plan parameters if performance targets are not met. The latter iterates and optimizes the plan until performance targets are achieved.

[0038] Understandably, this application's embodiments accurately pinpoint employee performance gaps, skill deficiencies, and job suitability needs through a demand diagnosis unit, avoiding resource misallocation and indiscriminate training. A customized solution generation unit, combined with an enterprise resource library and AI algorithms, generates solutions containing specific action items, resource support, and timelines, ensuring the improvement path is implementable and measurable. A multi-channel intelligent push unit delivers solutions and goals according to employee preferences, enhancing employee acceptance and willingness to implement. Furthermore, an execution process monitoring and dynamic optimization unit tracks progress in real time, sets achievement thresholds, and automatically analyzes obstacles and iterates solutions when expectations are not met. This ensures the continuity of performance improvement while flexibly addressing variables during execution, ultimately efficiently driving employee performance improvement and providing precise and dynamic human resource support for overall enterprise performance enhancement.

[0039] For example, Mr. Zhang, a B2B sales representative at a technology company, failed to meet his performance target last quarter, resulting in a 20% performance shortfall. The personalized intervention module of the HR intelligent management system was immediately activated: the needs diagnosis unit, by comparing his performance data, customer feedback, and the characteristics of his project management position—"frequent government-enterprise interactions and emphasis on long-term relationship maintenance"—precisely identified two major weaknesses: "insufficient depth in understanding customer needs" and "a single strategy for acquiring new customers." The customized solution generation unit, relying on the company's internal training resource library and AI matching algorithms, generated a four-week improvement plan. This plan included completing one online course on "Practical B2B Customer Needs Understanding" each week, conducting a customer follow-up review with senior sales mentor Mr. Li every two weeks, and carrying out two simulated government-enterprise negotiation exercises in the third week, along with clearly defined resource support. The multi-channel intelligent push unit, based on Zhang's preference for instant reception via WeChat and supplementary SMS reminders, synchronized the plan to his WeChat workbench, attaching a key target SMS message: "Increase new customer conversion rate by 15% within 4 weeks." The execution process monitoring and dynamic optimization unit collected data in real time. Through a visual dashboard, it showed that Zhang's simulated negotiation exercise score in the second week was only 65 points (below the 80-point threshold). After automatically analyzing the hindering factor of "insufficient understanding of the pain points of customers in the energy industry," the plan was immediately adjusted—adding three in-depth analysis courses of benchmark cases in the energy industry and changing the review mentor to a senior salesperson in the energy field. The iterated plan helped Zhang achieve a score of 92 points in the fourth week's exercise, and the new customer conversion rate increased by 18% by the end of the month, achieving the performance target.

[0040] In this embodiment of the application, the common problem identification and performance prediction module 500 includes, as follows: Figure 6 As shown, there are three units: common problem mining unit, optimization suggestion generation unit, and performance prediction unit.

[0041] The common problem mining unit identifies performance convergence deviations across departments / positions through clustering algorithms, pinpointing common management pain points at the enterprise level. The optimization suggestion generation unit, based on the identified common problems, combines industry practices and the enterprise strategy library to automatically match training supplements, process optimization, and human resource efficiency improvement solutions and push them to the responsible departments. The performance prediction unit integrates individual improvement trajectories and common problem improvement data, predicts the overall performance growth value of the enterprise through a performance prediction model, and outputs the growth range and key driving factors for future cycles to support strategic decision-making.

[0042] It is understood that the embodiments of this application utilize clustering algorithms through a common problem mining unit to identify performance convergence deviations across departments / positions, accurately pinpointing common management pain points at the enterprise level; the optimization suggestion generation unit, based on the mined common problems, combines industry practices and the enterprise strategy library to automatically match training supplements, process optimization, and human resource efficiency improvement solutions and push them to the responsible departments; the performance prediction unit integrates individual improvement trajectories and common problem improvement data, predicts the overall performance growth value of the enterprise through a performance prediction model, outputs the growth range and key driving factors for future cycles, supports strategic decision-making, improves the accuracy of common problem identification, the effectiveness of optimization suggestion implementation, and the scientific nature of performance prediction, promotes systematic improvement of human resource efficiency, provides data-driven decision-making basis for enterprise strategic adjustments and goal achievement, and promotes continuous improvement of organizational performance.

[0043] For example, after a medium-sized auto parts manufacturing company introduced this intelligent human resources management system, its common problem identification unit analyzed the monthly performance data of the three core departments—production, quality inspection, and logistics—using clustering algorithms. It discovered a convergence deviation where the production capacity achievement rate of these three departments had declined by more than 10% in recent quarters compared to previous periods, accurately pinpointing two common enterprise-level pain points: "insufficient proficiency in operating new equipment" and "lagging cross-departmental material coordination processes." The optimization suggestion generation unit then accessed the manufacturing equipment training case library and the company's internal process standards, automatically matching two solutions: "specialized training courses for new equipment operation" and "establishment of a digital Kanban system for material distribution," which were then pushed to the human resources department and production management department for implementation. One month after the implementation, the performance prediction unit integrated data on the improvement in trainees' operational proficiency (an average increase of 35%) and the reduction in material coordination time (a decrease of 40%). Using a built-in predictive model, it calculated that the company's overall production capacity achievement rate would increase by 5%-8% in the next quarter. The key driving factors were enhanced operational skills and improved process collaboration efficiency, providing core data support for management to formulate quarterly production expansion plans.

[0044] The proposed human resource intelligent management system for enterprise performance improvement utilizes a data acquisition module to comprehensively collect multi-dimensional data on strategic goals, operations, and employees, laying a solid foundation for analysis. A dynamic indicator management module breaks down strategy into departmental / job-level indicators using a strategic decoding algorithm, and automatically calibrates weights using a dynamic adjustment model to ensure indicators align with strategy. A performance attribution analysis module leverages AI algorithms to deeply explore the correlation between performance and data dimensions, accurately identifying key influencing factors and generating reports to address the challenge of inefficiency. A personalized intervention module uses an intelligent matching engine to push customized improvement plans, dynamically adjusting parameters until targets are met, addressing the pain point of how to improve efficiency. A common problem identification and performance prediction module uses clustering algorithms to locate common shortcomings within the enterprise and push optimization suggestions, simultaneously building a predictive model. This, combined with the effectiveness of individual and common improvements, predicts overall performance growth, significantly enhancing the targeting, scientific rigor, and foresight of performance improvement, facilitating efficient strategic implementation and continuous improvement in human resource efficiency. This addresses the problems of superficial attribution analysis, homogenized intervention plans, and insufficient predictability of common problems in existing technologies.

[0045] The following will illustrate a specific example of an intelligent human resource management system based on enterprise performance improvement, including: Group A, a leading domestic auto parts manufacturer (with annual revenue exceeding 8 billion yuan and 12,000 employees), introduced a "Human Resources Intelligent Management System Based on Enterprise Performance Improvement" in 2022. This effectively addressed pain points such as strategic implementation gaps, unclear performance-driven mechanisms, and insufficient talent efficiency. The following details the specific operational scenarios of the system's five core modules: In the initial stage of system launch, the data acquisition module first broke down the data barriers across the entire enterprise chain—by ​​connecting to ERP (operational data: quarterly order volume, raw material costs, yield rate), MES (production data: equipment OEE, production line downtime), OA (process data: approval timeliness, frequency of cross-departmental collaboration), and CRM (client data: customer complaint rate, on-time delivery rate) through API interfaces, and also integrating employee behavior data (enterprise WeChat communication records, LMS training system learning time, attendance system abnormal check-in rate) and competency data (annual skills assessment scores, project experience tag library), forming a database of 300+ dimensions covering "strategy-operation-employees". For example, the system automatically captures the real-time yield rate (fluctuation range 92%-95%) and equipment downtime (average 2.1 hours per day) of 32 production lines in Production Workshop 2 every day. It also collects the validity period of skill certificates of 187 employees in the workshop (23 welder certificates are about to expire), the mentorship records of the past three months (average 1.2 times per week), and data on accountability for quality accidents (41% of rework was caused by operational errors), providing multi-source heterogeneous data support for subsequent analysis.

[0046] Based on the massive amounts of data collected, the dynamic indicator management module initiated strategic decoding and indicator iteration. The group's medium- and long-term strategic goal is to "increase the market share of new energy vehicle parts to 25% and reduce the quality cost ratio to 12% by 2024." The system uses natural language processing (NLP) to analyze the strategic text, combined with industry benchmarking (top competitors' quality cost is 10%, and the growth rate of new energy business is 30%) and internal resource endowment (potential for existing production line transformation and technical team reserves), and uses linear programming algorithm to decompose the strategy into departmental indicators: the production center added "first-pass yield of new energy products" (weight 35%, the original traditional product pass rate weight was reduced to 25%) and "progress of intelligent equipment transformation" (weight 20%); the human resources department added "proportion of highly skilled personnel" (weight 25%, the original basic skills certification weight was reduced to 15%); and the quality department added "timeliness of customer complaints" (weight 30%, the raw material inspection omission rate weight was reduced to 20%). In Q2 of 2023, due to a surge in complaints about delayed delivery of a certain new energy battery casing product (up 50% quarter-on-quarter), the system automatically triggered indicator adjustments by monitoring the customer satisfaction data of the business unit where the product was located in real time (from 89 points to 82 points): increasing the weight of the business unit's "emergency order response speed" from 15% to 25%, while reducing the weight of "regular order production cost" (from 20% to 15%), to ensure that the indicators are always in sync with strategic priorities.

[0047] The performance attribution analysis module is based on multi-dimensional data modeling to delve into the key driving factors behind performance. In Q1 2023, the group's overall production efficiency decreased by 8% year-on-year (OEE dropped from 85% to 78%). The system called the random forest algorithm, inputting 120 variables including production data (equipment failure rate, mold changeover time), employee data (proportion of new employees, skill matching degree), and management data (rotation frequency of team leaders), and outputting an attribution report: The main reason for the decline in efficiency of Production Department 1 was that the proportion of new employees was as high as 42% (industry average 25%), and their "equipment debugging skills" assessment score was only 61 points (the passing score is 75 points). At the same time, the average tenure of team leaders was less than 1 year (insufficient experience led to a 40% increase in the time required to handle abnormalities). Further cross-analysis revealed a significant negative correlation between "effective mentoring time for new employees" and "operational error rate of new employees in the first month" (correlation coefficient -0.78), verifying that "inadequate implementation of the mentorship system" was a key influencing factor. The report prompted the group to revise the "New Employee Training and Management Measures", which stipulates that mentors must complete 40 hours of specialized training and pass the assessment and certification, and the mentoring time will be included in the performance evaluation (accounting for 15%).

[0048] For specific problems, the personalized intervention module pushes customized solutions through an intelligent matching engine. Taking Production Department 1 as an example, based on its diagnosis of "short skills for new employees + lack of experience for team leaders," the system matched an intervention package of "tiered training + shadowing program": new employees complete online theoretical courses (20 class hours) + VR equipment simulation operation (3 times a week, 1 hour each time) + offline mentorship (0.5 hours daily) in the first 3 months of their employment; team leaders participate in "abnormal handling sand table exercises" (once a month) and set "apprentice pass rate" as a promotion bonus. After 3 months of intervention, the error rate of new employees in the first month dropped from 35% to 12%, but because some veteran employees resisted the new training process (participation rate was only 65%), the system detected that the "training completion rate" did not reach the expected threshold (80%), and automatically adjusted the plan: increased training points rewards (which can be redeemed for vacation or physical goods), and linked training participation to the department head's KPI (accounting for 10%). After adjustments, the error rate of new employees in the fourth month further decreased to 8%, and the OEE of Production Unit 1 rebounded to 83%, exceeding the target value by 3 percentage points.

[0049] The Common Problem Identification and Performance Prediction module uncovers systemic risks at the organizational level and proactively addresses them. The system analyzes departmental data using the DBSCAN clustering algorithm and identifies "delayed cross-departmental requirement transmission" as a common problem—the average time for the R&D department to transmit new product drawings to the production department is 7 days (the industry best is 3 days), leading to insufficient production preparation and extended trial production cycles. Further correlation analysis shows that the requirement transmission delay is highly correlated with "lack of standardized templates" and "unclear responsible interfaces" (R²=0.82). Therefore, optimization suggestions are proposed: develop a cross-departmental digital requirement management platform (with built-in standardized templates), and clearly define the three levels of responsibility and timely milestones for "requirement submission-confirmation-implementation." Meanwhile, based on the effects of individual interventions (such as an 8% increase in the efficiency of Production Department 1) and the progress of common problem improvements (reducing the time for demand transmission to 4 days), the system constructs a linear regression prediction model and calculates that if the two improvements are continuously promoted throughout the year, the group's overall on-time delivery rate can be increased from 88% to 93%, the annual quality cost can be reduced by RMB 120 million (20% over the target value), and the revenue share of the new energy business is expected to increase from 18% to 22%, providing quantitative support for achieving the strategic goals in 2024.

[0050] Through the operation of this system, Group A's overall performance improved significantly in 2023: employee efficiency increased by 15%, key personnel retention rate rose from 72% to 85%, customer complaint rate decreased by 30%, and the annual quality and cost control target was achieved two quarters ahead of schedule. More importantly, the system enabled full-cycle management from "post-event assessment" to "pre-event prediction, in-event intervention, and post-event improvement," building a data-driven and agile performance improvement ecosystem for the company.

[0051] In summary, this application's embodiments, through data integration, dynamic indicator decomposition, and AI attribution analysis, accurately pinpoint core issues such as strategic implementation gaps and performance-driven ambiguity, promoting targeted resource allocation and enabling a management leap from experience-based decision-making to data-driven approaches. The personalized intervention module addresses individual performance bottlenecks through diagnosis, solutions, and iteration, while the common problem identification and prediction module captures systemic risks at the organizational level and plans ahead, ultimately driving a leap in overall enterprise performance. Its role extends beyond short-term indicator improvement; it constructs a full-cycle intelligent management ecosystem encompassing strategy, execution, feedback, and optimization, providing a replicable digital path for enterprises to respond to market changes and maintain long-term competitiveness.

[0052] Next, with reference to the accompanying drawings, a method for a human resource intelligent management system based on enterprise performance improvement proposed in this application is described.

[0053] like Figure 7 As shown, the method of this intelligent human resource management system based on enterprise performance improvement includes the following steps: In step S101, the company's medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data are collected.

[0054] Understandably, the implementation of this application, by collecting the company's medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data, forms the core foundation and prerequisite for the human resource intelligent management system to improve corporate performance. It can connect the data links between corporate strategy, current operations, and individual employees, providing a top-level basis for subsequent strategic decoding algorithms to ensure that the decomposed departmental and job-level performance indicators do not deviate from the company's development direction. It can also provide multi-dimensional materials for AI attribution analysis, accurately uncovering the intrinsic relationship between performance and employee work output, behavioral characteristics, and capability levels. This data can also support the push of customized solutions by the intelligent matching engine, as well as the clustering algorithm to identify common problems in the company and build performance prediction models. This ensures that the decisions and adjustments of the entire management system are based on real data, avoiding subjective judgment bias, and ultimately achieving precise implementation from individual performance optimization to overall corporate performance growth.

[0055] In step S102, based on the company's medium- and long-term strategic goals and operational data, the company's medium- and long-term strategic goals are broken down into departmental and job-level performance indicators through a strategic decoding algorithm. A dynamic adjustment model for the indicators is constructed to automatically adjust the weights of the performance indicators. At the same time, combined with employee work data, behavioral data, and capability data, an AI attribution analysis algorithm is used to mine the correlation between performance and each data dimension, identify key performance influencing factors, and generate a performance influencing factor report.

[0056] Key performance influencing factors refer to core factors or variables that have a significant driving or constraining effect on the achievement of key performance indicators of an organization or individual.

[0057] It is understood that the embodiments of this application accurately identify employee behaviors, competency elements and work patterns that have a decisive impact on performance results through a data-driven approach. This enables enterprises to move away from the traditional vague management that relies on experience and instead make precise interventions and resource allocations to key links that truly affect performance. This effectively improves the return on investment in human capital and provides a scientific basis and decision support for the accurate achievement of strategic goals and the continuous optimization of human resource allocation.

[0058] For example, in the sales team of an e-commerce company, the company's long-term strategic goal is to "increase the quarterly sales of high-margin products by 30%". The intelligent management system first collects the company's revenue data for high-margin products over the past three quarters, regional market competitor dynamics, and other operational data, as well as sales staff's customer follow-up records, product recommendation frequency, customer communication duration, and product professional certification results. Through AI attribution analysis algorithms, it was found that "frequency of high-margin product recommendations", "duration of in-depth customer needs analysis", and "product professional certification pass rate" have a correlation of 82%, 78%, and 75% with sales performance, respectively, making them core key performance indicators. In contrast, the correlation of "total customer visits," which is emphasized in traditional assessments, is only 41%. The performance impact factor report clearly shows that high-performing sales staff recommended high-margin products in 65% of their sales (compared to only 32% for ordinary sales staff), spent an average of over 15 minutes exploring customer needs (compared to less than 8 minutes for ordinary sales staff), and achieved 100% pass the advanced product certification (compared to only 68% for ordinary sales staff). This provides a precise basis for the subsequent rollout of customized solutions such as "high-margin product training + customer needs analysis template tools".

[0059] In step S103, based on the performance indicator weights and performance impact factor reports, a customized performance improvement plan is pushed through the intelligent matching engine. If the performance does not reach the expected threshold after the plan is implemented, the plan parameters are automatically adjusted until the performance reaches the target, and the performance data of all employees is obtained.

[0060] Among them, the intelligent matching engine is a tool that uses artificial intelligence technology to achieve efficient and accurate matching of target objects by analyzing features and rules.

[0061] It is understood that, through the deep integration of performance indicator weights, employee performance influencing factor reports, and multi-dimensional data, this application's embodiments accurately identify the personalized improvement needs of employees or departments at different positions and levels by relying on algorithmic models, and dynamically generate customized performance improvement plans that are adapted to their actual scenarios, effectively avoiding the inefficiency of traditional one-size-fits-all intervention models. Its data-driven intelligent matching mechanism not only improves the fit between the plan and the employee's skill gaps, but also enhances the feasibility of the measures, providing enterprises with a precise implementation path from strategy to individuals, significantly improving the pertinence and success rate of performance improvement, and powerfully promoting the coordinated development of organizational goals and individual growth.

[0062] For example, Li, an employee in the sales department of a manufacturing company, had his department's quarterly performance indicators, after strategic decoding, focused on "the proportion of sales revenue from high-margin products" (weight 40%) and "new customer conversion rate" (weight 35%). AI attribution analysis showed that Li's key influencing factors were "insufficient knowledge of high-margin products" (correlation 0.78) and "lack of depth in understanding customer needs" (correlation 0.65). Based on his historical performance data (high-margin product sales accounted for only 12% in the past 3 months), ability assessment (product knowledge test score of 72 points), and behavioral trajectory (average time spent filling out customer needs questionnaires < 5 minutes), the intelligent matching engine accurately matched a customized solution from the company's performance improvement solution library: pushing "quick learning micro-courses on high-margin product technical parameters" (15 minutes of fragmented learning per day), binding "in-depth customer needs interview templates" (mandatory filling of 10 core needs fields), and "weekly senior sales follow-up coaching". In the first month of the program's implementation, Li's sales of high-margin products increased to 21%, but the new customer conversion rate only slightly increased to 8% (target 10%). The system automatically triggered an adjustment mechanism—adding a "Customer Needs Pain Point Case Review Meeting" (twice a week) and increasing the frequency of follow-up coaching to twice a week. In the following month, both indicators reached the targets of 25% and 11% respectively, and the engine simultaneously generated an "Individual Improvement Effect Evaluation Report," providing data support for common optimization suggestions for the company (such as strengthening training on customer needs analysis tools). The entire process achieved intelligent matching of "diagnosis-solution-execution-iteration," ensuring that improvement measures were highly aligned with employees' individual weaknesses.

[0063] In step S104, the performance data of all employees are used to identify common problems in the enterprise through clustering algorithms, and targeted human resource efficiency optimization suggestions are pushed. At the same time, a performance prediction model is constructed to predict the overall performance growth value of the enterprise based on the individual improvement effect and the improvement of common problems in the enterprise.

[0064] Among them, the performance prediction model is an analytical tool built using historical data, key indicators and influencing factors. It is a structured methodology that uses quantitative methods to infer the future performance of an organization, individual or project.

[0065] It is understood that the embodiments of this application, by employing a performance prediction model, can address situations in enterprise performance management where individual differences are significant, common problems are complex, and the multi-factor relationships exhibit non-linear characteristics. By integrating multi-source performance data, a dynamic performance evaluation system is constructed, and the improvement effects of individual performance and the progress of common enterprise problems are analyzed. The system quantifies the correlation strength and dynamic evolution patterns between multiple factors and overall enterprise performance, effectively avoiding the problems of traditional models failing to capture the dynamism of performance parameters across multiple dimensions and failing to accurately characterize the non-linear relationships and dynamic evolution of multiple factors. This provides a clearer basis for correlation characteristics and dynamic pattern references for subsequent optimization of human resource strategies and control of performance risks, thereby improving the accuracy of performance management prediction and adjustment and the effectiveness of dynamic adjustments.

[0066] For example, in a manufacturing company, its performance prediction model operates as follows: Assuming that individual improvement data shows that after AI-customized skills training, assembly line employees' average daily output increased from 120 pieces to 145 pieces (efficiency improvement of 20.8%), and the product defect rate decreased from 3% to 1.2% (quality improvement of 60%); at the same time, after the company's common problem of "delayed response to cross-departmental demands" was addressed through human resource efficiency optimization suggestions (such as building a cross-departmental digital collaboration platform and adjusting assessment indicators to favor collaboration), the R&D-production demand docking cycle was shortened from 72 hours to 24 hours, and the project delay rate decreased from 18% to 5%. The performance prediction model integrates individual efficiency and quality improvement data with cross-departmental collaborative improvement indicators. It combines historical data showing a correlation between "a 15% increase in capacity utilization and a 10% increase in on-time delivery rate leading to approximately 8-10% annual revenue growth" with current market demand growth expectations (industry growth rate of 6%). Ultimately, it predicts that the company's overall revenue will grow by 11%-13% in the next year, and the net profit margin will increase by 2.5-3 percentage points compared to the current year. The key driving factors are identified as the synergistic effect of "breakthrough in production efficiency" and "optimization of delivery capabilities," providing quantitative basis for companies to formulate their next-stage strategies.

[0067] The method for an intelligent human resource management system based on enterprise performance improvement proposed in this application comprehensively collects multi-dimensional data on strategic goals, operations, and employees through a data acquisition module, laying a solid foundation for analysis. A dynamic indicator management module uses a strategic decoding algorithm to break down strategies into departmental / job-level indicators, and automatically calibrates weights using a dynamic adjustment model to ensure indicators align with the strategy. A performance attribution analysis module relies on AI algorithms to deeply explore the correlation between performance and data dimensions, accurately identify key influencing factors, and generate reports, solving the problem of inefficiency. A personalized intervention module uses an intelligent matching engine to push customized improvement plans, dynamically adjusting parameters until targets are met, addressing the pain point of how to improve efficiency. A common problem identification and performance prediction module uses clustering algorithms to locate common shortcomings within the enterprise and push optimization suggestions, simultaneously building a prediction model. Combining individual and common improvement results, it predicts overall performance growth, significantly enhancing the pertinence, scientific rigor, and foresight of performance improvement, facilitating efficient strategic implementation and continuous improvement of human resource efficiency. This solves the problems of superficial attribution analysis, homogenized intervention plans, and insufficient predictability of common problems in existing technologies.

[0068] The following will illustrate the method of a human resource intelligent management system based on enterprise performance improvement through a specific embodiment, including: Taking the implementation of a human resource intelligent management system based on enterprise performance improvement at a large-scale domestic auto parts manufacturing enterprise (with over 30,000 employees covering 12 categories of positions including R&D, production, and sales) as an example, this paper fully demonstrates the implementation path and effectiveness of this method. In early 2024, the company established a medium-to-long-term strategic goal of achieving 45% revenue from new energy components and maintaining a core product qualification rate of 99.8% from 2024 to 2026. To address the challenges of the disconnect between traditional performance management and strategy, and the lag in human resource efficiency analysis, the company initiated the construction of a human resource intelligent management system, implementing it throughout the process by adhering to four core stages.

[0069] The system first constructs a comprehensive data acquisition hub, seamlessly integrating with enterprise ERP systems, MES production systems, CRM customer management systems, and employee mobile apps via open APIs to collect multi-dimensional data in real time: at the strategic level, it synchronizes the company's three-year strategic plan and quarterly strategic review reports; operational data covers 23 indicators, including monthly revenue, capacity utilization, and raw material loss rate for each product line, with the new energy component segment detailed down to the daily output of a single model and the frequency of dealer replenishment; employee work data is collected differentiated according to job type, with production staff recording equipment data via IoT sensors. Operation time, piecework output, and quality inspection results are integrated into the project management tools for R&D positions, including task completion rate, patent application progress, and prototype iteration count. For sales positions, data is linked to the POS system, including average order value, number of new customer contracts, and channel expansion effectiveness. Behavioral data includes dynamic information such as attendance rate from the attendance system, course completion time from the training platform, and cross-departmental communication frequency via WeChat. Competency data includes static indicators such as skill certificate level, 360-degree evaluation score, performance ratings over the years, and the completeness of career development plans, forming a "digital profile" for each individual, with an average daily increase of 1.2 million data entries.

[0070] Entering the strategic decoding and attribution analysis phase, the system's Moka-style strategic decoding engine first performs semantic analysis on the enterprise's strategic text, automatically extracting core keywords such as "new energy revenue growth" and "quality upgrade." Combining this with operating data from the past three years, a regression model is constructed, breaking down the enterprise-level goals into three levels of performance indicators: the R&D department undertakes departmental KPIs such as "new energy core component R&D cycle ≤ 180 days" and "average annual growth of patent applications of 30%"; the production department implements indicators such as "new energy component production capacity increase of 60%" and "defect rate ≤ 0.2%"; and the sales department undertakes the task of "expanding new energy customers by 150 per year." This is further refined to the job level—such as production line operators' "daily output of new energy components ≥ 200 pieces" and "equipment inspection accuracy rate of 100%"; and R&D engineers' "module reuse rate ≥ 45%". The dynamic adjustment model for indicators has a built-in industry volatility factor library. When the price of battery raw materials rises in the second quarter of 2024, leading to a decrease in the gross profit of new energy components, the system automatically lowers the weight of the "gross profit margin of new energy products" indicator for sales positions from 20% to 12%, and simultaneously raises the weight of "new customer repurchase rate" to 25%, ensuring that the indicators are adapted to the actual business situation. Meanwhile, the AI ​​attribution analysis module used the random forest algorithm to mine 120,000 employee data points and generate a multi-dimensional correlation graph. It found that the performance of production positions was most correlated with "skill level (correlation coefficient 0.78)," "monthly training duration (correlation coefficient 0.65)," and "equipment maintenance frequency (correlation coefficient 0.59)." The core influencing factors for the performance of R&D positions were "number of cross-departmental collaborations (correlation coefficient 0.72)" and "past project experience (correlation coefficient 0.68)." Sales positions were significantly affected by "customer profile matching degree (correlation coefficient 0.81)" and "regional market saturation (correlation coefficient 0.63)." The final output, the "Performance Influence Factor White Paper," included a ranking of key driving factors for 18 job categories and suggestions for improvement priorities.

[0071] Based on the above analysis results, the intelligent matching engine launched a customized solution push mechanism, establishing a three-dimensional matching model of "indicator weight - influencing factor - solution": For employees with insufficient skills in production positions (accounting for 18%), a combination of "online skills courses + mentorship + skills points redemption" was pushed, along with practical simulation training using IoT devices; for inefficient R&D personnel (accounting for 12%), a "cross-departmental project collaboration platform access + weekly joint meeting reminders + collaboration contribution value incentive" solution was configured; and for sales teams with low customer matching rates (accounting for 21%), "AI customer profiling system training + regional market analysis report + customized sales script templates" were pushed. The system sets performance expectation thresholds (e.g., a 99.7% pass rate after skills improvement for production staff) and monitors the implementation effect of the plan through real-time data: For example, after 120 employees on a certain production line initially participated in online training, the pass rate only increased to 99.2%, failing to reach the threshold. The system immediately triggered parameter adjustments, automatically adding modules for "1 hour of daily offline practical training" and "equipment failure simulation exercises," extending the training cycle from 4 weeks to 6 weeks. Simultaneously, the system integrated with the intelligent attendance system to ensure training participation, ultimately raising the pass rate for this group to 99.85%. In another instance, after the first round of collaborative implementation, a project team in the R&D department still failed to meet the target even with a 10% reduction in the task completion cycle. System analysis revealed insufficient compatibility of communication tools, so the system switched to an AR visual collaboration platform and matched a dedicated project assistant to coordinate resources. Ultimately, the project cycle was further shortened by 18%, achieving a 100% pass rate. By the end of 2024, the system had generated 23,000 personalized plans, with over 4,200 plan iterations and adjustments. The average performance score of all employees increased by 22% compared to the previous year, with the most significant improvement (31%) observed in the performance of new energy component production staff.

[0072] In the common problem identification and performance prediction stage, the system used the K-means clustering algorithm to group the performance data of more than 30,000 employees, and finally aggregated them into three core clusters: Cluster 1 (accounting for 27%) is characterized by "skill gap type", which is concentrated in the production line. Its characteristics are that new employees account for more than 60%, skill level ≤3, and training coverage rate is less than 50%; Cluster 2 (accounting for 22%) is characterized by "inefficient collaboration type", which is mainly distributed in the cross-departmental departments of R&D and sales. The frequency of cross-departmental communication is 40% lower than the average, and the project collaboration delay rate is more than 25%; Cluster 3 (accounting for 18%) is characterized by "incentive misalignment type", which is mainly composed of highly skilled production technicians. The top 20% of the performance scores are only 55% of the salary percentile, and the average number of promotion opportunities is less than 0.3 times per year. To address these common issues, the system offers three optimization suggestions: First, design a "mentorship + skills allowance" mechanism for the "skill gap" group, aiming to achieve a 100% skills qualification rate for new employees within one year. Second, build an enterprise-level digital collaboration platform for departments with "inefficient collaboration," embedding AI task allocation and progress warning functions. Third, introduce a "performance-salary-promotion" linkage system for "incentive misalignment" talent, setting rules such as "top 10% performance will be given priority for promotion" and "salary increase upon skills certification upgrade." Simultaneously, an LSTM performance prediction model was constructed, integrating individual improvement data (e.g., an average increase of 8% in the pass rate of trained employees) with expected improvements in common issues (e.g., the collaboration platform is expected to shorten project cycles by 15%). After inputting the 2025 business forecast data, the overall enterprise performance growth forecast was obtained: the revenue share of new energy components will reach 42% (3 percentage points short of the strategic target), and the pass rate of core products will reach 99.7%. After a second optimization suggestion (an additional investment of 12 million yuan in new energy R&D and the addition of 30 automated equipment), the parameters were adjusted, and the forecast values ​​were updated to a revenue share of 46% and a pass rate of 99.82%, providing data support for management's strategic decision-making. In the year since the system was implemented, labor disputes have decreased by 78%, the efficiency of time-based accounting has increased by 65%, and the revenue of new energy components has increased by 58% year-on-year, fully verifying the practical value of intelligent management methods.

[0073] In summary, this application's embodiments break down information barriers through comprehensive data integration, constructing a precise mapping between employee digital profiles and strategic goals, thus solving the problem of the disconnect between traditional performance and strategy. Relying on AI-powered strategic decoding and dynamic adjustment mechanisms, strategic goals are broken down and adapted to the environment from the enterprise level to the job level, ensuring that goals are aligned with actual business conditions. Personalized solution delivery and real-time iteration based on data mining significantly improve employee performance, directly empowering business growth. Furthermore, common cluster analysis and performance prediction functions accurately pinpoint organizational weaknesses and provide optimization suggestions, offering data support for enterprise decision-making. Ultimately, this improves management efficiency and accelerates strategic implementation, becoming a core engine for activating human efficiency and anchoring goals in the digital transformation of medium and large-sized manufacturing enterprises, providing a replicable practical model.

[0074] Figure 8A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0075] When the processor 802 executes the program, it implements the method of the intelligent human resource management system based on enterprise performance improvement provided in the above embodiments.

[0076] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0077] The memory 801 is used to store computer programs that can run on the processor 802.

[0078] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0079] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0080] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0081] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0082] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for a human resource intelligent management system based on enterprise performance improvement.

[0083] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the aforementioned method for a human resource intelligent management system based on enterprise performance improvement.

[0084] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0085] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0086] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0087] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0088] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0089] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A human resource intelligent management system based on enterprise performance improvement, characterized in that: include: The module includes a data acquisition module, a dynamic indicator management module, a performance attribution analysis module, a personalized intervention module, and a common problem identification and performance prediction module; among them, The data acquisition module is used to collect the company's medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data. The dynamic indicator management module is used to decompose the company's medium- and long-term strategic goals into department-level and job-level performance indicators through a strategic decoding algorithm, construct a dynamic indicator adjustment model, and automatically adjust the weights of performance indicators. The performance attribution analysis module is used to mine the correlation between performance and various data dimensions through AI attribution analysis algorithms, identify key performance influencing factors, and generate a performance influencing factor report. The personalized intervention module is used to push customized performance improvement plans through the intelligent matching engine. If the performance does not reach the expected threshold after the plan is implemented, the plan parameters are automatically adjusted until the performance meets the target. The common problem identification and performance prediction module is used to identify common problems of enterprises through clustering algorithms, push targeted human resource efficiency optimization suggestions, and build a performance prediction model to predict the overall performance growth value of enterprises based on individual improvement effects and the improvement of common problems of enterprises.

2. The intelligent human resource management system based on enterprise performance improvement according to claim 1, characterized in that, The data acquisition module includes an enterprise strategic business data acquisition unit and an employee comprehensive data acquisition unit. The enterprise strategic business data acquisition unit is used to simultaneously acquire the enterprise's medium- and long-term strategic goals and core business indicators such as revenue, cost, and market share, providing a top-level basis and business foundation for performance indicator decomposition and analysis. The employee comprehensive data acquisition unit is used to integrate and capture information on employee task progress, output quality, behavioral performance, skill level, and training results, supporting individual / team performance evaluation, attribution analysis, and the formulation of personalized improvement plans.

3. The intelligent human resource management system based on enterprise performance improvement according to claim 1, characterized in that, The dynamic indicator management module includes a strategic goal analysis unit, a strategic decoding unit, a dynamic adjustment model unit, and an indicator calibration unit. The strategic goal analysis unit is responsible for structurally decomposing the company's medium- and long-term strategic goals, extracting key strategic elements, target values, and time nodes to form quantifiable strategic benchmarks. The strategic decoding unit uses a strategic decoding algorithm to transform high-level strategic goals layer by layer into departmental and job-level performance indicators, clarifying the definition, calculation logic, responsible parties, and horizontal collaborative relationships of each level of indicator. The dynamic adjustment model unit constructs a dynamic adjustment model for indicators based on real-time operating data and changes in the external environment, automatically optimizing the weight allocation of different performance indicators. The indicator calibration unit is used to periodically link performance attribution analysis and common problem identification results to verify the alignment between the indicator system and strategic goals, and to propose correction suggestions for indicators that deviate from the strategic direction.

4. The intelligent human resource management system based on enterprise performance improvement according to claim 1, characterized in that, The performance attribution analysis module includes a correlation calculation unit, a key influencing factor screening unit, and a report generation unit. The correlation calculation unit uses an AI attribution algorithm to quantify the correlation strength between performance results and employee capability data, behavioral data, and enterprise operational data, generating a correlation matrix. The key influencing factor screening unit combines the correlation matrix with business scenario weights, using threshold filtering and significance testing to select key factors that play a decisive role in performance from highly correlated factors, excluding secondary interfering factors. The report generation unit integrates key influencing factors, quantified correlation results, and attribution logic into a structured report, presented visually through heatmaps and influence path diagrams.

5. The intelligent human resource management system based on enterprise performance improvement according to claim 1, characterized in that, The personalized intervention module includes a needs diagnosis unit, a customized solution generation unit, a multi-channel intelligent push unit, and an execution process monitoring and dynamic optimization unit. The needs diagnosis unit accurately identifies individual improvement needs by analyzing employee performance gaps, skill deficiencies, and job characteristics. The customized solution generation unit, based on the needs analysis, combines the enterprise resource library with AI matching algorithms to generate customized performance improvement plans that include specific action items, resource support, and timelines. The multi-channel intelligent push unit selects the optimal channel to push the plan based on employee reach preferences and synchronizes key objectives. The execution process monitoring and dynamic optimization unit collects plan execution data in real time, tracks progress through a visual dashboard, sets performance achievement thresholds, and automatically analyzes hindering factors, adjusts plan parameters, and iteratively optimizes until performance targets are met if expectations are not met after execution.

6. The intelligent human resource management system based on enterprise performance improvement according to claim 1, characterized in that, The common problem identification and performance prediction module includes a common problem mining unit, an optimization suggestion generation unit, and a performance prediction unit. The common problem mining unit uses clustering algorithms to identify performance convergence deviations across departments / positions, pinpointing common management pain points at the enterprise level. The optimization suggestion generation unit, based on the identified common problems, combines industry practices and the enterprise strategy library to automatically match training supplements, process optimization, and human resource efficiency improvement solutions, and pushes them to the responsible departments. The performance prediction unit integrates individual improvement trajectories and common problem improvement data, predicts the overall enterprise performance growth value through a performance prediction model, and outputs the growth range and key driving factors for future cycles to support strategic decision-making.

7. A method for applying to a human resource intelligent management system based on enterprise performance improvement as described in any one of claims 1-6, characterized in that, include: Collect enterprise's medium- and long-term strategic goals, operational data, employee work data, behavioral data, and capability data; Based on the company's medium- and long-term strategic goals and operational data, the strategic decoding algorithm decomposes the company's medium- and long-term strategic goals into departmental and job-level performance indicators, constructs a dynamic adjustment model for indicators, automatically adjusts the weights of performance indicators, and combines the employee's work data, behavioral data, and ability data to use AI attribution analysis algorithms to mine the correlation between performance and each data dimension, identify key performance influencing factors, and generate a performance influencing factor report. Based on the performance indicator weights and the performance impact factor report, a customized performance improvement plan is pushed through an intelligent matching engine. If the performance does not reach the expected threshold after the plan is implemented, the plan parameters are automatically adjusted until the performance reaches the target, and the performance data of all employees is obtained. The performance data of all employees are used to identify common problems in the enterprise through clustering algorithms, and targeted suggestions for optimizing human resource efficiency are pushed. At the same time, a performance prediction model is built to predict the overall performance growth of the enterprise based on the individual improvement effect and the improvement of common problems in the enterprise.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method of the intelligent human resource management system based on enterprise performance improvement as described in claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the method of the intelligent human resource management system based on enterprise performance improvement as described in claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the method of the intelligent human resource management system based on enterprise performance improvement as described in claim 7.

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