Digital human performance management method and system based on robot process automation

Abstract

The invention relates to the technical field of robots, in particular to a digital human performance management method and system based on robot process automation, and the method comprises the steps: abstracting a performance management period into a digital transaction process with clear context, constraint conditions and state evolution; in the process, through unified limitation of an automatic access range, an index caliber and rule application, stable generation of a cross-system performance index is realized, and on this basis, a performance portrait result and a post knowledge structure are aligned to form a diagnosis conclusion and an enabling scheme which can be directly driven and executed. Furthermore, the enabling scheme is converted into an execution action capable of landing in an existing system through robot process automation, and the execution result is re-evaluated in the same transaction context, so that the execution process, the re-evaluation result and the initial performance state form continuous association.

Description

Technical Field

[0001] This invention relates to the field of robotics, and more particularly to a digital human resource performance management method and system based on robotic process automation. Background Technology

[0002] With the continuous improvement of organizational informatization and digitalization, human resource performance management is gradually shifting from relying on human experience and post-event statistics to data-driven process management based on information systems. In scenarios such as healthcare, public services, and large organizations, job performance-related data is widely distributed across multiple business and management systems, including both statistical data reflecting business results and process data reflecting execution processes and standardization.

[0003] In recent years, some systems have begun to introduce robotic process automation (RPA) technology for cross-system data collection and performance indicator generation, which has alleviated the problems of high cost and low efficiency of manual data processing to some extent. Meanwhile, some systems are attempting to conduct comprehensive analysis of job performance through performance profiling or rule models. However, from an engineering implementation perspective, existing solutions still generally suffer from structural deficiencies: performance data collection, indicator calculation, performance analysis, development empowerment, and result verification often exist as independent modules, lacking a unified process carrier and constraint mechanism. This makes it difficult to maintain consistency in data definitions, rule application, and result interpretation between different stages. The automatically generated performance conclusions and empowerment suggestions lack complete records of their formation path, making it difficult to reconstruct the data conditions and rule premises upon which they are based in the event of compliance audits or management disputes. The execution process of empowerment measures is usually detached from the performance system, and the execution status lacks a systematic connection with subsequent performance changes, making it difficult to form a closed loop at the engineering level for "evaluation-execution-re-evaluation." Furthermore, under the requirements of strong compliance and data localization, performance management systems need to operate long-term without relying on external intelligent services and cope with real-world situations such as job adjustments and rule changes. Traditional result-centered design approaches are unable to meet these complex constraints.

[0004] Therefore, there is an urgent need for a technical solution that can incorporate the entire performance management process based on automated data collection into unified constraints and continuous control while ensuring data security and compliance, in order to address the shortcomings of existing systems in terms of consistency, traceability, and closed-loop execution. Summary of the Invention

[0005] The purpose of this invention is to provide a digital human performance management method and system based on robotic process automation to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A digital human resource performance management method based on robotic process automation is characterized by the following steps: Create a performance transaction unit, and based on the job identifier associated in the transaction, perform the instantiation operation of the transaction constraint configuration, including reading the performance indicator configuration record that the job is allowed to use, then loading the rule set that matches the job and the current performance cycle, matching the rule set with the indicator set, generating the transaction constraint configuration, writing the transaction constraint configuration into the created performance transaction unit, and marking it as the initialization completed state; The Robotic Process Automation module reads the transaction constraint configuration associated with the current position through the performance transaction unit, generates a list of indicator items that the position is allowed to generate within the current performance cycle and a basic indicator set for the position's performance profile, and executes the automated process item by item according to the indicator item list; Based on job performance profiles and job knowledge graphs, each performance indicator is mapped to one or more nodes. The indicator results are aggregated at the node level to form a structured set of capability deviation diagnosis results. Then, using the node identifiers in the capability deviation diagnosis results set as indexes, the capability deviation diagnosis results are matched and combined with resource paths according to the pre-set empowerment scheme assembly rules to generate empowerment scheme objects. The set of capability deviation diagnosis results and the empowerment plan objects are written into the performance transaction unit. The structured empowerment tasks in the empowerment plan objects are transformed into operations that can be implemented in the existing system through the robotic process automation module. After the agreed period arrives, a re-evaluation indicator set is generated through the same caliber of the basic indicator set of the job performance profile, forming a closed loop.

[0007] Preferably, when the performance transaction unit is created, the specific steps of the instantiation operation include accessing the performance indicator configuration module to read the performance indicator configuration records that are allowed to be used for the position; and then accessing the rule engine configuration module to load the rule set that matches the position and the current performance cycle.

[0008] Preferably, before executing any performance-related task, the robotic process automation module reads the constraint configuration in the performance transaction unit through the transaction identifier, and determines the business system interface to be accessed, the calculated indicator results, and the results to be written into the transaction unit accordingly.

[0009] Preferably, the transaction constraint configuration is in the form of a structured configuration record, listing a list of indicator items that are allowed to be generated in the current performance cycle for this position, including a set of directly executable data entry information, statistical caliber constraints and result writing mapping rules for each indicator.

[0010] Preferably, the transaction constraint configuration configures multiple data retrieval paths for key indicators. The robot automation module obtains statistical results according to the multiple data retrieval paths and forms a multi-source statistical original value set for the indicator in memory. After data retrieval is complete, consistency processing is performed on the multi-source statistical results in memory. The specific steps include: For each indicator, the robot automation module calculates the difference measure of the indicator based on the numerical differences between different data acquisition paths. This measure reflects the stability of the current statistical results. The difference measure serves as an internal control quantity when generating the final indicator results. It is used to transform the uncertainty brought about by multiple data acquisition paths into a controllable influencing factor. Subsequently, the original statistical values ​​are combined with the difference measure to generate the basic indicator set for the job performance profile.

[0011] Preferably, the job knowledge graph is stored in the system as structured graph data in a pre-configured manner. The job knowledge graph includes job competency subgraphs that are directly related to the job. The job competency subgraphs are rooted at job nodes and expand downwards to competency nodes and knowledge nodes. Each node has a clear identifier and describes the degree of competency or knowledge required by the job through graph relationships.

[0012] As a preferred approach, empowerment resource paths associated with nodes in the set of capability deviation diagnosis results are retrieved from the job knowledge graph. These empowerment resource paths exist in a templated structure, including resource identifiers, applicable capability node identifiers, and execution order constraint information.

[0013] As a preferred approach, during the execution cycle of the transformation and implementation operation, the robotic process automation module polls or subscribes to the status changes of external systems at a preset rhythm, advances the task from created to completed or confirmed, and adds each status change to the execution record of the performance transaction unit.

[0014] As a preferred option, when writing the set of capability deviation diagnosis results and the empowerment scheme object into the performance transaction unit, the performance transaction unit is associated with the current performance cycle and job object through the transaction identifier. The transaction identifier is used to write the empowerment execution action, execution status record, re-evaluation data retrieval action and re-evaluation result into a unified performance transaction unit, so that the entire closed-loop process can be replayed with the same transaction object in the future.

[0015] In another aspect of the present invention, a digital human resource performance management system based on robotic process automation is also provided, comprising: The digital performance transaction data initialization module is used to create performance transaction units. Based on the job identifier associated in the transaction, it performs the instantiation operation of transaction constraint configuration, including reading the performance indicator configuration records that the job is allowed to use, then loading the rule set that matches the job and the current performance cycle, matching the rule set with the indicator set, generating transaction constraint configuration, writing the transaction constraint configuration into the created performance transaction unit, and marking it as initialization completed. The job performance profile generation module is used by the robotic process automation module to read the transaction constraint configuration associated with the current job through the performance transaction unit, generate a list of indicator items that the job is allowed to generate in the current performance cycle and a basic indicator set for job performance profile, and execute the automated process item by item according to the indicator item list; The diagnostic results generation module is used to map each performance indicator to one or more nodes based on the job performance profile and job knowledge graph. At the node level, the indicator results are aggregated to form a structured set of capability deviation diagnostic results. Then, using the node identifiers in the capability deviation diagnostic results set as indexes, the capability deviation diagnostic results are matched and combined with resource paths according to the pre-set empowerment scheme assembly rules to generate empowerment scheme objects. The reassessment and output execution module is used to write the set of capability deviation diagnosis results and empowerment plan objects into the performance transaction unit. The robotic process automation module transforms the structured empowerment tasks in the empowerment plan objects into operations that can be implemented in the existing system. After the agreed period arrives, a reassessment indicator set is generated using the same caliber of the job performance profile basic indicator set, forming a closed loop.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention, from the perspective of engineering process control, comprehensively restructures the digital human resource performance management process, unifying the previously fragmented processes of performance data generation, analysis, decision-making, and execution verification into a single controlled framework. This invention abstracts a performance management cycle into a digital transaction process with a clear context, constraints, and state evolution. Within this process, by uniformly defining the scope of automated data retrieval, indicator definitions, and applicable rules, it achieves stable generation of cross-system performance indicators. Based on this, it aligns performance profile results with job knowledge structures, forming diagnostic conclusions and empowerment plans that can directly drive execution. Furthermore, this invention uses robotic process automation to transform empowerment plans into actionable steps that can be implemented in existing systems, and re-evaluates the execution results within the same transaction context, creating a continuous link between the execution process, the re-evaluation results, and the initial performance state. Through the above methods, this invention achieves unified control over the entire process of performance management, from evaluation and diagnosis to execution and verification, without changing the existing business system structure. This enables performance results to have a clear formation path and a replayable execution trajectory, and allows for stable operation in complex business and compliance scenarios. Thus, it provides a digital human resource performance management technology solution with a closed structure, logical self-consistency, and practical engineering feasibility. Attached Figure Description

[0017] Figure 1 This is a flowchart of a digital human resource performance management method based on robotic process automation in a specific embodiment of the present invention; Figure 2 A diagram illustrating the composition of a digital human resource performance management system based on robotic process automation in a specific embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] refer to Figure 1 As shown, this invention proposes a digital human resource performance management method based on robotic process automation, including: S1: Create a performance transaction unit. Based on the job identifier associated in the transaction, perform instantiation of transaction constraint configuration, including reading the performance indicator configuration records allowed for the job, then loading the rule set that matches the job and the current performance cycle, matching the rule set with the indicator definition set, generating transaction constraint configuration, writing the transaction constraint configuration into the created performance transaction unit, and marking it as initialization complete. Specifically, this includes: In practice, when a performance cycle arrives or a performance management task is triggered, the performance management platform calls the transaction creation module in the background to generate a new performance transaction unit, denoted as . This creation process is typically completed through a service interface. For example, the performance management service sends a request to the transaction management service, containing the current performance cycle identifier, as well as the identifiers of the target position and personnel. Upon receiving the request, the transaction management service generates a corresponding transaction record in its database and returns a unique transaction identifier. This transaction identifier is then written to the performance transaction unit. This serves as the core context parameter for subsequent robotic process automation and analysis tasks to be passed between systems. In this way, whether an automation script calls a business system or an analysis module executes rule judgments, this transaction identifier can be used to locate the same performance management process.

[0020] In the performance transaction unit Once created, the system immediately performs an instantiation operation for transaction constraint configuration based on the job identifier associated with the transaction. This operation first accesses the performance indicator configuration module to read the performance indicator configuration records allowed for the job. These configuration records are typically maintained in advance by human resources or performance management personnel and stored in a structured format. For example, each record clearly identifies which indicators can be used in performance evaluation for a particular job and their corresponding calculation methods. Subsequently, the system accesses the rule engine configuration module to load a set of rules matching the job and the current performance cycle. These rules are used to subsequently determine the performance indicator status or trigger further processing. Both types of configurations are loaded into memory through the system interface as the basic data for subsequent filtering and combination.

[0021] After loading is complete, the system performs matching processing on the rule set and indicator definition set in memory to generate transaction constraint configuration, denoted as . This process does not involve complex calculations. Instead, it filters configuration items based on fields such as rule identifiers and job identifiers, retaining only those that simultaneously meet the job suitability criteria. The relationship between these criteria can be formally represented as follows: ; in, This represents the set of job-related rules that have been loaded. This represents the set of performance indicators for the loaded positions. At the implementation level, the system filters this set by traversing the configuration list and performing conditional checks, generating the final result. It is a set of structured configuration items used to explicitly define the range of rules and metrics that can be referenced in subsequent steps. The generated transaction constraint configuration. Written into the performance transaction unit In this context, the data is persistently stored as a fixed component of the transaction. Subsequent robotic process automation modules will read the data using the transaction identifier before performing any performance-related data collection or calculation tasks. Constraint Configuration Based on this, it decides which business system interfaces need to be accessed, which metric results need to be calculated, and which results need to be written to the transaction unit.

[0022] For example, when an automated script extracts data from a business system, it will only target... The defined indicators are aggregated and calculated, and the results are written to the performance transaction unit in structured field format. Instead of writing the original business details data into the transaction unit, the transaction unit remains a carrier of results and state.

[0023] After completing the transaction constraint configuration write, the system will perform a performance transaction unit. The initialization is now complete. With clearly defined job relationships, rule scope, and indicator constraints, this framework can directly serve as the contextual basis for the automated generation of job performance profile indicator sets in subsequent robotic processes. Through the implementation of this step, the performance management process gains a clear, stable, and sustainable starting point at the system level, providing clear engineering support for the sequential advancement of subsequent digital human resource performance management processes.

[0024] S2: The Robotic Process Automation (RPA) module reads the transaction constraint configuration associated with the current job position through the performance transaction unit, generates a list of indicator items allowed to be generated for the job position within the current performance cycle, and a basic indicator set for the job performance profile. Based on the indicator item list, it executes the automated process item by item, specifically including: Configuration around transaction constraints The defined metric definitions and rule scope are controlled by the robotic process automation module, which executes a controlled data acquisition and metric generation process. The core objective of this step is to transform job-related business results scattered across multiple business systems into a set of structured basic indicators that can be directly used for subsequent performance diagnosis. This ensures that these indicators maintain consistency with transaction constraints in terms of generation path, statistical definitions, and writing methods, thereby providing a stable and sustainable data foundation for the entire performance management process.

[0025] In practice, the robotic process automation module first locates the performance transaction unit by transaction identifier after startup. and from Read the transaction constraint configuration associated with the current job position. In transaction constraint configuration The system uses a structured configuration record to clearly list the indicators that are allowed to be generated for this position within the current performance cycle. Each indicator corresponds to a set of directly executable data retrieval entry information, statistical constraints, and result writing mapping rules. The Robotic Process Automation (RPA) module executes the automation process item by item according to this indicator list, always adhering to transaction constraint configurations throughout the execution process. As a basis for judgment, ensure that every data retrieval and aggregation operation occurs within the defined range.

[0026] During the generation of each metric, the Robotic Process Automation module will configure according to transaction constraints. The system configures the data retrieval entry points and selects the corresponding business system access method. For example, when the business system provides a statistical interface, the automation module retrieves the statistical results of the indicator within a specified period and organizational scope through interface calls; when the business system only provides a report page, the automation module obtains the report file through page element identification and download actions, and parses the statistical values ​​corresponding to the indicator from the file. In business systems such as healthcare and public health, it is common for the same indicator to have multiple statistical entry points. Multiple data retrieval paths will be configured for key indicators. The automation module will obtain statistical results according to these paths and form a multi-source statistical original value set for the indicator in memory.

[0027] After data retrieval is complete, the system does not directly write the original statistical values ​​to the transaction unit. Instead, it first performs consistency processing on the multi-source statistical results in memory. For each indicator, the automation module calculates a difference measure based on the numerical differences between different data retrieval paths. This difference measure reflects the stability of the current statistical results. It is not stored as an independent data item but serves as an internal control variable when generating the final indicator results, transforming the uncertainty brought about by multi-entry data retrieval into a controllable influencing factor. Subsequently, the system combines the original statistical values ​​and the difference measure to form the basic indicator set for job performance profiling. The combination relationship can be expressed as: ; in, The basic set of indicators representing job performance profiles exists in the form of a structured set; Indicates transaction constraint configuration A performance indicator identifier defined in the specification, which originates from the indicator item record in the performance indicator configuration module; The robotic process automation module is based on The data entry point and statistical scope are the original statistical values ​​of the indicator obtained or summarized from the business system side; This indicates the difference measure formed during the data collection process from multiple entry points, calculated by the automated module based on the deviation between the statistical results from different entry points; This represents the set of transaction constraint configurations that have been fixed in the current performance transaction unit. By introducing an exponential reduction term, when the statistical results from multiple entry points are consistent, the indicator value remains at its original level. However, when there are differences in the statistical results, the influence of the indicator in the performance profile decreases smoothly as the differences increase. This better reflects the reality of complex statistical data sources and large fluctuations in statistical methods in business scenarios such as healthcare and public health. (In the basic indicator set) After assembly, the performance management platform is configured according to transaction constraints. The write mapping rules defined in [the document] will [implement] Write to performance transaction unit The corresponding structured storage area. This write process associates transaction identifiers with indicator identifiers, establishing a clear correspondence between each indicator result and the current performance cycle and job role. After the write is complete, the performance transaction unit... It also includes transaction constraint configuration. and the basic indicator set for job performance profiles It is in a state that can be directly read and used by subsequent diagnostic steps based on job knowledge graphs.

[0028] Through the above process, driven by transaction constraints, this step involves the Robotic Process Automation module to complete cross-system data retrieval, controlled aggregation, and structured writing. This process compresses the job performance results in complex business systems into a set of consistent, stable, and controllable basic indicators, which are then solidified within the performance transaction unit. This provides a direct and reliable input basis for subsequent performance diagnosis and empowerment execution steps.

[0029] S3: Based on job performance profiles and job knowledge graphs, each performance indicator is mapped to one or more nodes. At the node level, indicator results are aggregated to form a structured set of capability deviation diagnosis results. Then, using the node identifiers in this set as indexes, and following pre-defined empowerment scheme assembly rules, the capability deviation diagnosis results are matched and combined with resource paths to generate empowerment scheme objects, specifically including: In this step, by focusing on the same transaction context, the stable job performance profile results are transformed into locatable and actionable capability improvement targets. This provides a unique and actionable intermediate result for subsequent empowerment execution driven by robotic process automation. Therefore, the object of processing is no longer business data or metrics themselves, but rather the job capability structure deviations implied by the metrics.

[0030] In its implementation, the system first locates the performance transaction unit by using the transaction identifier. And extract the complete set of basic indicators for job performance profiles from it. Job Performance Profile Basic Indicator Set Each indicator is in a structured form, including indicator identifiers and configurations based on transaction constraints in step two. The generated indicator result value. This indicator result value already implicitly includes the result of cross-entry consistency processing, and therefore is considered a reliable characterization of the actual performance status of the position in this step.

[0031] The system then uses performance transaction units. The job identifier is used as a query condition to load the job competency subgraph directly associated with that job from the job knowledge graph. This subgraph is rooted at the job node and expands downwards to competency and knowledge nodes. Each node has a clear identifier and describes the degree of competency or knowledge required for the job through graph relationships. The job knowledge graph is stored in the system as structured graph data, such as through a graph database or equivalent data structure, and is maintained by the organization in the early stages based on job descriptions, competency models, and policy regulations.

[0032] During the alignment process, the system does not perform reasoning on the entire knowledge graph, but rather... Starting with the indicators in the graph, each performance indicator is mapped to one or more capability or knowledge nodes based on the pre-configured "indicator-capability" relationship in the graph. This relationship is used to explain which type of capability or knowledge performance of the position is mainly reflected by the change of a certain indicator. For example, the timeliness of business completion is mapped to the execution standard capability node, and the quality pass rate is mapped to the professional knowledge mastery capability node.

[0033] By aggregating indicator results at the capability node level, a structured set of capability deviation diagnostic results is formed. Set of diagnostic results for ability deviation The generation of this value is not a simple threshold judgment, but rather a comprehensive characterization of multiple indicators on the same competency dimension by combining the multi-indicator characteristics of the job competency requirements. Its calculation process can be expressed as: ; in, A set of diagnostic results representing abilities or knowledge levels; This represents an identifier for a specific ability or knowledge node in the job knowledge graph that is associated with the current job position. This indicates that the node is mapped in the knowledge graph configuration. A subset of performance indicators, derived from the basic indicator set. ; Indicators The indicator result value generated in step two is used to reflect the degree of completion or achievement of the corresponding indicator; Indicators At the capability node The influence weight is determined by the job knowledge graph configuration and is used to reflect the differences in the importance of different indicators in this capability dimension. Represents a node The degree of dispersion of the results of the associated indicators is used to reflect the consistency of the performance of each indicator under the same capability dimension; This represents a coefficient used to adjust the degree of influence of consistency items. This coefficient is set in association with the job type in the job knowledge graph configuration. By introducing a dispersion item, when multiple indicators under a certain capability dimension show significant divergence, this capability deviation is further amplified in the diagnostic results, which is more in line with the business characteristics of scenarios such as medical care and public health, where "capability instability itself is a risk signal".

[0034] In generating a set of capability deviation diagnostic results Subsequently, the system uses the capability deviation diagnostic results set The system uses capability or knowledge nodes as indexes to retrieve associated empowerment resource paths from the job knowledge graph. These empowerment resource paths exist in a templated structure, typically containing resource identifiers, applicable capability node identifiers, and execution order constraints, such as corresponding training course numbers, practical task template numbers, or system learning path numbers. Following pre-defined empowerment scheme assembly rules, the system matches and combines capability deviation diagnosis results with resource paths one-to-one, instantiating and generating empowerment scheme objects. . It is a collection of structured objects, each of which corresponds to a specific empowerment task, clearly identifying the target capability node, associated resource identifiers, and execution requirements, but does not contain specific teaching content or original documents.

[0035] After instantiation is complete, the system will collect the capability deviation diagnosis results. With the empowerment solution target Included in the performance transaction unit. And it is associated with the current performance cycle and job object through a transaction identifier. At this time, the performance transaction unit Internally, the system has transitioned from the "indicator results layer" to the "capability diagnosis and empowerment preparation layer." Subsequent steps only need to be based on... The structured information in the module can trigger specific enabling actions by the robotic process automation module, without the need for further reasoning at the indicator or capability level.

[0036] S4 writes the set of capability deviation diagnosis results and empowerment plan objects into the performance transaction unit. Through the robotic process automation module, it transforms the structured empowerment tasks in the empowerment plan objects into operations that can be implemented in the existing system. After the agreed period, it generates a re-evaluation indicator set using the same caliber as the basic indicator set of the job performance profile, forming a closed-loop link, specifically including: Through the robotic process automation module The structured empowerment tasks are transformed into operations that can be implemented in existing systems, and after the agreed period, a re-evaluation indicator set is generated using the same criteria as the basic indicator set generated in step two. This creates a closed-loop "execution-re-evaluation" chain within the same transaction context. This step always revolves around the transaction identifier: the transaction identifier is used to write the enabling execution action, execution status record, re-evaluation data retrieval action, and re-evaluation result into a unified... This allows the entire closed-loop process to be replayed using the same transaction object in subsequent iterations.

[0037] After the robotic process automation module is started, it locates the performance transaction unit by transaction identifier. And read the enabling scheme object within it. Set of diagnostic results for ability deviation Empowerment Solution Targets Provides a list of directly executable tasks, each task described with structured fields including target capability or knowledge node identifier, resource reference identifier, and execution order or triggering conditions; and a collection of capability deviation diagnostic results. It provides information on the strength of deviations in capability or knowledge dimensions to determine task priorities and execution window arrangements on the execution side. For example, when the deviation of a certain capability node is higher, the automation module prioritizes the tasks associated with that node in the task queue and records their priority and planned execution time in the transaction. Subsequently, the automation module sequentially empowers the target objects of its solution. The task execution and assignment actions are as follows: When the resource reference points to the training system, a learning task is created via an interface call, and a personnel identifier and task identifier are bound; when the resource reference points to the process platform, a practice task work order is created, and the responsible person, deadline rule, and acceptance person are specified; when the resource reference points to the system learning path, a structured to-do list is generated in the management platform, and the corresponding system entry reference identifier is attached. Each assignment action generates an execution record and writes it to the performance transaction unit. The record must include at least the task identifier, the external system receipt identifier, and the current execution status. The external system receipt identifier is the task number returned by the training system or process platform, which is used by the automation module to query the task completion status.

[0038] During the execution cycle, the Robotic Process Automation (RPA) module polls or subscribes to the status changes of external systems at a preset rhythm, advancing the task from "created" to "completed" or "confirmed," and appending each status change to the performance transaction unit. The execution log is recorded. This status acquisition can be achieved in three ways in engineering implementation: calling the task query interface of an external system to read the status field; in systems that only provide a user interface for querying, an automated script opens the task details page, captures the status text, and parses it into a structured status code; in systems that provide file export capabilities, a task completion list is periodically exported and updated based on the task number. This is achieved by writing status changes into the performance transaction unit. In the subsequent re-evaluation phase, the process information of "to what extent has the execution been completed" can be read within the same transaction object, so that the closed loop is not just about calculating re-evaluation indicators, but puts the execution process and result calculation on the same transaction chain.

[0039] Once the enabling execution window ends or the agreed re-evaluation time point is reached, the system triggers re-evaluation data retrieval within the same transaction context. The Robotic Process Automation module generates the re-evaluation indicator set using the indicator generation path of S2. Re-evaluate and directly reuse the retrieved data. The existing transaction constraint configuration and the data retrieval entry and statistical caliber configuration used in step two enable the re-evaluation index set to be configured. With basic indicator set Under the same comparable benchmark; and in conjunction with the set of capability deviation diagnostic results For capabilities or knowledge nodes with more significant deviations, prioritize generating indicator results that map more strongly to these nodes, thereby allowing for faster observation of the impact of empowerment actions on key weaknesses in business scenarios. Re-evaluate the indicator set. Once generated, it is written into the performance transaction unit. and the benchmark set within the same transaction Establish corresponding relationships through indicator identification.

[0040] Reassessment phase This forms a structured closed-loop effect value, denoted as... This is used to characterize the change relative to the baseline after execution at the transaction level, and incorporates "execution completion degree" as a smoothing adjustment term into the same expression, so that the more complete the execution, the smaller the change. The more significant the contribution, the less effective the implementation. Changes are naturally suppressed. Empowerment solution objects The calculation can be expressed as: ; in, This represents the closed-loop effect value formed within the performance transaction unit; This represents the completion rate coefficient, determined by the Robotic Process Automation module based on performance transaction units. The task status recorded in the database is summarized, and the value increases as the task progresses from created to completed. This indicates that S2 is written to the performance transaction unit. A set of basic indicators for job performance profiling; express medium indicators The corresponding baseline result value; Represents the set of reassessment indicators Indicators The corresponding result value; This represents the number of metrics in the basic metric set. This expression aggregates the differences between metrics into a result that can be directly written to a transaction, and utilizes... By coupling the "execution process" and the "re-evaluation result" into the same numerical value, the closed-loop effect not only reflects the change in the result, but also reflects the impact of the execution progress on the reliability of the result.

[0041] Complete closed-loop effect value After the calculation, the system will re-evaluate the indicator set. With closed-loop effect value Included in the performance transaction unit. and performance transaction unit The process progresses to the closed-loop completion state, ensuring that the same transaction object simultaneously contains a set of benchmark metrics. Diagnostic results Target audience for the empowerment solution Empowering execution process recording and re-evaluation indicator set and closed-loop effect value In practical engineering, this write operation can be completed through a single transaction update operation. Write to the transaction result field, Write metrics to the table associated with the transaction identifier to ensure that any subsequent viewing, auditing, or review can be located through the same transaction identifier to trace the entire chain from diagnosis to execution to reassessment.

[0042] refer to Figure 2 As shown, in the second invention of this invention, a digital human resource performance management system based on robotic process automation is proposed, comprising: The contact behavior data acquisition module is used to acquire contact response data output by the sensing structure inside the placement head when the placement head starts to move towards the soft module and prepares to press the magnet. The acquisition continues throughout the entire placement action until the placement action ends and the magnet enters a stable bonding state. By recording the contact response data in chronological order, the contact behavior sequence of the entire placement process is obtained, and the bonding behavior curve is obtained after relativization. The chip magnet mounting process risk assessment module is used to calculate the transient change sequence of elastic rebound or local unstable contact generated by the soft module during the pressing process and the mounting process risk score through the bonding behavior curve during the mounting process of chip magnets of display soft module. It outputs the mounting process risk status indicator and then writes the risk status indicator along with the corresponding mounting sequence number into the detection record of the current station. The patch magnet flux distribution detection module is used to read the risk status indicator through the flux detection station, analyze the flux data of the same soft module, and then construct the post-mount status analysis quantity to output the post-mount flux status judgment result. The quality result judgment output module is used to combine the risk status identifier and the magnetic flux status judgment result to calculate the placement quality score, which comprehensively reflects the combined influence of the placement process and the placement result, and maps the placement quality score into a structured placement quality result code to output the placement quality result.

[0043] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A digital human resource performance management method based on robotic process automation, characterized in that, Includes the following steps: Create a performance transaction unit, and based on the job identifier associated in the transaction, perform the instantiation operation of the transaction constraint configuration, including reading the performance indicator configuration record that the job is allowed to use, then loading the rule set that matches the job and the current performance cycle, matching the rule set with the indicator set, generating the transaction constraint configuration, writing the transaction constraint configuration into the created performance transaction unit, and marking it as the initialization completed state; The Robotic Process Automation module reads the transaction constraint configuration associated with the current position through the performance transaction unit, generates a list of indicator items that the position is allowed to generate within the current performance cycle and a basic indicator set for the position's performance profile, and executes the automated process item by item according to the indicator item list; Based on job performance profiles and job knowledge graphs, each performance indicator is mapped to one or more nodes. The indicator results are aggregated at the node level to form a structured set of capability deviation diagnosis results. Then, using the node identifiers in the capability deviation diagnosis results set as indexes, the capability deviation diagnosis results are matched and combined with resource paths according to the pre-set empowerment scheme assembly rules to generate empowerment scheme objects. The set of capability deviation diagnosis results and the empowerment plan objects are written into the performance transaction unit. The structured empowerment tasks in the empowerment plan objects are transformed into operations that can be implemented in the existing system through the robotic process automation module. After the agreed period arrives, a re-evaluation indicator set is generated through the same caliber of the basic indicator set of the job performance profile, forming a closed loop.

2. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, When the performance transaction unit is created, the specific steps of the instantiation operation include accessing the performance indicator configuration module to read the performance indicator configuration records that are allowed for the position; then accessing the rule engine configuration module to load the rule set that matches the position and the current performance cycle.

3. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, Before executing any performance-related task, the Robotic Process Automation module reads the constraint configuration in the performance transaction unit through the transaction identifier, and determines the business system interface to be accessed, the calculated indicator results, and the results to be written into the transaction unit accordingly.

4. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, The transaction constraint configuration is structured and records a list of indicators that are allowed to be generated for this position within the current performance cycle. Each indicator corresponds to a set of directly executable data entry information, statistical caliber constraints, and result writing mapping rules.

5. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, The transaction constraint configuration configures multiple data retrieval paths for key indicators. The robot automation module obtains statistical results according to the multiple data retrieval paths and forms a multi-source statistical original value set for the indicator in memory. After data retrieval is complete, consistency processing is performed on the multi-source statistical results in memory. The specific steps include: For each indicator, the robot automation module calculates the difference measure of the indicator based on the numerical differences between different data acquisition paths. This measure reflects the stability of the current statistical results. The difference measure serves as an internal control quantity when generating the final indicator results. It is used to transform the uncertainty brought about by multiple data acquisition paths into a controllable influencing factor. Subsequently, the original statistical values ​​are combined with the difference measure to generate the basic indicator set for the job performance profile.

6. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, The job knowledge graph is stored in the system in the form of structured graph data through pre-configuration. The job knowledge graph includes job competency subgraphs that are directly related to the job. The job competency subgraphs are rooted at job nodes and expand downwards to competency nodes and knowledge nodes. Each node has a clear identifier and describes the degree of competency or knowledge required by the job through graph relationships.

7. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, Retrieve empowerment resource paths associated with nodes in the capability deviation diagnosis result set from the job knowledge graph. The empowerment resource paths exist in a templated structure, including resource identifiers, applicable capability node identifiers, and execution order constraint information.

8. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, During the execution cycle of the transformation and implementation operation, the Robotic Process Automation module polls or subscribes to the status changes of external systems according to a preset rhythm, advances the task from created to completed or confirmed, and adds each status change to the execution record of the performance transaction unit.

9. The digital human resource performance management method based on robotic process automation according to claim 1, characterized in that, When writing the set of capability deviation diagnosis results and the empowerment plan object into the performance transaction unit, the performance transaction unit is associated with the current performance cycle and job object through the transaction identifier. The transaction identifier is used to write the empowerment execution action, execution status record, re-evaluation data retrieval action and re-evaluation result into a unified performance transaction unit, so that the entire closed-loop process can be replayed with the same transaction object in the future.

10. A digital human resource performance management system based on robotic process automation, characterized in that: include: The digital performance transaction data initialization module is used to create performance transaction units. Based on the job identifier associated in the transaction, it performs the instantiation operation of transaction constraint configuration, including reading the performance indicator configuration records that the job is allowed to use, then loading the rule set that matches the job and the current performance cycle, matching the rule set with the indicator set, generating transaction constraint configuration, writing the transaction constraint configuration into the created performance transaction unit, and marking it as initialization completed. The job performance profile generation module is used by the robotic process automation module to read the transaction constraint configuration associated with the current job through the performance transaction unit, generate a list of indicator items that the job is allowed to generate in the current performance cycle and a basic indicator set for job performance profile, and execute the automated process item by item according to the indicator item list; The diagnostic results generation module is used to map each performance indicator to one or more nodes based on the job performance profile and job knowledge graph. At the node level, the indicator results are aggregated to form a structured set of capability deviation diagnostic results. Then, using the node identifiers in the capability deviation diagnostic results set as indexes, the capability deviation diagnostic results are matched and combined with resource paths according to the pre-set empowerment scheme assembly rules to generate empowerment scheme objects. The reassessment and output execution module is used to write the set of capability deviation diagnosis results and empowerment plan objects into the performance transaction unit. The robotic process automation module transforms the structured empowerment tasks in the empowerment plan objects into operations that can be implemented in the existing system. After the agreed period arrives, a reassessment indicator set is generated using the same caliber of the job performance profile basic indicator set, forming a closed loop.

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