Performance score management method and system for enterprise employees

Abstract

The invention provides a performance score management method and system for enterprise employees. According to the method, a target key achievement tree structure from an enterprise strategy layer to an execution layer is constructed, and employee post responsibilities and key achievement contribution degrees are dynamically associated, so that the enterprise employee performance is accurately evaluated. A directional voiceprint recognition technology in a multi-mode environment perception sensor array is adopted to collect voice active duration and interaction frequency of employees, and a mapping relation is established through timestamps and task nodes. And then, inputting the contribution degree of the key achievement and cooperation participation data into an alliance chain smart contract, and writing associated data into a block chain account book. And finally, based on the contribution trajectory in the block chain account book, combining a cross-period contribution attenuation coefficient and a collaborative network density, and dynamically generating a performance score by adjusting a time weight coefficient and a cross-department collaborative coefficient. According to the technical scheme provided by the invention, the efficiency and fairness of performance score management can be improved.

Description

Technical Field

[0001] This application relates to the technical field of performance score management, and particularly to a method and system for managing the performance scores of enterprise employees. Background Art

[0002] In the modern enterprise environment, with the increasing complexity of organizational structures and the growing need for cross-departmental collaboration, accurately evaluating employee performance has become a key challenge in enterprise management. Enterprises need a method that can dynamically reflect the contribution of employees in different time periods, different projects, and cross-departmental collaborations. This not only requires the system to quantify the direct work results of employees but also capture the performance of enterprise employees in indirect contributions such as teamwork and knowledge sharing. In addition, to ensure the fairness and transparency of the evaluation system, the proposed method should be able to provide detailed and verifiable data support.

[0003] Currently, some enterprises adopt performance management systems based on big data analysis to meet the above requirements. These systems integrate information from multiple internal data sources of the enterprise, such as email communication records, task assignments and completion status in project management tools, etc., and use complex algorithm models to calculate the work contributions and collaboration levels of each employee. This solution can meet the enterprise's performance management needs to a certain extent. Especially for those enterprises that attach importance to data analysis and digital transformation, this method provides a relatively objective evaluation basis and helps to identify potential problem areas.

[0004] However, this existing solution based on big data analysis has certain limitations. First, due to relying on multiple data sources, the accuracy and integrity of the system highly depend on the quality and integration ability of the data. Any data omission or error may lead to evaluation deviation. Second, such systems often lack real-time performance and cannot immediately reflect the latest contribution of employees. Especially in a rapidly changing work environment, delayed data updates may lead to lagging decisions. Finally, although big data analysis can provide quantitative evaluation results, it may be inadequate in explaining employee behavior patterns and the reasons behind them, and it is difficult to comprehensively capture the actual work performance and personal development needs of employees. Summary of the Invention

[0005] This application provides a method and system for managing the performance scores of enterprise employees to solve the problems of low efficiency and insufficient fairness in the management of performance scores in the prior art.

[0006] In a first aspect, this application provides a method for managing the performance scores of enterprise employees, including: Establish a tree structure of objective key results from the enterprise strategic layer to the execution layer, and dynamically associate the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and an inter-departmental collaboration coefficient; Collect the voice active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, generate collaboration participation data in combination with the sound source localization function, and establish a mapping between the collaboration participation data and the task nodes in the objective key results tree structure through timestamps; Input the contribution degree of key results and the collaboration participation data into the consortium chain smart contract, and use the event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger. The event trigger mechanism includes a zero-knowledge proof protocol; Based on the contribution track in the blockchain ledger, construct a traceable system including a cross-cycle contribution attenuation coefficient and a collaboration network density, and generate a performance score by dynamically adjusting the time weight coefficient and the inter-departmental collaboration coefficient.

[0007] Optionally, the step of using the event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger includes: Extract the time weight coefficient in the contribution degree of key results and the timing mark in the collaboration participation data according to the timestamp threshold, and generate a task event identifier including an inter-departmental task dependency matrix; Perform hierarchical mapping on the contribution degree of key results and the collaboration participation data based on the task event identifier, and generate a tree-shaped data block including the time weight coefficient, the inter-departmental collaboration coefficient, and the semantic association degree; Verify the integrity of the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block through the zero-knowledge proof protocol, generate a data verification proof including the hierarchical mapping relationship, bind the data verification proof with the task event identifier, and broadcast it to the consortium chain nodes; According to the consensus result of the consortium chain nodes, write the verified tree-shaped data block into the blockchain ledger in timestamp order. The blockchain ledger stores the hierarchical mapping relationship, the cross-cycle contribution attenuation coefficient, and the task event identifier.

[0008] Optionally, the step of verifying the integrity of the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block through the zero-knowledge proof protocol, and generating a data verification proof including the hierarchical mapping relationship includes: Perform structural encoding on the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block to generate a multi-dimensional polynomial representation, and generate a first commitment parameter based on the coefficient vector of the multi-dimensional polynomial representation; Extract the parent-child relationship identifiers of each node in the tree-shaped data block according to the topological order of the hierarchical mapping relationship, construct a set of constraint conditions associated with the cross-departmental collaboration coefficient, and generate a second commitment parameter according to the boundary values in the set of constraint conditions; Input the first commitment parameter and the second commitment parameter into a predefined challenge generator to generate a challenge factor sequence, and perform a linear combination operation on the cross-departmental collaboration coefficient and the semantic association degree to generate a set of response factors; Based on the set of response factors and the hierarchical mapping relationship, construct a proof object including polynomial constraint relationships and the parent-child relationship identifiers, and embed the hash digest of the hierarchical mapping relationship to generate a data verification proof.

[0009] Optionally, the performing a linear combination operation on the cross-departmental collaboration coefficient and the semantic association degree to generate a set of response factors includes: Extract the parent-child relationship identifiers, the components of the cross-departmental collaboration coefficient, and the components of the semantic association degree of each node in the tree-shaped data block according to the topological order of the hierarchical mapping relationship; Based on the index order of the challenge factor sequence, generate a polynomial weight factor associated with the time weight coefficient and the elements of the task dependency matrix for the node, and perform a dot product operation on the components of the cross-departmental collaboration coefficient and the components of the semantic association degree to generate an intermediate linear parameter; According to the hierarchical depth of the parent-child relationship identifier, superimpose a dynamic offset on the intermediate linear parameter, and the dynamic offset is generated by hash chain iteration based on the parent-child relationship identifier and the elements of the task dependency matrix; Aggregate the intermediate linear parameters and the dynamic offsets at the same level according to the time sequence markers to generate cross-verification parameters, and construct a response factor sequence according to the aggregation order; Based on the topological path of the tree-shaped data block, reversely aggregate the response factor sequence according to the hierarchical depth to generate a set of response factors.

[0010] Optionally, the constructing a traceable system including the cross-period contribution attenuation coefficient and the collaboration network density based on the contribution trace in the blockchain ledger includes: Extract the historical data of the time weight coefficient, the cross-departmental collaboration coefficient, and the semantic association degree from the blockchain ledger, and divide the cross-period task sequence according to the time window; Based on the time stamp interval of the time window and the attenuation rate of the time weight coefficient, calculate the contribution attenuation ratio of the cross-departmental collaboration coefficient in consecutive time windows to generate a cross-period contribution attenuation coefficient; Construct a collaborative network structure based on the node connection relationship of the cross-departmental task dependency matrix in the cross-cycle task sequence and the cumulative weight of the semantic association degree, and generate the collaborative network density by superimposing the node connection relationship and the semantic association degree; Input the cross-cycle contribution attenuation coefficient and the collaborative network density into the consortium chain smart contract to construct a traceable system including the cross-cycle contribution attenuation coefficient and the collaborative network density.

[0011] Optionally, the constructing a collaborative network structure based on the node connection relationship of the cross-departmental task dependency matrix in the cross-cycle task sequence and the cumulative weight of the semantic association degree includes: Decompose the cross-departmental task dependency matrix into a set of node connection pairs within each time window of the cross-cycle task sequence, and extract the timing mark and the corresponding semantic association degree component of each node connection pair; Calculate the time attenuation weight of the node connection pair according to the interval of the timing mark and the cross-cycle contribution attenuation coefficient, and perform weighted accumulation with the semantic association degree component to generate a dynamic cumulative weight; Based on the set of node connection pairs, extract the child node clusters sharing the same parent node, and generate the collaboration intensity parameter between the child node clusters according to the dynamic weight of the child node clusters and the historical data of the cross-departmental collaboration coefficient; According to the collaboration intensity parameter and the task dependency direction in the cross-departmental task dependency matrix, construct a two-way connection relationship for each child node cluster, and generate a collaborative network subgraph based on the weight distribution of the two-way connection relationship; Superimpose the collaborative network subgraph and the dynamic cumulative weight across cycles in the order of time windows, merge the connection edge weights between the same child node clusters, and construct a collaborative network structure.

[0012] Optionally, the generating a performance score by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient includes: Perform weighted aggregation of the time weight coefficient and the cross-departmental collaboration coefficient according to the job responsibility weight to generate an initial performance component, and perform cross-node coupling calibration based on the semantic association degree of adjacent nodes in the collaborative network; Along the hierarchical topological path of the tree structure, accumulate the calibrated initial performance components of the task nodes at the same level in time sequence to generate a performance score including cross-cycle attenuation compensation and collaboration gain.

[0013] In a second aspect, the present application provides a performance score management system for enterprise employees, including: Composition module, establish a tree structure of objective key results from the enterprise strategic level to the execution level, dynamically associate the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and an inter-departmental collaboration coefficient; Collection module, collect the voice active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, generate collaboration participation data in combination with the sound source localization function, and establish a mapping between the collaboration participation data and the task nodes in the objective key results tree structure through timestamps; Trigger module, input the contribution degree of key results and the collaboration participation data into the consortium chain smart contract, and use the event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger, and the event trigger mechanism includes a zero-knowledge proof protocol; Construction module, based on the contribution track in the blockchain ledger, construct a traceable system including a cross-cycle contribution attenuation coefficient and a collaboration network density, and generate a performance score by dynamically adjusting the time weight coefficient and the inter-departmental collaboration coefficient.

[0014] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for managing the performance score of enterprise employees as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium, storing a computer program, and when the computer program is executed by a computer, it implements a method for managing the performance score of enterprise employees as described in the first aspect.

[0016] In the embodiment of the present application, a tree structure of objective key results from the enterprise strategic level to the execution level is established, the job responsibilities of enterprise employees are dynamically associated with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and an inter-departmental collaboration coefficient; the voice active duration and interaction frequency of the enterprise employees are collected through the directional voiceprint recognition function in the multi-modal environment perception sensor array, and the collaboration participation data is generated in combination with the sound source localization function, and the collaboration participation data is mapped to the task nodes in the objective key results tree structure through timestamps; the contribution degree of key results and the collaboration participation data are input into the consortium chain smart contract, and the event trigger mechanism is used to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger, and the event trigger mechanism includes a zero-knowledge proof protocol; based on the contribution track in the blockchain ledger, a traceable system including a cross-cycle contribution attenuation coefficient and a collaboration network density is constructed, and a performance score is generated by dynamically adjusting the time weight coefficient and the inter-departmental collaboration coefficient.

[0017] The technical solution of this application has the following beneficial effects: By dynamically associating the job responsibilities of employees with the degree of key result contribution, especially considering the time weight coefficient and the cross-departmental collaboration coefficient, this application achieves comprehensive coverage from the enterprise strategy to the execution level. This method not only helps to clearly demonstrate the specific contribution of each employee's work to the achievement of enterprise goals, but also can flexibly adjust the evaluation criteria according to the time and cross-departmental cooperation situation. Using the directional voiceprint recognition function in the multi-modal environment perception sensor array, the voice activities and communication frequencies of employees can be accurately recorded, and the collaboration participation data can be generated by combining the sound source localization. The effect of this step is to objectively and quantitatively evaluate the participation degree and influence of employees in team collaboration, providing strong data support for performance evaluation. After associating the degree of key result contribution with the collaboration participation data using the event trigger mechanism, the data is written into the blockchain, ensuring the security and immutability of the data. The application of the zero-knowledge proof protocol further protects data privacy while ensuring the authenticity of the data. This step effectively improves the transparency and credibility of the performance evaluation process. Based on the contribution track in the blockchain ledger, considering factors such as the cross-cycle contribution attenuation coefficient and the collaboration network density, the time weight coefficient and the cross-departmental collaboration coefficient are dynamically adjusted to generate the performance score. This step makes the performance evaluation more fair and just, and can reflect the long-term performance trend of employees and the contribution changes at different stages.

[0018] Furthermore, by extracting the time weight coefficient in the degree of key result contribution and the timing mark in the collaboration participation data according to the timestamp threshold to generate the task event identifier, and based on this, hierarchical mapping is performed to generate a tree-shaped data block containing the time weight coefficient, the cross-departmental collaboration coefficient, and the semantic association degree. The zero-knowledge proof protocol is used to verify the integrity of the cross-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block, and the data verification proof is bound to the task event identifier and then broadcast to the consortium chain nodes. Finally, the verified data is written into the blockchain ledger in chronological order according to the consensus result. This process not only enhances the integrity and security of the data, but also improves the accuracy and fairness of the performance evaluation, and at the same time promotes effective communication and collaboration among different departments.

[0019] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 shows a flowchart of a method for managing performance scores of enterprise employees provided by this application; Figure 2 shows a schematic structural diagram of a system for managing performance scores of enterprise employees provided by this application; Figure 3 shows a schematic structural diagram of a computing device provided by this application. Detailed implementation manners

[0022] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application.

[0023] In some processes described in the specification and claims of this application and the above-mentioned accompanying drawings, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this text or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this text are used to distinguish different messages, devices, modules, etc., do not represent the sequence, and do not limit that "first" and "second" are of different types.

[0024] This solution focuses on constructing a tree structure of objective key results from the enterprise strategy to the execution layer, and dynamically associates the employee's job responsibilities with the contribution degree of key results. This method particularly considers the influence of time weight coefficient and cross-departmental collaboration coefficient, aims to comprehensively reflect the specific contributions of each employee to the realization of enterprise goals, and ensures that the evaluation criteria are flexibly adjusted according to time and cross-departmental cooperation situations.

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0026] Figure 1 provides a flowchart of a method for managing performance scores of enterprise employees for an embodiment of this application, as Figure 1 shown, the method includes: 101. Establish a tree structure of objective key results from the corporate strategic level to the execution level, dynamically associating the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and an inter-departmental collaboration coefficient; In this step, the tree structure of objective key results is a systematic method for refining the long-term strategic objectives of an enterprise into specific and actionable tasks.

[0027] The time weight coefficient measures the importance of task completion in different time periods. For example, urgent tasks may have a higher time weight.

[0028] The inter-departmental collaboration coefficient reflects the degree of cooperation of employees with other departments in the process of achieving the goal, which includes aspects such as communication frequency and resource sharing.

[0029] Job responsibilities clarify the specific work content that each employee needs to undertake, while the contribution degree of key results is an indicator calculated based on time weight and inter-departmental collaboration, used to evaluate the actual contribution of employees to achieving corporate goals.

[0030] In the embodiment of this application, first, clarify the corporate strategic objectives and decompose them into multiple specific and quantifiable sub-objectives. Then, set corresponding time weight coefficients for each sub-objective and assign values according to their urgency and importance. Next, determine the scope of responsibilities of each position and analyze how these responsibilities support the achievement of the above sub-objectives. By collecting collaboration data between teams, calculate the inter-departmental collaboration coefficient to reflect the work coordination between departments. Finally, combine the time weight coefficient and the inter-departmental collaboration coefficient, calculate the contribution degree of key results of each employee, and integrate it into the tree structure to form a system that clearly shows the objectives and their associations at each level from strategy to execution.

[0031] A manufacturing enterprise plans to launch a new product line. First, define the main stages such as product design, production preparation, and market promotion as sub-objectives. For each stage, set different time weights according to urgency and impact. Subsequently, clarify the responsibilities of departments such as R & D, production, and sales, and record the cooperation between departments to calculate the inter-departmental collaboration coefficient. Finally, based on these data, construct a tree structure to show the process of the entire project from planning to execution, as well as the role and contribution of each employee in this process.

[0032] 102. Collect the voice active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, generate collaboration participation data in combination with the sound source localization function, and establish a mapping between the collaboration participation data and the task nodes in the tree structure of objective key results through timestamps; In this step, the multi-modal environmental perception sensor array consists of multiple sensing technologies and can capture various information such as audio and video simultaneously.

[0033] The directional voiceprint recognition function focuses on identifying the voice characteristics in a specific direction, thereby recording the voice active duration and interaction frequency.

[0034] The collaborative participation data refers to the data collected through the directional voiceprint recognition function and sound source localization function in the multi-modal environmental perception sensor array, including the voice active duration, interaction frequency of employees, and the specific location information where these activities occur. This data is used to quantify the degree of interaction and cooperation mode among team members.

[0035] The sound source localization function determines the location of the sound source, which helps to generate detailed collaborative participation data.

[0036] The timestamp marks the specific time point when the event occurs, ensuring that the data corresponds precisely to the task nodes in the target key result tree structure.

[0037] The task node refers to the specific task unit in the target key result tree structure. Each node represents a specific actionable task or goal. The task node not only contains the information of the task itself but also associates parameters such as time weight coefficient and cross-departmental collaboration coefficient to measure the importance of the task and the impact of its completion on the achievement of the overall goal.

[0038] In the embodiment of the present application, first, a multi-modal environmental perception sensor array is installed in the office area, and the directional voiceprint recognition function is started to monitor the conversations among employees. Then, the sound source localization technology is used to determine the positional relationship between the two parties in the conversation, and further analyze the interaction mode among team members. Next, the timestamp is used to associate these interaction data with specific task nodes to form a detailed collaborative participation record. Finally, all the records are summarized to create a data set that comprehensively reflects the collaboration situation among employees for subsequent analysis.

[0039] Continuing with the example of a manufacturing enterprise, during the new product development period, a sensor array is deployed in the meeting room. By analyzing the speaking frequency and location of each member in the meeting, it is possible to understand which members have more direct communication. After these data are marked with timestamps, they are associated with different stages of product development, demonstrating the actual contributions of team members in the project progress.

[0040] 103. Input the key result contribution degree and the collaborative participation data into the consortium chain smart contract, and adopt an event trigger mechanism to write the associated data of the key result contribution degree and the collaborative participation data into the blockchain ledger. The event trigger mechanism includes a zero-knowledge proof protocol; In this step, the key result contribution is a comprehensive indicator calculated based on the time weight coefficient and the cross-departmental collaboration coefficient. It is used to evaluate employees' specific contributions to the realization of corporate goals and reflects the performance of employees in executing the tasks they are responsible for within a specific time period.

[0041] Consortium chain smart contracts are automatically executed contracts maintained by multiple pre-authorized nodes and are suitable for application scenarios that require multi-party trust and transparency.

[0042] An event-triggered mechanism is an automated process that automatically performs related actions when specific conditions are met.

[0043] The zero-knowledge proof protocol allows the authenticity of data to be verified without revealing the specific content, protecting data privacy while ensuring data accuracy.

[0044] Linked data is the result of combining key result contribution with collaborative participation data, including employee work results and their performance in teamwork. This data helps to fully understand the employee's work efficiency and contribution to teamwork, and is transparent and immutable through blockchain technology.

[0045] A blockchain ledger is a distributed database that is used to securely store transaction records.

[0046] In the embodiment of the present application, first, the key achievement contribution and collaboration participation data are integrated into the smart contract. Then, an event trigger mechanism is used to check whether the conditions for writing into the blockchain ledger are met. Then, the authenticity and integrity of the data are verified using a zero-knowledge proof protocol. Once the verification is successful, the data is recorded in chronological order on the blockchain ledger to ensure that the data cannot be tampered with and is open and transparent.

[0047] During the marketing phase of new products, team members’ performance data is integrated into smart contracts. When the scheduled performance evaluation cycle is reached, the event trigger mechanism is activated. After verification by the zero-knowledge proof protocol, all data is securely recorded on the blockchain for management review, while ensuring data security and fairness.

[0048] 104. Based on the contribution track in the blockchain account book, a traceability system including a cross-cycle contribution attenuation coefficient and a collaborative network density is constructed, and a performance score is generated by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient.

[0049] In this step, the inter-period contribution decay coefficient is used to measure the extent to which the impact of early contributions on current performance evaluation gradually weakens over time.

[0050] Collaboration network density represents the strength of connections and the frequency of interactions between members within a team.

[0051] Dynamic adjustment means continuously optimizing the time weight coefficient and cross - departmental collaboration coefficient according to the actual situation to more accurately reflect the comprehensive performance of employees throughout the project cycle.

[0052] The traceability system is a system built based on the blockchain ledger, used to record the contribution trajectory of employees throughout the work cycle, including factors such as the cross - cycle contribution attenuation coefficient and the collaboration network density. This system allows for the dynamic adjustment of the time weight coefficient and the cross - departmental collaboration coefficient, ensuring that performance evaluation can accurately reflect the long - term performance changes and actual contributions of employees.

[0053] The cross - departmental collaboration coefficient is a parameter used to measure the degree of cooperation of employees with other departments during the task - completion process. This includes, but is not limited to, activities such as communication frequency, resource sharing, and joint problem - solving.

[0054] The performance score is a value dynamically adjusted based on the time weight coefficient, the cross - departmental collaboration coefficient, and other relevant factors (such as the cross - cycle contribution attenuation coefficient and the collaboration network density) to objectively evaluate the work performance of employees.

[0055] In the embodiments of this application, first, historical data is extracted from the blockchain ledger to analyze the contribution changes of employees in different time periods. Then, the cross - cycle contribution attenuation coefficient is calculated based on the time interval and the attenuation rate of the time weight coefficient. Next, a collaboration network model is constructed based on the cross - departmental task - dependence matrix to quantify the collaboration intensity among team members. Finally, combining these analysis results, the time weight coefficient and the cross - departmental collaboration coefficient are dynamically adjusted to generate the final performance score.

[0056] At the review meeting after the product release, the management noticed some employees who made significant contributions in the early stage but had reduced contributions in the later stage by analyzing the data on the blockchain. Their performance scores were adjusted according to the cross - cycle contribution attenuation coefficient. At the same time, considering the high collaboration network density within the team, the scores of those employees who actively participated in cross - departmental cooperation were appropriately increased.

[0057] In summary, steps 101 to 104 not only achieve the effective decomposition and monitoring of the enterprise's strategic goals, but also objectively and fairly evaluate the work performance of each employee, promote efficient cross - departmental collaboration, improve the overall efficiency of the team, and at the same time enhance the enterprise's ability to quickly respond to market changes. Through this systematic method, the enterprise can better understand its own operation status and make corresponding strategic adjustments.

[0058] To further improve the transparency and credibility of performance evaluation, this solution uses an event-trigger mechanism combined with a zero-knowledge proof protocol to associate the key result contribution degree with the collaboration participation data and write it into the blockchain ledger. This process first extracts the correlation coefficient based on the timestamp threshold to generate a task event identifier, then performs hierarchical mapping to generate a tree-shaped data block, and finally verifies and broadcasts it to the consortium chain nodes to improve the transparency and credibility of performance evaluation. In some embodiments, the step of using the event-trigger mechanism to write the associated data of the key result contribution degree and the collaboration participation data into the blockchain ledger in step 103 includes: 201. Extract the time weight coefficient in the key result contribution degree and the timing mark in the collaboration participation data according to the timestamp threshold to generate a task event identifier including an inter-departmental task dependency matrix; In step 201, the timestamp threshold is a standard for screening data within a specific time period, which helps to determine which data meets the requirements of the current analysis. The time weight coefficient in the key result contribution degree reflects the time urgency and importance of task completion. The timing mark in the collaboration participation data records the specific moments of employee interaction. The inter-departmental task dependency matrix describes the cooperation requirements between different departments and their interdependent relationships. The task event identifier is a unique identifier generated from the above information and is used to identify and track specific task events.

[0059] In the embodiments of the present application, first, the key result contribution degree and the collaboration participation data that meet the requirements are screened according to the preset timestamp threshold, and then the time weight coefficient and the timing mark are extracted from these data. Then, analyze the task dependency situation between different departments to form a matrix, and generate a task event identifier in combination with the above information. The final result is a task event identifier containing all relevant information, laying a foundation for subsequent data processing.

[0060] 202. Perform hierarchical mapping on the key result contribution degree and the collaboration participation data based on the task event identifier to generate a tree-shaped data block including the time weight coefficient, the inter-departmental collaboration coefficient, and the semantic association degree; In step 202, the task event identifier serves as the core element for performing hierarchical mapping on the key result contribution degree and the collaboration participation data. The tree-shaped data block is a structured data form that includes information such as the time weight coefficient, the inter-departmental collaboration coefficient, and the semantic association degree. The semantic association degree measures the correlation strength of the data content and helps to understand the internal connection between different data points. The hierarchical mapping relationship describes the logical connection method between the nodes in the tree-shaped data block.

[0061] In the embodiments of the present application, first, relevant data is organized into a hierarchical structure using task event identifiers. Then, the time weight coefficient and cross - department collaboration coefficient of each node are calculated, and the semantic association degree between nodes is evaluated. Next, this information is integrated to form a tree - shaped data block. Finally, through this structured representation method, the data is made more manageable and analyzable, ensuring data consistency and integrity.

[0062] 203. Verify the integrity of the cross - department collaboration coefficient and semantic association degree in the tree - shaped data block through a zero - knowledge proof protocol, generate a data verification proof containing a hierarchical mapping relationship, and broadcast the data verification proof bound to the task event identifier to the consortium blockchain nodes; In step 203, the zero - knowledge proof protocol is a cryptographic technique that allows verification of data authenticity without revealing specific content. The data verification proof is a file generated through this protocol to confirm the integrity and accuracy of the data within the tree - shaped data block. The hierarchical mapping relationship describes the logical connection method between nodes in the tree - shaped data block.

[0063] In the embodiments of the present application, first, the zero - knowledge proof protocol is applied to verify the cross - department collaboration coefficient and semantic association degree within the tree - shaped data block to ensure their authenticity. Then, a data verification proof containing a hierarchical mapping relationship is generated and bound to the task event identifier. Finally, this verification result is broadcast to the consortium blockchain nodes to ensure data security and immutability while maintaining data privacy protection.

[0064] 204. According to the consensus result of the consortium blockchain nodes, write the verified tree - shaped data block into the blockchain ledger in timestamp order. The blockchain ledger stores the hierarchical mapping relationship, the cross - cycle contribution decay coefficient, and the task event identifier.

[0065] In step 204, the consortium blockchain node consensus result refers to the common confirmation of the authenticity and consistency of a certain data by multiple nodes in the network. The blockchain ledger is a distributed database used to securely store transaction records and other important information. The hierarchical mapping relationship, the cross - cycle contribution decay coefficient, and the task event identifier are all recorded in the blockchain ledger for long - term reference.

[0066] In the embodiments of the present application, first, collect the consensus feedback from the consortium blockchain nodes. Once the verification is successful, write the tree - shaped data block into the blockchain ledger in chronological order. Then, update the ledger content by adding new hierarchical mapping relationships, cross - cycle contribution decay coefficients, and task event identifiers. The purpose of this is to ensure that all operations are publicly transparent and cannot be tampered with, thereby improving data reliability and security.

[0067] The following is a specific example: A manufacturing enterprise plans to launch a new product line. At the initial stage of the project, based on the preset timestamp threshold, it screens out the key result contribution and collaboration participation data within the validity period and generates task event identifiers. Then, it uses these identifiers to perform hierarchical mapping on the data and create a tree-shaped data block. Next, it verifies the data integrity through a zero-knowledge proof protocol and broadcasts the verification result to the consortium chain nodes. Finally, according to the node consensus result, the verified data is recorded in the blockchain ledger in chronological order. This process not only improves the transparency and reliability of performance evaluation, but also promotes efficient collaboration among team members, enhances the overall execution power of the enterprise and the market response speed.

[0068] In summary, steps 201 to 204 can accurately reflect the work performance of each employee and their contribution to the achievement of team goals by establishing a highly transparent and tamper-proof performance evaluation system, thus promoting fair and just human resource management decisions. At the same time, this process greatly improves the team collaboration efficiency and enhances the market response ability and competitiveness of the enterprise.

[0069] To further improve the accuracy and security of data verification, this solution performs integrity verification on the cross-departmental collaboration coefficient and semantic association degree in the tree-shaped data block through structure encoding and polynomial representation, and then generates a data verification proof. This process includes generating commitment parameters, extracting parent-child relationship identifiers, constructing a challenge factor sequence, and a response factor set to ensure the authenticity and integrity of the data. In some embodiments, the integrity verification of the cross-departmental collaboration coefficient and semantic association degree in the tree-shaped data block through the zero-knowledge proof protocol in step 203 to generate a data verification proof including a hierarchical mapping relationship includes: 301. Perform structure encoding on the cross-departmental collaboration coefficient and semantic association degree in the tree-shaped data block to generate a multi-dimensional polynomial representation, and generate a first commitment parameter based on the coefficient vector of the multi-dimensional polynomial representation; In step 301, structure encoding is a technology that converts the original data into a form convenient for processing and verification. The multi-dimensional polynomial representation is a way to express complex data structures through mathematical methods, which can describe the mutual relationship between data. The first commitment parameter is a value generated from the coefficient vector of the multi-dimensional polynomial and is used as part of the subsequent verification process. The cross-departmental collaboration coefficient measures the degree of cooperation between different departments, while the semantic association degree evaluates the correlation strength of the data content.

[0070] In the embodiments of the present application, first, a specific algorithm is used to perform structural encoding on the cross - department collaboration coefficient and semantic correlation degree in the tree - shaped data block, and convert them into a multi - dimensional polynomial form. Then, based on the coefficient vectors of these polynomials, the first commitment parameter is calculated. The algorithms used in this process may include linear algebra methods and numerical analysis techniques. The final result is a numerical value representing the characteristics of the original data, which serves as the basis for subsequent verification.

[0071] 302. Extract the parent - child relationship identifiers of each node in the tree - shaped data block according to the topological order of the hierarchical mapping relationship, construct a set of constraint conditions associated with the cross - department collaboration coefficient, and generate a second commitment parameter according to the boundary values in the set of constraint conditions; In step 302, the topological order of the hierarchical mapping relationship refers to the logical connection order between nodes in the tree - shaped data block. The parent - child relationship identifier is used to describe the hierarchical relationship between nodes and helps to understand the data structure. The set of constraint conditions is a set of rules that define the specific requirements that the cross - department collaboration coefficient should meet. The second commitment parameter is generated from the boundary values in this set of constraint conditions and is used in the verification process.

[0072] In the embodiments of the present application, first, according to the topological order of the hierarchical mapping relationship, the parent - child relationship identifiers of each node are extracted, and a set of constraint conditions associated with the cross - department collaboration coefficient is constructed. Then, a second commitment parameter is generated according to the boundary values in this set of constraint conditions. This step uses the topological sorting algorithm in graph theory to determine the order between nodes and applies linear programming methods to generate the set of constraint conditions. The final result is a parameter that can reflect the internal consistency of the data and provides a basis for subsequent verification.

[0073] 303. Input the first commitment parameter and the second commitment parameter into a predefined challenge generator to generate a challenge factor sequence, and perform a linear combination operation on the cross - department collaboration coefficient and the semantic correlation degree to generate a set of response factors; In step 303, the challenge generator is an algorithm tool used to generate a random number sequence to test the authenticity and consistency of data. The challenge factor sequence is a random number sequence generated by the challenge generator and is used to test the reliability of the data. The set of response factors is the result set obtained by performing a linear combination operation on the cross - department collaboration coefficient and the semantic correlation degree and serves as a key part of the verification.

[0074] In the embodiments of the present application, first, the first commitment parameter and the second commitment parameter are input into a predefined challenge generator to obtain a challenge factor sequence. Then, the challenge factor sequence is used to perform a linear combination operation on the cross-department collaboration coefficient and the semantic association degree to obtain a set of response factors. In this process, a pseudo-random number generation algorithm in cryptography and a linear transformation method in linear algebra are used. The final result is a rigorously calculated data set, enhancing the security and reliability of data verification.

[0075] 304. Based on the set of response factors and the hierarchical mapping relationship, construct a proof object including polynomial constraint relationships and the parent-child relationship identifier, and embed the hash digest of the hierarchical mapping relationship to generate a data verification proof.

[0076] In step 304, the proof object is a verification file constructed from the set of response factors and the hierarchical mapping relationship. The polynomial constraint relationship refers to the mathematical relationship that the elements in the set of response factors need to satisfy to ensure data consistency. The hash digest is an encryption technology used to generate a unique identifier for data. The data verification proof is the finally generated verification file, which contains all necessary verification information to ensure the authenticity and immutability of the data.

[0077] In the embodiments of the present application, first, based on the set of response factors and the hierarchical mapping relationship, construct a proof object including polynomial constraint relationships and the parent-child relationship identifier. Secondly, embed the proof object with the hash digest of the hierarchical mapping relationship. Finally, obtain a data verification proof. This step uses a hash function and digital signature technology in cryptography to ensure the authenticity and integrity of the data. The final result is a comprehensive and reliable verification file, providing guarantee for the entire verification process.

[0078] The following is a specific example: A manufacturing enterprise plans to launch a new product line. At the initial stage of the project, first, use an advanced algorithm to perform structural encoding on the cross-department collaboration coefficient and the semantic association degree within the tree-shaped data block, and calculate the first commitment parameter. Then, extract the parent-child relationship identifier according to the topological order of the hierarchical mapping relationship and generate the second commitment parameter. Then, perform a linear combination operation on the cross-department collaboration coefficient and the semantic association degree to obtain a set of response factors. Finally, based on the set of response factors and the hierarchical mapping relationship, generate a data verification proof. This process not only improves the accuracy of data verification but also enhances the trust among team members and promotes the smooth progress of the project.

[0079] In summary, steps 301 to 304 verify the key data in the tree-shaped data block by adopting a zero-knowledge proof protocol, ensuring the authenticity and integrity of the data while protecting data privacy. This improves the transparency and credibility of the performance evaluation system, enhances team collaboration efficiency, helps enterprises make more fair and just human resource management decisions, and thus enhances the overall competitiveness of the enterprise. This method enables the enterprise to operate efficiently even in a complex cross-departmental cooperation environment, ensuring that the efforts of each employee can be accurately recorded and recognized.

[0080] To solve the problems of low cross-departmental collaboration efficiency and unclear semantic association of information, the solution involves performing a linear combination operation on the cross-departmental collaboration coefficient and the semantic association degree to generate a set of response factors. This process generates polynomial weight factors for each node according to the topological order of the hierarchical mapping relationship, and superimposes a dynamic offset, and finally generates cross-validation parameters by aggregating and marking in chronological order, so as to support efficient data verification and processing. In some embodiments, the linear combination operation on the cross-departmental collaboration coefficient and the semantic association degree described in step 303 to generate a set of response factors includes: 401. Extract the parent-child relationship identifier, the component of the cross-departmental collaboration coefficient, and the component of the semantic association degree of each node in the tree-shaped data block according to the topological order of the hierarchical mapping relationship; In step 401, the parent-child relationship identifier refers to the connection mark between a node and its direct superior or subordinate in the data structure, which is used to determine the position and level of the node in the tree structure. The component of the cross-departmental collaboration coefficient is a specific value for evaluating the closeness of cooperation between different departments, which is used to quantify the cooperation level between departments. The component of the semantic association degree is an index for measuring the semantic relevance between information contents, which is used to reflect the semantic connection strength between different information segments.

[0081] In the embodiment of the present application, first, according to the pre-defined hierarchical mapping relationship, the entire tree-shaped data block is traversed in topological order to extract the parent-child relationship identifier of each node. Then, according to these identifiers, the components of the cross-departmental collaboration coefficient and the semantic association degree corresponding thereto are collected. Then these data are integrated to form a preliminary data set for subsequent processing. Finally, a data set containing information related to each node is obtained, providing a basis for further calculation.

[0082] 402. Generate polynomial weight factors associated with the time weight coefficient and the elements of the task dependency matrix for the node according to the index order of the challenge factor sequence, and perform a dot product operation on the component of the cross-departmental collaboration coefficient and the component of the semantic association degree to generate intermediate linear parameters; In step 402, the challenge factor sequence is composed of a series of factors that may affect the project progress, and the index order refers to the order in which these factors are considered. The polynomial weight factor is a parameter generated by mathematical methods based on the time weight coefficient (indicating the importance of a task within a specific time period) and the task dependency matrix element (describing the degree of interdependence between tasks), and is used to adjust the components of the cross-departmental collaboration coefficient and the semantic association degree. The dot product operation is a multiplication operation between vectors, and the result is a scalar, which is used here to calculate the intermediate linear parameter.

[0083] In the embodiment of the present application, first, according to the index order of the challenge factor sequence, the time weight coefficient of each node is calculated, and the polynomial weight factor is constructed by combining the task dependency matrix elements. Then, these weight factors are used to perform a dot product operation on the components of the cross-departmental collaboration coefficient and the semantic association degree to generate an intermediate linear parameter. Finally, the results of all nodes are aggregated to form a set of intermediate linear parameter sets as the basis for the next step.

[0084] 403. According to the hierarchical depth of the parent-child relationship identifier, a dynamic offset is superimposed on the intermediate linear parameter, and the dynamic offset is generated by hash chain iteration based on the parent-child relationship identifier and the task dependency matrix element; In step 403, the hierarchical depth of the parent-child relationship identifier reflects the hierarchical position of a node in its affiliated tree structure. The dynamic offset is a variable generated by hash chain iteration based on the parent-child relationship identifier and the task dependency matrix element, and is used to adjust the intermediate linear parameter to make it more accurately reflect the actual situation.

[0085] In the embodiment of the present application, first, the hierarchical depth of the node is determined according to the parent-child relationship identifier, and then the corresponding dynamic offset is generated by the hash chain iteration algorithm according to this depth and the task dependency situation. Then, these offsets are superimposed on the intermediate linear parameter, and the adjusted parameter can better reflect the complexity in the actual business scenario. Finally, a set of adjusted intermediate linear parameters is formed and ready to enter the next step.

[0086] 404. Aggregate the intermediate linear parameters and the dynamic offsets at the same level according to the time sequence marks to generate cross-validation parameters, and construct a response factor sequence according to the aggregation order; In step 404, the cross-validation parameter refers to a series of parameter values obtained by aggregating the intermediate linear parameter and the time sequence mark at the same level. The time sequence mark represents the order in which events occur and is used to ensure the correct sorting during parameter aggregation. The response factor sequence is a set of values arranged according to the aggregation order, comprehensively reflecting the information processing situation of each node.

[0087] In the embodiments of the present application, first, the intermediate linear parameters within the same level are marked and aggregated in the order of occurrence to form cross-validation parameters. Then, a response factor sequence is constructed according to the aggregation order of these parameters. The purpose of doing this is to ensure the coherence and accuracy of the information flow. Finally, an ordered response factor sequence is obtained, which is ready for the last step of processing.

[0088] 405. Based on the topological path of the tree-shaped data block, the response factor sequence is reversely aggregated by hierarchical depth to generate a response factor set.

[0089] In step 405, the topological path refers to the connection method between the internal nodes of the tree-shaped data block. Reverse aggregation refers to the process from the leaf nodes upwards to the root node, which is used to generate the response factor set. This set comprehensively reflects all the key information about cross-departmental collaboration and semantic association in the entire tree-shaped data block. The response factor set is a set of comprehensive indicators obtained by reversely aggregating the response factors of each node according to their positions in the tree structure, which is used to comprehensively reflect the overall situation of cross-departmental collaboration efficiency and semantic association degree.

[0090] In the embodiments of the present application, first, the complete topological path from the leaf nodes to the root node in the tree-shaped data block is determined. Then, in the reverse order of the hierarchical depth, the response factors generated by each node are sequentially aggregated. Here, a bottom-up algorithm is adopted to ensure that the response factors of each node can take into account the influence of all its child nodes. Then, through iterative calculation, the response factors of adjacent levels are gradually merged, and finally a response factor set covering the entire tree structure is formed. The purpose of doing this is to accurately capture the collaboration status among departments and its impact on the overall project progress.

[0091] The following is a specific example: A manufacturing enterprise plans to launch a new product line. When operating specifically, first, a tree-shaped data model is established according to the product development process, covering multiple links such as design, procurement, and production. Then, the above steps are completed in sequence, and the interaction between each link is analyzed by using the cross-departmental collaboration coefficient and semantic association degree. The finally formed response factor set can help the enterprise identify potential problems and optimize resource allocation, improving the market launch speed and competitiveness of new products.

[0092] In summary, steps 401 to 405 effectively improve the cross - departmental collaboration efficiency during the new product line development process in manufacturing enterprises. The enterprise can not only clearly understand the collaboration efficiency and information correlation degree among various links, but also promptly identify potential bottlenecks and make rapid adjustments. This greatly increases the speed and success rate of new product line launch, reduces risks brought by poor communication or improper collaboration, optimizes resource allocation, and thus enhances the enterprise's market competitiveness. The whole process emphasizes the importance of effective management of information flow and cross - departmental coordination, providing the enterprise with a systematic method to improve overall operational efficiency.

[0093] To solve the problems of difficult quantification of contribution degree and unclear collaboration network structure in cross - departmental collaboration, the solution emphasizes extracting historical data from the blockchain ledger to construct a traceable system including cross - cycle contribution decay coefficient and collaboration network density. The specific steps include dividing cross - cycle task sequences, calculating contribution decay ratios, constructing collaboration network structures, and inputting these elements into the consortium chain smart contract to dynamically track the long - term performance and contribution changes of employees. In some embodiments, constructing a traceable system including cross - cycle contribution decay coefficient and collaboration network density based on the contribution trajectory in the blockchain ledger in step 104 includes: 501. Extract historical data of time weight coefficient, cross - departmental collaboration coefficient, and semantic association degree from the blockchain ledger, and divide cross - cycle task sequences according to time windows; In step 501, the time weight coefficient is a numerical value measuring the importance of tasks within a specific time period, used to reflect the impact degree of different time points on the overall goal. The cross - departmental collaboration coefficient is an evaluation value of the cooperation tightness among departments, used to quantify the collaboration level among departments. The semantic association degree is an index measuring the meaning correlation between information contents, used to reflect the connection strength between information segments. Historical data refers to the dataset of all the above - mentioned parameters recorded over a past period of time. A time window is a method of dividing a continuous time series into several segments, used to analyze the change trends within different time periods.

[0094] In the embodiment of the present application, first, all data related to the time weight coefficient, cross - departmental collaboration coefficient, and semantic association degree are extracted from the blockchain ledger. Then, according to a predetermined time interval, these data are divided into different time windows to form cross - cycle task sequences. Then, by integrating these data, a historical database that can reflect the long - term collaboration situation among departments is established. What is finally obtained is a set of task sequences arranged in chronological order, providing a basis for subsequent steps.

[0095] 502. Calculate the contribution decay ratio of the cross - departmental collaboration coefficient within consecutive time windows based on the time - stamp interval of the time window and the decay rate of the time weight coefficient, and generate a cross - cycle contribution decay coefficient; In step 502, the timestamp interval is the time difference between two adjacent time windows, which is used to calculate the decay rate of the contribution over time. The decay rate is a speed parameter that describes how the contribution gradually decreases over time and is used to adjust the contribution ratio of the cross-department collaboration coefficient. The contribution decay ratio is the change ratio of the cross-department collaboration coefficient calculated within consecutive time windows based on the decay rate of the time weight coefficient, and it is used to measure the change of the contribution in different time periods. The cross-cycle contribution decay coefficient is an indicator generated by comprehensively considering the above factors and is used to reflect the contribution change across multiple cycles.

[0096] In the embodiments of the present application, first, the timestamp interval of each time window is determined, and the contribution decay ratio within each window is calculated according to a preset decay rate formula. Then, these ratios are used to adjust the cross-department collaboration coefficient to generate the cross-cycle contribution decay coefficient. Finally, the results of all time windows are summarized to form a set of coefficients that can reflect the contribution changes of each department over a long time span. The purpose of doing this is to more accurately evaluate the contribution changes of each department in the long term.

[0097] 503. Based on the node connection relationship of the cross-department task dependency matrix in the cross-cycle task sequence and the cumulative weight of the semantic association degree, construct a collaboration network structure, and generate the collaboration network density by superimposing the node connection relationship and the semantic association degree; In step 503, the cross-department task dependency matrix is a matrix representing the mutual task dependency relationships between different departments, and the node connection relationship refers to the connection situation represented by each element in this matrix. The cumulative weight is the result of weighted summation of all relevant information fragments based on the semantic association degree and is used to enhance the connection between nodes. The collaboration network structure is a network model jointly composed of the node connection relationship and the cumulative weight and is used to depict the collaboration mode between departments. The collaboration network density is an indicator measuring the tightness of the connection between nodes in this network and reflects the overall collaboration efficiency.

[0098] In the embodiments of the present application, first, an initial collaboration network structure is constructed according to the task dependency matrix in the cross-cycle task sequence. Then, the collaboration network density is calculated by superimposing the cumulative weight of the node connection relationship and the semantic association degree. This process uses algorithms in graph theory to handle complex connection relationships. Finally, what is obtained is a set of data that can comprehensively reflect the collaboration mode and its tightness between departments, laying a foundation for the subsequent application of the intelligent contract.

[0099] 504. Input the cross-cycle contribution decay coefficient and the collaboration network density into the consortium chain intelligent contract to construct a traceable system including the cross-cycle contribution decay coefficient and the collaboration network density.

[0100] In step 504, the cross-cycle contribution decay coefficient is an indicator used to quantify the contribution changes of each department over a long time span, reflecting the changing trend of contributions in different time periods. The collaborative network density is an indicator measuring the tightness of connections between nodes in the inter-departmental collaborative network, reflecting the overall collaborative efficiency and the smoothness of information flow. The consortium chain smart contract is an automated protocol based on blockchain technology that can automatically execute preset rules, ensuring data transparency, immutability, and promoting trust-based cooperation among multiple parties.

[0101] In the embodiments of this application, first, the calculated cross-cycle contribution decay coefficient and the collaborative network density are passed as input parameters to the consortium chain smart contract. Then, according to the pre-set algorithm logic, the smart contract starts to process these data, ensuring the authenticity and consistency of the data through its built-in verification mechanism. Next, the smart contract adjusts the weight allocation of each department in future projects based on the contribution decay coefficient and optimizes the resource allocation strategy using the collaborative network density to enhance the team's collaborative efficiency. Finally, a traceable system containing the cross-cycle contribution decay coefficient and the collaborative network density is generated. This system not only records historical contribution data but also provides a scientific basis for future decision-making.

[0102] The following is a specific example: A manufacturing enterprise plans to launch a new product line. During the product development process, first, it collects the collaborative data of each department in past projects and classifies them according to time windows. Then, it calculates the contribution decay ratio of each department in each stage to evaluate its contribution changes throughout the product life cycle. Next, based on the task dependencies and information exchange among departments, it constructs a collaborative network model and analyzes the network density. Finally, it inputs these data into the consortium chain smart contract to achieve transparent management of the entire new product line development process.

[0103] In summary, through steps 501 to 504, by constructing a traceable system containing the cross-cycle contribution decay coefficient and the collaborative network density, the enterprise realizes refined management of internal cross-departmental collaboration. This system can not only accurately evaluate the contribution changes of each department in the long term but also effectively depict the collaborative network structure among departments, identify and solve potential cooperation bottlenecks. With the transparency of blockchain technology and the automated execution ability of smart contracts, the enterprise enhances the fairness and efficiency of the decision-making process, significantly improves the overall efficiency of the new product line from planning to market launch, and further strengthens its market competitiveness. The entire solution provides the enterprise with a comprehensive and systematic set of tools, promoting information sharing and improving collaborative efficiency.

[0104] To address the issues of low efficiency and difficulty in quantification in cross - departmental collaboration, this solution focuses on constructing a collaborative network structure by analyzing the node connection relationships and the cumulative weights of semantic association degrees in the cross - departmental task dependency matrix. This process includes decomposing the set of node connection pairs, calculating the time - decay weights, generating the collaboration intensity parameters between sub - node clusters, and based on this, constructing the bidirectional connection relationships and the collaborative network sub - graph. Finally, the connection - edge weights between the same sub - node clusters are merged to form a complete collaborative network structure. In some embodiments, constructing the collaborative network structure based on the node connection relationships and the cumulative weights of the semantic association degrees in the cross - departmental task dependency matrix in step 503 includes: 601. Decompose the cross - departmental task dependency matrix into a set of node connection pairs within each time window of the cross - cycle task sequence, and extract the timing tags and corresponding semantic association degree components of each node connection pair; In step 601, the cross - departmental task dependency matrix is a matrix representing the mutual dependency relationships between tasks of different departments. The set of node connection pairs is the set of direct connections between nodes within each time window extracted from this matrix. The timing tag is a label used to identify the order in which node connections occur, ensuring that the information flow is processed in the correct time sequence. The semantic association degree component is a measure of the relevance of the information content between nodes, used to enhance the connection between nodes.

[0105] In the embodiments of this application, first, determine the timing tag interval of each node connection pair, and calculate the time - decay weight of each connection pair according to a preset time - decay weight formula. Then, use these weights to perform weighted accumulation on the semantic association degree components to generate the dynamic cumulative weight. Finally, summarize the results of all node connection pairs to form a set of weights that can reflect the long - term connection strength between nodes, providing a basis for subsequent steps.

[0106] 602. Calculate the time - decay weight of the node connection pair according to the interval of the timing tags and the cross - cycle contribution decay coefficient, and perform weighted accumulation with the semantic association degree component to generate the dynamic cumulative weight; In step 602, the timing tag interval refers to the time difference between two adjacent node connection events, used to calculate the time - decay weight. The time - decay weight is an influence factor describing the gradual decrease of node connection over time, adjusted based on the cross - cycle contribution decay coefficient. The dynamic cumulative weight is a comprehensive index generated by weighted accumulation of the time - decay weight and the semantic association degree component of the node connection pair, used to measure the tightness of the connection between nodes.

[0107] In the embodiments of the present application, first, the timing mark intervals of each node connection pair are determined, and the time decay weights of each connection pair are calculated according to a preset time decay weight formula. Then, these weights are used to perform weighted accumulation on the semantic association degree components to generate dynamic cumulative weights. Finally, the results of all node connection pairs are summarized to form a set of weight sets that can reflect the long-term connection strength between nodes, providing a basis for subsequent steps.

[0108] 603. Based on the set of node connection pairs, extract sub-node clusters that share the same parent node, and generate a collaboration strength parameter between the sub-node clusters according to the historical data of the dynamic weights and cross-departmental collaboration coefficients of the sub-node clusters; In step 603, the sub-node clusters that share the same parent node refer to a group of nodes that have a common superior node at the same level. The dynamic weight is based on the dynamic cumulative weight calculated in the previous step. The collaboration strength parameter is an index generated according to the historical data of the dynamic weights and cross-departmental collaboration coefficients of the sub-node clusters, and is used to evaluate the cooperation tightness between the sub-node clusters.

[0109] In the embodiments of the present application, first, all sub-node clusters that share the same parent node are identified. This process uses statistical analysis methods to handle complex inter-node relationships. Then, according to the dynamic weights of all sub-node clusters and the historical cross-departmental collaboration coefficients, the collaboration strength parameters between the sub-node clusters are calculated. Finally, a set of data that can comprehensively reflect the collaboration mode and its tightness between the sub-node clusters is obtained, laying a foundation for the next step.

[0110] 604. According to the collaboration strength parameter and the task dependency direction in the cross-departmental task dependency matrix, construct a two-way connection relationship for each sub-node cluster, and generate a collaboration network sub-graph based on the weight distribution of the two-way connection relationship; In step 604, the two-way connection relationship is a reciprocal connection model established based on the collaboration strength parameter and the task dependency direction between the sub-node clusters. This relationship not only considers the directionality of information or resources flowing from one node to another, but also considers the reverse feedback or support, thus forming a more compact cooperation mode. The collaboration network sub-graph is a local network model generated based on the weight distribution of the two-way connection relationship, and is used to depict the specific collaboration situation between the sub-node clusters, and is used to more carefully analyze the collaboration efficiency and potential bottlenecks between departments.

[0111] In the embodiments of the present application, first, a bidirectional connection relationship is constructed for each sub-node cluster according to the collaboration intensity parameter and the task dependence direction between sub-node clusters. Then, based on the weight distribution of these connection relationships, a graph theory algorithm is used to generate a sub-graph of the collaboration network. In this process, by analyzing historical data and current task requirements, the optimal connection method and weight allocation are determined. Finally, the results of all sub-node clusters are summarized to form a set of charts that can clearly display the collaboration network structure between sub-node clusters, providing preparations for subsequent integration.

[0112] 605. Superimpose the sub-graph of the collaboration network and the dynamic cumulative weight across cycles in the order of time windows, merge the connection edge weights between the same sub-node clusters, and construct a collaboration network structure.

[0113] In step 605, superimposing across cycles refers to the process of merging the sub-graphs of the collaboration network and the dynamic cumulative weight in different time periods in the order of time windows. The purpose is to integrate data in different time periods to comprehensively reflect long-term trends and change laws. Merging the connection edge weights between the same sub-node clusters is to optimize the network structure to ensure more efficient information flow. The connection edge weight represents the importance or strength of the connection between two nodes in the collaboration network structure. When merging the connections between the same sub-node clusters, the structure of the entire network is optimized by adjusting these weights to ensure that key connections receive due attention, while redundant connections are appropriately weakened or removed.

[0114] In the embodiments of the present application, first, the sub-graphs of the collaboration network in each time period and the dynamic cumulative weight are superimposed across cycles in the order of time windows. This step uses data fusion technology to process data in different time periods to ensure that long-term trends are accurately reflected. Then, the connection edge weights between the same sub-node clusters are merged to optimize the entire collaboration network structure. By comparing the weight changes at different time points, the most effective connection paths are identified and the weights are adjusted accordingly. Finally, a complete collaboration network structure is obtained, which not only records historical collaboration situations but also provides a scientific basis for future decisions.

[0115] The following is a specific example: A manufacturing enterprise plans to launch a new product line. During the product development process, it first collects the collaboration data of each department in past projects and decomposes it into a set of node connection pairs within multiple time windows. Then, it calculates the time decay weight of each node connection pair to generate a dynamic cumulative weight. Subsequently, it superimposes these subgraphs and the dynamic cumulative weight across cycles in the order of time windows to construct a complete collaborative network structure. For example, a two-way connection is established between the design phase and the production phase to ensure timely feedback on the design scheme and rapid resolution of production problems. Subsequently, it superimposes these subgraphs and the dynamic cumulative weight across cycles in the order of time windows, merges the connection edge weights between the same sub-node clusters, such as enhancing the communication channels between the design and marketing departments, constructs a complete collaborative network structure, and optimizes resource allocation and information flow.

[0116] In summary, in steps 601 to 605, the enterprise has achieved refined management of internal cross-departmental collaboration. This system can not only accurately evaluate the collaboration changes of each department in the long term, but also effectively depict the collaborative network structure between departments, identify and solve potential cooperation bottlenecks. With this systematic method, the enterprise has enhanced the rationality and efficiency of resource allocation, shortened the product listing time, and improved its market competitiveness. The entire solution provides the enterprise with a comprehensive and systematic set of tools, promoting information sharing and improving collaboration efficiency.

[0117] To address the problem of insufficient attention to the time dimension and cross-departmental collaboration in the traditional performance evaluation system, this solution generates performance scores by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient. This involves weighted aggregation of the initial performance components and calibration based on the semantic correlation degree of adjacent nodes in the collaboration network. Subsequently, the calibrated components of task nodes at the same level are accumulated hierarchically along the tree structure, and finally, a performance score that comprehensively considers cross-cycle decay compensation and collaboration gain is generated. In some embodiments, generating the performance score by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient as described in step 104 includes: 701. Weightedly aggregate the time weight coefficient and the cross-departmental collaboration coefficient according to the job responsibility weight to generate an initial performance component, and perform cross-node coupling calibration based on the semantic correlation degree of adjacent nodes in the collaboration network; In step 701, the time weight coefficient refers to the proportional factor for adjusting the performance score according to the time of task completion, including data such as urgency and importance, and is used to measure the value of work results in different time periods. The cross-departmental collaboration coefficient is an indicator for measuring the degree of cooperation among departments, covering aspects such as communication efficiency and resource sharing, and aims to reflect the impact of team collaboration level on overall performance. The job responsibility weight is the different weight assigned according to the core functions of the employee's position, reflecting the contribution degree of each position to the achievement of enterprise goals. These parameters jointly act in the process of generating the initial performance component, ensuring that the performance evaluation is more fair and reasonable.

[0118] In the embodiment of the present application, first, the job responsibility weight is determined based on the importance and nature of the employee's position, and then the time weight coefficient of each task is calculated, considering its urgency and importance to the company's strategy. Then, the cross-departmental collaboration coefficient is evaluated, and specific values are obtained by analyzing the communication efficiency and resource sharing situation among departments. Finally, the above coefficients are weighted and aggregated according to the job responsibility weight to obtain the initial performance component, and the result is calibrated using the semantic correlation degree between nodes in the collaboration network to improve the accuracy of performance evaluation.

[0119] 702. Along the hierarchical topological path of the tree structure, the calibrated initial performance components of the task nodes at the same level are cumulatively added in time sequence to generate a performance score including cross-period decay compensation and collaboration gain.

[0120] In step 702, the hierarchical topological path of the tree structure refers to the hierarchical structure diagram formed by the task assignment and reporting relationships within the organization, showing the task transfer process from the high level to the grass-roots level. The task nodes at the same level represent various tasks at the same management level. The cross-period decay compensation is a compensation mechanism for the decreasing value of work in the early stage of long-term projects over time, while the collaboration gain reflects the additional benefits brought by team cooperation.

[0121] In the embodiment of the present application, first, along the hierarchical path of the tree structure in the organizational structure, all task nodes at the same level are identified. Then, for the calibrated initial performance components on each node, cumulative processing is performed in the order of occurrence time. Then, a cross-period decay compensation mechanism is introduced during the cumulative process to ensure that the efforts over a long time span are fairly recognized. Finally, considering the gain effect brought by team collaboration, the final performance score is integrated to comprehensively reflect the work performance of employees.

[0122] The following is a specific example: When a manufacturing enterprise plans to launch a new product line, the above method can be adopted to accurately evaluate the contributions of various departments and individuals involved in the project. First, determine the roles and responsibilities of each position in the new product line project and set the corresponding weight of job responsibilities. Subsequently, according to the project schedule and market expectations, determine the time weight coefficient for each stage task. At the same time, monitor and record the communication frequency and resource flow between departments as the basis for calculating the cross-departmental collaboration coefficient. Next, perform weighted aggregation on the time weight coefficient and cross-departmental collaboration coefficient by combining the weight of job responsibilities to obtain a preliminary performance score. On this basis, further examine the mutual influence between task nodes and adjust the performance score. Finally, based on the cumulative effect of various tasks during the entire project cycle and the additional value generated by teamwork, give the final performance evaluation result.

[0123] In summary, steps 701 to 702 can not only accurately quantify the work achievements of individual employees, but also fully consider the impact of factors such as time urgency and cross-departmental collaboration on overall performance, providing an enterprise with a more scientific and reasonable performance management system, which helps to motivate employees to actively engage in work, promote the cooperation atmosphere within the enterprise, and thus improve the overall competitiveness of the enterprise.

[0124] Figure 2 The accompanying drawing is a schematic structural diagram of a performance score management system for enterprise employees provided by an embodiment of the present application, as Figure 2 shown. The system includes: A composition module 21 that establishes a target key result tree structure from the enterprise strategic layer to the execution layer, and dynamically associates the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and a cross-departmental collaboration coefficient; An acquisition module 22 that collects the voice active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, generates collaboration participation data in combination with the sound source localization function, and the collaboration participation data is mapped to the task nodes in the target key result tree structure through timestamps; A trigger module 23 that inputs the contribution degree of key results and the collaboration participation data into the alliance chain smart contract, and writes the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger by using an event trigger mechanism, and the event trigger mechanism includes a zero-knowledge proof protocol; A construction module 24 that constructs a traceable system including a cross-cycle contribution attenuation coefficient and a collaboration network density based on the contribution trajectory in the blockchain ledger, and generates a performance score by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient.

[0125] Figure 2 The described performance score management system for enterprise employees can executeFigure 1 A performance score management method for enterprise employees described in the illustrated embodiment, the implementation principle and technical effects of which will not be elaborated further. For a performance score management system for enterprise employees in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to this method, and will not be elaborated herein.

[0126] In a possible design, Figure 2 A performance score management method for enterprise employees in the illustrated embodiment can be implemented as a computing device, such as Figure 3 shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0127] The processing component 32 is used for the Figure 1 A performance score management method for enterprise employees in the above

[0128] embodiment. Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0129] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0130] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0131] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0132] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0133] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0134] The embodiments of the present application also provide a computer storage medium storing a computer program, which when executed by a computer can implement the above-mentioned Figure 1 performance score management method for enterprise employees shown in the embodiments.

[0135] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0136] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0137] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A performance score management method for enterprise employees, characterized in that, Including: Establish a target key result tree structure from the enterprise strategic layer to the execution layer, and dynamically associate the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results consists of a time weight coefficient and an inter-departmental collaboration coefficient; Collect the speech active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, and generate collaboration participation data in combination with the sound source localization function. The collaboration participation data is mapped to the task nodes in the target key result tree structure through timestamps; Input the contribution degree of key results and the collaboration participation data into the consortium chain smart contract, and use the event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger. The event trigger mechanism includes a zero-knowledge proof protocol; Based on the contribution track in the blockchain ledger, construct a traceable system including a cross-cycle contribution attenuation coefficient and a collaboration network density, and generate a performance score by dynamically adjusting the time weight coefficient and the inter-departmental collaboration coefficient.

2. The method according to claim 1, wherein The step of using the event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger includes: Extract the time weight coefficient in the contribution degree of key results and the time sequence mark in the collaboration participation data according to the timestamp threshold, and generate a task event identifier including an inter-departmental task dependency relationship matrix; Perform hierarchical mapping on the contribution degree of key results and the collaboration participation data based on the task event identifier, and generate a tree-shaped data block including the time weight coefficient, the inter-departmental collaboration coefficient, and the semantic association degree; Verify the integrity of the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block through the zero-knowledge proof protocol, generate a data verification proof including the hierarchical mapping relationship, and broadcast the data verification proof bound with the task event identifier to the consortium chain nodes; According to the consensus result of the consortium chain nodes, write the verified tree-shaped data block into the blockchain ledger in timestamp order. The blockchain ledger stores the hierarchical mapping relationship, the cross-cycle contribution attenuation coefficient, and the task event identifier.

3. The method according to claim 2, characterized in that, The step of verifying the integrity of the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block through the zero-knowledge proof protocol and generating a data verification proof including the hierarchical mapping relationship includes: Perform structural encoding on the inter-departmental collaboration coefficient and the semantic association degree in the tree-shaped data block to generate a multi-dimensional polynomial representation, and generate a first commitment parameter based on the coefficient vector of the multi-dimensional polynomial representation; Extract the parent-child relationship identifiers of each node in the tree-shaped data block according to the topological order of the hierarchical mapping relationship, construct a set of constraint conditions associated with the inter-departmental collaboration coefficient, and generate a second commitment parameter according to the boundary values in the set of constraint conditions; Input the first commitment parameter and the second commitment parameter into a predefined challenge generator to generate a challenge factor sequence, and perform a linear combination operation on the inter-departmental collaboration coefficient and the semantic association degree to generate a set of response factors; Based on the response factor set and the hierarchical mapping relationship, construct a proof object including polynomial constraint relationships and the parent-child relationship identifiers, and embed the hash digest of the hierarchical mapping relationship to generate a data verification proof.

4. The method according to claim 3, wherein The linear combination operation on the cross-department collaboration coefficient and the semantic association degree to generate a response factor set includes: Extract the parent-child relationship identifiers, components of the cross-department collaboration coefficient, and components of the semantic association degree of each node in the tree-shaped data block according to the topological order of the hierarchical mapping relationship; Based on the index order of the challenge factor sequence, generate a polynomial weight factor associated with the time weight coefficient and the task dependency matrix elements for the node, and perform a dot product operation on the components of the cross-department collaboration coefficient and the components of the semantic association degree to generate intermediate linear parameters; According to the hierarchical depth of the parent-child relationship identifiers, superimpose a dynamic offset on the intermediate linear parameters, and the dynamic offset is generated by iterating the hash chain based on the parent-child relationship identifiers and the task dependency matrix elements; Aggregate the intermediate linear parameters and the dynamic offset of the same level according to the time sequence markers to generate cross-verification parameters, and construct a response factor sequence according to the aggregation order; Based on the topological path of the tree-shaped data block, reverse-aggregate the response factor sequence according to the hierarchical depth to generate a response factor set.

5. The method according to claim 1, characterized in that, Based on the contribution trace in the blockchain ledger, construct a traceable system including cross-cycle contribution attenuation coefficient and collaboration network density, including: Extract the historical data of the time weight coefficient, cross-department collaboration coefficient, and semantic association degree from the blockchain ledger, and divide the cross-cycle task sequence according to the time window; Based on the time stamp interval of the time window and the attenuation rate of the time weight coefficient, calculate the contribution attenuation ratio of the cross-department collaboration coefficient in consecutive time windows to generate a cross-cycle contribution attenuation coefficient; Based on the node connection relationship of the cross-department task dependency matrix in the cross-cycle task sequence and the cumulative weight of the semantic association degree, construct a collaboration network structure, and generate a collaboration network density by superimposing the node connection relationship and the semantic association degree; Input the cross-cycle contribution attenuation coefficient and the collaboration network density into the consortium chain smart contract to construct a traceable system including the cross-cycle contribution attenuation coefficient and the collaboration network density.

6. The method according to claim 5, wherein Based on the node connection relationship of the cross-department task dependency matrix in the cross-cycle task sequence and the cumulative weight of the semantic association degree, construct a collaboration network structure, including: Decompose the cross-department task dependency matrix into a set of node connection pairs in each time window of the cross-cycle task sequence, and extract the time sequence markers of each node connection pair and the corresponding semantic association degree components; According to the interval of the time sequence markers and the cross-cycle contribution attenuation coefficient, calculate the time attenuation weight of the node connection pair, and perform weighted accumulation with the semantic association degree components to generate a dynamic cumulative weight; Based on the set of node connection pairs, extract the sub-node clusters sharing the same parent node, and generate the collaboration strength parameters between the sub-node clusters according to the dynamic weights of the sub-node clusters and the historical data of the cross-department collaboration coefficient. Construct a two-way connection relationship for each sub-node cluster according to the collaboration intensity parameter and the task dependence direction in the cross-departmental task dependence matrix, and generate a collaboration network sub-graph based on the weight distribution of the two-way connection relationship; Superimpose the collaboration network sub-graph and the dynamic cumulative weight across cycles in the order of time windows, merge the connection edge weights between the same sub-node clusters, and construct a collaboration network structure.

7. The method according to claim 1, characterized in that The generating of the performance score by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient includes: Weightedly aggregate the time weight coefficient and the cross-departmental collaboration coefficient according to the job responsibility weight to generate an initial performance component, and perform cross-node coupling calibration based on the semantic association degree of adjacent nodes in the collaboration network; Along the hierarchical topological path of the tree structure, cumulatively add the calibrated initial performance components of the task nodes at the same level in chronological order to generate a performance score including cross-cycle decay compensation and collaboration gain.

8. A performance score management system for enterprise employees, characterized in that, Including: A composition module that establishes a tree structure of objective key results from the enterprise strategic layer to the execution layer, and dynamically associates the job responsibilities of enterprise employees with the contribution degree of key results, where the contribution degree of key results is composed of a time weight coefficient and a cross-departmental collaboration coefficient; An acquisition module that collects the voice active duration and interaction frequency of the enterprise employees through the directional voiceprint recognition function in the multi-modal environment perception sensor array, generates collaboration participation data in combination with the sound source localization function, and the collaboration participation data is mapped to the task nodes in the objective key results tree structure through time stamps; A trigger module that inputs the contribution degree of key results and the collaboration participation data into the consortium chain smart contract, and uses an event trigger mechanism to write the associated data of the contribution degree of key results and the collaboration participation data into the blockchain ledger, and the event trigger mechanism includes a zero-knowledge proof protocol; A construction module that constructs a traceable system including a cross-cycle contribution decay coefficient and a collaboration network density based on the contribution track in the blockchain ledger, and generates a performance score by dynamically adjusting the time weight coefficient and the cross-departmental collaboration coefficient.

9. A computing device, characterized in that, Including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for managing the performance score of enterprise employees as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, Stores a computer program, and when the computer program is executed by the computer, it implements a method for managing the performance score of enterprise employees as described in any one of claims 1 to 7.

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