Multi-dimensional hierarchical work assessment management method and system

Abstract

The invention discloses a multi-dimensional hierarchical work assessment management method and system, and relates to the field of enterprise management, and the method comprises the steps: obtaining an organizational structure tree diagram, and converting the membership of a strategic decision-making layer, a department management layer and a basic execution layer into a hierarchical database; the method comprises the following steps: extracting an annual strategic target based on a hierarchical database, disassembling the annual strategic target into a quarterly OKR of a department management layer, then dividing the quarterly OKR into a monthly KPI of a basic execution layer, forming a three-level index mapping table, carrying out weight distribution on the three-level index mapping table by adopting an AHP analytic hierarchy process, and generating a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor. According to the method, an organization structure tree diagram is converted into a hierarchical database, a three-level index mapping table is constructed, an AHP analytic hierarchy process is adopted for weight distribution, and a strategic decision-making layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor are generated; and further constructing a three-body entangled state through quantum bit coding to realize cross-level collaborative adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of enterprise management, and in particular to a multi-dimensional hierarchical work assessment management method and system. BACKGROUND

[0002] In modern enterprise management, the design and implementation of performance assessment system directly affect the efficiency of achieving organizational strategic goals. Traditional work assessment methods mainly adopt a hierarchical decomposition mode based on balanced scorecard or key performance indicators, which breaks down enterprise strategic goals into department-level tasks and further allocates them to individual job responsibilities, forming a three-level target management system. This technical solution calculates the weight of each level by linear weighting and uses a regular manual review mechanism to ensure the rationality of the indicators. Such methods have formed standardized processes in terms of goal alignment and data traceability, and can meet basic management needs. However, as the size of the enterprise expands and the complexity of the business increases, traditional methods face significant challenges in dynamic adaptability. When the organizational structure is adjusted or the strategic direction is changed, existing systems have difficulty in quickly responding and recalibrating cross-level indicator correlations, resulting in deviations between assessment results and actual situations.

[0003] The existing technical system has optimization space in realizing cross-level coordination, especially in handling the nonlinear correlation between the strategic layer, department layer, and execution layer. The traditional linear weight distribution method cannot accurately quantify the conduction efficiency of cross-level influence. When a department needs to temporarily adjust OKR due to external environmental changes, the existing system cannot automatically trigger the synchronous modification of associated strategic goals and individual KPIs, and still relies on manual intervention. This limitation is essentially due to the insufficient modeling of the coupling relationship between levels in traditional methods, which fails to establish a dynamic weight feedback mechanism. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a multi-dimensional hierarchical work assessment management method to solve the problems of insufficient cross-level dynamic coordination and difficult multi-source data fusion in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a multi-dimensional hierarchical work assessment management method, which comprises,

[0008] Obtaining an organizational structure tree diagram, converting the membership relationships of the strategic decision layer, department management layer, and basic execution layer into a hierarchical database;

[0009] The annual strategic target is extracted based on the hierarchical database, is disassembled into quarterly OKR of the department management layer, is further disassembled into monthly KPI of the basic execution layer, a three-level index mapping table is formed, the three-level index mapping table is subjected to weight distribution by using the AHP hierarchical analysis method, a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor are generated;

[0010] The strategic decision layer weight vector, the department management layer weight matrix and the basic execution layer weight tensor are coded into quantum bits, a three-body entangled state of the strategic decision layer index, the department management layer index and the basic execution layer index is constructed, cross-level collaborative adjustment is triggered through quantum measurement, and adjusted weight data are obtained;

[0011] According to the adjusted weight data, multi-source evaluation data of the strategic decision layer, the department management layer and the basic execution layer are collected from an ERP module, a CRM module and a code repository and subjected to normalization processing, and a standardized data table is generated;

[0012] The topological insulator algorithm is implanted in the standardized data table, and pure data flow is obtained;

[0013] The pure data flow is input into a hierarchical evaluation model, DEA data envelopment analysis is used for the strategic decision layer, entropy-weight-TOPSIS algorithm is used for the department management layer, and XGBoost evaluation is used for the basic execution layer, and an evaluation score table with hierarchical identification is generated.

[0014] As a preferred scheme of the multi-dimensional hierarchical work evaluation management method, the organizational structure tree diagram is obtained, the membership relationship of the strategic decision layer, the department management layer and the basic execution layer is converted into a hierarchical database, including the following steps,

[0015] The organizational structure tree diagram is exported through an enterprise OA module, the hierarchical depth of the organizational structure tree diagram is analyzed, and the membership relationship of the strategic decision layer, the department management layer and the basic execution layer is obtained;

[0016] The hierarchical correlation strength is quantified by using a dynamic weight adjacency matrix algorithm, and the strategic decision layer, the department management layer and the basic execution layer are converted into a hierarchical database by using a Cypher statement of a Neo4j graph database.

[0017] As a preferred scheme of the multi-dimensional hierarchical work evaluation management method, the annual strategic target is extracted based on the hierarchical database, is disassembled into quarterly OKR of the department management layer, is further disassembled into monthly KPI of the basic execution layer, a three-level index mapping table is formed, the three-level index mapping table is subjected to weight distribution by using the AHP hierarchical analysis method, a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor are generated, including the following steps,

[0018] Query the annual target of the strategic decision layer node from the hierarchical database to obtain an original strategic target, use a regular expression to extract a quantitative index from the original strategic target to obtain a structured index;

[0019] According to the actual completion rate data of the historical strategic indicators of the CRM module, the historical completion rate of each department to each item in the structured index is calculated to obtain a department capability score;

[0020] The structured index OKR is disassembled to obtain a department OKR list;

[0021] According to the target value of the department OKR list and the weight ratio of the employee contribution data, a personal KPI list is calculated;

[0022] The structured index, the department OKR list and the personal KPI list are associated to obtain a three-level index mapping table;

[0023] A quantum state comparison matrix is generated based on the three-level index mapping table, a phase estimation algorithm is performed on the quantum comparison matrix on an IBM quantum processor to obtain quantum state eigenvalues;

[0024] The quantum state eigenvalues are converted into a strategic decision layer weight vector, a department layer weight matrix and an execution layer weight tensor.

[0025] As a preferred scheme of the multi-dimensional hierarchical work assessment management method, the strategic decision layer weight vector, the department management layer weight matrix and the basic execution layer weight tensor are encoded into quantum bits, a three-body entangled state of the strategic decision layer index, the department management layer index and the basic execution layer index is constructed, cross-level collaborative adjustment is triggered through quantum measurement to obtain adjusted weight data, including the following steps,

[0026] The strategic decision layer weight vector is encoded into the amplitude state of one quantum bit to obtain a quantum state, the department management layer weight matrix is loaded onto two quantum bits through a controlled rotation gate to obtain an entangled state, and the basic execution layer weight tensor is loaded onto three quantum bits through a multi-control gate as a three-level conditional probability to obtain a three-body entangled state;

[0027] Based on the three-body entangled state, a reduced density matrix is obtained to verify the entanglement entropy, a verification flag bit is obtained, and the three-body entangled state is measured to obtain a measurement result statistical table;

[0028] According to the measurement result statistical table, a strategic-department covariance and a department-execution covariance are obtained to obtain a covariance matrix;

[0029] Based on the median in the covariance matrix, a cross-level management correlation strength critical value is obtained;

[0030] When the strategy-department covariance is greater than the cross-level management correlation strength critical value, cross-level coordinated adjustment is triggered, and the adjusted weight data is obtained.

[0031] As a preferred scheme of the multi-dimensional hierarchical work assessment management method, wherein: according to the adjusted weight data, multi-source assessment data of the strategic decision layer, the department management layer and the basic execution layer are collected from the ERP module, the CRM module and the code repository and normalized to generate a standardized data table, including the following steps,

[0032] According to the adjusted weight data, a structured dictionary is obtained, and for the strategic indicator list in the structured dictionary, the market share performance data is obtained using the performance indicator acquisition interface of the ERP module, and the strategic decision layer assessment data is obtained.

[0033] Using the department ID list in the structured dictionary, the quarterly targets and achievement completion rates of each department are queried through the CRM module interface to obtain the department management layer assessment data.

[0034] Based on the execution unit mapping table in the weight dictionary, the code contribution density of each execution unit is formed by scanning the code version control unit submission log to obtain the basic execution layer assessment data.

[0035] The strategic decision layer assessment data, the department management layer assessment data and the basic execution layer assessment data are normalized to generate a standardized data table.

[0036] As a preferred scheme of the multi-dimensional hierarchical work assessment management method, wherein: a topological insulator algorithm is implanted in the standardized data table to obtain a pure data stream, including the following steps,

[0037] A hierarchical label is added to the standardized data table to obtain a labeled graph structure.

[0038] The invariant of the boundary edge connecting different levels is calculated on the labeled graph structure to obtain an abnormal edge list.

[0039] The abnormal edge is obtained by analyzing the three-level indicator mapping table, the abnormal edge is removed according to the abnormal edge list, a purified adjacency matrix is obtained and spectral clustering is performed to obtain a three-level node ID list.

[0040] The three-level node ID list is screened to obtain a pure data stream.

[0041] As a preferred scheme of the multi-dimensional hierarchical work assessment management method, the pure data stream is input into the hierarchical evaluation model, DEA data envelope analysis is used for the strategic decision layer, entropy weight-TOPSIS algorithm is used for the department management layer, and XGBoost evaluation is used for the basic execution layer, and a hierarchical identification score table is generated, including the following steps,

[0042] Based on the pure data stream, the hierarchical label field is split into strategic decision layer data, department layer data and execution layer data, the resource input quantity and performance index achievement value of each strategic indicator are extracted from the strategic decision layer data, a decision unit matrix is constructed, and the strategic decision layer DEA is obtained;

[0043] The linear programming problem of the strategic decision layer DEA is solved on the quantum processor, the relative efficiency value of the strategic indicator is formed, and the strategic efficiency score is obtained;

[0044] Based on the department layer data, the information entropy and dynamic weight are obtained, and the entropy weight matrix and department weight matrix are formed;

[0045] Based on the department weight matrix, weighted standardization processing is performed, the distance between the positive ideal solution and the negative ideal solution is obtained, and the department ranking is generated;

[0046] The average submission quantity and the centrality of the code submission network are extracted from the code submission record and the task response time original data in the execution layer data, the feature matrix execution layer matrix is obtained, the employee performance sorting model is trained using the feature matrix execution layer matrix, and the XGBoost model is obtained;

[0047] Based on the XGBoost model, the performance scores of all employees are predicted, and the execution layer performance score is obtained;

[0048] The strategic efficiency score, the department ranking and the execution layer performance score are fused, and the hierarchical identification score table is obtained.

[0049] In the second aspect, the present application provides a multi-dimensional hierarchical work assessment management system, comprising a data acquisition module, which acquires an organizational structure tree diagram and converts the affiliation of the strategic decision layer, the department management layer and the basic execution layer into a hierarchical database;

[0050] The disassembly module extracts annual strategic objectives based on the hierarchical database, disassembles them into quarterly OKRs of the department management layer, and further divides them into monthly KPIs of the basic execution layer, forms a three-level index mapping table, and distributes weights to the three-level index mapping table using AHP hierarchical analysis method, to generate a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor;

[0051] A weight distribution module encodes the strategic decision layer weight vector, the department management layer weight matrix and the basic execution layer weight tensor into quantum bits, constructs a three-body entangled state of the strategic decision layer index, the department management layer index and the basic execution layer index, triggers cross-level collaborative adjustment through quantum measurement, and obtains adjusted weight data;

[0052] A cross-level module collects multi-source evaluation data of the strategic decision layer, the department management layer and the basic execution layer from the ERP module, the CRM module and the code repository according to the adjusted weight data, and performs normalization processing to generate a standardized data table;

[0053] A data cleaning module implants a topological insulator algorithm in the standardized data table to obtain a pure data stream;

[0054] A hierarchical evaluation module inputs the pure data stream into a hierarchical evaluation model, adopts DEA data envelopment analysis for the strategic decision layer, adopts entropy-weighted TOPSIS algorithm for the department management layer, and adopts XGBoost evaluation for the basic execution layer to generate an evaluation score table with layer identification.

[0055] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program is executed by the processor to implement any step of the multi-dimensional hierarchical work evaluation management method according to the first aspect of the present application.

[0056] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement any step of the multi-dimensional hierarchical work evaluation management method according to the first aspect of the present application.

[0057] The present application has the following beneficial effects: by converting the organizational structure tree graph into a hierarchical database and constructing a three-level index mapping table, using AHP hierarchical analysis method for weight distribution, generating strategic decision layer weight vector, department management layer weight matrix and basic execution layer weight tensor, further constructing three-body entangled state through quantum bit coding to realize cross-level collaborative adjustment, and collecting evaluation data from multi-source systems based on the adjusted weight data, and inputting the purified data into the hierarchical evaluation model based on the topological insulator algorithm, finally generating an evaluation score table with layer identification, the method innovatively applies quantum computing and topological principles to the field of performance evaluation, and solves the technical problems of traditional methods in cross-level dynamic collaboration and multi-source data fusion. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0059] Fig. 1 Flow chart of the multi-dimensional hierarchical work assessment management method.

[0060] Fig. 2 Schematic diagram of the file encryption system.

[0061] Fig. 3 Schematic diagram of the annual strategic target disintegration.

[0062] Fig. 4 Schematic diagram of obtaining the organizational structure tree diagram and converting it into a hierarchical database. DETAILED DESCRIPTION

[0063] In order to make the above objectives, features and advantages of the present application more apparent and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0064] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other ways not described herein, and the skilled in the art can make similar extensions without departing from the spirit of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0065] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0066] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides a multi-dimensional hierarchical work assessment management method, comprising the following steps:

[0067] S1, obtain an organizational structure tree diagram, and convert the membership relationships of the strategic decision-making layer, the department management layer and the basic execution layer into a hierarchical database.

[0068] S1.1, export the organizational structure tree diagram through the enterprise OA module, parse the hierarchical depth of the organizational structure tree diagram, and obtain the membership relationships of the strategic decision-making layer, the department management layer and the basic execution layer.

[0069] Further, the organizational structure tree diagram data including node ID, parent node ID and node type is obtained from the enterprise OA interface, the tree diagram is traversed using the breadth-first search algorithm, the strategic decision layer nodes, department management layer nodes and basic execution layer nodes are marked according to the node type field, the path length from the root node to each leaf node is counted, the nodes with the path length equal to 1 are classified as the strategic decision layer, the nodes with the path length equal to 2 are classified as the department management layer, and the nodes with the path length greater than or equal to 3 are classified as the basic execution layer, and a hierarchical relationship table including node ID, level label and affiliation is output, in which the strategic decision layer nodes establish a management relationship with the directly subordinate department management layer nodes, the department management layer nodes establish an execution relationship with the subordinate basic execution layer nodes, and a complete three-level affiliation relationship of strategic decision layer-department management layer-basic execution layer is formed.

[0070] S1.2, the hierarchical correlation strength is quantified by a dynamic weight adjacency matrix algorithm, and the strategic decision layer, the department management layer and the basic execution layer are converted into a hierarchical database by combining a Cypher statement of a Neo4j graph database.

[0071] Specifically, the expression is,

[0072]

[0073] wherein W ij is the hierarchical correlation weight between the current node i and the target node j, β is a hierarchical attenuation coefficient, d ij is the management span between the current node i and the target node j, i is the current node, and j is the target node.

[0074] S2, annual strategic targets are extracted based on the hierarchical database, are decomposed into quarterly OKRs of the department management layer, and are further divided into monthly KPIs of the basic execution layer, so as to form a three-level index mapping table, and the three-level index mapping table is subjected to weight distribution by using an AHP hierarchical analysis method, so as to generate a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor.

[0075] S2.1, the annual target of the strategic decision layer node is queried from the hierarchical database, so as to obtain an original strategic target, and a regular expression is used to extract a quantitative index from the original strategic target, so as to obtain a structured index.

[0076] Further, a Cypher query statement is executed from the hierarchical database to retrieve the annual target text recorded by the strategic decision layer node, so as to obtain an original strategic target set including natural language description, a regular expression pattern matching percentage value and a key index name are constructed using a Python re module, a market share value is matched, quantitative index pairs are extracted from the original strategic target text, and a structured index is generated.

[0077] S2.2, according to the actual completion rate data of the historical strategic indicators of the CRM module, the historical completion rate of each department to each item in the structured indicators is calculated to obtain the department capability score.

[0078] Specifically, the expression is,

[0079]

[0080] Wherein, R k is the historical completion rate score of the target department k to the current strategic indicator, T is the total number of statistical periods, t is the statistical period index, A kt is the actual completion value of the target department k in the tth period, G kt is the target value of the target department k in the tth period, λ is the time decay coefficient, and k is the target department.

[0081] S2.3, the structured indicator OKR is disassembled to obtain the department OKR list.

[0082] Specifically, the expression is,

[0083]

[0084] Wherein, OKR k is the final OKR value of the target department k, Sv is the target value of the strategic indicator v, n is the total number of departments participating in the allocation, α is the historical performance adjustment coefficient, R k is the historical completion rate score of the target department k, R m is the total sum of the historical scores of the departments, v is the strategic indicator, and m is the department index;

[0085] S2.4, according to the target value of the department OKR list and the weight ratio of the employee contribution data, the personal KPI list is calculated.

[0086] Specifically, the expression is,

[0087]

[0088] Wherein, KPI h is the KPI target value of the employee h, F h is the job level weight of the employee h, C h is the historical contribution rate of the employee h, P is the total number of target departments, p is the total number of target department indexes, C p is the personal contribution value.

[0089] S2.5, the structured indicators, department OKR list and personal KPI list are associated to obtain a three-level indicator mapping table.

[0090] Further, the strategic target entries in the structured index are associated and matched with the department target entries in the department OKR list through the department ID field, establishing a one-to-many mapping relationship from the strategic decision layer to the department management layer, each department target entry in the department OKR list is associated and matched with the employee performance indicators in the personal KPI list through the employee ID field, establishing a one-to-many mapping relationship from the department management layer to the basic execution layer, using the pandas library of Python to vertically merge the associated data of the three levels, generating a three-level index mapping table containing strategic decision layer index name, department management layer OKR description, and basic execution layer KPI value.

[0091] S2.6, generate a quantum state comparison matrix based on the three-level index mapping table, perform a phase estimation algorithm on the quantum comparison matrix on an IBM quantum processor to obtain quantum state eigenvalues.

[0092] Specifically, the expression is,

[0093]

[0094] Wherein, A is a quantum state comparison matrix, is a set of strategic decision layer indicators, is a set of department layer indicators, and ε is a set of execution layer indicators, is a three-level correlation weight, max(Y) is a weight normalization factor, ι is the index of the strategic decision layer indicator, is the index of the department management layer indicator, is the index of the basic execution layer indicator.

[0095] Specifically, the expression is,

[0096]

[0097] Wherein, U(A) is a linear transformation of the quantum state comparison matrix A, B is the number of quantum state eigenvalues, b is the quantum state eigenvalue index, η b is the quantum state eigenstate probability amplitude, θ b is the characteristic phase of the quantum state of the bth quantum state eigenvalue, and θ is the characteristic phase of the quantum state.

[0098] S2.7, convert the quantum state eigenvalues into a strategic decision layer weight vector, a department layer weight matrix, and an execution layer weight tensor.

[0099] Further, the real part of the quantum state eigenvalue output by the quantum processor is extracted to obtain a feature vector set containing phase information. According to the pre-defined hierarchical index rule in the quantum state comparison matrix, the feature vector set is divided into three groups of sub-vectors, strategic decision layer feature sub-vector, department layer feature sub-vector, and execution layer feature sub-vector. The Gram-Schmidt orthogonalization method is used to normalize each group of sub-vectors to ensure that the sum of the weights of each level is 1. The normalized strategic decision layer feature sub-vector is converted into a weight vector, the department layer feature sub-vector is rearranged into a two-dimensional weight matrix according to the department ID, and the execution layer feature sub-vector is constructed into a three-dimensional weight tensor according to the employee ID and task ID. During the conversion process, double-precision floating-point numbers are used to retain 15 significant digits to avoid the accumulation of measurement errors inherent in quantum computing. The final output strategic decision layer weight vector, department layer weight matrix, and execution layer weight tensor are stored in HDF5 format, preserving the complete quantum state eigenvalue mapping relationship.

[0100] S3, encode the strategic decision layer weight vector, department management layer weight matrix, and basic execution layer weight tensor into quantum bits to construct a three-body entangled state of strategic decision layer indicators, department management layer indicators, and basic execution layer indicators. Through quantum measurement, trigger cross-level collaborative adjustment to obtain adjusted weight data.

[0101] S3.1, encode the strategic decision layer weight vector into the amplitude state of a quantum bit to obtain a quantum state. Load the department management layer weight matrix through a controlled rotation gate to two quantum bits to obtain an entangled state. Use the basic execution layer weight tensor as a three-level conditional probability and load it through a multi-control gate to three quantum bits to obtain a three-body entangled state.

[0102] Further, the strategic decision layer weight vector is mapped to the state vector of a single quantum bit through quantum amplitude encoding technology to generate a strategic decision layer quantum state. Each ground state probability amplitude corresponds to a component of the original weight vector. The singular value decomposition is performed on the department management layer weight matrix, and the left singular vector is used as a control signal to act on two quantum bits through a controlled rotation gate operation to realize the preparation of the department management layer entangled state. The joint state probability distribution of the two quantum bits matches the row-column correlation of the weight matrix. After the basic execution layer weight tensor is subjected to a tensor expansion operation, a three-quantum bit multi-control gate circuit is used to realize conditional phase rotation to construct a three-body entangled state network between the strategic decision layer quantum state, the department management layer entangled state, and the basic execution layer quantum state. The quantum circuit design is realized using the IBM Qiskit framework. The angle parameter of the controlled rotation gate is determined by the singular value of the weight matrix, and the phase parameter of the multi-control gate is obtained by calculating the modulus value of the weight tensor. A three-body entangled state is obtained.

[0103] S3.2, based on the three-body entangled state, the reduced density matrix is obtained to verify the entanglement entropy, and the verification flag is obtained. The three-body entangled state is measured, and the measurement result statistics table is obtained.

[0104] Further, the density operator of the reduced density matrix is reconstructed by quantum state tomography technology, the von Neumann entropy is used to obtain the entanglement entropy of each subsystem, the department management layer and the basic execution layer, and the verification flag is obtained based on the entanglement entropy. The three-body entangled state that passes the verification is projected and measured under the basis, and the quantum measurement experiment is repeated 1000 times. The joint measurement results of the strategic decision layer quantum state, the department management layer entangled state and the basic execution layer quantum state are counted, and a measurement result statistics table containing the frequency of each state is generated. Quantum error mitigation techniques are used to correct the decoherence effect during measurement. The measurement result statistics table records the ratio of the number of each basis state measurements to the total number of measurements.

[0105] S3.3, according to the measurement result statistics table, the strategic-department covariance and department-execution covariance are obtained, and the covariance matrix is obtained.

[0106] Further, the joint probability distribution analysis is performed on the measurement values of the strategic decision layer quantum state, the department management layer entangled state and the basic execution layer quantum state in the measurement result statistics table. The covariance of the measurement values of the strategic decision layer quantum state and the department management layer entangled state is obtained, the strategic-department covariance is obtained, the covariance of the measurement values of the department management layer entangled state and the basic execution layer quantum state is formed, and the department-execution covariance is obtained. The strategic-department covariance and the department-execution covariance are arranged in order according to the hierarchy to construct a 2x2 symmetric covariance matrix. The diagonal elements of the matrix are the variances of the measurement values of each level, and the non-diagonal elements are the cross-level covariances. The construction process of the covariance matrix is realized by using Python NumPy library to ensure that the numerical calculation accuracy reaches double floating point standard. Covariance matrix.

[0107] S3.4, based on the median in the covariance matrix, the cross-level management correlation strength threshold is obtained.

[0108] Further, the non-diagonal elements (strategic-department covariance and department-execution covariance) of the covariance matrix are sorted, and the median of the sorted numerical sequence is extracted as the cross-level management correlation strength threshold. The two covariance values are arranged in ascending order to generate an ordered sequence. When the sequence length is even, the average of the middle two numbers is taken, and when the sequence length is odd, the middle value is directly taken. The cross-level management correlation strength threshold is used for subsequent judgment of whether to trigger weight adjustment. When any covariance value is lower than the threshold, it is determined that the cross-level correlation is insufficient.

[0109] S3.5, when the strategic-department covariance is greater than the cross-level management correlation strength threshold, the cross-level collaborative adjustment is triggered, and the adjusted weight data is obtained.

[0110] Further, the adjustment coefficient is calculated according to the difference ratio of the strategy-department covariance and the critical value, the adjustment coefficient calculation formula is (covariance-critical value) / critical value, the adjustment coefficient is used to simultaneously scale the strategic decision-making layer weight vector and the department management layer weight matrix, the scaling ratio of the strategic decision-making layer weight vector is 1+0.5 adjustment coefficient, the scaling ratio of the department management layer weight matrix is 1+0.3 adjustment coefficient, the base execution layer weight tensor is not directly adjusted, but is indirectly affected by the change of the department management layer weight matrix, and the adjusted strategic decision-making layer weight vector and department management layer weight matrix need to be normalized to ensure that the sum of the weights of each level is 1, and the normalized weight data and the original base execution layer weight tensor together constitute the adjusted weight data set.

[0111] S4、According to the adjusted weight data, the multi-source evaluation data of the strategic decision-making layer, the department management layer and the base execution layer are collected from the ERP module, the CRM module and the code repository and normalized to generate a standardized data table.

[0112] S4.1、According to the adjusted weight data, the structured dictionary is obtained, for the strategic indicator list in the structured dictionary, the performance data of the market share is obtained using the performance indicator acquisition interface of the ERP module, and the evaluation data of the strategic decision-making layer is obtained.

[0113] Further, by calling the BAPI_KPI_GETLIST interface provided by the ERP module, the strategic indicator name list in the structured dictionary is input as a query parameter to obtain the market share performance data of each strategic indicator, the performance data returned by the interface is matched and associated with the strategic decision-making layer node ID in the structured dictionary to form a strategic decision-making layer evaluation data set containing node ID, indicator name and market share value. During the data matching process, integrity check is implemented, when any strategic indicator is found without returning performance data, default value is automatically filled and abnormal state is marked, the strategic decision-making layer evaluation data set is stored in JSON format, the original timestamp and value unit information returned by the ERP interface are retained to ensure that the data can be correctly parsed during subsequent normalization processing, the performance data acquisition process records detailed request parameters and response logs, which are used to trace the data source and handle abnormal situations, and the finally generated strategic decision-making layer evaluation data set is used as an input component of the standardized data table.

[0114] S4.2、Using the department ID list in the structured dictionary, the quarterly targets and achievement completion rates of each department are queried through the CRM module interface to obtain the department management layer evaluation data.

[0115] Further, using the department ID list recorded in the structured dictionary as an input parameter, the Salesforce API interface SOQL query service provided by the CRM module is called, a query request containing the department ID, quarterly time range and target type fields is sent, and the API interface returns the target value list and actual completion value list of each department in the specified quarter, the quarterly target completion rate of each department is obtained, the department management layer assessment data set is generated, and each record in the data set contains five fields of department ID, target name, target value, actual value and completion rate, arranged in ascending order of department ID, and the department management layer assessment data is obtained.

[0116] S4.3, based on the execution unit mapping table in the weight dictionary, scanning the code version control unit submission log, forming the code contribution density of each execution unit, and obtaining the basic execution layer assessment data.

[0117] Further, based on the correspondence between the employee ID and the code repository account recorded in the execution unit mapping table in the weight dictionary, the Git command line tool is used to execute the log analysis command, and the code submission records in the specified time range are scanned. For the employee account of each execution unit, the number of code submissions, the number of new code lines and the number of deleted code lines are counted, and the code contribution density index is obtained. The employee ID, code submission times and code contribution density are associated to obtain the basic execution layer assessment data.

[0118] S4.4, the strategic decision layer assessment data, the department management layer assessment data and the basic execution layer assessment data are normalized to generate a standardized data table.

[0119] Further, the market share value in the strategic decision layer assessment data, the quarterly target completion rate in the department management layer assessment data and the code contribution density in the basic execution layer assessment data are converted into standard values in the interval [0, 1] by using the Min-Max normalization method respectively. The department management layer index normalization formula is (completion rate-minimal completion rate) / (maximum completion rate-minimal completion rate), and the basic execution layer index normalization formula is (contribution density-minimal density) / (maximum density-minimal density). The three types of normalized data are vertically merged according to the hierarchical relationship to generate a standardized data table containing three key fields of strategic decision layer node ID, department management layer department ID and basic execution layer employee ID.

[0120] S5, implanting topological insulator algorithm in the standardized data table to obtain pure data stream.

[0121] S5.1, adding hierarchical labels to the standardized data table to obtain a labeled graph structure.

[0122] Further, according to the hierarchical relationship recorded in the standardized data table, the strategic decision-making layer node and the directly subordinate department management layer node establish a management edge, the department management layer node and the subordinate basic execution layer node establish an execution edge, a labeled directed graph structure is constructed, the weight attribute of the edge comes from the normalized index value recorded in the standardized data table, the weight of the strategic decision-making layer-department management layer edge is the market share standard value, the weight of the department management layer-basic execution layer edge is the target completion rate standard value, the graph structure is stored by using the DiGraph class of the NetworkX library, the node attribute includes the node ID, the hierarchical label and the original index value, the edge attribute includes the weight value and the data source mark, the labeled graph structure is stored by using the JSON format serialization, and the complete topological connection relationship and attribute data are reserved.

[0123] S5.2, calculate the invariant of the boundary edge connecting different levels on the labeled graph structure to obtain an abnormal edge list.

[0124] Specifically, the expression is,

[0125]

[0126] Wherein, Z2 is the abnormal edge list, φ is the cross-level management correlation edge, is the parent level node, and Λ is the child level node, is the edge node weight.

[0127] S5.3, obtain the abnormal edge by analyzing the three-level index mapping table, remove the abnormal edge according to the abnormal edge list, obtain the purified adjacency matrix and perform spectral clustering to obtain a three-level node ID list.

[0128] Further, by analyzing the correlation between the indexes recorded in the three-level index mapping table, the edge with a weight value lower than 30% of the median of the same level edge weight is marked as an abnormal edge, an abnormal edge list is generated, according to the edge identifier in the abnormal edge list, the corresponding row and column elements in the adjacency matrix of the labeled graph structure are removed, a purified adjacency matrix is obtained, the normalized Laplacian matrix transformation is performed on the purified adjacency matrix, the first three eigenvectors are obtained as the spectral feature space, the K-means algorithm is used for clustering, the number of clusters is set to 3 corresponding to the three levels of strategic decision-making layer, department management layer and basic execution layer, and the node ID of each cluster in the clustering result constitutes a three-level node ID list. The node ID in the list is stored according to the original hierarchical label. In the spectral clustering process, the random initialization seed and the 10-time iteration convergence strategy are adopted to ensure the stability of the clustering result, and the three-level node ID list is obtained.

[0129] S5.4, screen the three-level node ID list to obtain a pure data stream.

[0130] Further, based on the node identifier recorded in the three-level node ID list, the complete data record of the corresponding node is extracted from the standardized data table, including the market share standard value of the strategic decision layer node, the target completion rate standard value of the department management layer node, and the code contribution density standard value of the basic execution layer node. The data records of the three types of nodes are reorganized according to the hierarchical relationship, with the strategic decision layer node data as the root node, the department management layer node data as the intermediate node, and the basic execution layer node data as the leaf node. A tree structure data flow is constructed, each node in the tree structure data flow contains four fields of node ID, hierarchical label, index value and parent node ID, and is stored in Protocol Buffers serialization format. During the data flow construction process, hierarchical consistency verification is implemented to ensure that each department management layer node has and only has one strategic decision layer parent node, and each basic execution layer node has and only has one department management layer parent node. Finally, a pure data flow is generated.

[0131] S6. Input the pure data flow into the hierarchical evaluation model, use DEA data envelopment analysis for the strategic decision layer, use entropy-TOPSIS algorithm for the department management layer, and use XGBoost evaluation for the basic execution layer to generate an evaluation score table with hierarchical identification.

[0132] S6.1, based on the pure data flow, the data is split into strategic decision layer data, department layer data and execution layer data according to the hierarchical label field. From the strategic decision layer data, the resource input and performance index achievement value of each strategic index are extracted, a decision unit matrix is constructed, and the strategic decision layer DEA is obtained.

[0133] Furthermore, based on the node data marked as "strategy" in the clean data stream, the resource input and performance indicator achievement fields are extracted for each strategic decision-making level node. Resource input includes three dimensions: capital input, human resource input, and technology input. Performance indicator achievement includes two dimensions: market share achievement and profit margin achievement. Each strategic decision-making level node is treated as a decision-making unit, with resource input as the input variable and performance indicator achievement as the output variable. A decision-making unit matrix is ​​constructed, where rows correspond to different strategic decision-making level nodes, columns correspond to input and output variables, and matrix element values ​​are standardized values ​​for each node. The number of rows and columns of the indicator value and the decision unit matrix are the sum of the number of nodes in the strategic decision layer and the number of input and output variables, respectively. Missing values ​​in the matrix are filled with the median of the same indicator. The completed decision unit matrix serves as the input data for the strategic decision layer DEA and is used for subsequent efficiency value calculation. The decision unit matrix is ​​stored in CSR sparse matrix format to optimize storage and computation efficiency under large-scale data. Data range verification is implemented during matrix construction to ensure that all input and output variable values ​​are within the range of [0,1]. The final generated strategic decision layer DEA input matrix contains a complete mapping relationship between node IDs and indicator values, and retains the hierarchical identification information in the original pure data stream.

[0134] S6.2 Solve the linear programming problem of the strategic decision layer DEA on a quantum processor to form the relative efficiency value of the strategic indicators and obtain the strategic efficiency score.

[0135] Furthermore, the linear programming problem of the strategic decision-making layer DEA is transformed into a quadratic unconstrained binary optimization form. Quantum annealing computation is performed on the D-Wave quantum processor. The quantum annealing process solves the objective function minimization problem. The objective function includes the sum of squares of input and output constraint terms and an efficiency maximization term. Hamiltonian encoding adopts a one-hot encoding strategy. The low-energy state solution returned by the quantum annealer is converted into an efficiency value through classical post-processing. The efficiency value θ∈[0,1] of each strategic decision-making layer node represents its relative efficiency level. The efficiency value calculation results are stored in association with the node ID to generate a list of strategic efficiency scores. The efficiency scores in the list retain 6 decimal places of precision. Decoherence time monitoring is implemented during the quantum computing process to obtain the strategic efficiency score.

[0136] S6.3. Based on departmental data, information entropy and dynamic weights are derived to form an entropy-weight matrix and a departmental weight matrix.

[0137] Specifically, the expression is,

[0138]

[0139] in, For the first Information entropy of the indicator is the proportion of the target department k on the index σ, χ is the number of departments, is the index index,

[0140]

[0141] wherein, is the weight of the first index.

[0142] S6.4, based on the department weight matrix, weighted standardization processing is carried out, the distance between the positive ideal solution and the negative ideal solution is obtained, and the department ranking is generated.

[0143] Further, the entropy weight matrix, the department weight matrix and the standardized value matrix of the department layer data are subjected to Hadamard product operation to obtain a weighted standardized decision matrix. In the weighted standardized decision matrix, the optimal value and the worst value of each index are determined as the positive ideal solution and the negative ideal solution. The positive ideal solution is composed of the maximum value of the benefit type index and the minimum value of the cost type index. The negative ideal solution is composed of the minimum value of the benefit type index and the maximum value of the cost type index. The Euclidean distance of each department to the positive ideal solution and the Euclidean distance to the negative ideal solution are obtained. The distance formula is the square root of the sum of squares. According to the relative closeness, the departments are sorted. The larger the closeness value, the better the comprehensive performance of the department. The department ranking result is arranged in descending order of closeness. A department management layer ranking list containing department ID, closeness value and ranking is generated, and the department ranking is generated.

[0144] S6.5, for the code submission record and task response time original data in the execution layer data, the average submission amount and the centrality of the code submission network are extracted to obtain a feature matrix execution layer matrix. The feature matrix execution layer matrix is used to train an employee performance sorting model to obtain an XGBoost model.

[0145] Further, the number of code submissions and task response time of each employee are extracted from the execution layer data, the average submission amount in a 30-day sliding window is obtained, the employee collaboration network graph is constructed based on the code version control log, the feature vector centrality of each employee node is calculated, the average submission amount and centrality are taken as the feature vector, and the feature matrix execution layer matrix is composed of the historical performance score of the employee, the XGBoost ranking model is trained using the feature matrix execution layer matrix, the target function is defined as the pairwise ranking loss, the relative ranking of employee performance is optimized, the XGBoost model training adopts the early stopping strategy, and the training is terminated when the NDCG index of the validation set does not improve for 3 consecutive rounds, the trained XGBoost model can predict the performance score of the new employee sample, and generate an execution layer performance score list containing employee ID, predicted score and ranking, the feature importance score is recorded during the model training process, the contribution of code contribution features and task response features is balanced, and finally the XGBoost model is generated as an execution layer evaluation tool, which together with the strategic efficiency score and department management layer ranking forms a hierarchical identification performance score table, and the XGBoost model is obtained.

[0146] S6.6, predicting the performance score of all employees based on the XGBoost model, and obtaining the execution layer performance score.

[0147] Further, the trained XGBoost model is loaded into the prediction environment, the average submission amount and code submission network centrality features of each employee in the feature matrix execution layer matrix are input, the XGBoost model outputs the original prediction score of each employee, the score is linearly mapped to the [0, 100] interval through Min-Max normalization, and the execution layer performance score is obtained. The execution layer performance score list contains three fields of employee ID, normalized score and percentile ranking, and is arranged in descending order of score. The score consistency check is implemented during the prediction process, and the review mechanism is automatically triggered when the difference between multiple prediction results of the same employee is more than 5 points. Finally, the generated execution layer performance score list is combined with the strategic efficiency score and department management layer ranking to form a complete hierarchical identification performance score table, and the complete mapping relationship between the model input features and output scores is recorded during the prediction process.

[0148] S6.7, the strategic efficiency score, department ranking and execution layer performance score are fused to obtain a hierarchical identification performance score table.

[0149] Further, the strategic decision layer node efficiency value in the strategic efficiency score list, the department closeness value in the department management layer ranking list, and the employee normalized score in the execution layer performance score list are matched according to the hierarchical relationship. The strategic decision layer node efficiency value and the closeness value of the directly subordinate department management layer node are weighted and summed. The weight uses the corresponding component in the quantum synergistically adjusted strategic decision layer weight vector. The comprehensive score of the department management layer node and the performance score of the subordinate grassroots execution layer employee are weighted and summed. The weight uses the corresponding value in the department management layer weight matrix. The fused score table contains three groups of identification fields of strategic decision layer node ID, department management layer department ID, and grassroots execution layer employee ID, as well as the original score, weighted score, and final examination score of each level. The score table is organized according to the tree structure of the strategic decision layer-department management layer-grassroots execution layer. The Parquet columnar storage format is used to retain the complete hierarchical association index. The score range verification is implemented in the score fusion process to ensure that the weighted examination score falls within the [0, 100] interval. The examination score table with hierarchical identification is obtained.

[0150] The embodiment also provides a multi-dimensional hierarchical work examination management system, comprising:

[0151] A data acquisition module acquires an organizational structure tree diagram and converts the membership relationship of the strategic decision layer, the department management layer, and the grassroots execution layer into a hierarchical database.

[0152] Based on the hierarchical database, annual strategic targets are extracted, decomposed into quarterly OKRs of the department management layer, and further divided into monthly KPIs of the grassroots execution layer to form a three-level index mapping table. The AHP hierarchical analysis method is used to distribute weights of the three-level index mapping table to generate a strategic decision layer weight vector, a department management layer weight matrix, and a grassroots execution layer weight tensor.

[0153] A weight distribution module encodes the strategic decision layer weight vector, the department management layer weight matrix, and the grassroots execution layer weight tensor into quantum bits, constructs a three-body entangled state of the strategic decision layer index, the department management layer index, and the grassroots execution layer index, triggers cross-level synergistic adjustment through quantum measurement, and obtains adjusted weight data.

[0154] A cross-level module collects multi-source examination data of the strategic decision layer, the department management layer, and the grassroots execution layer from an ERP module, a CRM module, and a code repository and performs normalization processing to generate a standardized data table according to the adjusted weight data.

[0155] A data cleaning module implants a topological insulator algorithm in the standardized data table to obtain a pure data stream.

[0156] The hierarchical evaluation module inputs the pure data stream into a hierarchical evaluation model, adopts DEA data envelope analysis for the strategic decision-making layer, adopts an entropy weight-TOPSIS algorithm for the department management layer, adopts XGBoost evaluation for the basic execution layer, and generates an evaluation score table with hierarchical identification.

[0157] The embodiment also provides a computer device suitable for the multi-dimensional hierarchical work evaluation management method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the multi-dimensional hierarchical work evaluation management method proposed in the above embodiment.

[0158] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0159] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the multi-dimensional hierarchical work evaluation management method proposed in the above embodiment. The storage medium can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0160] To sum up, the application converts the organizational structure tree diagram into a hierarchical database and constructs a three-level index mapping table, adopts the AHP analytic hierarchy process to distribute weights, generates a strategic decision layer weight vector, a department management layer weight matrix and a basic-level execution layer weight tensor, further realizes cross-level collaborative adjustment through quantum bit coding to construct a three-body entangled state, and collects evaluation data from multiple source systems based on the adjusted weight data, inputs the topological insulator algorithm to purify the evaluation data and inputs the hierarchical evaluation model, and finally generates an evaluation score table with hierarchical identification.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and are not limiting. Although the application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, which should be covered in the scope of the claims of the application.

Claims

1. A multi-dimensional, hierarchical performance evaluation management method, characterized in that: comprising, obtain an organizational structure tree diagram, and convert the affiliation relationships of the strategic decision-making layer, the department management layer, and the basic execution layer into a hierarchical database; extract annual strategic targets from the hierarchical database, decompose them into quarterly OKRs of the department management layer, and further decompose them into monthly KPIs of the basic execution layer to form a three-level index mapping table, and perform weight distribution on the three-level index mapping table using the AHP hierarchical analysis method to generate a strategic decision-making layer weight vector, a department management layer weight matrix, and a basic execution layer weight tensor; encode the strategic decision-making layer weight vector, the department management layer weight matrix, and the basic execution layer weight tensor into quantum bits, construct a three-body entangled state of the strategic decision-making layer index, the department management layer index, and the basic execution layer index, trigger cross-level collaborative adjustment through quantum measurement, and obtain adjusted weight data; collect multi-source evaluation data of the strategic decision-making layer, the department management layer, and the basic execution layer from an ERP module, a CRM module, and a code repository, and perform normalization processing to generate a standardized data table according to the adjusted weight data; plant a topological insulator algorithm in the standardized data table to obtain pure data flow; input the pure data flow into a hierarchical evaluation model, perform DEA data envelopment analysis on the strategic decision-making layer, perform entropy weight-TOPSIS algorithm on the department management layer, and perform XGBoost evaluation on the basic execution layer to generate an evaluation score table with hierarchical identifiers.

2. The multi-dimensional hierarchical work assessment management method of claim 1, wherein: obtain an organizational structure tree diagram, and convert the affiliation relationships of the strategic decision-making layer, the department management layer, and the basic execution layer into a hierarchical database, including the following steps, export the organizational structure tree diagram through an enterprise OA module, analyze the hierarchical depth of the organizational structure tree diagram, and obtain the affiliation relationships of the strategic decision-making layer, the department management layer, and the basic execution layer; quantify the hierarchical correlation strength through a dynamic weight adjacency matrix algorithm, and convert the strategic decision-making layer, the department management layer, and the basic execution layer into a hierarchical database using Cypher statements of a Neo4j graph database.

3. The multi-dimensional hierarchical work assessment management method of claim 2, wherein: extract annual strategic targets from the hierarchical database, decompose them into quarterly OKRs of the department management layer, and further decompose them into monthly KPIs of the basic execution layer to form a three-level index mapping table, and perform weight distribution on the three-level index mapping table using the AHP hierarchical analysis method to generate a strategic decision-making layer weight vector, a department management layer weight matrix, and a basic execution layer weight tensor, including the following steps, query the annual target of the strategic decision-making layer node from the hierarchical database to obtain an original strategic target, extract quantified indexes from the original strategic target using a regular expression to obtain structured indexes; calculate the historical completion rates of each department for each item in the structured indexes according to the actual completion rate data of historical strategic indexes of the CRM module to obtain department capability scores; decompose the structured indexes OKR to obtain a department OKR list; calculate individual KPI lists according to the target values of the department OKR list and the weight ratio of employee contribution data; associate the structured indexes, the department OKR list, and the individual KPI list to obtain a three-level index mapping table; generate a quantum state comparison matrix based on the three-level index mapping table, and perform a phase estimation algorithm on the quantum comparison matrix on an IBM quantum processor to obtain quantum state eigenvalues; Convert the quantum state eigenvalue into a strategic decision layer weight vector, a department layer weight matrix, and an execution layer weight tensor.

4. The multi-dimensional hierarchical work assessment management method of claim 3, wherein: Encode the strategic decision layer weight vector, the department management layer weight matrix, and the execution layer weight tensor into quantum bits, construct a three-body entangled state of the strategic decision layer index, the department management layer index, and the execution layer index, trigger cross-level collaborative adjustment through quantum measurement, and obtain adjusted weight data, including the following steps, Encode the strategic decision layer weight vector into the amplitude state of a quantum bit to obtain a quantum state, load the department management layer weight matrix onto two quantum bits through a controlled rotation gate to obtain an entangled state, and load the execution layer weight tensor as a three-level conditional probability onto three quantum bits through a multi-control gate to obtain a three-body entangled state. Based on the three-body entangled state, obtain the reduced density matrix to verify the entanglement entropy, obtain the verification flag bit, measure the three-body entangled state, and obtain a measurement result statistics table. According to the measurement result statistics table, obtain the strategic-department covariance and the department-execution covariance to obtain a covariance matrix. Based on the median in the covariance matrix, obtain the cross-level management correlation strength threshold. When the strategic-department covariance is greater than the cross-level management correlation strength threshold, trigger cross-level collaborative adjustment to obtain adjusted weight data.

5. The multi-dimensional hierarchical job assessment management method of claim 4, wherein: According to the adjusted weight data, collect multi-source evaluation data of the strategic decision layer, the department management layer, and the execution layer from the ERP module, the CRM module, and the code repository and perform normalization processing to generate a standardized data table, including the following steps, According to the adjusted weight data, obtain a structured dictionary, use the performance index acquisition interface of the ERP module to obtain market share performance data for the strategic index list in the structured dictionary, and obtain strategic decision layer evaluation data. Use the department ID list in the structured dictionary to query the quarterly targets and achievement completion rates of each department through the CRM module interface to obtain department management layer evaluation data. Based on the execution unit mapping table in the weight dictionary, scan the code version control unit submission log to form the code contribution density of each execution unit, and obtain the execution layer evaluation data. Normalize the strategic decision layer evaluation data, the department management layer evaluation data, and the execution layer evaluation data to generate a standardized data table.

6. The multi-dimensional hierarchical work assessment management method of claim 5, wherein: Implant a topological insulator algorithm in the standardized data table to obtain a pure data stream, including the following steps, Add a hierarchical label to the standardized data table to obtain a labeled graph structure; Calculate the invariant of the boundary edges connecting different levels on the labeled graph structure to obtain an abnormal edge list; Obtain abnormal edges by analyzing the three-level index mapping table, remove abnormal edges according to the abnormal edge list, obtain a purified adjacency matrix, and perform spectral clustering to obtain a three-level node ID list; Screen the three-level node ID list to obtain a pure data stream.

7. The multi-dimensional hierarchical job assessment management method of claim 6, wherein: Input the pure data stream into a hierarchical evaluation model, use DEA data envelopment analysis for the strategic decision layer, use entropy-weighted TOPSIS algorithm for the department management layer, and use XGBoost evaluation for the execution layer to generate an evaluation score table with hierarchical identification, including the following steps, Based on the pure data stream, the hierarchical label field is split into strategic decision layer data, department layer data and execution layer data. The resource input and performance index achievement value of each strategic indicator are extracted from the strategic decision layer data, a decision unit matrix is constructed, and the strategic decision layer DEA is obtained; Solving the linear programming problem of the strategic decision layer DEA on the quantum processor forms the relative efficiency value of the strategic indicator and obtains the strategic efficiency score; Based on the department layer data, the information entropy and dynamic weight are obtained to form the entropy weight matrix and department weight matrix; Based on the department weight matrix, weighted standardization processing is performed to obtain the distance between the positive ideal solution and the negative ideal solution, and the department ranking is generated; For the code submission record and task response time original data in the execution layer data, the average submission amount and the centrality of the code submission network are extracted to obtain the feature matrix execution layer matrix. The employee performance ranking model is trained using the feature matrix execution layer matrix to obtain the XGBoost model; Based on the XGBoost model, the performance scores of all employees are predicted to obtain the execution layer performance score; The strategic efficiency score, department ranking and execution layer performance score are fused to obtain the evaluation score table with hierarchical identification.

8. A multi-dimensional hierarchical work assessment management system based on the multi-dimensional hierarchical work assessment management method according to any one of claims 1 to 7, characterized by: It includes, The data acquisition module acquires the organizational structure tree diagram, and converts the membership relationship of the strategic decision layer, the department management layer and the basic execution layer into a hierarchical database; The disassembly module extracts the annual strategic target based on the hierarchical database, disassembles it into the quarterly OKR of the department management layer, and further divides it into the monthly KPI of the basic execution layer to form a three-level index mapping table. The AHP hierarchical analysis method is used to allocate weights to the three-level index mapping table to generate a strategic decision layer weight vector, a department management layer weight matrix and a basic execution layer weight tensor; The weight allocation module encodes the strategic decision layer weight vector, the department management layer weight matrix and the basic execution layer weight tensor into quantum bits, constructs a three-body entangled state of the strategic decision layer indicator, the department management layer indicator and the basic execution layer indicator, and obtains adjusted weight data through quantum measurement triggered cross-level collaborative adjustment; The cross-level module collects multi-source evaluation data of the strategic decision layer, the department management layer and the basic execution layer from the ERP module, the CRM module and the code repository based on the adjusted weight data and performs normalization processing to generate a standardized data table; The data cleaning module implants a topological insulator algorithm in the standardized data table to obtain a pure data stream; The hierarchical evaluation module inputs the pure data stream into a hierarchical evaluation model, uses DEA data envelopment analysis for the strategic decision layer, uses entropy weight-TOPSIS algorithm for the department management layer, and uses XGBoost evaluation for the basic execution layer to generate an evaluation score table with hierarchical identification. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the multi-dimensional hierarchical work evaluation management method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the multi-dimensional hierarchical work evaluation management method of any one of claims 1-7.

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