Enterprise management data processing method, device and equipment and readable storage medium

Abstract

The invention relates to an enterprise management data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an enterprise management index, a subjective weight of the enterprise management index and respective original data of each target enterprise under the enterprise management index; determining the objective weight of the enterprise management index according to the original data of each target enterprise under the enterprise management index; based on a comprehensive weight obtained according to the subjective weight and the objective weight and original data of each target enterprise under the enterprise management index, determining a vector cosine distance and a grey correlation coefficient of each target enterprise; and obtaining a close degree value of each target enterprise according to the vector cosine distance and the grey correlation coefficient of each target enterprise, and determining a management evaluation result of each target enterprise based on the close degree value of each target enterprise. By adopting the method, the enterprise management evaluation precision can be improved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for enterprise management data processing. Background Technology

[0002] In assessing the level of enterprise management modernization, the evaluation system comprehensively measures various indicators of enterprise management modernization, providing detailed data support for governments and research institutions. This helps in formulating more targeted policies and promoting economic development. For example, enterprises can use the evaluation system for self-assessment, develop improvement plans, and promote management innovation; official departments can identify differences in management levels between industries and regions and formulate policies to promote improvement; intermediary organizations can provide guidance and assistance through systematic diagnosis and promote best practices. The evaluation system provides continuous and systematic data support, revealing the patterns and trends of enterprise management modernization, and providing scientific decision-making basis for enterprises and governments, forming a positive interaction between theory and practice.

[0003] However, the current indicator weighting is unreasonable and cannot effectively distinguish the differences in enterprise management characteristics, resulting in low accuracy of enterprise management evaluation based on enterprise management data. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing enterprise management data that can improve the accuracy of enterprise management evaluation, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for processing enterprise management data, including:

[0006] Obtain enterprise management indicators, subjective weights of enterprise management indicators, and raw data for each target enterprise under the enterprise management indicators; subjective weights are determined based on fuzzy hierarchical analysis algorithm;

[0007] Based on the entropy weight algorithm, the objective weights of enterprise management indicators are determined according to the original data of each target enterprise under the enterprise management indicators.

[0008] The comprehensive weight of enterprise management indicators is obtained based on subjective and objective weights.

[0009] Based on the comprehensive weight and the original data of each target enterprise under the enterprise management indicators, the vector cosine distance and grey relational coefficient of each target enterprise are determined.

[0010] Based on the vector cosine distance and grey relational coefficient of each target enterprise, the proximity value of each target enterprise is obtained, and the management evaluation result of each target enterprise is determined based on the proximity value of each target enterprise.

[0011] Secondly, this application also provides an enterprise management data processing device, comprising:

[0012] The data acquisition module is used to acquire enterprise management indicators, the subjective weights of the enterprise management indicators, and the original data of each target enterprise under the enterprise management indicators; the subjective weights are determined based on the fuzzy hierarchical analysis algorithm.

[0013] An objective weight determination module is used to determine the objective weight of the enterprise management indicator based on the entropy weight algorithm and the original data of each target enterprise under the enterprise management indicator.

[0014] The comprehensive weight determination module is used to obtain the comprehensive weight of the enterprise management indicators based on the subjective weight and the objective weight;

[0015] The parameter determination module is used to determine the vector cosine distance and grey relation coefficient of each of the target enterprises based on the comprehensive weight and their respective original data under the enterprise management indicators.

[0016] The proximity value determination module is used to obtain the proximity value of each target enterprise based on the vector cosine distance and grey relational coefficient of each target enterprise, and to determine the management evaluation result of each target enterprise based on the proximity value of each target enterprise.

[0017] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the method provided in the first aspect above.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method provided in the first aspect above.

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in the method provided in the first aspect above.

[0020] The aforementioned enterprise management data processing methods, devices, computer equipment, computer-readable storage media, and computer program products determine the comprehensive weight based on the subjective and objective weights of enterprise management indicators. Combining the original data of each target enterprise under the enterprise management indicators, they determine the vector cosine distance and grey relational coefficient for each target enterprise. Based on the vector cosine distance and grey relational coefficient, they obtain the proximity value for each target enterprise to determine its respective management evaluation result. The reasonableness of the weights can be ensured through subjective and objective weights, and the proximity value determined by combining the vector cosine distance and grey relational coefficient can improve the discriminative power of the proximity value, thereby enhancing the accuracy of enterprise management evaluation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is an application environment diagram of an enterprise management data processing method in one embodiment;

[0023] Figure 2 This is a flowchart illustrating an enterprise management data processing method in one embodiment;

[0024] Figure 3 This is a flowchart illustrating the process of determining the vector cosine distance and grey relational coefficient in one embodiment;

[0025] Figure 4 This is a flowchart illustrating an enterprise management data processing method in another embodiment;

[0026] Figure 5 This is a flowchart illustrating the process of determining subjective weights in one embodiment;

[0027] Figure 6 This is a structural block diagram of an enterprise management data processing device in one embodiment;

[0028] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0030] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0031] The enterprise management data processing method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0032] Terminal 102 can obtain enterprise management indicators, their subjective weights, and the raw data of each target enterprise under these indicators. For example, users can upload enterprise management indicators, their subjective weights, and the raw data of each target enterprise under these indicators through terminal 102, which then sends these data to server 104. The raw data can include data in various formats, such as text and tables. Server 104 can determine the comprehensive weight based on the subjective and objective weights of the enterprise management indicators. Combining this with the raw data of each target enterprise under these indicators, server 104 determines the vector cosine distance and grey relational coefficient for each target enterprise. Based on the vector cosine distance and grey relational coefficient, server 104 obtains the proximity value for each target enterprise to determine its management evaluation result.

[0033] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0034] In one exemplary embodiment, such as Figure 2 As shown, an enterprise management data processing method is provided. This method is executed by a computer device, specifically a terminal or server, or both. In this embodiment, the method is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 202 to 210. Wherein:

[0035] Step 202: Obtain enterprise management indicators, subjective weights of enterprise management indicators, and original data of each target enterprise under enterprise management indicators; subjective weights are determined based on fuzzy hierarchical analysis algorithm.

[0036] Among them, enterprise management indicators can be indicators used to evaluate and process enterprise management data. These indicators can be constructed based on a modern management indicator system, and may include, but are not limited to, at least one of the following: intelligent manufacturing system coverage (%), management information system integration (system interconnection ratio), risk prediction accuracy (%), number of management levels (number of levels), person-job matching degree, and core talent retention rate (%). Subjective weights can be determined for enterprise management indicators based on the fuzzy hierarchical analysis algorithm. The fuzzy hierarchical analysis algorithm (FAHP) is a fuzzy extension of the analytic hierarchy process (AHP), introducing fuzzy mathematics to handle uncertainty and subjective judgment differences in decision-making. Its core lies in replacing the judgment matrix of AHP with a fuzzy consistency matrix, solving problems such as difficulty in consistency testing and scaling that does not conform to human thinking. Subjective weights are quantitative weights obtained by qualitatively judging the importance of each indicator in enterprise management indicators based on expert experience, representing the expert's judgment on the relative importance between indicators.

[0037] The target company is an enterprise that requires enterprise management data processing, such as an organization that needs management evaluation. The raw data is the data corresponding to the target company under the enterprise management indicators. For each target company, there may be corresponding raw data under different enterprise management indicators. For example, for target company A, it may include raw data A1 under indicator 1 and raw data A2 under indicator 2; for target company B, it may include raw data B1 under indicator 1 and raw data B2 under indicator 2.

[0038] Optionally, the server can obtain pre-built enterprise management indicators, which may include multiple indicators. The server can obtain the subjective weights of the enterprise management indicators, which can be determined for each indicator based on a fuzzy hierarchical analysis algorithm. The server can obtain the original data of each target enterprise under the enterprise management indicators; that is, for each target enterprise, it can iterate to obtain its original data under the enterprise management indicators.

[0039] Step 204: Based on the entropy weight algorithm, determine the objective weights of the enterprise management indicators according to the original data of each target enterprise under the enterprise management indicators.

[0040] The Entropy Weight Method, also known as the Entropy Weight Algorithm, is an objective weighting method based on information entropy theory. It determines weights by calculating the dispersion of indicators and is suitable for multi-indicator comprehensive evaluation systems. The core principle of the Entropy Weight Method is that the greater the degree of variation of an indicator (the lower the entropy), the more information it provides, and the higher its weight. Objective weights are determined by the Entropy Weight Method based on the dispersion of the enterprise's original data. Indicators with lower information entropy have higher weights. Objective weights depend on the original data of each target enterprise under its respective enterprise management indicators, allowing for an objective measurement of the information content of these indicators.

[0041] For example, for each target company's original data under its respective enterprise management indicators, the server can determine the objective weights of the enterprise management indicators based on the entropy weight algorithm. For instance, the server can pre-build an objective weight model, which can be a pre-trained artificial neural network model, such as a large language model. The server can input the original data of each target company under its respective enterprise management indicators into the objective weight model, so that the objective weight model can assign weights based on the entropy weight algorithm to obtain the objective weights of the enterprise management indicators.

[0042] Step 206: Based on subjective and objective weights, obtain the comprehensive weights of the enterprise management indicators.

[0043] The overall weight can be obtained based on subjective weights and objective weights. For example, the server can merge subjective weights and objective weights, such as by weighted fusion of subjective weights and objective weights, to obtain the overall weight of the enterprise management indicator.

[0044] Step 208: Based on the comprehensive weight and the original data of each target enterprise under the enterprise management indicators, determine the vector cosine distance and grey relational coefficient of each target enterprise.

[0045] The vector cosine distance measures the spatial angular difference (directional convergence) between the enterprise indicator vector and the ideal solution vector. The enterprise indicator vector can be constructed based on the original data of each target enterprise under its respective enterprise management indicators. The vector cosine distance focuses on the structural balance between indicators and is greatly affected by weight allocation. The value range of the vector cosine distance is [-1, 1], with a larger value indicating greater directional consistency. For example, if an industry benchmark enterprise prioritizes "innovation investment > cost control," and enterprise A has a high proportion of capital investment but weak innovation, its vector cosine distance will decrease. The grey relational coefficient measures the absolute difference (magnitude similarity) between the original data and the ideal solution of each enterprise under its respective enterprise management indicators. The grey relational coefficient focuses on the absolute value performance of the indicators and is greatly affected by numerical differences. The grey relational coefficient can range from (0,1), with a larger value indicating a smaller absolute difference. For example, if the industry's optimal labor cost rate is 15% and Company B's is 20%, the correlation coefficient reflects the specific difference between the two. Combining the vector cosine distance and the grey relational coefficient can prevent companies from receiving inflated scores due to "correct direction but flawed execution" or "meeting numerical targets but losing strategic direction," thus reducing the accuracy of the evaluation.

[0046] Optionally, the server can calculate the vector cosine distance and grey relational coefficient for each target enterprise based on the comprehensive weight and their respective original data under the enterprise management indicators. For example, the server can construct a target vector for each target enterprise based on the comprehensive weight and their respective original data under the enterprise management indicators. This target vector can be constructed by sequentially arranging the original data of each target enterprise under the enterprise management indicators. The server can also construct a reference vector based on the comprehensive weight and their respective original data under the enterprise management indicators, serving as the ideal solution vector. Based on the reference vector and the target vectors of each target enterprise, the server can then obtain the vector cosine distance and grey relational coefficient for each target enterprise.

[0047] Step 210: Based on the vector cosine distance and grey relational coefficient of each target enterprise, obtain the proximity value of each target enterprise, and determine the management evaluation result of each target enterprise based on the proximity value of each target enterprise.

[0048] Among them, the Relative Closeness value is a core quantitative indicator for measuring the performance of enterprise operation and management. The Relative Closeness value can be calculated based on the vector cosine distance and grey relational coefficient of each target enterprise. For example, the Relative Closeness value of each target enterprise can be calculated by weighting the vector cosine distance and grey relational coefficient of each target enterprise, so as to determine the management evaluation results of each target enterprise through the Relative Closeness value.

[0049] Optionally, for each target company, the server can calculate a proximity value based on the vector cosine distance and grey relational coefficient. For example, the proximity value can be obtained by weighting the vector cosine distance and grey relational coefficient. The server can then sort the proximity values ​​of each target company and obtain the management evaluation results for each target company based on the sorting results.

[0050] In the above-mentioned enterprise management data processing method, the comprehensive weight is determined based on the subjective and objective weights of the enterprise management indicators. Combining the original data of each target enterprise under the enterprise management indicators, the vector cosine distance and grey relational coefficient of each target enterprise are determined. Based on the vector cosine distance and grey relational coefficient, the closeness value of each target enterprise is obtained to determine the management evaluation result of each target enterprise. The reasonableness of the weight can be ensured by subjective and objective weights. The closeness value is determined by combining the vector cosine distance and grey relational coefficient, which can improve the distinguishability of the closeness value, thereby improving the evaluation accuracy of enterprise management.

[0051] In an exemplary embodiment, based on the entropy weight algorithm, the objective weights of enterprise management indicators are determined according to the original data of each target enterprise under the enterprise management indicators. This includes: transforming the original data of each target enterprise under the enterprise management indicators to obtain the intermediate data of each target enterprise under the enterprise management indicators; performing dimensionless processing on the intermediate data of each target enterprise under the enterprise management indicators to obtain the target data of each target enterprise under the enterprise management indicators; determining the entropy weights of the enterprise management indicators based on the target data of each target enterprise under the enterprise management indicators; and obtaining the objective weights of the enterprise management indicators based on the entropy weights of the enterprise management indicators.

[0052] Intermediate data can be obtained by transforming the original data. For example, incomparable factors within the industry can be eliminated from the original data to obtain intermediate data. Dimensionless processing removes the dimensions of the data to ensure comparability. Target data can be obtained by performing dimensionless processing on the intermediate data, meaning the target data may be dimensionless.

[0053] For example, the server can transform each piece of raw data to obtain intermediate data corresponding to each piece of raw data. For instance, the server can map each piece of raw data according to a preset mapping algorithm to obtain intermediate data corresponding to each piece of raw data. The server can perform dimensionless processing on each piece of intermediate data to remove the dimensions of each piece of intermediate data, obtaining the target data. The server can determine the entropy weights of enterprise management indicators based on each piece of target data; for example, it can determine the entropy weights of each indicator in the enterprise management indicators based on the target data, and the server can obtain the objective weights of the enterprise management indicators based on the entropy weights of each indicator in the enterprise management indicators.

[0054] In some embodiments, weight setting is a crucial step in constructing a performance evaluation system. The scientific and reasonable setting of standard values ​​helps managers conduct quantitative analysis of enterprise operations. This study employs an objective weighting method, namely the entropy weighting method, to objectively measure the amount of information in the indicators and determine the degree of influence of a particular piece of information on the overall research question based on the amount of information, thereby determining the weight. The weight is positively correlated with the dispersion of the information. Specifically:

[0055] Construct the original data matrix based on the original data. ,

[0056] (1)

[0057] In the formula, m is the number of evaluation objects, that is, the number of target enterprises; n is the number of specific evaluation indicators, that is, the total number of each indicator in the enterprise management indicators; i=1,2,…,m; j=1,2,…,n. The original data for the i-th target enterprise under the j-th indicator; This represents the raw data for the m-th target company under the n-th indicator.

[0058] Construct a standardized decision matrix Y = (y)m. To eliminate the dimensions of the indicators and reduce errors, first eliminate the influence of incomparable factors in the industries of the appropriate indicators in the original matrix X, and obtain matrix X' = ( )m, as shown in the following formula.

[0059] (2)

[0060] In the formula, This represents the intermediate data after transforming the original data of the i-th target company under the j-th indicator; k is the industry appropriateness value, which can be represented by the average value of all listed companies in the industry for the j-th indicator in the current year, excluding ST (Special Treatment) stocks. Based on this, the range method is used to perform dimensionless processing on the positive indicators, negative indicators, and the appropriateness indicator after eliminating industry factors, respectively, to obtain the decision matrix Y, as shown in the following formula.

[0061] (3)

[0062] (4)

[0063] Among them, the calculation of equation (3) The target data for the positive indicator is represented by the target data for the i-th target enterprise under the j-th indicator; Equation (4) calculates... The target data for the negative indicator represents the target data for the i-th target enterprise under the j-th indicator.

[0064] The entropy weight H of the j-th index is calculated as shown in formula (5).

[0065] (5)

[0066] In the formula, The entropy weight of the j-th index; This refers to the corrected data corresponding to the target data of the i-th target enterprise under the j-th indicator. Specifically, to avoid the entropy weight being meaningless when calculating the logarithm, the standardized target data needs to be corrected by shifting Y upward by b units. Thus, the corrected proportion is obtained. , Slightly greater than Positive integer values.

[0067] Therefore, the objective weight of the j-th indicator can be calculated. as follows,

[0068] (6)

[0069] In the formula, Let W be the difference coefficient of the j-th indicator, where W∈[0,1] and ∑W=1.

[0070] In this embodiment, the server transforms and dimensionlessly processes the original data to obtain the target data. Based on the target data, the entropy weight of the enterprise management indicators is determined, and the objective weight of the enterprise management indicators is determined based on the entropy weight of the enterprise management indicators. The enterprise management indicators can be objectively weighted based on the entropy weight algorithm, which can ensure the reliability of the objective weight.

[0071] In one exemplary embodiment, such as Figure 3 As shown, the process of determining the vector cosine distance and grey relational coefficient involves determining the vector cosine distance and grey relational coefficient for each target enterprise based on the comprehensive weights and their respective original data under the enterprise management indicators, including steps 302 to 308. Wherein:

[0072] Step 302: Based on the original data of each target enterprise under the enterprise management indicators, construct the normalized data of each target enterprise under the enterprise management indicators.

[0073] Normalized data can be obtained by performing normalization processing on each original data set separately. For example, for the original data of each target company under its respective enterprise management indicators, the server can perform normalization processing on each original data set to obtain the normalized data of each target company under its respective enterprise management indicators.

[0074] Step 304: Based on the comprehensive weight and the normalized data of each target enterprise under the enterprise management indicators, obtain the weighted data of each target enterprise under the enterprise management indicators.

[0075] Optionally, for normalized data, the server can perform weighted processing on the normalized data of each target enterprise under the enterprise management indicators according to the comprehensive weight, so as to obtain the weighted data of each target enterprise under the enterprise management indicators.

[0076] Step 306: Based on the weighted data of each target enterprise under the enterprise management indicators, determine the reference vector of the enterprise management indicators.

[0077] The reference vector can be determined based on the weighted data of each target company under its respective enterprise management indicators. The reference vector can serve as the ideal solution vector. For example, the server can determine the reference vector for the enterprise management indicators based on the weighted data of each target company under its respective enterprise management indicators. The reference vector can include positive and negative ideal solution vectors.

[0078] Step 308: Based on the weighted data and reference vectors of each target enterprise under the enterprise management indicators, determine the vector cosine distance and grey relational coefficient of each target enterprise.

[0079] Optionally, the server can calculate the vector cosine distance and grey relational coefficient for each target enterprise based on its weighted data and reference vector under the enterprise management indicators. For example, the server can construct the target vector for each target enterprise based on its weighted data under the enterprise management indicators, and obtain the vector cosine distance and grey relational coefficient for each target enterprise based on the reference vector and the target vector of each target enterprise.

[0080] In this embodiment, the server normalizes the original data, determines the weighted data based on the comprehensive weight, and determines the reference vector based on the weighted data. Based on the reference vector and each original data, the server determines the vector cosine distance and grey relational coefficient of each target enterprise, which can ensure the comparability of the vector cosine distance and grey relational coefficient.

[0081] In an exemplary embodiment, the method for determining the vector cosine distance and grey relational coefficient of each target enterprise based on its weighted data and reference vector under enterprise management indicators includes: constructing target vectors for each target enterprise based on its weighted data under enterprise management indicators; calculating the cosine distance based on the target vectors and reference vectors of each target enterprise to obtain the vector cosine distance of each target enterprise; and obtaining the grey relational coefficient of each target enterprise based on its target vectors and reference vectors using a grey relational algorithm.

[0082] The target vector can be constructed based on the weighted data of each target enterprise under the enterprise management indicators, and used to calculate with the reference vector to determine the gray relation coefficient of each target enterprise.

[0083] For example, the server can construct target vectors for each target company based on their respective weighted data under enterprise management indicators. For instance, the server can sequentially combine the weighted data of each target company under its respective enterprise management indicators to obtain target vectors. The server can calculate the cosine distance between each target company's target vector and reference vector to obtain the vector cosine distance for each target company. The server can also calculate the grey relational coefficient for each target company based on its respective target vector and reference vector using a grey relational algorithm.

[0084] In some embodiments, the process of determining the vector cosine distance and the grey relational coefficient may include:

[0085] 1) Homogenization matrix. This is used to eliminate incomparable factors in the industry matrix. = Based on this, the influence of cost indicators is eliminated, and a convergence process is performed as follows:

[0086] (7)

[0087] 2) Normalized decision matrix. To improve data comparability, the convergence matrix is ​​normalized to obtain normalized data. A normalized matrix can be constructed based on normalized data. .

[0088] (8)

[0089] 3) Multiply the normalized matrix by the corresponding index weight values ​​(comprehensive weight) to obtain the weighted normalized matrix. . This represents the weighted data for the i-th target enterprise under the j-th indicator.

[0090] (9)

[0091] in, The comprehensive weight of the j-th indicator; This represents the normalized data for the i-th target enterprise under the j-th indicator.

[0092] 4) Determine the positive and negative ideal solutions, and use the cosine of the vector angle to determine the optimal and worst performance. The cosine of the vector angle is an extension of the cosine theorem in multidimensional space, reflecting the degree of correlation between different vectors. The server can consider the vector of the set of spatial points formed by the performance evaluation index data corresponding to the weighted normalized matrix as the performance vector, denoted as vector. The eigenvectors (reference vectors) formed by the positive and negative ideal solutions calculated by the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method are denoted as the optimal ideal performance vectors. (Positive ideal vector) and worst-case ideal performance vector (Negative ideal vector).

[0093] (10)

[0094] (11)

[0095] in, The weighted data representing the i-th target enterprise under the j-th positive indicator; The weighted data representing the i-th target enterprise under the j-th negative indicator.

[0096] 5) Calculate the optimal and worst-case ideal distances. An improvement to the traditional TOPSIS method is made using vector cosine distance. Instead of using Euclidean distance to determine the similarity between samples, cosine values ​​are used to reflect the differences between individuals, and the degree of similarity changes with the cosine value.

[0097] (12)

[0098] (13)

[0099] in, The optimal vector cosine distance for the i-th target enterprise; The worst vector cosine distance for the i-th target firm.

[0100] Grey relational analysis can analyze data with small samples and limited information, systematically analyzing the correlation between different factors, and is easy to operate. This study draws on the relevant definition of grey relational analysis, constructs a weighted normalized matrix using the entropy weight method to calculate weights, and determines the grey relational coefficient between the i-th factor in performance evaluation and the corresponding evaluation indicators for positive and negative ideal performance. .

[0101] (14)

[0102] (15)

[0103] In the formula, The weighted data representing the i-th target enterprise under the j-th indicator; The weighted data representing the j-th positive index; The weighted data representing the j-th negative index; Let be the grey relational coefficient between the i-th target enterprise and the positive ideal performance; ε is the grey relational coefficient between the i-th target enterprise and the negative ideal performance; ε is the discrimination coefficient, which ranges from [0, 1] and is usually taken as 0.5 in practice.

[0104] In this embodiment, the server can construct target vectors based on the weighted data of each target enterprise under enterprise management indicators, and calculate the vector cosine distance and grey relational coefficients by combining the reference vectors, which can ensure the accuracy of the vector cosine distance and grey relational coefficients.

[0105] In an exemplary embodiment, the proximity value of each target enterprise is obtained based on its respective vector cosine distance and grey relational coefficient, including: determining the cosine distance weight and the relational coefficient weight; and weighting and fusing the vector cosine distance and grey relational coefficient of each target enterprise according to the cosine distance weight and the relational coefficient weight to obtain the proximity value of each target enterprise.

[0106] The cosine distance weight is the weight of the cosine distance between the vectors, and the correlation coefficient weight is the weight of the grey correlation coefficient. The cosine distance weight and the correlation coefficient weight can be configured according to actual needs; for example, they can both be set to 0.5.

[0107] Optionally, the server can determine the pre-configured cosine distance weight and correlation coefficient weight, and based on the cosine distance weight and correlation coefficient weight, perform weighted fusion of the vector cosine distance and grey correlation coefficient of each target enterprise to obtain the proximity value of each target enterprise.

[0108] In some embodiments, the process of determining the proximity value includes:

[0109] The grey relational method is used to improve the closeness of TOPSIS. According to formula (16), the correlation values ​​of the cosine distance between the vector angle and the grey relational degree are processed to be dimensionless.

[0110] (16)

[0111] In the formula, .

[0112] While considering the cosine distance of vectors, we also consider the grey relational degree, and perform weighted dimensionless processing on the correlation values ​​of TOPSIS and grey relational methods.

[0113] (17)

[0114] (18)

[0115] In the formula, Let be the positive fusion parameters for the i-th target enterprise. (Right now () represents the dimensionless cosine distance of the worst vector for the i-th target enterprise; (Right now () represents the dimensionless optimal vector cosine distance for the i-th target enterprise; Let α be the gray relation coefficient between the dimensionless processing of the i-th target enterprise and the positive ideal performance; α+β=1, where α and β reflect the degree of tendency towards position shape. Generally, α and β are considered to be equally important, and α=β=0.5 is usually taken.

[0116] according to , The relative closeness between the level of management modernization and the ideal level can be calculated:

[0117] (19)

[0118] in, Let be the proximity value of the i-th target enterprise. The proximity value is calculated using the improved TOPSIS model. And sort them. The larger the value, the higher the level of modernization in the company's management.

[0119] In this embodiment, the server performs weighted fusion of the vector cosine distance and grey relational coefficient for each target enterprise by using cosine distance weight and correlation coefficient weight, which can ensure the reliability of the proximity value and thus improve the accuracy of enterprise management evaluation.

[0120] In an exemplary embodiment, the comprehensive weight of the enterprise management indicator is obtained based on subjective weight and objective weight, including: determining the weighting coefficients of subjective weight and objective weight respectively; and obtaining the comprehensive weight of the enterprise management indicator based on the subjective weight and objective weight, and the weighting coefficients of subjective weight and objective weight respectively.

[0121] The weighting coefficients can be pre-configured according to actual needs. For example, the weighting coefficients for both subjective and objective weights can be configured to be 0.5. Optionally, the server can obtain the weighting coefficients for both subjective and objective weights, and then perform a weighted fusion of the subjective and objective weights based on these coefficients to obtain the comprehensive weight of the enterprise management indicator. For example, when evaluating the modernization level of enterprise A, experts use FAHP to subjectively assign weights to the enterprise management indicator of "degree of digital transformation" (resulting in a subjective weight of 0.35); and calculate the objective weight (0.28) based on the data using the entropy weight method. If the weighting coefficients for both subjective and objective weights are 0.5, the server can determine that the final comprehensive weight of the enterprise management indicator is (0.35 + 0.28) / 2 = 0.315.

[0122] In this embodiment, the server calculates a comprehensive weight by weighting the subjective and objective weights according to their respective weighting coefficients. This comprehensive weight ensures the rationality of the weights and helps improve the evaluation accuracy of enterprise management.

[0123] In an exemplary embodiment, the enterprise management data processing method further includes: comparing each indicator in the enterprise management indicators pairwise, and constructing a judgment matrix based on the comparison results; determining the initial indicator weights of each indicator based on the judgment matrix; determining the membership matrix of each indicator; and obtaining the subjective weights of the enterprise management indicators based on the initial indicator weights and the membership matrix.

[0124] The fuzzy hierarchical analysis algorithm combines the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation Method (FCM) to transform the fuzziness of language into numerical weights. AHP mathematizes the decision-making process by dividing it into levels such as objectives, effects, and indicators, thus providing a simple and effective solution for complex multi-objective decision problems. Fuzzy comprehensive evaluation is a mathematical modeling method applied to decision-making and evaluation problems, primarily used to handle situations involving fuzziness and uncertainty. This method transforms uncertain information into fuzzy sets, membership functions, and fuzzy operations to achieve comprehensive analysis of multiple evaluation factors. In fuzzy comprehensive evaluation, decision-makers define the weight and membership function of each evaluation factor to describe the contribution of each factor to different evaluation levels. Fuzzy comprehensive evaluation provides an effective tool for handling complex multi-factor decision problems, especially when facing imperfect data and fuzzy information, demonstrating significant advantages and thus finding widespread application in many fields.

[0125] The judgment matrix can include pairwise comparisons of the various enterprise management indicators, such as each element corresponding to the importance comparison of two indicators. The initial indicator weights are quantified based on the pairwise comparison results of each indicator; for example, the initial indicator weights can be determined using the geometric mean method. The membership matrix can include the membership degrees of each indicator, which can be determined based on expert fuzzy evaluation.

[0126] Optionally, enterprise management indicators may include multiple indicators. The server can compare the importance of each indicator pairwise to obtain comparison results. The server can then construct a judgment matrix based on these results. For example, enterprise management indicators may include three indicators: organizational resources (A1), execution capability (A2), and innovation effectiveness (A3). The server can compare the importance of these three indicators pairwise, with expert scoring determining the specific criteria. The server can then construct a judgment matrix based on the comparison results. The server can determine the initial indicator weights for each indicator based on the judgment matrix. For example, the server can calculate the initial indicator weights for each indicator using the geometric mean method based on the comparison results in the judgment matrix. The server can determine the membership matrix for each indicator, where the membership degree of each indicator can be determined based on expert fuzzy evaluations of each indicator. The server can combine the initial indicator weights with the membership matrix to obtain the subjective weights of each indicator, thereby obtaining the subjective weights of the enterprise management indicators.

[0127] In some embodiments, as shown in Table 1 below, the server can determine the judgment matrix by comparing each indicator in the enterprise management indicators pairwise according to the scaling methods listed in Table 1 (1-9). .

[0128] Table 1

[0129] the difference Equally important Slightly important Obviously important Strongly important Extremely important 1-9 scale 1 3 5 7 9

[0130] The even numbers in the middle represent the median of two adjacent scales. For example, a scale of 6 indicates that the importance is between significant (5) and strong (7).

[0131] Secondly, the judgment matrix is ​​standardized into a standardized matrix. The calculation formula is as follows:

[0132] (20)

[0133] in, For the standardized matrix The elements in the table represent the standardized comparison results. This represents the importance comparison result between the i-th and j-th indicators; n is the total number of indicators in the enterprise management indicators.

[0134] Finally, the weighting coefficient of the i-th indicator is calculated according to the following formula:

[0135] (twenty one)

[0136] in, is the weighting coefficient of the i-th indicator in the enterprise management indicators.

[0137] The server can perform a consistency check on the weighted coefficients. After passing the consistency check, the initial weight of each indicator can be obtained based on the weighted coefficients of each indicator.

[0138] Furthermore, the server can determine the membership matrix of each indicator based on the fuzzy scores given by experts. For example, (regarding the importance of indicator A1): Expert 1: considers A1 "relatively important" → Membership: High (0.7), Medium (0.3); Expert 2: considers A1 "moderately important" → Membership: High (0.4), Medium (0.6). The server can combine the initial indicator weights and the membership matrix to obtain the subjective weights of the enterprise management indicators.

[0139] By combining the Analytic Hierarchy Process (AHP) with fuzzy comprehensive evaluation, an AHP-Fuzzy Comprehensive Evaluation Model is constructed. First, the AHP is used to determine the initial weights of each indicator. Then, fuzzy comprehensive evaluation is used to comprehensively score the energy storage applicability under various typical scenarios. This transforms the evaluation process from a single qualitative perspective to a combination of qualitative and quantitative methods, thus providing a more accurate and comprehensive evaluation result.

[0140] In this embodiment, the server can compare each indicator in the enterprise management indicators pairwise, and determine the initial indicator weight of each indicator based on the judgment matrix constructed according to the comparison results. Combined with the membership matrix of each indicator, the subjective weight of the enterprise management indicators is obtained. This can transform the evaluation process from a single qualitative perspective to a combination of qualitative and quantitative methods, which can ensure the reliability of the subjective weight.

[0141] This application also provides an application scenario in which the above-described enterprise management data processing method is applied. Specifically, the application of the enterprise management data processing method in this scenario is as follows:

[0142] In the process of modernization, the modernization of enterprise management is an important component of national modernization. A deep understanding of the current state of enterprise management modernization is crucial for a comprehensive understanding of national conditions and enhancing international competitiveness. Therefore, developing a scientific evaluation index system for enterprise management modernization (hereinafter referred to as the "evaluation system") has become an urgent need. The evaluation system can comprehensively measure various indicators of enterprise management modernization, providing detailed data support for governments and research institutions, which helps to formulate more targeted policies and promote economic development. Simultaneously, state-owned enterprises can use the evaluation system for self-assessment, develop improvement plans, and promote management innovation; governments can identify differences in management levels between industries and regions and formulate promotional policies; intermediary organizations can provide guidance and assistance through systematic diagnosis and promote best practices. The evaluation system provides continuous and systematic data support, revealing the laws and trends of enterprise management modernization, and providing scientific decision-making basis for enterprises and governments, forming a positive interaction between theory and practice. Therefore, the development and application of an evaluation index system for enterprise management modernization is of great significance for deepening the modernization of state-owned enterprise management and promoting the establishment and improvement of management systems.

[0143] Currently, research on enterprise management evaluation focuses on the enterprise management evaluation system, its application, and development trends in enterprise management. Environmental trends directly affecting enterprise operation and management include new regulations and policies on enterprise management in various regions, as well as domestic and international cases of enterprise management innovation. Literature review reveals that there are many design principles and research findings in the evaluation standards and systems for enterprise management both domestically and internationally that can be learned from. However, there is no ready-made system that can be directly used to evaluate the overall state of modernization of enterprise management in my country. Existing systems either only evaluate a specific part of enterprise management, lack clear benchmarks making them inconvenient to operate, or lack specificity for local enterprises.

[0144] In response to the urgent need to improve the modernization level of management in state-owned enterprises, this application proposes a management modernization evaluation method based on an improved ideal value approximation ranking method using fuzzy hierarchical analysis and entropy weight method—the FAHP-Entropy Weight Method-TOPSIS model. This method mainly consists of three parts: establishing an indicator system, assigning weights, and evaluation.

[0145] (1) Using the MODERN model, a management indicator system for state-owned enterprises was constructed, covering six aspects: Method, Organization and resource, Direction, Execution, Retrospection, and Newness. This resulted in a management capability and management system modernization indicator system with 6 primary indicators, 19 secondary indicators, and 70 tertiary indicators.

[0146] (2) Use fuzzy hierarchical analysis (FAHP) to assign weights to each indicator and calculate the subjective weight of the indicator.

[0147] (3) Calculate the objective weights using the entropy weight method.

[0148] (4) The comprehensive weight is obtained by weighted average of subjective and objective weights. The improved TOPSIS model is used to evaluate each indicator by calculating the vector cosine distance and grey relational degree. The relative superiority and inferiority ranking of the indicators is calculated to assess the management modernization level of each indicator.

[0149] Enterprise management is a complex system, and its evaluation involves multiple aspects. How to establish a parameter-based indicator system to reflect the overall level and structural characteristics of management modernization has become a fundamental research topic in evaluating enterprise management modernization. This application proposes a management modernization level evaluation model based on the MODERN model and the FAHP-entropy weight method-improved TOPSIS management level evaluation model. Specifically, as follows... Figure 4 As shown, the enterprise management data processing method of this application includes:

[0150] Step 1: Establish a management system and management capability modernization indicator system based on the MODERN model.

[0151] By collecting data on the management objectives and current status of state-owned enterprises (SOEs), and addressing the identified management problems and internal management structures, six main evaluation perspectives were established. These perspectives, along with six aspects—Method, Organization and Resources, Direction, Execution, Retrospection, and Newness—were used to construct a management indicator system for SOEs. This resulted in a management capability and modernization indicator system comprising 6 primary indicators, 19 secondary indicators, and 70 tertiary indicators.

[0152] Step 2: Subjective weighting based on the FAHP model.

[0153] like Figure 5 As shown, a combination of the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation Method (FCM) is used. First, a judgment matrix is ​​constructed, the weight vector is calculated, and the consistency check is performed. Then, the indicator weights are determined, the membership matrix is ​​calculated using expert scoring, and finally, the subjective weights are calculated.

[0154] Step 3: Calculate the objective weights based on the entropy weight method.

[0155] The decision matrix of the indicator system for different enterprise management levels is calculated using industry adaptability. The entropy weight of each indicator is then calculated through the decision matrix to determine the objective weight of the indicator.

[0156] Step 4: Improved TOPSIS evaluation model based on vector cosine angle and grey relational analysis.

[0157] First, the weight vectors of each indicator are determined based on the entropy weight method. Then, the positive and negative ideal solutions are determined, and the optimal and worst performances are determined using the cosine of the vector angle. Finally, a weighted normalized matrix is ​​constructed using a comprehensive weight combining subjective and objective weights, and the grey relational coefficients between the i-th performance and the positive and negative ideal performances with respect to their corresponding evaluation indicators are determined. The grey relational method is used to improve the TOPSIS closeness, and the correlation values ​​between the cosine distance of the vector angle and the grey relational degree are dimensionless according to the comprehensive closeness calculation formula.

[0158] The combined FAHP-Entropy Weight Method-TOPSIS evaluation model proposed in this application achieves complementary advantages among the various models, effectively improving the evaluation effect and objectivity. The FAHP-Entropy Weight Method-TOPSIS evaluation model, by assigning weights to enterprise indicators and calculating evaluation indicators, effectively combines subjective and objective weighting methods to assign importance weights to each indicator. Then, by improving the TOPSIS model through vector cosine angle and grey relational degree calculations, the calculated relative proximity has a more significant discriminative effect, which is beneficial for obtaining a ranking of superior and inferior indicators and improving the evaluation accuracy of management modernization level.

[0159] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0160] Based on the same inventive concept, this application also provides an enterprise management data processing apparatus for implementing the enterprise management data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more enterprise management data processing apparatus embodiments provided below can be found in the limitations of the enterprise management data processing method described above, and will not be repeated here.

[0161] In one exemplary embodiment, such as Figure 6 As shown, an enterprise management data processing device 600 is provided, including: a data acquisition module 602, an objective weight determination module 604, a comprehensive weight determination module 606, a parameter determination module 608, and a proximity value determination module 610, wherein:

[0162] The data acquisition module 602 is used to acquire enterprise management indicators, the subjective weights of the enterprise management indicators, and the original data of each target enterprise under the enterprise management indicators; the subjective weights are determined based on the fuzzy hierarchical analysis algorithm.

[0163] The objective weight determination module 604 is used to determine the objective weight of the enterprise management indicator based on the entropy weight algorithm and the original data of each target enterprise under the enterprise management indicator.

[0164] The comprehensive weight determination module 606 is used to obtain the comprehensive weight of the enterprise management indicators based on the subjective weight and the objective weight;

[0165] The parameter determination module 608 is used to determine the vector cosine distance and grey relation coefficient of each of the target enterprises based on the comprehensive weight and their respective original data under the enterprise management indicators.

[0166] The proximity value determination module 610 is used to obtain the proximity value of each of the target enterprises based on their respective vector cosine distance and grey relational coefficient, and to determine the management evaluation result of each of the target enterprises based on their respective proximity values.

[0167] In some embodiments, the objective weight determination module 604 is further configured to: transform the original data of each target enterprise under the enterprise management indicator to obtain the intermediate data of each target enterprise under the enterprise management indicator; perform dimensionless processing on the intermediate data of each target enterprise under the enterprise management indicator to obtain the target data of each target enterprise under the enterprise management indicator; determine the entropy weight of the enterprise management indicator based on the target data of each target enterprise under the enterprise management indicator; and obtain the objective weight of the enterprise management indicator according to the entropy weight of the enterprise management indicator.

[0168] In some embodiments, the parameter determination module 608 is further configured to: construct normalized data for each of the target enterprises under the enterprise management indicators based on their respective original data; obtain weighted data for each of the target enterprises under the enterprise management indicators based on the comprehensive weight and their respective normalized data; determine a reference vector for the enterprise management indicators based on their respective weighted data; and determine the vector cosine distance and grey relational coefficient for each of the target enterprises based on their respective weighted data and the reference vector.

[0169] In some embodiments, the parameter determination module 608 is further configured to construct a target vector for each of the target enterprises based on their respective weighted data under the enterprise management indicators; calculate the cosine distance between the target vectors of each target enterprise and the reference vector to obtain the vector cosine distance between each of the target enterprises; and obtain the gray relational coefficient between each target enterprise based on the target vectors of each target enterprise and the reference vector through a gray relational algorithm.

[0170] In some embodiments, the proximity value determination module 610 is further configured to determine the cosine distance weight and the correlation coefficient weight; and to perform weighted fusion of the vector cosine distance and grey correlation coefficient of each of the target enterprises according to the cosine distance weight and the correlation coefficient weight, so as to obtain the proximity value of each of the target enterprises.

[0171] In some embodiments, the comprehensive weight determination module 606 is further configured to determine the weighting coefficients of the subjective weight and the objective weight respectively; and to obtain the comprehensive weight of the enterprise management indicator based on the subjective weight and the objective weight, and the weighting coefficients of the subjective weight and the objective weight respectively.

[0172] In some embodiments, a subjective weight determination module is further included, which is used to compare each indicator in the enterprise management indicators pairwise and construct a judgment matrix based on the comparison results; determine the initial indicator weight of each indicator based on the judgment matrix; determine the membership matrix of each indicator; and obtain the subjective weight of the enterprise management indicators based on the initial indicator weight and the membership matrix.

[0173] Each module in the aforementioned enterprise management data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0174] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores various data involved in enterprise management data processing methods. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements an enterprise management data processing method.

[0175] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0176] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0177] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0178] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0179] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0180] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0181] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0182] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An enterprise management data processing method characterized by comprising: The method comprises: obtaining enterprise management indicators, subjective weights of the enterprise management indicators, and original data of each target enterprise under the enterprise management indicators; the subjective weights are determined based on a fuzzy analytic hierarchy process algorithm; determining objective weights of the enterprise management indicators based on an entropy weight algorithm and the original data of each target enterprise under the enterprise management indicators; obtaining comprehensive weights of the enterprise management indicators according to the subjective weights and the objective weights; determining vector cosine distances and grey correlation coefficients of each target enterprise based on the comprehensive weights and the original data of each target enterprise under the enterprise management indicators; obtaining closeness values of each target enterprise according to the vector cosine distances and the grey correlation coefficients of each target enterprise, and determining management evaluation results of each target enterprise based on the closeness values of each target enterprise.

2. The method of claim 1, wherein, The method comprises: converting the original data of each target enterprise under the enterprise management indicators to obtain intermediate data of each target enterprise under the enterprise management indicators; performing dimensionless processing on the intermediate data of each target enterprise under the enterprise management indicators to obtain target data of each target enterprise under the enterprise management indicators; determining entropy weights of the enterprise management indicators based on the target data of each target enterprise under the enterprise management indicators; obtaining the objective weights of the enterprise management indicators according to the entropy weights of the enterprise management indicators.

3. The method of claim 1, wherein, The method comprises: constructing normalized data of each target enterprise under the enterprise management indicators based on the original data of each target enterprise under the enterprise management indicators; obtaining weighted data of each target enterprise under the enterprise management indicators according to the comprehensive weights and the normalized data of each target enterprise under the enterprise management indicators; determining a reference vector of the enterprise management indicators based on the weighted data of each target enterprise under the enterprise management indicators; determining vector cosine distances and grey correlation coefficients of each target enterprise according to the weighted data of each target enterprise under the enterprise management indicators and the reference vector.

4. The method of claim 3, wherein, The method comprises: constructing a target vector of each target enterprise according to the weighted data of each target enterprise under the enterprise management indicators; determining vector cosine distances and grey correlation coefficients of each target enterprise according to the target vector of each target enterprise and the reference vector. The vector cosine distance of each target enterprise is obtained by performing cosine distance calculation based on the target vector of each target enterprise and the reference vector. The grey correlation coefficient of each target enterprise is obtained by a grey correlation algorithm based on the target vector of each target enterprise and the reference vector.

5. The method of claim 1, wherein, The steps of obtaining the closeness value of each target enterprise based on the vector cosine distance and the grey correlation coefficient of each target enterprise include: determining the cosine distance weight and the correlation coefficient weight; performing weighted fusion on the vector cosine distance and the grey correlation coefficient of each target enterprise respectively according to the cosine distance weight and the correlation coefficient weight, to obtain the closeness value of each target enterprise.

6. The method of claim 1, wherein, The steps of obtaining the comprehensive weight of the enterprise management index based on the subjective weight and the objective weight include: determining the weighting coefficients of the subjective weight and the objective weight; obtaining the comprehensive weight of the enterprise management index based on the subjective weight and the objective weight and the weighting coefficients of the subjective weight and the objective weight.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: comparing each index in the enterprise management index with each other, and constructing a judgment matrix based on the comparison result; determining the initial index weight of each index based on the judgment matrix; determining the membership degree matrix of each index; obtaining the subjective weight of the enterprise management index based on the initial index weight and the membership degree matrix.

8. An enterprise management data processing apparatus characterized by comprising: The device includes: a data acquisition module configured to acquire enterprise management indexes, a subjective weight of the enterprise management indexes, and original data of each target enterprise under the enterprise management indexes; the subjective weight is determined based on a fuzzy analytic hierarchy process algorithm; an objective weight determination module configured to determine an objective weight of the enterprise management indexes based on an entropy weight algorithm and the original data of each target enterprise under the enterprise management indexes; a comprehensive weight determination module configured to obtain a comprehensive weight of the enterprise management indexes based on the subjective weight and the objective weight; a parameter determination module configured to determine a vector cosine distance and a grey correlation coefficient of each target enterprise based on the comprehensive weight and the original data of each target enterprise under the enterprise management indexes; a closeness value determination module configured to obtain a closeness value of each target enterprise based on the vector cosine distance and the grey correlation coefficient of each target enterprise, and determine a management evaluation result of each target enterprise based on the closeness value of each target enterprise. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 7.

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