Production activity comprehensive evaluation method and device based on multi-dimensional indexes, and storage medium

Abstract

The invention mainly provides a production activity comprehensive evaluation method based on multi-dimensional indexes. The method comprises the following steps: carrying out dimension division on enterprise production activity data according to six indexes including safety, quality, cost, delivery, environment and mastitis; respectively defining a first-level index and a second-level index decomposed according to the first-level index under each dimension, and formulating calculation logic of each index; utilizing correlation analysis to find out correlation indexes with high correlation degree with the first-level indexes from a large number of second-level indexes; calculating the weight corresponding to each correlation index through the information entropy of the correlation indexes; by establishing a comprehensive evaluation model, index scores of all dimensions are calculated, actual data of monthly production activities are visually presented, and enterprises are guided to carry out production process evaluation. According to the technical scheme provided by the invention, the purpose of comprehensively evaluating the operation condition of the enterprise can be achieved by defining the indexes of multiple dimensions.

Description

Technical Field

[0001] The present application belongs to the technical field of enterprise operation evaluation, and specifically relates to a method, device and storage medium for comprehensive evaluation of production activities based on multi-dimensional indicators. Technical Background

[0002] In some highly competitive and technologically innovative industries, product quality, production efficiency, cost control, delivery capabilities and environmental impact are key indicators for measuring corporate competitiveness. In order to improve these indicators, manufacturing companies often need to adopt advanced management methods and innovative technologies to maintain corporate competitiveness. In the face of massive amounts of production and operation data, if traditional quality control, analysis and accounting management methods are still used, it is obviously lacking in systematicness and comprehensiveness, and it is difficult to accurately evaluate and optimize key indicators of corporate operations.

[0003] Some of the more advanced existing methods also optimize business operations from a single dimension of indicators, such as the Six Sigma method for quality control, the ABC (Activity-Based Costing) method for cost accounting, and Lean Manufacturing for production efficiency. Although these methods are effective in specific areas, they lack a comprehensive framework to simultaneously consider multiple aspects of safety, quality, cost, delivery, and environment involving the entire business operation, which may lead to the sacrifice of performance in another dimension when optimizing one dimension. In addition, these existing methods often lack flexibility and are difficult to adapt to rapidly changing market conditions and diverse customer needs.

[0004] Technical content

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method, device and storage medium for comprehensive evaluation of production activities based on multi-dimensional indicators. By defining indicators of multiple dimensions, key parameters of the production operation process are modeled, algorithms that adapt to different data types and analysis requirements are implemented, and analysis data is provided in a visual manner to achieve the purpose of comprehensively evaluating the operating status of the enterprise.

[0006] In order to achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for comprehensive evaluation of production activities based on multi-dimensional indicators, the steps of the method comprising:

[0007] Divide the enterprise production activity data into six indicator dimensions: safety, quality, cost, delivery, environment, and morale;

[0008] Under each dimension, define the primary indicators and the secondary indicators decomposed from the primary indicators, and formulate the calculation logic of each indicator;

[0009] Using correlation analysis to find out the correlation indicators with high correlation with the corresponding primary indicators from a large number of secondary indicators;

[0010] Obtaining weights corresponding to the correlation indicators by calculating the information entropy of the correlation indicators;

[0011] The monthly indicator scoring targets of all the related indicators are calculated by weighted average with the corresponding weights to obtain the secondary indicator scores, and the scores of the primary indicators are calculated based on the scores of the secondary indicators and the corresponding weights;

[0012] The actual data of monthly production activities is presented through the scoring of the above indicators, and the cumulative value from January of the year to the present time is calculated to guide enterprises in evaluating their production processes.

[0013] Optionally, the method of using correlation analysis to find out correlation indicators with a high correlation with the corresponding primary indicators from a large number of secondary indicators includes:

[0014] Define the overall correlation coefficient ρ as the ratio of the covariance between two variables X and Y and the product of their standard deviations

[0015] Among them, variable X is the secondary indicator, and variable Y is the primary indicator to which the secondary indicator belongs;

[0016] As well as the sample covariance and standard deviation, we get the sample correlation coefficient γ

[0017]

[0018] According to the absolute value of the correlation coefficient γ, extremely strong correlation indicators, strong correlation indicators, moderate correlation indicators and weak correlation indicators are selected from high to low.

[0019] Optionally, the obtaining a weight corresponding to each of the correlation indicators by calculating the information entropy of the correlation indicators includes:

[0020] The correlation index is subjected to range normalization processing X1,X2,X3,...,X k is the correlation index, where X i ={x1,x2,...,x n}, X i Provides historical data for each secondary indicator;

[0021] Calculate the standardized information entropy E of each correlation index j , in

[0022]

[0023] The information entropy E1, E2, ..., E k, calculate the weight Get the weight W of each correlation index i .

[0024] Optionally, the monthly indicator scoring targets of all the correlation indicators are calculated by weighted average with corresponding weights to obtain the secondary indicator scores, and the scores of the primary indicators are calculated according to the scores of the secondary indicators and the corresponding weights, including constructing a comprehensive evaluation model: if there are n correlation indicators, recorded as X (n) , the evaluation training matrix E of multi-dimensional indicators is represented as: E = [X1W1, X2W2, X3W3, ..., X n W n ];

[0025] The annual multi-dimensional indicator scoring data matrix is ​​represented as follows:

[0026]

[0027] The indicator scores are calculated by the evaluation training matrix E and the scoring data matrix D.

[0028] Optionally, the score value of the normalization factor in the process of constructing the comprehensive evaluation model is:

[0029] Optionally, the first-level indicators of the safety dimension include but are not limited to the annual number of lost working days; the first-level indicators of the quality dimension include but are not limited to the first-time pass rate FTT and the process capability index CPK; the first-level indicators of the cost dimension include but are not limited to the poor quality cost rate; the first-level indicators of the delivery dimension include but are not limited to the production plan completion rate and the work-in-process inventory; the first-level indicators of the environmental dimension include but are not limited to unit energy consumption; the first-level indicators of the morale dimension include but are not limited to the employee turnover rate.

[0030] In a second aspect, the embodiment of the present application provides a comprehensive evaluation device for production activities based on multi-dimensional indicators, including:

[0031] The indicator storage module is used to store the first-level indicators defined in each of the six indicator dimensions of safety, quality, cost, delivery, environment, and morale, as well as the second-level indicators decomposed from the first-level indicators and the calculation logic of each indicator;

[0032] A correlation calculation module, used to find out the correlation indicators with high correlation with the corresponding primary indicators from a large number of the secondary indicators through correlation analysis;

[0033] A weight calculation module, used to obtain the weight corresponding to each of the correlation indicators by calculating the information entropy of the correlation indicators;

[0034] An indicator scoring module is used to store and construct a comprehensive evaluation model, wherein the evaluation model includes calculating the weighted average of the monthly indicator scoring targets of all the relevant indicators and the corresponding weights to obtain the secondary indicator scores, and calculating the scores of each primary indicator based on the scores of each secondary indicator and the corresponding weights;

[0035] The display module is used to present the actual data of monthly production activities based on the indicator scores, and to present the cumulative values ​​from January of the current year to the present time, so as to guide the enterprise in conducting production process evaluation.

[0036] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including a computer-readable storage medium storing a computer program and a processor. When the computer program is read and executed by the processor, a comprehensive assessment method for production activities based on multi-dimensional indicators as described in any one of the first aspects above is implemented.

[0037] The technical solution of this application can avoid the single-dimensional indicator evaluation in the existing technology, and establish an indicator quantitative evaluation model from six dimensions: safety, quality, cost, delivery, environment and morale, so as to fully cover the comprehensive perspective of enterprise production and operation. In addition, the process key parameter modeling in this technical solution can provide algorithms that adapt to different data types and analysis needs according to the situation of different enterprises. Finally, through the visualization of the monthly and annual trend changes of the indicator evaluation, the production and operation of the enterprise can be evaluated more objectively.

[0038] Instruction Manual

[0039] Figure 1 An overall flow chart of a comprehensive evaluation method for production activities based on multi-dimensional indicators provided in an embodiment of the present application;

[0040] Figure 2 A flowchart of a method for comprehensive evaluation of production activities based on multi-dimensional indicators provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0042] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0043] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0044] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0045] like Figure 1 to Figure 2 As shown, an embodiment of the present invention provides a method for comprehensive evaluation of production activities based on multi-dimensional indicators, the method comprising:

[0046] Step S1: Divide the enterprise production activity data into six indicator dimensions: safety, quality, cost, delivery, environment, and morale.

[0047] Step S2: Under each dimension, define the primary indicators and the secondary indicators decomposed from the primary indicators, and formulate the calculation logic of each indicator.

[0048] Among them, the safety (S) dimension is to ensure that safety measures are in place during the production process, including: reduction of work-related accidents indicators, annual loss of work days indicators, etc. The quality (Q) dimension, including the first pass rate (FTT) and the process capability index CPK, are important indicators for measuring product quality. A high first pass rate means a low scrap rate in the production process, and the CPK index reflects the stability and predictability of the production process. The cost (C) dimension, including indicators such as equipment comprehensive efficiency, material consumption and poor quality cost rate, are directly related to production costs and the economic benefits of the enterprise. By quantitatively analyzing such indicators, enterprises can reduce costs while maintaining product quality. The delivery (D) dimension includes: production plan completion rate and work-in-process inventory management, which are important indicators for evaluating delivery capabilities. Quick response to market and customer orders and effective inventory management can help companies improve customer satisfaction. The environment (E) dimension includes indicators such as unit product energy consumption and waste disposal, which can improve the efficiency of enterprise resource utilization. The morale (M) dimension includes: employee turnover rate, etc., which can help improve the overall performance of the enterprise.

[0049] The above six dimensions can cover all aspects of the evaluation of the enterprise to the greatest extent, and are universal, and can evaluate the operation of the enterprise as a whole, making it convenient to check for omissions and fill in the gaps. Of course, the first-level indicators defined above can be expanded or adjusted according to the characteristics of different enterprises, and are used here as a preferred reference. After defining the first-level indicators, it is necessary to further decompose multiple second-level indicators and the calculation rules of each second-level indicator to avoid the evaluation of the indicators being too vague or out of touch with reality. Specifically, this embodiment takes the rubber production industry as an example to establish first-level indicators and second-level indicators, with reference to the following Tables 1 and 2.

[0050]

[0051] Table 1

[0052]

[0053] Table 2

[0054] Step S3: Use correlation analysis to find correlation indicators with a high correlation with the corresponding primary indicators from a large number of secondary indicators.

[0055] Specifically, the correlation coefficient analysis is adopted to define the overall correlation coefficient ρ, which is defined as the ratio of the covariance between two variables X and Y and the product of their standard deviations. Variable X is a secondary indicator, and variable Y is a primary indicator to which the secondary indicator belongs.

[0056] And, estimate the sample covariance and standard deviation to obtain the sample correlation coefficient γ.

[0057]

[0058] When the two indicator variables are linearly correlated and there is no influence of extreme values, γ can express the strength of the linear correlation between the two indicator variables. The larger the absolute value of γ, the stronger the correlation between the indicators. The following principles are followed:

[0059] ① When γ>0, it indicates that the two indicator variables are positively correlated, that is, the larger the value of one indicator variable, the larger the value of the other indicator variable; ② When γ<0, it indicates that the two indicator variables are negatively correlated, that is, the larger the value of one indicator variable, the smaller the value of the other indicator variable; ③ When γ=0, it indicates that the two indicator variables are not linearly correlated and are not included in the indicator evaluation system of this application.

[0060] The correlation strength of the γ variable is determined by the following range: extremely strong correlation (0.8-1.0), strong correlation (0.6-0.8), moderate correlation (0.4-0.6), weak correlation (0.2-0.4), and no correlation (0.0-0.2). Enterprises can select the corresponding secondary correlation indicators within the required correlation strength according to the values ​​of γ and ρ. Therefore, each enterprise can select relevant indicators in a targeted manner according to its own production and operation conditions, and can dynamically adjust them according to development conditions to improve the accuracy and reliability of model indicator evaluation.

[0061] Step S4: Obtain the weight corresponding to each correlation indicator by calculating the information entropy of the correlation indicator.

[0062] Based on the relevant secondary indicators obtained above, the weights are objectively calculated using the amount of information carried by the data and the variability of the indicators. Define the information entropy of the indicator. The smaller the information entropy of an indicator, the greater the variability of the indicator, the more information it provides, the greater the role it can play in the comprehensive evaluation, and the greater the weight. On the contrary, if the information entropy of the indicator is larger, it means that the indicator has a small degree of variability, less information, a small role in the comprehensive evaluation, and a small weight. The specific steps for determining the weight are as follows:

[0063] S41. Data standardization:

[0064] The obtained secondary indicators with high correlation X1, X2, X3, ..., X k , where X i ={x1,x2,...,x n}, Xi is the historical data of each secondary indicator, and the range is standardized. Get the standardized data of each dimension indicator Y1,Y2,Y3,...,Y k .

[0065] S42, information entropy E of each correlation index j calculate:

[0066] in

[0067] S43. Calculation of weights of various correlation indicators:

[0068] The information entropy of each secondary index of correlation is obtained in turn: E1, E2, ..., E k , calculate the corresponding weight Get the weight W of each relevant secondary indicator i .

[0069] Step S5: Calculate the weighted average of the monthly indicator scoring targets and corresponding weights of all relevant indicators to obtain the secondary indicator scores, and calculate the scores of each primary indicator based on the scores of each secondary indicator and the corresponding weights.

[0070] Specifically, it includes establishing a comprehensive evaluation model: if there are n correlation indicators, recorded as X (n) , the evaluation training matrix E of multi-dimensional indicators is represented as: E = [X1W1, X2W2, X3W3, ..., X n W n ];

[0071] The annual multi-dimensional indicator scoring data matrix is ​​represented as follows:

[0072]

[0073] Among them, the score value of the normalization factor is:

[0074] The indicator scores are calculated by the evaluation training matrix E and the scoring data matrix D.

[0075] Step S6: Visually present the actual data of monthly production activities through indicator scoring, and calculate the cumulative value from January of the current year to the present time to guide the enterprise to conduct production process evaluation.

[0076] For example, the safety facility integrity rate, the monthly target is 100%, the actual value is 100%, and the indicator score is 100 points. The safety hazard rectification rate, the monthly target is 100%, the actual completion is 95%, and the indicator score is 95 points. Furthermore, the monthly score of the safety dimension is calculated according to the weight of each indicator. If the score is low, the indicator of this dimension is improved.

[0077] For example, the historical operating data of the safety facility integrity rate is 90, 92, 95, 88, 96, 93, 91, 94, 89, 97, 95, 90, the maximum value = 97 and the minimum value = 88. After the range standardization process, we get Y1, Y2, Y3, ..., Y k0.222, 0.444, 0.778, 0.889, 0.556, 0.333, 0.667, 0.111, 1, 0.778, 0.222. According to p ij Calculating the logic, we get p as 0.222, 0, 0.778, 0.444, 0.889, 0.556, 0.333, 0.667, 0.111, 1, 0.233, 0.333. The calculated weight is 0.6808.

[0078] Similarly, the weight of the safety hazard rectification rate is calculated to be 0.3192, and the weights of each secondary indicator are obtained. The comprehensive score = (safety facility integrity rate score × weight) + (safety hazard rectification rate score × weight), and the comprehensive score = (100 × 0.6808) + (95 × 0.3192) = 50 + 47.5 = 98.404. The lower the score, the worse the overall assessment of SQCDEM is, and the secondary indicators with larger weights need to be improved.

[0079] In summary, this technical solution can provide a comprehensive assessment method for the production industry to evaluate and optimize these key dimensions, and the evaluation indicators can be adjusted in real time and dynamically to adapt to the development characteristics of different enterprises or the operational priorities of enterprises at different stages, providing more reliable indicator evaluation values ​​and visual display.

[0080] In addition, the embodiment of the present application provides a comprehensive evaluation device for production activities based on multi-dimensional indicators, including:

[0081] The indicator storage module is used to store the six indicator dimensions of safety, quality, cost, delivery, environment, and morale, the first-level indicators defined in each dimension, the second-level indicators decomposed from the first-level indicators, and the calculation logic of each indicator;

[0082] A correlation calculation module is used to find correlation indicators with high correlation with the corresponding primary indicators from a large number of secondary indicators through correlation analysis;

[0083] A weight calculation module, used to obtain the weight corresponding to each correlation indicator by calculating the information entropy of the correlation indicator;

[0084] The indicator scoring module is used to store and build a comprehensive evaluation model. The evaluation model includes calculating the weighted average of the monthly indicator scoring targets of all relevant indicators and the corresponding weights to obtain the secondary indicator scores, and calculating the scores of each primary indicator based on the scores of each secondary indicator and the corresponding weights;

[0085] The display module is used to present the actual data of monthly production activities based on the indicator scores, and to present the cumulative values ​​from January of the current year to the present time, so as to guide enterprises in evaluating the production process.

[0086] The technical effect that can be achieved by this embodiment is basically the same as the technical effect that can be achieved by the aforementioned comprehensive evaluation method of production activities based on multi-dimensional indicators, and will not be repeated here.

[0087] At the same time, an embodiment of the present application provides a computer-readable storage medium, including a computer-readable storage medium storing a computer program and a processor. When the computer program is read and executed by the processor, a comprehensive evaluation method for production activities based on multi-dimensional indicators as described above is implemented.

[0088] The technical effect that can be achieved by this embodiment is basically the same as the technical effect that can be achieved by the aforementioned comprehensive evaluation method of production activities based on multi-dimensional indicators, and will not be repeated here.

[0089] Since the system / device described in the above embodiments of the present invention is a system / device used to implement the method of the above embodiments of the present invention, a person skilled in the art can understand the specific structure and deformation of the system / device based on the method described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the method of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0090] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0092] The electronic device includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0093] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0094] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0095] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A comprehensive evaluation method for production activities based on multi-dimensional indicators, characterized by include: Divide the enterprise production activity data into six indicator dimensions: safety, quality, cost, delivery, environment, and morale; Under each dimension, define the primary indicators and the secondary indicators decomposed from the primary indicators, and formulate the calculation logic of each indicator; Using correlation analysis to find out the correlation indicators with high correlation with the corresponding primary indicators from a large number of the secondary indicators; Obtaining weights corresponding to the correlation indicators by calculating the information entropy of the correlation indicators; The monthly indicator scoring targets of all the related indicators are calculated by weighted average with the corresponding weights to obtain the secondary indicator scores, and the scores of the primary indicators are calculated based on the scores of the secondary indicators and the corresponding weights; The actual data of monthly production activities is presented through the scoring of the above indicators, and the cumulative value from January of the year to the present time is calculated to guide enterprises in evaluating their production processes.

2. A comprehensive evaluation method for production activities based on multi-dimensional indicators as claimed in claim 1, characterized in that The method of using correlation analysis to find correlation indicators with a high correlation with the corresponding primary indicators from a large number of secondary indicators includes: The overall correlation coefficient ρ is defined as the ratio of the covariance between two variables X and Y and the product of their standard deviations. Among them, variable X is the secondary indicator, and variable Y is the primary indicator to which the secondary indicator belongs; As well as the sample covariance and standard deviation, we get the sample correlation coefficient γ According to the absolute value of the correlation coefficient γ, extremely strong correlation indicators, strong correlation indicators, moderate correlation indicators and weak correlation indicators are selected from high to low.

3. A comprehensive evaluation method for production activities based on multi-dimensional indicators as claimed in claim 1, characterized in that The step of obtaining the weight corresponding to each of the correlation indicators by calculating the information entropy of the correlation indicators includes: The correlation index is subjected to range normalization processing X1,X2,X3,...,X k is the correlation index, where X i ={x1,x2,...,x n }, X i Provides historical data for each secondary indicator; Calculate the standardized information entropy E of each correlation index j , in The information entropy E1, E2, ..., E k , calculate the weight Get the weight W of each correlation index i .

4. A comprehensive evaluation method for production activities based on multi-dimensional indicators as claimed in claim 1, characterized in that The monthly indicator scoring targets of all the related indicators are calculated by weighted average with the corresponding weights to obtain the secondary indicator scores, and the scores of the primary indicators are calculated according to the scores of the secondary indicators and the corresponding weights, including the construction of a comprehensive evaluation model: If there are n correlation indicators, denoted as X (n) , the evaluation training matrix E of multi-dimensional indicators is represented as: E = [X1W1, X2W2, X3W3, ..., X n W n ]; The annual multi-dimensional indicator scoring data matrix is ​​represented as follows: The indicator scores are calculated by the evaluation training matrix E and the scoring data matrix D.

5. A comprehensive evaluation method for production activities based on multi-dimensional indicators as claimed in claim 4, characterized in that The score value of the normalization factor in the process of constructing the comprehensive evaluation model is:

6. As claimed in claim 1, a comprehensive evaluation method for production activities based on multi-dimensional indicators, the first-level indicators of the safety dimension include but are not limited to the annual number of lost working days; the first-level indicators of the quality dimension include but are not limited to the first time pass rate FTT and the process capability index CPK; the first-level indicators of the cost dimension include but are not limited to the defective quality cost rate; the first-level indicators of the delivery dimension include but are not limited to the production plan completion rate and the work-in-progress inventory; the first-level indicators of the environment dimension include but are not limited to unit energy consumption; the first-level indicators of the morale dimension include but are not limited to employee turnover rate.

7. A comprehensive evaluation device for production activities based on multi-dimensional indicators, characterized in that include: The indicator storage module is used to store the first-level indicators defined in each of the six indicator dimensions of safety, quality, cost, delivery, environment, and morale, as well as the second-level indicators decomposed from the first-level indicators and the calculation logic of each indicator; A correlation calculation module, used to find out the correlation indicators with high correlation with the corresponding primary indicators from a large number of the secondary indicators through correlation analysis; A weight calculation module, used to obtain the weight corresponding to each of the correlation indicators by calculating the information entropy of the correlation indicators; An indicator scoring module is used to store and construct a comprehensive evaluation model, wherein the evaluation model includes calculating the weighted average of the monthly indicator scoring targets of all the relevant indicators and the corresponding weights to obtain the secondary indicator scores, and calculating the scores of each primary indicator based on the scores of each secondary indicator and the corresponding weights; The display module is used to present the actual data of monthly production activities based on the indicator scores, and to present the cumulative values ​​from January of the current year to the present time, so as to guide the enterprise in conducting production process evaluation.

8. A computer-readable storage medium, characterized in that: It comprises a computer-readable storage medium storing a computer program and a processor. When the computer program is read and executed by the processor, the method for comprehensive evaluation of production activities based on multi-dimensional indicators as claimed in any one of claims 1 to 6 is implemented.

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