An intelligent manufacturing capability maturity evaluation method, device and equipment

Abstract

The application provides a kind of intelligent manufacturing capability maturity evaluation method, device and equipment, the method comprises: the intelligent manufacturing capability maturity evaluation index system of multiple different dimensions covering target enterprise is constructed;Obtain the evaluation information set of the evaluation expert set to target enterprise;Based on evaluation information set, construct probability language decision matrix framework;Based on BWM algorithm, evaluation information set is handled, to obtain the weight of different evaluation attributes involved in probability language decision matrix framework, form probability language decision matrix based on weight and probability language decision matrix framework;Based on VIKOR algorithm, probability language terms in probability language decision matrix are handled, and the intelligent manufacturing capability maturity level evaluation result of target enterprise is obtained.The method of the application can objectively and accurately collect expert evaluation information to evaluate the intelligent manufacturing capability maturity of enterprise.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of computer, and particularly relates to an intelligent manufacturing capability maturity evaluation method, device and equipment. BACKGROUND

[0002] At present, the industry is also in the process of a new round of transformation and upgrading, and will take intelligent manufacturing as the key direction of research and development. Intelligent manufacturing refers to deploying emerging technologies into manufacturing assets to form an integrated, autonomous and flexible environment to respond to changing customer needs. Intelligent manufacturing not only brings a lot of benefits to enterprises, such as reducing costs, improving quality, standardizing work processes, etc., but also promotes multi-dimensional integration development of enterprises and realizes enterprise transformation and upgrading. Therefore, in the current era of rapid development of information technology, vigorously and continuously developing intelligent manufacturing is the best way to help enterprises improve their competitiveness. In the process of intelligent manufacturing construction, regional governments, manufacturing industries and manufacturing enterprises urgently need relevant evaluation indexes and evaluation methods to evaluate the intelligent manufacturing capability maturity level (Intelligent Manufacturing Capability Maturity, IMCM) and determine the specific stage of development, so as to better recognize the development characteristics and shortcomings of themselves. On this basis, combined with the actual situation and important intelligent manufacturing development influencing factors, a more effective intelligent manufacturing development plan suitable for the region is formulated.

[0003] Comprehensive domestic and foreign research finds that although scholars have made certain achievements in the research of IMCM, there are still many aspects to be improved, especially the lack of unified definition of the concept of IMCM. In view of the development level and implementation path of intelligent manufacturing, scholars and institutions from different angles have put forward corresponding theoretical models, such as the intelligent manufacturing readiness level model proposed by Jung, the IMPULS industry 4.0 readiness (IR) model proposed by Flatt, the model of smart industry readiness index (SIRI), the readiness assessment model for industry 4.0 (RAMI) and the intelligent manufacturing capability maturity model, etc.

[0004] The current intelligent manufacturing development is still in the exploratory stage. Different enterprises lack a unified understanding of intelligent manufacturing, and their positioning and development of intelligent manufacturing are unclear. Therefore, effective methods are needed to guide the distributed implementation and systematic promotion of intelligent manufacturing construction. Among them, finding a scientific intelligent manufacturing capability evaluation method has become a key problem. Intelligent manufacturing capability maturity can quantify the development of enterprise intelligent manufacturing. Through model evaluation, it helps enterprises to continuously promote intelligent manufacturing. Therefore, many researchers have explored the intelligent manufacturing capability maturity evaluation method and the construction of the evaluation model. A digital transformation capability maturity model (DX-CMM) is proposed to help improve enterprise intelligent manufacturing capability by providing current digital transformation capability / maturity determination, gap analysis derivation, and standardized ways to create a comprehensive improvement roadmap. Wagire uses fuzzy analytic hierarchy process to analyze industrial 4.0 maturity evaluation model, and uses automobile parts manufacturing enterprises to implement evaluation. Colli proposes a new 360 digital maturity evaluation method based on learning theory model, which can make the evaluation results of industrial 4.0 capability maturity specific. Lee proposes an intelligent factory intelligent evaluation framework based on the concept of operation management, which uses network analysis to comprehensively evaluate the intelligent level of the factory. Guo

[18] proposes an intelligent manufacturing system evaluation model and algorithm based on pattern recognition and big data, and gives reasonable suggestions for intelligent manufacturing system construction.

[0005] In summary, around the problem of intelligent manufacturing capability maturity, scholars mainly explore from the aspects of concept characteristics, index system and evaluation method. Although some research results have been achieved, the overall research is still in the process of exploration, and there is still room for improvement. For the research of intelligent manufacturing capability maturity index system, most scholars choose the research object as a certain link of production, logistics and other intelligent manufacturing, and ignore the consideration of all factors of intelligent manufacturing, so that the evaluation results obtained by using these indexes cannot comprehensively and accurately reflect the intelligent manufacturing capability maturity of enterprises. In terms of evaluation method, most of the existing researches use traditional evaluation methods such as AHP method and factor analysis method. Although these methods retain most of the original information of the indexes, the determination of the weight is influenced by the experience of experts, which is inevitably subjective. Moreover, the language granularity between experts is not considered, and the fuzziness and hesitation of expert evaluation information are not taken into account. SUMMARY

[0006] The technical problem to be solved by the present application is to provide an intelligent manufacturing capability maturity evaluation method, device and equipment which can objectively and accurately collect expert evaluation information to evaluate the intelligent manufacturing capability maturity of enterprises.

[0007] The content of the application comprises an intelligent manufacturing capability maturity evaluation method, comprising:

[0008] Building an intelligent manufacturing capability maturity evaluation index system covering multiple different dimensions of the target enterprise, including manufacturing foundation dimension, digital intelligence dimension, value performance dimension and strategic organization dimension;

[0009] Obtaining an evaluation information set of the target enterprise based on the intelligent manufacturing capability maturity evaluation index system, a preset evaluation probability language term set by an evaluation expert set, the evaluation expert set containing experts in different fields, and the evaluation information set including evaluation information given by the evaluation expert set based on different evaluation attributes;

[0010] Building a probability language decision matrix framework based on the evaluation information set;

[0011] Processing the evaluation information set based on the BWM algorithm to obtain the weights of different evaluation attributes involved in the probability language decision matrix framework, and forming a probability language decision matrix based on the weights and the probability language decision matrix framework;

[0012] Processing the probability language terms of different evaluations in the probability language decision matrix based on the VIKOR algorithm to obtain the intelligent manufacturing capability maturity level evaluation result of the target enterprise.

[0013] In some embodiments, the maturity evaluation index system contains multiple indicators at different levels, each level containing multiple indicators related to manufacturing foundation dimension, digital intelligence dimension, value performance dimension and strategic organization dimension.

[0014] In some embodiments, the method further comprises:

[0015] Building a probability language term set L={l α (p α )∣l α ∈S,p α ≥1,α=1,2,…,#L},

[0016] Wherein, l α (p α ) is the basic constituent element of the probability language term set, l α is the language term, p α is the probability information of the corresponding language term, #L represents the cardinality of the probability language term set L, and S is the preset language term set

[0017] In some embodiments, the method further comprises:

[0018] Determining an evaluation expert set Z={z1,z2,…,zG};

[0019] determining a weight vector of each expert in the set of evaluation experts, forming a weight vector set υ = (v1, v2, …, vn), which satisfies the condition and satisfies the condition G

[0020] determining a probability linguistic term set of the set of evaluation experts based on the set of evaluation experts and the weight vector set:

[0021]

[0022] wherein q g is the probability information of the linguistic term l g in L γ and can be given by the following formula:

[0023]

[0024] wherein, is the probability information of the linguistic term l g provided by the expert z

[0025] In some embodiments, the constructing the probability linguistic decision matrix framework based on the set of evaluation information comprises:

[0026] determining the evaluation attributes involved based on the set of evaluation information, and constructing a set of evaluation attributes;

[0027] determining a set of intelligent manufacturing maturity matching the set of evaluation attributes;

[0028] constructing the probability linguistic decision matrix framework based on the set of evaluation information, the set of evaluation attributes, and the set of intelligent manufacturing maturity.

[0029] In some embodiments, the processing the set of evaluation information based on the BWM algorithm to obtain the weight of different evaluation attributes involved in the probability linguistic decision matrix framework comprises:

[0030] obtaining the optimal evaluation attribute and the worst evaluation attribute selected and determined by the set of evaluation experts;

[0031] obtaining the importance comparison results between different evaluation attributes and the optimal evaluation attribute by the set of evaluation experts, and forming a first probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results;

[0032] obtaining the importance comparison results between different evaluation attributes and the worst evaluation attribute by the set of evaluation experts, and forming a second probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results;​​

[0033] The scores of the first probability language vector and the second probability language vector are calculated based on a score formula to obtain the first vector and the second vector, respectively;

[0034] A weight vector solving model is constructed: The l bi , and the l wi are the first vector and the second vector of the i-th evaluation attribute, respectively, the w i is the weight of the i-th evaluation attribute, and the ξ is a judgment value. The weight vector solving model is solved by finding the minimum ξ to obtain a reasonable distribution of the weight vector;

[0035] The weights of different evaluation attributes are determined based on the first and second vectors and the VIKOR algorithm participating in the solving of the weight vector solving model.

[0036] In some embodiments, the VIKOR algorithm is used to process the probability language terms of different evaluations in the probability language decision matrix to obtain the intelligent manufacturing capability maturity level evaluation result of the target enterprise, including:

[0037] The probability language terms of different evaluations in the probability language decision matrix are processed based on a score function to calculate and determine the scores of the target enterprise on each evaluation attribute;

[0038] The positive ideal value and the negative ideal value of the target enterprise on each evaluation attribute are determined based on the scores;

[0039] The maximum group utility value and the minimum individual regret value of the target enterprise on different intelligent manufacturing capability maturity levels are calculated based on the positive ideal value and the negative ideal value;

[0040] The comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise is calculated based on the maximum group utility value and the minimum individual regret value.

[0041] In some embodiments, the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise is calculated based on the maximum group utility value and the minimum individual regret value, including:

[0042] The maximum group utility value and the minimum individual regret value are brought into the following formula to calculate the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise:

[0043]

[0044] In the formula, Q j is the comprehensive evaluation value of the target enterprise j, S j is the maximum group utility value of the target enterprise j, and R jis the minimum individual regret value of the target enterprise j, θ is a decision mechanism adjustment coefficient in [0, 1], θ>0.5 represents that the decision is mainly based on the principle of maximizing group utility, θ<0.5 represents that the decision is mainly based on the principle of minimizing individual regret, and the values of S, Q and R are inversely proportional to the intelligent manufacturing capability maturity.

[0045] Another embodiment of the present application also provides an intelligent manufacturing capability maturity evaluation device, comprising:

[0046] A first construction module is configured to construct an intelligent manufacturing capability maturity evaluation index system covering multiple different dimensions of a target enterprise, wherein the multiple different dimensions include manufacturing foundation dimension, digital intelligence dimension, value performance dimension and strategic organization dimension.

[0047] An obtaining module is configured to obtain a set of evaluation information of the target enterprise based on the intelligent manufacturing capability maturity evaluation index system and a set of preset evaluation probability linguistic terms by a set of evaluation experts, wherein the set of evaluation experts includes experts in different fields, and the set of evaluation information includes evaluation information given by the set of evaluation experts based on different evaluation attributes.

[0048] A second construction module is configured to construct a probability linguistic decision matrix framework according to the set of evaluation information.

[0049] A first processing module is configured to process the set of evaluation information according to a BWM algorithm to obtain weights of different evaluation attributes involved in the probability linguistic decision matrix framework, and to form a probability linguistic decision matrix based on the weights and the probability linguistic decision matrix framework.

[0050] A second processing module is configured to process probability linguistic terms of different evaluations in the probability linguistic decision matrix according to a VIKOR algorithm to obtain an intelligent manufacturing capability maturity level evaluation result of the target enterprise.

[0051] Another embodiment of the present application also provides an electronic device, comprising

[0052] at least one processor; and

[0053] a memory in communication with the at least one processor; wherein

[0054] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the intelligent manufacturing capability maturity evaluation method according to any one of the above embodiments.

[0055] The beneficial effects of the present application include comprehensively considering indexes in four dimensions of manufacturing foundation dimension, digital intelligence dimension, value performance dimension and strategic organization dimension of enterprises, and constructing an intelligent manufacturing maturity evaluation index system according to the indexes, constructing an evaluation probability language term set considering semantic information, preference attitude and hesitation mentality of experts, making the determined weight of each evaluation attribute more accurate and realistic based on the algorithm for participating in the evaluation index weight based on the probability representation. In addition, a probability language decision matrix is constructed based on the index attribute weight and expert evaluation information, and an intelligent manufacturing maturity evaluation model driven by expert knowledge is established based on the matrix and combined with the VIKOR algorithm, which is used for quickly and effectively evaluating the intelligent manufacturing maturity of enterprises, so that the specific grade of the intelligent manufacturing maturity of different enterprises can be determined, high reference value data can be obtained, and the enterprises can understand the shortcomings in the process of changing from traditional manufacturing to intelligent manufacturing through the determination process, thereby promoting the high-quality and efficient improvement of the manufacturing development level.

[0056] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0057] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0058] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate embodiments of the present application, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0059] Figure 1 The flowchart of the intelligent manufacturing capability maturity evaluation method of the present application.

[0060] Figure 2 The application flowchart of the intelligent manufacturing capability maturity evaluation method of the present application.

[0061] Figure 3 The structure block diagram of the intelligent manufacturing capability maturity evaluation device of the present application. DETAILED DESCRIPTION

[0062] In the following, specific embodiments of the present application will be described in detail with the help of the drawings, but not as a limitation of the present application.

[0063] It is to be understood that various modifications can be made to the embodiments disclosed herein. Consequently, the description herein is not to be considered as limiting, but merely as a description of exemplary embodiments. Other modifications will be readily apparent to those skilled in the art with the benefit of this disclosure.

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and, together with the general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0065] These and other characteristics of the present application will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings.

[0066] It should also be understood that, although the present application has been described above in terms of certain embodiments, many other modifications will be apparent to those skilled in the art in light of the teachings herein, and such modifications are intended to fall within the scope of the application as defined by the appended claims.

[0067] The above and other aspects, features, and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which:

[0068] Specific embodiments of the present disclosure are described hereinafter, with reference to the drawings; however, it will be understood that the disclosed embodiments are merely examples of the present disclosure, which can be embodied in various ways. Well-known and / or redundant functions and structures are not described in detail to avoid obscuring the present disclosure unnecessarily. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but are merely as a basis for the claims and a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriate detailed structure.

[0069] The specification can use phrases such as "in one embodiment", "in another embodiment", "in yet another embodiment", or "in other embodiments", which can refer to one or more of the same or different embodiments under the present disclosure.

[0070] Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0071] As Figure 1 shown, the embodiments of the present application provide a method for evaluating intelligent manufacturing capability maturity, comprising:

[0072] S1: constructing an intelligent manufacturing capability maturity evaluation index system covering a plurality of different dimensions of a target enterprise, the plurality of different dimensions comprising a manufacturing foundation dimension, a digital intelligence dimension, a value performance dimension, and a strategic organization dimension;

[0073] S2: Obtain an evaluation expert set based on the intelligent manufacturing capability maturity evaluation index system, a preset evaluation probability language term set, and evaluation information of a target enterprise, the evaluation expert set including experts in different fields, and the evaluation information set including evaluation information given by the evaluation expert set based on different evaluation attributes (i.e., different evaluation dimensions);

[0074] S3: Construct a probability language decision matrix architecture based on the evaluation information set;

[0075] S4: Process the evaluation information set based on the BWM algorithm to obtain weights of different evaluation attributes involved in the probability language decision matrix architecture, and form a probability language decision matrix based on the weights and the probability language decision matrix architecture;

[0076] S5: Process different evaluation probability language terms in the probability language decision matrix based on the VIKOR algorithm to obtain an intelligent manufacturing capability maturity level evaluation result of the target enterprise.

[0077] Based on the above content, it can be known that the method proposed in this embodiment is actually an intelligent manufacturing capability maturity evaluation method based on expert knowledge driving, which includes representing expert knowledge structure by using a probability language term set, solving weights of capability indexes at different levels by using the BWM method, and quantitatively evaluating the intelligent manufacturing implementation capability of an enterprise by using the VIKOR method, so as to realize comprehensive integration of evaluation information from qualitative to quantitative, thereby providing reference opinions for the development of intelligent manufacturing of an enterprise.

[0078] Exemplarily, the embodiment first constructs an evaluation index system capable of covering multiple different dimensions, which is used for experts to evaluate the intelligent manufacturing capability maturity of the target enterprise. The index system can include multiple evaluation indexes respectively related to different dimensions, and the experts can evaluate the target enterprise based on the evaluation indexes. When evaluating, in order to ensure uniform specifications and be able to describe multiple psychological factors of the experts during evaluation, the embodiment uses a probability language term set for evaluation to make the experts evaluate, that is, the evaluation language is uniform, and the evaluation information represented by the same evaluation language is consistent. At this time, the system obtains evaluation information that can be directly processed. That is, at this time, the system enters the data processing level. Specifically, after the system obtains the evaluation information in the embodiment, a probability language decision matrix architecture is constructed based on the evaluation information, and then the obtained evaluation information is processed based on the BWM algorithm to determine the weights of different evaluation attributes involved in the constructed architecture, that is, the system analyzes and processes the evaluation information based on the BWM algorithm to determine the weight of the evaluation index for different evaluation attributes, that is, the degree of attention should be given, and the attention degree is positively correlated with the influence of the index of the evaluation attribute on the actual maturity of the enterprise. When the weights of each different evaluation attribute are adjusted and determined, the system can update the constructed decision matrix architecture based on the weights to obtain the final decision matrix. In order to avoid that the obtained decision matrix has limitations when making decisions, the system in the embodiment also processes the different evaluation probability language terms in the decision matrix using the VIKOR method to comprehensively analyze the evaluation results under each evaluation attribute, that is, to comprehensively analyze the evaluation results under each dimension, and finally obtain the intelligent manufacturing capability maturity evaluation result that can be consistent with the actual situation of the enterprise. Therefore, the method in the embodiment first obtains the professional evaluation data of experts in each field by limiting the evaluation language, and then the system analyzes and processes the obtained evaluation data in multiple layers. The processing process is impossible for humans to participate, and is a process of data analysis and data processing realized by the system based on machine learning capability. Finally, an evaluation result that discards the subjective color of experts, retains the professional evaluation of experts, and can objectively describe the actual manufacturing capability of the enterprise is obtained, so that the evaluation result is more consistent with the actual situation of the enterprise and has more reference significance.

[0079] The above scheme of the embodiment comprehensively considers the indexes in the manufacturing foundation dimension, the digital intelligence dimension, the value performance dimension, and the strategic organization dimension, and accordingly constructs an intelligent manufacturing maturity evaluation index system. Meanwhile, the evaluation probability language term set is constructed by considering the semantic information, preference attitude, and hesitation mentality of experts, and the algorithm for participating in the evaluation index weight based on the probability expression of the evaluation information is used to make the weight of each evaluation attribute more accurate and realistic. In addition, the probability language decision matrix is constructed based on the index attribute weight and the expert evaluation information, and the intelligent manufacturing maturity evaluation model driven by expert knowledge is established based on the matrix and combined with the VIKOR algorithm, so that the intelligent manufacturing maturity of enterprises can be quickly and effectively evaluated through the evaluation model. Therefore, the method provided in the embodiment can provide convenience for different enterprises to determine the specific level of intelligent manufacturing maturity, so that the enterprises can obtain evaluation data with high reference value, help the enterprises understand the deficiencies in the process of transforming from traditional manufacturing to intelligent manufacturing through the determination process, and promote the high-quality and efficient improvement of the manufacturing development level.

[0080] Specifically, the method of the embodiment constructs a five-level intelligent manufacturing capability maturity model, and the set formed by the five levels is G IMCM , which can be expressed as: G IMCM ={G1, G2, G3, G4, G5}, in the formula, G1 represents the planning level, G2 represents the specification level, G3 represents the integration level, G4 represents the optimization set, and G5 represents the leading level. Different maturity levels have different characteristics and different judgment conclusions.

[0081] The maturity evaluation index system includes multiple indexes of different levels, and each level includes multiple indexes about the manufacturing foundation dimension, the digital intelligence dimension, the value performance dimension, and the strategic organization dimension.

[0082] For example, the evaluation index system in the embodiment involves four dimensions of manufacturing foundation, digital construction, product benefit, and enterprise planning, 16 secondary evaluation indexes, and 50 tertiary indexes. According to the requirements of maturity, each index can be divided into five levels, and the intelligent manufacturing production requirements can be evaluated according to the corresponding intelligent manufacturing dimensions, and the overall intelligent manufacturing capability of the enterprise can be evaluated. The embodiment expands the value performance dimension based on the development needs of the enterprise, considers four secondary indexes of administrative management, cost performance, economic benefit, and innovation capability, and constructs three-level indexes for the strategic organization dimension from strategic planning and talent construction. The embodiment also expands the sub-indexes of emerging industry, including eight tertiary indexes of personalized customization, remote control, collaborative manufacturing, green sustainability, maintenance and repair, human factors engineering, agile development, and man-machine integration. Of course, the specific index content is not limited to the above, and other indexes can be added according to the actual situation of the enterprise.

[0083] In an embodiment, as shown in Figure 2 Fig. 1, when constructing the probabilistic language term set, the method comprises:

[0084] S6: Constructing the probabilistic language term set L = {l α (p α ) | l α ∈ S, p α ≥ 1, α = 1, 2, …, #L},

[0085] wherein l α (p α ) is the basic constituent element of the probabilistic language term set, l α is the language term, p α is the probability information of the corresponding language term, #L represents the cardinality of the probabilistic language term set L, and S is the preset language term set

[0086] Based on the establishment of the probabilistic language term set, the uncertainty psychology and hesitation attitude of the evaluation experts during the evaluation can be better presented, and even the evaluation preferences of the evaluation experts can be presented.

[0087] Further, the method further comprises:

[0088] S7: Determining the evaluation expert set Z = {z1, z2, …, z G};

[0089] S8: Determining the weight vector of each expert in the evaluation expert set, forming the weight vector set υ = (v1, v2, …, v G ), wherein the weight vector satisfies the condition and the condition

[0090] S9: Determining the probabilistic language term set of the evaluation expert set based on the evaluation expert set and the weight vector set:

[0091]

[0092] In the formula, L is the expert group probability information, l γ is the language term of the expert group, p γ is the language term probability of the expert group, q g is the probability information of the language term l g in L γ , and can be given by the following formula:

[0093]

[0094] wherein, is the language term provided by the expert z g ​The formula represents that if expert 1 has (1, 2, 3) evaluations and expert 2 has only (1, 2) evaluations, the evaluation of expert 2 is completed by default as (1, 2, 3), but the probability of 3 is 0.

[0095] In another embodiment, the constructing a probabilistic linguistic decision matrix framework based on the evaluation information set comprises:

[0096] S10: determining the evaluation attributes involved based on the evaluation information set, and constructing an evaluation attribute set;

[0097] S11: determining a set of intelligent manufacturing maturity matching the evaluation attribute set;

[0098] S12: constructing a probabilistic linguistic decision matrix framework based on the evaluation information set, the evaluation attribute set, and the set of intelligent manufacturing maturity.

[0099] Exemplarily, the probabilistic linguistic decision matrix framework and the subsequent formed probabilistic linguistic decision matrix are essentially used for describing the intelligent manufacturing capability maturity (IMCM) problem of an enterprise. Specifically, in the intelligent manufacturing capability maturity rating process of a manufacturing enterprise, multiple field experts usually participate in the rating work, and in order to ensure the scientificity of the evaluation process, the overall capability of the enterprise is usually comprehensively evaluated from multiple angles (evaluation attributes). Let Z = {z1, z2, …, zn} be the set of field experts, G R = {r1, r2, …, rm} be the set of reference linguistic terms for evaluation, C = {c1, c2, …, cm} be the set of intelligent manufacturing maturity, A = {a1, a2, …, an} be the set of evaluation attributes, and the weight vector of the evaluation attributes be w = (w1, w2, …, wn). m n n When each evaluation expert evaluates the intelligent manufacturing capability of the enterprise, the expert needs to give the corresponding qualitative evaluation information on different evaluation attributes. The evaluation information of the expert on the intelligent manufacturing capability maturity of the enterprise can be comprehensively expressed as a set of probabilistic linguistic terms, and the evaluation information of all experts is further integrated to obtain the following probabilistic linguistic decision matrix (framework) (PLDM):

[0100]

[0101] Continuing to combine Figure 2 the evaluation information set based on the BWM algorithm to obtain the weights of different evaluation attributes involved in the probabilistic linguistic decision matrix framework, comprising:

[0102] ​​​S13: obtaining the optimal evaluation attribute and the worst evaluation attribute determined by the evaluation expert set selection;

[0103] S14: obtaining the importance comparison results between different evaluation attributes and the optimal evaluation attribute by the evaluation expert set, and forming a first probability language vector corresponding to each evaluation attribute based on the importance comparison results;

[0104] S15: obtaining the importance comparison results between different evaluation attributes and the worst evaluation attribute by the evaluation expert set, and forming a second probability language vector corresponding to each evaluation attribute based on the importance comparison results;

[0105] S16: calculating the scores of the first probability language vector and the second probability language vector based on the score formula respectively to obtain a first vector and a second vector respectively;

[0106] S17: constructing a weight vector solving model: The l bi , l wi are the first vector and the second vector of the i-th evaluation attribute, the w i is the weight of the i-th evaluation attribute, and the ξ is a judgment value. The solving model solves the reasonable distribution of the weight vector by finding the minimum ξ. The weight vector is relatively larger when the minimum value of the relative language proportion vector is relatively larger, and the weight is balanced by the absolute value, which is close to 0, indicating that the weight distribution is more reasonable.

[0107] S18: determining the weights of different evaluation attributes based on the first vector, the second vector, and the planning algorithm participating in the solution of the weight vector solving model.

[0108] Exemplarily, the above steps of the embodiment are based on a score function, and the BWM method is used to design attribute weights in a probability language environment. The BWM method is essentially a subjective method, which can better reflect the will and wishes of the decision maker, and has simple calculation, clear thinking, good consistency, and easy consensus.

[0109] Regarding the score function, for example, assume that S is a given language term set, L = {l α (p α ) | a = 1, 2, …, #L} is a defined probability language term set. The score function of L is defined as:

[0110] r α is the subscript of the language term l α ; based on the definition of the score function, the dispersion function of L is defined as:

[0111] Based on the scoring function and the deviation function, different probabilistic language term sets can be compared in size, according to the following rules:

[0112] 1) If S(L1) < S(L2), then

[0113] 2) If S(L1)>S(L2), then

[0114] 3) If S(L1) = S(L2), then compare their deviations: ① If D(L1) < D(L2), then ②If D(L1)>D(L2), then ③If D(L1)=D(L2), then L1~L2.

[0115] Furthermore, based on the definitions of the scoring function and the deviation function, a distance measure between any two probabilistic language term sets can be given:

[0116] Assumption For linguistic terminology set, probabilistic linguistic terminology set and Let L1 and L2 be defined on it, and satisfy the condition of equal cardinality, i.e., #L1 = #L2. Then the distance measure between L1 and L2 can be defined as:

[0117]

[0118] S(L1) and S(L2) are the score functions of the probabilistic language term sets L1 and L2, respectively; D(L1) and D(L2) are the deviation functions of the probabilistic language term sets L1 and L2, respectively.

[0119] Furthermore, when calculating the weights of the evaluation attributes, the following are included:

[0120] After discussion and consensus by the expert group, the evaluation attributes C = {c1, c2, ..., c...} were evaluated. n In this process, select the best and worst attributes and use them as reference attributes.

[0121] The expert panel discussed and compared the importance of all evaluation attributes and the optimal attribute, then compiled expert opinions to form the first probability language vector L. b =(L b1 ,L b2 ,…,L bn );

[0122] The expert panel discussed and compared the importance of all evaluation attributes and the worst-case attribute, then compiled expert opinions to form a second probability language vector l. w =(l w1 ,lw2 ,…,l wn );

[0123] The score of the first probability language vector L b =(L b1 ,L b2 ,…,L bn ) is calculated and normalized to obtain the first vector l b =(l b1 ,l b2 ,…,l bn ); wherein

[0124] The score of the second probability language vector L w =(L w1 ,L w2 ,…,L wn ) is calculated and normalized to form the second vector l w =(l w1 ,l w2 ,…,l wn ), wherein,

[0125] The following solution model of the weight vector w=(w1,w2,…,w n ) is established:

[0126] min w ξ

[0127] s.t.|w i -l bi |≤ξ,|w i -l wi |≤ξ,

[0128]

[0129] The above model is solved using mathematical programming software in combination with the first vector and the second vector to obtain the corresponding evaluation attribute weight w=(w1,w2,…,w n ).

[0130] In an embodiment, the VIKOR algorithm is used to process the probability language terms in the probability language decision matrix to obtain the intelligent manufacturing capability maturity level evaluation result of the target enterprise, including:

[0131] S19: The probability language terms in the probability language decision matrix are processed based on the score function to calculate the score of the target enterprise on each evaluation attribute;

[0132] S20: determining positive ideal values and negative ideal values of the target enterprise on each evaluation attribute based on the scores;

[0133] S21: calculating maximum group utility values and minimum individual regret values of the target enterprise on different intelligent manufacturing capability maturity levels based on the positive ideal values and the negative ideal values;

[0134] S22: calculating a comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values.

[0135] The calculation of the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values comprises:

[0136] S23: bringing the maximum group utility values and the minimum individual regret values into the following formula to calculate the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise:

[0137]

[0138] In the formula, Q j is the comprehensive evaluation value of the target enterprise j, S j is the maximum group utility value of the target enterprise j, R j is the minimum individual regret value of the target enterprise j, and θ ∈ [0, 1] is a decision mechanism adjustment coefficient, θ > 0.5 indicating that the decision is mainly based on the principle of maximizing group utility, and θ < 0.5 indicating that the decision is mainly based on the principle of minimizing individual regret. The values of S, Q and R are inversely proportional to the intelligent manufacturing capability maturity.

[0139] Specifically, the VIKOR method is a multi-attribute decision-making method based on ideal points. The principle is to comprehensively rank the candidate individuals according to the principle of maximizing group utility and minimizing individual regret, and the ranking result can effectively avoid the generation of reverse order and is more reasonable. When calculating the comprehensive evaluation value based on this method, it includes:

[0140] The probability language terms of different evaluations in the probability language decision matrix are processed based on the score function to calculate and determine the scores of the target enterprise on each evaluation attribute. Then, based on the score, the positive ideal values and the negative ideal values of each evaluation attribute of the candidate individual, i.e., the target enterprise, are determined.

[0141]

[0142] Then, the maximum group utility value S j and the minimum individual regret value R j, respectively, are:

[0143]

[0144] After that, the comprehensive evaluation value Q of the enterprise IMCM level of the target enterprise can be calculated j :

[0145]

[0146] Based on the above calculation, the comprehensive evaluation values corresponding to different evaluation attributes and different evaluation levels can be obtained. For example, the system needs to be repeatedly executed based on the method described above to obtain the comprehensive evaluation values corresponding to different evaluation attributes and different evaluation levels. After obtaining the comprehensive evaluation values corresponding to each evaluation attribute and different evaluation levels, the system can also sort multiple comprehensive evaluation values, so that the target enterprise can know the intelligent manufacturing maturity evaluation level of the enterprise in which dimensions is higher and which dimensions is weaker, and then more targetedly adjust the enterprise itself.

[0147] As shown in Figure 3 , another embodiment of the present application simultaneously provides an intelligent manufacturing capability maturity evaluation device, comprising:

[0148] A first construction module is configured to construct an intelligent manufacturing capability maturity evaluation index system covering multiple different dimensions of a target enterprise, wherein the multiple different dimensions include manufacturing foundation dimension, digital intelligence dimension, value performance dimension, and strategic organization dimension.

[0149] An obtaining module is configured to obtain an evaluation information set of the target enterprise based on the intelligent manufacturing capability maturity evaluation index system and a preset evaluation probability language term set by an evaluation expert set, wherein the evaluation expert set includes experts in different fields, and the evaluation information set includes evaluation information given by the evaluation expert set based on different evaluation attributes.

[0150] A second construction module is configured to construct a probability language decision matrix framework according to the evaluation information set.

[0151] A first processing module is configured to process the evaluation information set according to a BWM algorithm to obtain weights of different evaluation attributes involved in the probability language decision matrix framework, and to form a probability language decision matrix based on the weights and the probability language decision matrix framework.

[0152] A second processing module is configured to process probability language terms of different evaluations in the probability language decision matrix according to a VIKOR algorithm to obtain an intelligent manufacturing capability maturity level evaluation result of the target enterprise.

[0153] In some embodiments, the maturity evaluation index system includes multiple levels of indicators, each level containing multiple indicators related to manufacturing foundation, digital intelligence, value effectiveness, and strategic organization.

[0154] In some embodiments, the apparatus further includes:

[0155] The third building module is used to construct the probabilistic language terminology set L = {l α (p α )∣l α ∈S,p α ≥1, α=1,2,…,#L},

[0156] Among them, l α (p α ) is a basic building block of the probabilistic language terminology set, l α For linguistic terms, p α For the probability information of the corresponding linguistic terms, #L represents the cardinality of the probabilistic linguistic term set L, and S is the preset linguistic term set.

[0157] In some embodiments, the apparatus further includes:

[0158] The first determining module is used to determine the set of evaluation experts Z = {z1, z2, ..., z}. G};

[0159] The second determining module is used to determine the weight vector of each expert in the evaluation expert set, forming a weight vector set υ = (v1, v2, ..., v G The weight vector satisfies the condition and also satisfies the condition.

[0160] The third determining module is used to determine the probabilistic language terminology set of the evaluation expert set based on the evaluation expert set and the weight vector set.

[0161]

[0162] In the formula, q g For L g Chinese language terminology γ The probability information can be given by the following formula:

[0163]

[0164] in, For expert z g Provided language terminology The probability information.

[0165] In some embodiments, the constructing a probabilistic linguistic decision matrix framework based on the set of evaluation information comprises:

[0166] determining evaluation attributes involved based on the set of evaluation information, and constructing a set of evaluation attributes;

[0167] determining a set of intelligent manufacturing maturity matching the set of evaluation attributes;

[0168] constructing a probabilistic linguistic decision matrix framework based on the set of evaluation information, the set of evaluation attributes, and the set of intelligent manufacturing maturity.

[0169] In some embodiments, the processing the set of evaluation information based on the BWM algorithm to obtain weights of different evaluation attributes involved in the probabilistic linguistic decision matrix framework comprises:

[0170] obtaining the optimal evaluation attribute and the worst evaluation attribute selected and determined by the set of evaluation experts;

[0171] obtaining the importance comparison results between different evaluation attributes and the optimal evaluation attribute by the set of evaluation experts, and forming a first probabilistic linguistic vector corresponding to each evaluation attribute based on the importance comparison results;

[0172] obtaining the importance comparison results between different evaluation attributes and the worst evaluation attribute by the set of evaluation experts, and forming a second probabilistic linguistic vector corresponding to each evaluation attribute based on the importance comparison results;

[0173] calculating scores of the first probabilistic linguistic vector and the second probabilistic linguistic vector based on a score formula, to obtain the first vector and the second vector, respectively;

[0174] constructing a weight vector solving model: the l bi , the l wi the first vector and the second vector of the i-th evaluation attribute, respectively, the w i the weight of the i-th evaluation attribute, the ξ is a judgment value, and the solving model solves the reasonable distribution of the weight vector by finding the smallest ξ;

[0175] determining the weights of different evaluation attributes based on the first vector, the second vector, and the planning algorithm participating in the solving of the weight vector solving model.

[0176] In some embodiments, the processing the probabilistic linguistic terms of different evaluations in the probabilistic linguistic decision matrix based on the VIKOR algorithm to obtain the intelligent manufacturing capability maturity level evaluation result of the target enterprise comprises:

[0177] processing the probability linguistic terms of different evaluations in the probability linguistic decision matrix based on a score function to calculate scores of the target enterprise on each evaluation attribute;

[0178] determining positive ideal values and negative ideal values of the target enterprise on each evaluation attribute based on the scores;

[0179] calculating maximum group utility values and minimum individual regret values of the target enterprise on different intelligent manufacturing capability maturity levels based on the positive ideal values and the negative ideal values;

[0180] calculating a comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values.

[0181] In some embodiments, the calculating the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values comprises:

[0182] the maximum group utility value and the minimum individual regret value are brought into the following formula to calculate the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise:

[0183]

[0184] In the formula, Q j is the comprehensive evaluation value of the target enterprise j, S j is the maximum group utility value of the target enterprise j, R j is the minimum individual regret value of the target enterprise j, θ∈[0,1] is a decision mechanism adjustment coefficient, θ>0.5 represents that the decision is mainly based on the principle of maximizing group utility, θ<0.5 represents that the decision is mainly based on the principle of minimizing individual regret, and the values of S, Q and R are inversely proportional to the intelligent manufacturing capability maturity.

[0185] Another embodiment of the present application also provides an electronic device, comprising:

[0186] at least one processor; and,

[0187] a memory in communication connection with the at least one processor; wherein,

[0188] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the intelligent manufacturing capability maturity evaluation method as described in any one of the above embodiments.

[0189] Another embodiment of the present application also provides a storage medium comprising a stored program, wherein the device comprising the storage medium performs the intelligent manufacturing capability maturity evaluation method according to any one of the above embodiments when the program is run.

[0190] The embodiment of the present application also provides a computer program product tangibly stored on a computer readable medium and comprising computer readable instructions which, when executed, cause at least one processor to perform the intelligent manufacturing capability maturity evaluation method in the above embodiments. It should be understood that each scheme in the present embodiment has the corresponding technical effects in the above method embodiments, which will not be repeated here.

[0191] It should be noted that the computer storage medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable medium may, for example, but not limited to, be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer readable signal medium can include a data signal carrying computer readable program code in a baseband or as a part of a carrier wave. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium that can send, propagate or transmit a program configured for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained on the computer readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, cable, RF, etc., or any suitable combination of the above.

[0192] One or more embodiments of the present application are intended to cover all such alternatives, modifications, and variations as fall within the scope of the present application. Accordingly, any one or more of the omitted, modified, equivalent replacements, improvements, and the like, made in the spirit and principles of one or more embodiments of the present application, should be included in the scope of the present application. The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims.

Claims

1. A method for evaluating intelligent manufacturing capability maturity, characterized in that, The method comprises the following steps: constructing an intelligent manufacturing capability maturity evaluation index system covering multiple different dimensions of a target enterprise, the multiple different dimensions including a manufacturing foundation dimension, a digital intelligence dimension, a value performance dimension, and a strategic organization dimension; obtaining a set of evaluation information of the target enterprise based on the intelligent manufacturing capability maturity evaluation index system, a set of preset evaluation probability linguistic terms, and a set of evaluation experts, the set of evaluation experts including experts in different fields, and the set of evaluation information including evaluation information given by the set of evaluation experts based on different evaluation attributes; constructing a probability linguistic decision matrix framework based on the set of evaluation information; processing the set of evaluation information based on a BWM algorithm to obtain weights of different evaluation attributes involved in the probability linguistic decision matrix framework, and forming a probability linguistic decision matrix based on the weights and the probability linguistic decision matrix framework; processing probability linguistic terms of different evaluations in the probability linguistic decision matrix based on a VIKOR algorithm to obtain an intelligent manufacturing capability maturity level evaluation result of the target enterprise; The method further comprises: Constructing a probabilistic language terminology set , wherein, is a basic building element of a probabilistic language term set, is a language term, is the probability information of the corresponding language term, denotes the cardinality of the probabilistic language term set , is a pre-defined language term set , denotes the corresponding evaluation term of the language term set; The processing of the set of evaluation information based on the BWM algorithm to obtain the weights of the different evaluation attributes involved in the probability linguistic decision matrix framework comprises: obtaining optimal evaluation attributes and worst evaluation attributes selected and determined by the set of evaluation experts; obtaining importance comparison results between different evaluation attributes and the optimal evaluation attributes by the set of evaluation experts, and forming a first probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results; obtaining importance comparison results between different evaluation attributes and the worst evaluation attributes by the set of evaluation experts, and forming a second probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results; calculating scores of the first probability linguistic vector and the second probability linguistic vector based on a score formula to obtain a first vector and a second vector, respectively; A weight vector solving model is constructed: , , are respectively the first vector and the second vector of the first evaluation attribute, i is the weight of the first evaluation attribute, i is the judgment value, the weight vector solving model solves the reasonable distribution of the weight vector by finding the minimum .​​ participating in solving of a weight vector solving model based on the first vector, the second vector, and a programming algorithm to determine the weights of the different evaluation attributes.

2. The intelligent manufacturing capability maturity evaluation method according to claim 1, characterized in that, The maturity evaluation index system comprises multiple indexes at different levels, and each level comprises multiple indexes related to the manufacturing foundation dimension, the digital intelligence dimension, the value performance dimension, and the strategic organization dimension. 3.The intelligent manufacturing capability maturity evaluation method according to claim 1, characterized in that, The method further comprises: determining a set of evaluation experts ; determining a weight vector of each of the evaluation experts in the set of evaluation experts, forming a set of weight vectors , the weight vectors satisfying the condition and satisfying the condition ; determining a set of probability linguistic terms of the set of evaluation experts based on the set of evaluation experts and a set of weight vectors: wherein is the expert group probability information, is the expert group language term, is the expert group language term probability, is the expert language term probability information, and can be given by ​ wherein, is an expert in the language terminology provided with probability information, is an expert in the language terminology set. 4.The intelligent manufacturing capability maturity evaluation method according to claim 1, characterized in that, The construction of the probability linguistic decision matrix framework based on the set of evaluation information comprises: determining evaluation attributes involved based on the set of evaluation information, and constructing a set of evaluation attributes; determining a set of intelligent manufacturing capabilities matched with the set of evaluation attributes; constructing the probability linguistic decision matrix framework based on the set of evaluation information, the set of evaluation attributes, and the set of intelligent manufacturing capabilities. 5.The intelligent manufacturing capability maturity evaluation method according to claim 1, characterized in that, The processing of the probability linguistic terms of different evaluations in the probability linguistic decision matrix based on the VIKOR algorithm to obtain the intelligent manufacturing capability maturity level evaluation result of the target enterprise comprises: processing the probability linguistic terms of different evaluations in the probability linguistic decision matrix based on a score function to calculate scores of the target enterprise on each evaluation attribute; determining positive ideal values and negative ideal values of the target enterprise on each evaluation attribute based on the scores; calculating maximum group utility values and minimum individual regret values of the target enterprise on different intelligent manufacturing capability maturity levels based on the positive ideal values and the negative ideal values; calculating a comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values. 6.The intelligent manufacturing capability maturity evaluation method according to claim 5, characterized in that, The calculation of the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise based on the maximum group utility values and the minimum individual regret values comprises: The maximum group utility values and the minimum individual regret values are brought into the following formula to calculate the comprehensive evaluation value of the intelligent manufacturing capability maturity level of the target enterprise: In the formula, The comprehensive evaluation value of the target enterprise , The maximum group utility value of the target enterprise , The minimum individual regret value of the target enterprise , The decision mechanism adjustment coefficient Indicates that the decision is mainly based on the principle of maximizing group utility Indicates that the decision is mainly based on the principle of minimizing individual regret , And The value is inversely proportional to the intelligent manufacturing capability maturity. 7.An intelligent manufacturing capability maturity evaluation apparatus characterized by comprising: comprises: The first construction module is configured to construct an intelligent manufacturing capability maturity evaluation index system covering multiple different dimensions of a target enterprise, wherein the multiple different dimensions include a manufacturing foundation dimension, a digital intelligence dimension, a value performance dimension, and a strategic organization dimension. The obtaining module is configured to obtain a set of evaluation information of a target enterprise based on a set of pre-set evaluation probability linguistic terms by a set of evaluation experts, wherein the set of evaluation experts includes experts in different fields, and the set of evaluation information includes evaluation information given by the set of evaluation experts based on different evaluation attributes. The second construction module is configured to construct a probability linguistic decision matrix framework according to the set of evaluation information. The first processing module is configured to process the set of evaluation information according to a BWM algorithm to obtain weights of different evaluation attributes involved in the probability linguistic decision matrix framework, and to form a probability linguistic decision matrix based on the weights and the probability linguistic decision matrix framework. The second processing module is configured to process probability linguistic terms of different evaluations in the probability linguistic decision matrix according to a VIKOR algorithm to obtain an intelligent manufacturing capability maturity level evaluation result of the target enterprise. The device further comprises: a third constructing module configured to construct a probability language term set , wherein, is a basic building element of a probabilistic language term set, is a language term, is the probability information of the corresponding language term, denotes the cardinality of the probabilistic language term set , is a pre-defined language term set , denotes the corresponding evaluation terms of the language term set; The processing of the set of evaluation information according to the BWM algorithm to obtain the weights of different evaluation attributes involved in the probability linguistic decision matrix framework comprises: obtaining optimal evaluation attributes and worst evaluation attributes selected and determined by the set of evaluation experts; obtaining importance comparison results between different evaluation attributes and the optimal evaluation attributes by the set of evaluation experts, and forming a first probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results; obtaining importance comparison results between different evaluation attributes and the worst evaluation attributes by the set of evaluation experts, and forming a second probability linguistic vector corresponding to each evaluation attribute based on the importance comparison results; calculating scores of the first probability linguistic vector and the second probability linguistic vector based on a score formula to obtain a first vector and a second vector, respectively; Construct a weight vector solution model: The , The first i The first and second vectors of the evaluation attributes, the For the first i The weights of each evaluation attribute, the To determine the numerical value, the weight vector solution model finds the minimum value. Solve for the reasonable distribution of the weight vector; determining the weights of different evaluation attributes based on the first vector, the second vector, and a programming algorithm participating in the solution of the weight vector solution model.

8. An electronic device, comprising: comprises at least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the intelligent manufacturing capability maturity evaluation method according to any one of claims 1-6.

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