A method for analyzing complex interaction of product sustainable design factors

Abstract

The application discloses a kind of to product sustainable design factor complex interaction analysis method, comprising: obtaining the document related to the product designed from internet big data, and after processing, high-frequency word is obtained;High-frequency word is used as sustainable design practice factor in sustainable design practice, the optimal factor is determined and directed graph is constructed to determine the highest level factor;LoRA model is constructed, the product image of the similar product of the designed product is collected, these product images are input into DeepBooru model, and the image label of each product image is obtained;Product image and image label are used as sample, training set is constructed and input into LoRA model for training, and the trained LoRA model is saved;Then from all the highest level factors of the directed graph, factor is selected to generate product design scheme using model, and the optimal product design scheme is determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy vehicle design, and particularly relates to a method for analyzing complex interaction of sustainable design factors of products. BACKGROUND

[0002] In the global economic environment, sustainable development has become an important issue of common concern for the international community, especially in response to environmental degradation and limited resources. With the increasingly serious environmental problems, including global warming, air quality deterioration and energy crisis, environmental challenges force governments and businesses to seek more green and sustainable solutions. Among many solutions, the promotion of new energy vehicles (NEV) is considered an effective challenge to the traditional fossil fuel-dependent model and a key technological innovation to achieve carbon neutrality in the transportation sector. Studies have shown that new energy vehicles have significant advantages in reducing the carbon footprint of cities, improving energy efficiency, and promoting the transformation of energy consumption structure. The popularity of new energy vehicles is not only seen as a technological revolution, but also a key component of the overall plan for sustainable consumption and production. The design, production and commercialization of new energy vehicles involve numerous challenges and opportunities at the intersection of technology, consumer behavior and environmental policy.

[0003] However, despite the increasing maturity of technology, there are still significant differences in the popularity of new energy vehicles and the widespread acceptance of consumers around the world, which is partly due to regional characteristics of market policies, economic incentives and consumer behavior. The new energy vehicle market has experienced rapid development. Through a series of policy support, it effectively promotes the research and development and market promotion of new energy vehicles; these policies not only promote technological progress, but also accelerate the market transition from traditional vehicles to new energy vehicles. However, the market acceptance of new energy vehicles still faces challenges between consumer expectations and product design, and consumer awareness, emotions and psychological expectations of new energy vehicles greatly influence their purchase decisions. In addition, the concept of sustainable design has not been fully accepted by the market in the automotive industry, which requires designers to consider not only the feasibility of technology, but also the needs of consumers for aesthetics and comfort. New energy vehicles are designed for consumers, and in an era centered on consumers, it is still a challenge for sustainable management personnel and enterprises to better understand and analyze consumers' inner demands for sustainable design while correlating and analyzing the deep relationship between factors from a forward-looking and practical perspective. SUMMARY

[0004] The purpose of the present application is to provide a method for analyzing the complex interaction of sustainable design factors of products, which provides a new solution for product design and improves design efficiency.

[0005] In order to achieve the above-mentioned task, the present application adopts the following technical solutions:

[0006] A method for analyzing complex interaction of product sustainable design factors, comprising:

[0007] Step 1, obtaining documents related to the designed product from Internet big data, and performing data mining on the documents, screening out product-related words, and performing data processing on the words to obtain high-frequency words;

[0008] Step 2, taking the high-frequency words as sustainable design practice factors in sustainable design practice, determining the optimal sustainable design practice factor by calculating the evaluation value of all sustainable design practice factors; and constructing a directed graph based on the optimal sustainable design practice factor and determining the highest level factor in the directed graph;

[0009] Step 3, constructing a LoRA model, collecting product images of similar products of the designed product, inputting the product images into a DeepBooru model to obtain image labels of each product image; taking the product images and image labels as samples, constructing a training set and inputting it into the LoRA model for training, and saving the trained LoRA model;

[0010] Then, from all the highest level factors of the directed graph, select a high level factor that a product designer pays attention to, select all optimal sustainable design practice factors associated with the selected high level factor from the directed graph, take the high level factor and all optimal sustainable design practice factors as input information, and generate a product design scheme through the LoRA model; adjust one or the optimal sustainable design practice factor in the input information, and generate a new product design scheme through the LoRA model after each adjustment;

[0011] Step 4, determining the optimal product design scheme from all generated product design schemes.

[0012] Further, the data processing of the words to obtain high-frequency words comprises:

[0013] Step 11, calculating the word frequency TF(t, d):

[0014]

[0015] Wherein, f t,d represents the frequency of the product-related word t in the document d; f t′,d represents the frequency of any word t' in the document d;

[0016] Step 12, calculating the inverse document frequency IDF(t, D):

[0017]

[0018] Where D is a document set, d′ represents a document, and |D| represents the total number of documents in the document set; for a word t, |{d′∈D:t∈d′}| provides the total number of documents in the document set D containing word t;

[0019] Step 13, calculate the term frequency-inverse document frequency (TF-IDF) value:

[0020] TF-IDF = TF(t,d) × IDF(t,D)

[0021] Calculate the TF-IDF value for all product-related words, sort all TF-IDF values, and select the top m words with the highest TF-IDF values ​​as high-scoring product-related words.

[0022] Furthermore, the process of determining the optimal sustainable design practice factor by calculating the evaluation values ​​of all sustainable design practice factors includes:

[0023] First, construct the decision matrix D, where the elements d in the matrix are... ij This represents the score of the i-th sustainable design practice factor on the j-th criterion; then, an intuitionistic fuzzy number is introduced for each element d in the decision matrix. ij Intuitive fuzzy number processing is applied to obtain the intuitive fuzzy decision matrix, including membership degree u. ij non-membership degree v ij The determination and the degree of hesitation π ij The calculation, where π ij =1-u ij -v ij ; where membership degree represents the degree of positive evaluation of factor i under criterion j, non-membership degree represents the degree of negative evaluation of factor i under criterion j, and hesitation degree represents the uncertainty of the evaluation of factor i under criterion j.

[0024] Next, the intuitionistic fuzzy decision matrix is ​​standardized, and the standardized intuitionistic fuzzy number is expressed as follows: Next, the weights of the criteria are objectively determined using the entropy weight method. Then, for each sustainable design practice factor, its comprehensive evaluation value S under all criteria is calculated. i The calculation formula is:

[0025]

[0026] Where n is the number of criteria, m is the number of sustainable design practice factors, and w j Let μ' represent the weight of the j-th criterion. ij ,v′ ij,π′ ij These represent the standardized membership degree, non-membership degree, and hesitation degree, respectively; μ′ kj v′ kj This represents the membership degree and non-membership degree of the k-th factor under the j-th criterion after standardization.

[0027] Based on the comprehensive evaluation value S i All sustainable design practice factors are ranked, and the top n sustainable design practice factors with the highest comprehensive evaluation values ​​are selected as the optimal sustainable design practice factors.

[0028] Furthermore, the criteria include market adaptability, industry relevance of the product, and the complexity of sustainable practices.

[0029] Furthermore, the construction of the directed graph based on optimal sustainable design practice factors, and the determination of the highest-level factors in the directed graph, includes:

[0030] The optimal sustainable design practice factors were assigned the number β. p (p = 1, 2, ..., n), and then construct an adjacency matrix A based on the relationships between sustainable design practice factors, where the elements a in adjacency matrix A are... pq Indicating sustainable design practice factor β p and β q The logical relationship between them is β. p Effects on β q If a pq =1, then it means β p With β q There exists a relationship between β p to β q The relationship; if a pq =0, then it means β p With β q There is no relationship between β and β p to β q Direct relationship;

[0031] The reachability matrix R is obtained by performing Boolean operations on the adjacency matrix A. M :

[0032] (A+I) k-1 ≠(A+I) k =(A+I) k+1 =R M

[0033] Where I is defined as the identity matrix of the same order as A, and k is the minimum power that reaches the final stable state of the reachable matrix;

[0034] Next, from β pThe set of all optimal sustainable design practice factors that can be reached from the starting point is denoted as the reachable set R(β p ), and the set of all optimal sustainable design practice factors that can reach β p is denoted as the predecessor set A(β p ); after obtaining the reachable matrix R M , it can be seen from any sustainable design practice factor which other factors can be directly or indirectly affected by the factor;

[0035] The intersection of R(β p ) and A(β p ) is R(β p )∩A(β p ), and R(β p )∩A(β p ) is the set of all factors that can reach β p and can reach β p ; a directed graph is drawn based on the reachable matrix, which is used to represent the logical relationship between optimal sustainable design practice factors and determine the highest level factors in the directed graph:

[0036] If the intersection of the reachable set of a certain optimal sustainable design practice factor and its predecessor set is equal to its reachable set itself, then the factor and all optimal sustainable design practice factors in the reachable set are the highest level factors; all highest level factors are determined in the same way.

[0037] Further, the determination of the optimal product design scheme from all generated product design schemes comprises:

[0038] Step 4.1, set multiple evaluation criteria for the product, then the decision maker scores each generated product design scheme from different evaluation criteria, and uses the scores to build an evaluation decision matrix E, where the element E ij in the decision matrix represents the evaluation value of the i-th product design scheme with respect to the j-th evaluation criterion;

[0039] Step 4.2, normalize the decision matrix; after normalization, the elements are denoted as R ij ; for the j-th evaluation criterion, the difference between the maximum and minimum values in the corresponding E ij is selected as the difference value s

[0040] Step 4.3, evaluate the difference between each product design scheme and other product design schemes:

[0041]

[0042] where P j(i,i') represents the difference value between the i-th and i'-th product design scheme, R i′j represents the normalized value of the evaluation value of the i'-th product design scheme with respect to the j-th evaluation criterion;

[0043] Step 4.4, for the i-th and i'-th product design scheme, calculate the preference value under each evaluation criterion:

[0044]

[0045] wherein π(i,i') represents the preference value of the i-th product design scheme with respect to the i'-th product design scheme, w j represents the preset weight of the j-th evaluation criterion, and u represents the number of evaluation criteria;

[0046] Step 4.5, for each product design scheme i, calculate its positive flow and negative flow:

[0047]

[0048] wherein i'≠i, φ + (i), φ - (i) are the positive flow and negative flow of the product design scheme respectively; and v represents the number of product design schemes.

[0049] Step 4.6, for each product design scheme i, calculate its net flow φ(i), which is the difference value between the positive flow and the negative flow:

[0050] φ(i) = φ + (i) - φ - (i)

[0051] Among all product design schemes, the product design scheme with the maximum net flow φ(i) value is selected as the optimal product design scheme under the high-level factor concerned by the product designer.

[0052] A terminal device comprising a processor, a memory and a computer program stored in the memory; characterized in that when the processor executes the computer program, the method for analyzing the complex interaction relationship of product sustainable design factors is realized.

[0053] A computer readable storage medium, the medium storing a computer program; characterized in that when the computer program is executed by a processor, the method for analyzing the complex interaction relationship of product sustainable design factors is realized.

[0054] Compared with the prior art, the present application has the following technical features:

[0055] This invention provides an analysis of the intertwined relationships and impacts of factors in the sustainable industrial design process of products, supported by big data and natural language processing technologies. Based on this analysis, it offers a product design solution that comprehensively considers consumer sustainable design needs and Sustainable Design Practices (SDP). This helps enterprise managers and designers better understand the complex causal relationships in the sustainable design process, determine the optimal product design solution, and improve product design efficiency and user experience. It organically combines knowledge from operations research, design, neural networks and intelligent science, and consumer behavior to a certain extent, fully leveraging the greatest advantages of interdisciplinary collaboration in the field of sustainability. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the method of the present invention;

[0057] Figure 2 This is a schematic diagram of a directed graph in one embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of a product design scheme generated in one embodiment of the present invention. Detailed Implementation

[0059] See appendix Figure 1 This invention provides a method for analyzing the complex interactive relationships of sustainable design factors in products, comprising the following steps:

[0060] Step 1: Obtain documents related to the designed product from internet big data, perform data mining on them, filter out words related to the product, and process the words to obtain high-scoring words.

[0061] Step 11, calculate TF(t,d). Term frequency (TF) defines the probability of a word appearing in a document, while inverse document frequency (IDF) measures the specificity of a word.

[0062]

[0063] Among them, f t,d This indicates the frequency of product-related terms (t) in document (d); f t′,d Represents the frequency of any word t′ in document d, |{f t′,D :t′∈d}| gives the total number of words in document d.

[0064] Step 12, calculate the inverse document frequency (IDF) (t,D):

[0065]

[0066] where D is the document set, d' represents a document, and |D| represents the total number of documents in the document set; for a word t, |{d'∈D:t∈d'}| provides the total number of documents in the document set D containing the word t.

[0067] Step 13, calculate the term frequency-inverse document frequency TF-IDF value as:

[0068] TF-IDF = TF(t, d) x IDF(t, D)

[0069] Calculate the TF-IDF value of all product-related words, and sort all TF-IDF values, and select the top m words with the highest TF-IDF value as the high-score words related to the product.

[0070] In an embodiment of the present application, for example, when the product to be designed is a new energy vehicle, the document set related to the new energy vehicle is obtained from the Internet data, and the words in the document are analyzed, and finally 18 high-score words are obtained, including interior, appearance, power, sound insulation, comfort, cost performance, etc.

[0071] Step 2, the high-frequency words are used as sustainable design practice factors in sustainable design practice SDP, the evaluation value of all sustainable design practice factors is calculated to determine the optimal sustainable design practice factor; the directed graph is constructed based on the optimal sustainable design practice factor, and the highest level factor in the directed graph is determined.

[0072] Step 2.1, determine the optimal sustainable design practice factor.

[0073] First, construct the decision matrix D, where the elements d ij in the matrix represent the score of the i th sustainable design practice factor under the j th criterion, where the criterion is the adaptability of the Chinese market, the relevance of the industry where the product is located, and the complexity of the sustainable practice; then introduce the intuitionistic fuzzy number, and obtain the intuitionistic fuzzy decision matrix by processing each element d ij in the decision matrix; this involves determining the membership degree u ij and the non-membership degree v ij and calculating the hesitation degree π ij , where π ij =1-u ij -v ij ; where the membership degree represents the positive evaluation degree of the i th factor under the j th criterion, the non-membership degree represents the negative evaluation degree of the i th factor under the j th criterion, and the hesitation degree represents the uncertainty of the evaluation of the i th factor under the j th criterion.

[0074] Then, normalize the intuitionistic fuzzy decision matrix, and the normalized intuitionistic fuzzy number is represented as Then the weight of each criterion is determined by entropy weight method, and then the comprehensive evaluation value S of each sustainable design practice factor is calculated under all criteria i , and the calculation formula is as follows:

[0075]

[0076] wherein n is the number of criteria, m is the number of sustainable design practice factors, w j represents the weight of the jth criterion, μ′ ij , v′ ij , π′ ij respectively represent the normalized membership degree, non-membership degree and hesitation degree; μ′ kj , v′ kj represent the normalized membership degree and non-membership degree of the kth factor under the jth criterion.

[0077] Based on the comprehensive evaluation value S i , all sustainable design practice factors are sorted, and the top n sustainable design practice factors with the highest comprehensive evaluation value are selected as the optimal sustainable design practice factors.

[0078] Step 2.2, a directed graph is constructed to determine the highest level factor.

[0079] The optimal sustainable design practice factors obtained are numbered β p (p = 1, 2, …, n), and then an adjacency matrix A is constructed according to the relationship between the sustainable design practice factors, wherein the element a pq in the adjacency matrix A represents the logical relationship between the sustainable design practice factors β p and β q , i.e. β p influences β q ; if a pq = 1, it means that there is a relationship from β p to β q between β p and β q ; if a pq = 0, it means that there is no direct relationship from β p to β q between β p and β q ; for example, two factors are “sound insulation” and “comfort”, since sound insulation affects comfort, the value of the logical relationship between them is 1.

[0080] Boolean operation is performed according to the adjacency matrix A to obtain the reachable matrix R M :

[0081] (A+I)k-1 ≠(A+I) k =(A+l) k+1 =R M

[0082] Where I is defined as the identity matrix of the same order as A, and k is the smallest power that reaches the final stable state of the reachable matrix (where no more reachability relations are added).

[0083] Next, from β p Starting from a point, the set of all optimal sustainable design practices that can be reached is denoted as the reachability set R(β). p ), and all possible destinations to β p The set of optimal sustainable design practice factors is represented by the prior set A(β). p The reachability matrix R is obtained from this. M Then, starting from any sustainable design practice factor, we can examine which other factors it directly or indirectly affects; the reachable set R(β) p ) is precisely the set used to describe this concept, that is, from a specific factor β p Starting point: the set of all factors that can be reached along the directed chain of relationships.

[0084] With R(β) p ) and A(β) p Find the intersection R(β) of the two. p )∩A(β p If R(β) p )∩A(β p ) is factor β p It can be achieved, and it is also possible to achieve β. p The set of all factors; if R(β) p )∩A(β p )=R(β p If R(β) p The factors in the set are at the highest level among all factors.

[0085] A directed graph is drawn based on the reachability matrix. The directed graph is used to represent the logical relationships between factors of optimal sustainable design practices, and the highest-level factor in the directed graph is determined:

[0086] If the intersection of the reachable set of a certain optimal sustainable design practice factor and its antecedent set is equal to the reachable set itself, then the factor and all optimal sustainable design practice factors within the corresponding reachable set are the highest-level factors; the same method is used to determine all highest-level factors.

[0087] Step 3, constructing the LoRA model, collecting product images of similar products of the designed product from the Internet and the like, inputting the product images into the DeepBooru model to obtain image labels of each product image; taking the product images and the image labels as samples, constructing a training set and inputting into the LoRA model for training, and saving the trained LoRA model; wherein the LoRA model is an existing model, which will not be described again.

[0088] Then, from all the highest level factors of the directed graph, a high level factor of interest of a product designer is selected, all optimal sustainable design practice factors associated with the selected high level factor are selected from the directed graph, and the high level factor and all the optimal sustainable design practice factors are collectively taken as input information to generate a product design scheme (i.e., a product image) through the LoRA model; by adjusting (including deleting, replacing, etc.) one or more optimal sustainable design practice factors in the input information, a new product design scheme is generated through the LoRA model after each adjustment.

[0089] In one embodiment of the present application, when designing a new energy vehicle, the product design scheme therein is as shown in (a1) to (c4) of Figure 3 .

[0090] Step 4, determining the optimal product design scheme from all generated product design schemes; specifically as follows:

[0091] Step 4.1, setting multiple evaluation criteria for the product, and then the decision maker scores each product design scheme generated in step 3 from different evaluation criteria, and an evaluation decision matrix E is constructed using the scores, wherein the elements E ij in the decision matrix represent the evaluation value of the decision maker on the i th product design scheme with respect to the j th evaluation criterion; the evaluation criteria can be, for example, aesthetic appearance, practicality, etc., and each evaluation criterion is set to a specific score.

[0092] Step 4.2, normalizing the decision matrix; after normalization, the elements are represented as R ij ; for the j th evaluation criterion, the difference between the maximum and minimum values in the corresponding E ij is selected as the difference value s j of the j th evaluation criterion.

[0093] Step 4.3, evaluating the difference between each product design scheme and other product design schemes:

[0094]

[0095] wherein P j (i,i') represents the difference value between the i th and i' th product design schemes, and R i′jrepresents the normalized value of the evaluation value of the ith product design scheme with respect to the jth evaluation criterion.

[0096] Step 4.4, for the ith and ith product design schemes, calculate the preference value under each evaluation criterion:

[0097]

[0098] wherein, represents the preference value of the ith product design scheme with respect to the ith product design scheme, w j represents the preset weight of the jth evaluation criterion, and u represents the number of evaluation criteria.

[0099] Step 4.5, for each product design scheme i, calculate its positive flow and negative flow:

[0100]

[0101] wherein, i'≠i, represents the preference value of the ith product design scheme with respect to the ith product design scheme, and w + (i) and (i) are respectively the positive flow and the negative flow of the product design scheme; and v represents the number of product design schemes. - +

[0102] Step 4.6, for each product design scheme i, calculate its net flow φ(i), which is the difference between the positive flow and the negative flow:

[0103] φ(i) = φ + (i) - φ - (i)

[0104] Among all the product design schemes, the product design scheme with the maximum net flow φ(i) value is selected as the optimal product design scheme under the high-level factor concerned by the product designer; by replacing different high-level factors and repeating the above steps, the optimal product design schemes corresponding to different high-level factors concerned can be obtained.

[0105] The above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for analyzing the complex interactive relationships of sustainable design factors in products, characterized in that, The method comprises the following steps: Step 1, obtaining documents related to the designed product from Internet big data, and performing data mining on the documents to screen out words related to the product, and performing data processing on the words to obtain high-score words; Step 2, taking the high-score words as sustainable design practice factors in sustainable design practice, determining optimal sustainable design practice factors by calculating the evaluation values of all sustainable design practice factors, and constructing a directed graph based on the optimal sustainable design practice factors and determining the highest level factors in the directed graph; Step 3, constructing a LoRA model, collecting product images of similar products of the designed product, inputting the product images into a DeepBooru model to obtain image labels of each product image, taking the product images and the image labels as samples, constructing a training set and inputting the training set into the LoRA model for training, and saving the trained LoRA model; Then, from all the highest level factors in the directed graph, a high level factor concerned by a product designer is selected, all optimal sustainable design practice factors associated with the selected high level factor are selected from the directed graph, the high level factor and all the optimal sustainable design practice factors are taken as input information together, and a product design scheme is generated by the LoRA model; by adjusting one or more optimal sustainable design practice factors in the input information, a new product design scheme is generated by the LoRA model after each adjustment; Step 4, determining an optimal product design scheme from all generated product design schemes.

2. The method for analyzing complex interrelationship of product sustainable design factors according to claim 1, wherein, The data processing on the words to obtain high-score words comprises the following steps: Step 11, Calculate word frequency : wherein, denotes a product related term in a document appears; denotes any term in a document appears; Step 12, calculate inverse document frequency : wherein, is a collection of documents, denotes a document, denotes the total number of documents in the collection; for a vocabulary , provides the total number of documents in the collection of documents containing the vocabulary . Step 13, calculating the TF-IDF value as: The TF-IDF values of all product-related words are calculated, and all the TF-IDF values are sorted, and the top m TF-IDF values are selected as high-score words related to the product.

3. The method for analyzing complex interrelationship of product sustainable design factors according to claim 1, wherein, The determination of the optimal sustainable design practice factors by calculating the evaluation values of all sustainable design practice factors comprises: Firstly, a decision matrix is constructed where the element in the matrix denotes the score of the th sustainable design practice factor on the th criterion; then the intuitionistic fuzzy number is introduced, and the intuitionistic fuzzy number processing is applied to each element in the decision matrix to obtain an intuitionistic fuzzy decision matrix; including the determination of membership and non-membership and the calculation of hesitation , where ; wherein the membership degree indicates the positive evaluation degree of the i th factor under the i th criterion, the non-membership degree indicates the negative evaluation degree of the j th factor under the j th criterion, and the hesitation degree indicates the uncertainty of the evaluation of the factor i under the criterion j ; Then the intuitionistic fuzzy decision matrix is normalized, and the normalized intuitionistic fuzzy number is denoted as , and then the weights of criteria are determined objectively by entropy weight method, and then for each sustainable design practice factor, the comprehensive evaluation value under all criteria is calculated , and the calculation formula is: wherein, is the number of criteria, is the number of sustainable design practice factors, denotes the weight of the th criterion, denotes the normalized membership, non-membership and hesitancy, respectively; , denotes the normalized membership, non-membership of the k th factor under the j th criterion; based on the comprehensive evaluation value , all sustainable design practice factors are sorted, and the top n sustainable design practice factors with the highest comprehensive evaluation value are selected as the optimal sustainable design practice factors.

4. The method for analyzing complex interrelationship of product sustainable design factors according to claim 3, wherein, The criteria are market adaptability, relevance of the product to the industry, and complexity of sustainable practice.

5. The method for analyzing complex interrelationship of product sustainable design factors of claim 1, wherein, The construction of the directed graph based on the optimal sustainable design practice factors and the determination of the highest level factors in the directed graph comprise: Setting a number to the acquired optimal sustainable design practice factors Then constructing an adjacency matrix according to the relationship between the sustainable design practice factors Wherein the element In the adjacency matrix Indicates the logical relationship between the sustainable design practice factors And Namely Influence If , it indicates that there is a relationship from To Between And If , it indicates that there is no direct relationship from To Between And ​ According to the adjacency matrix Boolean operation is performed to obtain the reachable matrix : wherein is defined as the identity matrix of the same order as is the minimum power for reaching the final stable state of the reachable matrix​ Next, from the set of all possible optimal sustainable design practice factors that can be reached from is denoted as the reachable set , while the set of all possible optimal sustainable design practice factors that can reach is denoted as the predecessor set ; where the reachable matrix is obtained, one can see from any sustainable design practice factor which other factors it can directly or indirectly affect; With And Find the intersection of both Then Is a factor Achievable, yet able to achieve The full set of factors; directed graph based on the reachability matrix, the directed graph is used to represent the logical relationship between the optimal sustainable design practice factors, and determine the highest level factors in the directed graph: If the intersection of the reachable set of a certain optimal sustainable design practice factor and the predecessor set thereof is equal to the reachable set itself, the factor and all the optimal sustainable design practice factors in the reachable set are the highest level factors; all the highest level factors are determined by the same method.

6. The method for analyzing complex interrelationship of product sustainable design factors of claim 1, wherein, The determination of the optimal product design scheme from all generated product design schemes comprises: Step 4.1, set multiple evaluation criteria of the product, then the decision maker scores each product design scheme generated from different evaluation criteria, and builds an evaluation decision matrix using the scores E , elements in the decision matrix E ij represent the evaluation value of the decision maker on the first i product design scheme about the first j evaluation criterion; Step 4.2: Normalize the decision matrix; its elements after normalization are represented as follows: R ij ; Regarding the first j Each evaluation criterion, from the corresponding E ij Choose the difference between the maximum and minimum values ​​as the first j Differences in evaluation criteria ; Step 4.3, evaluating the difference between each product design scheme and other product design schemes: wherein, represents the difference value between the first and the second product design schemes, represents the normalized value of the evaluation value of the first product design scheme with respect to the first j evaluation criterion. Step 4.

4. For each of the product design alternatives, the preference value under each evaluation criterion is calculated: and the 2nd product design alternative. wherein, represents a preference value of an i-th product design scheme relative to a j-th product design scheme, represents a preset weight of an i-th evaluation criterion, j u represents a number of evaluation criteria;​​​ Step 4.5, for each product design solution , calculate its positive and negative flows: Wherein, , , Respectively, the positive flow and negative flow of the product design scheme; Indicates the number of product design schemes; Step 4.6, for each product design solution , calculate its net flow , i.e. the difference between the positive and negative flows: In all product design schemes, the product design scheme with the maximum net flow is selected as the optimal product design scheme under the high-level factor of the product designer's attention.

7. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that, The processor executes the computer program to realize the method for analyzing the complex interaction relationship of product sustainable design factors according to any one of claims 1-6.

8. A computer readable storage medium having stored therein a computer program; characterized in that, The computer program is executed by the processor to realize the method for analyzing the complex interaction relationship of product sustainable design factors according to any one of claims 1-6.

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