A pushing method and system of domain innovation method preferred sequences

Abstract

The application discloses a pushing method and system of a field innovation method optimal sequence, and comprises the following steps of the method: a user determines an application scene of a field innovation method according to an actual problem; a search analysis module imports the selected application scene into a applicability evaluation module; the applicability evaluation module performs applicability evaluation on field innovation methods in a database according to the application scene, and obtains five field innovation method optimal sequences with the maximum applicability; and a content pushing module pushes the name, definition and applicability value information of the field innovation methods in the optimal sequence to the user. The application utilizes a method combining analytic hierarchy process, entropy value method and the technique for order preference by similarity to ideal solution to determine the total applicability of the field innovation method, and pushes the method optimal sequence according to the user demand, and is suitable for the field of rail transit equipment and provides support for selecting field innovation methods in the field.

Description

Technical Field

[0001] This invention relates to the field of knowledge delivery, specifically to a method and system for delivering a preferred sequence of innovative methods in this field. Background Technology

[0002] The domain-specific innovation methods extracted from numerous innovative achievements in the field of rail transit equipment can effectively guide various innovation activities in this field. However, the application scenarios of these domain-specific innovation methods vary greatly, and the sources of information are complex and diverse, resulting in significant differences in the adaptability of each method to different scenarios. This makes it difficult for innovators to select the most applicable method from among the many available domain-specific innovation methods. Summary of the Invention

[0003] To address the aforementioned technical problems in the application of domain innovation methods, this invention provides a method and system for pushing a preferred sequence of domain innovation methods, which supports enterprises or individuals in selecting domain innovation methods during innovation activities.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is: a method for pushing preferred sequences in a field-innovative method, comprising the following steps:

[0005] Step 1: Users determine the application scenarios for domain-specific innovative methods based on actual problems;

[0006] Step 2: The search and analysis module imports the selected application scenarios into the applicability evaluation module;

[0007] Step 3: The applicability evaluation module evaluates the applicability of the domain innovation methods in the database based on the application scenario, and obtains the preferred sequence of the 5 domain innovation methods with the highest applicability;

[0008] Step 4: The content push module pushes the name, definition, and applicability value of the innovative methods in this preferred sequence to the user.

[0009] The specific steps of step 3 in the above-mentioned domain innovation method preferred sequence push method are as follows:

[0010] Step 3-1: Determine the applicability evaluation indicators for the application scenarios of domain innovation methods; establish an applicability evaluation indicator system, including the target layer, dimension layer, and indicator layer;

[0011] Step 3-2: Determine the weights of the dimension layer indicators using fuzzy hierarchical analysis; determine the weights of the indicator layer indicators using the entropy method; combine the weights of the dimension layer indicators determined by fuzzy hierarchical analysis and the weights of the indicator layer indicators determined by the entropy method to determine the overall weights of the indicator layer.

[0012] Step 3-3: Use the approximation of ideal value ranking method to obtain the applicability of innovation methods in each field to the indicators of the indicator layer; combine the overall weight of the indicators of the indicator layer to obtain the total applicability value of innovation methods in each field.

[0013] A system for pushing a preferred sequence of domain innovation methods includes an application scenario selection module, a search and analysis module, an applicability evaluation module, a content push module, and a feedback module. The application scenario selection module allows users to determine the application scenario of a domain innovation method based on a real-world problem context, including scenario indicators across five dimensions: product type, innovation object, innovation chain link, innovation category, and innovation procedure. The user then selects one of these five determined application scenario indicators. The search and analysis module imports the application scenario into the applicability evaluation module to obtain a preferred sequence of domain innovation methods. The content push module then pushes this preferred sequence to the user, who selects an innovation method from the preferred sequence to apply. The applicability evaluation module evaluates the applicability of domain innovation methods in the database based on the application scenario, obtaining the five preferred sequences of domain innovation methods with the highest applicability. The content push module then pushes the name, definition, and applicability value information of the domain innovation methods in this preferred sequence to the user. After applying the innovation method, the user can evaluate the application effect of the method in the feedback module.

[0014] The beneficial effects of this invention are as follows: This invention utilizes a combination of the analytic hierarchy process (AHP), entropy method, and approximation of ideal value ranking method to determine the overall applicability of innovative methods in a field, and can push the optimal method sequence according to user needs. It is applicable to the field of rail transit equipment and provides strong support for the selection of innovative methods in this field. Attached Figure Description

[0015] Figure 1 This is a flowchart of the push method of the present invention;

[0016] Figure 2 This is a diagram of the evaluation index system for application scenarios of innovative methods in the field.

[0017] Figure 3 This is a structural diagram of the push system of the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to embodiments.

[0019] This invention provides a method for pushing a preferred sequence of domain innovation methods, which constructs a knowledge database of domain innovation methods; by establishing a domain innovation method applicability evaluation system, the applicability value of each method corresponding to the application scenario is calculated; thus, users can obtain the corresponding preferred sequence of domain innovation methods according to the actual application scenario. This invention is described below using the field of rail transit equipment as an example; it should be understood that this invention is not limited thereto. The invention will be further described below with reference to embodiments.

[0020] Example 1:

[0021] like Figure 1 The diagram shows a flowchart illustrating the construction process of the push method for the optimal sequence of domain innovation methods, including the following steps:

[0022] 1. Construct a knowledge database of domain-specific innovative methods. Utilize a MySQL database to store the content of domain-specific innovative methods, categorizing the content into four dimensions: method name, method definition, method application steps, and method source.

[0023] 2. Users determine the application scenarios for domain innovation methods based on actual problems. Users are required to select one indicator from each of the five dimensions—product type, innovation object, innovation chain link, innovation category, and innovation procedure—as the application scenario, represented as dictionary AS = {product type: indicator 1; innovation object: indicator 2; innovation chain link: indicator 3; innovation category: indicator 4; innovation procedure: indicator 5}.

[0024] 3. Evaluate the applicability of domain innovation methods based on application scenarios to obtain applicability values ​​for these methods, including the following steps:

[0025] 3.1 Constructing an evaluation index system. Constructing an evaluation index system for the application scenarios of innovative methods in the field, such as... Figure 2 As shown, it includes a target layer, a dimension layer, and a metric layer. The dimension layer consists of five dimensions of the application scenario, including product type, innovation object, innovation chain link, innovation category, and innovation procedure; the metric layer consists of specific indicators under each dimension.

[0026] Among them, the product type is a collection of the main products of rail transit equipment enterprises, including electric locomotives, shunting locomotives, EMUs, urban rail and maglev trains, etc.

[0027] The innovation targets are specific innovative components of rail transit equipment, including electrical systems, braking systems, driver's cabs, car bodies, and bogies.

[0028] The innovation chain comprises the different stages of rail transit equipment development, production, and service. The development stage includes: demand analysis, conceptual design, detailed design, and design verification; the production stage includes process design, material preparation, production organization, and product delivery; and the service stage includes pre-sales service, after-sales service, and operation and maintenance support.

[0029] Innovation categories refer to the types of innovations in the manufacturing process of rail transit equipment, specifically technological innovation and management innovation.

[0030] The innovation process is the procedure for identifying, analyzing, and solving problems during the innovation of rail transit equipment.

[0031] The application scenario can then be represented as the dictionary AS = {Product type: indicator 1; Innovation object: indicator 2; Innovation chain link: indicator 3; Innovation category: indicator 4; Innovation procedure: indicator 5}.

[0032] 3.2 Determining Indicator Weights. A combined weighting method using fuzzy hierarchical analysis (AHP) and entropy method was employed to determine the indicator weights. To reduce the subjectivity of human evaluation, fuzzy hierarchical analysis was used to determine the weights of the dimension layer indicators, while entropy method was used to determine the weights of the indicator layer indicators. Finally, a product method was used to combine the indicator weights from the dimension layer and the indicator layer to determine the overall weight of the indicator layer.

[0033] Step 1: Determine the weights of the dimension layer indicators using fuzzy hierarchical analysis. The specific steps are as follows:

[0034] S1: The relative importance of the dimensional level indicators to the evaluation of the applicability of domain innovation methods is compared pairwise to obtain the fuzzy judgment matrix X = (x ij ) 5×5 , 0≤x ij ≤1,x ij =x ik -x jk +0.5, and after transformation, the fuzzy consistency judgment matrix A = (a ij ) 5×5 The transformation relationship is as follows: Where, x i x j , respectively, are the sums of the elements in the i-th and j-th rows of the fuzzy judgment matrix X, and n is the number of evaluation indicators.

[0035] In fuzzy hierarchical analysis, a scale of 0.1-0.9 is used to evaluate the relative importance between two indicators, i.e., x. ij The values ​​are shown in Table 1.

[0036] Table 1. Scale values ​​and meanings of fuzzy judgment matrix

[0037] Scale value meaning 0.1 Indicator i is very unimportant compared to indicator j. 0.3 Indicator i is not important relative to indicator j. 0.5 Indicator i is just as important as indicator j. 0.7 Indicator i is more important than indicator j. 0.9 Indicator i is much more important than indicator j. 0.2、0.4、0.6、0.8 The median of the two adjacent judgments above

[0038] For example, when comparing product type and innovation object pairwise, if it is believed that product type is less important than innovation object when evaluating the applicability of domain innovation methods to application scenarios, the score is 0.3.

[0039] S2: Calculate the weights of the dimension-level indicators. The formula for calculating the weights of the dimension-level indicators is:

[0040]

[0041] Step 2: Determine the weights of the indicators in the indicator layer using the entropy method. The specific steps are as follows:

[0042] S1: If there are m evaluators participating in the scoring, and l evaluation indicators, then the evaluators' scores for each indicator constitute the initial judgment matrix, S = (s ij ) m×l The initial judgment matrix is ​​normalized to obtain the standardized judgment matrix S′=(s′ ij ) m×l The normalization formula is:

[0043]

[0044] Where, min{s j} represents the minimum value in the j-th column of the initial judgment matrix; max{s j} represents the maximum value of the j-th column of the initial judgment matrix.

[0045] S2: Calculate the information entropy of the indicators. The weight of the objective score of the i-th evaluator under the j-th indicator is... Then the information entropy of the j-th indicator is

[0046] S3: Calculate the indicator weights at the indicator layer. The formula for calculating the indicator weights at the indicator layer is:

[0047]

[0048] Step 3: Determine the overall weight of the indicator layer. In the previous steps, the FAHP and EM methods were used to calculate the indicator weights of the dimension layer and indicator layer, respectively. Therefore, the formula for calculating the overall weight of each indicator in the indicator layer is: in, The total weight of the indicators in the indicator layer; w i ,w′ ij These are the indicator weights in the dimension layer and the indicator layer, respectively.

[0049] 3.3 Calculate the overall applicability of domain innovation methods. The applicability values ​​of domain innovation methods to the indicators at the indicator layer are obtained based on the approximation of ideal values ​​ranking method. If the application scenario of the domain innovation method is determined, the overall applicability of the domain innovation method is obtained by combining the overall weights of the indicators obtained above. The specific steps are as follows:

[0050] S1: If there are m innovative methods in a given domain, and the number of indicators in the indicator layer is n, then the initial evaluation matrix is: And determine the innovation method with the greatest positive and negative applicability based on the assessed value:

[0051]

[0052]

[0053] Among them, the domain innovation method for the maximum positive and negative applicability corresponding to each indicator in the indicator layer. Each from the initial evaluation matrix It consists of the maximum and minimum values ​​of each column of elements.

[0054] S2: Calculate the distance between the innovation method and the maximum positive and negative applicability method in each field:

[0055]

[0056] S3: The applicability of each method is If a specific application scenario for a domain innovation method is determined, the overall applicability of each method in that scenario can be determined. The optimal sequence of domain innovation methods is then obtained by ranking the overall applicability values. The formula for calculating the overall applicability E is as follows:

[0057]

[0058] The above steps yield the optimal sequence of five domain innovation methods with the highest total applicability, and the dictionary CG consisting of their total applicability values: CG = {Domain Innovation Method 1: Total Applicability Value 1; Domain Innovation Method 2: Total Applicability Value 2; Domain Innovation Method 3: Total Applicability Value 3; Domain Innovation Method 4: Total Applicability Value 4; Domain Innovation Method 5: Total Applicability Value 5}.

[0059] 4. Based on the applicability values ​​of innovative methods in each field, a sequence of optimal methods is obtained, and the names, definitions, and applicability values ​​of each party are pushed to the user.

[0060] 5. After a user selects a method from the preferred sequence of innovative methods in the field pushed by the system, they can evaluate the application effect of the method. The evaluation indicators are the five application scenario options selected in step ③.

[0061] For example, if the user selects the application scenario AS = {Product type: electric locomotive; Innovation object: vehicle body; Innovation chain link: conceptual design; Innovation category: technological innovation; Innovation procedure: problem solving}, then after the user selects a method from the preferred sequence to apply, the application effect of the method can be evaluated. The evaluation indicators are electric locomotive, vehicle body, conceptual design, technological innovation, and problem solving.

[0062] Example 2:

[0063] like Figure 3 The diagram shows the structural composition of the push system of this invention:

[0064] This push system includes an application scenario selection module, a search and analysis module, an applicability evaluation module, a content push module, and a feedback module. The specific functions of each module are as follows:

[0065] ① In the application scenario selection module of the system, users need to determine the application scenario of the domain innovation method based on the actual problem situation, including five dimensions of scenario indicators: product type, innovation object, innovation chain link, innovation category and innovation procedure, and select the five application scenario indicators determined above.

[0066] ② The search and analysis module imports the application scenario into the applicability evaluation module to obtain the preferred sequence of innovative methods in the field. The content push module pushes this preferred sequence to the user, and the user selects an innovative method from the preferred sequence to apply it.

[0067] ③ The applicability evaluation module evaluates the applicability of domain innovation methods in the database based on the application scenario, and obtains the optimal sequence of 5 domain innovation methods with the highest applicability;

[0068] ④ The content push module pushes the name, definition, and applicability value of the innovative methods in this preferred sequence to the user.

[0069] ⑤ After applying the innovative method, users can evaluate the effectiveness of the method in the feedback module.

Claims

1. A method for pushing optimal sequences in a domain-innovative approach, characterized in that, Includes the following steps: Step 1: Users determine the application scenarios for domain-specific innovative methods based on actual problems; Step 2: The search and analysis module imports the selected application scenarios into the applicability evaluation module; Step 3: The applicability evaluation module evaluates the applicability of the domain innovation methods in the database based on the application scenario, and obtains the optimal sequence of the 5 domain innovation methods with the highest applicability; Step 4: The content push module pushes the name, definition, and applicability value of the innovative methods in this preferred sequence to the user; The specific steps of step 3 are as follows: Step 3-1: Determine the applicability evaluation indicators for the application scenarios of domain innovation methods; establish an applicability evaluation indicator system, including the target layer, dimension layer, and indicator layer; Step 3-2: Determine the weights of the dimension layer indicators using fuzzy hierarchical analysis; determine the weights of the indicator layer indicators using the entropy method; combine the weights of the dimension layer indicators determined by fuzzy hierarchical analysis and the weights of the indicator layer indicators determined by the entropy method to determine the overall weights of the indicator layer. Step 3-3: Use the approximation of ideal value ranking method to obtain the applicability of each field's innovation method to the indicator layer indicators; combine the overall weight of the indicator layer indicators to obtain the total applicability value of each field's innovation method.

2. The method for pushing the preferred sequence of domain innovation methods according to claim 1, characterized in that, The application scenarios in step 1 include scenario indicators in five dimensions: product type, innovation object, innovation chain link, innovation category, and innovation procedure.

3. The method for pushing the preferred sequence of domain innovation methods according to claim 1, characterized in that, The specific steps for determining the weights of the dimension layer indicators using fuzzy hierarchical analysis in step 3-2 are as follows: S1: The fuzzy judgment matrix is ​​obtained by pairwise comparison of the relative importance of the dimensional level indicators to the evaluation of the applicability of domain innovation methods. The fuzzy consistency judgment matrix can be obtained after transformation. The transformation relationship is as follows: , where: x i x j These are the sums of the elements in the i-th and j-th rows of the fuzzy judgment matrix X, respectively, and n is the number of evaluation indicators. S2: Calculate the weights of the dimension-level indicators. The formula for calculating the weights of the dimension-level indicators is as follows: 。 4. The method for pushing the preferred sequence of domain innovation methods according to claim 1, characterized in that, The specific steps for determining the weights of the index layer using the entropy method in step 3-2 are as follows: S1: If there are m evaluators participating in the scoring, and l evaluation indicators, then the evaluators' scores for each indicator constitute the initial judgment matrix. The initial judgment matrix is ​​normalized to obtain the standardized judgment matrix. The normalization formula is: ； in, This represents the minimum value in the j-th column of the initial judgment matrix; This represents the maximum value in the j-th column of the initial judgment matrix; S2: Calculate the information entropy of the indicators, where the weight of the objective score of the i-th evaluator under the j-th indicator is... Then the information entropy of the j-th indicator is ; S3: Calculate the indicator weights at the indicator layer. The formula for calculating the indicator weights at the indicator layer is as follows: 。 5. The method for pushing the preferred sequence of the domain innovation method according to claim 3 or 4, characterized in that, The specific steps for determining the overall weight of the indicator layer in step 3-2 are as follows: The formula for calculating the overall weight of each indicator in the indicator layer is as follows: ,in, This represents the overall weight of the indicators in the indicator layer; where, This represents the weight of the i-th metric in the dimension layer. This indicates the weight of the j-th indicator corresponding to the i-th dimension in the indicator layer.

6. The method for pushing the preferred sequence of the domain innovation method according to claim 5, characterized in that, The specific steps for calculating the overall applicability of the domain innovation method in step 3-3 are as follows: S1: If there are g types of domain innovation methods and n indicators in the indicator layer, then the initial evaluation matrix is: And determine the innovation method with the greatest positive and negative applicability based on the assessed value: ； Among them, the domain innovation method for the maximum positive and negative applicability corresponding to each indicator in the indicator layer. Each from the initial evaluation matrix It consists of the maximum and minimum values ​​of each column of elements; S2: Calculate the distance between the innovation method and the maximum positive and negative applicability method in each field: ； in, and These represent the positive and negative ideal innovation method distances between the i-th innovation method and the j-th indicator, respectively. and They are respectively and The j-th element in the middle; S3: The applicability of innovation methods in various fields is If a specific application scenario for a domain innovation method is determined, the overall applicability of each method in that scenario can be determined. The optimal sequence of domain innovation methods is obtained based on the ranking of their overall applicability values. The formula for calculating the overall applicability E is as follows: ； in, This indicates the applicability of the i-th innovative method to the j-th indicator; This represents the total applicability of the i-th innovative method; This represents the overall weight of the i-th innovative method corresponding to the j-th indicator; This indicates the applicability of the i-th innovative method to the j-th indicator.

7. A push system for optimizing sequences using a domain-innovative method, characterized in that: It includes an application scenario selection module, a search and analysis module, an applicability evaluation module, a content push module, and a feedback module; The application scenario selection module is used by users to determine the application scenario of the domain innovation method based on the actual problem situation, including scenario indicators in five dimensions: product type, innovation object, innovation chain link, innovation category and innovation procedure. The user selects the five application scenario indicators determined above. The search and analysis module imports the application scenario into the applicability evaluation module to obtain the preferred sequence of domain innovation methods. The content push module pushes this preferred sequence to the user, and the user selects an innovation method from the preferred sequence to apply it. The applicability evaluation module evaluates the applicability of domain innovation methods in the database based on application scenarios, obtaining a preferred sequence of five domain innovation methods with the highest applicability. The content push module pushes the names, definitions, and applicability values ​​of the domain innovation methods in this preferred sequence to the user. After applying the innovation methods, the user can evaluate the application effect of the methods in the feedback module. The specific steps for obtaining the preferred sequence of five domain innovation methods with the highest applicability are as follows: determining the applicability evaluation index for the application scenarios of the domain innovation methods; establishing an applicability evaluation index system, including a target layer, a dimension layer, and an index layer; using fuzzy hierarchical analysis to determine the index weights of the dimension layer; using the entropy method to determine the index weights of the index layer; and combining the index weights of the dimension layer determined by fuzzy hierarchical analysis and the index weights of the index layer determined by the entropy method to determine the overall weight of the index layer. The applicability of innovation methods in each field to the indicators of the indicator layer is obtained by using the ranking method of approximating ideal values; combined with the overall weight of the indicators of the indicator layer, the total applicability value of innovation methods in each field is obtained.

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