Electronic and electrical architecture evaluation method, device, equipment and medium

Abstract

The embodiment of the invention provides an electronic and electrical architecture evaluation method and device, equipment and a medium, and relates to the technical field of automotive electronics. The method comprises the steps that an evaluation model is obtained, the evaluation model comprises N hierarchies, and N is a positive integer; judging matrixes corresponding to N-1 levels of the evaluation model are constructed, the order of the judging matrixes is equal to the number of model elements of the corresponding levels, and items in the judging matrixes are importance scale values obtained by comparing row model elements and column model elements of the judging matrixes; and obtaining an evaluation result according to the feature vectors of the judgment matrixes corresponding to the N-1 hierarchies. Therefore, a qualitative evaluation standard is converted into a quantitative numerical value by utilizing the judgment matrix corresponding to the hierarchy in the evaluation model. By calculating the ratio of the model elements, the judgment matrix can reflect the relative importance and priority among different elements, so that the evaluation is more objective and accurate, and the reliability of the evaluation result is reflected.

Description

Technical Field

[0001] The present application relates to the field of automotive electronics technology, and in particular to an evaluation method, device, equipment and medium for electronic and electrical architecture. Background Art

[0002] Electrical and Electronic Architecture (EEA) is an important part of the car, covering all electrical and electronic systems inside the car, including sensors, controllers, communication networks, etc. The main responsibility of the electrical and electronic architecture is to manage and coordinate the above electrical and electronic systems to ensure that they can work together to meet the various functional requirements of the car. With the rapid improvement of the degree of intelligence and electrification in the automotive industry, the core position of EEA in automobiles has become increasingly prominent. EEA is not only related to the realization of automobile functions, but also directly affects the performance and manufacturing cost of the car. Therefore, the evaluation process of EEA requires a faster and more effective analysis method.

[0003] In related technologies, EEA is usually evaluated based on statistical analysis methods such as statistical test method and set value statistical iteration method. These methods will conduct quantitative analysis on multiple indicators such as production cost, scientific research input, benefit output, etc., and then further analyze and evaluate the quantitative analysis results to determine the advantages and disadvantages of EEA.

[0004] However, when evaluating EEA through statistical analysis methods, the indicator data used is usually the company's historical statistical data, which cannot be dynamically associated with the current EEA development and design process in real time, resulting in a certain lag in the evaluation results. In addition, if the historical statistical data is missing or wrong, the EEA evaluation results will also be problematic, reducing the reliability of the evaluation results. Summary of the invention

[0005] Based on the above problems, the present application provides an electronic and electrical architecture evaluation method, device, equipment and medium, which can enhance the reliability of the evaluation results.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] In a first aspect, the present application provides a method for evaluating an electronic and electrical architecture, the method comprising:

[0008] Obtain an evaluation model, wherein the evaluation model includes N levels, where N is a positive integer;

[0009] Constructing judgment matrices corresponding to the N-1 levels of the evaluation model, respectively, wherein the order of the judgment matrix is ​​equal to the number of model elements of the corresponding level, and the items in the judgment matrix are importance scale values ​​compared with the row model elements and column model elements of the judgment matrix;

[0010] An evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

[0011] Optionally, obtaining the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively includes:

[0012] Performing consistency check on the judgment matrices corresponding to the N-1 levels respectively;

[0013] If the consistency check passes, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

[0014] Optionally, the performing consistency check on the judgment matrices corresponding to the N-1 levels respectively includes:

[0015] Determine the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to each of the N-1 levels;

[0016] Determine the average random consistency index according to the orders of the judgment matrices corresponding to the N-1 levels respectively;

[0017] Performing a consistency check according to a ratio of the consistency index to the average random consistency index;

[0018] If the consistency check passes, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels, including:

[0019] If the ratio of the consistency index to the average random consistency index is lower than a preset value, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

[0020] Optionally, determining the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels respectively includes:

[0021] The consistency index is determined according to the ratio of the first value and the second value of the judgment matrix corresponding to the N-1 levels, wherein the first value is the difference between the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels and the order of the judgment matrix corresponding to the N-1 levels, and the second value is the difference between the order of the judgment matrix corresponding to the N-1 levels and 1; the calculation formula of the consistency index is as follows:

[0022] CI=(λ max -n) / (n-1)

[0023] Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix.

[0024] Optionally, obtaining the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively includes:

[0025] Normalizing the eigenvectors of the judgment matrices corresponding to the N-1 levels to obtain standard influence weight vectors of the judgment matrices corresponding to the N-1 levels;

[0026] Obtaining an evaluation result according to the standard influence weight vectors of the judgment matrices corresponding to the N-1 levels respectively;

[0027] The formula for the normalization process is as follows:

[0028]

[0029] Among them, b i is the standard influence weight vector of the i-th level, a i is the feature vector of the i-th level, and n is a positive integer.

[0030] Optionally, the evaluation result is obtained as follows:

[0031]

[0032] Among them, Z is the evaluation result, b i ' is the standard influence weight vector of the i-th model element at level B, c ij For the B level i 'The standard influence weight vector of the jth model element, d jk For the C level ij The corresponding standard influence weight vector of the model element, C>B.

[0033] In a second aspect, the present application provides an evaluation device for electronic and electrical architecture, the device comprising: an acquisition module, a construction module and an evaluation module;

[0034] The acquisition module is used to acquire an evaluation model, wherein the evaluation model includes N levels, where N is a positive integer;

[0035] The construction module is used to construct judgment matrices corresponding to the N-1 levels of the evaluation model, the order of the judgment matrix is ​​equal to the number of model elements of the corresponding level, and the items in the judgment matrix are importance scale values ​​compared with the row model elements and column model elements of the judgment matrix;

[0036] The evaluation module is used to obtain the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

[0037] Optionally, the evaluation module specifically includes: a verification submodule and an evaluation submodule;

[0038] The check submodule is used to perform consistency check on the judgment matrices corresponding to the N-1 levels respectively;

[0039] The evaluation submodule is used to obtain an evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively if the consistency check passes.

[0040] Optionally, the syndrome module specifically includes: a first syndrome module, a second syndrome module and a third syndrome module;

[0041] The first check submodule is used to determine the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels respectively;

[0042] The second check submodule is used to determine the average random consistency index according to the orders of the judgment matrices corresponding to the N-1 levels respectively;

[0043] The third check submodule is used to perform consistency check according to the ratio of the consistency index and the average random consistency index;

[0044] The evaluation submodule is specifically used to obtain an evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively if the ratio of the consistency index to the average random consistency index is lower than a preset value.

[0045] Optionally, the first check submodule is specifically used to determine the consistency index according to the ratio of the first value and the second value of the judgment matrix corresponding to the N-1 levels, the first value is the difference between the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels and the order of the judgment matrix corresponding to the N-1 levels, and the second value is the difference between the order of the judgment matrix corresponding to the N-1 levels and 1; the calculation formula of the consistency index is as follows:

[0046] CI=(λ max -n) / (n-1)

[0047] Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix.

[0048] Optionally, the evaluation submodule specifically includes a first evaluation submodule and a second evaluation submodule;

[0049] The first evaluation submodule is used to normalize the characteristic vectors of the judgment matrices corresponding to the N-1 levels to obtain the standard influence weight vectors of the judgment matrices corresponding to the N-1 levels;

[0050] The formula for the normalization process is as follows:

[0051]

[0052] Among them, b i is the standard influence weight vector of the i-th level, a i is the feature vector of the i-th level, and n is a positive integer;

[0053] The second evaluation submodule is used to obtain an evaluation result according to the standard influence weight vectors of the judgment matrices corresponding to the N-1 levels respectively.

[0054] Optionally, the evaluation result is obtained as follows:

[0055]

[0056] Among them, Z is the evaluation result, b i ' is the standard influence weight vector of the i-th model element at level B, c ij For the B level i 'The standard influence weight vector of the jth model element, d jk For the C level ij The corresponding standard influence weight vector of the model element, C>B.

[0057] In a third aspect, the present application provides an electronic and electrical architecture evaluation device, including: a memory and a processor;

[0058] The memory is used to store programs;

[0059] The processor is used to implement the steps of the above-mentioned electronic and electrical architecture evaluation method when executing the computer program.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned electronic and electrical architecture evaluation method are implemented.

[0061] Compared with the prior art, this application has the following beneficial effects:

[0062] The embodiment of the present application provides an evaluation method, device, equipment and medium for electronic and electrical architecture, the method comprising: obtaining an evaluation model, the evaluation model comprising N levels, N being a positive integer; constructing judgment matrices corresponding to the N-1 levels of the evaluation model, the order of the judgment matrix being equal to the number of model elements of the corresponding level, and the items in the judgment matrix being the importance scale values ​​of the row model elements and column model elements of the judgment matrix; obtaining an evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels. Thus, the qualitative evaluation criteria are converted into quantitative values ​​using the judgment matrix corresponding to the levels in the evaluation model. By calculating the ratios between the model elements, the judgment matrix can reflect the relative importance and priority between different elements, making the evaluation more objective and accurate, and reflecting the reliability of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0064] Figure 1 A flowchart of an electronic and electrical architecture evaluation method provided in an embodiment of the present application;

[0065] Figure 2 A schematic diagram of an evaluation model structure provided in an embodiment of the present application;

[0066] Figure 3 A schematic diagram of an electronic and electrical architecture evaluation device provided in an embodiment of the present application;

[0067] Figure 4 An interactive timing diagram of an electronic and electrical architecture evaluation device provided in an embodiment of the present application;

[0068] Figure 5 A structural diagram of an electronic and electrical architecture evaluation device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] As described above, currently, EEA is usually evaluated based on statistical analysis methods such as statistical test method and set-valued statistical iteration method. Statistical test method is an accurate EEA evaluation method. Its core idea is to evaluate the performance, reliability and other relevant indicators of products or systems by collecting and analyzing test data. It has the characteristics of simple simulation algorithm, flexible simulation process and high accuracy of simulation results. This method is widely used in engineering, scientific research, quality management and other fields. Set-valued statistical iteration method is an efficient EEA evaluation method. By simulating and iterating multiple possible solutions designed by EEA, it can quickly screen out solutions with superior performance and reasonable cost. The advantage of set-valued statistical iteration method is that it can automatically adjust weights through iteration, avoiding the subjectivity and arbitrariness of artificial setting. At the same time, this method can also handle some fuzzy and uncertain information, making the evaluation results more objective and accurate. These methods will further analyze and evaluate the quantitative analysis results after quantitatively analyzing multiple indicators such as production cost, scientific research input, and benefit output to judge the advantages and disadvantages of EEA.

[0070] However, when evaluating EEA through statistical analysis methods, the indicator data used are usually the company's historical statistical data, which cannot be dynamically associated with the current EEA development and design process in real time, resulting in a certain lag in the evaluation results. In addition, if the historical statistical data is missing or wrong, the EEA evaluation results will also be problematic, which will not only increase the company's evaluation costs, but also reduce the reliability of the evaluation results.

[0071] After research, the inventor proposed an evaluation method, device, equipment and medium for electronic and electrical architecture, the method comprising: obtaining an evaluation model, the evaluation model comprising N levels, N being a positive integer; constructing judgment matrices corresponding to the N-1 levels of the evaluation model, the order of the judgment matrix being equal to the number of model elements of the corresponding level, and the items in the judgment matrix being the importance scale values ​​of the row model elements and column model elements of the judgment matrix; obtaining the evaluation result according to the eigenvectors of the judgment matrix corresponding to the N-1 levels. Thus, the qualitative evaluation criteria are converted into quantitative values ​​using the judgment matrix corresponding to the level in the evaluation model. By calculating the ratio between the model elements, the judgment matrix can reflect the relative importance and priority between different elements, making the evaluation more objective and accurate, and reflecting the reliability of the evaluation results. Furthermore, since the scheme is based on a hierarchical structure, the number of levels and the setting of model elements can be easily adjusted according to actual needs. This flexibility enables the scheme to adapt to the evaluation needs of different fields and scenarios.

[0072] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0073] See also Figure 1 , which is a flow chart of an electronic and electrical architecture evaluation method provided in an embodiment of the present application. The electronic and electrical architecture evaluation method includes:

[0074] S101: Build an evaluation model based on the EEA design platform.

[0075] First, in the EEA design platform, an evaluation model is constructed according to the Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process refers to a method that assists decision makers in making decisions and evaluating complex systems by decomposing complex problems into several levels and factors and conducting qualitative and quantitative analysis between these factors. The evaluation model refers to a model for evaluating the electronic and electrical architecture of a vehicle, which includes at least the target layer, the criterion layer, and the solution layer. The target layer reflects the evaluation purpose of the decision-making problem. The criterion layer reflects the intermediate links or evaluation criteria required to achieve the evaluation purpose. The solution layer reflects the solution or measures to be evaluated.

[0076] See also Figure 2 , which is a schematic diagram of an evaluation model structure provided in an embodiment of the present application. The evaluation model includes a target layer A, a criterion layer B, a sub-criteria layer C and a solution layer D.

[0077] In the embodiments disclosed in the present application, the evaluation purpose reflected in the target layer A is to evaluate the automotive electronic and electrical architecture. The evaluation criteria reflected in the criterion layer B may include one or more of platform reliability, variant management, cost control, controller system and wiring harness design. The solution layer D reflects the fully distributed architecture and the centralized distributed architecture. Among them, if all vehicle controllers communicate as independent nodes in the network, it is fully distributed; if the controllers are divided into groups and the network is set up nearby, and network communication is carried out in the form of groups, it is centralized.

[0078] The sub-criteria layer C embodies the sub-criteria corresponding to the evaluation criteria in the criterion layer. In the embodiments disclosed in the present application, the sub-criteria corresponding to platform reliability may include fault failure handling and functional safety level, the sub-criteria corresponding to variant management may include network topology variants and electronic control unit (ECU) variants, the sub-criteria corresponding to cost control may include design cost and maintenance cost, the sub-criteria corresponding to the controller system may include function allocation and the number of core platform pieces, and the sub-criteria corresponding to harness design may include harness layout environment and harness space layout.

[0079] It should be noted that the evaluation objectives in the target layer, the evaluation criteria in the criteria layer, the sub-criteria in the sub-criteria layer, and the architecture in the solution layer are collectively referred to as model elements. Model elements can be uniquely identified in the EEA design platform through a universally unique identifier (UUID).

[0080] It should be noted that the evaluation model disclosed in the above embodiment is only an example. In practical applications, the criterion layer can allow the evaluation model to establish any number of layers of criteria and any number of branches of sub-criteria under a criterion based on the model level expansion. This application does not limit the specific evaluation model.

[0081] S102: Construct a judgment matrix according to the structure of the evaluation model.

[0082] After the evaluation model is built, it is necessary to build a judgment matrix based on the structure of the evaluation model. The judgment matrix is ​​a square matrix whose number of rows and columns is equal to the number of model elements at that level.

[0083] Since the evaluation model disclosed in the embodiment of the present application includes four layers, namely, target layer A, criterion layer B, sub-criterion layer C, and solution layer D, a judgment matrix corresponding to criterion layer B is first constructed. In some specific implementations, the judgment matrix corresponding to criterion layer B can be shown in Table 1 below:

[0084] Table 1

[0085]

[0086]

[0087] It should be noted that the value of each cell in the judgment matrix represents the importance scale value of the row model element compared with the column model element. This method can be used to quantify the relative importance of the model elements. 1 means that the two model elements are equally important, 9 means that one model element is extremely important than the other model element, and other numbers represent different degrees of importance between the two. Generally speaking, according to the principle of hierarchical analysis, the number of model elements in each layer should not exceed 9, that is, the order of the judgment matrix in each layer should not exceed 9.

[0088] In some examples, the EEA evaluation value for "Platform Reliability - Cost Control" is 1 / 2, so platform reliability is not as important as cost control, and the difference in importance between the two is not large. In other examples, the EEA evaluation value for "Controller System - Variant Management" is 1 / 7, so the controller system is not as important as variant control, and the difference in importance between the two is large.

[0089] Generally speaking, the judgment matrix is ​​symmetrical, and the model elements on the diagonal are 1 (because the model elements are always equally important compared to themselves), while the values ​​of other cells require relevant technical personnel to make comprehensive judgments based on the company's historical data.

[0090] Secondly, construct a judgment matrix corresponding to the sub-criteria layer C. In some specific implementations, the judgment matrix corresponding to the sub-criteria layer C can be shown in Tables 2 to 6 below:

[0091] Table 2

[0092] Platform reliability Failure handling Functional safety level Failure handling 1 1 Functional safety level 1 1

[0093] Table 3

[0094] Variant Management Network topology variants ECU variants Network topology variants 1 1 / 3 ECU variants 3 1

[0095] Table 4

[0096] Cost Control Design Cost Maintenance costs Design Cost 1 5 Maintenance costs 1 / 5 1

[0097] Table 5

[0098] Controller system Function allocation Number of core platforms Function allocation 1 1 / 3 Number of core platforms 3 1

[0099] Table 6

[0100] Wiring harness design Wiring harness layout environment Wiring harness space layout Wiring harness layout environment 1 1 / 3 Wiring harness space layout 3 1

[0101] Finally, a judgment matrix corresponding to solution layer D is constructed. In some specific implementations, the judgment matrix corresponding to solution layer D may be as shown in Tables 7 to 16 below:

[0102] Table 7

[0103] Failure handling Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 1 / 3 Centralized distributed architecture 3 1

[0104] Table 8

[0105] Functional safety level Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 3 Centralized distributed architecture 1 / 3 1

[0106] Table 9

[0107]

[0108]

[0109] Table 10

[0110] ECU variants Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 1 / 5 Centralized distributed architecture 5 1

[0111] Table 11

[0112] Design Cost Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 1 / 3 Centralized distributed architecture 3 1

[0113] Table 12

[0114] Maintenance costs Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 1 / 5 Centralized distributed architecture 5 1

[0115] Table 13

[0116] Function allocation Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 3 Centralized distributed architecture 1 / 3 1

[0117] Table 14

[0118]

[0119]

[0120] Table 15

[0121] Wiring harness layout environment Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 1 / 3 Centralized distributed architecture 3 1

[0122] Table 16

[0123] Wiring harness space layout Fully distributed architecture Centralized distributed architecture Fully distributed architecture 1 2 Centralized distributed architecture 1 / 2 1

[0124] It should be noted that in the hierarchical analysis method, there is usually no requirement for the order of constructing judgment matrices at different levels. It is only necessary to ensure the logic and consistency of the hierarchical structure and fully consider the relative importance and dependencies between elements when constructing the judgment matrix.

[0125] S103: Perform consistency check on the judgment matrix.

[0126] The purpose of consistency check is to ensure that the model elements in the judgment matrix satisfy certain logical relationships, so that the subsequent evaluation results are more reliable and effective. Therefore, after the construction of the judgment matrix is ​​completed, each judgment matrix needs to be checked for consistency. If the consistency check passes, step S104 can be executed. If the consistency check fails, it is necessary to readjust the values ​​in the matrix until the consistency check passes.

[0127] In some specific implementations, the judgment matrix may be checked for consistency through the following steps S31-S34:

[0128] S31: Obtain the maximum eigenvalue λ of the judgment matrix max and the eigenvector A.

[0129] In some examples, the maximum eigenvalue λ of the judgment matrix can be solved based on the eigenvalue decomposition (EVD) method. max and eigenvector A. EVD refers to the method of decomposing the judgment matrix into the product of matrices represented by its eigenvalues ​​and eigenvectors. EVD is more accurate than the approximate algorithm used in the general hierarchical analysis method.

[0130] It should be noted that the present application does not limit the specific solution method, and the power method, Jacobi method, special AHP software, etc. may also be used to solve the problem.

[0131] S32: According to the maximum eigenvalue λ max , determine the consistency index (Consistency Index, CI).

[0132] The consistency index is a quantitative index used to measure the degree of inconsistency of the judgment matrix. Specifically, the calculation formula of the consistency index can be shown as the following formula (1):

[0133] CI=(λ max -n) / (n-1) (1)

[0134] Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix. The larger the CI value, the more serious the inconsistency of the judgment matrix.

[0135] S33: Determine the average random consistency index (Random Index, RI) according to the order of the judgment matrix.

[0136] The average random consistency index is a constant related to the order n of the judgment matrix, representing the average value of the consistency index of the randomly generated judgment matrix. In some specific implementations, the corresponding relationship between the average random consistency index and the order of the judgment matrix can be shown in Table 17 below:

[0137] Table 17

[0138] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45

[0139] S34: Determine a consistency ratio (Consistency Ratio, CR) according to the consistency index and the average random consistency index.

[0140] The consistency ratio is used to determine whether the consistency of the judgment matrix is ​​acceptable. If the value of the consistency ratio is less than the preset value (usually 0.1), the consistency of the judgment matrix is ​​considered acceptable. If the value of the consistency ratio is greater than or equal to the preset value, the consistency of the judgment matrix is ​​considered unacceptable, and the judgment matrix needs to be adjusted and re-checked for consistency.

[0141] Specifically, the calculation formula of the consistency ratio can be shown as the following formula (2):

[0142] CR=CI / RI(2)

[0143] Among them, CR is the consistency ratio, CI is the consistency index, and RI is the average random consistency index.

[0144] S104: Normalize the eigenvectors of the judgment matrix to obtain a standard influence weight vector.

[0145] After the feature vector A is obtained through the above step S31, the feature vector A can also be normalized so that the sum of the elements in the vector is 1, so that the data is more intuitive. Specifically, the calculation formula for the normalization process can be shown as the following formula (3):

[0146]

[0147] Among them, b i is the standard influence weight vector of the i-th level, which indicates the relative importance of each model element relative to the model elements of the previous level. i is the feature vector of the i-th level, and n is a positive integer.

[0148] S105: Comprehensively judge the standard influence weight vectors of all levels of the matrix to generate a ladder influence weight table.

[0149] If there are multiple levels in the evaluation model, the standard impact weight vectors of each level need to be synthesized to obtain the final overall weight. This step is usually implemented by multiplying each level, starting from the highest level, and synthesizing the weights layer by layer until the overall weight of the lowest level (scheme level) relative to the highest level (target level) is obtained. In some specific implementations, the ladder impact weight table can be shown in Table 18 below:

[0150] Table 18

[0151]

[0152]

[0153] S106: Determine the evaluation result according to the ladder impact weight table.

[0154] The total weight calculated according to the ladder influence weight table is the evaluation result. The larger the total weight, the higher the relative importance of the factor or solution in the entire decision-making structure. In some specific implementations, the calculation formula of the total weight can be shown as the following formula (4):

[0155]

[0156] Among them, Z is the total weight (i.e., the evaluation result), b i ' is the standard influence weight vector of the i-th model element in the criterion layer B, c ij b is the standard layer B i 'The standard influence weight vector of the jth model element, d jk C is the sub-criteria layer C ij The standard influence weight vector of the corresponding model elements.

[0157] According to the above Table 18, the total weights of the fully distributed architecture D1 and the centralized distributed architecture D2 are 0.3923 and 0.6077 respectively, indicating that the centralized distributed architecture D2 is a better architectural design solution.

[0158] In summary, the present application discloses an evaluation method for an electronic and electrical architecture, the method comprising: obtaining an evaluation model, the evaluation model comprising N levels, N being a positive integer; constructing judgment matrices corresponding to the N-1 levels of the evaluation model, the order of the judgment matrix being equal to the number of model elements of the corresponding level, and the items in the judgment matrix being the importance scale values ​​of the row model elements and column model elements of the judgment matrix; obtaining an evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels. Thus, the qualitative evaluation criteria are converted into quantitative values ​​using the judgment matrix corresponding to the level in the evaluation model. By calculating the ratio between the model elements, the judgment matrix can reflect the relative importance and priority between different elements, making the evaluation more objective and accurate, and reflecting the reliability of the evaluation results. Furthermore, since the scheme is based on a hierarchical structure, the number of levels and the setting of model elements can be easily adjusted according to actual needs. This flexibility enables the scheme to adapt to the evaluation needs of different fields and scenarios.

[0159] See also Figure 3 , which is a schematic diagram of an electronic and electrical evaluation device provided in an embodiment of the present application. The electronic and electrical evaluation device 300 includes: an acquisition module 301, a construction module 302 and an evaluation module 303;

[0160] An acquisition module 301 is used to acquire an evaluation model, where the evaluation model includes N levels, where N is a positive integer;

[0161] A construction module 302 is used to construct judgment matrices corresponding to the N-1 levels of the evaluation model, wherein the order of the judgment matrix is ​​equal to the number of model elements of the corresponding level, and the items in the judgment matrix are importance scale values ​​compared with the row model elements and column model elements of the judgment matrix;

[0162] The evaluation module 303 is used to obtain the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

[0163] In some specific implementations, the evaluation module 303 specifically includes: a verification submodule and an evaluation submodule;

[0164] The check submodule is used to perform consistency check on the judgment matrices corresponding to the N-1 levels respectively;

[0165] The evaluation submodule is used to obtain the evaluation result according to the eigenvectors of the judgment matrix corresponding to the N-1 levels respectively if the consistency check passes.

[0166] In some specific implementations, the check submodule specifically includes: a first check submodule, a second check submodule, and a third check submodule;

[0167] The first check submodule is used to determine the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to each of the N-1 levels;

[0168] The second check submodule is used to determine the average random consistency index according to the order of the judgment matrix corresponding to the N-1 levels respectively;

[0169] A third check submodule is used to perform consistency check according to the ratio of the consistency index and the average random consistency index;

[0170] The evaluation submodule is specifically used to obtain the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels if the ratio of the consistency index to the average random consistency index is lower than a preset value.

[0171] In some specific implementations, the first check submodule is specifically used to determine the consistency index according to the ratio of the first value and the second value of the judgment matrix corresponding to the N-1 levels, the first value is the difference between the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels and the order of the judgment matrix corresponding to the N-1 levels, and the second value is the difference between the order of the judgment matrix corresponding to the N-1 levels and 1; the calculation formula of the consistency index is as follows:

[0172] CI=(λmax -n) / (n-1)

[0173] Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix.

[0174] In some specific implementations, the evaluation submodule specifically includes a first evaluation submodule and a second evaluation submodule;

[0175] The first evaluation submodule is used to normalize the eigenvectors of the judgment matrices corresponding to the N-1 levels to obtain the standard influence weight vectors of the judgment matrices corresponding to the N-1 levels;

[0176] The normalization formula is as follows:

[0177]

[0178] Among them, b i is the standard influence weight vector of the i-th level, a i is the feature vector of the i-th level, and n is a positive integer;

[0179] The second evaluation submodule is used to obtain the evaluation result according to the standard influence weight vectors of the judgment matrix corresponding to the N-1 levels respectively.

[0180] In some specific implementations, the formula for obtaining the evaluation result is as follows:

[0181]

[0182] Among them, Z is the evaluation result, b i ' is the standard influence weight vector of the i-th model element at level B, c ij For the B level i 'The standard influence weight vector of the jth model element, d jk For the C level ij The corresponding standard influence weight vector of the model element, C>B.

[0183] In summary, the present application provides an evaluation device for an electronic and electrical architecture, which uses a judgment matrix corresponding to the levels in the evaluation model to convert qualitative evaluation criteria into quantitative values. By calculating the ratios between model elements, the judgment matrix can reflect the relative importance and priority between different elements, making the evaluation more objective and accurate, and reflecting the reliability of the evaluation results. Furthermore, since the device is based on a hierarchical structure, the number of levels and the setting of model elements can be easily adjusted according to actual needs. This flexibility enables the solution to adapt to the evaluation needs of different fields and scenarios.

[0184] See also Figure 4 , which is an interactive timing diagram of an electronic and electrical architecture evaluation device provided in an embodiment of the present application. The electronic and electrical architecture evaluation device can be located within the EEA design platform or outside the EEA design platform. If it is located outside the EEA design platform, the specific interactive timing is as follows: First, in response to the user opening the electronic and electrical architecture evaluation device, the electronic and electrical architecture evaluation device reads the judgment matrix generated by the EEA design platform model and successfully starts the electronic and electrical architecture evaluation device; secondly, the user assigns values ​​to the judgment matrix based on the assignment standard through the electronic and electrical architecture evaluation device. Optionally, the assignment can be obtained through expert scoring and other methods to ensure the objectivity of the evaluation as much as possible. After the assignment, the electronic and electrical architecture evaluation device is responsible for data consistency verification; finally, if the data consistency verification passes, the electronic and electrical architecture evaluation device performs column vector normalization processing on the judgment matrix, generates a standard ladder weight summary table and calculates the final weight result, and finally writes the result data into the EEA design platform.

[0185] Furthermore, the present application also discloses an electronic and electrical architecture evaluation device. Figure 5 , which is a structural diagram of an electronic and electrical architecture evaluation device provided in an embodiment of the present application. It should be noted that the content in the figure cannot be regarded as any limitation on the scope of use of the present application. The electronic and electrical architecture evaluation device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input and output interface 25 and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the method disclosed in any of the aforementioned embodiments. In addition, the electronic and electrical architecture evaluation device 20 in this embodiment can specifically be an electronic computer.

[0186] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic and electrical architecture evaluation device 20; the communication interface 24 can create a data transmission channel between the electronic and electrical architecture evaluation device 20 and external devices, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0187] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0188] The operating system 221 is used to manage and control the hardware devices on the electronic and electrical architecture evaluation device 20 and the computer program 222, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the method performed by the electronic and electrical architecture evaluation device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0189] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the aforementioned disclosed method. The specific steps of the method can refer to the corresponding contents disclosed in the aforementioned embodiments, and will not be repeated here.

[0190] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0191] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for evaluating an electronic and electrical architecture, characterized in that: The method comprises: Obtain an evaluation model, wherein the evaluation model includes N levels, where N is a positive integer; Constructing judgment matrices corresponding to the N-1 levels of the evaluation model, respectively, wherein the order of the judgment matrix is ​​equal to the number of model elements of the corresponding level, and the items in the judgment matrix are importance scale values ​​compared with the row model elements and column model elements of the judgment matrix; An evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

2. The method according to claim 1, characterized in that The evaluation result is obtained according to the characteristic vectors of the judgment matrices corresponding to the N-1 levels, including: Performing consistency check on the judgment matrices corresponding to the N-1 levels respectively; If the consistency check passes, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

3. The method according to claim 2, characterized in that The performing consistency check on the judgment matrices corresponding to the N-1 levels respectively includes: Determine the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to each of the N-1 levels; Determine the average random consistency index according to the orders of the judgment matrices corresponding to the N-1 levels respectively; Performing a consistency check according to a ratio of the consistency index to the average random consistency index; If the consistency check passes, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels, including: If the ratio of the consistency index to the average random consistency index is lower than a preset value, an evaluation result is obtained according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

4. The method according to claim 3, characterized in that The determining of the consistency index according to the maximum eigenvalue of the judgment matrix corresponding to each of the N-1 levels includes: The consistency index is determined according to the ratio of the first value and the second value of the judgment matrix corresponding to the N-1 levels, wherein the first value is the difference between the maximum eigenvalue of the judgment matrix corresponding to the N-1 levels and the order of the judgment matrix corresponding to the N-1 levels, and the second value is the difference between the order of the judgment matrix corresponding to the N-1 levels and 1; the calculation formula of the consistency index is as follows: CI=(λ max −n) / (n−1) Among them, CI is the consistency index, λ max is the maximum eigenvalue, and n is the order of the judgment matrix.

5. The method according to claim 2, characterized in that: The evaluation result is obtained according to the characteristic vectors of the judgment matrices corresponding to the N-1 levels, including: Normalizing the eigenvectors of the judgment matrices corresponding to the N-1 levels to obtain standard influence weight vectors of the judgment matrices corresponding to the N-1 levels; Obtaining an evaluation result according to the standard influence weight vectors of the judgment matrices corresponding to the N-1 levels respectively; The formula for the normalization process is as follows: Among them, b i is the standard influence weight vector of the i-th level, a i is the feature vector of the i-th level, and n is a positive integer.

6. The method according to claim 5, characterized in that The formula for obtaining the evaluation result is as follows: Among them, Z is the evaluation result, b i ' is the standard influence weight vector of the i-th model element at level B, c ij For the B level i 'Standard influence weight vector of the jth model element, d jk For the C level ij The corresponding standard influence weight vector of the model element, C>B.

7. An evaluation device for electronic and electrical architecture, characterized in that: The device comprises: an acquisition module, a construction module and an evaluation module; The acquisition module is used to acquire an evaluation model, wherein the evaluation model includes N levels, where N is a positive integer; The construction module is used to construct judgment matrices corresponding to the N-1 levels of the evaluation model, the order of the judgment matrix is ​​equal to the number of model elements of the corresponding level, and the items in the judgment matrix are importance scale values ​​compared with the row model elements and column model elements of the judgment matrix; The evaluation module is used to obtain the evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively.

8. The device according to claim 7, characterized in that The evaluation module specifically includes: a verification submodule and an evaluation submodule; The check submodule is used to perform consistency check on the judgment matrices corresponding to the N-1 levels respectively; The evaluation submodule is used to obtain an evaluation result according to the eigenvectors of the judgment matrices corresponding to the N-1 levels respectively if the consistency check passes.

9. An electronic and electrical architecture evaluation device, characterized in that: include: Memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the method according to any one of claims 1 to 6.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the method according to any one of claims 1 to 6 is implemented.

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