CN113762795A - Industrial chain diagnosis method and system based on hierarchical analysis - Google Patents
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Abstract
The application provides an industrial chain diagnosis method and system based on hierarchical analysis, which are applied to the field of industrial chain modernization and comprise the following steps: acquiring a knowledge graph of an industrial chain to be detected; processing the to-be-detected industrial chain knowledge graph according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system; screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network; and analyzing and diagnosing the power level network of the industrial chain knowledge graph diagnosis index system according to the mixed layered Bayesian network to obtain an industrial chain analysis and diagnosis result. According to the method, the index system is established, the mixed Bayesian network is adopted for analysis and calculation, and the final industrial chain analysis and diagnosis result is obtained, so that the strength of the nodes in the industrial chain knowledge graph corresponding to the index is judged, visual industrial chain information is provided for local governments, and the development of industrial chain modernization is facilitated.
Description
Technical Field
The invention relates to the field of industrial chain modernization, in particular to an industrial chain diagnosis method and system based on hierarchical analysis.
Background
The promotion of industrial chain modernization is an important link for constructing a new development pattern, the modernization development of the industrial chain is more and more emphasized, the modernization of the industrial chain needs to be adapted to the high-quality development requirement, the outstanding problems in the current industrial chain modernization process are clarified, the attack and robustness of the high-level industrial foundation and the modernization of the industrial chain are well established, and powerful support is provided for constructing the new development pattern.
The industrial chain modernization mainly comprises chain building, strong chain, chain supplementing and chain extending, however, in the real work, a problem faced by the government is that the strong and weak links of a certain local industrial chain are difficult to distinguish, and the strong person is strong and the weak person is weak. Because the concept is novel, in the prior art, the technical schemes adopted for solving the government problems are not complete and perfect, and the application range is limited.
Disclosure of Invention
The application provides an industrial chain diagnosis method and system based on hierarchical analysis, and aims to solve the problems that the strength of an industrial chain is difficult to distinguish in government work and the specific content of the strength is difficult to know.
In order to achieve the purpose, the invention adopts the following technical scheme:
an industry chain diagnosis method based on hierarchical analysis comprises the following steps:
acquiring a knowledge graph of an industrial chain to be detected;
processing the to-be-detected industrial chain knowledge graph according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, wherein the industrial chain graph diagnosis index system has specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
and analyzing and diagnosing the power level network of the industrial chain knowledge graph diagnosis index system according to the mixed layered Bayesian network to obtain an industrial chain analysis and diagnosis result.
Preferably, the analyzing and diagnosing the industry chain knowledge graph diagnosis index system power-level network according to the hybrid hierarchical bayesian network to obtain an industry chain analyzing and diagnosing result includes:
performing index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
comprehensively analyzing the power level network of the industrial chain knowledge graph diagnosis index system according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industrial chain level comprehensive evaluation result;
and acquiring the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to a maximum membership principle to obtain an industrial chain analysis diagnosis result.
Preferably, the index weight calculation of the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a bayesian network to obtain a diagnosis index comprehensive weight includes:
layering the power level network of the industrial chain knowledge graph diagnosis index system according to an analytic hierarchy process, comparing indexes of each layer after layering process through a 1-9 scale method and a preset judgment rule to obtain a ranking result of the index weight of each layer, summarizing the ranking results to obtain a result matrix, and calculating a characteristic vector of the result matrix according to a square root method to obtain a first calculation result;
constructing the Bayesian network according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, and performing parameter calculation on the first Bayesian network according to a parameter machine learning algorithm to obtain a second calculation result;
and combining the first calculation result and the second calculation result to obtain the comprehensive weight of the diagnosis index.
Preferably, the comprehensive analysis is performed on the power level network of the industry chain knowledge graph diagnosis index system according to the fuzzy theory and the diagnosis index weight coefficient, so as to obtain a comprehensive evaluation result of the industry chain level, and the method comprises the following steps:
calculating the membership degree of the specific indexes to an evaluation set according to a membership degree function to obtain a membership degree data set so as to form a fuzzy evaluation matrix, summarizing the comprehensive weights of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to a scheme of four grades, namely high, general and low;
calculating the fuzzy evaluation matrix and the comprehensive weight matrix according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, summarizing to obtain a fuzzy comprehensive evaluation matrix, and calculating the specific index according to an analytic hierarchy process to obtain an index weight distribution matrix;
and calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a horizontal comprehensive evaluation result of the industrial chain.
A hierarchical analysis based industry chain diagnostic system comprising:
an acquisition module: the method comprises the steps of obtaining a knowledge graph of an industrial chain to be detected;
an index system construction module: the industrial chain knowledge graph diagnosis index system is provided with specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
the graph convolution network computing module: the system is used for screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
industry chain analysis and diagnosis module: and the power level network analysis and diagnosis module is used for analyzing and diagnosing the industry chain knowledge graph diagnosis index system power level network according to the mixed layered Bayesian network to obtain an industry chain analysis and diagnosis result.
Preferably, the industry chain analysis and diagnosis module includes:
a comprehensive weight calculation module: the system is used for carrying out index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
a fuzzy theory evaluation module: the power level network of the industrial chain knowledge graph diagnosis index system is comprehensively analyzed according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industrial chain level comprehensive evaluation result;
industry chain contrasts analysis module: the method is used for obtaining the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to the maximum membership principle to obtain the analysis and diagnosis result of the industrial chain.
Preferably, the comprehensive weight calculating module includes:
the first calculation module of index weight: the system comprises an industrial chain knowledge graph diagnosis index system power level network, a first calculation result, a second calculation result and a third calculation result, wherein the industrial chain knowledge graph diagnosis index system power level network is subjected to hierarchical processing according to an analytic hierarchy process, indexes of each layer subjected to hierarchical processing are compared through a 1-9 scale method and a preset judgment rule to obtain a sequencing result of index weight of each layer, the sequencing results are collected to obtain a result matrix, and eigenvector calculation is performed on the result matrix according to a square root method to obtain the first calculation result;
the index weight second calculation module: the Bayesian network is constructed according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, and the first Bayesian network is subjected to parameter calculation according to a parameter machine learning algorithm to obtain a second calculation result;
an index weight combining module: and the comprehensive weight of the diagnostic index is obtained by combining the first calculation result and the second calculation result.
Preferably, the fuzzy theory evaluation module includes:
a first matrix building module: the evaluation set is used for calculating the membership degree of the specific indexes to the evaluation set according to a membership degree function to obtain a membership degree data set so as to form a fuzzy evaluation matrix, and summarizing the comprehensive weights of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to the scheme of four grades, namely high, general and low;
a second matrix construction module: the comprehensive evaluation matrix is used for calculating the fuzzy evaluation matrix and the comprehensive weight matrix according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, summarizing to obtain a fuzzy comprehensive evaluation matrix, and calculating the specific index according to an analytic hierarchy process to obtain an index weight distribution matrix;
an evaluation result calculation module: and the comprehensive evaluation matrix is used for calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a comprehensive evaluation result of the industrial chain level.
A hierarchical analysis based industry chain diagnostic system comprising a memory and a processor, the memory storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a hierarchical analysis based industry chain diagnostic method as claimed in any one of the preceding claims.
A computer-readable storage medium storing a computer program which, when executed by a computer, implements a hierarchical analysis-based industry chain diagnostic method as recited in any one of the above.
The invention has the following beneficial effects:
according to the method, the knowledge graph data of the industrial chain to be detected is processed according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, so that the index system applied in the technical scheme accords with the economics principle and the industrial chain development rule, and the accuracy of an industrial chain diagnosis result is improved; in the calculation process of the comprehensive weight of the index, an analytic hierarchy process and a Bayesian network are used, the robustness of the whole system is improved, the anti-interference capability is improved, and the analysis is performed on a multi-level basis, so that the analysis result is more comprehensive, and the accuracy of the final diagnosis result is improved; the industrial chains in different fields have different industrial chain knowledge maps, and different industrial chain analysis and diagnosis results can be obtained through the technical scheme according to the different industrial chain knowledge maps, so that the technical scheme has wider applicability; according to the method, an index system is established, hierarchical analysis, a Bayesian network and a fuzzy evaluation method are adopted for analysis and calculation, a final industrial chain analysis and diagnosis result is obtained, different information of each index is displayed in the result, level judgment is carried out according to an actual industrial chain, and therefore the strength of nodes in an industrial chain knowledge graph corresponding to the index is judged, visual industrial chain information is provided for local governments, and development of industrial chain modernization is facilitated.
Drawings
FIG. 1 is a flowchart of an embodiment of the present invention for implementing a hierarchical analysis-based industrial chain diagnosis method
FIG. 2 is a flowchart of a method for analyzing and diagnosing an industry chain knowledge graph diagnosis index system power-level network according to a hybrid hierarchical Bayesian network to obtain an industry chain analysis and diagnosis result according to an embodiment of the present invention
FIG. 3 is a schematic diagram of an industrial chain diagnostic system based on hierarchical analysis according to an embodiment of the present invention
FIG. 4 is a diagram of an industrial chain analysis and diagnosis module 400 in an industrial chain diagnosis system based on hierarchical analysis according to an embodiment of the present invention
FIG. 5 is a schematic diagram of an integrated weight calculation module 410 in an industry chain diagnosis system based on hierarchical analysis according to an embodiment of the present invention
FIG. 6 is a schematic diagram of a fuzzy theory evaluation module 420 in an industrial chain diagnostic system based on hierarchical analysis according to an embodiment of the present invention
FIG. 7 is a schematic diagram of an electronic device implementing a hierarchical analysis-based industry chain diagnostic system according to an embodiment of the present invention
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work based on the embodiments of the present invention belong to the protection scope of the present invention.
The terms "first," "second," and the like in the claims and in the description of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order, it being understood that the terms so used are interchangeable under appropriate circumstances and are merely used to describe a distinguishing manner between similar elements in the embodiments of the present application and that the terms "comprising" and "having" and any variations thereof are intended to cover a non-exclusive inclusion such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, and the terms used herein in the specification of the present application are for the purpose of describing particular embodiments only and are not intended to limit the present application.
Example 1
As shown in fig. 1, a method for diagnosing an industry chain based on hierarchical analysis includes the following steps:
s11, acquiring a knowledge graph of the industrial chain to be detected;
s12, processing the to-be-detected industrial chain knowledge graph according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, wherein the industrial chain graph diagnosis index system has specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
s13, screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
and S14, analyzing and diagnosing the power level network of the industry chain knowledge graph diagnosis index system according to the mixed hierarchical Bayesian network to obtain an industry chain analysis and diagnosis result.
In embodiment 1, first, a to-be-measured industrial chain knowledge graph of a designated area is obtained, an industrial chain can be evaluated from three dimensions of competitiveness, stability and risk resistance according to an economic principle, and according to a development rule of the industrial chain, the modernization of the industrial chain needs to be built, the industrial chain knowledge graph mainly comprises four core contents of chain building, strong chain, chain supplementing and chain delaying, based on the four core contents, the obtained to-be-measured industrial chain knowledge graph is analyzed, corresponding indexes are determined according to nodes in the corresponding graph, the indexes are summarized to obtain an industrial chain graph diagnosis index system, so that the index system contains specific indexes and index values obtained by mapping all nodes in the knowledge graph, and LSTM and CNN neural network models in the graph convolutional neural network model are mainly used for filtering and screening the indexes, because the indexes are further obtained through the economic principle, according to the economic principle, many indexes can be included in the definition of the development index of the industrial chain, but some indexes may be on data expression or not be prominent or are repeated with other indexes, so that the optimization in the indexes is needed, the standardized data of the primarily selected indexes are input value matrixes of a neural network, the weighted identity matrixes in analysis are collected as output value matrixes, the indexes are screened according to the condition that the cumulative contribution rate of average influence values (MIV) is more than 85 percent, and an index system after secondary screening is obtained, wherein the index system has 3 large dimensions, 12 first-level indexes, 27 second-level indexes and 57 third-level indexes, namely an industrial chain knowledge graph diagnosis index system power-level network, then the index system power-level network is diagnosed according to the industrial chain knowledge graph through an analytic hierarchy method and a Bayesian network, and analyzing by a fuzzy evaluation method, and diagnosing to obtain a final industrial chain analysis and diagnosis result. The beneficial effect of this embodiment is: processing the knowledge graph data of the industrial chain to be detected according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, so that the index system applied in the technical scheme conforms to the economics principle and the industrial chain development rule, and the accuracy of an industrial chain diagnosis result is improved; invalid indexes are filtered out through the screening of the neural network model on the index system, and the precision of the index system is improved, so that the final diagnosis result of the industrial chain is more accurate, information data with higher credibility can be provided for the government, and the government can conveniently build the modernization of the industrial chain; the industrial chains in different fields have different industrial chain knowledge maps, and different industrial chain analysis and diagnosis results can be obtained through the technical scheme according to the different industrial chain knowledge maps, so that the technical scheme has wider applicability; the industrial chain knowledge graph is the basis of the technical scheme, the knowledge graph refers to the association of upstream and downstream nodes in the constructed industrial chain, and each industrial chain node can be further evaluated only after the construction of the industrial chain knowledge graph and the cleaning of the upstream and downstream relations among products are completed, so that the conclusion of the advantages and disadvantages of each node can be obtained for government decision reference, therefore, the industrial chain knowledge graph has a very good technical effect by taking the industrial chain knowledge graph as the basis, and the finally obtained diagnosis result also has very high credibility; according to the method, an index system is established, hierarchical analysis, a Bayesian network and a fuzzy evaluation method are adopted for analysis and calculation, a final industrial chain analysis and diagnosis result is obtained, different information of each index is displayed in the result, level judgment is carried out according to an actual industrial chain, and therefore the strength of nodes in an industrial chain knowledge graph corresponding to the index is judged, visual industrial chain information is provided for local governments, and development of industrial chain modernization is facilitated.
Example 2
As shown in fig. 2, a method for analyzing and diagnosing a power-level network of an industry chain knowledge graph diagnosis indicator system according to a hybrid hierarchical bayesian network to obtain an industry chain analysis and diagnosis result includes the following steps:
s21, performing index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
s22, comprehensively analyzing the power level network of the industry chain knowledge graph diagnosis index system according to the diagnosis index comprehensive weight according to a fuzzy theory to obtain an industry chain level comprehensive evaluation result;
and S23, acquiring the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to the maximum membership principle to obtain an industrial chain analysis diagnosis result.
In embodiment 2, after hierarchical processing of a diagnostic index system, the evaluation indexes are compared pairwise by a scale method of 1 to 9 according to guidance opinions of experts in the field, a ranking result of the evaluation index weights is obtained layer by layer, so that a judgment matrix is obtained, for the judgment matrix, in order to ensure the accuracy of the judgment matrix, consistency check is performed, a selected square root method is used for solving feature vectors, the solved result is used as a first calculation result, and then the first calculation result is processed through a bayesian network, the bayesian network mainly comprises two aspects of structure learning and parameter learning, the network structure is a directed acyclic graph describing the relationship between nodes, conditional probability is used as a parameter for depicting the dependency relationship between the nodes and father nodes, the bayesian network is constructed according to the diagnostic index system, learning Bayesian network parameters by historical data, calculating objective weights of Bayesian network node indexes, taking the calculation results as second calculation results, combining the first calculation results with the second calculation results for summary calculation to obtain comprehensive weights of diagnosis indexes in a diagnosis index system, constructing an evaluation set, selecting one of the evaluation sets as an evaluation set with 4 evaluation levels, namely { high, general, low }, constructing a fuzzy evaluation matrix, selecting a proper membership function model, calculating the membership degree of each specific index to the evaluation set by using the relationship between the original data membership degree function of each specific index and the membership degree function, constructing the fuzzy evaluation matrix by using the calculation results, constructing the comprehensive weight matrix by using the comprehensive weights of the diagnosis indexes as a basis, and then calculating the comprehensive weight matrix according to the calculation method of the matrix, multiplying the comprehensive weight matrix and the fuzzy evaluation matrix to obtain a fuzzy comprehensive evaluation result of a first-level index, wherein each level of index is processed by the fuzzy evaluation matrix and the comprehensive weight matrix, the fuzzy comprehensive evaluation result of the first-level index is only obtained by calculation, firstly, the evaluation of each link is carried out on a first-level large dimension, secondly, the evaluation score of the final whole industrial chain is convenient to calculate, the fuzzy comprehensive evaluation matrix is constructed according to the obtained fuzzy comprehensive evaluation result of the first-level index, then, the comprehensive weight of the diagnosis index is processed according to an analytic hierarchy process to obtain the weight coefficient of the first-level index, the index weight distribution matrix is constructed according to the weight coefficient, then, the fuzzy comprehensive evaluation matrix and the index weight distribution matrix are multiplied according to the calculation method of the matrix to obtain the final fuzzy comprehensive evaluation result, and the evaluation result represents the industrial chain level comprehensive evaluation result, and finally, acquiring the actual industrial chain level of the local area, comparing the actual industrial chain operation current situation with the industrial chain level comprehensive evaluation result, and determining the development level of the local industrial chain according to the maximum membership principle to obtain the final industrial chain analysis diagnosis result. The beneficial effect of this embodiment is: in the calculation process of the comprehensive weight of the index, an analytic hierarchy process and a Bayesian network are used, the robustness of the whole system is improved, the anti-interference capability is improved, and the analysis is performed on a multi-level basis, so that the analysis result is more comprehensive, and the accuracy of the final diagnosis result is improved; according to the method, an index system is established, hierarchical analysis, a Bayesian network and a fuzzy evaluation method are adopted for analysis and calculation, a final industrial chain analysis and diagnosis result is obtained, different information of each index is displayed in the result, level judgment is carried out according to an actual industrial chain, and therefore the strength of nodes in an industrial chain knowledge graph corresponding to the index is judged, visual industrial chain information is provided for local governments, and development of industrial chain modernization is facilitated.
Example 3
As shown in fig. 3, an industry chain diagnosis system based on hierarchical analysis includes:
the acquisition module 100: the method comprises the steps of obtaining a knowledge graph of an industrial chain to be detected;
index architecture building Module 200: the industrial chain knowledge graph diagnosis index system is provided with specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
the graph convolution network calculation module 300: the system is used for screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
the industry chain analysis diagnostic module 400: and the power level network analysis and diagnosis module is used for analyzing and diagnosing the industry chain knowledge graph diagnosis index system power level network according to the mixed layered Bayesian network to obtain an industry chain analysis and diagnosis result.
One implementation of the above embodiment is: firstly, in an acquisition module 100, acquiring an industrial chain knowledge graph to be detected, then, in an index system construction module 200, processing the industrial chain knowledge graph data to be detected according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, wherein the industrial chain graph diagnosis index system has specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph, then, in a graph convolution network calculation module 300, screening the specific indexes and the index values according to a graph convolution neural network to obtain an industrial chain knowledge graph diagnosis index system power level network, and finally, in an industrial chain analysis diagnosis module 400, analyzing and diagnosing the industrial chain knowledge graph diagnosis index system power level network according to a mixed layered Bayesian network to obtain an industrial chain analysis diagnosis result.
Example 4
As shown in fig. 4, an industry chain analysis diagnosis module 400 in an industry chain diagnosis system based on hierarchical analysis includes:
the integrated weight calculation module 410: the system is used for carrying out index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
fuzzy theory evaluation module 420: the power level network of the industrial chain knowledge graph diagnosis index system is comprehensively analyzed according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industrial chain level comprehensive evaluation result;
industry chain contrastive analysis module 430: the method is used for obtaining the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to the maximum membership principle to obtain the analysis and diagnosis result of the industrial chain.
One implementation of the above embodiment is: firstly, in a comprehensive weight calculation module 410, index weight calculation is carried out on the industry chain knowledge graph diagnosis index system power level network according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight, then in a fuzzy theory evaluation module 420, comprehensive analysis is carried out on the industry chain knowledge graph diagnosis index system power level network according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industry chain level comprehensive evaluation result, finally in an industry chain comparison analysis module 430, an industry chain operation current situation is obtained, and the industry chain operation current situation and the industry chain level comprehensive evaluation result are compared according to a maximum membership principle to obtain an industry chain analysis diagnosis result.
Example 5
As shown in fig. 5, an integrated weight calculation module 410 in a hierarchical analysis-based industry chain diagnostic system includes:
the index weight first calculation module 411: the system comprises an industrial chain knowledge graph diagnosis index system power level network, a first calculation result, a second calculation result and a third calculation result, wherein the industrial chain knowledge graph diagnosis index system power level network is subjected to hierarchical processing according to an analytic hierarchy process, indexes of each layer subjected to hierarchical processing are compared through a 1-9 scale method and a preset judgment rule to obtain a sequencing result of index weight of each layer, the sequencing results are collected to obtain a result matrix, and eigenvector calculation is performed on the result matrix according to a square root method to obtain the first calculation result;
the index weight second calculation module 412: the Bayesian network is constructed according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, and the first Bayesian network is subjected to parameter calculation according to a parameter machine learning algorithm to obtain a second calculation result;
the index weight combining module 413: and the comprehensive weight of the diagnostic index is obtained by combining the first calculation result and the second calculation result.
One implementation of the above embodiment is: firstly, in an index weight first calculation module 411, performing layered processing on the industry chain knowledge graph diagnosis index system power level network according to an analytic hierarchy process, comparing each layer of indexes after layered processing through a scale method of 1-9 and a preset judgment rule to obtain a sequencing result of each layer of index weight, summarizing the sequencing results to obtain a result matrix, performing feature vector calculation on the result matrix according to a square root method to obtain a first calculation result, then in an index weight second calculation module 412, constructing the Bayesian network according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, performing parameter calculation on the first Bayesian network according to a parameter machine learning algorithm to obtain a second calculation result, and finally combining the first calculation result and the second calculation result in an index weight combination module 413, and obtaining the comprehensive weight of the diagnosis index.
Example 6
As shown in fig. 6, a fuzzy theory evaluation module 420 in an industry chain diagnosis system based on hierarchical analysis includes:
the first matrix building module 421: the evaluation set is used for calculating the membership degree of the specific indexes to the evaluation set according to a membership degree function to obtain a membership degree data set so as to form a fuzzy evaluation matrix, and summarizing the comprehensive weights of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to the scheme of four grades, namely high, general and low;
the second matrix building module 422: the comprehensive evaluation matrix is used for calculating the fuzzy evaluation matrix and the comprehensive weight matrix according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, summarizing to obtain a fuzzy comprehensive evaluation matrix, and calculating the specific index according to an analytic hierarchy process to obtain an index weight distribution matrix;
the evaluation result calculation module 423: and the comprehensive evaluation matrix is used for calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a comprehensive evaluation result of the industrial chain level.
One implementation of the above embodiment is: first, in a first matrix building module 421, the membership of the specific index to the evaluation set is calculated according to a membership function to obtain a membership data set, to form a fuzzy evaluation matrix, and summarizing the comprehensive weight of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to a scheme of four grades of high, general and low, then, in a second matrix construction module 422, the fuzzy evaluation matrix and the comprehensive weight matrix are calculated according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, the fuzzy comprehensive evaluation result is summarized to obtain a fuzzy comprehensive evaluation matrix, the specific indexes are calculated according to an analytic hierarchy process to obtain an index weight distribution matrix, and finally in an evaluation result calculation module 423, and calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a horizontal comprehensive evaluation result of the industrial chain.
Example 7
As shown in fig. 7, an electronic device comprises a memory 701 and a processor 702, wherein the memory 701 is used for storing one or more computer instructions, and the one or more computer instructions are executed by the processor 702 to implement any one of the methods described above.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the electronic device described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
A computer readable storage medium storing a computer program which, when executed, causes a computer to implement any of the methods as described above.
Illustratively, a computer program may be divided into one or more modules/units, one or more modules/units are stored in the memory 701 and executed by the processor 702, and the I/O interface transmission of data is performed by the input interface 705 and the output interface 706 to accomplish the present invention, and one or more of the modules/units may be a series of computer program instruction segments describing the execution of the computer program in a computer device.
The computer device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The computer device may include, but is not limited to, the memory 701 and the processor 702, and those skilled in the art will appreciate that the present embodiment is merely an example of a computer device and is not a limitation of computer devices, and may include more or less components, or combine certain components, or different components, for example, the computer device may further include an input 707, a network access device, a bus, etc.
The processor 702 may be a Central Processing Unit (CPU), other general-purpose processor 702, a digital signal processor 802 (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general purpose processor 702 may be a microprocessor 702 or the processor 702 may be any conventional processor 702 or the like.
The storage 701 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. The memory 701 may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard) or the like provided on the computer device, further, the memory 701 may also include both an internal storage unit and an external storage device of the computer device, the memory 701 is used for storing a computer program and other programs and data required by the computer device, the memory 701 may also be used for temporarily storing the program code in the output unit 708, and the aforementioned storage media include various media capable of storing program codes, such as a usb disk, a removable hard disk, a read only memory ROM703, a random access memory RAM704, a disk and an optical disk.
The above description is only an embodiment of the present invention, but the technical features of the present invention are not limited thereto, and any changes or modifications within the technical field of the present invention by those skilled in the art are covered by the claims of the present invention.
Claims (10)
1. An industrial chain diagnosis method based on hierarchical analysis is characterized by comprising the following steps:
acquiring a knowledge graph of an industrial chain to be detected;
processing the to-be-detected industrial chain knowledge graph according to an economics principle and an industrial chain development rule to obtain an industrial chain graph diagnosis index system, wherein the industrial chain graph diagnosis index system has specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
and analyzing and diagnosing the power level network of the industrial chain knowledge graph diagnosis index system according to the mixed layered Bayesian network to obtain an industrial chain analysis and diagnosis result.
2. The method for diagnosing the industrial chain based on the hierarchical analysis according to claim 1, wherein the analyzing and diagnosing the industrial chain knowledge graph diagnosis index system power-level network according to the hybrid hierarchical bayesian network to obtain an industrial chain analyzing and diagnosing result comprises:
performing index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
comprehensively analyzing the power level network of the industrial chain knowledge graph diagnosis index system according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industrial chain level comprehensive evaluation result;
and acquiring the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to a maximum membership principle to obtain an industrial chain analysis diagnosis result.
3. The method as claimed in claim 2, wherein the step of performing index weight calculation on the industry chain knowledge graph diagnosis index system power-level network according to the analytic hierarchy process and the bayesian network to obtain a diagnosis index comprehensive weight comprises:
layering the power level network of the industrial chain knowledge graph diagnosis index system according to an analytic hierarchy process, comparing indexes of each layer after layering process through a 1-9 scale method and a preset judgment rule to obtain a ranking result of the index weight of each layer, summarizing the ranking results to obtain a result matrix, and calculating a characteristic vector of the result matrix according to a square root method to obtain a first calculation result;
constructing the Bayesian network according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, and performing parameter calculation on the first Bayesian network according to a parameter machine learning algorithm to obtain a second calculation result;
and combining the first calculation result and the second calculation result to obtain the comprehensive weight of the diagnosis index.
4. The industrial chain diagnosis method based on the hierarchical analysis according to claim 3, wherein the comprehensive analysis is performed on the industrial chain knowledge graph diagnosis index system power level network according to the fuzzy theory and the diagnosis index weight coefficient to obtain an industrial chain level comprehensive evaluation result, and the method comprises the following steps:
calculating the membership degree of the specific indexes to an evaluation set according to a membership degree function to obtain a membership degree data set so as to form a fuzzy evaluation matrix, summarizing the comprehensive weights of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to a scheme of four grades, namely high, general and low;
calculating the fuzzy evaluation matrix and the comprehensive weight matrix according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, summarizing to obtain a fuzzy comprehensive evaluation matrix, and calculating the specific index according to an analytic hierarchy process to obtain an index weight distribution matrix;
and calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a horizontal comprehensive evaluation result of the industrial chain.
5. A hierarchical analysis-based industry chain diagnostic system for implementing the bayesian network-based industry chain diagnostic method of claim 1, comprising:
an acquisition module: the method comprises the steps of obtaining a knowledge graph of an industrial chain to be detected;
an index system construction module: the industrial chain knowledge graph diagnosis index system is provided with specific indexes and index values which can be obtained by mapping each node in the industrial chain knowledge graph;
the graph convolution network computing module: the system is used for screening the specific indexes and the index values according to the graph convolution neural network to obtain an industry chain knowledge graph diagnosis index system power level network;
industry chain analysis and diagnosis module: and the power level network analysis and diagnosis module is used for analyzing and diagnosing the industry chain knowledge graph diagnosis index system power level network according to the mixed layered Bayesian network to obtain an industry chain analysis and diagnosis result.
6. The system of claim 5, wherein the industry chain analysis diagnosis module comprises:
a comprehensive weight calculation module: the system is used for carrying out index weight calculation on the power-level network of the industry chain knowledge graph diagnosis index system according to an analytic hierarchy process and a Bayesian network to obtain a diagnosis index comprehensive weight;
a fuzzy theory evaluation module: the power level network of the industrial chain knowledge graph diagnosis index system is comprehensively analyzed according to the fuzzy theory and the diagnosis index comprehensive weight to obtain an industrial chain level comprehensive evaluation result;
industry chain contrasts analysis module: the method is used for obtaining the current operation situation of the industrial chain, and comparing the current operation situation of the industrial chain with the comprehensive evaluation result of the industrial chain level according to the maximum membership principle to obtain the analysis and diagnosis result of the industrial chain.
7. The system of claim 6, wherein the integrated weight calculation module comprises:
the first calculation module of index weight: the system comprises an industrial chain knowledge graph diagnosis index system power level network, a first calculation result, a second calculation result and a third calculation result, wherein the industrial chain knowledge graph diagnosis index system power level network is subjected to hierarchical processing according to an analytic hierarchy process, indexes of each layer subjected to hierarchical processing are compared through a 1-9 scale method and a preset judgment rule to obtain a sequencing result of index weight of each layer, the sequencing results are collected to obtain a result matrix, and eigenvector calculation is performed on the result matrix according to a square root method to obtain the first calculation result;
the index weight second calculation module: the Bayesian network is constructed according to the industry chain knowledge graph diagnosis index system power level network to obtain a first Bayesian network, and the first Bayesian network is subjected to parameter calculation according to a parameter machine learning algorithm to obtain a second calculation result;
an index weight combining module: and the comprehensive weight of the diagnostic index is obtained by combining the first calculation result and the second calculation result.
8. The system of claim 7, wherein the fuzzy theory evaluation module comprises:
a first matrix building module: the evaluation set is used for calculating the membership degree of the specific indexes to the evaluation set according to a membership degree function to obtain a membership degree data set so as to form a fuzzy evaluation matrix, and summarizing the comprehensive weights of the diagnosis indexes to obtain a comprehensive weight matrix, wherein the evaluation set is not limited to the scheme of four grades, namely high, general and low;
a second matrix construction module: the comprehensive evaluation matrix is used for calculating the fuzzy evaluation matrix and the comprehensive weight matrix according to a matrix algorithm to obtain a fuzzy comprehensive evaluation result, summarizing to obtain a fuzzy comprehensive evaluation matrix, and calculating the specific index according to an analytic hierarchy process to obtain an index weight distribution matrix;
an evaluation result calculation module: and the comprehensive evaluation matrix is used for calculating the fuzzy comprehensive evaluation matrix and the index weight distribution matrix according to a matrix algorithm to obtain a comprehensive evaluation result of the industrial chain level.
9. A hierarchical analysis based industry chain diagnostic system comprising a memory and a processor, the memory storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement a hierarchical analysis based industry chain diagnostic method as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer, implements a hierarchical analysis-based industry chain diagnostic method according to any one of claims 1 to 4.
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