A method and system for identifying functional faults of PCB circuits

Abstract

The present invention discloses a functional fault identification method and system for a PCB circuit, comprising the following steps: step S1, constructing all components into a component operation analysis matrix based on electrical signal characteristics; step S2, performing matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association attributes of the component electrical network, and performing community analysis on the component network based on the adjacency matrix to obtain multiple groups of component clusters; step S3, setting an electrical signal acquisition point at a central component to detect the electrical signal characteristics of the central component in real time, and then locating the component cluster that produces the functional fault based on the electrical signal characteristics of the central component. The present invention realizes the unified identification and analysis of functional faults by dividing discrete components with functional associations into the same cluster, and low-density functional fault monitoring realizes hidden mining of functional fault areas, avoids occupying a large amount of monitoring hardware, and reduces the amount of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of PCB circuit fault identification, and in particular to a functional fault identification method and system for a PCB circuit. Background Art

[0002] With the development of modern electronic technology, the integration of PCB circuits is getting higher and higher, which makes the design of PCB circuits more and more complex. How to effectively monitor the status of PCB circuits to ensure their reliable operation has become a research hotspot in the current circuit design field.

[0003] In recent years, the rapid development of artificial intelligence and computer technology has provided a new theoretical basis for PCB circuit fault diagnosis technology, and has produced a knowledge-based diagnosis method. This method does not require an accurate mathematical model of the object and has "intelligent" characteristics, so it is a very vital method. Knowledge-based fault diagnosis methods can be mainly divided into: expert system fault diagnosis method, fuzzy fault diagnosis method, fault tree fault diagnosis method, neural network fault diagnosis method, SVM fault diagnosis method, information fusion fault diagnosis method, etc.

[0004] Knowledge-based fault diagnosis methods have high practical significance for complex circuit systems and nonlinear systems. Since this method fully considers human intelligence factors and is more in line with natural reasoning about actual systems, it is a very promising diagnostic method. However, some theories of this type of method are not yet mature, and there are not many successful applications in the actual process of circuit systems. Many aspects of this method need further research. The diagnostic model constructed by the existing knowledge-based diagnostic method cannot further explore the fault in the process of fault identification to obtain the hidden functional faults. In addition, the existing technology monitors the circuit system with high density, which will occupy a large amount of hardware, thereby generating a large amount of monitoring data and increasing the amount of data processing. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for identifying functional faults of PCB circuits, so as to solve the technical problems that the prior art cannot further explore the PCB circuit faults to obtain the hidden functional faults, and the high-density monitoring of the circuit system will occupy a large amount of hardware, thereby generating a large amount of monitoring data and increasing the data processing volume.

[0006] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0007] A method for identifying functional faults of a PCB circuit comprises the following steps:

[0008] Step S1, monitoring the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and constructing all components into a component operation analysis matrix based on the electrical signal characteristics;

[0009] Step S2, performing matrix conversion on the component operation analysis matrix to obtain an adjacency matrix characterizing the functional association characteristics between components, and performing community analysis on the electrical connection network formed by components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups, so as to realize the classification of discrete components with functional association into the same group for unified identification and analysis of functional faults;

[0010] Step S3, perform centrality measurement within multiple component groups to obtain the central component in each component group, and set an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locate the component group that causes functional failures based on the electrical signal characteristics of the central component, and realize hidden mining of functional failure areas through low-density functional failure monitoring.

[0011] As a preferred solution of the present invention, constructing all components into a component operation analysis matrix based on electrical signal characteristics includes:

[0012] Encode and quantize each component into the matrix element x of the component operation analysis matrix i , and quantify the electrical signal characteristics of each component into the element characteristic quantity y of the matrix element ij ;

[0013] Combine the matrix elements x i and the element characteristic y ij The component operation analysis matrix is ​​constructed as S={x i | i∈[1,N]},x i ={y ij |j∈[1,M]};

[0014] Among them, x i is the ith component or the ith matrix element, y ij is the jth component in the electrical signal feature of the i-th component or the j-th element feature of the i-th matrix element, N is the total number of components, M is the total category of element features, and i and j are counting variables.

[0015] As a preferred solution of the present invention, the component operation analysis matrix is ​​transformed into an adjacency matrix that characterizes the functional association characteristics between components, including:

[0016] Calculate the correlation between each matrix element in the component operation matrix S and the other matrix elements in turn, and generate the element correlation matrix , the correlation is measured by cosine similarity, and the correlation calculation formula is: ;

[0017] Based on the preset correlation threshold D, the element correlation matrix is ​​binarized to generate the adjacency matrix , the binary conversion formula is: ;

[0018] Among them, i1∈[1,N], i2∈[1,N], N is the total number of components, M is the total category of element features, , are the i1th and i2th matrix elements of the component operation matrix S, respectively. is the adjacency matrix No. Line Matrix elements of columns, is the similarity matrix Line Matrix elements of the columns.

[0019] As a preferred solution of the present invention, a community analysis is performed on the electrical connection network formed by components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups, including:

[0020] Set the objective function of community analysis and the number of iterations of the genetic algorithm, and linearly calibrate the objective function to construct the fitness function of the genetic algorithm;

[0021] Circularly executing the genetic algorithm based on the adjacency matrix until reaching the number of iterations, multiple groups of partition communities are output, and the multiple groups of partition communities are used as multiple groups of component groups, wherein the fitness function is used to select genetic individuals after each execution of the genetic algorithm;

[0022] The method for setting the objective function includes:

[0023] The degree of external functional association between different component groups is quantified as the first optimization goal to limit the component scale of the component group. The function expression of the first optimization goal is:

[0024] ;

[0025] The degree of internal functional association within the same component group is quantified as the second optimization goal to limit the miniaturization of the component group. The function expression of the second optimization goal is:

[0026] ;

[0027] The objective function of community analysis is obtained by minimizing the first optimization objective and the second optimization objective. The functional expression of the objective function is:

[0028] ;

[0029] in, is the matrix element set of the kth component group divided in the component operation analysis matrix S, , , , , are the i1th and i2th matrix elements of the component operation matrix S, respectively, and m is the total number of component groups after the component operation analysis matrix S is divided;

[0030] The functional expression of the fitness function is:

[0031] ;

[0032] Where F is the fitness function value, α and β are weight coefficients, α∈[0,1], β∈[0,1], α+β=1, and δ is the correction constant.

[0033] As a preferred solution of the present invention, the centrality measurement is performed within multiple component groups to obtain the central component in each component group, including:

[0034] In each component group, direct centrality analysis and indirect centrality analysis are performed on each component to obtain the direct centrality and indirect centrality of each component, and the direct centrality and indirect centrality are weightedly combined as the centrality of each component;

[0035] In each component group, the components are sorted from high to low according to the centrality, and the components ranked in the top L are selected as the central components in each component group;

[0036] The direct centrality is to count the number of components in the component group to which each component belongs whose correlation is greater than or equal to the correlation threshold D. The calculation formula of the direct centrality is:

[0037]

[0038] In the formula, R r is the direct centrality of the rth component in the component group, u ro is the matrix element corresponding to the rth component in the component group and the oth component in the component group in the adjacency matrix U, h is the total number of components in the component group, o is a counting variable, in u ro When it is equal to 1, the value is 1. in u ro When it is equal to 0, the value is 0;

[0039] The indirect centrality is the sum of the direct centralities of each component whose correlation degree is greater than or equal to the correlation threshold D in the component group to which it belongs. The calculation formula of the indirect centrality is:

[0040]

[0041] In the formula, Z r is the indirect centrality of the rth component in the component group, R ry is the direct centrality of the rth component of the component group to which the component y has a correlation greater than or equal to the correlation threshold D.

[0042] The calculation formula of the centrality is:

[0043]

[0044] Where P r is the centrality, A is the weight of the direct centrality, and B is the weight of the indirect centrality, A∈[0,1], B∈[0,1], A+B=1.

[0045] As a preferred solution of the present invention, when the controller CPU in the PCB circuit is operating normally, operation monitoring points are set at all components to obtain electrical signal characteristics of all components, and after the central component is determined, the operation monitoring points at the central component are removed, and the operation monitoring points at the central component are set as electrical signal collection points to detect the electrical signal characteristics of the central component in real time.

[0046] As a preferred solution of the present invention, the method of locating the component group causing the functional failure based on the electrical signal characteristics of the central component includes:

[0047] The electrical signal characteristics of the central components are similarly matched with the electrical signal characteristics of the central components when the controller CPU in the PCB circuit is operating normally, and the similarity matching results are compared with the preset threshold, where:

[0048] If the similarity matching result is lower than the preset threshold, the central component is marked as a faulty component;

[0049] If the similarity matching result is higher than or equal to the preset threshold, the central component is marked as a non-faulty component;

[0050] All components of the component group to which the faulty component belongs except the faulty component are marked as hidden faulty components.

[0051] As a preferred solution of the present invention, before calculating each component of the electrical signal feature, each component is normalized to eliminate dimensional errors.

[0052] As a preferred solution of the present invention, the present invention provides a functional fault identification device for a PCB circuit. According to the functional fault identification method for a PCB circuit, the device includes:

[0053] The a priori analysis unit (1) is used to monitor the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and construct all components into a component operation analysis matrix based on the electrical signal characteristics, perform matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association characteristics between the components, and perform community analysis on the electrical connection network formed by the components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups;

[0054] The fault mining unit (2) performs centrality measurement within multiple component groups to obtain the central component in each component group, and sets an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locates the component group that causes the functional fault based on the electrical signal characteristics of the central component.

[0055] As a preferred solution of the present invention, the priori analysis unit and the fault mining unit both include an electrical signal monitoring device, and the electrical signal monitoring device is used to collect electrical signal characteristics.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention performs matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association attributes of the component network, and performs community analysis on the component network based on the adjacency matrix to obtain multiple groups of component groups, so as to realize the division of discrete components with functional associations into the same group for unified identification and analysis of functional faults, locate the component group that causes functional faults based on the electrical signal characteristics of the central component, and realize hidden mining of functional fault areas through low-density functional fault monitoring, thereby avoiding occupying a large amount of monitoring hardware and reducing the amount of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0059] Figure 1 A flow chart of a functional fault identification method provided by an embodiment of the present invention;

[0060] Figure 2 This is a structural block diagram of an identification device provided in an embodiment of the present invention.

[0061] The numbers in the figure represent the following:

[0062] 1- Priori analysis unit; 2- Fault mining unit. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] like Figure 1 As shown, the present invention provides a method for identifying functional faults of a PCB circuit, comprising the following steps:

[0065] Step S1, monitoring the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and constructing all components into a component operation analysis matrix based on the electrical signal characteristics;

[0066] All components are constructed into a component operation analysis matrix based on electrical signal characteristics, including:

[0067] Encode and quantize each component into the matrix element x of the component operation analysis matrix i , and quantify the electrical signal characteristics of each component into the element characteristic quantity y of the matrix element ij ;

[0068] Combine matrix elements x i and element characteristic y ij Construct the component operation analysis matrix S={x i | i∈[1,N]},x i ={y ij |j∈[1,M]};

[0069] Among them, x i is the ith component or the ith matrix element, y ij is the jth component in the electrical signal feature of the i-th component or the j-th element feature of the i-th matrix element, N is the total number of components, M is the total category of element features, and i and j are counting variables.

[0070] Element characteristic yij It is a summary of various characteristics of electrical signals, such as current characteristics (amplitude and phase), voltage characteristics (amplitude and phase), power characteristics, etc. The specific implementation depends on the actual situation. These characteristics are used to mine the functional correlation of each component in the PCB circuit and search for potential functional correlations between components.

[0071] Step S2, performing matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association characteristics between components, and performing community analysis on the electrical connection network formed by components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups, so as to realize the classification of discrete components with functional association into the same group for unified identification and analysis of functional faults;

[0072] The component operation analysis matrix is ​​transformed into an adjacency matrix that characterizes the functional association characteristics between components, including:

[0073] Calculate the correlation between each matrix element in the component operation matrix S and the other matrix elements in turn, and generate the element correlation matrix , the correlation is measured by cosine similarity, and the correlation calculation formula is:

[0074]

[0075] Based on the preset correlation threshold D, the element correlation matrix is ​​converted into a binary matrix to generate an adjacency matrix , the binary conversion formula is: ;

[0076] Among them, i1∈[1,N], i2∈[1,N], N is the total number of components, M is the total category of element features, , are the i1th and i2th matrix elements of the component operation matrix S, respectively. is the adjacency matrix No. Line Matrix elements of columns, is the similarity matrix Line Matrix elements of the columns.

[0077] The element features are extracted from the running components, reflecting the running characteristics of the components. By calculating the similarity of the running characteristics, we can understand the correlation characteristics between components and finally construct an adjacency matrix that characterizes the functional association between components, such as: , then it represents the matrix element that represents the component operation matrix and There is a correlation between them, that is, the matrix elements of the component operation matrix When an anomaly occurs, it will be associated with the matrix element that can be traced back to the component. , components Functional failures can be traced back to components , , then it represents the matrix element of the component operation matrix and There is no correlation between the matrix elements of the component operation matrix. Anomalies occur with matrix elements of the component operation matrix There is no impact and there is no functional relationship between the two.

[0078] Based on the adjacency matrix, the community analysis of the electrical connection network formed by components in the PCB circuit is performed to obtain multiple groups of component groups, including:

[0079] Set the objective function of community analysis and the number of iterations of the genetic algorithm, and linearly calibrate the objective function to construct the fitness function of the genetic algorithm;

[0080] Circularly executing the genetic algorithm based on the adjacency matrix until reaching the number of iterations, multiple groups of partition communities are output, and the multiple groups of partition communities are used as multiple groups of component groups, wherein the fitness function is used to select genetic individuals after each execution of the genetic algorithm;

[0081] The objective function setting method includes:

[0082] Quantifying the degree of external functional association between different component groups is used as the first optimization goal to limit the component scale of the component group. The function expression of the first optimization goal is:

[0083] ;

[0084] Quantifying the degree of internal functional association within the same component group is used as the second optimization goal to limit the miniaturization of the component group. The function expression of the second optimization goal is:

[0085] ;

[0086] The objective function of community analysis is obtained by minimizing the first optimization objective and the second optimization objective. The functional expression of the objective function is:

[0087] ;

[0088] in, is the matrix element set of the kth component group divided in the component operation analysis matrix S, , , , , are the i1th and i2th matrix elements of the component operation matrix S, respectively, and m is the total number of component groups after the component operation analysis matrix S is divided;

[0089] The functional expression of the fitness function is:

[0090] ;

[0091] Where F is the fitness function value, α and β are weight coefficients, α∈[0,1], β∈[0,1], α+β=1, and δ is the correction constant.

[0092] By using the community analysis method of complex networks to perform community division on the adjacency matrix that represents the functionality of components, components with functional associations can be divided into the same component group. All components belonging to the same component group have functional association attributes. For example, once the function of a component is abnormal and causes a functional failure, the association can be traced within the component group to which it belongs to obtain the remaining components that are affected by the association and are in a functional abnormality. Therefore, it is necessary to monitor the components at the association center within each component group, and then discover the hidden components with functional failures within the group through functional associations.

[0093] Step S3, perform centrality measurement within multiple component groups to obtain the central component in each component group, and set an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locate the component group that causes functional failures based on the electrical signal characteristics of the central component, and realize hidden mining of functional failure areas through low-density functional failure monitoring.

[0094] The centrality measurement is performed within multiple groups of components to obtain the central components in each group of components, including:

[0095] In each component group, direct centrality analysis and indirect centrality analysis are performed on each component to obtain the direct centrality and indirect centrality of each component, and the direct centrality and indirect centrality are weightedly combined as the centrality of each component;

[0096] In each component group, the components are sorted from high to low according to the centrality, and the components ranked in the top L are selected as the central components in each component group;

[0097] Direct centrality is to count the number of components in the component group to which each component belongs whose correlation is greater than or equal to the correlation threshold D. The calculation formula of direct centrality is:

[0098] ;

[0099] In the formula, R r is the direct centrality of the rth component in the component group, u ro is the matrix element corresponding to the rth component in the component group and the oth component in the component group in the adjacency matrix U, h is the total number of components in the component group, o is a counting variable, in u ro When it is equal to 1, the value is 1. in u ro When it is equal to 0, the value is 0;

[0100] The indirect centrality is the sum of the direct centralities of each component whose correlation degree in each component group is greater than or equal to the correlation threshold D. The calculation formula of the indirect centrality is:

[0101] ;

[0102] In the formula, Z r is the indirect centrality of the rth component in the component group, R ry is the direct centrality of the rth component of the component group to which the component y has a correlation greater than or equal to the correlation threshold D.

[0103] The calculation formula of centrality is:

[0104] ;

[0105] Where P r is the centrality, A is the weight of the direct centrality, and B is the weight of the indirect centrality, A∈[0,1], B∈[0,1], A+B=1.

[0106] The higher the centrality of a component, the higher the degree of functional correlation with the remaining components in the group, that is, the more components that are functionally correlated with it. An electrical signal collection point is set at the central component for real-time monitoring. If a functional failure occurs in the central component, the possibility of functional failure in the remaining components in the group where the central component is located is higher. The correlation can be mined, low-density laying of electrical signal collection points can be achieved, and efficient fault mining can be achieved.

[0107] When the controller CPU in the PCB circuit operates normally, operation monitoring points are set at all components to obtain the electrical signal characteristics of all components, and after the central component is determined, the operation monitoring point at the central component is removed and the operation monitoring point at the central component is set as an electrical signal collection point to detect the electrical signal characteristics of the central component in real time.

[0108] Based on the electrical signal characteristics of the core components, the component group that causes functional failure is located, including:

[0109] The electrical signal characteristics of the central components are similarly matched with the electrical signal characteristics of the central components when the controller CPU in the PCB circuit is operating normally, and the similarity matching results are compared with the preset threshold, where:

[0110] If the similarity matching result is lower than the preset threshold, the central component is marked as a faulty component;

[0111] If the similarity matching result is higher than or equal to the preset threshold, the central component is marked as a non-faulty component;

[0112] All components of the component group to which the faulty component belongs except the faulty component are marked as hidden faulty components.

[0113] Before calculating the various components of the electrical signal characteristics, each component is normalized to eliminate dimensional errors.

[0114] like Figure 2 As shown, based on the above controller functional fault identification method, the present invention provides a functional fault identification device for a PCB circuit, the device comprising:

[0115] A priori analysis unit 1 is used to monitor the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and construct all components into a component operation analysis matrix based on the electrical signal characteristics, perform matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association characteristics between components, and perform community analysis on the electrical connection network formed by the components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups;

[0116] Fault mining unit 2 performs centrality measurement within multiple component groups to obtain the central component in each component group, and sets an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locates the component group that causes functional faults based on the electrical signal characteristics of the central component.

[0117] Both the priori analysis unit and the fault mining unit include an electrical signal monitoring device, which is used to collect electrical signal characteristics.

[0118] The present invention performs matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association attributes of the component network, and performs community analysis on the component network based on the adjacency matrix to obtain multiple groups of component groups, so as to realize the division of discrete components with functional associations into the same group for unified identification and analysis of functional faults, locate the component group that causes functional faults based on the electrical signal characteristics of the central component, and realize hidden mining of functional fault areas through low-density functional fault monitoring, thereby avoiding occupying a large amount of monitoring hardware and reducing the amount of data processing.

[0119] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for identifying functional faults of a PCB circuit, characterized in that: The following steps are involved: Step S1, monitoring the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and constructing all components into a component operation analysis matrix based on the electrical signal characteristics; The method of constructing all components into a component operation analysis matrix based on the electrical signal characteristics includes: Encode and quantize each component into the matrix element x of the component operation analysis matrix i , and quantify the electrical signal characteristics of each component into the element characteristic quantity y of the matrix element ij ; Combine the matrix elements x i and the element characteristic y ij The component operation analysis matrix is ​​constructed as S = {x i |i∈[1,N]},x i ={y ij |j∈[1,M]}; Among them, x i is the ith component or the ith matrix element, y ij is the jth element characteristic quantity of the jth component or the ith matrix element in the electrical signal characteristic quantity of the i-th component, N is the total number of components, M is the total category of element characteristics, i and j are counting variables; Step S2, performing matrix conversion on the component operation analysis matrix to obtain an adjacency matrix characterizing the functional association characteristics between components, and performing community analysis on the electrical connection network formed by components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups, so as to realize the classification of discrete components with functional association into the same group for unified identification and analysis of functional faults; The component operation analysis matrix is ​​transformed into an adjacency matrix that characterizes the functional association characteristics between components, including: Calculate the correlation between each matrix element in the component operation matrix S and the other matrix elements in turn, and generate the element correlation matrix The correlation is measured by cosine similarity, and the correlation calculation formula is: Based on the preset correlation threshold D, the element correlation matrix is ​​binarized to generate the adjacency matrix The binary conversion formula is: Among them, i1∈[1,N], i2∈[1,N], N is the total number of components, M is the total category of element features, are the i1th and i2th matrix elements of the component operation matrix S, respectively. is the matrix element of the i1th row and i2th column of the adjacency matrix U, is the matrix element of the i1th row and i2th column of the similarity matrix; Based on the adjacency matrix, a community analysis is performed on the electrical connection network formed by components in the PCB circuit to obtain multiple groups of component groups, including: Set the objective function of community analysis and the number of iterations of the genetic algorithm, and linearly calibrate the objective function to construct the fitness function of the genetic algorithm; Circularly executing the genetic algorithm based on the adjacency matrix until reaching the number of iterations, multiple groups of partition communities are output, and the multiple groups of partition communities are used as multiple groups of component groups, wherein the fitness function is used to select genetic individuals after each execution of the genetic algorithm; The method for setting the objective function includes: The degree of external functional association between different component groups is quantified as the first optimization goal to limit the component scale of the component group. The function expression of the first optimization goal is: The degree of internal functional association within the same component group is quantified as the second optimization goal to limit the miniaturization of the component group. The function expression of the second optimization goal is: The objective function of community analysis is obtained by minimizing the first optimization objective and the second optimization objective. The functional expression of the objective function is: Among them, C k is the matrix element set of the kth component group divided in the component operation analysis matrix S, are the i1th and i2th matrix elements of the component operation matrix S, respectively, and m is the total number of component groups after the component operation analysis matrix S is divided; The functional expression of the fitness function is: F=-(αf1+βf2)+max(αf1+βf2)+δ; Where F is the fitness function value, α and β are weight coefficients, α∈[0,1], β∈[0,1], α+β=1, and δ is the correction constant; Step S3, perform centrality measurement within multiple component groups to obtain the central component in each component group, and set an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locate the component group that causes functional failures based on the electrical signal characteristics of the central component, and realize hidden mining of functional failure areas through low-density functional failure monitoring.

2. The method for identifying functional faults of a PCB circuit according to claim 1, characterized in that: The centrality measurement is performed within multiple component groups to obtain the central component in each component group, including: In each component group, direct centrality analysis and indirect centrality analysis are performed on each component to obtain the direct centrality and indirect centrality of each component, and the direct centrality and indirect centrality are weightedly combined as the centrality of each component; In each component group, the components are sorted from high to low according to the centrality, and the components ranked in the top L are selected as the central components in each component group; The direct centrality is to count the number of components in the component group to which each component belongs whose correlation is greater than or equal to the correlation threshold D. The calculation formula of the direct centrality is: In the formula, R r is the direct centrality of the rth component in the component group, u ro is the matrix element corresponding to the rth component in the component group and the oth component in the component group in the adjacency matrix U, h is the total number of components in the component group, o is a counting variable, J(u ro ==1) in u ro When it is equal to 1, the value is 1, J(u ro ==1) in u ro When it is equal to 0, the value is 0; The indirect centrality is the sum of the direct centralities of each component whose correlation degree is greater than or equal to the correlation threshold D in the component group to which it belongs. The calculation formula of the indirect centrality is: WITH r =∑R ry ; In the formula, Z r is the indirect centrality of the rth component in the component group, R ry is the direct centrality of the rth component of the component group to which the component y has a correlation greater than or equal to the correlation threshold D. The calculation formula of the centrality is: P r =AR r +BZ r ; Where P r is the centrality, A is the weight of the direct centrality, B is the weight of the indirect centrality, A∈[0,1], B∈[0,1], A+B=1.

3. The method for identifying functional faults of a PCB circuit according to claim 2, characterized in that: When the controller CPU in the PCB circuit operates normally, operation monitoring points are set at all components to obtain the electrical signal characteristics of all components, and after the central component is determined, the operation monitoring point at the central component is removed, and the operation monitoring point at the central component is set as an electrical signal collection point to detect the electrical signal characteristics of the central component in real time.

4. A method for identifying functional faults of a PCB circuit according to claim 3, characterized in that: The method of locating the component group causing the functional failure based on the electrical signal characteristics of the central component includes: The electrical signal characteristics of the central components are similarly matched with the electrical signal characteristics of the central components when the controller CPU in the PCB circuit is operating normally, and the similarity matching results are compared with the preset threshold, where: If the similarity matching result is lower than the preset threshold, the central component is marked as a faulty component; If the similarity matching result is higher than or equal to the preset threshold, the central component is marked as a non-faulty component; All components of the component group to which the faulty component belongs except the faulty component are marked as hidden faulty components.

5. A method for identifying functional faults of a PCB circuit according to claim 4, characterized in that: Before calculating each component of the electrical signal feature, each component is normalized to eliminate dimensional errors.

6. A functional fault identification device for a PCB circuit, characterized in that: According to a method for identifying functional faults of a PCB circuit according to any one of claims 1 to 5, the device comprises: A priori analysis unit (1) is used to monitor the electrical signal characteristics of all components when the controller CPU in the PCB circuit is operating normally, and construct all components into a component operation analysis matrix based on the electrical signal characteristics, perform matrix conversion on the component operation analysis matrix to obtain an adjacency matrix that characterizes the functional association characteristics between the components, and perform community analysis on the electrical connection network formed by the components in the PCB circuit based on the adjacency matrix to obtain multiple groups of component groups; The fault mining unit (2) performs centrality measurement within multiple component groups to obtain the central component in each component group, and sets an electrical signal collection point at the central component to detect the electrical signal characteristics of the central component in real time, and then locates the component group that causes functional faults based on the electrical signal characteristics of the central component.

7. A functional fault identification device for a PCB circuit according to claim 6, characterized in that: The priori analysis unit and the fault mining unit both include an electrical signal monitoring device, and the electrical signal monitoring device is used to collect electrical signal characteristics.

Images