A model-based reliability assessment method for complex aircraft electrical circuit networks

Abstract

The present invention discloses a reliability assessment method for a complex electrical circuit network of an aircraft based on a model, comprising: abstracting the circuit structure of the electrical circuit network of the aircraft into a model diagram; using a Monte Carlo algorithm to perform multiple cycles of random failure simulation on the connection path of any two nodes, and performing connectivity checks between the nodes; in the case of determining that there is connectivity between the nodes, using a Laplace algorithm to perform an approximate calculation of the total resistance of the electrical circuit network, if the total resistance is less than a given resistance threshold, it indicates that the electrical circuit network is reliable, otherwise it is unreliable; repeating the random failure simulation step and the reliability assessment step until a specified number of cycles is reached, recording the connectivity, total resistance, and failure probability of each simulation, and finally outputting a reliable probability as a reliability index of the electrical circuit network. The present invention can accurately identify fault areas and quantify the reliability of electrical circuit networks with high efficiency and low cost.

Description

Technical Field

[0001] The present invention belongs to the field of engineering technology and relates to a model-based aircraft complex electrical circuit network reliability assessment method, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] At present, traditional metal structure aircraft are gradually replaced by composite materials, which makes the traditional power supply network design gradually replaced by composite materials, and the traditional power supply network design and management methods may no longer be applicable. In order to make up for the defects of low metal content and uneven distribution of composite aircraft, an electrical circuit network is added to provide the aircraft with a reliable electrical path and zero potential. The electrical circuit network consists of metal structures (such as metal strips, wires, and fasteners), which are used to safely guide lightning pulses away from the aircraft and provide grounding for non-critical electrical systems, thereby avoiding ground potential fluctuations and excessive ground loop voltage drops, ensuring power supply quality and normal equipment operation, and ensuring the overall robustness and reliability of the aircraft electrical circuit network. Therefore, the reliability assessment research of the electrical circuit network helps to understand and solve the electrical system challenges faced by aircraft and improve the reliability and safety of its design.

[0003] As a prior art, Boeing applied for an invention patent (US20090323539A1) "RELIABILITY ESTIMATION METHODS FOR LARGE NETEORKED SYSTEMS" in 2009. This patent application involves a reliability evaluation method for electrical circuit networks based on reliability block diagrams, which reflects the working status of aircraft metal components through a combination of nodes and links. The patent application introduces an approximate calculation method, including path and cut set enumeration, which is used to calculate the upper and lower limits of system reliability, and uses a binary decision diagram to represent the upper and lower limits of reliability.

[0004] However, when the system is large or complex, the enumeration process of paths and cut sets becomes time-consuming and complicated. In addition, binary decision diagrams are a data structure for Boolean functions. When dealing with reliability problems, these data structures need to be effectively constructed and managed. As the scale of the system increases, more computing and storage resources are required to maintain these data structures, limiting the scalability of the method. These processes require a lot of computing resources and time, which increases the computational cost and limits the applicability of the method in practical engineering applications. Summary of the invention

[0005] The purpose of the present invention is to provide a model-based aircraft complex electrical circuit network reliability assessment method, computer equipment, computer readable storage medium and computer program product, which can accurately identify fault areas and quantify the reliability of the electrical circuit network with high efficiency and low cost.

[0006] In order to achieve the above object, one aspect of the present invention provides a model-based aircraft complex electrical circuit network reliability assessment method, comprising:

[0007] Step S1: abstract the metal structural elements of the aircraft as nodes, abstract the cable connections as edges, use graph theory methods to abstract the circuit structure of the electrical circuit network as a model graph, and use a connectivity check algorithm to perform connectivity analysis between nodes to determine the integrity of the electrical circuit network;

[0008] Step S2: Based on the model graph, the Monte Carlo algorithm is used to perform multiple cycles of random failure simulation on the connection path between any two nodes. In each cycle, the failure time of the nodes and edges between the connection paths is determined by random sampling, and the nodes and edges that fail in the simulation are judged by the failure probability. The nodes and edges judged to be failed are removed from the model graph, and the connectivity between the nodes is checked using the connectivity check algorithm;

[0009] Step S3: When it is determined that there is connectivity between the nodes, the total resistance of the electrical circuit network is approximately calculated using the Laplace algorithm, and it is determined whether the total resistance is less than a given resistance threshold. If the total resistance is less than the given resistance threshold, it indicates that the electrical circuit network is reliable, otherwise it is unreliable.

[0010] Step S4: Repeat steps S2 and S3 until the specified number of cycles is reached, record the connectivity, total resistance and failure probability of each simulation, and finally output the reliability probability as the reliability index of the electrical circuit network.

[0011] Preferably, in step S1, a weight is assigned to each edge to represent the resistance of the cable, and an adjacency matrix and a weight matrix are constructed, wherein each element in the adjacency matrix represents whether there is a connection relationship between nodes, and the weight matrix records the weight of the connection.

[0012] Preferably, a union-find algorithm is used as the connectivity checking algorithm.

[0013] Preferably, in step S2, the failure time of nodes and edges is determined as follows :

[0014]

[0015] in, is a random number uniformly distributed in the interval [0, 1], are the failure rates of nodes and edges.

[0016] Preferably, in step S2, the failure probability is calculated as follows:

[0017]

[0018] in, is the cumulative distribution function, which represents the failure time The probability of being less than or equal to the set failure time threshold.

[0019] Preferably, in step S3, the total resistance of the electrical circuit network is calculated based on the resistance between the nodes, wherein the resistance between any two nodes is calculated as follows: and The resistance between :

[0020]

[0021] in, Represents the Laplace matrix with the first Row and The determinant of the submatrix after the columns is, Represents the Laplace matrix Row and The remainder of a column.

[0022] Another aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0023] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0024] Another aspect of the present invention provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0025] The model-based aircraft complex electrical circuit network reliability assessment method, computer device, computer-readable storage medium and computer program product according to the above aspects of the present invention can accurately identify fault areas and quantify the reliability of the electrical circuit network with high efficiency and low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings used in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work:

[0027] Figure 1is a flow chart of a model-based aircraft complex electrical circuit network reliability assessment method according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of a model-based aircraft complex electrical circuit network reliability assessment method according to an embodiment of the present invention;

[0029] Figure 3 It is a structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. 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.

[0031] An embodiment of the present invention provides a model-based aircraft complex electrical circuit network reliability assessment method, such as Figure 1 As shown, the method of the embodiment of the present invention includes steps S1 to S4. Figure 2 The schematic diagram of the present invention describes in detail each step of the method of the embodiment of the present invention.

[0032] In step S1, the electrical circuit network is modeled. Specifically, the circuit topology of the aircraft electrical circuit network is abstracted into a model graph using graph theory methods, and the connectivity analysis is performed using graph theory algorithms to identify breakpoints and unconnected nodes in the electrical circuit network to ensure network integrity. Among them, by collecting and analyzing data on metal structural elements and their connecting cables, the model graph is constructed using graph theory methods, establishing the basis for designing, optimizing, analyzing and visualizing the electrical circuit network.

[0033] In step S2, Monte Carlo simulation and fault simulation are performed. Specifically, based on the model graph, the Monte Carlo algorithm is used to perform multiple cycles of random failure simulation on any two nodes. In each cycle, according to the failure rate of metal components and connecting cables in the known electrical circuit network, the failure time of nodes and edges is determined by random sampling to simulate the failure of circuit connection. The connectivity of the two nodes under the current circuit structure is analyzed using a connectivity check algorithm to identify the fault area. In the electrical circuit network, the connectivity path from the grounding point to a certain metal component is generally analyzed. In this step, the Monte Carlo algorithm and connectivity check are combined to further quantitatively evaluate the reliability of the electrical circuit network, and the controllability of the network structure is judged by the simulation results.

[0034] In step S3, the total resistance of the electrical circuit network is calculated based on the Laplace algorithm and reliability evaluation is performed. Combined with a large number of Monte Carlo simulation results, the reliability index of the electrical circuit network is output to provide a quantitative decision support tool for design optimization and fault prevention. In the Monte Carlo simulation, only after determining that the circuit is connected, the total resistance of the electrical circuit network is approximated based on the Laplace matrix. Due to the failure of a node or edge in the connection path between the two nodes, the resistance structure of the circuit changes, the resistance of the new circuit is calculated, and it is determined whether it is within the given resistance threshold range to evaluate the reliability of the electrical circuit network. In this step, by approximating the resistance value of the electrical circuit network and analyzing its convergence, it is possible to determine whether the connected resistance value is still within the controllable range after the node failure, and the reliability of the electrical circuit network is evaluated accordingly.

[0035] In step S4, steps S2 and S3 are repeated multiple times until the number of cycles is reached, the operating state of the resistor network under different failure combinations is simulated, and the connectivity, total resistance value and failure probability of each simulation are calculated and recorded. Finally, the Monte Carlo simulation results are summarized and the reliability probability is calculated as the reliability index of the electrical circuit network.

[0036] As an example, taking a complex electrical circuit network of an aircraft as an example, the reliability assessment is performed using the method of the embodiment of the present invention.

[0037] Step S1: Electrical circuit network modeling based on graph theory

[0038] In the process of modeling the electrical circuit network, the physical structure and electrical characteristics of the electrical circuit network are comprehensively analyzed from three aspects: material properties, structural layout, and connection methods, to achieve a graph-theoretic abstract representation of the topological structure of the electrical circuit network. Metal structural elements such as luggage racks, cabin floor rails, and beams are abstracted as nodes, cable connections are abstracted as edges, and weights are assigned to each edge to represent the resistance of the cable, to construct an adjacency matrix and a weight matrix. Specifically, the adjacency matrix is ​​an N×N matrix in which each element Indicates whether there is a connection relationship between node i and node j. If there is a connection relationship, it is represented by 1, otherwise it is represented by 0. The weight matrix records the weight or specific parameters of the connection, such as the resistance of the connecting cable, or some fixed structures.

[0039] In the process of connectivity analysis of model graphs, a variety of algorithms can be used, including depth-first search (DFS), breadth-first search (BFS), and union-find. For graphs with weighted edges, when it is necessary to obtain specific information about the path and locate potential fault nodes, DFS and BFS algorithms are used. However, they are usually not as good as the optimization performance of union-find in terms of the average time complexity of connectivity detection. When it is necessary to quickly determine the connectivity of large-scale graphs, the union-find algorithm is used. This method is particularly suitable for fast connectivity determination of large-scale graph structures, especially in scenarios where edges and nodes in the graph change frequently. For example, when performing Monte Carlo simulation analysis, the electrical circuit network of an aircraft may contain thousands of metal structural components, and frequent deletion of edges and nodes is a common operation. In this case, fast and accurate connectivity determination is crucial to improving algorithm efficiency, and union-find becomes a key tool for improving the efficiency of the simulation process due to its efficient connectivity detection capability. Table 1 shows the process of connectivity determination of the three algorithms.

[0040] Table 1 Connectivity algorithm

[0041]

[0042] Step S2: Monte Carlo simulation and fault simulation

[0043] In the reliability analysis of electrical circuit networks, the Monte Carlo algorithm is widely used because of its advantages in dealing with randomness and uncertainty. The algorithm evaluates the overall performance and connectivity of the system by simulating a large number of random events, and can provide in-depth statistical analysis results. For the electrical circuit network of the present invention, the Monte Carlo algorithm can comprehensively consider the working state of the system under various random failure conditions, thereby providing a probabilistic evaluation of the reliability of the system.

[0044] First, an appropriate failure time distribution model is selected to describe the failure characteristics of aircraft metal components or connecting cables. In many cases, the exponential distribution is a commonly used model, which assumes that the failure rate of the simulated object is is constant. The failure time of the simulation object It follows an exponential distribution, and its probability density function for:

[0045] ( )

[0046] The mean time between failures (MTBF) can be calculated by the following integral:

[0047]

[0048] Cumulative distribution function Indicates the expiration time The probability of being less than or equal to a certain time point t is calculated as:

[0049] ( )

[0050] To generate random failure times from an exponential distribution , the embodiment of the present invention uses uniformly distributed random numbers The inverse function law of . is uniformly distributed on the interval [0, 1], then the failure time The calculation formula is:

[0051]

[0052] Set a failure time threshold T, if In the range [0, T], the simulation object is considered to fail before time T; if > T , it is considered that the simulation object is still in normal working condition at time T.

[0053] Next, we simulate the random failure of any node or edge in the connection path between any two nodes (such as the aircraft metal component T and the ground point S) to obtain different failure combinations. Furthermore, we check the connectivity between the two nodes. If there is connectivity, we will use the Laplace algorithm to approximate the total resistance of the circuit after the change. R .

[0054] Step S3: Calculate the total resistance of the circuit based on the Laplace algorithm and perform reliability evaluation

[0055] In the reliability analysis of an electrical circuit network, even if connectivity between any two nodes exists, the circuit organization structure may change due to the failure of edges or nodes in their connection paths. This change may affect the performance of the circuit, so it is necessary to approximate the overall resistance of the circuit to determine whether it remains within a controllable range. To this end, an embodiment of the present invention proposes an algorithm based on a Laplace matrix for approximating the total resistance of a complex electrical circuit network and evaluating the reliability of the network after being disturbed.

[0056] The Laplace matrix is ​​a key concept in graph theory and plays a vital role in circuit analysis. In an electrical circuit network, the elements of the Laplace matrix are defined based on the connections between nodes: the elements on the diagonal represent the degree of the node (that is, the number of edges directly connected to the node), and the off-diagonal elements represent the connection status between nodes, which is 1 if the two nodes are connected and 0 if they are not connected. This structure allows the Laplace matrix to describe the topology of the circuit in detail, that is, how the components are connected.

[0057] The eigenvalues ​​and eigenvectors of the Laplacian matrix contain information about the physical properties and connectivity of the electrical circuit network. Among them, the smallest eigenvalue is always 0, and the corresponding eigenvector is a vector with all elements 1, which represents the connectivity of the electrical circuit network as a whole. If the electrical circuit network is fully connected, then the multiplicity of this 0 eigenvalue is 1; if the network consists of multiple connected components, then the multiplicity of the 0 eigenvalue will be equal to the number of connected components. In addition to the smallest eigenvalue, the size and distribution of other eigenvalues ​​can also provide additional information about the connectivity of the network. The second smallest eigenvalue of the Laplacian matrix, the algebraic connectivity, provides key information for the connectivity and partitioning of the electrical circuit network: it quantifies the global connectivity of the network. The larger its value, the denser the connection between the network nodes and the less likely the network is to be partitioned; the algebraic connectivity close to zero suggests that the network may be easy to partition or have bottlenecks. In summary, the Laplacian matrix can not only capture the topological structure of the electrical circuit network, but also provide a deep understanding of the network connectivity and physical properties through its eigenvalues ​​and eigenvectors. Compared with other methods such as matrix inversion to calculate the equivalent resistance of the circuit, simulation and other methods, the Laplace algorithm is suitable for processing large electrical circuit networks. It uses matrix operations to approximate the total resistance of the circuit, avoiding the complexity of detailed simulation of the entire network or solving large-scale equations.

[0058] First, simplify the complex electrical circuit network into a model that satisfies the resistance distance theory in graph theory, that is, a network structure composed of unit resistors. This can be achieved by converting all resistors into multiples of the basic unit resistor. In this process, determining the value of equivalent resistors is usually based on the principle of converting all resistors into multiples of the same unit resistor. This can be achieved by finding the lowest common multiple of the resistance values ​​in the circuit or setting a unit resistance value that is easy to calculate (such as 1Ω), and then converting each resistance value into an integer multiple of this unit resistance. If the original resistance value cannot be converted directly, it can be approximated by circuit transformations such as resistor division or parallel connection. This method not only simplifies the mathematical model of the circuit, but also ensures the similarity in electrical characteristics between the idealized network and the original network by maintaining the linear relationship between the resistance values. Any two points in the resistance network and Resistance between It can be approximately calculated by the following formula:

[0059]

[0060] in, Represents the Laplace matrix with the first Row and The determinant of the submatrix after the column, and Represents the Laplace matrix Row and The remainder of a column.

[0061] Then, the total resistance of the electrical loop network is calculated based on the resistance between the nodes. The infinite equal-value grid resistor network is simulated and approximated using Python software. The convergence of the total resistance is observed by continuously expanding the order 𝑛 of the network, and the results are compared with the true value to verify the effectiveness and accuracy of the algorithm. Although the Laplace algorithm performs well in global graph analysis, its time complexity and computational efficiency are also important considerations for large-scale graphs. The calculation of the Laplace matrix involves solving eigenvalues ​​and eigenvectors, a process that usually has high computational complexity. However, in practical applications, the Laplace matrices of many electrical loop networks are sparse, and the properties of sparse matrices and optimization algorithms can be used to reduce computational costs and improve computational efficiency.

[0062] In order to optimize the calculation process, an embodiment of the present invention proposes a method for storing the calculation results in a dictionary. In this process, each time the resistance of a circuit is calculated, the result is stored in the dictionary with the unique identifier of the circuit as the key. When the resistance of the same circuit structure needs to be calculated, the dictionary is first queried. If the corresponding calculation result exists, the result is directly obtained; otherwise, the Laplace matrix calculation is performed and the result is stored in the dictionary. This method avoids repeated calculations and improves calculation efficiency through efficient dictionary query operations. It is particularly suitable for situations where the same topology structure frequently appears in large-scale electrical circuit network analysis.

[0063] In Monte Carlo simulation, only after the circuit is determined to be connected, the total resistance is approximated based on the Laplace matrix. If the total resistance is less than a given threshold, it means that the electrical circuit network is reliable, otherwise it is unreliable.

[0064] Finally, in step S4, by statistically analyzing a large number of simulation results, the reliability probability and performance evaluation of the circuit can be obtained. The method of the embodiment of the present invention not only provides a quantitative reliability evaluation of the electrical circuit network under random failure conditions, but also helps understand the response of the system under different failure modes, providing important decision support for system optimization and maintenance.

[0065] In summary, the method of the embodiment of the present invention is guided by the general idea of ​​reliability assessment of the electrical circuit network. With the advancement of fault simulation and diagnosis, the quantitative requirements for network structure safety and reliability are gradually realized, and the design is guided in a data-driven manner to ensure the effective implementation of electrical circuit network optimization and fault prevention measures. The method of the embodiment of the present invention constructs a circuit topology model, abstracts metal components and connecting wires into nodes and edges in graph theory, and considers their connection relationship and resistance characteristics; then, the Monte Carlo method is used to simulate random failure events in the circuit, evaluate the connectivity between nodes and the fault propagation path, and at the same time, the Laplace algorithm is used to approximate the total resistance of the circuit and analyze the overall electrical characteristics of the electrical circuit network; finally, the reliability and performance indicators of the circuit are evaluated by analyzing the Monte Carlo simulation results and the total resistance of the circuit.

[0066] The method of the embodiment of the present invention proposes an abstract modeling technology based on graph theory, realizes the accurate mathematical expression of the topological structure of the aircraft electrical circuit network, and provides a solid foundation for the optimization and analysis of the network; realizes a Monte Carlo fault simulation method, determines the failure time of the component by random sampling, simulates the failure of the circuit connection, and then identifies the fault area, thereby enhancing the ability of network fault diagnosis; proposes a set of data-driven reliability evaluation tools, quantifies the reliability index of the electrical circuit network by using the Laplace matrix and Monte Carlo simulation results, and realizes the scientific formulation of design optimization and fault prevention measures. The method of the embodiment of the present invention provides a systematic reliability analysis method through graph theory modeling and Monte Carlo simulation, which can accurately identify the fault area and quantify the reliability of the electrical circuit network, thereby providing scientific decision support for the design and maintenance of the electrical system, enhancing the robustness of the system and reducing potential operational risks.

[0067] The above describes the application of the method of the embodiment of the present invention in the electrical circuit network of an aircraft, and can also be applied to industries with similar scenarios such as aviation, aerospace, ships, weapons, automobiles, rail transportation, etc.

[0068] An embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 3As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store operating parameter data of each framework. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps of the method of the embodiment of the present invention are implemented.

[0069] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0070] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of the embodiment of the present invention are implemented.

[0071] An embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps of the method of the embodiment of the present invention when executed by a processor.

[0072] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A model-based reliability assessment method for complex electrical circuit networks of aircraft, characterized in that: include: Step S1: abstract the metal structural elements of the aircraft as nodes, abstract the cable connections as edges, use graph theory methods to abstract the circuit structure of the electrical circuit network as a model graph, and use a connectivity check algorithm to perform connectivity analysis between nodes to determine the integrity of the electrical circuit network; Step S2: Based on the model graph, the Monte Carlo algorithm is used to perform multiple cycles of random failure simulation on the connection path between any two nodes. In each cycle, the failure time of the nodes and edges between the connection paths is determined by random sampling, and the nodes and edges that fail in the simulation are judged by the failure probability. The nodes and edges judged to be failed are removed from the model graph, and the connectivity between the nodes is checked using the connectivity check algorithm; Step S3: When it is determined that there is connectivity between the nodes, the total resistance of the electrical circuit network is approximately calculated using the Laplace algorithm, and it is determined whether the total resistance is less than a given resistance threshold. If the total resistance is less than the given resistance threshold, it indicates that the electrical circuit network is reliable, otherwise it is unreliable. Step S4: Repeat steps S2 and S3 until the specified number of cycles is reached, record the connectivity, total resistance and failure probability of each simulation, and finally output the reliability probability as the reliability index of the electrical circuit network.

2. The method according to claim 1, characterized in that In step S1, a weight is assigned to each edge to represent the resistance of the cable, and an adjacency matrix and a weight matrix are constructed, wherein each element in the adjacency matrix represents whether there is a connection relationship between nodes, and the weight matrix records the weight of the connection.

3. The method according to claim 1 or 2, characterized in that The union-find algorithm is used as the connectivity check algorithm.

4. The method according to claim 1 or 2, characterized in that: In step S2, the failure time of nodes and edges is determined as follows : in, is a random number uniformly distributed in the interval [0, 1], are the failure rates of nodes and edges.

5. The method according to claim 4, characterized in that In step S2, the failure probability is calculated as follows: in, is the cumulative distribution function, which represents the failure time The probability of being less than or equal to the set failure time threshold.

6. The method according to claim 1 or 2, characterized in that: In step S3, the total resistance of the electrical loop network is calculated based on the resistance between the nodes, wherein any two nodes are calculated as follows and The resistance between : in, Represents the Laplace matrix with the first Row and The determinant of the submatrix after the columns is, Represents the Laplace matrix Row and The remainder of a column.

7. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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