Power distribution network topology identification method based on node power and voltage amplitude

Abstract

The invention relates to a power distribution network topology identification method based on node power and voltage amplitude. The method comprises the following steps: S1, generating an active power coefficient matrix YP and a reactive power coefficient matrix YQ based on load flow calculation; s2, solving YP and YQ through a linear regression algorithm by using observation data of multiple groups of node power and voltage amplitude; s3, performing threshold screening on the YP and the YQ to generate a topological matrix YTopo; s4, updating the YTopo through iterative optimization until the matrix converges; and S5, performing symmetry correction on the converged YTopo, and outputting a final topological structure. According to the method, data noise and load change in the power distribution network can be effectively handled, and the topology identification precision and robustness are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network topology identification, and particularly to a distribution network topology identification method based on node power and voltage amplitude. Background Art

[0002] At present, a large number of distributed energy sources (such as photovoltaic and wind power) are gradually connected to the distribution network, providing new opportunities for optimizing the energy structure and consuming renewable energy. However, the large-scale access of such distributed energy sources has also significantly increased the uncertainty of the distribution network operation, such as problems like voltage over-limit, reverse power flow, and three-phase imbalance. Such changes pose higher requirements for the planning, dispatching, and operation and maintenance of the distribution network.

[0003] In power system analysis, the topological structure and line parameters of the distribution network are basic data for functions such as power flow calculation, fault location, and state estimation, and are also important bases for the planning and operation of the distribution network. However, due to the limited coverage of measurement devices in medium and low voltage distribution networks and the complex distribution of various devices, the existing topological information may be incomplete or inconsistent with the actual operation situation. Based on these problems, the identification of topology and line parameters has gradually become an important research direction for distribution networks.

[0004] However, the existing technologies have problems such as lagging information update, low redundancy of measurement data, and dependence on high-cost devices in the distribution network topology identification, and it is difficult to meet the dynamics and complexity brought by the access of distributed energy sources. In addition, the existing methods are insufficient in accuracy under the conditions of data noise and load fluctuation, and the real-time performance and robustness also need to be improved. Summary of the Invention

[0005] The purpose of the present invention is to provide a distribution network topology identification method based on node power and voltage amplitude, which can effectively cope with data noise and load changes in the distribution network and improve the accuracy and robustness of topology identification.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is: a distribution network topology identification method based on node power and voltage amplitude, including the following steps:

[0007] S1. Generate an active power coefficient matrix Y P and a reactive power coefficient matrix Y Q ;

[0008] S2. Use multiple groups of observed data of node power and voltage amplitude to solve for Y P and Y Q ;

[0009] S3. For Y P and Y QPerform threshold screening to generate the topological matrix Y Topo ;

[0010] S4. Update Y through iterative optimization Topo until the matrix converges;

[0011] S5. Perform symmetry correction on the converged Y Topo and output the final topological structure.

[0012] Furthermore, in the power flow calculation of the power system in polar coordinates, the calculation formulas for active power and reactive power are shown in Formulas (1) and (2):

[0013]

[0014] where P i and Q i respectively represent the injected active power and reactive power of node i, U i and U j respectively represent the voltage amplitudes of node i and node j, n represents the total number of nodes, G ij and B ij respectively represent the real part (conductance) and imaginary part (susceptance) of the admittance matrix Y between node i and node j, δ ij represents the phase angle difference between the voltages of node i and node j, δ ij =δ i -δ j ;

[0015] Writing the above formulas in matrix form gives:

[0016] [P]=[U]⊙([Y P ·[U]) (3)

[0017] [Q]=[U]⊙([Y Q ·[U]) (4)

[0018] where [P] and [Q] respectively represent the column vectors of node active power and reactive power, with a dimension of n×1; [U] represents the column vector of node voltage amplitudes, with a dimension of n×1; ⊙ is the dot product operator, indicating element-wise multiplication of matrices; [Y P and [Y Q respectively represent the active power coefficient matrix and the reactive power coefficient matrix, with a dimension of n×n; the calculation formulas for the coefficient matrices [Y P and [Y Q are as follows:

[0019] Y P(i,j) =G ij cos(δ ij )+B ijsin(δ ij ) (5)

[0020] Y Q(i,j) =G ij sin(δ ij )-B ij cos(δ ij ) (6)

[0021] When nodes i and j are not connected, the admittance matrix Y ij =0, and its real part G ij and imaginary part B ij are both 0. According to the formula, regardless of the value of δ ij , at this time Y P(i,j) and Y Q(i,j) are both equal to 0;

[0022] When nodes i and j are connected, the admittance matrix Y ij ≠0, and G ij and B ij are both not equal to 0. Assuming that Y P(i,j) and Y Q(i,j) are both equal to 0 at the same time, then it is required that However obviously does not hold. Therefore, this equation has no valid solution. Therefore, Y P(i,j) and Y Q(i,j) cannot be 0 at the same time;

[0023]

[0024] Combining Y P(i,j) and Y Q(i,j) to judge the connection situation between nodes i and j. When Y P(i,j) and Y Q(i,j) are 0, then the two nodes are not connected. When Y P(i,j) and Y Q(i,j) are not equal to 1, the two nodes are connected.

[0025] Furthermore, based on the observed values of the active power, reactive power, and voltage amplitude of each node, according to formulas (3) and (4), the Y P and Y Q matrices are estimated by linear regression;

[0026] [Y P =[P / U][U] T ([U][U] T ) -1 (8)

[0027] [Y Q =[Q / U][U] T ([U][U] T )-1 (9)

[0028] Since the number of unknowns in the Y P and Y Q matrices is greater than the number of constraint equations, multiple sets of P, Q, and U data are used to improve the accuracy of the estimation results.

[0029] Furthermore, to avoid the equations (8) and (9) being underdetermined, the number of data sets M required is greater than or equal to the number of system nodes n.

[0030] Furthermore, to more accurately identify the topological structure, a threshold ε is introduced to filter noise, and the elements in the power coefficient matrix are screened and corrected through formula (10);

[0031]

[0032] If the absolute value of an element in the solved active power coefficient matrix Y P is less than the set threshold ε, the corresponding element in the topological matrix Y PTopo is 0, otherwise it is 1; the same method is used to obtain the topological matrix Y Q of the Y QTopo matrix;

[0033] Then the topology of the admittance matrix Y is:

[0034] Y Topo = Y PTopo ∪ Y QTopo (11).

[0035] Furthermore, by iteratively optimizing and updating Y Topo , the newly obtained Y Topo is compared with the Y Topo obtained in the previous cycle. If Y Topo has changed, the new Y Topo is used as a new constraint condition to estimate the power coefficient matrix again, thereby gradually reducing the influence of noise and improving the accuracy of the estimation until Y Topo no longer changes, indicating that the iteration has converged and the preliminary identification of the topological matrix is completed.

[0036] Furthermore, the new constraint condition is that for the elements where Y Topo = 0, the corresponding elements of Y P and Y Q are also equal to 0.

[0037] Furthermore, when nodes i and j are connected, both Y Topo(i,j) and Y Topo(j,i) are 1, so Y Topois a symmetric matrix, and further improve Y according to symmetry Topo as follows:

[0038]

[0039] The present invention also provides a computer device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which implement the above method when the computer program instructions are executed by the processor.

[0040] The present invention also provides a computer-readable storage medium, on which computer program instructions are stored, and characterized in that the above method is implemented when the computer program instructions are executed by a processor.

[0041] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for identifying the topology of a distribution network based on node power and voltage amplitude for the actual needs in the topology identification of a distribution network. This method constructs a power coefficient matrix through power flow calculation and regression analysis of multiple sets of power observation data, and determines the connection relationship between nodes by combining threshold screening and iterative optimization algorithms. Therefore, this method can effectively cope with data noise and load changes, while reducing the dependence on high-cost measurement equipment and improving the accuracy and robustness of topology identification. This method can realize high-precision real-time identification of the topology structure of a distribution network in the scenario of dynamic access of distributed energy, is applicable to various operation scenarios of medium- and low-voltage distribution networks, and provides reliable technical support for the planning, scheduling, and operation and maintenance of distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flowchart of the implementation of the method for identifying the topology of a distribution network based on node power and voltage amplitude in an embodiment of the present invention;

[0043] Figure 2 is a structure diagram of an IEEE-33 node distribution network in an embodiment of the present invention;

[0044] Figure 3 is the topology identification result of the node admittance matrix in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present invention will be further described below with reference to the drawings and embodiments.

[0046] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0047] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] As Figure 1 shown, this embodiment provides a method for identifying the topology of a distribution network based on node power and voltage amplitude, including the following steps:

[0049] S1. Generate an active power coefficient matrix Y P and a reactive power coefficient matrix Y Q ;

[0050] S2. Use the observed data of multiple groups of node power and voltage amplitude to solve for Y P and Y Q through a linear regression algorithm;

[0051] S3. Perform threshold screening on Y P and Y Q to generate a topology matrix Y Topo ;

[0052] S4. Update Y Topo by iterative optimization until the matrix converges;

[0053] S5. Perform symmetry correction on the converged Y Topo to ensure that Y Topo (i,j) = Y Topo (j,i), and output the final topology structure.

[0054] 1. Construct a power coefficient matrix

[0055] In the polar coordinate power flow calculation of the power system, the calculation formulas for active power and reactive power are as shown in Formulas (1) and (2):

[0056]

[0057] Among them, P i and Q i respectively represent the injected active power and reactive power of node i, U i and U j respectively represent the voltage amplitudes of node i and node j, n represents the total number of nodes, G ij and B ij respectively represent the real part (conductance) and imaginary part (susceptance) of the admittance matrix Y between node i and node j, δij represents the phase angle difference between the voltages of node i and node j, δ ij = δ i - δ j .

[0058] Writing the above formula in matrix form gives:

[0059] [P] = [U] ⊙ ([Y P · [U]) (3)

[0060] [Q] = [U] ⊙ ([Y Q · [U]) (4)

[0061] where [P] and [Q] represent the column vectors of active power and reactive power of the nodes, with dimensions n×1; [U] represents the column vector of the voltage magnitudes of the nodes, with dimensions n×1; ⊙ is the dot product operator, indicating the multiplication of corresponding elements of the matrices; [Y P and [Y Q represent the active power coefficient matrix and the reactive power coefficient matrix respectively, with dimensions n×n.

[0062] The calculation formulas for the coefficient matrices [Y P and [Y Q are as follows:

[0063] Y P(i,j) = G ij cos(δ ij ) + B ij sin(δ ij ) (5)

[0064] Y Q(i,j) = G ij sin(δ ij ) - B ij cos(δ ij ) (6)

[0065] When node i and node j are not connected, the admittance matrix Y ij = 0, and its real part G ij and imaginary part B ij are both 0. According to the formula, no matter how large δ ij is, at this time Y P(i,j) and Y Q(i,j) are both equal to 0.

[0066] When node i and node j are connected, the admittance matrix Y ij ≠ 0, and G ij and B ij are both not equal to 0. Assuming that Y P(i,j) and Y Q(i,j) are both equal to 0 at the same time, then it is required that However is clearly not valid, so this equation has no valid solution. Therefore, Y P(i,j) and Y Q(i,j) cannot be 0 at the same time.

[0067]

[0068] In summary, Y P(i,j) and Y Q(i,j) can be combined to judge the connection situation between nodes i and j. When Y P(i,j) and Y Q(i,j) is 0, the two nodes are not connected. When Y P(i,j) and Y Q(i,j) is not equal to 1, the two nodes are connected.

[0069] 2. Solve the power coefficient matrix

[0070] Based on the observed values of the active power, reactive power, and voltage amplitude of each node, according to formulas (3) and (4), estimate the Y P and Y Q matrix through linear regression.

[0071] [Y P = [P / U][U] T ([U][U] T ) -1 (8)

[0072] [Y Q = [Q / U][U] T ([U][U] T ) -1 (9)

[0073] Since the number of unknowns in the Y P and Y Q matrices is much larger than the number of constraint equations, there will be a large error in the estimation results. Therefore, multiple sets of P, Q, U data are used to improve the accuracy of the estimation results.

[0074] Although the δ corresponding to each set of data is different, in the actual operation of the power system, δ ij usually only fluctuates within a very small range. Therefore, the changes in the Y P and Y Q matrices corresponding to different sets of data are also small. On the other hand, since δ ii = 0, the diagonal elements of the power coefficient matrices for each set remain constant, Y P(i,i) = G ii , Y Q(i,i) = -B ii . For two unconnected nodes, their corresponding Y P(i,j) and Y Q(i,j)also remains constant, Y P(i,j) = Y Q(i,j) = 0. In summary, the power coefficient matrices Y P and Y Q corresponding to different groups have little difference, and the accuracy of the linearly regressed estimated power coefficient matrix can be improved by multiple groups of P, Q, U data. To avoid the equations (8) and (9) being underdetermined equations, the number of data groups M required should be greater than or equal to the number of system nodes n.

[0075] 3. Topology Identification

[0076] Topology identification is an important process in power system analysis to determine the connection relationships between network nodes. Due to measurement errors and uncertainties in regression analysis, the estimated power coefficient matrix may contain noise, resulting in non-zero power coefficients corresponding to unconnected branches.

[0077] To more accurately identify the topology, a threshold ε is introduced to filter the noise, and the elements in the power coefficient matrix are screened and corrected through formula (10);

[0078]

[0079] As shown in formula (10), if the absolute value of the element in the solved active power coefficient matrix Y P is less than the set threshold ε, then the corresponding element in the topology matrix Y PTopo is 0, otherwise it is 1. The topology matrix Y Q of the Y QTopo matrix is obtained using the same method;

[0080] According to the analysis in formula (7), when the corresponding elements in Y PTopo and Y QTopo are both 0, then the corresponding element in the nodal admittance matrix Y is also 0, and the two corresponding nodes are not connected. Therefore, the topology of the admittance matrix Y is:

[0081] Y Topo = Y PTopo ∪ Y QTopo (11).

[0082] By iteratively optimizing and updating Y Topo , the newly obtained Y Topo is compared with the Y Topo obtained in the previous cycle. If Y Topo has changed, then the new Y Topo is used as the new constraint condition (i.e., for the elements where Y Topo = 0, Y P and Y QThe corresponding element is also equal to 0), the power coefficient matrix is estimated again, thereby gradually reducing the influence of noise and improving the accuracy of the estimation. Until Y Topo no longer changes, which indicates that the iteration has converged and the preliminary identification of the topological matrix is completed.

[0083] When node i and node j are connected, Y Topo(i,j) and Y Topo(j,i) are both 1, so Y Topo is a symmetric matrix. According to the symmetry, Y Topo is further improved as follows:

[0084]

[0085] 4. Validity verification

[0086] To verify the effectiveness of the method proposed in the present invention, the IEEE-33 node distribution network is selected as the test system in this embodiment. The topology of the IEEE-33 node distribution network is as Figure 2 shown. Its structure consists of 32 main branches and 5 tie lines (represented by dashed lines), having typical distribution network characteristics.

[0087] Assume that the acquisition interval of the smart meter is 15 minutes, then the amount of data that each meter can record in a day is 96 groups (M = 96). To obtain the data required for multiple identifications, the power of each node is made to fluctuate between 0.8 and 1.2 times the initial power to simulate the load fluctuations at different times, and the power flow calculation is performed using matpower to generate the required PQU data. To be closer to the actual measurement conditions, 2% random perturbation is added to the generated power flow calculation data to simulate the influence of measurement errors on the data.

[0088] The topological identification of the nodal admittance matrix is as Figure 3 shown, where the yellow squares represent the elements with Y Topo = 1, and the blue squares represent the elements with Y Topo = 0. The numbers of the corresponding rows and columns of each element are the node numbers at both ends of the branch. For example, the corresponding square of Y Topo (2,19) is yellow, indicating that Y Topo (2,19) = 1, then there is a connection line between node 2 and node 19. Comparing Figure 2 and Figure 3 , it can be found that the identified network topology is the same as the original network topology, indicating the effectiveness of the method.

[0089] This embodiment also provides a computer device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the above method is implemented.

[0090] This embodiment also provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method is implemented.

[0091] The method for identifying the topology of a distribution network based on node power and voltage amplitude provided by the present invention can achieve high-precision and robust topology identification under load fluctuations and measurement noise conditions by constructing a power coefficient matrix and combining a threshold screening and iterative optimization algorithm; it does not rely on high-cost equipment and only uses the existing SCADA system and smart meter data, greatly reducing costs; at the same time, it adapts to the dynamic access of distributed energy and complex topological structures, has real-time update and wide applicability, and significantly improves the planning, scheduling, and operation and maintenance efficiency of the distribution network.

[0092] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified function in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0094] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the specified function in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps specified in one process or a plurality of processes and / or boxes Figure 1 in one box or a plurality of boxes Figure 1 of the functions specified in the process or processes and / or boxes.

[0096] As mentioned above, the above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any other form. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for identifying the topology of a distribution network based on node power and voltage amplitude, characterized in that Including the following steps: S1. Generate the active power coefficient matrix Y P and the reactive power coefficient matrix Y Q ; S2. Using the observed data of multiple sets of node power and voltage magnitudes, solve for Y through a linear regression algorithm P and Y Q ; S3. Perform threshold screening on Y P and Y Q to generate a topological matrix Y Topo ; S4. Update Y through iterative optimization Topo until the matrix converges; S5, after convergence, Y Topo Perform symmetry correction and output the final topology.

2. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 1, characterized in that, In the power flow calculation of the power system in polar coordinates, the calculation formulas for active power and reactive power are shown in Formulas (1) and (2) as follows: Among them, P i and Q i denote the injected active power and reactive power of node i, U i and U j Represent the voltage amplitude of node i and node j respectively, n represents the total number of nodes, G ij and B ij denote the real part (conductance) and imaginary part (susceptance) of the admittance matrix Y between node i and node j, δ ij The phase angle difference between the voltages at nodes i and j is δ ij =δ i -δ j ; Writing the above formulas in matrix form gives: [P] = [U] ⊙ ([Y P · [U]) (3) [Q] = [U] ⊙ ([Y Q · [U]) (4) Among them, [P] and [Q] respectively represent the column vectors of the active power and reactive power of the nodes, with the dimension of n×1; [U] represents the column vector of the node voltage amplitude, with the dimension of n×1; ⊙ is the dot product operator, indicating the multiplication of the corresponding elements of the matrix; [Y P and [Y Q respectively represent the active power coefficient matrix and the reactive power coefficient matrix, with the dimension of n×n; the calculation formulas of the coefficient matrices [Y P and [Y Q are as follows: Y P(i,j) = G ij cos(δ ij ) + B ij sin(δ ij ) (5) Y Q(i,j) = G ij sin(δ ij ) - B ij cos(δ ij ) (6) When nodes i and j are not connected, the admittance matrix Y ij =0, its actual part G ij and the imaginary part B ij are all 0. According to the formula, no matter δ ij How big is Y? P(i,j) and Y Q(i,j) are all equal to 0; When nodes i and j are connected, the admittance matrix Y ij ≠0, G ij and B ij are both not equal to 0. Assume that Y P(i,j) and Y Q(i,j) are both equal to 0 simultaneously, then it is required that However it is obviously not valid. Therefore, this equation has no valid solution. Thus, Y P(i,j) and Y Q(i,j) cannot be 0 simultaneously; Combined with Y P(i,j) and Y Q(i,j) to determine the connection situation between nodes i and j. When Y P(i,j) and Y Q(i,j) is 0, the two nodes are not connected. When Y P(i,j) and Y Q(i,j) is not equal to 1, the two nodes are connected.

3. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 1, characterized in that, Based on the observed values of the active power, reactive power, and voltage magnitude of each node, according to formulas (3) and (4), estimate the Y matrix through linear regression P and Y Q matrix; [Y P =[P / U][U] T ([U][U] T ) -1 (8) [Y Q =[Q / U][U] T ([U][U] T ) -1 (9) Since Y P and Y Q the number of unknowns in the matrix is greater than the number of constraint equations, so multiple sets of P, Q, and U data are used to improve the accuracy of the estimation result.

4. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 3, characterized in that To avoid Formulas (8) and (9) from being underdetermined equations, the number of data sets M required is greater than or equal to the number of system nodes n.

5. The method for identifying distribution network topology based on node power and voltage amplitude according to claim 1, characterized in that: To identify the topological structure more accurately, a threshold ε is introduced to filter out noise, and the elements in the power coefficient matrix are screened and corrected through Formula (10); If the absolute value of the element in the solved active power coefficient matrix Y P is less than the set threshold ε, the corresponding element in the topology matrix Y PTopo is 0, otherwise it is 1; the topology matrix Y Q of the Y QTopo matrix is obtained using the same method; Then the topology of the admittance matrix Y is: Y Topo = Y PTopo ∪ Y QTopo (11).

6. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 1, characterized in that Update Y through iterative optimization Topo , the newly obtained Y Topo and Y obtained in the last cycle Topo For comparison, if Y Topo If there is a change, the new Y Topo As a new constraint, the power coefficient matrix is estimated again, thereby gradually reducing the noise impact and improving the accuracy of the estimation until Y Topo If there is no more change, it means that the iteration has converged and the preliminary identification of the topological matrix is completed.

7. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 6, characterized in that, The new constraint is that for the elements where Y Topo = 0, the corresponding elements of Y P and Y Q are also equal to 0.

8. A method for identifying the topology of a distribution network based on node power and voltage amplitude according to claim 1, characterized in that, When node i and node j are connected, Y Topo(i,j) and Y Topo(j,i) are both 1. Therefore, Y Topo is a symmetric matrix. According to the symmetry, further improve Y Topo as follows:

9. A computer device, characterized in that: Including: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method according to any one of Claims 1-8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of Claims 1-8 is implemented.

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