A power inspection method and system

Abstract

A power inspection method and system, the method comprising: Step 1, obtaining line tower coordinate data to obtain line information of each line; Step 2, dividing the lines into first-class lines and second-class lines according to the line information to obtain a weighted connected graph; Step 3, calculating the minimum weight matrix between each substation in the line according to the coordinates of each substation in the line; Step 4, calculating the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix; Step 5, calculating the relative optimal path of the first-class lines and the relative optimal path of the second-class lines according to the weighted connected graph, the minimum weight matrix between substations, and the perfect matching result to obtain the final path planning result. The present invention judges whether the connected graph is disconnected by introducing the Floyd algorithm, and can obtain a relatively short inspection path when searching for inspection objects based on the graph theory network algorithm, and is more suitable for inspection areas with more overhead lines and fewer underground cables.

Description

Technical Field

[0001] The present invention belongs to the technical field of power inspection path planning, and specifically relates to a power inspection method and system. Background Art

[0002] For transmission lines of 35 kV and above, preventing external damage is the key work, which is inseparable from the regular inspection of the line corridor. Manual inspection has low efficiency and uncontrollable quality, while the currently frequently mentioned unmanned aerial vehicle (UAV) autonomous inspection is restricted by its battery life. The battery life of the inspection robot in the present invention is much higher than that of the UAV, because the UAV consumes work done by gravity and resistance, while the inspection robot consumes work done by rolling friction, which is much less than that of the UAV. By using the inspection robot and reasonable and comprehensive path planning, the inspection frequency can be increased, and the tree-line distance can be measured incidentally, which is especially suitable for the central and western regions of China with more overhead lines and fewer underground cables.

[0003] In the prior art, there are already various methods for path planning of power line inspection. The main technical problems existing in the existing path planning methods mainly include:

[0004] 1. When solving, it is necessary to traverse the paths of all target points. Since there are paths between any two nodes in the power line, traversing the paths of all target points requires a large amount of cost, resulting in a high complexity of the solution algorithm;

[0005] 2. In the existing path planning methods, each device in the line is usually regarded as a node for path planning, which not only increases the planning cost but also is not applicable to the path planning of power line inspection in areas with more overhead lines and fewer underground cables;

[0006] 3. In the prior art, known algorithms are usually used for solution. For example, the minimum weight perfect matching algorithm is only applicable to the case of fewer odd-degree points and cannot be used for the case of more odd-degree points;

[0007] 4. When the prior art conducts path planning, it usually uses the method of randomly selecting one of the nearest lines as the next inspection object for planning, making the result have a large randomness, which is not conducive to obtaining a shorter inspection route, thereby increasing the inspection cost. Summary of the Invention

[0008] To solve the deficiencies in the prior art, the present invention provides a power inspection method, which can overcome the technical problems in the prior art, such as high algorithm complexity, high planning cost and inspection cost, and inapplicability to the power line inspection path planning in areas with more overhead lines and fewer underground cables.

[0009] The present invention adopts the following technical solutions.

[0010] A power inspection method includes:

[0011] Step 1: Obtain the coordinate data of line poles and towers to get the line information of each line.

[0012] Step 2: Divide the lines into the first type of lines and the second type of lines according to the line information to obtain a weighted connected graph.

[0013] Step 3: Calculate the minimum weight matrix between each substation in the line according to the coordinates of the substations in the line.

[0014] Step 4: Calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix.

[0015] Step 5: Calculate the relatively optimal path of the first type of lines and the relatively optimal path of the second type of lines according to the weighted connected graph, the minimum weight matrix between substations, and the perfect matching result to obtain the final path planning result.

[0016] Preferably, the line information includes: line length, starting position, and ending position.

[0017] Preferably, the specific content of Step 2 includes:

[0018] Step 2.1: Assume that the total number of lines is n, the starting point of the line is a substation, and the ending point is a substation or the cable goes underground. Classify the lines with both ends being substations into the first type of lines, and classify the lines with one end being a substation and the other end being the cable going underground into the second type of lines.

[0019] Step 2.2: Count the number of lines with a certain substation as the starting point or the ending point in each substation. If the number of lines is odd, define this substation as an odd-degree point; if the number of lines is even, define this substation as an even-degree point.

[0020] Judge whether the number of substations defined as odd-degree points in the area to be inspected is odd. If it is odd, select the shortest lines associated with each odd-degree point substation and classify them into the second type of lines.

[0021] Step 2.3: For the first type of lines, use the substations associated with the first type of lines as points, the first type of lines as edges, and the length of each line as the weight to construct a weighted connected graph G. The obtained weighted connected graph G is: {line name: starting substation, ending substation, weight}.

[0022] Preferably, the specific content of Step 3 includes:

[0023] Step 3.1: Calculate the distance matrix d between each substation n*n , where n is the total number of substations, and the distance matrix d n*nThe element d[i,j] in the i-th row and j-th column represents the distance between substation i and substation j. The value of d[i,j] is equal to the line weight value between substation i and substation j. If there are multiple lines between substation i and substation j, then d[i,j] takes the shortest line weight value. If there is no line between substation i and substation j, then d[i,j] is infinity. The weight value between substation i and substation i is 0, so d[i,i] is 0;

[0024] Step 3.2, set the minimum weight matrix D n*n , and the initial value of each element is infinity. D[i,j] represents the distance weight value between substation i and substation j;

[0025] Step 3.3, set the path matrix P n*n , the path matrix P n*n The initial value of each element in is 0. The element P[i,j] in the path matrix P n*n in the i-th row and j-th column represents the maximum number of the intermediate node on the shortest path from substation i to substation j. If P[i,j] is 0, it means there is no intermediate node between substation i and j;

[0026] Step 3.4, assign initial values to each element in the minimum weight matrix D n*n : D[i,j]=d[i,j]. D[i,j] represents the element in the i-th row and j-th column of the minimum weight matrix D n*n ;

[0027] Step 3.5, let k = 1, traverse all i and j. If D[i,k]+D[k,j]<D[i,j], then:

[0028] D[i,j]=D[i,k]+D[k,j], P[i,j]=k;

[0029] Step 3.6, stop traversing when k = n to obtain the final weight matrix D and path matrix P. Otherwise, let k = k + 1 and return to step 3.5 to continue traversing.

[0030] Preferably, the specific steps of step 4 include:

[0031] Step 4.1, define the substation as an odd-degree point or an even-degree point according to step 2.2, and judge whether the number of substations defined as odd-degree points in the area to be inspected is odd. If it is odd, after removing this line, classify this odd-degree point substation as an even-degree point substation; if it is even, enter step 4.2;

[0032] Step 4.2, if the number of odd-degree points is even, denote the number of odd-degree points as n_odd, set a threshold T, the initial value of the threshold T can be set as the length of a single line, set the minimum sum of weights s_min of the perfect match, the initial value can be set to be greater than the total length of the lines in the area to be inspected, set a perfect match list cxx, the initial value is an empty list with a length of n_odd;

[0033] Step 4.3, determine whether the threshold T is less than the maximum value of the elements in the weight matrix D. If so, go to Step 4.4; otherwise, go to Step 4.13;

[0034] Step 4.4, set an incidence matrix M n_odd*n_odd , the initial value of each element in the incidence matrix is 0. Traverse all odd-degree point substations. According to the weight matrix D, if the distance between substation i and substation j is less than T, then the element M[i, j] in the i-th row and j-th column of the incidence matrix is 1. There is no association between the same substation, so M[i, i]=0;

[0035] Step 4.5, set a match list cx, the initial value is an empty list with a length of n_odd. When the i-th element cx[i]=j in the match list, it means that the i-th substation is matched with the j-th substation. Set a loop variable count, the initial value is 1;

[0036] Step 4.6, determine whether there is a 0 in the match list cx. If so, search for the position where 0 first appears in the match list cx and record it as the first position u, update the value of the loop variable count to count + 1, and go to Step 4.7; if not, go to Step 4.11;

[0037] Step 4.7, set a mark list vis, the initial value is an empty list with a length of n_odd;

[0038] Step 4.8, according to the first position u, the mark list vis, the match list cx, and the incidence matrix M, use an improved Hungarian algorithm to calculate the augmenting path starting from substation u, and obtain the return values: logical value flag, the second position u1 of the substation, the mark list vis, and the match list cx;

[0039] Step 4.9, judge the conditions: cx[u1]=0 and flag = 1. If both conditions are satisfied, use the second position u1 of the substation, the mark list vis, the match list cx, and the incidence matrix M as parameters, and use an improved Hungarian algorithm to calculate the augmenting path starting from the second position u1 of the substation, and obtain the return values: logical value flag, the second position u1 of the substation, the mark list vis, and the match list cx;

[0040] If the two conditions are not both satisfied, go to Step 4.10; otherwise, repeat Step 4.9;

[0041] Step 4.10, if count is greater than n_odd 3 , then stop the loop and go to Step 4.11; otherwise, go to Step 4.6;

[0042] Step 4.11, determine whether there is a 0 in the matching list cx. If there is no 0, it indicates that cx is a perfect match for odd-degree points. Calculate the sum s of the weights of the perfect match according to the weight matrix D. Further, determine whether the sum s of the weights of the perfect match is less than s_min. If the sum s of the weights of the perfect match is less than s_min, then update the values of s_min and cxx with the sum s of the weights of the perfect match and the matching list cx respectively;

[0043] If there is a 0, it indicates that there are still unmatched odd-degree point substations. At this time, s_min and cxx remain unchanged;

[0044] Step 4.12, update the threshold T to T + 0.1 and go to Step 4.3 until the value of the threshold T is greater than or equal to the maximum value of the elements of the weight matrix D to end the loop;

[0045] Step 4.13, obtain the final s_min and the perfect matching result cxx.

[0046] Preferably, in Steps 4.8 to 4.9, an improved Hungarian algorithm is used to calculate the augmenting path starting from the second position u1 of the substation, which specifically includes:

[0047] Input parameters: the first position u, the marking list vis, the matching list cx, and the incidence matrix M;

[0048] Set flag = 0, flag is a logical value, and let the second position u1 of the substation = u;

[0049] Traverse all odd-degree point substations. When traversing to substation v, if M[u, v] = 1 and vis[v] = 0, it indicates that there is a connection between substation u and substation v, and v has not been visited. Then set flag = 1, vis[v] = 1, update v as visited, and go to the next step to determine whether cx[v] is 0; if M[u, v] = 1 and vis[v] = 0 do not hold simultaneously, then continue to judge the next substation v + 1;

[0050] Determine whether the v-th element cx[v] in the matching list is 0. If it is 0, it indicates that the v-th substation is not matched. Let cx[u]=v and cx[v]=u, match substation u and substation v, end the function process, and return flag, u1, vis, cx. If it is not 0, it indicates that the v-th substation has been matched. Let u1 = cx[v], cx[v]=0, disconnect the matching between substation v and the previous substation u1, and rematch it. Let cx[u]=v and cx[v]=u, match the first position u of the substation and substation v, end the function process, and obtain the return values of flag, u1, vis, and cx.

[0051] If no situation satisfying the above conditions is obtained after traversing all odd-degree substation, output flag, u1, vis, and cx as the return values.

[0052] Preferably, step 5 specifically includes:

[0053] Step 5.1, construct a new weighted connected graph G* according to the weighted connected graph G, the minimum weight matrix D between substations, and the perfect matching result of odd-degree substations:

[0054] For even-degree substations, add the perfect matching result obtained in step 4.2.4 to graph G to construct a new weighted connected graph G*. The new weighted connected graph G* is an Euler graph, and use the Fleury algorithm to calculate the relatively optimal path for traversing all first-class lines;

[0055] Step 5.2, simplify the second-class line into a loop. When inspecting, go from the starting point of the line to the end point and then return the same way. The inspection mileage is 2 times the length of the line;

[0056] Step 5.3, combine the path planning results of the first-class and second-class lines to obtain the final path planning result.

[0057] Preferably, step 5.1 specifically includes:

[0058] Step 5.1.1, add the perfect matching result obtained in step 4 as duplicate edges to graph G to construct a new weighted connected graph G*. The new weighted connected graph G* is an Euler graph, and each point is an even-degree point. The data format of the new weighted connected G* is: {line name: starting substation, ending substation, weight};

[0059] Step 5.1.2, set the Euler path p, with the initial value being an empty list, and set the line list line_list, with the initial value being all the lines in G*;

[0060] Step 5.1.3, starting from any point, add it to p;

[0061] Step 5.1.4, check whether there is a line in line_list. If there is, go to Step 5.1.5; if not, go to Step 5.1.7;

[0062] Step 5.1.5, record the last value of p as the substation state, obtain the lines connected to state, and store them in the list list_1;

[0063] Step 5.1.6, traverse the lines in list_1, use the line i and G* in them as parameters, and use the judgment function to make a judgment. If the judgment function returns True, add the line i to p, add the substation at the other end of i to p, delete the line i from line_list and G* respectively, and return to Step 5.1.4; if the judgment function returns False, continue to judge the line i+1 in the same way;

[0064] Step 5.1.7, obtain the final Euler path p.

[0065] Preferably, the judgment function in Step 5.1.6 has the following judgment logic:

[0066] (1) Input the parameters i and G*;

[0067] (2) Temporarily delete i from G* to obtain a new G**;

[0068] (3) Calculate the distance matrix d** between each substation point in G**. d**[i, j] represents the distance between substation i and substation j, that is, the line weight value between substation i and substation j. If there are multiple lines between substation i and substation j, d**[i, j] takes the shortest line weight value. If there is no line between substation i and substation j, d**[i, j] is infinity, and the weight value between substation i and substation i is 0, that is, d**[i, i] is 0;

[0069] (4) According to the calculation steps of the Floyd algorithm in Steps 4.2.3.2 to 4.2.3.6, calculate the minimum weight matrix D** of each substation point in G**;

[0070] (5) Judge whether there is an infinite value in D**. If there is, it means that G** is no longer connected at this time, and the line i is a bridge of G*, and the function returns False; if not, it means that G** is still connected at this time, and the line i may or may not be a bridge of G*, and the function returns True.

[0071] Preferably, the Euler path p in Step 5.1.7 specifically includes:

[0072] The format of the Euler path p is: point - line - point... line - point, where the starting point and the ending point are the same. If points m and n are the "duplicate edges" calculated in step 4, it indicates that there may be no line between points m and n. At this time, judge according to the path matrix P obtained in step 3.6. If P[m, n] = k and P[m, k] = 0, it indicates that the path from point m to point n is m - k - n.

[0073] The present invention also provides a power inspection system for implementing the power inspection method, including: an information acquisition module, a weighted connected graph construction module, a minimum weight matrix calculation module, a perfect matching result calculation module, and a path planning module;

[0074] The information acquisition module is used to acquire the coordinate data of the line towers to obtain the line information of each line;

[0075] The weighted connected graph construction module is used to divide the lines into the first - type lines and the second - type lines according to the line information to obtain a weighted connected graph;

[0076] The minimum weight matrix calculation module is used to calculate the minimum weight matrix between each substation according to the coordinates of the substations in the line;

[0077] The perfect matching result calculation module is used to calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix;

[0078] The path planning module is used to calculate the relatively optimal path of the first - type lines and the relatively optimal path of the second - type lines according to the weighted connected graph, the minimum weight matrix between the substations, and the perfect matching result to obtain the final path planning result.

[0079] The present invention also provides a terminal, including a processor and a storage medium;

[0080] The storage medium is used to store instructions;

[0081] The processor is used to operate according to the instructions to execute the steps of the power inspection method.

[0082] The present invention also provides a computer - readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the power inspection method.

[0083] The beneficial effects of the present invention are as follows. Compared with the prior art, the present invention simplifies the complex power grid line distribution into an undirected connected graph. Only the starting point or the ending point of the line is considered as a node, and the line between the nodes is the necessary route. At the same time, only the substation is counted as a node. If one end of the power line is an underground cable, the underground end is not counted as a node. This not only makes the method more applicable to the power inspection in areas with more overhead lines and fewer underground cables, but also can reduce the complexity of the algorithm and the time cost.

[0084] The present invention improves the Hungarian algorithm to solve the minimum weight perfect matching of non-bipartite graphs, which can be applicable to the case of any number of odd-degree points.

[0085] The present invention improves the Fleury algorithm to adapt to the path planning of power inspection. If the undirected connected graph is no longer connected after removing a certain line, then this line is a bridge of the undirected connected graph. When judging how to determine the bridge of the undirected connected graph, the present invention introduces the Floyd algorithm for judgment. If there is an infinite value in the obtained distance matrix, it means that the current graph is not connected, and this method has no relevant disclosure in the prior art.

[0086] Based on the graph theory network algorithm, the present invention can theoretically obtain a relatively short inspection path when looking for inspection objects. The present invention uses inspection robots for inspection, and the inspection quality is higher than that of manual inspection. Moreover, the battery life of the inspection robot is much higher than that of the unmanned aerial vehicle, because the unmanned aerial vehicle consumes work done by gravity and resistance, while the inspection robot consumes work done by rolling friction, which is much smaller than that of the unmanned aerial vehicle.

[0087] The present invention innovates the path planning algorithm and theoretically obtains a relatively short inspection path. It simplifies the complex power grid line distribution into an undirected connected graph. To reduce the time complexity of the algorithm, only the starting point or the ending point of the line is considered as a node, and the line between them is the necessary route. It improves the Hungarian algorithm to solve the minimum weight perfect matching of non-bipartite graphs, which can be applicable to the case of any number of odd-degree points. It improves some steps of the Fleury algorithm to adapt to the path planning model of power inspection. The path planning result obtained by the present invention can traverse all the poles and the line channels between the poles, so as to achieve a relatively short inspection path at the theoretical level. Brief Description of the Drawings

[0088] Figure 1 is the flowchart of the power inspection method in the present invention;

[0089] Figure 2 is the schematic diagram of the power inspection of the inspection robot in the present invention;

[0090] Figure 3 is the structural diagram of the power inspection system in the present invention. Detailed Embodiments

[0091] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0092] As Figure 1 shown, the present invention proposes a power inspection method, which includes the following steps:

[0093] Step 1: Obtain the coordinate data of the line towers to obtain the line information of each line;

[0094] Among them, the line information includes: line length, starting position, and ending position. The information of each line in the present invention is planned to obtain the relatively optimal path for the inspection robot to traverse each line.

[0095] Further preferably, as Figure 2 shown, this application uses a power inspection robot for inspection, specifically including:

[0096] Install guide wires between two adjacent towers;

[0097] Among them, the position where the guide wires are installed needs to meet the following conditions: below the lowest conductor, and the vertical distance from the lowest conductor needs to meet certain conditions according to the line voltage level. The distance for a 35kV line is greater than 2m, the distance for a 110kV line is greater than 4m, the distance for a 220kV line is greater than 4.5m, and the distance for a 500kV line is greater than 7m.

[0098] Design a power inspection robot and a robot charging station for inspecting power towers:;

[0099] Specifically, the power inspection robot consists of the following parts: wheels, battery, communication device, pan-tilt camera, millimeter-wave radar, and megaphone. The wheels are located above the robot and can roll along the guide rail; the battery can support the robot to travel from the substation and back to the substation with a battery life of at least 30 km; the communication device is used to transmit the video images back to the background in real time; the pan-tilt camera is for wide-angle shooting and can be remotely zoomed for shooting the channel conditions; the millimeter-wave radar is used to measure the distance to the trees below and for inspection obstacle avoidance; the megaphone is used to talk to the construction workers who may cause damage to the line below the line.

[0100] The robot charging station is located in the substation, and the inspection robot can enter the charging station by itself for autonomous charging.

[0101] Step 2: Divide the lines into the first type of lines and the second type of lines according to the line information to obtain a weighted connected graph;

[0102] Specifically, the present invention is applicable to lines with a substation as the starting point, a substation as the ending point, or lines where the cable enters the ground, such as in open areas with more overhead lines and fewer underground cables, and classifies the lines according to the ending points of the lines. Step 2 specifically includes:

[0103] Step 2.1: Let the total number of lines be n, with a substation as the starting point and a substation or cable entering the ground as the ending point. Classify the lines with substations at both ends as the first type of lines, and classify the lines with a substation at one end and a cable entering the ground at the other end as the second type of lines;

[0104] Step 2.2: Count the number of lines starting from or ending at each substation in each substation. If the number of lines is odd, define the substation as an odd-degree point; if the number of lines is even, define the substation as an even-degree point;

[0105] Judge whether the number of substations defined as odd-degree points in the area to be inspected is odd. If it is odd, take the shortest lines associated with each odd-degree point substation and classify them as the second type of lines;

[0106] Step 2.3: For the first type of lines, use the substations associated with the first type of lines as points, the first type of lines as edges, and the length of each line as the weight to construct a weighted connected graph G. The obtained weighted connected graph G is: {line name: starting substation, ending substation, weight}.

[0107] Step 3: Calculate the minimum weight matrix between each substation according to the coordinates of each substation in the line;

[0108] Step 3 specifically includes:

[0109] Step 3.1: Calculate the distance matrix d n*n , where n is the total number of substations. The element d[i, j] in the distance matrix d n*n represents the distance between substation i and substation j. The value of d[i, j] is equal to the line weight between substation i and substation j. If there are multiple lines between substation i and substation j, d[i, j] takes the shortest line weight. If there is no line between substation i and substation j, d[i, j] is infinity. The weight between substation i and substation i is 0, so d[i, i] is 0;

[0110] Step 3.2: Set the minimum weight matrix D n*n , with the initial value of each element being infinity. D[i, j] represents the distance weight between substation i and substation j;

[0111] Step 3.3: Set the path matrix P n*n , and the path matrix P n*nThe initial values of each element in n*n are 0. The element P[i, j] in the path matrix P

[0112] represents the maximum number of the intermediate nodes on the shortest path from substation i to substation j. If P[i, j] is 0, it means there is no intermediate node between substation i and substation j; n*n Step 3.4, assign initial values to each element in the minimum weight matrix D n*n : D[i, j] = d[i, j]. D[i, j] represents the element in the i-th row and j-th column of the minimum weight matrix D

[0113] Step 3.5, let k = 1, traverse all i and j. If D[i, k] + D[k, j] < D[i, j], then:

[0114] D[i, j] = D[i, k] + D[k, j], P[i, j] = k;

[0115] Step 3.6, stop traversing when k = n, and obtain the final weight matrix D and path matrix P. Otherwise, let k = k + 1, and return to Step 3.5 to continue traversing.

[0116] Step 4, calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix;

[0117] Step 4 specifically includes:

[0118] Step 4.1, define the substations as odd-degree points or even-degree points according to Step 2.2, and judge whether the number of substations defined as odd-degree points in the area to be inspected is odd. If it is odd, after removing this line, classify this odd-degree point substation as an even-degree point substation; if it is even, enter Step 4.2;

[0119] For odd-degree point substations, construct a complete weighted graph G1, and process the complete weighted graph G1 based on the improved relevant graph theory network algorithm to obtain the perfect matching result with the minimum weight; that is, match all odd-degree points in pairs to make the sum of the weights between two odd-degree points the smallest. This perfect matching result is the "repeated edge" of graph G;

[0120] Step 4.2, if the number of odd-degree points is even, denote the number of odd-degree points as n_odd, set a threshold T, and the initial value of the threshold T is relatively small, which can be set as the length of a single line, such as 8 km;

[0121] Set the minimum sum of weights s_min of the perfect matching, with the initial value being a relatively large value, which can be set to be greater than the total length of the lines in the area to be inspected, such as 2000 km. Set the perfect matching list cxx, with the initial value being an empty list and the length being n_odd;

[0122] For the perfect matching list cxx, when the i-th element cxx[i] = j in it, it means that the i-th substation is matched with the j-th substation;

[0123] Step 4.3, determine whether the threshold T is less than the maximum value of the elements of the weight matrix D. If so, go to Step 4.4; otherwise, go to Step 4.13;

[0124] Step 4.4, set the incidence matrix M n_odd*n_odd , the initial value of each element in the incidence matrix is 0. Traverse all the substations with odd degrees. According to the weight matrix D, if the distance between substation i and substation j is less than T, then the element M[i, j] in the i-th row and j-th column of the incidence matrix is 1. If there is no association between the same substations, then M[i, i] = 0;

[0125] Step 4.5, set the matching list cx, with the initial value being an empty list and the length being n_odd. When the i-th element cx[i] = j in the matching list, it means that the i-th substation is matched with the j-th substation. Set the loop variable count, with the initial value being 1;

[0126] Step 4.6, determine whether there is a 0 in the matching list cx. If there is, search for the position where the 0 first appears in the matching list cx and record it as the first position u. Update the value of the loop variable count to count + 1, and go to Step 4.7; if not, go to Step 4.11;

[0127] Step 4.7, set the marking list vis, with the initial value being an empty list and the length being n_odd;

[0128] Step 4.8, according to the first position u, the marking list vis, the matching list cx, and the incidence matrix M, use the improved Hungarian algorithm to calculate the augmenting path starting from substation u, and obtain the return values: logical value flag, the second position u1 of the substation, the marking list vis, and the matching list cx;

[0129] Step 4.9, judge the conditions: cx[u1] = 0 and flag = 1. If both conditions are satisfied, then use the second position u1 of the substation, the marking list vis, the matching list cx, and the incidence matrix M as parameters, and use the improved Hungarian algorithm to calculate the augmenting path starting from the second position u1 of the substation, and obtain the return values: logical value flag, the second position u1 of the substation, the marking list vis, and the matching list cx;

[0130] If the two conditions are not both satisfied, go to Step 4.10; otherwise, repeat Step 4.9;

[0131] Among them, the original Hungarian algorithm can only calculate the maximum matching of a bipartite graph, that is, the point set is divided into two groups in advance, while the odd-degree points are distributed in a non-bipartite graph. The improved Hungarian algorithm of the present invention is applicable to the maximum matching of a non-bipartite graph, that is, the point set is not grouped. The matching results of both are related to the point set order, and the algorithm obtains the maximum number of matching results. What the present invention needs is a perfect matching result, that is, the pairwise matching results of an even number of odd-degree point sets. The present invention sets different thresholds T to try to select edges with smaller weights for matching, and ensures that the obtained result is a perfect matching through step 4.11, and finally obtains a perfect matching result with relatively small weights through iterative update. Specifically, in steps 4.8 to 4.9 of the present invention, an improved Hungarian algorithm is used to calculate the augmenting path starting from the second position u1 of the substation:

[0132] Input parameters: the first position u, the marked list vis, the matching list cx, and the incidence matrix M;

[0133] Set flag = 0, flag is a logical value, and let the second position u1 of the substation be u;

[0134] Traverse all substations with odd degrees. When traversing to substation v, if M[u, v] = 1 and vis[v] = 0, it indicates that there is a connection between substation u and substation v, and v has not been visited. Then set flag = 1, vis[v] = 1, update v as visited, and transfer to the next step to determine whether cx[v] is 0; if M[u, v] = 1 and vis[v] = 0 do not hold simultaneously, then continue to judge the next substation v + 1;

[0135] Judge whether the v-th element cx[v] in the matching list is 0. If it is 0, it means that the v-th substation has not been matched. Let cx[u] = v, cx[v] = u, match substation u and substation v, and the function process ends, returning flag, u1, vis, cx; if it is not 0, it means that the v-th substation has been matched. Let u1 = cx[v], cx[v] = 0, disconnect the matching between substation v and the previous substation u1, and rematch it. Let cx[u] = v, cx[v] = u, match the first position u of the substation and substation v, and the function process ends, obtaining the return values of flag, u1, vis, cx;

[0136] If the above conditions are not met after traversing all substations with odd degrees, then output flag, u1, vis, cx as the return values.

[0137] Step 4.10, if count is greater than n_odd 3 , then stop the loop, transfer to step 4.11, otherwise transfer to step 4.6;

[0138] Step 4.11: Determine whether there is a 0 in the matching list cx. If there is no 0, it indicates that cx is a perfect matching of odd-degree points. Calculate the sum of weights s of the perfect matching according to the weight matrix D. Further, determine whether the sum of weights s of the perfect matching is less than s_min. If the sum of weights s of the perfect matching is less than s_min, update the values of s_min and cxx with the sum of weights s of the perfect matching and the matching list cx respectively;

[0139] If there is a 0, it indicates that there are still unmatched odd-degree point substations. At this time, s_min and cxx remain unchanged;

[0140] Step 4.12: Update the threshold T to T + 0.1 and go to Step 4.3 until the value of the threshold T is greater than or equal to the maximum value of the elements of the weight matrix D to end the loop;

[0141] Step 4.13: Obtain the final s_min and the perfect matching result cxx.

[0142] Step 5: Calculate the relatively optimal paths of the first type of lines and the relatively optimal paths of the second type of lines according to the weighted connected graph, the minimum weight matrix between substations, and the perfect matching result, and obtain the final path planning result.

[0143] Among them, Step 5 specifically includes:

[0144] Step 5.1: Construct a new weighted connected graph G* according to the weighted connected graph G, the minimum weight matrix D between substations, and the perfect matching result of odd-degree point substations:

[0145] For even-degree point substations, add the perfect matching result obtained in Step 4.2.4 to the graph G to construct a new weighted connected graph G*. The new weighted connected graph G* is an Euler graph. Use the Fleury algorithm to calculate the relatively optimal paths of all the first type of lines; all the points of the obtained new weighted connected graph G* are even-degree points, so the graph G* is an Euler graph. According to the properties of Euler graphs, when choosing the next line each time, give priority to choosing non-"bridge" lines. If there are no non-"bridge" lines to choose, then choose "bridge" lines (if a certain line is a "bridge", after deleting this line, the graph will no longer be connected). In this way, a non-repeating path that traverses all lines can always be found. Based on the above idea, when using the function to judge in the present invention, taking the lines in list_1 as parameters, there will always be a certain line such that the function return value is True, so that the entire algorithm process can proceed stably to reach Step 5.1.7. The present invention considers deleting the line between two nodes when going from the current node to the next node, forming a new graph with the remaining points and lines and calculating the distances between all points. If there are two points in the new graph that cannot be reached, then the line between i and point j is a "bridge" of the original graph, otherwise this line is not a "bridge".

[0146] Specifically, step 5.1 specifically includes:

[0147] Step 5.1.1: Add the perfect matching result obtained in step 4 as duplicate edges to graph G to construct a new weighted connected graph G*. The new weighted connected graph G* is an Eulerian graph, and each vertex is an even-degree vertex. The data format of the new weighted connected graph G* is: {line name: starting substation, ending substation, weight value};

[0148] Step 5.1.2: Set the Euler path p with an initial value of an empty list, and set the line list line_list with an initial value of all the lines in G*;

[0149] Step 5.1.3: Starting from any vertex, add it to p;

[0150] Step 5.1.4: Determine whether there is a line in line_list. If there is, go to step 5.1.5; if not, go to step 5.1.7;

[0151] Step 5.1.5: Denote the last value of p as the substation state, obtain the lines connected to state, and store them in the list list_1;

[0152] Step 5.1.6: Traverse the lines in list_1. Using the line i in it and G* as parameters, make a judgment with a judgment function. If the judgment function returns True, add the line i to p, add the substation at the other end of i to p, delete the line i from both line_list and G* respectively, and return to step 5.1.4; if the judgment function returns False, continue to judge the line i + 1 in the same way;

[0153] The judgment function in step 5.1.6 has the following judgment logic:

[0154] (1) Input the parameters i and G*;

[0155] (2) Temporarily delete i from G* to obtain a new G**;

[0156] (3) Calculate the distance matrix d** between each substation vertex in G**. d**[i, j] represents the distance between substation i and substation j, that is, the weight value of the line between substation i and substation j. If there are multiple lines between substation i and substation j, then d**[i, j] takes the shortest line weight value. If there is no line between substation i and substation j, then d**[i, j] is infinity. The weight value between substation i and substation i is 0, that is, d**[i, i] is 0;

[0157] (4) According to the calculation steps of the Floyd algorithm in steps 4.2.3.2 to 4.2.3.6, calculate the minimum weight matrix D** of each substation point in G**;

[0158] (5) Determine whether there is an infinite value in D**. If it exists, it indicates that G** is no longer connected at this time, and line i is a bridge of G*. The function returns False; if it does not exist, it indicates that G** is still connected at this time, and line i may or may not be a bridge of G*. The function returns True.

[0159] Step 5.1.7, obtain the final Euler path p.

[0160] Among them, obtaining the Euler path p specifically includes:

[0161] The format of the Euler path p is: point - line - point... line - point, where the starting point and the ending point are the same. If point m and point n are the "duplicate edges" calculated in step 4, it indicates that there may be no line between point m and point n. At this time, judge according to the path matrix P obtained in step 3.6. If P[m, n] = k and P[m, k] = 0, it indicates that the path from point m to point n is m - k - n.

[0162] Step 5.2, simplify the second - type lines into a loop. During the inspection, go from the starting point of the line to the ending point and then return along the original route. The inspection mileage is 2 times the length of the line;

[0163] Step 5.3, combine the path planning results of the first - type and second - type lines to obtain the final path planning result.

[0164] Further preferably, the background YOLO algorithm real - time recognizes the external damage hidden dangers in the inspection video and gives an alarm.

[0165] Use the existing YOLO online recognition algorithm to recognize the video images transmitted in real - time by the inspection robot during the inspection process, frame out the engineering identification objects such as engineering vehicles, construction site greenhouses, and dust - proof nets, and let the staff judge whether a work order needs to be issued for processing.

[0166] In order to verify the actual effect of the method proposed in the present invention, a region with 65 substations, 221 transmission lines of 35 kV and above in a certain city is selected as the area to be inspected. The length of general lines is between 1 and 15 km, and very few lines exceed 20 km. The total length of the lines is 1888 km.

[0167] After calculation, the maximum value of the elements in the weight matrix D is 56.8 km, the number of substation nodes with odd degrees is 28, and the total mileage of the final path planning is 2945 km, exceeding the actual mileage by 56%. However, most of the repeated distances are consumed on the second type of lines. The second type of lines is as long as 962 km, and 1924 km is consumed for round-trip inspection. This part of the mileage is inevitable, while the repeated mileage of the first type of lines is only 95 km, indicating the rationality and superiority of the path planning algorithm. If a single inspection robot cannot complete the task within the specified time, the lines in a certain city can be segmented by area, and multiple inspection robots can be used. Each area adopts the path planning method of the present invention.

[0168] As Figure 3 shown, the present invention also proposes a power inspection system for implementing the above power inspection method. The power inspection system includes: an information acquisition module, a weighted connected graph construction module, a minimum weight matrix calculation module, a perfect matching result calculation module, and a path planning module;

[0169] The information acquisition module is used to acquire the coordinate data of line poles and towers to obtain the line information of each line;

[0170] The weighted connected graph construction module is used to divide the lines into the first type of lines and the second type of lines according to the line information to obtain a weighted connected graph;

[0171] The minimum weight matrix calculation module is used to calculate the minimum weight matrix between each substation according to the coordinates of each substation in the line;

[0172] The perfect matching result calculation module is used to calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix;

[0173] The path planning module is used to calculate the relatively optimal path of the first type of lines and the relatively optimal path of the second type of lines according to the weighted connected graph, the minimum weight matrix between substations, and the perfect matching result to obtain the final path planning result.

[0174] The beneficial effect of the present invention is that, compared with the prior art, the present invention simplifies the complex power grid line distribution into an undirected connected graph, only considers taking the starting point or the ending point of the line as a node, takes the line between nodes as the necessary route, and only counts the substation as a node. If one end of the power line is an underground cable, the underground end is not counted as a node. This not only makes the method more suitable for power inspection in areas with more overhead lines and fewer underground cables, but also can reduce the complexity of the algorithm and the time cost.

[0175] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.

[0176] A computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0177] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. The network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0178] Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A power inspection method, characterized in that: include: Step 1, obtaining line tower coordinate data to obtain line information of each line; Step 2: divide the lines into first-category lines and second-category lines according to the line information, and obtain a weighted connected graph; The step 2 specifically includes: Step 2.1, assuming that the total number of lines is n, the starting point of the line is the substation, and the end point is the substation or the cable is buried underground. Lines with both ends being substations are classified as first-class lines, and lines with one end being a substation and the other end being a cable buried underground are classified as second-class lines; Step 2.2, count the number of lines in each substation that start or end at the substation. If the number of lines is an odd number, the substation is defined as an odd-degree point; if the number of lines is an even number, the substation is defined as an even-degree point; Determine whether the number of substations defined as odd-degree points in the area to be inspected is an odd number. If it is an odd number, take the shortest line associated with each odd-degree point substation and classify it as a second-category line; Step 2.3, for the first type of line, the substation associated with the first type of line is used as a point, the first type of line is used as an edge, and the length of each line is used as a weight to construct a weighted connected graph G. The obtained weighted connected graph G is: {line name: starting substation, end substation, weight}; Step 3, calculating the minimum weight matrix between each substation according to the coordinates of each substation in the line; Step 4, obtaining a perfect matching result with the minimum weight according to the line division result and the minimum weight matrix calculation; Step 5: Calculate the relative optimal path of the first type of line and the relative optimal path of the second type of line according to the weighted connectivity graph, the minimum weight matrix between substations and the perfect matching result to obtain the final path planning result.

2. A power inspection method according to claim 1, characterized in that: The route information includes: route length, starting point location and end point location.

3. The power inspection method according to claim 1, characterized in that: The step 3 specifically includes: Step 3.1, calculate the distance matrix dn*n between each substation, where n is the total number of substations. The element d[i,j] in the i-th row and j-th column of the distance matrix dn*n represents the distance between substation i and substation j. The value of d[i,j] is equal to the line weight between substation i and substation j. If there are multiple lines between substation i and substation j, d[i,j] takes the shortest line weight. If there is no line between substation i and substation j, d[i,j] is infinite. If the weight between substation i and substation i is 0, d[i,i] is 0. Step 3.2, set the minimum weight matrix Dn*n, the initial value of each element is infinite, D[i,j] represents the distance weight between substation i and substation j; Step 3.3, set the path matrix Pn*n, the initial value of each element in the path matrix Pn*n is 0, the element P[i,j] in the i-th row and j-th column of the path matrix Pn*n represents the maximum number of the intermediate nodes on the shortest path from substation i to substation j. If P[i,j] is 0, it means that there is no intermediate node between substations i and j; Step 3.4, assign initial values to each element in the minimum weight matrix Dn*n: D[i,j]=d[i,j], where D[i,j] represents the element in the i-th row and j-th column of the minimum weight matrix Dn*n; Step 3.5, let k = 1, traverse all i and j, if D[i,k]+D[k,j]<D[i,j], then: D[i,j]=D[i,k]+D[k,j], P[i,j]=k; Step 3.6, stop traversing when k = n, obtain the final weight matrix D and path matrix P, otherwise let k = k + 1, and return to Step 3.5 to continue traversing.

4. A power inspection method according to claim 1, characterized in that: The specific steps of Step 4 include: Step 4.1, define the substation as an odd-degree point or an even-degree point according to Step 2.2, and judge whether the number of substations defined as odd-degree points in the area to be inspected is odd. If it is odd, after removing this line, classify this odd-degree point substation as an even-degree point substation; if it is even, go to Step 4.2; Step 4.2, if the number of odd-degree points is even, denote the number of odd-degree points as n_odd, set a threshold T, the initial value of the threshold T can be set as the length of a single line, set the minimum weight sum s_min of the perfect match, the initial value can be set to be greater than the total length of the lines in the area to be inspected, set a perfect match list cxx, the initial value is an empty list with a length of n_odd; Step 4.3, judge whether the threshold T is less than the maximum value of the elements in the weight matrix D. If so, go to Step 4.4, otherwise go to Step 4.13; Step 4.4, set an incidence matrix Mn_odd*n_odd, the initial value of each element in the incidence matrix is 0, traverse all odd-degree point substations, according to the weight matrix D, if the distance between substation i and substation j is less than T, then the element M[i,j] in the i-th row and j-th column of the incidence matrix is 1, and there is no association between the same substation, then M[i,i]=0; Step 4.5, set a match list cx, the initial value is an empty list with a length of n_odd, when the i-th element cx[i]=j in the match list, it means that the i-th substation is matched with the j-th substation, set a loop variable count, the initial value is 1; Step 4.6, judge whether there is a 0 in the match list cx. If so, search for the position where 0 first appears in the match list cx and record it as the first position u, update the value of the loop variable count to count + 1, and go to Step 4.7; if not, go to Step 4.11; Step 4.7, set a marker list vis, the initial value is an empty list with a length of n_odd; Step 4.8, according to the first position u, the marker list vis, the match list cx, and the incidence matrix M, use the improved Hungarian algorithm to calculate the augmenting path starting from substation u, and obtain the return values: logical value flag, the second position u1 of the substation, the marker list vis, and the match list cx; Step 4.9, judging condition: cx[u1]=0 and flag=1. If both conditions are met at the same time, the second position u1 of the substation, the tag list vis, the matching list cx, and the association matrix M are used as parameters, and the improved Hungarian algorithm is used to calculate the augmented path starting from the second position u1 of the substation, and the return value is: logical value flag, the second position u1 of the substation, the tag list vis, and the matching list cx; If the two conditions are not met at the same time, go to step 4.10, otherwise repeat step 4.9; Step 4.10, if count is greater than n_odd3, stop the loop and go to step 4.11, otherwise go to step 4.6; Step 4.11, determine whether there is 0 in the matching list cx. If there is no 0, it indicates that cx is a perfect match of an odd-degree point. The sum of the weights s of the perfect match is calculated according to the weight matrix D. It is further determined whether the sum of the weights s of the perfect match is less than s_min. If the sum of the weights s of the perfect match is less than s_min, the sum of the weights s of the perfect match and the matching list cx are used to update the values ​​of s_min and cxx respectively. If there is 0, it means there are unmatched odd-degree substations, and s_min and cxx remain unchanged; Step 4.12, update the threshold T to T+0.1 and go to step 4.3, until the value of the threshold T is greater than or equal to the maximum value of the elements of the weight matrix D, and the loop ends; Step 4.13, get the final s_min and perfect matching result cxx.

5. A power inspection method according to claim 4, characterized in that: In the steps 4.8 to 4.9, the improved Hungarian algorithm is used to calculate the augmented path starting from the second position u1 of the substation, which specifically includes: Input parameters: first position u, tag list vis, matching list cx, association matrix M; Set flag = 0, flag is a logical value, and let the second position of the substation u1 = u; Traverse all odd-degree substations. When traversing to substation v, if M[u,v]=1 and vis[v]=0, it means that substation u and substation v are connected, and v has not been visited. Then set flag=1, vis[v]=1, update v to be visited, and go to the next step to determine whether cx[v] is 0; if M[u, v] = 1 and vis[v] = 0 are not true at the same time, continue to determine the next substation v+1; Determine whether the vth element cx[v] in the matching list is 0. If it is 0, it indicates that the vth substation has not been matched. Let cx[u]=v, cx[v]=u, match substation u with substation v, the function process ends, and returns flag, u1, vis, and cx; if it is not 0, it indicates that the vth substation has been matched. Let u1=cx[v], cx[v]=0, disconnect the match between substation v and the previous substation u1, and rematch it. Let cx[u] = v, cx[v]=u, match the first position u of the substation with substation v, the function process ends, and obtains the return value of flag, u1, vis, and cx; If all the odd-degree substations are traversed and none of the above conditions are met, flag, u1, vis, and cx are output as return values.

6. A power inspection method according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1, construct a new weighted connectivity graph G* based on the weighted connectivity graph G, the minimum weight matrix D between substations, and the perfect matching result of the odd-degree substations: For even-degree substations, add the perfect matching result obtained in step 4.2.4 to graph G to construct a new weighted connected graph G*, which is an Euler graph. The Fleury algorithm is used to calculate the relative optimal path that traverses all first-class lines. Step 5.2: For the second type of line, simplify it into a loop. During inspection, go from the starting point of the line to the end point and then return along the original route. The inspection mileage is twice the length of the line. Step 5.3, combining the path planning results of the first and second types of routes to obtain the final path planning result.

7. A power inspection method according to claim 6, characterized in that: The step 5.1 specifically includes: Step 5.1.1, add the perfect matching result obtained in step 4 as a repeated edge to the graph G, and construct a new weighted connected graph G*. The new weighted connected graph G* is an Euler graph, and each point is an even-degree point. The data format of the new weighted connected graph G* is: {line name: starting substation, end substation, weight}; Step 5.1.2, set the Euler path p, the initial value is an empty list, set the line list line_list, the initial value is all the lines in G*; Step 5.1.3, take any point as the starting point and add it to p; Step 5.1.4, determine whether there is a line in line_list, if yes, go to step 5.1.5, if not, go to step 5.1.7; Step 5.1.5, record the last value of p as the substation state, obtain the line connected to state, and store it in list list_1; Step 5.1.6, traverse the lines in list_1, use line i and G* as parameters, and use the judgment function to make a judgment. If the judgment function returns True, add line i to p, add the substation at the other end of i to p, delete line i from line_list and G* respectively, and return to step 5.1.4; if the judgment function returns False, use the same method to continue to judge line i+1; Step 5.1.7, get the final Euler path p.

8. A power inspection method according to claim 7, characterized in that: The judgment function of step 5.1.6 has the following judgment logic: (1) Input parameters i and G*; (2) Temporarily delete i from G* to obtain a new G**; (3) Calculate the distance matrix d** between each substation in G**, where d**[i, j] represents the distance between substation i and substation j, that is, the line weight between substation i and substation j. If there are multiple lines between substation i and substation j, d**[i, j] takes the shortest line weight. If there is no line between substation i and substation j, d**[i, j] is infinite, and the weight between substation i and substation i is 0, that is, d**[i, i] is 0; (4) According to the calculation steps of the Floyd algorithm in steps 4.2.3.2 to 4.2.3.6, calculate the minimum weight matrix D** of each substation point in G**; (5) Determine whether there is an infinite value in D**. If so, it means that G** is no longer connected at this time, and line i is a bridge of G*, and the function returns False; if not, it means that G** is still connected at this time, and line i may or may not be a bridge of G*, and the function returns True.

9. A power inspection method according to claim 8, characterized in that: The Euler path p of step 5.1.7 specifically includes: The format of the Euler path p is: point-line-point...line-point, where the starting point and the end point are the same. If point m and point n are "duplicate edges" calculated in step 4, it indicates that there may be no line between point m and point n. At this time, according to the path matrix P obtained in step 3.6, if P[m, n] = k and P[m, k] = 0, it indicates that the path from point m to point n is mkn.

10. A power inspection system, used to implement the power inspection method according to any one of claims 1 to 9, characterized in that: include: Information acquisition module, weighted connectivity graph construction module, minimum weight matrix calculation module, perfect matching result calculation module and path planning module; The information acquisition module is used to obtain the line tower coordinate data and obtain the line information of each line; The weighted connectivity graph construction module is used to divide the lines into first-category lines and second-category lines according to the line information to obtain a weighted connectivity graph; The minimum weight matrix calculation module is used to calculate the minimum weight matrix between each substation according to the coordinates of each substation in the line; The perfect matching result calculation module is used to calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix; The path planning module is used to calculate the relative optimal path of the first type of line and the relative optimal path of the second type of line according to the weighted connectivity graph, the minimum weight matrix between substations and the perfect matching result to obtain the final path planning result.

11. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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