Electric power inspection method and system

Abstract

The invention discloses an electric power inspection method and system, and the method comprises the steps: 1, obtaining the coordinate data of a line tower, and obtaining the line information of each line; 2, dividing lines into a first type of lines and a second type of lines according to the line information to obtain a weighted connected graph; 3, calculating a minimum weight matrix among the substations according to the coordinates of the substations in the line; 4, calculating according to the line division result and the minimum weight matrix to obtain a perfect matching result with the minimum weight; and 5, calculating a relative optimal path of the first type of lines and a relative optimal path of the second type of lines according to the weighted connected graph, the minimum weight matrix between the transformer substations and the perfect matching result, and obtaining a final path planning result. According to the method, the Floyd algorithm is introduced to judge whether the connected graph is not connected or not, a relatively short inspection path can be obtained when the inspection object is searched based on the graph theory network algorithm, and the method is more suitable for an inspection area with more overhead lines and fewer underground cables.

Description

Technical Field

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

[0002] For transmission lines of 35kV and above, preventing external damage is the focus of work, which requires regular inspections of line channels. Manual inspections are inefficient and the quality is uncontrollable, and the autonomous inspections by drones that are often mentioned today are limited by their endurance. The endurance of the inspection robot in the present invention is much higher than that of drones, because drones consume work done by gravity and resistance, while the inspection robot consumes work done by rolling friction, which is much less than that of drones. The use of inspection robots and reasonable and comprehensive path planning can increase the number of inspections and measure the distance to the tree line at the same time. It is especially suitable for central and western China where there are many overhead lines and fewer underground cables.

[0003] In the prior art, there are many methods for path planning for power line inspection. The technical problems existing in the existing path planning methods mainly include: 1. When solving the problem, it is necessary to traverse the paths of all target points. Since there is a path between any two nodes in the power line, traversing the paths of all target points requires a lot of cost, resulting in high complexity of the solution algorithm; 2. In the existing path planning methods, each device in the line is usually used as a node for path planning, which not only increases the planning cost, but also is not suitable for path planning of power inspection in areas with more overhead lines and fewer underground cables; 3. In the prior art, known algorithms are usually used for solving, such as the minimum weight complete matching algorithm, which is only applicable to the case with fewer odd degree points and cannot be used for the case with more odd degree points; 4. When performing path planning, the existing technology usually adopts the method of randomly selecting one of the nearest routes as the next patrol object for planning, which makes the result more random, is not conducive to obtaining a shorter training route, and thus increases the patrol cost. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a power inspection method that can overcome the technical problems in the prior art such as high algorithm complexity, high planning cost and high inspection cost, and the unsuitability for power inspection line planning in areas with more overhead lines and fewer underground cables.

[0005] The present invention adopts the following technical solution.

[0006] A power inspection method, comprising: 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; 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.

[0007] Preferably, the route information includes: route length, starting point location and end point location.

[0008] Preferably, 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 the point, the first type of line is used as the edge, and the length of each line is used as the weight to construct a weighted connected graph G. The obtained weighted connected graph G is: {line name: starting substation, end substation, weight}.

[0009] Preferably, the step 3 specifically includes: Step 3.1, calculate the distance matrix d between each substation n*n , n is the total number of substations, the distance matrix d n*n The 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 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 D n*n, the initial value of each element is infinity, and D[i, j] represents the distance weight between substation i and substation j; 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 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 j; 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 in; 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, 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.

[0010] Preferably, 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, and the length is n_odd; Step 4.3, judge 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; 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 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. If there is no association between the same substation, then M[i, i]=0; Step 4.5, set the matching list cx, the initial value is an empty list, the length is n_odd, when the i element cx[i]=j in the matching list, it means that the i-th substation matches the j-th substation, set the loop variable count, the initial value is 1; Step 4.6, determine whether there is 0 in the matching list cx. If so, search for the first position of 0 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; Step 4.7, set the marker list vis, the initial value is an empty list, and the length is n_odd; Step 4.8, according to the first position u, the tag list vis, the matching list cx, and the association matrix M, the improved Hungarian algorithm is used to calculate the augmented path starting from the substation u, and the return value is obtained: the logical value flag, the second position u1 of the substation, the tag list vis, and the matching 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_odd 3 , then 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.

[0011] Preferably, in step 4.8 to step 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.

[0012] Preferably, 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.

[0013] Preferably, 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.

[0014] Preferably, 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.

[0015] Preferably, the Euler path p in 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 the "repeated edges" calculated in step 4, it means 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 means that the path from point m to point n is mkn.

[0016] The present invention also proposes an electric power inspection system for implementing the electric power inspection method, comprising: an information acquisition module, a weighted connectivity graph construction module, a minimum weight matrix calculation module, a perfect matching result calculation module and a 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.

[0017] The present invention also provides a terminal, comprising a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps of the power inspection method.

[0018] The present invention also proposes a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the power inspection method are implemented.

[0019] The beneficial effect of the present invention is that, compared with the prior art, the present invention simplifies the complex distribution of power grid lines into an undirected connected graph, only considers the starting point or end point of the line as a node, and considers the line between the nodes as a 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 suitable for power inspection in areas with more overhead lines and fewer underground cables, but also reduces the complexity of the algorithm and reduces the time cost. The present invention improves the Hungarian algorithm to solve the minimum weight of the non-bipartite graph completely matching, and is applicable to the case of any number of singular degree points; 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 the line is a bridge of the undirected connected graph. When determining the bridge of the undirected connected graph, the present invention introduces the Floyd algorithm for determination. If the obtained distance matrix has an infinite value, it indicates that the current graph is not connected. There is no related disclosure of this method in the prior art. The present invention can theoretically obtain a relatively short inspection path when searching for inspection objects based on a graph theory network algorithm; the present invention adopts an inspection robot for inspection, and the inspection quality is higher than that of manual inspection, and the endurance of the inspection robot is much higher than that of a drone, because the drone 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 drone; The present invention makes innovations in the path planning algorithm and theoretically obtains the relatively shortest inspection path: the complex distribution of power grid lines is simplified into an undirected connected graph. In order to reduce the time complexity of the algorithm, only the starting point or end point of the line is considered as a node, and the line in between is the necessary route. The improved Hungarian algorithm solves the minimum weight complete match of the non-bipartite graph and can be applied to the case of any number of odd-degree points. Some steps of the Fleury algorithm are improved to adapt to the path planning model of power inspection. The path planning result obtained by the present invention can traverse all the line channels between the towers and the towers, thereby achieving the relatively shortest inspection path at the theoretical level. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the power inspection method of the present invention; Figure 2 This is a schematic diagram of the power inspection of the inspection robot in the present invention; Figure 3 It is a structural diagram of the power inspection system in the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0022] like Figure 1 As shown, the present invention proposes a power inspection method, which includes the following steps: Step 1, obtaining line tower coordinate data to obtain line information of each line; The route information includes: route length, starting point position and end point position. The information planning of each route of the present invention obtains the relatively optimal path for the inspection robot to traverse each route.

[0023] Further preferably, Figure 2 As shown, this application uses an electric power inspection robot for inspection, specifically including: Install guide rails between two towers; Among them, the location for installing the guide line must meet the following requirements: it must be located below the lowest conductor, and the vertical distance from the lowest conductor must meet certain conditions according to the line voltage level. The distance for 35kV line must be greater than 2m, the distance for 110kV line must be greater than 4m, the distance for 220kV line must be greater than 4.5m, and the distance for 500kV line must be greater than 7m.

[0024] Design power inspection robots and robot charging stations for inspecting power poles and towers: Specifically, the power inspection robot consists of the following parts: wheels, batteries, communication devices, PTZ cameras, millimeter-wave radars, and loudspeakers. The wheels are located on top of the robot and can roll along the guide rails; the battery can support the robot to start from the substation and return to the substation, with a range of at least 30km; the communication device is used to transmit the video images back to the background in real time; the PTZ camera is a wide-angle camera that can be zoomed remotely and is used to shoot channel conditions; the millimeter-wave radar is used to measure the distance to the trees below and to avoid obstacles during inspection; the loudspeaker is used to communicate with construction workers below the line who may cause damage to the line.

[0025] The robot charging station is located in the substation, and the inspection robot can enter the charging station and charge itself.

[0026] Step 2: divide the lines into first-category lines and second-category lines according to the line information, and obtain a weighted connected graph; Specifically, the present invention is applicable to lines starting from a substation and ending at a substation or where the cables are buried underground, such as open areas with more overhead lines and fewer underground cables, and the lines are classified according to the end points of the lines. 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 the point, the first type of line is used as the edge, and the length of each line is used as the weight to construct a weighted connected graph G. The obtained weighted connected graph G is: {line name: starting substation, end substation, weight}.

[0027] Step 3, calculating the minimum weight matrix between each substation according to the coordinates of each substation in the line; Step 3 specifically includes: Step 3.1, calculate the distance matrix d between each substation n*n , n is the total number of substations, the distance matrix d n*n The 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 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 D n*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 P n*n , the path matrix P n*n The initial value of each element in is 0, and the path matrix P n*n The element P[i,j] in the i-th row and j-th column 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 D n*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 D n*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, 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.

[0028] Step 4, calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix; Step 4 specifically includes: Step 4.1, define 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; 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 so that the sum of the weights between two odd-degree points is the smallest, and this perfect matching result is the "repeated edge" of graph G; 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 to the length of a single line, such as 8 km; Set the minimum sum of weights s_min of the perfect matching, with an initial value of 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 a perfect matching list cxx, with an initial value of an empty list and a length of n_odd; 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; 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 M n_odd*n_odd , and 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. If there is no association between the same substations, then M[i, i] = 0; Step 4.5, set the matching list cx, the initial value is an empty list, the length is n_odd, when the i element cx[i]=j in the matching list, it means that the i-th substation matches the j-th substation, set the loop variable count, the initial value is 1; Step 4.6, determine whether there is 0 in the matching list cx. If so, search for the first position of 0 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; Step 4.7, set the marker list vis, the initial value is an empty list, and the length is n_odd; Step 4.8, according to the first position u, the tag list vis, the matching list cx, and the association matrix M, the improved Hungarian algorithm is used to calculate the augmented path starting from the substation u, and the return value is obtained: the logical value flag, the second position u1 of the substation, the tag list vis, and the matching 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; Among them, the original Hungarian algorithm can only calculate the maximum matching of bipartite graphs, that is, the point set is divided into two groups in advance, and the odd-degree point distribution is non-bipartite. The improved Hungarian algorithm of the present invention is suitable for the maximum matching of non-bipartite graphs, that is, there is no grouping of point sets. The matching results of both are related to the order of the point sets, and the algorithm obtains the maximum number of matching results. What the present invention needs is a perfect matching result, that is, an even number of odd-degree point sets are matched pairwise. The present invention sets different thresholds T to try to select edges with small weights for matching. Step 4.11 ensures that a perfect match is obtained, and iterative updates finally obtain a perfect matching result with a relatively small weight. Specifically, the improved Hungarian algorithm is used in steps 4.8 to 4.9 of the present invention to calculate the augmented path starting from the second position u1 of the substation: 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.

[0029] 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; 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.

[0030] 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.

[0031] Wherein, 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, the perfect matching result obtained in step 4.2.4 is added to graph G to construct a new weighted connected graph G*. The new weighted connected graph G* is an Euler graph. The Fleury algorithm is used to calculate the relative optimal path that traverses all first-class lines. All points in the new weighted connected graph G* are even-degree points, so graph G* is an Euler graph. According to the properties of the Euler graph, each time the next line is selected, non-"bridge" lines are given priority. If there are no non-"bridge" lines to choose from, then the "bridge" line is selected (if a line is a "bridge", after deleting the line, the graph will no longer be connected). In this way, a non-repetitive path that traverses all lines can always be found. Based on the above ideas, the present invention uses the lines in list_1 as parameters when using function judgment. There will always be a line that makes the function return value True, so that the entire algorithm flow can proceed stably to step 5.1.7. The present invention considers deleting the line between two nodes when going from the current node to the next node, using the remaining points and lines to form a new graph and calculating the distances between all points. If two points in the new graph cannot be reached, the line between i and point j is the bridge of the original graph, otherwise the line is not a bridge.

[0032] Specifically, step 5.1 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; 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.

[0033] Step 5.1.7, get the final Euler path p.

[0034] Among them, the Euler path p is obtained, which 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 the "repeated edges" calculated in step 4, it means 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 means that the path from point m to point n is mkn.

[0035] 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.

[0036] Further preferably, the background YOLO algorithm identifies the hidden dangers of external damage in the inspection video in real time and issues an alarm.

[0037] The existing YOLO online recognition algorithm is used to identify the video images transmitted back by the inspection robot in real time during the inspection process, and frame the engineering markers such as engineering vehicles, construction site greenhouses, and dust-proof nets. The staff can then decide whether a work order needs to be issued.

[0038] In order to verify the actual effect of the method proposed in the present invention, a city with 65 substations, 221 transmission lines of 35kV and above, generally with a line length of 1-15km, and very few lines exceeding 20km, with a total line length of 1888km, was selected as the inspection area.

[0039] After calculation, the maximum value of the element of the weight matrix D is 56.8km, the number of odd-point substations is 28, and the final total mileage of the path planning is 2945km, which exceeds the actual mileage by 56%. However, most of the repeated distance is consumed on the second-class line. The second-class line is as long as 962km, and the round-trip inspection consumes 1924km. This part of the mileage is inevitable, while the repeated mileage of the first-class line is only 95km, which shows the rationality and superiority of the path planning algorithm. If a single inspection robot cannot complete the task within the specified time, the lines of a city can be divided into areas, and multiple inspection robots can be used. Each area uses the path planning method of the present invention.

[0040] like Figure 3 As shown, the present invention also proposes an electric power inspection system for implementing the above-mentioned electric power inspection method, the electric power inspection system comprising: an information acquisition module, a weighted connectivity graph construction module, a minimum weight matrix calculation module, a perfect matching result calculation module and a 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.

[0041] The beneficial effect of the present invention is that, compared with the prior art, the present invention simplifies the complex distribution of power grid lines into an undirected connected graph, only considers the starting point or end point of the line as a node, and the line between the nodes as a 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 suitable for power inspection in areas with more overhead lines and fewer underground cables, but also reduces the complexity of the algorithm and reduces the time cost.

[0042] 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 carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0043] A computer-readable storage medium may be a tangible device that can hold and store instructions used 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 above. More specific examples (a non-exhaustive list) of computer-readable storage media 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 disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0044] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, 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 can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card 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.

[0045] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent 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 "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a 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., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A power inspection method, characterized in that: Including: Step 1: Obtain the coordinate data of line poles and towers to get the line information of each line. 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. Step 3: Calculate the minimum weight matrix between each substation according to the coordinates of the substations in the line. Step 4: Calculate the perfect matching result with the minimum weight according to the line division result and the minimum weight matrix. 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.

2. A power inspection method according to claim 1, wherein: The line information includes: line length, starting position and ending position.

3. A power inspection method according to claim 1, wherein: The specific steps of Step 2 include: Step 2.1: Assume 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. Step 2.2: Count the number of lines with this substation as the starting point or 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. 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 line associated with each odd-degree point substation and classify it into the second type of lines. 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}.

4. A power inspection method according to claim 1, wherein: The specific steps of Step 3 include: Step 3.1, calculate the distance matrix d between each substation n*n , n is the total number of substations, the distance matrix d n*n The 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 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 D n*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 P n*n , the path matrix P n*n The initial value of each element in is 0, and the path matrix P n*n The element P[i,j] in the i-th row and j-th column 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, for the minimum weight matrix D n*n Assign initial values ​​to each element in: D[i,j]=d[i,j], D[i,j] represents the minimum weight matrix D n*n The element in row i and column j in ; 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 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.

5. A power inspection method according to claim 3, wherein: 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. 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; Step 4.2, if the number of odd degree points is even, record the number of odd degree points as n_odd, set the threshold T, the initial value of which can be set to the length of a single line, set the minimum sum of weights of perfect matching s_min, the initial value of which can be set to be greater than the total length of the lines in the area to be inspected, set the perfect matching list cxx, the initial value of which is an empty list with a length of n_odd; Step 4.3, determine whether the threshold T is less than the maximum value of the element of the weight matrix D, if yes, go to step 4.4, otherwise go to step 4.13; Step 4.4, set the correlation matrix M n_odd*n_odd , the initial value of each element in the association matrix is ​​0, and all odd-degree substations are traversed. 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 association matrix is ​​1. If the same substation is not associated, then M[i,i]=0; Step 4.5, set the matching list cx, the initial value is an empty list, the length is n_odd, when the i element cx[i]=j in the matching list, it means that the i-th substation matches the j-th substation, set the loop variable count, the initial value is 1; Step 4.6, determine whether there is 0 in the matching list cx. If so, search for the first position of 0 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; Step 4.7, set the marker list vis, the initial value is an empty list, and the length is n_odd; Step 4.8, according to the first position u, the tag list vis, the matching list cx, and the association matrix M, the improved Hungarian algorithm is used to calculate the augmented path starting from the substation u, and the return value is obtained: the logical value flag, the second position u1 of the substation, the tag list vis, and the matching 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_odd 3 , then 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.

6. A power inspection method according to claim 5, 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.

7. The 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.

8. A power inspection method according to claim 7, 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.

9. A power inspection method according to claim 8, 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.

10. A power inspection method according to claim 9, 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.

11. A power inspection system, used to implement the power inspection method according to any one of claims 1 to 10, 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.

12. 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 1-10.

13. 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 10 are implemented.

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