Method and system for troubleshooting high-altitude wind power transmission line

Abstract

The invention relates to the technical field of line troubleshooting, and particularly discloses a troubleshooting method and system for a high-altitude wind power transmission line, and the method comprises the steps: obtaining power data containing time information based on a power detection point, and obtaining a power data matrix containing time information; determining a risk value of each point location according to the power data matrix containing the time information, marking an abnormal point location according to the risk value, obtaining a remote sensing image at the abnormal point location, and verifying the risk value of each abnormal point location according to the remote sensing image; and determining a risk area according to the verified risk value, determining a control instruction of the robot according to the risk area, and sending the control instruction to the robot. According to the method, the data obtained at the electric power detection points are counted, the counted data are analyzed to determine the risk values at different positions, the line is analyzed according to the risk values, the control instruction is synchronously determined, the robot inspects important areas firstly, and the inspection process of the robot is greatly optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of line troubleshooting, in particular to a fault troubleshooting method and system for high-altitude wind power transmission lines. BACKGROUND

[0002] The special geographical and climatic conditions (such as low air pressure, strong ultraviolet rays, large diurnal temperature difference, snow and freezing, etc.) in high-altitude areas can accelerate equipment aging and increase the risk of line faults, so it is necessary to combine conventional power system inspection techniques and special coping strategies in high-altitude environments in troubleshooting.

[0003] The existing inspection process mostly adopts manual inspection scheme, and in special environments, the manual inspection process has great risks, in addition, in bad weather, manual inspection is almost impossible, in order to solve this problem, many inspection robots appear in the prior art, but the energy of the inspection robot is limited, how to carry out more effective inspection under the condition of limited energy is the technical problem that the technical scheme of the present application wants to solve. SUMMARY

[0004] The purpose of the present application is to provide a fault troubleshooting method and system for high-altitude wind power transmission lines to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A fault troubleshooting method for high-altitude wind power transmission lines, the method comprising:

[0007] Obtain distribution information of dangerous areas according to historical weather data and topographic parameters, and determine power detection points according to the distribution information of dangerous areas;

[0008] Obtain power data containing time information based on the power detection points, and obtain a power data matrix containing time information;

[0009] Determine the risk value of each point according to the power data matrix containing time information, mark the abnormal point according to the risk value, obtain the remote sensing image at the abnormal point, and verify the risk value of each abnormal point according to the remote sensing image;

[0010] Determine the risk area according to the verified risk value, determine the control instruction of the robot according to the risk area, and send it to the robot.

[0011] As a further scheme of the present application: the step of obtaining distribution information of dangerous areas according to historical weather data and topographic parameters, and determining power detection points according to the distribution information of dangerous areas comprises:

[0012] Receive the inspection area input by the staff, and obtain the area map of the inspection area;

[0013] locating the to-be-inspected area in the area map according to preset area characteristics;

[0014] obtaining historical weather data of the to-be-inspected area, inputting the historical weather data into a preset identification model, and obtaining a weather abnormality degree;

[0015] selecting the to-be-inspected area with the weather abnormality degree reaching a preset abnormality threshold as a dangerous area, and determining a danger degree of the dangerous area according to the area characteristics and the weather abnormality degree;

[0016] determining the arrangement number of the power detection points in each area according to the danger degree, and randomly inserting the arrangement number of power detection points in the line; wherein, the danger degree of a non-to-be-inspected area adopts a preset default value.

[0017] As a further scheme of the application, the step of locating the to-be-inspected area in the area map according to preset area characteristics comprises:

[0018] contour recognition is performed on the area map to obtain area contours;

[0019] for any area contour, a preset number of points are selected in the contour as end points;

[0020] a navigation path from the nearest maintenance station to the end point is generated, and a navigation time length is calculated;

[0021] a characteristic value of all navigation time lengths is calculated, and an area contour with a characteristic value reaching a preset characteristic threshold is selected and marked as a to-be-inspected area.

[0022] As a further scheme of the application, the step of obtaining power data containing time information based on the power detection points to obtain a power data matrix containing time information comprises:

[0023] power data is obtained based on the power detection points, and a time label is inserted when the power data is obtained;

[0024] a three-dimensional detection area is created based on the area map, a grid is inserted in the three-dimensional detection area according to a grid tool, a matrix corresponding to the three-dimensional detection area is generated based on the grid, and the matrix is used as a matrix template; wherein, a cell size of the grid is a preset value, and an open adjustment port is provided;

[0025] for a certain moment, the latest power data of each power detection point at the moment is queried, a row and column position corresponding to the power detection point is queried in the matrix template, the latest power data is inserted into the row and column position, and a power data matrix is obtained.

[0026] As a further scheme of the present application: the step of determining the risk value of each point according to the power data matrix containing time information, marking the abnormal point according to the risk value, obtaining the remote sensing image at the abnormal point, and verifying the risk value of each abnormal point according to the remote sensing image comprises:

[0027] reading the power data matrix within a preset time range;

[0028] sequentially reading the power data matrix, and sequentially selecting points in the power data matrix;

[0029] centering on the point, cutting a sub-data matrix according to a preset size, inputting the sub-data matrix into a preset identification model, and determining an instantaneous risk value;

[0030] statistically obtaining all instantaneous risk values of the same point within the time range, and calculating a final risk value of the point;

[0031] fitting the final risk value of each point according to the final risk values of all points;

[0032] marking the point with a risk value greater than a preset risk value threshold as an abnormal point.

[0033] As a further scheme of the present application: the step of determining the risk area according to the verified risk value, determining the control instruction of the robot according to the risk area, and sending the control instruction to the robot comprises:

[0034] regionally dividing the investigation area according to the verified risk value to obtain a risk area, and synchronously determining a risk value mean;

[0035] determining the order of the risk area according to the risk value mean;

[0036] determining an investigation path based on the order, determining the control instruction of the robot based on the investigation path, and sending the control instruction to the robot;

[0037] wherein, the robot is internally provided with a fault identification module for fault investigation on the line.

[0038] The present application also provides a fault investigation system for a high-altitude wind power transmission line, which comprises:

[0039] a detection point determination module for obtaining distribution information of a dangerous area according to historical weather data and terrain parameters, and determining power detection points according to the distribution information of the dangerous area;

[0040] a space-time simulation module for obtaining power data containing time information based on the power detection points, and obtaining a power data matrix containing time information;

[0041] The risk value calculation module is configured to determine the risk value of each point according to the power data matrix containing time information, mark the abnormal point according to the risk value, obtain a remote sensing image at the abnormal point, and verify the risk value of each abnormal point according to the remote sensing image.

[0042] The control instruction sending module is configured to determine a risk area according to the verified risk value, determine a control instruction of the robot according to the risk area, and send the control instruction to the robot.

[0043] As a further scheme of the present application, the detection point determination module comprises:

[0044] The area map acquisition unit is configured to receive an investigation area input by a staff member and acquire an area map of the investigation area.

[0045] The area to be inspected positioning unit is configured to position an area to be inspected in the area map according to a preset area feature.

[0046] The abnormality degree calculation unit is configured to acquire historical weather data of the area to be inspected, input the historical weather data into a preset identification model, and obtain a weather abnormality degree.

[0047] The danger degree calculation unit is configured to select an area to be inspected whose weather abnormality degree reaches a preset abnormality degree threshold as a dangerous area, and determine a danger degree of the dangerous area according to the area feature and the weather abnormality degree.

[0048] The point insertion unit is configured to determine the arrangement number of power detection points in each area according to the danger degree, and randomly insert the arrangement number of power detection points in the line; wherein the danger degree of a non-inspected area adopts a preset default value.

[0049] As a further scheme of the present application, the space-time simulation module comprises:

[0050] The data acquisition unit is configured to acquire power data based on the power detection points, and insert a time label when acquiring the power data.

[0051] The template generation unit is configured to create a three-dimensional detection area based on the area map, insert a grid in the three-dimensional detection area according to a grid tool, generate a matrix corresponding to the three-dimensional detection area based on the grid, and take the matrix as a matrix template; wherein the cell size of the grid is a preset value, and an open adjustment port is provided.

[0052] The transfer matrix generation unit is configured to query the latest power data of each power detection point at a certain moment, query the row and column positions corresponding to the power detection points in the matrix template, and insert the latest power data into the row and column positions to obtain a power data matrix.

[0053] As a further scheme of the present application, the risk value calculation module comprises:

[0054] a matrix reading unit configured to read the power data matrix within a preset time range;

[0055] a point position selecting unit configured to sequentially read the power data matrix and sequentially select point positions in the power data matrix;

[0056] a risk identifying unit configured to center on the point positions, cut sub-data matrices according to a preset size, input the sub-data matrices into a preset identifying model, and determine instantaneous risk values;

[0057] a risk statistical unit configured to statistically determine all instantaneous risk values of the same point position within the time range and calculate a final risk value of the point position;

[0058] a risk fitting unit configured to fit the final risk value of each point position according to final risk values of all point positions;

[0059] a point position marking unit configured to mark point positions with a fitted risk value greater than a preset risk value threshold as abnormal point positions.

[0060] Compared with the prior art, the present application has the beneficial effects that: the present application sets power detection points according to terrain parameters and weather history data, statistically determines data acquired at the power detection points, analyzes the statistical data to determine risk values at different positions, analyzes a line according to the risk values, synchronously determines control instructions, and makes a robot first patrol important areas, thereby greatly optimizing the robot patrol process. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only some embodiments of the present application.

[0062] Figure 1 It is a flowchart of the fault troubleshooting method for the high-altitude wind power transmission line.

[0063] Figure 2 It is a first sub-flowchart of the fault troubleshooting method for the high-altitude wind power transmission line.

[0064] Figure 3 It is a second sub-flowchart of the fault troubleshooting method for the high-altitude wind power transmission line.

[0065] Figure 4 It is a third sub-flowchart of the fault troubleshooting method for the high-altitude wind power transmission line.

[0066] Figure 5 It is a fourth sub-flowchart of the fault troubleshooting method for the high-altitude wind power transmission line.

[0067] Figure 6 A high-altitude wind power transmission line fault troubleshooting system structure block diagram. DETAILED DESCRIPTION

[0068] In order to make the technical problems to be solved by the present application, technical solutions and beneficial effects clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0069] Figure 1 A flow chart of a high-altitude wind power transmission line fault troubleshooting method, in the embodiment of the present application, a high-altitude wind power transmission line fault troubleshooting method, the method comprises:

[0070] Step S100: obtaining distribution information of dangerous areas according to historical weather data and terrain parameters, and determining power detection points according to the distribution information of the dangerous areas;

[0071] The technical scheme of the present application is applied to the troubleshooting scene of the transmission line, generally a region is set in advance, called the troubleshooting region, and the technical scheme of the present application is only used in this region; the historical weather data and terrain parameters in the troubleshooting region are obtained, the dangerous areas that may exist in the troubleshooting region are determined, called the dangerous areas, the positions of all the dangerous areas are called the distribution information of the dangerous areas, and the power detection points can be determined by analyzing the distribution information of the dangerous areas.

[0072] Step S200: obtaining power data containing time information based on the power detection points, and obtaining a power data matrix containing time information;

[0073] The power detection equipment such as power meters and the like is installed at the power detection points, which can be used to obtain power data, and the obtained power data includes current, voltage, and power in a unit time and the like; the obtained power data is counted, data simulation is performed on positions without the power detection points, and a power data matrix containing power data at each time is obtained; each row and column position in the power data matrix corresponds to a position in the power line; in addition, the data simulation process includes a simulation process in the time domain and a simulation process in the space, which are collectively called time-space simulation.

[0074] As for how long to create a power data matrix, the time interval is set by the staff in advance.

[0075] Step S300: determining risk values of each point according to the power data matrix containing time information, marking abnormal points according to the risk values, obtaining remote sensing images at the abnormal points, and verifying the risk values of each abnormal point according to the remote sensing images;

[0076] After obtaining the power data matrix at each moment, the risk value at each point can be obtained by analyzing each power data matrix. The point with a high enough risk value is selected as an abnormal point. The abnormal point corresponds to the actual position in the power line. The remote sensing image of the actual position corresponding to the abnormal point is obtained. The risk value of each abnormal point is verified according to the remote sensing image, such as whether there is a foreign object or an abnormal phenomenon. In the high-altitude wind power transmission line, some line problems can easily occur due to temperature difference. These line problems are very obvious, such as trees falling on the line. It is worth mentioning that if no abnormality is found in the remote sensing image, the original risk value is retained, and if an abnormality is found, the original risk value is expanded.

[0077] Step S400: determining a risk area according to the verified risk value, determining a control instruction of the robot according to the risk area, and sending the control instruction to the robot;

[0078] Based on the verified risk value, the abnormal points are clustered to obtain multiple small areas in the troubleshooting area, which are called risk areas. Control instructions pointing to each risk area are generated and sent to the robot, which can control the robot to the specific risk area for close-range fault troubleshooting. Under the background of the prior art, the robots used for fault troubleshooting have sufficient capabilities. They are all equipped with multiple data acquisition modules and data recognition modules, which are sufficient to complete fault troubleshooting. The simplest one is only equipped with a data acquisition module and a data transmission module. The data collected by the data transmission module is fed back to the artificial end, and the data is checked by the artificial end. For the technical solution of the present application, only the troubleshooting results fed back by the artificial end need to be received.

[0079] Figure 2 The first sub-flow block diagram of the fault troubleshooting method for the high-altitude wind power transmission line comprises the steps of obtaining the distribution information of the dangerous area according to the historical weather data and the terrain parameters, and determining the power detection points according to the distribution information of the dangerous area.

[0080] Step S101: receiving the troubleshooting area input by the worker, and obtaining the area map of the troubleshooting area;

[0081] Step S102: positioning the to-be-detected area in the area map according to the preset area feature;

[0082] Step S103: obtaining the historical weather data of the to-be-detected area, inputting the historical weather data into a preset recognition model, and obtaining a weather abnormality degree;

[0083] Step S104: selecting the to-be-detected area with a weather abnormality degree reaching a preset abnormality degree threshold as a dangerous area, and determining the danger degree of the dangerous area according to the area feature and the weather abnormality degree;

[0084] Step S105: determining the number of power detection points arranged in each region according to the risk degree, and randomly inserting the number of power detection points in the line.

[0085] In an example of the technical scheme of the present application, the arrangement process of the power detection points is described, the staff input the investigation region, obtain the region map of the investigation region, locate the detection region in the region map according to the preset region feature, the region feature is the image feature of several preset regional types, including dangerous regional types such as valleys and mountains, and the region feature of each regional type is a predetermined feature; after determining the detection region, obtain the historical weather data of the detection region, input the historical weather data into a preset identification model to obtain a weather abnormality degree, select the detection region with a weather abnormality degree reaching a preset abnormality threshold as a dangerous region, and the weather abnormality degree and the region feature can be combined to determine the risk degree of the dangerous region; in general, the reference risk degree of each regional type is determined in advance when determining the region feature, and the reference risk degree and the weather abnormality degree (which is by default in the same dimension) are added after obtaining the weather abnormality degree to obtain the final risk degree; in addition, the weather abnormality degree can be set as a dimensionless coefficient, and the final risk degree is obtained by multiplying the coefficient and the reference risk degree; the number of power detection points in each region is determined according to the risk degree, and the number of power detection points is randomly inserted in the power line; this random insertion process generally sets a limit condition, that is, only one power detection point is arranged on a line.

[0086] Further, the step of locating the detection region in the region map according to the preset region feature comprises:

[0087] contour recognition is performed on the region map to obtain a region contour;

[0088] for any region contour, a preset number of points in the contour are selected as end points;

[0089] a navigation path from the nearest maintenance station to the end point is generated, and a navigation time is calculated;

[0090] a feature value of all navigation times is calculated, a region contour with a feature value reaching a preset feature threshold is selected, and is marked as a detection region.

[0091] Contour recognition is performed on the regional map to obtain a regional contour, for any regional contour, a preset number of point positions are selected in the contour as end points, a navigation path from the nearest maintenance station to the end points is generated, a navigation time length is calculated, a characteristic value of the navigation time length is calculated, the characteristic value of the navigation time length is used to represent the difficulty of passing through a certain region, the difficulty of passing through represents the degree of difficulty of reaching the region, the greater the characteristic value, the greater the difficulty of passing through, and the regional contour with a large enough characteristic value is also marked as a region to be inspected, which is equivalent to marking the region difficult to reach as a region that may exist in danger.

[0092] Figure 3 The second sub-flow block diagram of the fault troubleshooting method for the high-altitude wind power transmission line, the step of obtaining power data containing time information based on the power detection point to obtain a power data matrix containing time information includes:

[0093] Step S201: obtaining power data based on the power detection point, and inserting a time label when obtaining the power data;

[0094] Step S202: creating a three-dimensional detection area based on a regional map, inserting a grid in the three-dimensional detection area according to a grid tool, and generating a matrix corresponding to the three-dimensional detection area based on the grid as a matrix template; wherein the cell size of the grid is a preset value, and an open adjustment port is provided.

[0095] Step S203: querying the latest power data of each power detection point at a certain time, querying the row and column positions corresponding to the power detection point in the matrix template, inserting the latest power data into the row and column positions, and obtaining a power data matrix.

[0096] In one example of the technical scheme of the application, the generation process of the power data matrix is described, the power equipment is installed at the power detection point, the power data is obtained based on the power equipment, the time label is inserted when obtaining the power data, the three-dimensional detection area is created based on the regional map, the grid is inserted in the three-dimensional detection area according to the grid tool, and the matrix corresponding to the three-dimensional detection area is generated based on the grid as the matrix template; the three-dimensional detection area is to add a height on the regional map, and the obtained matrix template is also a three-dimensional matrix, the cell size of the grid is a preset value, and the open adjustment port is provided.

[0097] For a certain time, the latest power data of each power detection point at the time is queried, the row and column positions corresponding to the power detection point in the matrix template are queried, the latest power data is inserted into the row and column positions, and a power data matrix is obtained.

[0098] Figure 4The third sub-flow block diagram of the fault troubleshooting method for the high-altitude wind power transmission line, the step of determining the risk value of each point according to the power data matrix containing time information, marking the abnormal point according to the risk value, obtaining the remote sensing image at the abnormal point, and verifying the risk value of each abnormal point according to the remote sensing image comprises:

[0099] Step S301: reading the power data matrix within a preset time range;

[0100] Step S302: reading the power data matrix in sequence, and selecting points in sequence in the power data matrix;

[0101] Step S303: taking the point as the center, cutting a sub-data matrix according to a preset size, inputting the sub-data matrix into a preset identification model, and determining an instantaneous risk value;

[0102] Step S304: counting all instantaneous risk values of the same point within the time range, and calculating a final risk value of the point;

[0103] Step S305: fitting the final risk value of each point according to the final risk values of all points;

[0104] Step S306: marking the point whose fitted risk value is greater than a preset risk value threshold as an abnormal point.

[0105] All power data matrices containing time labels are read within a preset time range, the power data matrices are read in sequence, points are selected in sequence in the power data matrices, the points are taken as the center, sub-data matrices are cut according to a preset size, the sub-data matrices are input into a preset identification model, instantaneous risk values are determined, the identification principle comprises comparing the data with some threshold values, and also comprises calculating the gradient of the data, comparing the gradient with some threshold values, and the specific implementation is not limited; all instantaneous risk values of the same point within the time range are counted, a final risk value of the point is calculated, the final risk value of each point is fitted according to the final risk values of all points, and the point whose fitted risk value is greater than a preset risk value threshold is marked as an abnormal point.

[0106] The calculation process of the final risk value and the fitting process of the final risk value are described as follows:

[0107] The sum of the instantaneous risk value and one is calculated, the logarithm of the sum is calculated as a coefficient, the product of the coefficient and the instantaneous risk value is calculated, the calculated product is accumulated, and the final risk value is obtained, which is the calculation process of the final risk value.

[0108] For the fitting process, for any point, calculate the difference between its final risk value and the final risk value of the surrounding points, reduce the difference according to the preset ratio, make the current final risk value closer to the surrounding final risk value, and then obtain the fitted final risk value; it is worth mentioning that there are a plurality of point positions around a certain point position, which can obtain a fitting value by fitting with any point position, and the mean value of all fitting values can be used as the final fitting value.

[0109] Figure 5 The fourth sub-flow block diagram of the fault troubleshooting method for the high-altitude wind power transmission line, the step of determining the risk area according to the verified risk value and determining the control instruction of the robot according to the risk area and sending to the robot comprises:

[0110] Step S401: according to the verified risk value, the area of the troubleshooting area is divided into regions, and the risk area is obtained, and the mean value of the risk value is determined synchronously;

[0111] Step S402: determine the order of the risk area according to the mean value of the risk value;

[0112] Step S403: determine the troubleshooting path based on the order, determine the control instruction of the robot based on the troubleshooting path, and send to the robot.

[0113] In one example of the technical scheme of the application, the troubleshooting process is limited, the area of the troubleshooting area is divided into regions according to the verified risk value, the risk area is obtained, and the mean value of the risk value is determined synchronously, the order of the risk area is determined according to the mean value of the risk value, the troubleshooting path is determined based on the order, the control instruction of the robot is determined based on the troubleshooting path, and the robot is sent, wherein the robot is built-in fault recognition module, used for fault troubleshooting of the line.

[0114] Figure 6 The component structure block diagram of the fault troubleshooting system for the high-altitude wind power transmission line, in the embodiment of the application, a fault troubleshooting system for a high-altitude wind power transmission line, the system 10 comprises:

[0115] The detection point determination module 11 is used for obtaining the distribution information of the dangerous area according to the historical weather data and the terrain parameters, and determining the power detection point according to the distribution information of the dangerous area;

[0116] The space-time simulation module 12 is used for obtaining the power data containing time information based on the power detection point, and obtaining the power data matrix containing time information;

[0117] The risk value calculation module 13 is used for determining the risk value of each point according to the power data matrix containing time information, marking the abnormal point according to the risk value, obtaining the remote sensing image at the abnormal point, and verifying the risk value of each abnormal point according to the remote sensing image.

[0118] The control instruction sending module 14 is configured to determine a risk area according to the verified risk value, determine a control instruction of the robot according to the risk area, and send the control instruction to the robot.

[0119] Further, the detection point determination module 11 comprises:

[0120] The area map acquisition unit is configured to receive an investigation area input by a staff member, and acquire an area map of the investigation area.

[0121] The area to be inspected positioning unit is configured to position an area to be inspected in the area map according to a preset area feature.

[0122] The abnormality degree calculation unit is configured to acquire historical weather data of the area to be inspected, input the historical weather data into a preset identification model, and obtain a weather abnormality degree.

[0123] The danger degree calculation unit is configured to select an area to be inspected with a weather abnormality degree reaching a preset abnormality degree threshold as a dangerous area, and determine a danger degree of the dangerous area according to the area feature and the weather abnormality degree.

[0124] The point insertion unit is configured to determine a number of power detection points arranged in each area according to the danger degree, and randomly insert the number of power detection points in the line; wherein the danger degree of a non-inspected area adopts a preset default value.

[0125] Specifically, the space-time simulation module 12 comprises:

[0126] The data acquisition unit is configured to acquire power data based on the power detection points, and insert a time label when acquiring the power data.

[0127] The template generation unit is configured to create a three-dimensional detection area based on the area map, insert a grid in the three-dimensional detection area according to a grid tool, generate a matrix corresponding to the three-dimensional detection area based on the grid, and take the matrix as a matrix template; wherein a cell size of the grid is a preset value, and an open adjustment port is provided.

[0128] The transfer matrix generation unit is configured to query the latest power data of each power detection point at a certain moment, query a row and column position corresponding to the power detection point in the matrix template, and insert the latest power data into the row and column position to obtain a power data matrix.

[0129] Further, the risk value calculation module 13 comprises:

[0130] The matrix reading unit is configured to read the power data matrix within a preset time range.

[0131] The point selection unit is configured to read the power data matrix in sequence, and select points in sequence in the power data matrix.

[0132] a risk identification unit configured to center on a point, intercept a sub-data matrix according to a preset size, input the sub-data matrix into a preset identification model, and determine an instantaneous risk value;

[0133] a risk statistics unit configured to count all instantaneous risk values of the same point within a time range, and calculate a final risk value of the point;

[0134] a risk fitting unit configured to fit the final risk value of each point according to final risk values of all points;

[0135] a point marking unit configured to mark a point with a fitted risk value greater than a preset risk value threshold as an abnormal point.

[0136] The above merely describes preferred embodiments of the present application and is not intended to limit the present application, and any modification, equivalent replacement, and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for troubleshooting a high-altitude wind power transmission line, characterized in that, The method comprises: According to the historical weather data and the terrain parameters, the distribution information of the dangerous area is obtained, and the power detection point is determined according to the distribution information of the dangerous area; Based on the power detection point, the power data containing time information is obtained, and the power data matrix containing time information is obtained; According to the power data matrix containing time information, the risk value of each point is determined, the abnormal point is marked according to the risk value, the remote sensing image at the abnormal point is obtained, and the risk value of each abnormal point is verified according to the remote sensing image; According to the verified risk value, the risk area is determined, the control instruction of the robot is determined according to the risk area, and the robot is sent.

2. The method of claim 1, wherein, The step of obtaining the distribution information of the dangerous area according to the historical weather data and the terrain parameters, and determining the power detection point according to the distribution information of the dangerous area comprises: Receive the staff input of the investigation area, obtain the area map of the investigation area; According to the preset area feature, the to-be-inspected area is located in the area map; Obtain the historical weather data of the to-be-inspected area, input the historical weather data into the preset identification model, and obtain the weather abnormality degree; Select the to-be-inspected area with the weather abnormality degree reaching the preset abnormality threshold as the dangerous area, and determine the danger degree of the dangerous area according to the area feature and the weather abnormality degree; According to the danger degree, the arrangement number of power detection points in each area is determined, and the arrangement number of power detection points is randomly inserted in the line; wherein the danger degree of the non-inspected area adopts a preset default value.

3. The method of claim 2, wherein, The step of locating the to-be-inspected area in the area map according to the preset area feature comprises: Contour recognition is performed on the area map to obtain the area contour; For any area contour, a preset number of points in the contour are selected as the terminal point; A navigation path from the nearest maintenance station to the terminal point is generated, and the navigation time is calculated; Calculate the characteristic value of all navigation time, select the area contour with the characteristic value reaching the preset characteristic threshold, and mark it as the to-be-inspected area.

4. The method of claim 1, wherein, The step of obtaining the power data containing time information based on the power detection point, and obtaining the power data matrix containing time information comprises: Based on the power detection point, the power data is obtained, and the time label is inserted when the power data is obtained; Based on the area map, a three-dimensional detection area is created, a grid is inserted in the three-dimensional detection area according to a grid tool, a matrix corresponding to the three-dimensional detection area is generated based on the grid, and the matrix is used as a matrix template; wherein the cell size of the grid is a preset value, and an open adjustment port is provided; For a certain moment, the latest power data of each power detection point at that moment is queried, the row and column positions corresponding to the power detection point are queried in the matrix template, and the latest power data is inserted into the row and column positions to obtain the power data matrix.

5. The method of claim 1, wherein, The step of determining the risk value of each point according to the power data matrix containing time information, marking the abnormal point according to the risk value, obtaining the remote sensing image at the abnormal point, and verifying the risk value of each abnormal point according to the remote sensing image comprises: Read the power data matrix within a preset time range; Read the power data matrix in turn, and select the point in turn in it; Centered on the point, a sub-data matrix is cut according to a preset size, the sub-data matrix is input into a preset identification model, and the instantaneous risk value is determined. The statistical same point position in the time range of all instantaneous risk values, calculate the final risk value of the point position; According to the final risk value of all point positions, the final risk value of each point position is fitted; The point position with a fitted risk value greater than a preset risk value threshold is marked as an abnormal point position.

6. The method of claim 1, wherein, The step of determining the risk area according to the verified risk value, determining the control instruction of the robot according to the risk area, and sending to the robot includes: According to the verified risk value, the area of the investigation area is divided into regions to obtain the risk area, and the average value of the risk value is determined synchronously; According to the average value of the risk value, the order of the risk area is determined; Based on the order, the investigation path is determined, the control instruction of the robot is determined based on the investigation path, and the robot is sent; Wherein, the robot is built-in fault recognition module, for fault investigation on the line.

7. A fault locating system for a high altitude wind power transmission line, the system comprising: The system comprises: A detection point determination module is configured to obtain distribution information of a dangerous area based on historical weather data and terrain parameters, and determine power detection points based on the distribution information of the dangerous area; A space-time simulation module is configured to obtain power data containing time information based on the power detection points, and obtain a power data matrix containing time information; A risk value calculation module is configured to determine the risk value of each point position based on the power data matrix containing time information, mark abnormal point positions based on the risk value, obtain remote sensing images at the abnormal point positions, and verify the risk value of each abnormal point position based on the remote sensing images; A control instruction sending module is configured to determine a risk area based on the verified risk value, determine a control instruction of a robot based on the risk area, and send the control instruction to the robot.

8. The system for troubleshooting of high altitude wind power transmission lines as claimed in claim 7 wherein, The detection point determination module comprises: A region map acquisition unit is configured to receive an investigation region input by a worker, and acquire a region map of the investigation region; A to-be-inspected region positioning unit is configured to position a to-be-inspected region in the region map based on a preset region feature; An abnormality calculation unit is configured to obtain historical weather data of the to-be-inspected region, input the historical weather data into a preset identification model, and obtain a weather abnormality; A danger calculation unit is configured to select a to-be-inspected region with a weather abnormality reaching a preset abnormality threshold as a dangerous area, and determine a danger degree of the dangerous area based on the region feature and the weather abnormality; A point insertion unit is configured to determine the arrangement number of power detection points in each region based on the danger degree, and randomly insert the arrangement number of power detection points in the line; wherein, the danger degree of a non-to-be-inspected region adopts a preset default value.

9. The system for troubleshooting of high altitude wind power transmission lines as claimed in claim 7 wherein, The space-time simulation module comprises: A data acquisition unit is configured to acquire power data based on the power detection points, and insert a time label when acquiring the power data; A template generation unit is configured to create a three-dimensional detection area based on the region map, insert a grid in the three-dimensional detection area based on a grid tool, generate a matrix corresponding to the three-dimensional detection area based on the grid, and use the matrix as a matrix template; wherein, the cell size of the grid is a preset value, and an open adjustment port is provided; A transfer matrix generation unit is configured to query the latest power data of each power detection point at a certain moment, query the row and column positions corresponding to the power detection points in the matrix template, and insert the latest power data into the row and column positions to obtain a power data matrix.

10. The system for troubleshooting of high altitude wind power transmission lines as claimed in claim 7 wherein, The risk value calculation module comprises: A matrix reading unit is configured to read the power data matrix within a preset time range; A point selection unit is configured to sequentially read the power data matrix and sequentially select points in the power data matrix; A risk identification unit is configured to center on the points, cut a sub-data matrix according to a preset size, input the sub-data matrix into a preset identification model, and determine an instantaneous risk value; A risk statistical unit is configured to statistically determine all instantaneous risk values of the same point within the time range, and calculate a final risk value of the point; A risk fitting unit is configured to fit the final risk value of each point according to the final risk values of all points; A point marking unit is configured to mark the points with the fitted risk values greater than a preset risk value threshold as abnormal points.

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