Power transmission line hazard source distance measurement method and system based on point cloud space information fusion

Abstract

The invention relates to the technical field of hazard source distance measurement, and provides a power transmission line hazard source distance measurement method and system based on point cloud space information fusion, and the method comprises the steps: collecting the point cloud data of a target power transmission line through a laser point cloud device; the method comprises the following steps: acquiring images through monitoring equipment to obtain real-time monitoring images; the back-end server receives the monitoring image, selects a region of interest, and positions an accurate measurement starting region; filtering the point cloud data according to the starting area to obtain spatial point cloud analysis data; registering the point cloud analysis data and the monitoring image to generate a spatial information alignment model; and measuring the distance between a target hazard source and the power transmission line in the model, and outputting hazard distance quantitative data. The technical problem that the dangerous source cannot be accurately positioned due to the lack of accurate distance measurement and real-time dynamic identification of the external environment of the existing power transmission line monitoring equipment is solved, and the effects of carrying out dangerous source identification and distance measurement through the laser point cloud equipment and the image monitoring equipment and reducing the accident rate are realized.

Description

Technical Field

[0001] The present application relates to the technical field of hazard source distance measurement, and specifically to a method and system for hazard source distance measurement of power transmission lines based on point cloud spatial information fusion. Background Art

[0002] With the continuous expansion and complexity of the power transmission network, the safety of transmission lines has become increasingly important. Transmission lines usually pass through complex terrains such as mountains, forests, and rivers. External environmental factors such as tree growth, building construction, and natural disasters may pose a threat to the operation of the line, resulting in line tripping, short circuits, and other faults. These external sources of danger are often difficult to identify and measure in time, affecting the safety and stability of the transmission line. Most of the existing transmission line monitoring equipment uses cameras or sensors installed on transmission towers to collect images and perform simple environmental monitoring. However, traditional monitoring systems usually cannot achieve accurate ranging functions, making it difficult to accurately locate and evaluate external sources of danger, especially when facing dynamic sources of danger, and their response capabilities are weak. Summary of the invention

[0003] This application provides a transmission line hazard source ranging method and system based on point cloud spatial information fusion, aiming to solve the technical problem that existing transmission line monitoring equipment is unable to accurately locate hazard sources and provide timely warnings due to the lack of accurate ranging and real-time dynamic identification of the external environment.

[0004] In view of the above problems, the present application provides a method and system for measuring the distance of transmission line hazards based on point cloud spatial information fusion.

[0005] The first aspect disclosed in the present application provides a transmission line hazard source distance measurement method based on point cloud spatial information fusion, the method comprising: performing real-time point cloud data acquisition on a target transmission line through a laser point cloud device to obtain real-time point cloud data; performing image acquisition on the target transmission line through a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower connected to the target transmission line; a back-end server receives the real-time monitoring image transmitted back by the target monitoring device, and selects an area of ​​interest based on the real-time monitoring image to locate a precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server through a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source; the back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data; the back-end server aligns the spatial point cloud analysis data with the real-time monitoring image to obtain a spatial information alignment model; spatial distance measurement between the target hazard source and the target transmission line is performed in the spatial information alignment model, and quantitative data of the hazard distance is output.

[0006] Another aspect disclosed in the present application provides a transmission line hazard source ranging system based on point cloud spatial information fusion, the system comprising: a point cloud data acquisition unit: performing real-time point cloud data acquisition on a target transmission line through a laser point cloud device to obtain real-time point cloud data; an image acquisition unit: performing image acquisition on the target transmission line through a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower mechanically connected to the target transmission line; an area selection unit: a back-end server receives the real-time monitoring image sent back by the target monitoring device, and selects an area of ​​interest based on the real-time monitoring image. Box selection, positioning precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server through a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source; spatial data filtering unit: the back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data; data alignment unit: the back-end server aligns the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model; spatial distance measurement unit: performs spatial distance measurement between the target hazard source and the target transmission line in the spatial information alignment model, and outputs quantitative data of the hazard distance.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The above-mentioned transmission line hazard source distance measurement method based on point cloud spatial information fusion uses a laser point cloud device to collect real-time point cloud data of the target transmission line, and uses the target monitoring device to collect real-time monitoring images of the transmission line. These devices are pre-installed on the transmission tower connected to the transmission line to form the basis for data collection. The real-time monitoring image is transmitted to the back-end server through the wireless transmission relay module. The back-end server analyzes the image, selects the area of ​​interest, and determines the precise measurement start area covering the hazard source. Subsequently, the back-end server performs spatial data filtering on the real-time point cloud data according to the determined precise measurement start area, retains only the point cloud data related to the measurement start area, and generates spatial point cloud analysis data. After that, the server aligns the point cloud analysis data with the monitoring image to generate a spatial information alignment model. Through this model, the spatial distance between the hazard source and the transmission line can be accurately measured, and quantitative hazard spacing data can be output, providing scientific support for the safety assessment and risk management of the transmission line.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 It is a flow chart of a method for measuring the distance between dangerous sources of power transmission lines based on point cloud spatial information fusion in one embodiment.

[0011] Figure 2 This is an architecture diagram of a power transmission line hazard source ranging system based on point cloud spatial information fusion in one embodiment.

[0012] Explanation of the accompanying drawings: point cloud data acquisition unit 1, image acquisition unit 2, area selection unit 3, spatial data filtering unit 4, data registration unit 5, spatial distance measurement unit 6. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a transmission line hazard source distance measurement method and system based on point cloud spatial information fusion, thereby solving the technical problem that existing transmission line monitoring equipment is unable to accurately locate hazard sources and issue timely warnings due to the lack of accurate distance measurement and real-time dynamic identification of the external environment.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides a method for measuring the distance of transmission line danger sources based on point cloud spatial information fusion, the method comprising: The point cloud data of the target transmission line is collected in real time through the laser point cloud equipment to obtain real-time point cloud data.

[0017] In an embodiment of the present application, the system terminal scans the environment around the target transmission line through a laser point cloud device. The laser point cloud device captures the spatial information of the transmission line and its surrounding objects in real time by emitting a high-frequency laser beam and receiving its reflected signal. These signals are converted into point cloud data through the built-in processing unit of the device and transmitted to the system terminal to form real-time point cloud data. Each point in the real-time point cloud data contains specific three-dimensional coordinate information (such as X, Y, and Z positions). These point cloud data can accurately reflect the shape, position, and spatial distribution of the transmission line and its surrounding environment, providing a basis for subsequent analysis and processing. During the acquisition process, the laser point cloud device will operate continuously with high precision and high frequency to ensure that the captured spatial data is updated in real time, thereby reflecting the status of the transmission line and potential sources of danger in its surrounding area in real time.

[0018] The target transmission line is imaged by a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower mechanically connected to the target transmission line.

[0019] In one embodiment, the system terminal uses the target monitoring device to capture and collect image data around the transmission line in real time according to a preset frequency, wherein the target monitoring device and the laser point cloud device are pre-installed on the transmission tower, and the transmission tower is mechanically connected to the target transmission line. The two devices are usually in a fixed position on the transmission tower and can continuously monitor the operation status of the transmission line. The real-time monitoring images taken by the target monitoring device can obtain real-time environmental information around the transmission line and monitor whether there are potential sources of danger, such as trees, buildings or other objects that may threaten the safety of the line. These images will then be sent to the back-end server for processing and analysis to further determine the areas that need to be accurately measured.

[0020] Furthermore, the present application provides a method for collecting images of the target power transmission line by a target monitoring device, which includes: Interactively obtain basic grid data of the target transmission line; perform transmission line risk assessment based on the basic grid data to obtain a transmission line risk table; preset multi-level hazard source identification frequencies; after obtaining the target risk level of the target transmission line from the transmission line risk table, use the target risk level to traverse the multi-level hazard source identification frequencies to configure the image acquisition frequency of the target monitoring device; the target monitoring device performs intermittent image acquisition on the target transmission line with the image acquisition frequency as a constraint.

[0021] Preferably, the reserved port of the system terminal establishes a connection with the back-end server to interactively obtain the basic data of the power grid related to the target transmission line. These data include but are not limited to the power grid topology, line attribute data, etc. These data are the basis for subsequent risk assessment and analysis. Subsequently, the obtained basic data of the power grid are analyzed, and the risk of the transmission line is evaluated in combination with the transmission risk evaluation function. The evaluation criteria include but are not limited to factors such as the length of the line, the surrounding environment, and the historical fault frequency. The system terminal calculates the risk index of each transmission line by combining these factors, and generates a transmission line risk number table, listing the risk levels of different lines. In addition, the system terminal also sets multiple hazard source identification frequencies based on historical experience and expert advice, and each hazard source identification frequency corresponds to a risk level. For example, for a first-level risk area (such as a transmission line risk number of 0.8 to 1), a higher identification frequency is set (5 images are collected every 1 second). These frequencies are pre-defined and classified according to different risk levels to ensure that high-risk areas are monitored more frequently. After that, the system terminal extracts the transmission line risk number in the transmission line risk number table, matches the corresponding risk level, and then traverses the preset multi-level hazard source identification frequency according to the matching result to determine the appropriate image acquisition frequency. For example, if the risk level of a line is level one, the system terminal will select the corresponding acquisition frequency and configure the image acquisition parameters of the target monitoring device to ensure that it continuously monitors the transmission line at the set time interval. After configuring the image acquisition frequency, the target monitoring device performs intermittent image acquisition of the transmission line according to the set frequency interval. For example, the device is activated 5 times every 1 second, captures the environmental images around the line, and transmits these images to the back-end server for further analysis. This intermittent acquisition method can effectively save equipment resources while ensuring that high-risk areas are adequately monitored.

[0022] Furthermore, this application provides basic grid data, including: The basic power grid data includes power grid topology data, line attribute data, system operation data and historical maintenance data.

[0023] Optionally, the basic grid data of the target transmission line includes but is not limited to grid topology data, line attribute data, system operation data and historical maintenance data. Among them, the grid topology data describes the physical layout and structure of the entire transmission line, covering the spatial position and connection relationship of the line. It includes the connection between the transmission line and each node, for example, the configuration of key nodes such as substations and switch stations, and the electrical connection method between each node. Through these data, the specific location of each transmission line in the power grid and the topological relationship of adjacent lines can be clarified, which is convenient for analyzing the scope of power outage. The line attribute data provides key technical parameters of each transmission line. These parameters include the physical length of the line, the type of conductor (such as ACSR aluminum-clad steel core aluminum stranded wire, AAAC all-aluminum alloy conductor, etc.), the cross-sectional area of ​​the conductor, the electrical impedance characteristics of the conductor, etc. These attributes play an important role in evaluating the impedance characteristics of the transmission line. The system operation data includes the rated current value and maximum load capacity of each transmission line. The rated current indicates the current that the line can safely transmit under normal operating conditions, while the maximum load capacity refers to the maximum load that the line can carry under extreme conditions. This data is used to assess the transmission capacity of the line and help determine whether the line is at risk of overload. Historical maintenance data records relevant information about each transmission line during past operation and maintenance, including the frequency of historical faults, the repair time required for each fault, the measures taken during maintenance, and the remaining service life of the line. By analyzing this data, we can understand the health status and maintenance records of the line and help predict the risk of possible future faults.

[0024] Furthermore, the present application provides a method for performing a transmission line risk assessment based on the basic data of the power grid to obtain a transmission line risk table, including: A power outage scope analysis is performed based on the power grid topology data to obtain multiple power outage node ratios of multiple transmission lines; multiple line length parameters of the multiple transmission lines are extracted from the line attribute data, and impedance calculations are performed on the multiple transmission lines based on the line attribute data to obtain multiple line impedances; a transmission capacity evaluation is performed on the multiple transmission lines based on the system operation data to obtain multiple line transmission capacities; and multiple historical operation and maintenance frequencies, multiple remaining service lives, and multiple line operation cycles of the multiple transmission lines are obtained by calling the historical maintenance data.

[0025] Optionally, in the outage range analysis, the system terminal first identifies the nodes directly connected to each transmission line. These nodes usually include substations, switch stations, and distribution nodes. Each node may be indirectly connected to more subordinate nodes through other transmission lines. Subsequently, the direct outage nodes that are immediately affected by the line outage are identified. These nodes are power facilities directly connected to the faulty line, such as substations or switch stations. When the line is out of service, these nodes cannot obtain power. Then analyze the indirect outage nodes caused by the direct outage nodes. For example, a substation may supply power to multiple downstream nodes. When the substation is out of power due to the faulty line, these downstream nodes will also be indirectly out of power. The system terminal tracks these indirectly affected nodes according to the topological structure and gradually expands the outage range until all potentially affected nodes are found. After that, the number of outage nodes for each transmission line is counted, including nodes with direct and indirect outages, and then the number of outage nodes counted for each transmission line is calculated by ratio to the total number of nodes associated with the line to obtain the ratio of multiple outage nodes for multiple transmission lines.

[0026] In impedance calculation, the system terminal extracts the conductor type, line length, cross-sectional area and other parameters of each transmission line from the line attribute data, and then obtains the corresponding conductor resistivity according to the conductor type of each transmission line, and calculates the ratio of the product of the conductor resistivity of each transmission line and the corresponding line length to the corresponding conductor cross-sectional area to obtain the resistance value of each transmission line. Subsequently, the reactance value is calculated according to the operating frequency of each transmission line and the inductance of the line. The calculation method is: ; Where X is the reactance value; f is the operating frequency of the power grid, which is generally 50Hz or 60Hz; L is the inductance of the line, which is related to the geometric structure and distance of the conductor. After that, the system terminal multiplies the reactance value by the imaginary unit, and then adds the product to the resistance value to obtain multiple line impedances of multiple transmission lines.

[0027] In the transmission capacity evaluation, the system terminal uses the system operation data of each transmission line to evaluate its transmission capacity. This data includes the rated current and maximum load capacity of the line. The rated current refers to the safe transmission current of the line under normal operating conditions, while the maximum load capacity is the highest current value that the line can withstand in a short period of time. Based on these data, the system terminal uses the rated current or maximum load capacity to calculate the transmission capacity according to current business needs, and obtains multiple line transmission capacities of multiple transmission lines. The calculation method is ; Where P is the transmission capacity; V is the voltage level of the line, such as 110kV, 220kV, 500kV, etc.; I is the rated current or maximum load capacity. When the business demand is for normal operation, the rated current is used, and when the business demand is for emergency or extreme conditions, the maximum load capacity is used; It is the power factor, which is usually between 0.8 and 1, and can be 0.9 by default.

[0028] In addition, the system terminal also extracts the operation and maintenance history of each transmission line from the historical maintenance data, including the historical operation and maintenance frequency, the remaining service life and the line operation cycle. The historical operation and maintenance frequency reflects the health of the line, and the remaining service life is used to show the time window in which the line may fail in the future. This line operation cycle is the total time since the line was put into operation.

[0029] A transmission risk assessment function is pre-constructed, and the transmission risk assessment function is as follows: ; A plurality of transmission risk coefficients are obtained by synchronizing the plurality of power outage node ratios, a plurality of line length parameters, a plurality of line impedances, a plurality of line transmission capacities, a plurality of historical operation and maintenance frequencies, a plurality of remaining service lives and a plurality of line operation cycles of the plurality of transmission lines to the transmission risk evaluation function; and the plurality of transmission lines and the plurality of transmission risk coefficients are stored in association with each other to form the transmission line risk table.

[0030] Optionally, in order to quantify the transmission risk, the system terminal pre-builds a transmission risk assessment function, which takes into account multiple key parameters that affect the safety and stability of the transmission line to obtain a comprehensive risk value of the line. The transmission risk assessment function is as follows: ;in, is the transmission risk coefficient. The larger the transmission risk coefficient, the higher the risk of the transmission line. P is the power outage node ratio, which indicates the ratio of the number of transmission nodes directly or indirectly affected when the transmission line fails. L is the line length parameter, which indicates the physical length of the transmission line. Z is the line impedance, which indicates the resistance and reactance characteristics of the transmission line. C is the line transmission capacity, which indicates the maximum power that the transmission line can transmit under rated or extreme conditions. M is the historical operation and maintenance frequency, which indicates the frequency of failures and maintenance of the transmission line in history. R is the remaining service life, which indicates the time that the transmission line is expected to continue to operate. T is the line operation cycle, which indicates the length of time since the line was put into operation. The system terminal inputs the power outage node ratio, line length, impedance, transmission capacity, historical operation and maintenance frequency, remaining service life and operation cycle of each transmission line obtained by analysis into the risk assessment function to calculate the risk coefficient of each line. Then, each transmission line and its corresponding risk coefficient are associated and stored to generate a transmission line risk table. This table can help system terminals quickly identify high-risk lines for key monitoring.

[0031] The back-end server receives the real-time monitoring image transmitted back by the target monitoring device, and selects the area of ​​interest based on the real-time monitoring image to locate the precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server via a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source.

[0032] In one embodiment, the system terminal transmits the real-time monitoring images sent back by the target monitoring device to the back-end server through the wireless transmission relay module. After receiving these data, the back-end server will activate the pre-deployed two-dimensional identification model of the hazard source to select the area of ​​interest and locate the precise measurement start area. Each transmission tower is equipped with a wireless transmission relay module, which collects and transmits the data collected by the camera and sensor through a low-power wireless network such as LoRa or NB-IoT. During the data transmission process, the wireless transmission relay module not only plays the role of data transmission, but also can perform preliminary processing on the collected data. For example, the image data can be compressed to reduce the occupancy of the transmission bandwidth, and the sensor data can be filtered to ensure that only high-quality or useful information is transmitted to the server. This preprocessing mechanism can improve the efficiency and reliability of data transmission, especially in the case of limited bandwidth, to ensure the integrity and real-time performance of the data.

[0033] Furthermore, the present application provides that a backend server receives the real-time monitoring image transmitted back by the target monitoring device, and selects an area of ​​interest based on the real-time monitoring image to locate a precise measurement start area, including: A two-dimensional hazard source recognition model is pre-deployed in the back-end server, wherein the two-dimensional hazard source recognition model includes a cascaded hazard source selection layer and a hazard distance recognition layer; the hazard source selection layer of the two-dimensional hazard source recognition model is used to perform hazard source recognition selection on the real-time monitoring image, and if the real-time monitoring image includes the target hazard source, the hazard source selection layer outputs a real-time selection image; based on the real-time selection image, the hazard distance recognition layer is activated to perform image acquisition distance analysis and output real-time distance parameters; spatial positioning is performed according to the real-time selection image and the real-time distance parameters to obtain the precise measurement start area.

[0034] Optionally, in the backend server, the system terminal pre-arranges a two-dimensional hazard source recognition model, which is composed of a cascade of a hazard source selection layer and a hazard distance recognition layer, and both are built based on a CNN network. The hazard source selection layer is responsible for identifying and selecting potential hazard sources in the real-time monitoring image. If the real-time monitoring image contains a target hazard source, the selection layer will output a real-time image with a selection area (i.e., ROI, region of interest). These areas represent potential hazard sources and are used for further analysis. Subsequently, based on the output real-time selection image, the system terminal activates the hazard distance recognition layer in the two-dimensional hazard source recognition model, performs distance analysis on the ROI in the image, and calculates the actual distance between the hazard source and the monitoring device. In this process, the hazard distance recognition layer outputs a real-time distance parameter for quantifying the distance between the position of the hazard source in the image and the monitoring device. Afterwards, the system terminal performs spatial positioning based on the ROI in the real-time selection image and the obtained real-time distance parameter, combined with the location information of the monitoring device, through geometric projection, triangulation or other spatial positioning algorithms, and calculates the specific location of the hazard source in the real world. Taking geometric projection as an example, the system terminal calculates the horizontal distance and vertical height between the hazard source and the transmission line through geometric projection based on the installation height and field of view of the monitoring equipment, and then uses the installation height of the monitoring equipment as a reference, and combines the field of view of the monitoring equipment to map the ROI area identified in the image to the real space. In this way, the actual position of the hazard source can be determined by geometric projection. Then, the actual position of the determined hazard source is compared with the position of the transmission line to determine the distance between the hazard source and the transmission line, and then the distance between the hazard source and the transmission line is compared with the preset safety distance. If the distance between the hazard source and the transmission line is less than the preset safety distance, the system terminal will mark the area where the hazard source is located as a precise measurement start area, which is an area defined based on a two-dimensional plane to indicate the location of the hazard source that needs further monitoring and measurement. This start area provides a precise target for subsequent operations such as laser ranging.

[0035] Furthermore, the present application provides a two-dimensional hazard source identification model, including: Extract the equipment layout characteristics and equipment model information of the target monitoring equipment; extract and obtain the line layout characteristics of the target transmission line based on the basic data of the power grid; use the equipment layout characteristics, equipment model information and line layout characteristics as screening constraints to collect and obtain multiple sample dangerous intrusion images, wherein the multiple sample dangerous intrusion images include multiple sample dangerous source selection marks and multiple sample dangerous source distance marks; use the multiple sample dangerous intrusion images and multiple sample dangerous source selection marks as training data, and construct the dangerous source selection layer based on the CNN network; use the multiple sample dangerous intrusion images and multiple sample dangerous source distance marks as training data, and construct the dangerous distance identification layer based on the CNN network; complete the construction of the two-dimensional identification model of the dangerous source by cascading the dangerous source selection layer and the dangerous distance identification layer.

[0036] Optionally, in the process of constructing the two-dimensional identification model of the hazardous source, the system terminal first extracts the equipment layout characteristics and equipment model information of the target monitoring equipment, which includes the installation location, installation method, technical specifications of the equipment, etc., and then extracts the layout characteristics of the transmission line based on the basic data of the power grid, which includes but is not limited to the physical location and length of the transmission line. Subsequently, the layout characteristics, model information of the equipment and the layout characteristics of the transmission line are used as screening conditions to collect multiple sample dangerous intrusion images that match these conditions. Each sample image not only contains the image data of the hazardous source, but also marks the location of these hazardous sources (i.e., the box selection mark) and the distance between the hazardous source and the monitoring device (i.e., the distance mark). After that, a convolutional neural network (CNN) is used to construct a hazardous source box selection layer, including an input layer, an output layer, a convolution layer, a pooling layer, etc. Then, the weights and biases of the hazardous source box selection layer are initialized by using random numbers or the like, and multiple sample hazardous intrusion images and multiple sample hazardous source box selection marks are input as training data into the initialized hazardous source box selection layer for forward propagation to generate a real-time box selection image. Then, the cross entropy loss function and the intersection over union ratio are used to jointly calculate the loss value between the real-time box selection image and the corresponding sample hazard source box selection mark. Among them, the cross entropy loss function is used to measure the difference between the predicted hazard source category and the real category. The intersection over union ratio (IoU) is used to measure the degree of bounding box overlap. IoU represents the ratio of the intersection and union of the predicted bounding box and the real bounding box. By minimizing the IoU loss, the hazard source box selection layer will try to make the predicted bounding box overlap with the real labeled bounding box. Then, through the back propagation algorithm, the weights are continuously adjusted to gradually reduce the loss function value. Commonly used optimization algorithms such as Adam or SGD are used to update the parameters of the network. Through multiple training rounds, it is continuously iterated on a large amount of sample data until the accuracy of the hazard source box selection layer in selecting the hazard source in a given image is greater than or equal to the expected accuracy. After the training is completed, the system terminal verifies through the validation set (sample images that are not involved in the training) to check the performance of the hazard source box selection layer on the new data. If the accuracy is sufficient, the steps of adjusting the network structure and adding more sample data are performed. When the hazard source selection layer can stably output accurate selection results on the validation set, the system terminal outputs the current hazard source selection layer. At the same time, the system terminal also uses multiple sample dangerous intrusion images and multiple sample hazard source distance labels as training data. Through the same training method mentioned above, a hazard distance identification layer is constructed based on the CNN network to analyze and predict the actual distance between the hazard source and the monitoring equipment. Finally, by cascading the hazard source selection layer and the hazard distance identification layer, the system terminal completes the construction of the hazard source two-dimensional identification model. This model can simultaneously identify the hazard source in the image and calculate its distance from the transmission line for subsequent monitoring.

[0037] The back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data.

[0038] In one embodiment, in the back-end server, the system terminal determines the spatial measurement start area by analyzing the identified precise measurement start area, and performs spatial data filtering in this spatial measurement start area to generate high-precision spatial point cloud analysis data, which will be transmitted back to the back-end server via wireless transmission. The back-end server will receive and store this data for subsequent analysis, processing and further risk assessment. Through this process, a complete three-dimensional image of the measurement area can be obtained to ensure more accurate monitoring and assessment of hazardous sources and transmission lines.

[0039] Furthermore, the present application provides that the back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data, including: A standard three-dimensional space is constructed with the target monitoring device as a reference point, the transmission tower as a depth axis, and the spatial direction of the target transmission line as a horizontal axis; the maximum straight-line distance of the precise measurement starting area is collected, and the collection result is used as a point cloud filtering distance constraint; a frame selection space feature is output according to the shape feature of the target hazard source; after locating the precise measurement starting area in the standard three-dimensional space according to the equipment layout features and the real-time distance parameters, a spatial measurement starting area is framed based on the point cloud filtering distance constraint and the frame selection space feature; the real-time point cloud data is spatially filtered with the frame selection space starting area as a constraint to obtain the spatial point cloud analysis data.

[0040] Optionally, the system terminal sets the target monitoring device as the reference point, combines the depth of the transmission tower as the vertical axis, and uses the spatial direction of the target transmission line as the horizontal axis to construct a standard three-dimensional space model. This three-dimensional space is used to locate and analyze the precise measurement start area in detail. Subsequently, the identified precise measurement start area is analyzed to determine the maximum straight-line distance in the area. This distance represents the longest distance that needs to be covered during the filtering process. In order to ensure that the measurement coverage is sufficient, the system terminal sets this maximum straight-line distance as the point cloud filtering distance constraint to ensure that subsequent point cloud data processing only focuses on the relevant range of the measurement start area. After that, the system terminal analyzes the shape features of the hazard source (such as rectangle, circle, complex shape, etc.) based on the real-time monitoring image, and outputs the specific frame selection space features of the hazard source through the boundary extraction algorithm (RANSAC algorithm). These features describe the spatial range, direction and size of the hazard source. Then, the system terminal accurately locates the precise measurement start area in the constructed three-dimensional space based on the layout features of the monitoring equipment and the distance data between the hazard source and the transmission line collected in real time. In the precise measurement start area, the system terminal selects the point cloud data within the start area and the distance from the transmission line and the hazard source through the three-dimensional coordinates of the point cloud data, and removes the points beyond the filter distance constraint range, so as to ensure that the subsequent processing only focuses on the relevant regional point cloud data, and then combines the box selection space feature with the filter distance constraint to generate a more accurate spatial measurement area. This process ensures that the measurement start area contains both the hazard source and its influence range, and excludes irrelevant redundant areas. In this way, the system terminal uses the point cloud filter distance constraint as the boundary and the three-dimensional shape of the box selection space feature as a supplement to generate the final range of the spatial measurement start area. The shape of this spatial measurement start area may be a regular geometric body (such as a rectangle, a cylinder) or an irregular three-dimensional polygonal area, depending on the shape and influence range of the hazard source. The point cloud data inside it will be used for three-dimensional spatial analysis to ensure that data processing is concentrated in the key areas related to the hazard source, reduce the amount of calculation and improve the analysis accuracy. Finally, the system terminal uses the box-selected spatial measurement start area as a constraint condition to filter the real-time point cloud data. The purpose of filtering is to remove spatial points in the point cloud data that are not related to the measurement start area, and only retain the point cloud data in the start area. The filtering process is carried out by checking whether each point cloud point is located in the box-selected measurement start area (based on the three-dimensional spatial coordinates). Only points that meet the conditions will be retained to form a refined point cloud data set, that is, spatial point cloud analysis data. These data focus on key points in the precise measurement start area, reflect the three-dimensional characteristics of the area, and provide support for subsequent measurement, analysis, and risk assessment. Through this process, point cloud data can be efficiently screened and processed to ensure that only spatial information related to the precise measurement start area is focused on, thereby greatly improving the accuracy and efficiency of the analysis.

[0041] The back-end server registers the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model.

[0042] In one embodiment, in the back-end server, the system terminal registers the collected spatial point cloud analysis data (three-dimensional data) and the real-time monitoring image (two-dimensional image) from the monitoring device. The core goal of the registration is to integrate the information in the two-dimensional image with the three-dimensional point cloud data to ensure that the spatial positions and features of the two correspond accurately. Specifically, the system terminal screens the spatial point cloud analysis data according to the precise measurement start area, and then performs feature matching on the screening results and the real-time monitoring image to complete the registration and alignment of the real-time frame selection image and the spatial point cloud analysis data, ensuring that the objects in the monitoring image can be accurately mapped in the three-dimensional point cloud data. Through this registration, a spatial information alignment model can be generated, that is, an environmental map that combines two-dimensional images with three-dimensional point cloud data. This model can more intuitively display the environmental information of the measurement area, which is helpful for further analysis and risk assessment.

[0043] Furthermore, the present application provides that the backend server registers the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model, including: Feature points are extracted from the spatial point cloud analysis data to obtain a point cloud feature set; feature extraction is performed on the real-time framed image to obtain a registration feature set; feature matching is performed on the point cloud feature set and the registration feature set to locate the registration matrix; registration and alignment of the real-time framed image and the spatial point cloud analysis data are performed based on the registration matrix, and the spatial information alignment model is output.

[0044] Optionally, the system terminal extracts features from the collected spatial point cloud analysis data, identifies key points therein, such as terrain features, object contours, etc., and generates a point cloud feature set. At the same time, the system terminal extracts features from the real-time frame selection image, extracts key features related to the hazard source from the image, such as edges, corners, etc., and forms a registration feature set. Common algorithms for extracting feature points include SIFT (Scale Invariant Feature Transform), Harris Corner Detection, etc. Afterwards, the extracted point cloud feature points and image feature points are described. This process represents the local information of each feature point as a vector, which contains the geometric information or texture information of its surrounding environment. The description of point cloud features is usually combined with its three-dimensional geometric shape and local curvature information, while the description of image features is represented by information such as grayscale or color changes. Then, by comparing the description vector of each point cloud feature point with the description vector of each image feature point, the Euclidean distance is used to find matching feature point pairs with similar distances within the similar distance threshold. Based on the matched point pairs, the system terminal will minimize the similarity distance between the matched point pairs and calculate the registration matrix used to align the point cloud feature set and the registration feature set. This matrix usually includes translation, rotation and scaling operations, which are used to convert the feature points in the two-dimensional image to the corresponding positions in the three-dimensional point cloud space so that the two are aligned in the same spatial frame. Finally, based on this registration matrix, the system terminal will accurately register and align the real-time frame selection image with the spatial point cloud analysis data. After the registration is completed, a spatial information alignment model is output, which integrates the two-dimensional image and three-dimensional point cloud data. Using this alignment model, the system terminal can combine the safety threshold of the transmission line to accurately evaluate the distance between the hazard source and the transmission line, and judge the potential threat level of the hazard source to the transmission line based on the calculated distance and risk level rules, providing a basis for subsequent risk warnings.

[0045] The spatial distance between the target hazard source and the target transmission line is measured in the spatial information alignment model, and quantitative data of the hazard distance is output.

[0046] In one embodiment, in the generated spatial information alignment model, the system terminal accurately aligns the three-dimensional spatial position information of the target hazard source and the target transmission line, and uses this alignment model to calculate the actual distance between the hazard source and the transmission line. Specifically, the system terminal extracts the three-dimensional coordinates of the hazard source and the three-dimensional coordinates of the transmission line from the aligned model, and then measures the straight-line distance between the two through geometric calculations. This process ensures the accuracy of the spatial distance and outputs the measurement results in digital form as quantitative data of the hazard spacing. These quantitative data are used to evaluate the potential threat of the hazard source to the transmission line, and to determine whether the hazard source exceeds the safety threshold based on the size of the distance. If the distance is less than the preset safety distance, a corresponding warning will be issued.

[0047] In summary, the embodiments of the present application have at least the following technical effects: The embodiment of the present application collects real-time data of the transmission line and its surrounding environment through laser point cloud equipment and target monitoring equipment. The back-end server combines real-time monitoring images and point cloud data, and generates a spatial information alignment model through steps such as area of ​​interest selection, spatial data filtering, two-dimensional recognition model and risk assessment, so as to achieve accurate distance measurement between the hazard source and the transmission line and output quantitative hazard distance data. At the same time, by constructing the basic grid data and risk assessment model of the transmission line, the monitoring strategy is further optimized, the accuracy and reliability of hazard source identification and ranging are improved, and strong technical support is provided for the safe management of transmission lines. These technical effects jointly solve the technical problem that the existing transmission line monitoring equipment cannot accurately locate and timely warn the hazard source due to the lack of accurate ranging and real-time dynamic recognition of the external environment, and realizes the effect of reducing the accident rate by identifying and ranging the hazard source through laser point cloud equipment and image monitoring equipment.

[0048] Embodiment 2 is based on the same inventive concept as the power transmission line hazard source distance measurement method based on point cloud spatial information fusion in the above embodiment. Figure 2 As shown, the present application provides a transmission line hazard source ranging system based on point cloud spatial information fusion, the system comprising: a point cloud data acquisition unit 1: performing real-time point cloud data acquisition on a target transmission line through a laser point cloud device to obtain real-time point cloud data; an image acquisition unit 2: performing image acquisition on the target transmission line through a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower mechanically connected to the target transmission line; an area selection unit 3: a back-end server receives the real-time monitoring image returned by the target monitoring device, and selects an area of ​​interest based on the real-time monitoring image. Positioning precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server through a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source; spatial data filtering unit 4: the back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data; data alignment unit 5: the back-end server aligns the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model; spatial distance measurement unit 6: performs spatial distance measurement between the target hazard source and the target transmission line in the spatial information alignment model, and outputs quantitative data of the hazard distance.

[0049] Furthermore, the image acquisition unit 2 is also used to perform the following method: Interactively obtain basic grid data of the target transmission line; perform transmission line risk assessment based on the basic grid data to obtain a transmission line risk table; preset multi-level hazard source identification frequencies; after obtaining the target risk level of the target transmission line from the transmission line risk table, use the target risk level to traverse the multi-level hazard source identification frequencies to configure the image acquisition frequency of the target monitoring device; the target monitoring device performs intermittent image acquisition on the target transmission line with the image acquisition frequency as a constraint.

[0050] Furthermore, the image acquisition unit 2 is also used to perform the following method: The basic power grid data includes power grid topology data, line attribute data, system operation data and historical maintenance data.

[0051] Furthermore, the image acquisition unit 2 is also used to perform the following method: Perform a power outage range analysis based on the power grid topology data to obtain multiple power outage node ratios of multiple transmission lines; extract multiple line length parameters of the multiple transmission lines from the line attribute data, and perform impedance calculation on the multiple transmission lines based on the line attribute data to obtain multiple line impedances; perform transmission capacity evaluation on the multiple transmission lines based on the system operation data to obtain multiple line transmission capacities; obtain multiple historical operation and maintenance frequencies, multiple remaining service lives and multiple line operation cycles of the multiple transmission lines by calling the historical maintenance data; pre-construct a transmission risk evaluation function, which is as follows: ;in, is the transmission risk coefficient, P is the power outage node ratio, L is the line length parameter, Z is the line impedance, C is the line transmission capacity, M is the historical operation and maintenance frequency, R is the remaining service life, and T is the line operation cycle; by synchronizing the multiple power outage node ratios, multiple line length parameters, multiple line impedances, multiple line transmission capacities, multiple historical operation and maintenance frequencies, multiple remaining service lives and multiple line operation cycles of the multiple transmission lines to the transmission risk evaluation function, a plurality of transmission risk coefficients are obtained; the multiple transmission lines and the multiple transmission risk coefficients are associated and stored to form the transmission line risk table.

[0052] Furthermore, the area selection unit 3 is also used to perform the following method: A two-dimensional hazard source recognition model is pre-deployed in the back-end server, wherein the two-dimensional hazard source recognition model includes a cascaded hazard source selection layer and a hazard distance recognition layer; the hazard source selection layer of the two-dimensional hazard source recognition model is used to perform hazard source recognition selection on the real-time monitoring image, and if the real-time monitoring image includes the target hazard source, the hazard source selection layer outputs a real-time selection image; based on the real-time selection image, the hazard distance recognition layer is activated to perform image acquisition distance analysis and output real-time distance parameters; spatial positioning is performed according to the real-time selection image and the real-time distance parameters to obtain the precise measurement start area.

[0053] Furthermore, the area selection unit 3 is also used to perform the following method: Extract the equipment layout characteristics and equipment model information of the target monitoring equipment; extract and obtain the line layout characteristics of the target transmission line based on the basic data of the power grid; use the equipment layout characteristics, equipment model information and line layout characteristics as screening constraints to collect and obtain multiple sample dangerous intrusion images, wherein the multiple sample dangerous intrusion images include multiple sample dangerous source selection marks and multiple sample dangerous source distance marks; use the multiple sample dangerous intrusion images and multiple sample dangerous source selection marks as training data, and construct the dangerous source selection layer based on the CNN network; use the multiple sample dangerous intrusion images and multiple sample dangerous source distance marks as training data, and construct the dangerous distance identification layer based on the CNN network; complete the construction of the two-dimensional identification model of the dangerous source by cascading the dangerous source selection layer and the dangerous distance identification layer.

[0054] Furthermore, the spatial data filtering 4 is also used to perform the following method: A standard three-dimensional space is constructed with the target monitoring device as a reference point, the transmission tower as a depth axis, and the spatial direction of the target transmission line as a horizontal axis; the maximum straight-line distance of the precise measurement starting area is collected, and the collection result is used as a point cloud filtering distance constraint; a frame selection space feature is output according to the shape feature of the target hazard source; after locating the precise measurement starting area in the standard three-dimensional space according to the equipment layout features and the real-time distance parameters, a spatial measurement starting area is framed based on the point cloud filtering distance constraint and the frame selection space feature; the real-time point cloud data is spatially filtered with the frame selection space starting area as a constraint to obtain the spatial point cloud analysis data.

[0055] Furthermore, the data registration unit 5 is also used to perform the following method: Feature points are extracted from the spatial point cloud analysis data to obtain a point cloud feature set; feature extraction is performed on the real-time framed image to obtain a registration feature set; feature matching is performed on the point cloud feature set and the registration feature set to locate the registration matrix; registration and alignment of the real-time framed image and the spatial point cloud analysis data are performed based on the registration matrix, and the spatial information alignment model is output.

[0056] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0058] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion, characterized in that: The method comprises: The point cloud data of the target transmission line is collected in real time through the laser point cloud equipment to obtain real-time point cloud data; Capturing images of the target transmission line through a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower mechanically connected to the target transmission line; The back-end server receives the real-time monitoring image transmitted back by the target monitoring device, and selects the region of interest based on the real-time monitoring image to locate the precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server via a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source; The back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data; The backend server registers the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model; The spatial distance between the target hazard source and the target transmission line is measured in the spatial information alignment model, and quantitative data of the hazard distance is output.

2. The method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion according to claim 1, characterized in that: Before collecting images of the target transmission line by a target monitoring device, the method includes: Interactively obtaining basic power grid data of the target transmission line; Performing a transmission line risk assessment based on the basic power grid data to obtain a transmission line risk table; Preset the frequency of multi-level hazard source identification; After obtaining the target risk level of the target transmission line from the transmission line risk table, the image acquisition frequency configuration of the target monitoring device is configured by traversing the multi-level hazard source identification frequency using the target risk level; The target monitoring device performs intermittent image acquisition on the target power transmission line with the image acquisition frequency as a constraint.

3. The method for measuring the distance of transmission line danger sources based on point cloud spatial information fusion according to claim 2, characterized in that: The backend server receives the real-time monitoring image transmitted back by the target monitoring device, and selects the region of interest based on the real-time monitoring image to locate the precise measurement start area. The method includes: A two-dimensional hazard source identification model is pre-deployed in the back-end server, wherein the two-dimensional hazard source identification model includes a cascaded hazard source selection layer and a hazard distance identification layer; The hazard source selection layer of the hazard source two-dimensional identification model performs hazard source identification and selection on the real-time monitoring image. If the real-time monitoring image includes the target hazard source, the hazard source selection layer outputs a real-time selection image. Based on the real-time frame selection image, the danger distance recognition layer is activated to perform image acquisition distance analysis and output real-time distance parameters; The precise measurement start area is obtained by performing spatial positioning according to the real-time frame selection image and the real-time distance parameter.

4. The method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion according to claim 3, characterized in that: The method comprises: Extracting device layout characteristics and device model information of the target monitoring device; Extracting and obtaining the line layout characteristics of the target transmission line based on the basic data of the power grid; Taking the equipment layout features, equipment model information and line layout features as screening constraints, a plurality of sample dangerous intrusion images are collected and obtained, wherein the plurality of sample dangerous intrusion images include a plurality of sample dangerous source selection marks and a plurality of sample dangerous source distance marks; Using the multiple sample dangerous intrusion images and the multiple sample dangerous source selection marks as training data, constructing the dangerous source selection layer based on the CNN network; Using the plurality of sample dangerous intrusion images and the plurality of sample dangerous source distance identifiers as training data, constructing the dangerous distance recognition layer based on a CNN network; The construction of the two-dimensional identification model of the hazard source is completed by cascading the hazard source selection layer and the hazard distance identification layer.

5. The method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion according to claim 4, characterized in that: The back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data, and the method includes: A standard three-dimensional space is constructed by taking the target monitoring device as a reference point, the transmission tower as a depth axis, and the spatial direction of the target transmission line as a horizontal axis; The maximum straight-line distance is collected for the precise measurement start area, and the collection result is used as a point cloud filtering distance constraint; Outputting a frame selection space feature according to the shape feature of the target hazard source; After locating the precise measurement start area in the standard three-dimensional space according to the equipment layout characteristics and the real-time distance parameters, selecting the space measurement start area based on the point cloud filtering distance constraint and the selection space characteristics; The real-time point cloud data is spatially filtered with the frame-selected spatial measurement start area as a constraint to obtain the spatial point cloud analysis data.

6. The method for measuring the distance of transmission line danger sources based on point cloud spatial information fusion according to claim 2, characterized in that: The basic power grid data includes power grid topology data, line attribute data, system operation data and historical maintenance data.

7. The method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion according to claim 6, characterized in that: Based on the basic data of the power grid, a transmission line risk assessment is performed to obtain a transmission line risk table, and the method includes: Performing a power outage range analysis based on the power grid topology data to obtain multiple power outage node ratios of multiple transmission lines; Extracting and obtaining a plurality of line length parameters of the plurality of power transmission lines from the line attribute data, and performing impedance calculation on the plurality of power transmission lines based on the line attribute data to obtain a plurality of line impedances; Performing transmission capacity evaluation on the plurality of transmission lines based on the system operation data to obtain transmission capacities of the plurality of lines; Obtaining multiple historical operation and maintenance frequencies, multiple remaining service lives, and multiple line operation cycles of the multiple transmission lines from the historical maintenance data; A transmission risk assessment function is pre-constructed, and the transmission risk assessment function is as follows: ； in, is the transmission risk coefficient, P is the power outage node ratio, L is the line length parameter, Z is the line impedance, C is the line transmission capacity, M is the historical operation and maintenance frequency, R is the remaining service life, and T is the line operation cycle; A plurality of power transmission risk coefficients are obtained by synchronizing the plurality of power outage node ratios, a plurality of line length parameters, a plurality of line impedances, a plurality of line transmission capacities, a plurality of historical operation and maintenance frequencies, a plurality of remaining service lives and a plurality of line operation cycles of the plurality of transmission lines to the power transmission risk evaluation function; The plurality of transmission lines and the plurality of transmission risk coefficients are stored in association with each other to form the transmission line risk table.

8. The method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion according to claim 5, characterized in that: The backend server registers the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model, and the method includes: Extracting feature points from the spatial point cloud analysis data to obtain a point cloud feature set; Extracting features from the real-time frame-selected image to obtain a registration feature set; Performing feature matching on the point cloud feature set and the registration feature set to locate the registration matrix; The real-time frame selection image and the spatial point cloud analysis data are registered and aligned based on the registration matrix, and the spatial information alignment model is output.

9. The transmission line hazard source distance measurement system based on point cloud spatial information fusion is characterized by: The steps for implementing the method for measuring the distance of dangerous sources of power transmission lines based on point cloud spatial information fusion as described in any one of claims 1 to 8 include: Point cloud data acquisition unit: collect point cloud data of the target transmission line in real time through laser point cloud equipment to obtain real-time point cloud data; Image acquisition unit: acquiring images of the target transmission line through a target monitoring device to obtain a real-time monitoring image, wherein the target monitoring device and the laser point cloud device are pre-installed on a transmission tower mechanically connected to the target transmission line; Area selection unit: the back-end server receives the real-time monitoring image transmitted back by the target monitoring device, and selects the area of ​​interest based on the real-time monitoring image to locate the precise measurement start area, wherein the target monitoring device is indirectly connected to the back-end server via a wireless transmission relay module, wherein the precise measurement start area covers the target hazard source; Spatial data filtering unit: the back-end server performs spatial data filtering on the real-time point cloud data according to the precise measurement start area to obtain spatial point cloud analysis data; Data registration unit: the backend server registers the spatial point cloud analysis data and the real-time monitoring image to obtain a spatial information alignment model; Spatial distance measurement unit: performs spatial distance measurement between the target hazard source and the target transmission line in the spatial information alignment model, and outputs quantitative data of the hazard distance.

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