System for monitoring reflective film around transformer substation based on laser radar technology

Abstract

The invention discloses a system for monitoring a reflective film around a transformer substation based on a laser radar technology, and relates to the technical field of monitoring, and the system comprises a laser radar scanning module which is provided with a multi-line laser radar array, and carries out the three-dimensional point cloud data collection of the periphery of the transformer substation in a horizontal scanning mode; the reflection intensity analysis module is used for extracting an abnormal region with the reflection intensity higher than a preset threshold value in the point cloud data and generating coordinate information of a suspected target of the reflective film in combination with a spatial positioning algorithm; the multispectral verification module comprises a visible light camera and an infrared sensor and is used for carrying out image feature matching on the abnormal region and distinguishing a reflective film from a high-reflection interferent; the dynamic tracking module analyzes the drift trajectory of the reflective film through a time sequence, and predicts the probability that the reflective film invades the core area of the transformer substation based on meteorological data; and the grading alarm module triggers sound-light alarm, an unmanned aerial vehicle interception instruction and a power grid power-off protection signal according to the intrusion probability.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and specifically to a system for monitoring reflective films around a substation based on lidar technology. Background Art

[0002] As a disposable product made of aluminum-plastic composite, reflective films are economical, affordable, and easy to use, playing a huge role in improving the quality of apples. They are a commonly used product among fruit farmers. After apple picking, some reflective films cannot be quickly and timely cleaned up. The reflective films in the fruit orchards and woodlands around the substation pose a certain hazard to the line safety of the substation. The surface of the reflective film is coated with metallic aluminum and cannot be naturally degraded. If not properly handled later, there will be endless troubles and endanger the power safety. The surface of the reflective film is coated with a metallic reflective layer. In case of strong wind weather, the discarded reflective films may be blown onto the substation equipment or power supply lines, causing short circuit tripping and affecting the power consumption of people's production, life, and economic development.

[0003] Currently, the substation staff mainly rely on manual inspections to clean up the discarded reflective films in the surrounding fruit orchards. After discovery, manual cleaning is carried out. The disadvantages of this method are as follows: there is a long time difference in manual inspection, and it is impossible to quickly discover and clean up the reflective films around the substation in a short time, which is likely to cause danger; simply relying on manual inspection to discover and clean up reflective films is inefficient, time-consuming, and laborious; there is a problem that the coverage of manual inspection and cleaning of reflective films is not comprehensive, and in case of omissions and in case of weather such as strong wind, it is easy to cause danger, thus affecting the normal operation of substation equipment.

[0004] Based on the above analysis, in order to save manpower and ensure the rapid and accurate cleaning of reflective films in the fruit orchards around the substation, a method for quickly detecting and determining the position of reflective films in the area needs to be designed. This design uses lidar technology to continuously scan and emit laser 360 degrees, accurately determine the existence position and range size of reflective films in the area, and then arrange for staff to carry out cleaning work purposefully, which can effectively solve the problem of incomplete cleaning of reflective films and ensure the normal operation of the lines and equipment around the substation. Summary of the Invention

[0005] To solve the above technical problems, a system for monitoring reflective films around a substation based on lidar technology is provided. This technical solution solves the problems of the long time difference in manual inspection, the inability to quickly discover and clean up the reflective films around the substation in a short time, which is likely to cause danger; simply relying on manual inspection to discover and clean up reflective films is inefficient, time-consuming, and laborious; there is a problem that the coverage of manual inspection and cleaning of reflective films is not comprehensive, and in case of omissions and in case of weather such as strong wind, it is easy to cause danger, thus affecting the normal operation of substation equipment.

[0006] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A system for monitoring reflective films around a substation based on lidar technology, comprising:

[0008] Lidar scanning module: A multi-line lidar array is set up to collect three-dimensional point cloud data of the area around the substation in a horizontal scanning mode;

[0009] Reflection intensity analysis module: Used to extract abnormal areas in the point cloud data where the reflection intensity is higher than a preset threshold, and generate coordinate information of suspected reflective film targets in combination with a spatial positioning algorithm;

[0010] Multi-spectral verification module: Comprising a visible light camera and an infrared sensor, used to perform image feature matching on the abnormal areas to distinguish reflective films from high-reflection interference objects;

[0011] Dynamic tracking module: Analyze the drifting trajectory of the reflective film through time series, and predict the probability of its intrusion into the core area of the substation based on meteorological data;

[0012] Hierarchical alarm module: Trigger audible and visual alarms, UAV interception instructions, and power grid power-off protection signals according to the intrusion probability.

[0013] Preferably, the lidar scanning module specifically includes:

[0014] Multi-line lidar unit: Adopt a multi-line laser emitter to realize three-dimensional point cloud acquisition through multiple layers of vertically distributed laser beams;

[0015] Radar deployment unit: Arrayed on the top of the substation fence and high-point brackets, and realize full-angle horizontal scanning through a horizontal rotation mechanism, taking into account near-field details and far-field coverage.

[0016] Preferably, the reflection intensity analysis module specifically includes:

[0017] Threshold segmentation unit: Based on the material characteristics of the reflective film and the statistical reflection intensity of the environmental background, dynamically adjust the threshold; traverse the three-dimensional point cloud data, screen the point set with a reflection intensity higher than the threshold, and generate an initial candidate area;

[0018] Clustering analysis unit: Adopt density clustering to aggregate discrete high-reflection intensity points into continuous areas, excluding isolated noise points; based on the generated initial candidate area, calculate the convex hull and concave hull of the clustered point cloud to determine the geometric shape and spatial range of the reflective film target;

[0019] Spatial positioning unit: Combine the known lidar position, and calculate the three-dimensional coordinates of the target area based on the point cloud distance information; use the weighted centroid of the clustering area as the final coordinate of the reflective film target;

[0020] Coordinate transformation unit: Transforms the coordinates in the local coordinate system into the geodetic coordinate system and the engineering application coordinate system to ensure matching with the global map; Optimizes the coordinate stability through multi-view data fusion and dynamic positioning algorithms.

[0021] Preferably, the reflection intensity analysis module specifically includes:

[0022] Data preprocessing unit: Deploys local edge computing nodes to perform point cloud denoising, data compression, and feature extraction, reducing the cloud transmission load; Normalizes the reflection intensity according to the laser radar's transmission power and distance attenuation model to eliminate the influence of distance and angle on the intensity value.

[0023] Preferably, the multi-spectral verification module specifically includes:

[0024] Multi-spectral data acquisition unit: The visible light camera captures the RGB image of the target area through a high-resolution camera, recording the visual features of color, texture, and shape; The infrared sensor uses the infrared sensor to detect the radiation heat characteristics of the target and the reflectivity difference in the infrared band, identifying the surface thermal response difference between the reflective film and the high-reflection material.

[0025] Multi-spectral feature classification unit: Inputs the visible light imaging features and infrared sensing features into the deep learning model to generate a joint feature vector; Based on the reflective film sample library, trains the support vector machine and random forest models to learn the difference features between the reflective film and the interference objects; Introduces transfer learning technology to accelerate the model convergence using the pre-trained network.

[0026] Interference object exclusion unit: Adaptively adjusts the reflectivity discrimination threshold of the visible light imaging features and infrared sensing features according to the environmental light intensity; Combines the spatial positioning information to exclude the interference objects with similar reflection characteristics; Eliminates the influence of short-term high-reflection noise through continuous frame data comparison.

[0027] Result verification unit: Matches the multi-spectral classification result with the point cloud reflectivity data to improve the confidence level;

[0028] Maps the identified reflective film target to the three-dimensional space coordinate system and outputs the coordinates and confidence score.

[0029] Preferably, the dynamic tracking module specifically includes:

[0030] Reflective film trajectory data acquisition unit: Real-time captures the drifting trajectory point cloud data of the reflective film through the lidar and records its time series position coordinates;

[0031] Meteorological data synchronous acquisition unit: Integrates the real-time meteorological parameters of wind speed, wind direction, temperature, and humidity and aligns them with the trajectory data timestamp;

[0032] Time series trajectory modeling unit: Construct a time series model of the trajectory through parameters such as moving speed, acceleration, and direction angle to analyze the drifting law of the reflective film; Use Kalman filtering to predict the future short-term trajectory and optimize the prediction error by combining historical trajectory data; Combine the substation geographical fence to map the trajectory to the spatial coordinate system and calculate the real-time distance from the core area.

[0033] Preferably, the reflective film trajectory data acquisition unit specifically includes:

[0034] Reflective film trajectory data acquisition, record the position coordinates of the reflective film in the time series:

[0035] P(t i ) = [x(t i ), y(t i ), z(t i )]

[0036] In the formula, P(t i ) is the position coordinate of the reflective film at t i , x(t i ), y(t i ), z(t i ) are the coordinate components of the reflective film in the three-dimensional space, and t i is the timestamp;

[0037] Align the meteorological data with the trajectory data timestamp:

[0038] M(t i ) = [v w (t i ), θ w (t i ), T(t i ), H(t i )]

[0039] In the formula, M(t i ) is the meteorological data at time t i , v w (t i ), θ w (t i ), T(t i ), H(t i ) are the numerical values of wind speed, wind direction, temperature, and humidity at time t i respectively.

[0040] Preferably, the dynamic tracking module specifically includes:

[0041] Meteorological data fusion and risk modeling unit: Determine the contribution weights of wind speed and wind direction to the drift trajectory based on correlation analysis; use Monte Carlo simulation to comprehensively calculate the probability of the reflective film invading the core area by integrating meteorological prediction data and trajectory prediction results; dynamically adjust the invasion probability threshold according to the meteorological warning level to improve the sensitivity of the model.

[0042] Preferably, the meteorological data fusion and risk modeling unit specifically includes:

[0043] Use Monte Carlo simulation to comprehensively calculate the probability of the reflective film invading the core area by integrating meteorological prediction data and trajectory prediction results:

[0044]

[0045] In the formula, is the predicted drift trajectory value at time t; N is the number of random samples, usually taking 10^4 to 10^6 times; v i , θ i are the random values of wind speed and wind direction sampled from the meteorological prediction distribution; φ i are the physical parameters of the device, including the area of the reflective film and the drag coefficient.

[0046] Preferably, the hierarchical alarm module specifically includes:

[0047] Invasion risk classification and early warning unit: Classify the risk into low, medium, and high levels according to the probability value, and trigger the corresponding level response mechanism; mark the high-risk trajectory path through a visual map and push warning information to the operation and maintenance system; start emergency measures in combination with the prediction results;

[0048] Trigger alarm response unit: Emit a warning sound through a directional acoustic emitter to cover a predetermined range; project a red flashing light spot onto the target area using a high-brightness LED array;

[0049] UAV interception instruction generation: Generate an interception route based on the A* algorithm, and preferentially select the upwind flight path to offset the drift impact; send coordinates to the UAV control station through the LoRa wireless communication protocol;

[0050] Power grid power-off protection unit: Segment power-off strategy, and trigger the isolation of the surrounding three-level power grids in sequence according to the distance of the target from the core equipment;

[0051] Model optimization unit: Compare the actual invasion events with the prediction results, evaluate the accuracy of the model and correct the parameters; use virtual reality technology to generate drift scenarios under different meteorological conditions to enhance the generalization ability of the model.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] The present invention proposes to use lidar sensing technology to accurately determine the position of the residual reflective film. With a clear purpose for the staff, the cleaning is more efficient; it improves work efficiency, saves time and effort. In the past, the traditional method of cleaning the residual reflective film in the fruit forest around the substation relied solely on manual inspections and cleaning, which was prone to omissions and had problems of incomplete and untimely cleaning. By using lidar sensing technology, the link of personnel patrol is saved, time and effort are saved, and work efficiency is improved; the lidar system has high resolution, good concealment, and strong anti-interference ability, and can work continuously and effectively in daily work. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is an internal framework diagram of a system for monitoring reflective films around a substation based on lidar technology;

[0055] Figure 2 It is a basic principle flowchart of the lidar sensor working;

[0056] Figure 3 It is an external view of the lidar sensor;

[0057] Figure 4 It is a schematic diagram of the device for simulating the emission and reception of signals;

[0058] Figure 5 It is a schematic diagram of the lidar scanning range;

[0059] Figure 6 It is a schematic diagram of the principle of the device for simulating the emission and reception of signals;

[0060] Figure 7 It is a schematic diagram of the structure of the installation position of the lidar sensor. DETAILED DESCRIPTION OF THE INVENTION

[0061] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0062] Referring to Figure 1 as shown, a system for monitoring reflective films around a substation based on lidar technology includes:

[0063] Lidar scanning module: A multi-line lidar array is set up to collect three-dimensional point cloud data of the area around the substation in a horizontal scanning mode;

[0064] Reflection intensity analysis module: It is used to extract the abnormal areas in the point cloud data where the reflection intensity is higher than the preset threshold, and generate the coordinate information of the suspected targets of the reflective film in combination with the spatial positioning algorithm;

[0065] Multi - spectral verification module: It includes a visible - light camera and an infrared sensor, which are used to perform image feature matching on abnormal areas to distinguish reflective films from high - reflection interference objects;

[0066] Dynamic tracking module: Analyze the drifting trajectory of the reflective film through time series and predict the probability of its intrusion into the core area of the substation based on meteorological data;

[0067] Hierarchical alarm module: Trigger audible and visual alarms, drone interception instructions, and power grid power - off protection signals according to the intrusion probability.

[0068] It should be noted that the threshold segmentation unit of the reflection intensity analysis module includes:

[0069] Dynamically adjust the threshold: Dynamically adjust the threshold according to the reflection characteristics of the reflective film material and the statistical reflection intensity of the environmental background. For example, when the reflection intensity of the environmental background is low, the threshold can be appropriately reduced to more sensitively detect the reflective film; while in an environment with a high reflection intensity, the threshold needs to be increased to avoid misjudgment.

[0070] The spatial positioning unit includes: Calculate the three - dimensional coordinates by combining the lidar position. Using the known lidar position information and combining the distance information in the point - cloud data, determine the coordinates of the target area in three - dimensional space through geometric calculations, which usually involves triangulation or other methods of spatial geometry;

[0071] Use the weighted centroid as the final coordinate: Use the weighted centroid of the clustering area as the final coordinate of the reflective - film target. The calculation of the weighted centroid can consider the reflection intensity or other characteristics of each point, making the coordinates better represent the actual position of the target.

[0072] The interference - object exclusion unit in the multi - spectral verification module includes:

[0073] Adaptive adjustment of the reflectivity discrimination threshold: Adaptively adjust the reflectivity discrimination threshold of the visible - light imaging feature and the infrared induction feature according to the environmental light intensity, which helps to accurately distinguish the reflective film from the interference object under different lighting conditions;

[0074] Exclude interference objects by combining spatial positioning information: Combine spatial positioning information to exclude interference objects with similar reflection characteristics. By analyzing the position and distribution of the target in space, misjudgment can be further reduced;

[0075] Eliminate noise through continuous - frame data comparison: Eliminate the influence of transient high - reflection noise through continuous - frame data comparison. Transient high - reflection may be caused by environmental changes or other dynamic factors, and these noises can be effectively removed through time - series analysis;

[0076] Screening point set to generate initial candidate regions: Traverse each point in the 3D point cloud data, and filter out the points with reflection intensity higher than the current threshold to form an initial candidate region. This step is similar to threshold segmentation in image processing, which distinguishes foreground and background by comparing the gray value of each pixel with the threshold.

[0077] The reflective film trajectory data acquisition unit in the dynamic tracking module includes:

[0078] Lidar real-time capture: Real-time capture the drifting trajectory point cloud data of the reflective film through the lidar system; the lidar can provide high-precision 3D position information and record the position coordinates of the reflective film at different time points;

[0079] Time series position coordinate recording: Record the captured point cloud data in chronological order to form time series position coordinates, which helps to analyze the movement trajectory and speed change of the reflective film later.

[0080] The meteorological data synchronous acquisition unit includes:

[0081] Integrated meteorological parameter acquisition: Integrate meteorological sensors such as wind speed, wind direction, temperature and humidity to collect these meteorological parameters in real time. Meteorological conditions have a significant impact on the drift of the reflective film, so these data are crucial for accurately predicting its trajectory;

[0082] Timestamp alignment: Align the timestamp of the collected meteorological data with that of the trajectory data to ensure that the meteorological conditions and the position of the reflective film can be accurately associated at each time point during analysis.

[0083] The time series trajectory modeling unit includes:

[0084] Trajectory time series model construction: By analyzing parameters such as the moving speed, acceleration and direction angle of the reflective film, construct a time series model of the trajectory, which can help understand the drifting law of the reflective film and predict its future movement trend;

[0085] Kalman filter prediction and error optimization: Use the Kalman filter algorithm to predict the trajectory of the reflective film in the short term in the future. The Kalman filter can effectively process noise data and provide smoother and more accurate prediction results. At the same time, combine historical trajectory data to optimize the prediction error and improve the reliability of the prediction;

[0086] Geofence and spatial coordinate system mapping: Combine the geofence of the substation to map the trajectory data into the spatial coordinate system, and evaluate the intrusion risk by calculating the real-time distance between the reflective film and the core area of the substation.

[0087] The meteorological data fusion and risk modeling unit includes:

[0088] Correlation analysis to determine contribution weights: Determine the contribution weights of meteorological parameters such as wind speed and wind direction to the drifting trajectory of the reflective film through correlation analysis; This helps to understand the influence degree of different meteorological conditions on the movement of the reflective film.

[0089] Monte Carlo simulation to calculate the intrusion probability: Use the Monte Carlo simulation method to comprehensively combine meteorological prediction data and trajectory prediction results to calculate the probability of the reflective film intruding into the core area; Monte Carlo simulation obtains numerical results through repeated random sampling and can effectively evaluate the probabilities of different results in processes that are difficult to predict easily due to the intervention of random variables.

[0090] Dynamically adjust the intrusion probability threshold: Dynamically adjust the intrusion probability threshold according to the meteorological warning level to improve the sensitivity of the model; Under high-risk meteorological conditions, lower the threshold to more sensitively detect potential threats; Under low-risk conditions, raise the threshold to reduce false alarms.

[0091] In the grading alarm module, the intrusion risk grading and warning unit includes:

[0092] Risk grading and response mechanism: According to the calculated intrusion probability value, divide the risk into low, medium, and high levels, and trigger corresponding response mechanisms respectively; Low risk may only require monitoring, medium risk triggers a warning, and high risk activates emergency protection measures.

[0093] Visualized map and alarm information push: Mark the high-risk trajectory path through a visualized map and push alarm information to the operation and maintenance system to help operation and maintenance personnel timely understand the on-site situation and take corresponding measures.

[0094] Start emergency measures: Combine the prediction results and start corresponding emergency measures, such as notifying relevant personnel and preparing protective equipment.

[0095] The trigger alarm response unit includes:

[0096] Directional sound wave emitter: Emit a warning sound through a directional sound wave emitter to cover a predefined range to warn and disperse possible threats.

[0097] High-brightness LED array: Use a high-brightness LED array to project a red flashing light spot onto the target area to enhance the visual warning effect and improve attention and alertness.

[0098] The UAV interception unit includes:

[0099] Generate an interception flight path based on the A* algorithm: Generate an optimal interception flight path based on the A* algorithm, and preferentially select a headwind flight path to offset the influence of the drifting of the reflective film and improve the interception success rate.

[0100] LoRa wireless communication protocol to send coordinates: Send the target coordinates to the UAV control station through the LoRa wireless communication protocol to ensure the reliable transmission and timely response of the instructions.

[0101] The power grid power-off protection unit includes:

[0102] Segmented power-off strategy: According to the distance of the reflective film from the core equipment, trigger the isolation of the surrounding three-level power grid in sequence, and adopt a segmented power-off strategy to minimize the impact on the entire power grid and protect key equipment.

[0103] Reference Figure 2 As shown, the basic working principle of the lidar sensor includes: The lidar detection itself has very accurate ranging ability, and its ranging accuracy can reach several centimeters. The laser generator generates and emits a beam of light pulses, which hit an object and are reflected back, and finally received by the receiver. The receiver accurately measures the propagation time of the light pulse from emission to being reflected back. Since the light pulse travels at the speed of light, the receiver will always receive the previous reflected pulse before the next pulse is emitted. Given that the speed of light is known, the propagation time can be converted into a measurement of distance. Combining the height of the laser generator, the laser scanning angle, the position of the laser generator obtained from the GPS, and the laser emission direction obtained from the INS, the coordinates X, Y, Z of each ground light spot can be accurately calculated. The frequency of the laser beam emission can range from several pulses per second to tens of thousands of pulses per second. For example, in a system with a frequency of 10,000 pulses per second, the receiver will record 600,000 points in one minute. Generally speaking, the ground light spot spacing of the LIDAR system ranges from 2 to 4m.

[0104] Reference Figure 4 As shown, the equipment for simulating the transmission and reception of signals includes a laser transmitter, an optical receiver, an information processing system, etc.

[0105] Reference Figure 5 As shown, the schematic diagram of the lidar scanning range can set the maximum scanning distance and the minimum scanning distance, and use the angular resolution to specifically determine the position, approximate contour, range and other information of the target object.

[0106] Reference Figure 6 and Figure 7As shown in the figure, the design of the marked part in the figure: ① is a laser emitter that emits multiple light rays to scan the area it belongs to; ② is an optical receiver. The light rays emitted by the laser emitter are reflected back and received by the optical receiver, and then transmitted to the computer information processing system; ③ is the computer information processing system that compares the emitted light rays with the transmitted signals. After comparing with the preset target information reference value, it determines information such as the position and size range of the target object. At the same time, the computer terminal is connected to ④ the lidar sensor, and the rotation of the direction can be controlled. ⑤ is the automatic telescopic bracket of the lidar sensor. When the monitoring range of the lidar is lower than 500 meters around, the telescopic bracket can be telescoped up and down to control the monitoring of the lidar sensor within the preset range. ⑥ is the base fixing device of the lidar sensor, which is fixed to the top plane of the main control building of the substation. ⑦ is the top of the main control building of the substation, and the lidar sensor is installed at a corner above the top of the building.

[0107] Embodiment:

[0108] 1. Install lidar sensors at the four corner walls of the substation and the top of the main control building. Each radar sensing system continuously emits light beams to the ground in 360 degrees, can rotate freely up, down, left and right, and is responsible for monitoring the existence range, size, etc. of the targets within its own range.

[0109] 2. The specific working principle of the lidar sensing system: Lidar (Laser Radar) is a radar system that detects the position, distance and other characteristic quantities of targets by emitting laser beams. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal (target echo) reflected from the target with the emitted signal. After appropriate processing, relevant information about the target can be obtained, such as target distance, azimuth, height, speed, attitude, and even shape parameters, so as to detect, track and identify the target. It consists of a laser transmitter, an optical receiver, a turntable and an information processing system, etc. The laser converts the electrical pulse into an optical pulse and emits it. The optical receiver then restores the optical pulse reflected from the target into an electrical pulse and sends it to the display. The target we monitor using the lidar sensing system is the reflective film in the orchard outside the substation. First, we use the installed radar sensing system to specifically emit light rays to the known detectable reflective film. The receiver receives the signal reflected from the reflective film, compares the emitted signal with the received signal, and transmits it to the information processing system to obtain the basic information reference value of the target reference object (the reflectivity of the reflective film is generally about 0.9).

[0110] 3. Set the scanning range of the lidar sensing system to 500 meters nearby, start the daily 360-degree scanning, compare the transmitted signal with the emitted signal, and then transmit it to the information processing system for comparison and screening with the preset information reference value, so as to determine the specific position of the reflective film.

[0111] 4. Connect the lidar sensor to a computer or a mobile terminal. Using the factory default IP address of the lidar sensor, set the IP address of the computer or mobile terminal to the same IP segment as the lidar sensor. Enter the IP address of the lidar sensor in the browser of the computer or mobile terminal. A prompt to download a plugin will appear. Click to install. After the installation is complete and it is opened, the computer and mobile terminal can view the information transmitted from the lidar sensor, and the direction of the lidar sensor can be adjusted according to actual needs. If you need to specifically view the situation in a certain specific direction, the radar sensor can be adjusted to the corresponding angle for further detailed observation.

[0112] 5. After determining the specific position of the reflective film within the range, arrange relevant personnel to handle it in a timely manner. The information collected by the lidar sensor is presented in the form of a reflectivity map. The reflectivity map presents the reflectivity information in the data in the form of an image, mapping the reflectivity value of each point to the brightness of the image to form a grayscale image. The reflectivity map can intuitively display the reflectivity differences on the surfaces of different objects, thereby further analyzing and understanding the relevant information of the target object.

[0113] 6. Set voice broadcast reminders on the remote computer and mobile terminal. When the presence of a reflective film is detected, a voice broadcast reminder will be issued to facilitate the staff to pay attention through the computer and mobile terminal in a timely manner.

[0114] Generally speaking, to detect the reflective film around the substation using lidar sensing technology, first, the installed lidar sensor emits a light beam. The reflected signal is compared with the transmitted signal to process the data. The data is presented as a reflectivity map on the computer or mobile terminal, which can accurately determine the position, shape, etc. of the reflective film. At the same time, voice broadcast reminders are issued on the computer and mobile terminal to facilitate the staff to discover and handle it in a timely manner.

[0115] In summary, the advantages of the present invention are as follows: accurately discovering the target position and clarifying the purpose of work. The reflective films in the fruit forests around the substation are not cleaned in a timely and thorough manner and are scattered everywhere. Using lidar sensing technology can accurately determine the position of the remaining reflective films, with clear purposes for the staff and more efficient cleaning; improving work efficiency, saving time and effort. In the past, the traditional method of cleaning the remaining reflective films in the fruit forests around the substation relied solely on manual inspections and cleaning, which was prone to omissions and there were problems of incomplete and untimely cleaning. Using lidar sensing technology saves the link of personnel inspections, saves time and effort, and improves work efficiency; the lidar system has high resolution, good concealment, and strong anti-interference ability, and can work continuously and effectively in daily work.

[0116] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, various changes and improvements will occur to the present invention, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection required by the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for monitoring reflective film around substations based on laser radar technology, characterized in that: include: LiDAR scanning module: a multi-line LiDAR array is set up to collect 3D point cloud data around the substation in horizontal scanning mode; Reflection intensity analysis module: used to extract abnormal areas in point cloud data where the reflection intensity is higher than the preset threshold, and generate coordinate information of the suspected target of the reflective film in combination with the spatial positioning algorithm; Multispectral verification module: includes a visible light camera and an infrared sensor, which is used to match image features of abnormal areas and distinguish between reflective film and highly reflective interference objects; Dynamic tracking module: Analyze the drift trajectory of reflective film through time series, and predict the probability of its intrusion into the core area of ​​the substation based on meteorological data; Hierarchical alarm module: triggers sound and light alarms, drone interception commands and power grid power failure protection signals according to the intrusion probability.

2. According to claim 1, the system for monitoring the reflective film around the substation based on laser radar technology is characterized in that: The laser radar scanning module specifically includes: Multi-line LiDAR unit: uses a multi-line laser transmitter to achieve 3D point cloud acquisition through vertically distributed multi-layer laser beams; Radar deployment unit: The array is deployed on the top of the substation wall and high-point bracket, and a horizontal rotation mechanism is used to achieve full-angle horizontal scanning, taking into account both near-field details and far-field coverage.

3. The system for monitoring reflective film around a substation based on laser radar technology according to claim 2 is characterized in that: The reflection intensity analysis module specifically includes: Threshold segmentation unit: dynamically adjusts the threshold based on the reflective film material characteristics and the environmental background reflection intensity statistics; traverses the three-dimensional point cloud data, screens the point set with reflection intensity higher than the threshold, and generates the initial candidate area; Cluster analysis unit: Based on the generation of the initial candidate area, density clustering is used to aggregate discrete high-reflection intensity points into continuous areas and exclude isolated noise points; the convex hull and concave hull of the clustered point cloud are calculated to determine the geometric shape and spatial range of the reflective film target; Spatial positioning unit: Combined with the known laser radar position, based on the point cloud distance information, the three-dimensional coordinates of the target area are calculated; the weighted centroid of the clustered area is used as the final coordinate of the reflective film target; Coordinate conversion unit: Converts the coordinates in the local coordinate system into the geodetic coordinate system and engineering application coordinate system to ensure matching with the global map; optimizes coordinate stability through multi-perspective data fusion and dynamic positioning algorithm.

4. The system for monitoring reflective film around a substation based on laser radar technology according to claim 2 is characterized in that: The reflection intensity analysis module specifically includes: Data preprocessing unit: deploy local edge computing nodes to perform point cloud denoising, data compression and feature extraction to reduce cloud transmission load; normalize the reflection intensity according to the laser radar's transmission power and distance attenuation model to eliminate the influence of distance and angle on the intensity value.

5. The system for monitoring reflective film around a substation based on laser radar technology according to claim 3 is characterized in that: The multi-spectral verification module specifically includes: Multispectral data acquisition unit: The visible light camera captures the RGB image of the target area through a high-resolution camera, recording the visual characteristics of color, texture, and shape; the infrared sensor uses the infrared sensor to detect the radiation heat characteristics of the target and the difference in reflectivity in the infrared band, identifying the difference in surface thermal response between reflective film and highly reflective materials; Multispectral feature classification unit: Input visible light imaging features and infrared sensing features into the deep learning model to generate a joint feature vector; Based on the reflective film sample library, train the support vector machine and random forest model to learn the difference features between reflective film and interference; Introduce transfer learning technology and use pre-trained networks to accelerate model convergence; Interference elimination unit: Adaptively adjust the reflectivity discrimination threshold of visible light imaging features and infrared sensing features according to the ambient light intensity; eliminate interferences with similar reflective characteristics by combining spatial positioning information; eliminate the influence of short-term high reflective noise by comparing continuous frame data; Result verification unit: Match the multispectral classification results with the point cloud reflectivity data to improve the confidence level; map the identified reflective film target to a three-dimensional space coordinate system and output the coordinates and confidence score.

6. The system for monitoring reflective film around a substation based on laser radar technology according to claim 4 is characterized in that: The dynamic tracking module specifically includes: Reflective film trajectory data acquisition unit: uses laser radar to capture the drift trajectory point cloud data of the reflective film in real time and record its time series position coordinates; Meteorological data synchronization acquisition unit: integrates real-time meteorological parameters such as wind speed, wind direction, temperature and humidity, and aligns them with the trajectory data timestamp; Time series trajectory modeling unit: construct a time series model of the trajectory through moving speed, acceleration, and angular parameters to analyze the drift law of the reflective film; use Kalman filtering to predict future short-term trajectories, and optimize the prediction error by combining historical trajectory data; combine the substation geographic fence to map the trajectory to the spatial coordinate system and calculate the real-time distance to the core area.

7. The system for monitoring reflective film around a substation based on laser radar technology according to claim 4 is characterized in that: The reflective film trajectory data acquisition unit specifically includes: Acquire the reflective film trajectory data and record the position coordinates of the reflective film in the time series: P(t i )=[x(t i ),y(t i ),z(t i )] In the formula, P(t i ) is the reflective film at t i The position coordinates, x(t i ),y(t i ),z(t i ) is the coordinate component of the reflective film in three-dimensional space, t i is the timestamp; Align weather data with trajectory data timestamps: M(t i )=[v w (t i ),θ w (t i ),T(t i ),H(t i )] In the formula, M(t i ) is the time t i Meteorological data, v w (t i ),θ w (t i ),T(t i ),H(t i ) are wind speed, wind direction, temperature and humidity at time t i The numerical value of .

8. The system for monitoring reflective film around a substation based on laser radar technology according to claim 4 is characterized in that: The dynamic tracking module specifically includes: Meteorological data fusion and risk modeling unit: Determine the contribution weight of wind speed and wind direction to drift trajectory based on correlation analysis; use Monte Carlo simulation to integrate meteorological forecast data and trajectory prediction results to calculate the probability of reflective film invading the core area; dynamically adjust the intrusion probability threshold according to the meteorological warning level to improve the model sensitivity.

9. The system for monitoring reflective film around a substation based on laser radar technology according to claim 4 is characterized in that: The meteorological data fusion and risk modeling unit specifically includes: Monte Carlo simulation is used to combine meteorological forecast data and trajectory prediction results to calculate the probability of reflective film invading the core area: In the formula, is the predicted value of the drift trajectory at time t; N is the number of random samples, usually 10^4 to 10^6 times; v i ,θ i are random values ​​of wind speed and direction sampled from the weather forecast distribution; φ i It is the physical parameters of the equipment, including the reflective film area and the resistance coefficient.

10. The system for monitoring reflective film around a substation based on laser radar technology according to claim 5 is characterized in that: The hierarchical alarm module specifically includes: Intrusion risk classification and early warning unit: divides risks into three levels: low, medium and high according to probability values, triggering corresponding level response mechanisms; marks high-risk trajectory paths through visual maps, and pushes alarm information to the operation and maintenance system; initiates emergency measures based on prediction results; Trigger alarm response unit: emits warning sound through directional sound wave transmitter, covering a predetermined range; uses high-brightness LED array to project red flashing light spots to the target area; Drone interception unit: Generates interception routes based on the A* algorithm, giving priority to upwind flight paths to offset drift effects; sends coordinates to the drone control station via the LoRa wireless communication protocol; Grid power outage protection unit: Segmented power outage strategy, triggering the isolation of the surrounding three-level grids in sequence according to the distance of the target to the core equipment.

Images