Power transmission pole foundation pit scanning sonar detection device, system and method based on unmanned aerial vehicle

Abstract

The invention discloses a power transmission pole foundation pit scanning sonar detection device, system and method based on an unmanned aerial vehicle, and the method comprises the steps: receiving a detection task instruction inputted by terminal equipment, carrying out the initialization and path planning according to the received detection task instruction, and carrying out the calibration; setting an automatic program; generating a defect identification result and an execution signal; performing visualization processing on the acquisition process and the execution process to generate a detection report and a defect distribution diagram; and carrying out quality analysis and classification processing. In the implementation process of the technical scheme, the detection task instruction is input through the terminal equipment, accurate positioning and automatic detection of the power transmission pole foundation pit are achieved, the execution signal is generated according to the defect recognition result, the unmanned aerial vehicle automatically executes the corresponding defect processing operation, and therefore the efficiency and accuracy of foundation pit detection are improved; and meanwhile, the collected data is visually displayed through the visual processing module, and a visual detection report and a defect distribution diagram are generated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power engineering detection, in particular to a power transmission tower foundation pit scanning sonar detection device, system and method based on a UAV. BACKGROUND

[0002] The power transmission tower foundation pit is the basic structure of the power transmission line project, and its construction quality directly affects the stability of the tower and the safety of the line operation. Therefore, after the completion of the power engineering construction, it must be quality detected.

[0003] The existing detection methods mostly rely on manual measurement and visual inspection. For example, the detection personnel visually measures the size and shape of the foundation pit by using tools such as a tape measure, a level, a searchlight, etc. This method is not only inefficient, but also has great safety hazards in complex terrain or harsh environments. Moreover, manual measurement is greatly affected by subjective factors, and has limited ability to identify small defects (such as millimeter-level cracks, shallow collapse, etc.), which can easily cause missed detection or misjudgment. In recent years, some research has attempted to introduce automated equipment to optimize the detection process. For example, a ground robot is used to carry a laser scanner or an ultrasonic sensor to detect the foundation pit, or a fixed-wing UAV is used to carry a high-definition camera to detect the foundation pit above, which improves the detection efficiency.

[0004] However, in the implementation process of the above technical solutions, there are still some problems. For example, the ground robot is limited in moving in complex terrain and is difficult to cover all detection areas. Although the fixed-wing UAV carrying a high-definition camera can improve the detection efficiency and coverage, it still has a detection blind area in hidden areas or harsh weather conditions, and it is difficult to obtain the complete structure information of the foundation pit inside. Therefore, there is an urgent need for a lightweight, high-precision, multi-scene adaptive foundation pit detection scheme to solve the problem that the existing technology cannot efficiently and accurately detect the internal structure of the foundation pit in complex terrain and harsh weather conditions. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the defects of the prior art and provide a power transmission tower foundation pit scanning sonar detection device, system and method based on a UAV,

[0006] To solve the above technical problems, the technical solution of the present application is: a power transmission tower foundation pit scanning sonar detection method based on a UAV, comprising:

[0007] Receiving a detection task instruction input from a terminal device, the detection task instruction including the position coordinates of the foundation pit, the flight parameters of the UAV and the sonar parameters, and initializing and path planning according to the received detection task instruction, and calibrating;

[0008] An automatic program is set, including parsing the detection task instruction into an executable operation sequence, writing the path planning data and flight control parameters into the flight control system of the UAV according to a preset communication protocol, and enabling the UAV to autonomously perform flight and data collection;

[0009] According to the received detection data, defect identification is performed to generate a defect identification result, and an execution signal is generated according to the defect identification result, and the execution signal is sent to the UAV, and the UAV performs a defect processing operation according to the received execution signal;

[0010] After the UAV performs the defect processing operation according to the received execution signal, the collection process and the execution process are visualized to generate a detection report and a defect distribution map;

[0011] The quality of the power transmission pole foundation pit is analyzed, and classified processing is performed according to the analysis result.

[0012] In the implementation process of the technical scheme of the present application, the terminal device inputs the detection task instruction to realize accurate positioning and automatic detection of the power transmission pole foundation pit, and an execution signal is generated according to the defect identification result, so that the UAV automatically performs the corresponding defect processing operation, thereby improving the efficiency and accuracy of the foundation pit detection. At the same time, the visualized display module is used to visualize the collected data to generate an intuitive detection report and defect distribution map.

[0013] Further, in the path planning, a path generation algorithm based on a grid map is used, the terrain features of the foundation pit area and the sonar detection range are combined, the detection flight line spacing and the hovering point position are laid out, and the spacing of different hovering points is less than or equal to two-thirds of the sonar detection radius.

[0014] Further, the automatic program includes:

[0015] A takeoff point and a hovering point are generated, the hovering point including a flight height and a hovering time, the flight height being set according to the foundation pit depth and the sonar detection angle, and the hovering time being set according to the sonar data collection requirement;

[0016] After reaching the hovering point, the sonar detection unit starts multi-angle scanning, emits ultrasonic pulses at a set frequency and receives echo signals, collects the intensity, propagation time and phase information of the echo signals, and performs filtering, amplification and digitization processing on the collected echo signals through the sonar signal processing unit;

[0017] The sonar detection data and the real-time positioning information of the UAV are collected synchronously and stored in a data binding manner.

[0018] Further, the defect identification process further includes:

[0019] The probe data is pre-processed, including removing noise interference, correcting signal attenuation, and correcting probe deviation caused by flight attitude change;

[0020] Feature parameters are extracted from the pre-processed data, including peak intensity, pulse width, frequency distribution, and waveform symmetry of the echo signal, and are quantitatively processed to establish a feature database;

[0021] A standard feature template of the power transmission pole foundation pit is preset, which contains the feature parameter distribution range and statistical characteristics of the normal area, and after quantization of the standard feature template, it is compared with the data to be identified to determine whether the power transmission pole foundation pit has abnormal features;

[0022] Defect features of the power transmission pole foundation pit are extracted from the feature parameters, and a defect index of each foundation pit is calculated according to the extracted defect features, which considers the abnormality degree and spatial distribution characteristics of the echo signal, and is used to represent the integrity and stability of the foundation pit structure;

[0023] According to the calculation result of the defect index, the corresponding execution signal is generated, and the generated execution signal is sent to the unmanned aerial vehicle.

[0024] Further, through statistical analysis and pattern recognition method, the feature parameters are classified and clustered, the data groups with similar features are identified, and the feature patterns of potential defect areas are extracted, and the machine learning algorithm is used to train the data in the feature database to build a defect recognition model.

[0025] Further, the normalization method is used for quantitative processing to convert feature parameters of different dimensions to a unified scale, and through dimension reduction technology, the main change trend of the feature parameters is extracted, and the feature database not only stores the original feature data, but also records the feature vectors after quantitative and dimension reduction processing.

[0026] Further, the calculation of the defect index adopts the weighted summation method, and each defect feature is linearly superimposed according to its weight on the stability of the foundation pit, and finally a comprehensive evaluation value is obtained.

[0027] Further, the execution signal includes the cruise path adjustment instruction of the unmanned aerial vehicle, the sonar detection parameter optimization instruction and the data acquisition mode switching signal.

[0028] The power transmission pole foundation pit scanning sonar detection system based on unmanned aerial vehicle includes:

[0029] The instruction receiving module is used for receiving the probe task instruction input from the terminal device, which includes the position coordinates of the foundation pit, the flight parameters of the unmanned aerial vehicle and the sonar parameters, and performs initialization and path planning according to the received probe task instruction, and performs calibration;

[0030] An automatic control module is configured to set an automatic program, including parsing the detection task instruction into an executable operation sequence, writing the path planning data and flight control parameters into the flight control system of the UAV according to a preset communication protocol, and enabling the UAV to autonomously perform flight and data collection;

[0031] A defect identification module is configured to identify defects according to the received detection data, generate a defect identification result, and generate an execution signal according to the defect identification result, and send the execution signal to the UAV, which performs a defect processing operation according to the received execution signal;

[0032] A visualization processing module is configured to visualize the collection process and the execution process after the UAV performs the defect processing operation according to the received execution signal, and generate a detection report and a defect distribution map.

[0033] A quality analysis module is configured to analyze the quality of the power transmission tower foundation pit and perform classification processing according to the analysis result.

[0034] The unmanned aerial vehicle-based power transmission tower foundation pit scanning sonar detection device comprises:

[0035] An unmanned aerial vehicle platform, which is a multi-rotor unmanned aerial vehicle, is integrated with an obstacle avoidance sensor for avoiding obstacles during flight, and has a detachable battery compartment.

[0036] A communication unit is arranged on the unmanned aerial vehicle platform for data transmission and instruction interaction with a ground control terminal, and supports 4G / 5G communication protocol.

[0037] A sonar detection unit is fixed to the bottom of the unmanned aerial vehicle platform, which comprises a sonar probe and a sonar signal processing unit. The sonar probe is used to emit ultrasonic signals to the inside of the foundation pit and receive echo signals reflected by the internal structure of the foundation pit. The sonar signal processing unit amplifies, filters and digitizes the echo signals, and extracts the depth and shape information of the internal structure of the foundation pit.

[0038] The sonar detection unit further comprises a power supply unit, which comprises a battery protection shell with an opening on the side surface, and a battery installed inside the battery protection shell, which is used to provide power for the sonar probe and the sonar signal processing unit.

[0039] By adopting the above technical solution, the present invention has the following beneficial effects: by inputting detection task instructions through terminal equipment, it can achieve precise positioning and automated detection of power transmission pole foundation pits, and generate execution signals based on defect identification results, enabling the UAV to automatically perform corresponding defect handling operations, thereby improving the efficiency and accuracy of foundation pit detection; at the same time, the collected data can be visualized through the visualization processing module to generate intuitive detection reports and defect distribution maps. Attached Figure Description

[0040] Figure 1 This is a structural diagram of the UAV-based scanning sonar detection device for power pole foundation pits in this application.

[0041] Figure 2 This is a flowchart of the UAV-based scanning sonar detection method for power pole foundation pits in this application;

[0042] Figure 3 This is a block diagram of the UAV-based scanning sonar detection system for power pole foundation pits in this application.

[0043] The reference numerals in the attached figures are as follows:

[0044] 1. Unmanned aerial vehicle (UAV) platform; 2. Communication unit; 3. Sonar detection unit; 4. Power supply unit; 5. Battery protective casing. Detailed Implementation

[0045] This invention provides a scanning sonar detection device, system, and method for power transmission pole foundation pits based on unmanned aerial vehicles (UAVs). Those skilled in the art can refer to the content of this document and appropriately modify the process parameters to achieve the desired results. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and fall within the scope of protection of this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can clearly modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0046] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0047] Example 1: As Figure 1 As shown, this application also proposes a UAV-based scanning sonar detection method for power transmission pole foundation pits. This method is used to scan and detect the internal structure of the foundation pit using the aforementioned device, thereby obtaining high-precision three-dimensional structural information and identifying potential defects. The method includes the following steps:

[0048] Step 101: Receive the detection mission command input from the terminal device. The detection mission command includes the location coordinates of the pit, the flight parameters of the UAV and the sonar parameters. Perform initialization and path planning according to the received detection mission command, and perform calibration.

[0049] Before starting the detection mission, the user needs to input the detection mission command on the ground terminal device to start the drone and carry out the pit detection process. The detection mission command includes (but is not limited to) the location coordinates of the pit, which can be latitude and longitude information, depth range, etc., the drone's flight parameters, such as maximum flight altitude, flight speed, and hovering time, and sonar parameters, including the center frequency of sonar detection, scanning angle range, detection resolution, etc. The setting of these parameters can ensure that the drone flies stably above the target pit area and starts the detection mission.

[0050] After receiving the detection task instruction from the terminal device, the device needs to be initialized. The device initialization process includes self-testing and calibration of each unit to ensure that each module is operating normally. Among them, sonar calibration includes adjusting the transmission and reception sensitivity of the sonar probe and conducting echo tests on a known standard reflector to calibrate its time delay error and sensitivity to ensure the accuracy and consistency of subsequent detection data.

[0051] After initialization, path planning is performed based on the pit location coordinates and flight parameters in the detection mission instructions. Path planning includes determining the take-off and landing points of the UAV, the flight route, and the coverage path of the detection area to ensure that the UAV can complete the pit scanning task according to the preset trajectory. In the path planning, a path generation algorithm based on grid map is adopted. Combined with the terrain features of the pit area and the sonar detection range, the detection flight path spacing and hovering point positions are set. In this embodiment, the spacing between different hovering points is less than or equal to two-thirds of the sonar detection radius to ensure sufficient data overlap between adjacent detection areas, thereby improving the accuracy and completeness of 3D reconstruction. For example, when the sonar detection radius is 3 meters, the spacing between adjacent hovering points should be less than or equal to 2 meters to ensure effective coverage of the detection area and avoid data omissions or reconstruction blind spots due to excessive spacing.

[0052] In addition, during the path planning process, it is also necessary to comprehensively consider the drone's flight altitude, wind speed and direction, as well as surrounding environmental interference factors, and dynamically adjust the flight trajectory to ensure the continuity and stability of sonar detection data. For areas with complex terrain or obstacles, the path planning algorithm will automatically avoid them and replan a safe route based on the data collected by the obstacle avoidance sensor to prevent drone collisions or detection interruptions.

[0053] Path planning for UAVs is a relatively mature method in the existing technology, such as global path planning based on the A* algorithm or Dijkstra's algorithm. This embodiment will not be described in detail, but you can refer to the relevant technology for details.

[0054] Step 102: Set up an automation program, including parsing the probe mission instructions into an executable sequence of operations, and writing the path planning data and flight control parameters into the UAV's flight control system according to a preset communication protocol, so that the UAV can autonomously perform flight and data acquisition.

[0055] In addition to remotely controlling the drone to perform reconnaissance missions via terminal devices, the reconnaissance mission parameters can also be pre-programmed into the drone's local control system via a wireless communication module, enabling offline autonomous operation. This allows for stable execution of reconnaissance missions even in environments with no network coverage or weak signals. Specifically, this automation program includes:

[0056] Generate takeoff and hovering points. The hovering point includes flight altitude and hovering time. The flight altitude is set according to the depth of the pit and the sonar detection angle, and the hovering time is set according to the sonar data acquisition requirements.

[0057] In automatic flight mode, the UAV needs a clearly defined takeoff and hovering point. Upon reaching the takeoff point, the UAV ascends vertically at the set altitude and, after stabilizing, flies sequentially to each hovering point along a predetermined route. At each hovering point, the UAV automatically triggers its sonar detection equipment to collect data. During this process, other modules need to be coordinated. For example, while the sonar is operating, the UAV's positioning system calibrates its position and attitude in real time to ensure the consistency of the detection data. Simultaneously, the flight control system dynamically compensates for the flight attitude based on data from the inertial measurement unit (IMU) and the global navigation satellite system (GNSS) to prevent attitude deviations caused by external disturbances from affecting the accuracy of the sonar data. Throughout the detection process, the automated program caches the detection data from each hovering point in real time and, after the flight is completed, organizes the data and uploads it to the ground terminal equipment.

[0058] After reaching the hovering point, the sonar detection unit begins to perform multi-angle scanning, and emits ultrasonic pulses at a set frequency and receives echo signals. At the same time, it collects the intensity, propagation time and phase information of the echo signals, and filters, amplifies and digitizes the collected echo signals through the sonar signal processing unit to extract effective detection data.

[0059] The system simultaneously collects sonar detection data and UAV real-time positioning information, and binds and stores the data so that the spatial location and detection characteristics of the detection points can be accurately restored during subsequent data processing. Specifically, the data binding process adopts a timestamp alignment method to ensure that the sonar detection data and positioning information are consistent in time sequence. A high-precision clock synchronization mechanism ensures that the data acquisition time difference between different sensors is at the millisecond level, thereby effectively improving the spatial resolution and geometric accuracy of the data.

[0060] Step 103: Based on the received detection data, perform defect identification, generate defect identification results, generate execution signals based on the defect identification results, send the execution signals to the UAV, and the UAV performs defect processing operations according to the received execution signals;

[0061] After receiving the detection data from the UAV, the ground terminal equipment performs defect identification on the detection data and generates defect identification results. The defect identification process further includes:

[0062] The detection data is preprocessed, including removing noise interference, correcting signal attenuation, and correcting detection deviations caused by changes in flight attitude.

[0063] Sonar data contains noise, which may come from environmental interference, equipment errors, or signal reflection. Therefore, filtering algorithms are needed to denoise the data. Commonly used methods include moving average filtering, wavelet transform denoising, and adaptive filtering.

[0064] Simultaneously, signal attenuation correction is also required. Since the intensity of ultrasonic waves weakens during propagation due to medium absorption and scattering, it is necessary to use a model of the relationship between detection distance and signal intensity to compensate for the data. For example, an exponential attenuation model can be used to normalize the signal intensity to eliminate the influence of propagation distance on the detection results. In addition, changes in flight attitude may cause the sonar detection direction to be inconsistent with the normal direction of the target surface, thus introducing detection errors. Therefore, it is necessary to perform geometric correction on the detection data according to the attitude angle of the UAV to ensure the accuracy and consistency of the detection results.

[0065] Feature parameters are extracted from the preprocessed data, including the peak intensity, pulse width, frequency distribution and waveform symmetry of the echo signal, and then quantized to establish a feature database.

[0066] Sonar data from power transmission pole foundation pits contains defect-related feature information, necessitating feature parameter extraction. These parameters include key indicators such as peak intensity, pulse width, frequency distribution, and waveform symmetry of the echo signal, which are quantified into analyzable values ​​to establish a feature database. Specifically, statistical analysis and pattern recognition methods are used to classify and cluster the feature parameters, identifying data groups with similar characteristics. This allows for the extraction of feature patterns from potential defect areas, providing data support for subsequent defect identification. Building upon this, machine learning algorithms are used to train the data in the feature database, constructing a defect identification model. This model can automatically identify defect types based on input sonar feature parameters and output corresponding identification results.

[0067] After extracting the feature parameters, they need to be quantized and a feature database needs to be established. Specifically, the quantization process can use normalization methods to convert feature parameters with different dimensions into a uniform scale for subsequent analysis and modeling. For example, peak intensity and pulse width can be scaled proportionally to the range of 0 to 1 to eliminate the influence of differences in dimensions. At the same time, dimensionality reduction techniques such as principal component analysis (PCA) can be used to extract the main trends in the feature parameters, reduce redundant information, and improve the computational efficiency and recognition accuracy of the model. The established feature database not only stores the original feature data, but also records the feature vectors after quantization and dimensionality reduction, providing input data for subsequent defect classification and recognition.

[0068] A standard feature template for transmission pole foundation pits is preset. This template includes the distribution range and statistical characteristics of feature parameters in normal areas. After quantifying the standard feature template, it is compared with the data to be identified to determine whether there are abnormal features in the transmission pole foundation pits.

[0069] During construction, the foundation pit data of the power transmission pole will have a standard template. After the standard template is quantified, it will be used as a standard feature template for comparison and analysis with the actual detection data. When the feature parameters in the actual detection data deviate from the set threshold of the standard feature template, it will be judged that there is an anomaly or defect.

[0070] The defect features of the transmission pole foundation pit are extracted from the feature parameters, and the defect index of each foundation pit is calculated based on the extracted defect features. This index takes into account the degree of anomaly of the echo signal and its spatial distribution characteristics, and is used to characterize the integrity and stability of the foundation pit structure.

[0071] The defect characteristics in the feature parameters include (but are not limited to) the degree of distortion of the echo signal, energy attenuation rate, waveform asymmetry and multi-band response differences. These characteristics can effectively reflect the abnormal changes in the internal structure of the foundation pit and calculate the defect index. The defect index is calculated by weighted summation, which linearly superimposes each defect characteristic according to its weight in the stability of the foundation pit, and finally obtains a comprehensive evaluation value. The higher the value, the greater the possibility of defects in the foundation pit. The defect index comprehensively considers the multi-dimensional feature information of the foundation pit structure and can reflect its overall stability and potential risks.

[0072] Based on the calculation results of the defect index, corresponding execution signals are generated and sent to the drone;

[0073] After calculating the defect index, corresponding execution signals need to be generated based on the defect index. These execution signals include (but are not limited to) drone cruise path adjustment commands, sonar detection parameter optimization commands, and data acquisition mode switching signals. These signals are used to guide the drone to perform refined detection and data verification of abnormal areas. For example, when the defect index of a certain foundation pit exceeds a preset threshold, it is highly likely that there is a defect. At this time, corresponding control commands need to be generated to make the drone repeatedly scan the foundation pit and adjust the sonar detection frequency and scanning angle during the repeated scanning process to obtain higher resolution echo data. Then, the aforementioned analysis process is repeated to avoid safety hazards caused by misjudgment or missed detection. There is no need to restart the drone, which improves the detection efficiency of power transmission pole foundation pits. At the same time, it can also be linked with the ground monitoring system to upload the location information and defect index of abnormal foundation pits to the management center in real time, so that maintenance personnel can grasp the on-site situation in a timely manner and formulate corresponding disposal plans. In addition, the system can also automatically trigger an early warning mechanism based on the historical trend of the defect index to remind relevant personnel to pay close attention to potential risk areas and conduct regular re-inspections, thereby realizing closed-loop management of the entire process of power transmission pole foundation pit defect detection.

[0074] Step 104: After the UAV performs defect processing operations according to the received execution signals, the acquisition process and execution process are visualized to generate a detection report and a defect distribution map;

[0075] After the UAV completes the defect handling operation, the collected echo data and defect index information will be systematically integrated, and the internal structure of the foundation pit will be visualized through 3D modeling technology. A detection report and a defect distribution map will be generated. The detection report contains key information such as defect location, type, severity, and handling suggestions, and is presented intuitively through a combination of charts and text. The defect distribution map is based on a geographic information system (GIS), which combines the spatial coordinates of the foundation pit and the distribution of defect index to mark the specific location and risk level of abnormal areas, facilitating subsequent maintenance and repair work. At the same time, the system supports exporting the detection report and defect distribution map to multiple formats, such as PDF, DWG, or KML, to meet the information exchange needs of different departments, and data sharing and remote access can be achieved through a cloud platform.

[0076] Step 105: Conduct quality analysis on the transmission pole foundation pit and classify the results. By comprehensively analyzing the collected foundation pit structural data and defect index, assess the overall quality status of the foundation pit and classify it according to the type and severity of defects, dividing the foundation pit into minor defect area, moderate defect area and severe defect area.

[0077] Minor defect areas mainly manifest as tiny cracks or localized unevenness on the surface of the foundation pit, which usually have little impact on structural safety and only require regular inspections and surface repairs. Moderate defect areas include cracks of a certain depth or localized loose areas, which require reinforcement and subsequent re-inspection to ensure the repair effect. Severe defect areas show obvious structural damage or settlement, and must be reinforced or rebuilt immediately to prevent safety accidents. At the same time, relevant data should be included in the risk database for subsequent risk assessment and prevention measure development.

[0078] Example 2: Figure 2 As shown, this application also proposes a UAV-based scanning sonar detection system for power transmission pole foundation pits. This system operates the UAV-based scanning sonar detection method for power transmission pole foundation pits described in Embodiment 2. The system includes:

[0079] The instruction receiving module is used to receive the detection mission instructions input from the terminal device. The detection mission instructions include the location coordinates of the pit, the flight parameters of the UAV and the sonar parameters. The module performs initialization and path planning based on the received detection mission instructions and performs calibration.

[0080] The automation control module is used to set up automation programs, including parsing the detection mission instructions into an executable sequence of operations, and writing path planning data and flight control parameters into the UAV's flight control system according to a preset communication protocol, so that the UAV can autonomously perform flight and data acquisition.

[0081] The defect identification module is used to identify defects based on the received detection data, generate defect identification results, generate execution signals based on the defect identification results, and send the execution signals to the UAV. The UAV performs defect processing operations according to the received execution signals.

[0082] The visualization processing module is used to visualize the acquisition and execution processes after the UAV performs defect processing operations according to the received execution signals, and generate detection reports and defect distribution maps.

[0083] The quality analysis module is used to perform quality analysis on the foundation pits of transmission poles and to classify the results based on the analysis.

[0084] Example 3: Figure 3 As shown, this application proposes a UAV-based scanning sonar detection device for power pole foundation pits. This device is used for high-precision 3D modeling and defect identification of the internal structure of the foundation pit, thereby achieving comprehensive perception and intelligent analysis of the foundation pit status in concealed areas and complex terrain conditions. The device includes:

[0085] Unmanned aerial vehicle (UAV) platform 1 is a multi-rotor UAV with vertical take-off and landing and hovering functions. UAV platform 1 integrates obstacle avoidance sensors to avoid obstacles during flight, and UAV platform 1 has a detachable battery compartment for quick battery replacement.

[0086] Communication unit 2 is installed on the UAV platform and is used to realize data transmission and command interaction with the ground control terminal. Communication unit 2 supports 4G / 5G communication protocols and can achieve stable data transmission in different electromagnetic environments.

[0087] The sonar detection unit 3 is fixed to the bottom of the UAV platform. The sonar detection unit 3 includes a sonar probe and a sonar signal processing unit. The sonar probe is used to emit ultrasonic signals into the pit and receive echo signals reflected back from the internal structure of the pit. The sonar signal processing unit amplifies, filters and digitizes the echo signals to extract the depth and shape information of the internal structure of the pit.

[0088] The sonar detection unit 3 also includes a power supply unit 4, which includes a battery protective case 5. The side of the battery protective case 5 has an opening, and a battery is installed inside the battery protective case 5. The battery is used to provide power to the sonar probe and the sonar signal processing unit. Due to the opening design of the battery protective case 5, air circulation can be increased during operation, preventing the battery from overheating due to long-term operation, thereby ensuring the stable operation of the sonar detection unit.

[0089] In addition, the device also includes a positioning and attitude determination unit, a data processing unit, etc. (not shown in the figure). This part can be designed with reference to relevant modules in the prior art. For example, a combination of GPS and IMU navigation can be used to achieve high-precision three-dimensional positioning and attitude measurement, ensuring the flight stability of the UAV and the accuracy of the detection data in complex environments. This part will not be described in detail in this embodiment.

[0090] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles (UAVs), characterized in that: include: The system receives detection mission instructions from the terminal device, which include the location coordinates of the pit, UAV flight parameters, and sonar parameters. It then initializes and plans the path based on the received detection mission instructions and performs calibration. The automation program is set up, including parsing the detection mission instructions into an executable sequence of operations, and writing the path planning data and flight control parameters into the UAV's flight control system according to the preset communication protocol, so that the UAV can autonomously perform flight and data acquisition. Defects are identified based on the received detection data, resulting in a defect identification result. An execution signal is then generated based on the defect identification result and sent to the UAV. The UAV performs defect processing operations according to the received execution signal. After the drone performs defect processing operations according to the received execution signals, the acquisition process and execution process are visualized to generate a detection report and a defect distribution map; The quality of the transmission pole foundation pits was analyzed, and the results were classified and processed accordingly.

2. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 1, characterized in that: In path planning, a path generation algorithm based on grid maps is adopted. Combining the terrain features of the pit area with the sonar detection range, the spacing of detection flight paths and the location of hovering points are set. The spacing between different hovering points is less than or equal to two-thirds of the sonar detection radius.

3. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 1, characterized in that: The automation program includes: Generate takeoff and hovering points. The hovering point includes flight altitude and hovering time. The flight altitude is set according to the depth of the pit and the sonar detection angle, and the hovering time is set according to the sonar data acquisition requirements. After reaching the hovering point, the sonar detection unit begins to perform multi-angle scanning, and emits ultrasonic pulses at a set frequency and receives echo signals. At the same time, it collects the intensity, propagation time and phase information of the echo signals, and filters, amplifies and digitizes the collected echo signals through the sonar signal processing unit. Simultaneously collect sonar detection data and real-time positioning information of UAVs, and bind and store the data.

4. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 1, characterized in that: The defect identification process further includes: The detection data is preprocessed, including removing noise interference, correcting signal attenuation, and correcting detection deviations caused by changes in flight attitude. Feature parameters are extracted from the preprocessed data, including the peak intensity, pulse width, frequency distribution and waveform symmetry of the echo signal, and then quantized to establish a feature database. A standard feature template for transmission pole foundation pits is preset. This template includes the distribution range and statistical characteristics of feature parameters in normal areas. After quantifying the standard feature template, it is compared with the data to be identified to determine whether there are abnormal features in the transmission pole foundation pits. The defect features of the transmission pole foundation pit are extracted from the feature parameters, and the defect index of each foundation pit is calculated based on the extracted defect features. This index takes into account the degree of anomaly of the echo signal and its spatial distribution characteristics, and is used to characterize the integrity and stability of the foundation pit structure. Based on the calculation results of the defect index, corresponding execution signals are generated and sent to the drone.

5. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 4, characterized in that: By classifying and clustering feature parameters using statistical analysis and pattern recognition methods, data groups with similar features are identified, and feature patterns of potential defect areas are extracted. Machine learning algorithms are then used to train the data in the feature database to build a defect identification model.

6. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 4, characterized in that: The quantization process uses a normalization method to convert feature parameters of different dimensions into a unified scale, and uses dimensionality reduction techniques to extract the main trends of change in the feature parameters. The established feature database not only stores the original feature data, but also records the feature vectors after quantization and dimensionality reduction.

7. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 4, characterized in that: The defect index is calculated using a weighted summation method, which linearly superimposes each defect feature according to its weight in terms of its impact on the stability of the foundation pit, and finally obtains a comprehensive evaluation value.

8. The method for scanning sonar detection of power transmission pole foundation pits based on unmanned aerial vehicles according to claim 4, characterized in that: The execution signals include UAV cruise path adjustment commands, sonar detection parameter optimization commands, and data acquisition mode switching signals.

9. A UAV-based scanning sonar detection system for power transmission pole foundation pits, used to implement the UAV-based scanning sonar detection method for power transmission pole foundation pits as described in any one of claims 1 to 8, characterized in that: include: The instruction receiving module is used to receive the detection mission instructions input from the terminal device. The detection mission instructions include the location coordinates of the pit, the flight parameters of the UAV and the sonar parameters. The module performs initialization and path planning based on the received detection mission instructions and performs calibration. The automation control module is used to set up automation programs, including parsing the detection mission instructions into an executable sequence of operations, and writing path planning data and flight control parameters into the UAV's flight control system according to a preset communication protocol, so that the UAV can autonomously perform flight and data acquisition. The defect identification module is used to identify defects based on the received detection data, generate defect identification results, generate execution signals based on the defect identification results, and send the execution signals to the UAV. The UAV performs defect processing operations according to the received execution signals. The visualization processing module is used to visualize the acquisition and execution processes after the UAV performs defect processing operations according to the received execution signals, and generate detection reports and defect distribution maps. The quality analysis module is used to perform quality analysis on the foundation pits of transmission poles and to classify the results based on the analysis.

10. A UAV-based scanning sonar detection device for power transmission pole foundation pits, characterized in that: include: The drone platform (1) is a multi-rotor drone. The drone platform (1) integrates obstacle avoidance sensors to avoid obstacles during flight. The drone platform (1) also has a detachable battery compartment. Communication unit (2), which is set on the UAV platform (1) and is used to realize data transmission and command interaction with the ground control terminal. The communication unit supports 4G / 5G communication protocol; The sonar detection unit (3) is fixed at the bottom of the UAV platform. The sonar detection unit (3) includes a sonar probe and a sonar signal processing unit. The sonar probe is used to transmit ultrasonic signals into the pit and receive echo signals reflected back from the internal structure of the pit. The sonar signal processing unit amplifies, filters and digitizes the echo signals to extract the depth and shape information of the internal structure of the pit. The sonar detection unit (3) also includes a power supply unit (4), which includes a battery protective case (5) with openings on the side and a battery installed inside the battery protective case (5) for providing power to the sonar probe and the sonar signal processing unit.

Images