Unmanned aerial vehicle intelligent power line inspection system

Abstract

The invention provides an unmanned aerial vehicle intelligent power line inspection system, and relates to the technical field of power line inspection. The unmanned aerial vehicle intelligent power line inspection system comprises an unmanned aerial vehicle platform which comprises an unmanned aerial vehicle body, a flight control system, a power system and a navigation system; the sensor module comprises a high-definition camera, an infrared camera, a laser radar and an ultrasonic sensor; the data processing module comprises an embedded processor, a data storage unit and a data transmission module and is used for processing and analyzing the data acquired by the sensor; the intelligent algorithm module is used for intelligently analyzing and judging the state of the power line; and the communication module comprises a wireless communication module and a satellite communication module and is used for realizing data transmission and remote control between the unmanned aerial vehicle and a ground control center. The unmanned aerial vehicle is used for replacing manual inspection, so that the work of personnel in dangerous environments such as high altitude and complex terrains is reduced, and the personnel risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power line inspection, and in particular to an unmanned aerial vehicle intelligent power line inspection system. Background Art

[0002] With the continuous growth of global electricity demand and the increasing complexity of power networks, the safe and stable operation of power systems faces unprecedented challenges. As a key infrastructure for power transmission, the operating status of power lines is directly related to the reliability of power supply. The background technology of the UAV intelligent power line inspection system mainly involves the monitoring and maintenance of power lines. Traditional power line inspections usually rely on manual inspections or ground inspection vehicles, which have problems such as low efficiency, high cost, and great safety hazards. Especially for high-voltage power lines, manual inspections are not only complicated to operate but also highly dangerous. Therefore, improving inspection efficiency and ensuring the safety of staff have become urgent issues to be solved.

[0003] However, the traditional power line inspection method mainly relies on manual inspection, which has many limitations and is difficult to meet the requirements of modern power systems for efficiency, safety and intelligence. Manual inspection is a commonly used method in power line maintenance. Inspection personnel usually inspect power lines by walking, riding or using helicopters. This method has the problems of low efficiency, poor safety, incomplete data and high cost. Therefore, technicians in this field provide a drone intelligent power line inspection system to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a drone intelligent power line inspection system, which solves the problems of low efficiency, poor safety, incomplete data and high cost in traditional methods.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an unmanned aerial vehicle intelligent power line inspection system, comprising:

[0008] UAV platform, including the UAV body, flight control system, power system and navigation system;

[0009] Sensor modules, including high-definition cameras, infrared cameras, lidar and ultrasonic sensors, are used to obtain images, temperature and distance data of power lines;

[0010] A data processing module, including an embedded processor, a data storage unit and a data transmission module, for processing and analyzing the data collected by the sensor;

[0011] Intelligent algorithm module, used for intelligent analysis and judgment of the status of power lines;

[0012] Communication module, including wireless communication module and satellite communication module, used to realize data transmission and remote control between the UAV and the ground control center;

[0013] The ground control center, including monitoring terminals, data analysis platforms and databases, is used to monitor the flight status of drones in real time, analyze inspection data and generate inspection reports.

[0014] Preferably, the UAV platform comprises the following steps:

[0015] S1. Task reception and initialization: The ground control center sends inspection task instructions to the UAV through the communication module, including flight path, inspection area, task type, flight altitude and speed parameters. After the UAV receives the task instruction, the flight control system parses the task instruction, generates a specific flight plan, and establishes a communication connection with the ground control center to ensure the real-time and reliability of data transmission;

[0016] S2. Takeoff and climb: confirm that the drone is at the preset takeoff point, ensure that there are no obstacles in the takeoff area, check the status of the motor and propeller, ensure that the power system is working properly, confirm that the battery power is sufficient to meet the flight mission requirements, set the climb speed according to the mission requirements, monitor the altitude of the drone in real time, and ensure that it climbs to the set flight altitude as planned;

[0017] S3. Flight inspection: According to the mission instructions, the flight control system navigates according to the preset flight path. The flight control system adjusts the attitude and position of the drone in real time based on the sensor data, detects the image, temperature and distance through relevant sensors, and stores the relevant data;

[0018] S4. Landing and recovery: confirm that the drone is at the preset landing point and ensure that there are no obstacles in the landing area. After the drone lands, shut down the power system;

[0019] S5. Data analysis and report generation: pre-process the collected data and generate relevant reports;

[0020] S6. Safety and emergency response: monitor the overall status of the drone in real time, including the flight area, battery power, and communication connection, and execute corresponding emergency response measures according to the type of fault.

[0021] Preferably, the sensor module comprises the following steps:

[0022] S1. Sensor initialization and self-test. After the drone is started, each sensor in the sensor module is started in turn. According to the preset mission requirements, the sensor parameters are configured, including resolution, frame rate, measurement range and sampling frequency;

[0023] S2. Data collection: collect data on image, temperature and distance in sequence;

[0024] S3. Data preprocessing, calibrating the data, filtering the collected data, and improving the data quality;

[0025] S4. Data transmission: encode the pre-processed data, convert it into a format suitable for transmission, compress the data, reduce the amount of data transmission, improve the transmission efficiency, and transmit the data to the ground control center in real time through the wireless communication module;

[0026] S5. Data storage: store the collected data in the local storage unit of the drone, back up important data to prevent data loss, transmit the data to the cloud server for remote storage and backup, and synchronize the data with the database of the ground control center to ensure data consistency and real-time;

[0027] S6. Sensor calibration and maintenance: Regularly check the calibration of sensors to ensure the measurement accuracy of sensors, and record the calibration history and calibration parameters of sensors for subsequent tracking and analysis;

[0028] S7. Security and emergency processing: encrypt the transmitted data to prevent the data from being stolen and tampered with.

[0029] Preferably, the data processing module comprises the following steps:

[0030] S1. Data reception and verification. The data processing module receives data from the sensor module through a predetermined data interface, performs integrity checks on the received data to ensure that the data is not lost or damaged during transmission, verifies data consistency, ensures time synchronization and spatial alignment between different sensor data, verifies whether the data format meets expectations, and ensures that the data can be correctly parsed and processed;

[0031] S2. Data preprocessing: processing image data and distance data, removing environmental noise and sensor noise, correcting sensor data, eliminating system errors and measurement errors, fusing data from different sensors to improve data accuracy and reliability, associating related data, and establishing associations between data;

[0032] S3. Data storage and management: storing the pre-processed data in the local storage unit of the drone, transmitting the data to the database of the ground control center or the cloud server through the communication module for remote storage and backup, and indexing, data classification and data backup processing;

[0033] S4. Data analysis and processing: Based on deep learning algorithms, image data is identified and analyzed to identify defects, damage and foreign objects in power lines. Image data is segmented and processed to extract areas of interest. The temperature data collected by infrared thermal imagers is analyzed for distribution to identify overheated areas and temperature anomalies. Based on temperature data, abnormal heating of power lines is detected to identify potential fault points. Distance data collected by lidar and ultrasonic sensors is measured and analyzed to generate three-dimensional point cloud data.

[0034] S5. Intelligent analysis and fault diagnosis: Apply machine learning algorithms to analyze inspection data, identify the status and faults of power lines, establish a power line fault diagnosis model based on historical inspection data and fault data, verify and test the model, and then perform fault diagnosis;

[0035] S6. Result output and report generation, realizing data visualization, report generation and result feedback.

[0036] Preferably, the intelligent algorithm module comprises the following steps:

[0037] S1. Data preprocessing: data cleaning, data alignment and fusion, and data standardization;

[0038] S2. Feature extraction and selection: extract image features, sensor features and time series features respectively, then perform correlation analysis, dimensionality reduction processing and feature selection algorithm calculation to select the optimal feature subset;

[0039] S3. Model training and optimization: select a machine learning model for training, then prepare training data, train the model, and optimize hyperparameters, and finally evaluate the model.

[0040] S4. Model deployment and reasoning: serialize the trained model, convert it into a format suitable for deployment, integrate the model into the intelligent algorithm module of the drone platform, and perform real-time data processing, model reasoning, and result output;

[0041] S5. Fault diagnosis and prediction: Use the trained model to classify the status of power lines, identify the fault type, perform trend analysis on historical inspection data and real-time data, and predict the possibility of faults.

[0042] S6. Result feedback and optimization: The fault diagnosis and prediction results are fed back to the ground control center and relevant maintenance personnel in real time, and a detailed inspection report is generated based on the reasoning results, including fault description and maintenance suggestions.

[0043] Preferably, the communication module comprises the following steps:

[0044] S1. Communication initialization and self-test, the communication module starts self-test and configures the parameters of the communication network;

[0045] S2. Data transmission and reception, packaging data, encrypting sensitive data, and sending it through the wireless communication module;

[0046] S3. Communication link management: establish a connection before takeoff, verify the established communication connection, monitor the link, and switch the link according to the needs;

[0047] S4. Remote control and command execution, executing the commands issued from the ground;

[0048] S5. Data security and privacy protection, data encryption and access control;

[0049] S6. Emergency handling and fault recovery, real-time detection of the fault status of the communication link and the hardware and software of the communication module.

[0050] (III) Beneficial effects

[0051] The present invention provides a drone intelligent power line inspection system. It has the following beneficial effects:

[0052] 1. In the present invention, the UAV can quickly cover a large area of ​​power lines, especially in areas with complex terrain or difficult to reach, greatly improving the inspection efficiency. The UAV has autonomous flight capabilities and can automatically perform inspection tasks according to preset flight paths and mission plans, reducing human intervention.

[0053] 2. In the present invention, the intelligent algorithm module can automatically identify defects, damage, and foreign objects in the power lines. The system can analyze the inspection data, identify potential fault points, and make trend forecasts and provide early warnings. The intelligent algorithm module can continuously learn and optimize based on new inspection data and feedback results, and continuously improve the recognition accuracy and diagnostic precision.

[0054] 3. In the present invention, drones replace manual inspections, reducing the work of personnel in dangerous environments such as high altitudes and complex terrains, and reducing personnel risks. The ground control center can remotely control the drones and take quick measures in emergency situations, such as emergency landing and return, further ensuring personnel safety. Drone inspections reduce the demand for manual inspection personnel and reduce labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0056] Figure 2 It is a schematic diagram of the system flow of the UAV platform in the present invention;

[0057] Figure 3 It is a schematic diagram of the system flow of the sensor module in the present invention;

[0058] Figure 4 It is a system flow diagram of the data processing module in the present invention;

[0059] Figure 5 It is a system flow diagram of the intelligent algorithm module in the present invention;

[0060] Figure 6 It is a schematic diagram of the system flow of the communication module in the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Embodiment 1:

[0063] like Figure 1-6 As shown, an embodiment of the present invention provides a drone intelligent power line inspection system, comprising:

[0064] UAV platform, including the UAV body, flight control system, power system and navigation system;

[0065] Sensor modules, including high-definition cameras, infrared cameras, lidar and ultrasonic sensors, are used to obtain images, temperature and distance data of power lines;

[0066] A data processing module, including an embedded processor, a data storage unit and a data transmission module, for processing and analyzing the data collected by the sensor;

[0067] Intelligent algorithm module, used for intelligent analysis and judgment of the status of power lines;

[0068] Communication module, including wireless communication module and satellite communication module, used to realize data transmission and remote control between the UAV and the ground control center;

[0069] The ground control center, including monitoring terminals, data analysis platforms and databases, is used to monitor the flight status of drones in real time, analyze inspection data and generate inspection reports.

[0070] The UAV platform includes the following steps:

[0071] S1. Task reception and initialization: The ground control center sends inspection task instructions to the UAV through the communication module, including flight path, inspection area, task type, flight altitude and speed parameters. After the UAV receives the task instruction, the flight control system parses the task instruction, generates a specific flight plan, and establishes a communication connection with the ground control center to ensure the real-time and reliability of data transmission;

[0072] S2. Takeoff and climb: confirm that the drone is at the preset takeoff point, ensure that there are no obstacles in the takeoff area, check the status of the motor and propeller, ensure that the power system is working properly, confirm that the battery power is sufficient to meet the flight mission requirements, set the climb speed according to the mission requirements, monitor the altitude of the drone in real time, and ensure that it climbs to the set flight altitude as planned;

[0073] S3. Flight inspection: According to the mission instructions, the flight control system navigates according to the preset flight path. The flight control system adjusts the attitude and position of the drone in real time based on the sensor data, detects the image, temperature and distance through relevant sensors, and stores the relevant data;

[0074] S4. Landing and recovery: confirm that the drone is at the preset landing point and ensure that there are no obstacles in the landing area. After the drone lands, shut down the power system;

[0075] S5. Data analysis and report generation: pre-process the collected data and generate relevant reports;

[0076] S6. Safety and emergency response: monitor the overall status of the drone in real time, including the flight area, battery power, and communication connection, and execute corresponding emergency response measures according to the type of fault.

[0077] The sensor module includes the following steps:

[0078] S1. Sensor initialization and self-test. After the drone is started, each sensor in the sensor module is started in turn. According to the preset mission requirements, the sensor parameters are configured, including resolution, frame rate, measurement range and sampling frequency;

[0079] S2. Data collection: collect data on image, temperature and distance in sequence;

[0080] S3. Data preprocessing, calibrating the data, filtering the collected data, and improving the data quality;

[0081] S4. Data transmission: encode the pre-processed data, convert it into a format suitable for transmission, compress the data, reduce the amount of data transmission, improve the transmission efficiency, and transmit the data to the ground control center in real time through the wireless communication module;

[0082] S5. Data storage: store the collected data in the local storage unit of the drone, back up important data to prevent data loss, transmit the data to the cloud server for remote storage and backup, and synchronize the data with the database of the ground control center to ensure data consistency and real-time;

[0083] S6. Sensor calibration and maintenance: Regularly check the calibration of sensors to ensure the measurement accuracy of sensors, and record the calibration history and calibration parameters of sensors for subsequent tracking and analysis;

[0084] S7. Security and emergency processing: encrypt the transmitted data to prevent the data from being stolen and tampered with.

[0085] The data processing module includes the following steps:

[0086] S1. Data reception and verification. The data processing module receives data from the sensor module through a predetermined data interface, performs integrity checks on the received data to ensure that the data is not lost or damaged during transmission, verifies data consistency, ensures time synchronization and spatial alignment between different sensor data, verifies whether the data format meets expectations, and ensures that the data can be correctly parsed and processed;

[0087] S2. Data preprocessing: processing image data and distance data, removing environmental noise and sensor noise, correcting sensor data, eliminating system errors and measurement errors, fusing data from different sensors to improve data accuracy and reliability, associating related data, and establishing associations between data;

[0088] S3. Data storage and management: storing the pre-processed data in the local storage unit of the drone, transmitting the data to the database of the ground control center or the cloud server through the communication module for remote storage and backup, and indexing, data classification and data backup processing;

[0089] S4. Data analysis and processing: Based on deep learning algorithms, image data is identified and analyzed to identify defects, damage and foreign objects in power lines. Image data is segmented and processed to extract areas of interest. The temperature data collected by infrared thermal imagers is analyzed for distribution to identify overheated areas and temperature anomalies. Based on temperature data, abnormal heating of power lines is detected to identify potential fault points. Distance data collected by lidar and ultrasonic sensors is measured and analyzed to generate three-dimensional point cloud data.

[0090] S5. Intelligent analysis and fault diagnosis: Apply machine learning algorithms to analyze inspection data, identify the status and faults of power lines, establish a power line fault diagnosis model based on historical inspection data and fault data, verify and test the model, and then perform fault diagnosis;

[0091] S6. Result output and report generation, realizing data visualization, report generation and result feedback.

[0092] The intelligent algorithm module includes the following steps:

[0093] S1. Data preprocessing: data cleaning, data alignment and fusion, and data standardization;

[0094] S2. Feature extraction and selection: extract image features, sensor features and time series features respectively, then perform correlation analysis, dimensionality reduction processing and feature selection algorithm calculation to select the optimal feature subset;

[0095] S3. Model training and optimization: select a machine learning model for training, then prepare training data, train the model, and optimize hyperparameters, and finally evaluate the model.

[0096] S4. Model deployment and reasoning: serialize the trained model, convert it into a format suitable for deployment, integrate the model into the intelligent algorithm module of the drone platform, and perform real-time data processing, model reasoning, and result output;

[0097] S5. Fault diagnosis and prediction: Use the trained model to classify the status of power lines, identify the fault type, perform trend analysis on historical inspection data and real-time data, and predict the possibility of faults.

[0098] S6. Result feedback and optimization: The fault diagnosis and prediction results are fed back to the ground control center and relevant maintenance personnel in real time, and a detailed inspection report is generated based on the reasoning results, including fault description and maintenance suggestions.

[0099] The communication module includes the following steps:

[0100] S1. Communication initialization and self-test, the communication module starts self-test and configures the parameters of the communication network;

[0101] S2. Data transmission and reception, packaging data, encrypting sensitive data, and sending it through the wireless communication module;

[0102] S3. Communication link management: establish a connection before takeoff, verify the established communication connection, monitor the link, and switch the link according to the needs;

[0103] S4. Remote control and command execution, executing the commands issued from the ground;

[0104] S5. Data security and privacy protection, data encryption and access control;

[0105] S6. Emergency handling and fault recovery, real-time detection of the fault status of the communication link and the hardware and software of the communication module.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An unmanned aerial vehicle intelligent power line inspection system, characterized by: include: UAV platform, including the UAV body, flight control system, power system and navigation system; Sensor modules, including high-definition cameras, infrared cameras, lidar and ultrasonic sensors, are used to obtain images, temperature and distance data of power lines; A data processing module, including an embedded processor, a data storage unit and a data transmission module, for processing and analyzing the data collected by the sensor; Intelligent algorithm module, used for intelligent analysis and judgment of the status of power lines; Communication module, including wireless communication module and satellite communication module, used to realize data transmission and remote control between the UAV and the ground control center; The ground control center, including monitoring terminals, data analysis platforms and databases, is used to monitor the flight status of drones in real time, analyze inspection data and generate inspection reports.

2. The UAV intelligent power line inspection system according to claim 1 is characterized by: The UAV platform comprises the following steps: S1. Task reception and initialization: The ground control center sends inspection task instructions to the UAV through the communication module, including flight path, inspection area, task type, flight altitude and speed parameters. After the UAV receives the task instruction, the flight control system parses the task instruction, generates a specific flight plan, and establishes a communication connection with the ground control center to ensure the real-time and reliability of data transmission; S2. Takeoff and climb: confirm that the drone is at the preset takeoff point, ensure that there are no obstacles in the takeoff area, check the status of the motor and propeller, ensure that the power system is working properly, confirm that the battery power is sufficient to meet the flight mission requirements, set the climb speed according to the mission requirements, monitor the altitude of the drone in real time, and ensure that it climbs to the set flight altitude as planned; S3. Flight inspection: According to the mission instructions, the flight control system navigates according to the preset flight path. The flight control system adjusts the attitude and position of the drone in real time based on the sensor data, detects the image, temperature and distance through relevant sensors, and stores the relevant data; S4. Landing and recovery: confirm that the drone is at the preset landing point and ensure that there are no obstacles in the landing area. After the drone lands, shut down the power system; S5. Data analysis and report generation: pre-process the collected data and generate relevant reports; S6. Safety and emergency response: monitor the overall status of the drone in real time, including the flight area, battery power, and communication connection, and execute corresponding emergency response measures according to the type of fault.

3. The UAV intelligent power line inspection system according to claim 1 is characterized by: The sensor module comprises the following steps: S1. Sensor initialization and self-test. After the drone is started, each sensor in the sensor module is started in turn. According to the preset mission requirements, the sensor parameters are configured, including resolution, frame rate, measurement range and sampling frequency; S2. Data collection: collect data on image, temperature and distance in sequence; S3. Data preprocessing, calibrating the data, filtering the collected data, and improving the data quality; S4. Data transmission: encode the pre-processed data, convert it into a format suitable for transmission, compress the data, reduce the amount of data transmission, improve the transmission efficiency, and transmit the data to the ground control center in real time through the wireless communication module; S5. Data storage: store the collected data in the local storage unit of the drone, back up important data to prevent data loss, transmit the data to the cloud server for remote storage and backup, and synchronize the data with the database of the ground control center to ensure data consistency and real-time; S6. Sensor calibration and maintenance: Regularly check the calibration of sensors to ensure the measurement accuracy of sensors, and record the calibration history and calibration parameters of sensors for subsequent tracking and analysis; S7. Security and emergency processing: encrypt the transmitted data to prevent the data from being stolen and tampered with.

4. The UAV intelligent power line inspection system according to claim 1 is characterized by: The data processing module comprises the following steps: S1. Data reception and verification. The data processing module receives data from the sensor module through a predetermined data interface, performs integrity checks on the received data to ensure that the data is not lost or damaged during transmission, verifies data consistency, ensures time synchronization and spatial alignment between different sensor data, verifies whether the data format meets expectations, and ensures that the data can be correctly parsed and processed; S2. Data preprocessing: processing image data and distance data, removing environmental noise and sensor noise, correcting sensor data, eliminating system errors and measurement errors, fusing data from different sensors to improve data accuracy and reliability, associating related data, and establishing associations between data; S3. Data storage and management: storing the pre-processed data in the local storage unit of the drone, transmitting the data to the database of the ground control center or the cloud server through the communication module for remote storage and backup, and indexing, data classification and data backup processing; S4. Data analysis and processing: Based on deep learning algorithms, image data is identified and analyzed to identify defects, damage and foreign objects in power lines. Image data is segmented and processed to extract areas of interest. The temperature data collected by infrared thermal imagers is analyzed for distribution to identify overheated areas and temperature anomalies. Based on temperature data, abnormal heating of power lines is detected to identify potential fault points. Distance data collected by lidar and ultrasonic sensors is measured and analyzed to generate three-dimensional point cloud data. S5. Intelligent analysis and fault diagnosis: Apply machine learning algorithms to analyze inspection data, identify the status and faults of power lines, establish a power line fault diagnosis model based on historical inspection data and fault data, verify and test the model, and then perform fault diagnosis; S6. Result output and report generation, realizing data visualization, report generation and result feedback.

5. The UAV intelligent power line inspection system according to claim 1 is characterized by: The intelligent algorithm module includes the following steps: S1. Data preprocessing: data cleaning, data alignment and fusion, and data standardization; S2. Feature extraction and selection: extract image features, sensor features and time series features respectively, then perform correlation analysis, dimensionality reduction processing and feature selection algorithm calculation to select the optimal feature subset; S3. Model training and optimization: select a machine learning model for training, then prepare training data, train the model, and optimize hyperparameters, and finally evaluate the model. S4. Model deployment and reasoning: serialize the trained model, convert it into a format suitable for deployment, integrate the model into the intelligent algorithm module of the drone platform, and perform real-time data processing, model reasoning, and result output; S5. Fault diagnosis and prediction: Use the trained model to classify the status of power lines, identify the fault type, perform trend analysis on historical inspection data and real-time data, and predict the possibility of faults. S6. Result feedback and optimization: The fault diagnosis and prediction results are fed back to the ground control center and relevant maintenance personnel in real time, and a detailed inspection report is generated based on the reasoning results, including fault description and maintenance suggestions.

6. The UAV intelligent power line inspection system according to claim 1 is characterized by: The communication module comprises the following steps: S1. Communication initialization and self-test, the communication module starts self-test and configures the parameters of the communication network; S2. Data transmission and reception, packaging data, encrypting sensitive data, and sending it through the wireless communication module; S3. Communication link management: establish a connection before takeoff, verify the established communication connection, monitor the link, and switch the link according to the needs; S4. Remote control and command execution, executing the commands issued from the ground; S5. Data security and privacy protection, data encryption and access control; S6. Emergency handling and fault recovery, real-time detection of the fault status of the communication link and the hardware and software of the communication module.

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