Unmanned aerial vehicle power line inspection system and method

Abstract

The invention relates to the technical field of electric power, and discloses an unmanned aerial vehicle electric power line inspection system and method, intelligent and automatic inspection processes are realized through efficient cooperation of multiple modules, and compared with traditional manual inspection and single data acquisition means, the unmanned aerial vehicle electric power line inspection system has the advantages that multi-source data acquisition and real-time analysis are integrated, and the inspection efficiency is improved. The system can quickly respond and timely adjust the inspection path in a dynamic environment, greatly improves the inspection efficiency and accuracy, employs an advanced data analysis and calculation method, enhances the comprehensive monitoring of the state of the power equipment, timely recognizes the potential fault risk, reduces the possibility of equipment fault and power failure, and improves the inspection efficiency and accuracy. And the feedback module ensures rapid transmission of information, so that operation and maintenance personnel can obtain an inspection result in the first time, the decision process is accelerated, and the comprehensive application of the system improves the safety and reliability of the power line.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and particularly to an unmanned aerial vehicle (UAV) power line inspection system and method. Background Art

[0002] In the current era of rapid development of intelligent technologies, automated systems have become important tools for improving efficiency and reducing costs in various industries. In the power industry, the application of intelligent inspection technologies is gradually replacing traditional manual inspection methods and becoming an important means to improve the efficiency of power line maintenance. As an emerging inspection tool, UAV technology has efficient data acquisition capabilities and flexible mobility, and has become an indispensable technology in the field of power line inspection.

[0003] Traditional UAV technology has provided a certain degree of convenience in power line inspection, but there are still significant deficiencies. Traditional UAVs often rely on manual operation and single data acquisition methods, lacking intelligent data processing capabilities. The real-time performance and accuracy of inspection results are affected, and potential fault risks cannot be identified in a timely manner. In addition, traditional UAVs usually can draw inspection conclusions only after data analysis, resulting in a delay in the decision-making process and thus affecting the maintenance efficiency of power equipment.

[0004] Therefore, we propose an unmanned aerial vehicle power line inspection system and method to solve the above-mentioned problems. Summary of the Invention

[0005] The purpose of the present invention is to provide an unmanned aerial vehicle power line inspection system to solve the problems in the above-mentioned background art that traditional UAVs often rely on manual operation and single data acquisition methods and lack intelligent data processing capabilities. The real-time performance and accuracy of inspection results are affected, and potential fault risks cannot be identified in a timely manner.

[0006] To achieve the above purpose, the present invention provides the following technical solution: An unmanned aerial vehicle power line inspection system includes a first data acquisition module, a first data parsing module, a first data calculation module, a first data analysis module, a second data acquisition module, a second data parsing module, a second data calculation module, a second data analysis module, and a feedback module;

[0007] The first data acquisition module is used to obtain multi-source data for the UAV detection path, thereby generating a first multi-source data set;

[0008] The first data parsing module is used to preprocess the first multi-source data and reorganize the preprocessed first multi-source data, thereby generating a first data set, a second data set, and a third data set;

[0009] The first data calculation module is used to perform integrated calculation on the first data set, the second data set, and the third data set, so as to generate a path correction value LJX;

[0010] The first data analysis module is used to compare the path correction value LJX with a preset path intervention threshold LYZ, so as to generate a first comparison result, and determine whether the subsequent system needs to use the path correction value LJX according to the first comparison result;

[0011] The second data acquisition module is used to acquire multi-source data during the UAV inspection and organize it into a second multi-source data set;

[0012] The second data parsing module is used to preprocess the second multi-source data and reorganize it into an image data set and a sensor data set;

[0013] The second data calculation module is used to integrate the image data set, the sensor data set, and the path correction value LJX, so as to obtain a circuit anomaly coefficient DLZ;

[0014] The second data analysis module is used to compare the circuit anomaly coefficient DLZ with a preset circuit anomaly threshold DYZ value, so as to generate a second comparison result. According to the second comparison result, it is judged whether the current circuit is abnormal. If the first comparison result shows no abnormality, the circuit anomaly coefficient DLZ and the circuit anomaly threshold DYZ value are integrated and calculated to generate a circuit anomaly magnitude value DCZ, and the circuit anomaly magnitude value DCZ is compared with the circuit anomaly threshold DYZ value again to generate a re-comparison result. According to the re-comparison result, the abnormal level of the current circuit is judged;

[0015] The feedback module is used to send multiple data to the terminal for feedback operations.

[0016] Preferably, the first multi-source data set collected by the first data acquisition module includes the target height, the target curvature, the target distribution coefficient, the obstacle height, the obstacle distribution rate, the environmental wind speed, the environmental humidity, and the environmental temperature.

[0017] Preferably, the first data parsing module includes a first preprocessing unit and a first organizing unit. The first preprocessing unit is used to preprocess and dimensionlessize the first multi-source data set, and the first organizing unit is used to reorganize the preprocessed first multi-source data set into a first data set, a second data set, and a third data set;

[0018] The first data set includes the target height A, the target curvature B, and the target distribution coefficient C;

[0019] The second data set includes the obstacle height D and the obstacle distribution rate E;

[0020] The third data set includes ambient wind speed, ambient humidity, and ambient temperature;

[0021] Among them, the ambient wind speed is respectively recorded as F1, F2, F3,..., Fn according to the time stamp;

[0022] The ambient humidity is respectively recorded as G1, G2, G3,..., Gn according to the time stamp;

[0023] The ambient temperature is respectively recorded as H1, H2, H3,..., Hn according to the time stamp.

[0024] Preferably, the first data calculation module calculates and obtains the path correction value LJX through the following formula;

[0025]

[0026] In the formula, A is the target height, B is the target curvature, C is the target distribution coefficient, D is the obstacle height, and E is the obstacle distribution rate;

[0027] Fn, Gn, and Hn are the ambient wind speed, ambient humidity, and ambient temperature at the nth time stamp;

[0028] F1, G1, and H1 are the ambient wind speed, ambient humidity, and ambient temperature at the first time stamp;

[0029] a1, a2, and a3 are weight values, and the values of A1, a2, and a3 are adjusted and set by the user.

[0030] Preferably, the first comparison result generated by the first data analysis module is specifically as follows:

[0031] When LJX ≤ LYZ, it means that the current path correction value LJX does not need to intervene in subsequent calculations. When LJX > LYZ, it means that the current path correction value LJX needs to intervene in subsequent calculations.

[0032] Preferably, the second data acquisition module includes an image acquisition unit and a sensor acquisition unit;

[0033] The image acquisition unit is used to acquire the image data of the drone, including resolution, frame rate, viewing angle offset degree, and color depth;

[0034] The sensor acquisition unit is used to acquire the drone sensor data, including target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value;

[0035] The resolution, frame rate, viewing angle offset degree, color depth, target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value constitute the second multi-source data.

[0036] Preferably, the second data parsing module includes a second preprocessing unit and a second sorting unit;

[0037] The second preprocessing unit is used to preprocess and dimensionlessize the second multi-source data set, and the second sorting unit is used to reorganize the processed second multi-source data set into an image data set and a sensor data set;

[0038] The image data set includes resolution I, frame rate view angle offset degree J, and color depth K;

[0039] The sensor data set includes target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value;

[0040] Among them, the target temperature values are respectively recorded as L1, L1, L1,..., L1 according to the time stamps;

[0041] The measurement distances are respectively recorded as M1, M1, M1,..., M1 according to the time stamps;

[0042] The electromagnetic radiation intensities are respectively recorded as O1, O1, O1,..., O1 according to the time stamps;

[0043] The vibration values are respectively recorded as P1, P1, P1,..., P1 according to the time stamps.

[0044] Preferably, the second data calculation module calculates and obtains two circuit anomaly coefficients DLZ through the following formulas respectively;

[0045]

[0046] In the formula: DLZ1 is the circuit anomaly coefficient calculated by adding the path correction value LJX, and DLZ2 is the circuit anomaly coefficient calculated without adding the path correction value LJX;

[0047] I is the resolution, J is the frame rate view angle offset degree, and K is the color depth;

[0048] PL is the target temperature value intervention coefficient, PM is the measurement distance intervention coefficient, PO is the magnetic radiation intensity intervention coefficient, and PP is the vibration value intervention coefficient;

[0049] Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth time stamp;

[0050] Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth time stamp.

[0051] Preferably, the second data analysis module includes a first analysis unit and a second analysis unit;

[0052] The first analysis unit is used to generate a second comparison result, and the second analysis unit is used to generate a re-comparison result;

[0053] The specific content of the second comparison result is as follows:

[0054] When DLZ ≤ DYZ, it means that the current path value does not need to intervene in subsequent calculations. When DLZ > DYZ, it means that the current path value needs to intervene in subsequent calculations;

[0055] The specific content of the re-comparison result is as follows:

[0056] When DCZ ≤ 10% × DYZ, it means that the current circuit has a first-level abnormality;

[0057] When 10% × DYZ < DCZ ≤ 20% × DYZ, it means that the current circuit has a second-level abnormality;

[0058] When DCZ > 20% × DYZ, it means that the current circuit has a third-level abnormality;

[0059] The circuit abnormality magnitude value DCZ is obtained through the following formula:

[0060]

[0061] In the formula: DLZ is the circuit abnormality coefficient, including DLZ1 and DLZ1, and DYZ is the circuit abnormality threshold.

[0062] This application also includes a method for inspecting power lines of an unmanned aerial vehicle, and the specific steps are as follows:

[0063] S1. During the flight of the unmanned aerial vehicle, use the first data acquisition module to obtain multi-source data for the inspection path and generate a first multi-source data set;

[0064] S2. Use the first data parsing module to preprocess and dimensionlessize the obtained first multi-source data, remove noise and unify the data format, and re-organize the preprocessed data into a first data set, a second data set, and a third data set;

[0065] S3. Integrate and calculate the organized data through the first data calculation module to generate a path correction value LJX. Use the first data analysis module to compare the path correction value LJX with the preset path intervention threshold LYZLYZ to generate a first comparison result and determine whether path correction needs to be intervened;

[0066] S4. During the inspection of the unmanned aerial vehicle, use the second data acquisition module to collect image and sensor data and generate a second multi-source data set;

[0067] S5. The second multi-source data is preprocessed by the second data parsing module, sorted into an image data set and a sensor data set. The image data set, the sensor data set, and the path correction value LJX are integrated by the second data calculation module to calculate the circuit anomaly coefficient DLZ.

[0068] S6. Through the second data analysis module, the circuit anomaly coefficient DLZ is compared with the preset circuit anomaly threshold value DYZ to generate a second comparison result, and it is judged whether the current circuit is abnormal. If the first comparison result shows no abnormality, the circuit anomaly coefficient DLZ and the circuit anomaly threshold value DYZ are integrated and calculated to generate a circuit anomaly magnitude value DCZ, and then a re-comparison is performed to judge the circuit anomaly level.

[0069] S7. The feedback module is used to send the data collected and calculated during the inspection process to the terminal for feedback operations to ensure the traceability and timely response of the inspection results.

[0070] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0071] 1. Through the efficient cooperation of nine modules, the system realizes an intelligent and automated inspection process. Compared with traditional manual inspections and single data collection methods, the improvement effect is significant. First, the system integrates multi-source data collection and real-time analysis, can quickly respond in a dynamic environment, and timely adjust the inspection path, greatly improving the inspection efficiency and accuracy. Second, advanced data parsing and calculation methods are adopted to enhance the comprehensive monitoring of the power equipment status, timely identify potential fault risks, and reduce the possibility of equipment failures and power outages. In addition, the feedback module ensures the rapid transmission of information, enabling maintenance personnel to obtain inspection results in the first time, thus accelerating the decision-making process. The comprehensive application of this system improves the safety and reliability of power lines.

[0072] 2. The first data analysis module reflects significant improvement points and benefits through the generated first comparison result. When LJX is less than or equal to LYZ, the system determines that no intervention calculation is required for the current path; conversely, when LJX is greater than LYZ, the system will perform further path correction. This mechanism effectively improves the intelligence level of the inspection process. First, through clear threshold determination, it simplifies the decision-making process, enables the system to quickly respond to a dynamically changing environment, avoids unnecessary calculations, and improves the calculation efficiency. Second, the setting of this comparison result can help maintenance personnel quickly identify whether the inspection path needs to be adjusted, thereby reducing the complexity of manual intervention and judgment and reducing the operation risk. This intelligent judgment method not only improves the inspection efficiency but also ensures the safety and reliability of the drone in a complex environment, contributing to more efficient power line management and meeting the requirements of the modern power industry for automation and intelligence.

[0073] 3. The two data calculation modules obtain two circuit anomaly coefficients DLZ through formula calculation, which reflects multiple important improvement points and benefits. By calculating the circuit anomaly coefficients DLZ1 and DLZ2, the system can respectively evaluate the anomaly conditions of the circuit with and without adding the path correction value LJX. This dual evaluation mechanism enhances the system's comprehensive monitoring ability of the power line status, enabling the inspection personnel to more accurately judge whether there are potential faults in the line. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a system flow chart of the present invention.

[0075] Figure 2 It is a method step diagram of the present invention.

[0076] In the figure: 1. The first data acquisition module; 2. The first data parsing module; 21. The first preprocessing unit; 22. The first sorting unit; 3. The first data calculation module; 4. The first data analysis module; 5. The second data acquisition module; 51. The image acquisition unit; 52. The sensor acquisition unit; 6. The second data parsing module; 61. The second preprocessing unit; 62. The second sorting unit; 7. The second data calculation module; 8. The second data analysis module; 81. The first analysis unit; 82. The second analysis unit; 9. The feedback module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0078] Embodiment 1: Please refer to Figure 1 , a drone power line inspection system, including a first data acquisition module 1, a first data parsing module 2, a first data calculation module 3, a first data analysis module 4, a second data acquisition module 5, a second data parsing module 6, a second data calculation module 7, a second data analysis module 8, and a feedback module 9;

[0079] The first data acquisition module 1 is used to obtain multi-source data for the drone detection path, thereby generating a first multi-source data set;

[0080] The first data parsing module 2 is used to preprocess the first multi-source data and re-organize the preprocessed first multi-source data, thereby generating a first data set, a second data set, and a third data set;

[0081] The first data calculation module 3 is used to perform integrated calculations on the first data set, the second data set, and the third data set to generate a path correction value LJX;

[0082] The first data analysis module 4 is used to compare the path correction value LJX with a preset path intervention threshold LYZ to generate a first comparison result, and determine whether the subsequent system needs to use the path correction value LJX according to the first comparison result;

[0083] The second data acquisition module 5 is used to collect multi-source data during the drone inspection and organize it into a second multi-source data set;

[0084] The second data parsing module 6 is used to preprocess the second multi-source data and reorganize it into an image data set and a sensor data set;

[0085] The second data calculation module 7 is used to integrate the image data set, the sensor data set, and the path correction value LJX to obtain a circuit anomaly coefficient DLZ;

[0086] The second data analysis module 8 is used to compare the circuit anomaly coefficient DLZ with a preset circuit anomaly threshold DYZ value to generate a second comparison result. According to the second comparison result, it is judged whether the current circuit is abnormal. If the first comparison result shows no abnormality, the circuit anomaly coefficient DLZ and the circuit anomaly threshold DYZ value are integrated and calculated to generate a circuit anomaly magnitude value DCZ, and the circuit anomaly magnitude value DCZ is compared with the circuit anomaly threshold DYZ value again to generate a re-comparison result. According to the re-comparison result, the anomaly level of the current circuit is judged;

[0087] The feedback module 9 is used to send multiple data to the terminal for feedback operations.

[0088] In this embodiment: The first data acquisition module 1 is responsible for obtaining relevant data on the drone detection path from multiple sources. These data sources include geographical information, environmental conditions, flight parameters, etc. Thus, it can provide comprehensive and accurate basic information for subsequent data processing, ensuring that the drone can perform path planning based on the real environmental conditions during the inspection process, thereby improving the inspection efficiency and accuracy.

[0089] The first data parsing module 2 preprocesses and organizes the first multi-source data to generate a first data set, a second data set, and a third data set. The beneficial effect of this process is to eliminate data noise, standardize the data format, and ensure the reliability of subsequent analysis. At the same time, through classification and organization, it is convenient for subsequent modules to perform more effective calculations and analyses on specific data sets, optimizing the overall performance of the system.

[0090] The first data calculation module 3 integrates the first data set, the second data set, and the third data set for calculation to generate a path correction value LJX. Thus, by comprehensively integrating various types of data, it can accurately evaluate the rationality of the current flight path, promptly detect and correct potential route deviations. This not only improves the inspection accuracy of the UAV but also ensures the safety of the inspection mission.

[0091] The first data analysis module 4 compares the path correction value LJX with a preset path intervention threshold LYZ to generate a first comparison result. The beneficial effect of this module is to determine whether it is necessary to correct the inspection path of the UAV to cope with dynamically changing environmental factors. Through this mechanism, the system can achieve adaptive adjustment, thereby optimizing the inspection efficiency under different environmental conditions.

[0092] During the UAV inspection process, the second data acquisition module 5 real-time collects multi-source data and organizes it into a second multi-source data set, thereby being able to reflect the operating status of the power line and environmental changes in real time, ensuring the timeliness and reliability of the data, providing the latest basis for subsequent analysis, and enhancing the system's response ability to emergencies.

[0093] The second data parsing module 6 preprocesses the second multi-source data to generate an image data set and a sensor data set. Thus, it can improve the utilization rate of the data. Through effective data organization, it ensures that the analysis module can extract important information from it to help judge the operating status and potential risks of the power line.

[0094] The second data calculation module 7 integrates the image data set, the sensor data set, and the path correction value LJX to obtain a circuit anomaly coefficient DLZ, thereby being able to comprehensively integrate information from multiple data sources, accurately identify anomalies in the power line, promptly issue warnings, and reduce the risks of power equipment failures and power outages.

[0095] The second data analysis module 8 compares the circuit anomaly coefficient DLZ with a preset circuit anomaly threshold DYZ value to generate a second comparison result and determine whether the circuit is abnormal. If no abnormality occurs, this module will also calculate a circuit anomaly magnitude value DCZ and conduct another comparison. Thus, through a multi-level judgment mechanism, it can ensure the accurate identification of power line anomalies and provide clear decision-making basis for maintenance personnel.

[0096] The feedback module 9 sends multiple pieces of data to the terminal for feedback operations, thereby enabling the rapid transmission and sharing of information, allowing maintenance personnel to promptly understand the inspection results and equipment status. Through rapid feedback, it can effectively improve decision-making efficiency and shorten the problem handling time.

[0097] Through the efficient collaboration of nine modules, the system has achieved an intelligent and automated inspection process. Compared with traditional manual inspections and single data collection methods, the system integrates multi-source data collection and real-time analysis, can respond quickly in a dynamic environment, adjust the inspection path in a timely manner, greatly improving the efficiency and accuracy of inspections. By adopting advanced data parsing and calculation methods, it enhances the comprehensive monitoring of the status of power equipment, promptly identifies potential fault risks, and reduces the likelihood of equipment failures and power outages. The feedback module 9 ensures the rapid transmission of information, enabling operation and maintenance personnel to obtain inspection results in a timely manner, thus accelerating the decision-making process. The comprehensive application of this system has improved the safety and reliability of power lines.

[0098] Embodiment 2: Please refer to Figure 1 , the first multi-source data set collected by the first data collection module 1 includes the target height, target curvature, target distribution coefficient, obstacle height, obstacle distribution rate, environmental wind speed, environmental humidity, and environmental temperature.

[0099] In this embodiment: The first multi-source data set collected by the first data collection module 1, including the target height, target curvature, target distribution coefficient, obstacle height, obstacle distribution rate, environmental wind speed, environmental humidity, and environmental temperature, reflects multiple key improvements and benefits. First, the rich environmental and target data can provide comprehensive background information for the path planning of the drone, making the inspection path more reasonable and safe, and reducing the risks of collision and error. Second, the real-time monitoring of parameters such as environmental wind speed, humidity, and temperature helps to judge the flight conditions, optimize the flight height and speed, and improve the inspection efficiency. Third, through the accurate measurement of the target height and curvature, the recognition accuracy of the power line status can be improved, thus promptly discovering potential problems.

[0100] Embodiment 3: Please refer to Figure 1 , the first data parsing module 2 includes a first preprocessing unit 21 and a first sorting unit 22. The first preprocessing unit 21 is used to preprocess and dimensionlessize the first multi-source data set, and the first sorting unit 22 is used to reorganize the preprocessed first multi-source data set into a first data set, a second data set, and a third data set;

[0101] The first data set includes the target height A, target curvature B, and target distribution coefficient C;

[0102] The second data set includes the obstacle height D and obstacle distribution rate E;

[0103] The third data set includes the environmental wind speed, environmental humidity, and environmental temperature;

[0104] Among them, the environmental wind speed is respectively recorded as F1, F2, F3,..., Fn according to the time stamp;

[0105] The environmental humidity is respectively recorded as G1, G2, G3, ..., Gn according to the time stamps;

[0106] The environmental temperature is respectively recorded as H1, H2, H3, ..., Hn according to the time stamps.

[0107] In this embodiment: The first data analysis module 2 preprocesses and dimensionlessizes the first multi-source data set through the first preprocessing unit 21, and uses the first sorting unit 22 to reorganize the data into a first data set, a second data set and a third data set, reflecting significant improvement points and benefits. Preprocessing and dimensionlessization help to eliminate noise and inconsistencies in the data, improve data quality, provide a reliable basis for subsequent analysis, ensure the accuracy of inspection decisions, classify the data into three categories: target, obstacle and environment, facilitate targeted analysis and calculation by the system, and improve the efficiency of information extraction. The first data set focuses on target features and can more accurately evaluate the condition of the power line; the second data set focuses on obstacles to ensure the safety of the inspection path; the third data set monitors environmental changes in real time to facilitate dynamic adjustment of the flight strategy. This structured data sorting method not only improves the intelligent level of the inspection, but also enhances the system's adaptability to complex environments, thus realizing more efficient and safer operations in power line inspections.

[0108] Embodiment 4: Please refer to Figure 1 , the first data calculation module 3 calculates and obtains the path correction value LJX through the following formula;

[0109]

[0110] In the formula, A is the target height, B is the target curvature, C is the target distribution coefficient, D is the obstacle height, and E is the obstacle distribution rate;

[0111] Fn, Gn, and Hn are the environmental wind speed, environmental humidity, and environmental temperature at the nth time stamp;

[0112] F1, G1, and H1 are the environmental wind speed, environmental humidity, and environmental temperature at the first time stamp;

[0113] a1, a2, and a3 are weight values, and the values of A1, a2, and a3 are adjusted and set by the user.

[0114] In this embodiment: The first data calculation module 3 obtains the path correction value LJX through a calculation formula, which reflects several important improvement points and benefits. First of all, this formula comprehensively considers various factors such as the target height, target curvature, target distribution coefficient, and obstacle characteristics, making the calculation of the path correction value LJX more comprehensive and accurate. This comprehensive calculation ability can effectively improve the adaptability of the UAV in complex environments, thus ensuring the safety and effectiveness of the inspection path. Secondly, the real-time data of environmental wind speed, humidity, and temperature are introduced into the formula, allowing the system to adjust the flight path in real time according to dynamic environmental changes and optimize the inspection strategy. This real-time feedback mechanism can quickly respond to changes in external conditions and reduce the risk of accidents. In addition, the user-adjustable weight value provides flexibility for the system, allowing users to make personalized settings according to specific inspection requirements, further enhancing the applicability and user-friendliness of the system. These improvements jointly promote the intelligence and efficiency of the UAV inspection system in power line management, enabling it to better meet the high requirements of the modern power industry for safety and efficiency.

[0115] Embodiment Five: Please refer to Figure 1 , the specific first comparison result generated by the first data analysis module 4 is as follows:

[0116] When LJX ≤ LYZ, it means that the current path correction value LJX does not need to intervene in subsequent calculations. When LJX > LYZ, it means that the current path correction value LJX needs to intervene in subsequent calculations.

[0117] In this embodiment: Through the first comparison result generated by the first data analysis module 4, when the path correction value LJX is less than or equal to the preset threshold LYZ, the system determines that the current path does not need to be intervened for calculation; conversely, when LJX is greater than LYZ, the system will perform further path correction. This mechanism effectively improves the intelligence level of the inspection process. First of all, through clear threshold determination, it simplifies the decision-making process, enables the system to quickly respond to dynamically changing environments, avoids unnecessary calculations, and improves calculation efficiency. Secondly, the setting of this comparison result can help operation and maintenance personnel quickly identify whether the inspection path needs to be adjusted, thereby reducing the complexity of manual intervention and judgment, reducing operation risks. This intelligent judgment method not only improves inspection efficiency but also ensures the safety and reliability of the UAV in complex environments, helps to achieve more efficient power line management, and meets the requirements of the modern power industry for automation and intelligence.

[0118] Embodiment Six: Please refer to Figure 1 , the second data acquisition module 5 includes an image acquisition unit 51 and a sensor acquisition unit 52;

[0119] The image acquisition unit 51 is used to acquire the image data of the UAV, including resolution, frame rate, perspective deviation degree, and color depth;

[0120] The sensor acquisition unit 52 is used to acquire UAV sensor data including the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value;

[0121] The resolution, frame rate, perspective offset degree, color depth, target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value constitute the second multi-source data.

[0122] In this embodiment: Through the collaborative work of the image acquisition unit 51 and the sensor acquisition unit 52, the second data acquisition module 5 significantly improves the comprehensiveness and accuracy of data acquisition. The image acquisition unit 51 is responsible for obtaining high-quality image data, including resolution, frame rate, perspective offset degree, and color depth. These parameters can help the system more accurately identify the power line and its surrounding environment, improving the image analysis ability during the inspection process. At the same time, the sensor acquisition unit 52 is responsible for acquiring key data such as the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value, ensuring that the system can monitor the operating status of power equipment and environmental impacts in real time. The composition of this multi-source data set not only enhances the comprehensive monitoring ability of the power line but also helps the operation and maintenance personnel quickly identify potential faults and risks. In addition, the rich data types provide a more solid foundation for subsequent data parsing and analysis, contributing to improving the intelligence and automation level of the inspection. This improvement directly promotes the safety and efficiency of the power industry, meeting the development needs of modern smart grids.

[0123] Embodiment Seven: Please refer to Figure 1 , the second data parsing module 6 includes a second preprocessing unit 61 and a second sorting unit 62;

[0124] The second preprocessing unit 61 is used to preprocess and dimensionlessize the second multi-source data set, and the second sorting unit 62 is used to reorganize the processed second multi-source data set into an image data set and a sensor data set;

[0125] The image data set includes the resolution I, frame rate, perspective offset degree J, and color depth K;

[0126] The sensor data set includes the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value;

[0127] Among them, the target temperature values are respectively recorded as L1, L1, L1,..., L1 according to the time stamp;

[0128] The measurement distances are respectively recorded as M1, M1, M1,..., M1 according to the time stamp;

[0129] The electromagnetic radiation intensities are respectively recorded as O1, O1, O1,..., O1 according to the time stamp;

[0130] The vibration values are respectively recorded as P1, P1, P1, ..., P1 according to the timestamps.

[0131] In this embodiment: The second data parsing module 6, through the second preprocessing unit 61 and the second sorting unit 62, improves the quality and efficiency of data processing. The second preprocessing unit 61 preprocesses and dimensionlessizes the second multi-source data set to ensure the consistency and comparability of the data, thereby eliminating noise and deviation, laying a solid foundation for subsequent analysis. This process not only improves the data quality but also makes the results of data analysis more reliable, helping to more accurately identify the status and risks of power equipment.

[0132] The second sorting unit 62 reorganizes the processed data into an image data set and a sensor data set, enabling the system to conduct targeted analysis and decision-making. The image data set includes resolution, frame rate, viewing angle offset degree, and color depth, enhancing the image analysis ability and thus improving the monitoring accuracy of the power line and its surrounding environment. At the same time, the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value in the sensor data set can reflect the operating status of the power equipment in real time, helping the operation and maintenance personnel quickly identify potential faults and abnormal conditions.

[0133] Through the marking of timestamps, these data can provide trend analysis of dynamic changes, further enhancing the intelligent level of the patrol inspection. This systematic data sorting and processing method not only optimizes the efficiency of the patrol inspection process but also provides a more efficient and safe management solution for the power industry.

[0134] Embodiment VIII: Please refer to Figure 1 , the second data calculation module 7 calculates and obtains two circuit anomaly coefficients DLZ through the following formulas respectively;

[0135]

[0136] In the formula: DLZ1 is the circuit anomaly coefficient calculated by adding the path correction value LJX, and DLZ2 is the circuit anomaly coefficient calculated without adding the path correction value LJX;

[0137] I is the resolution, J is the frame rate viewing angle offset degree, K is the color depth;

[0138] PL is the target temperature value intervention coefficient, PM is the measurement distance intervention coefficient, PO is the magnetic radiation intensity intervention coefficient, and PP is the vibration value intervention coefficient;

[0139] Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth timestamp;

[0140] Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth timestamp.

[0141] In this embodiment: The second data calculation module 7 obtains two circuit anomaly coefficients DLZ through formula calculation, which reflects multiple important improvement points and benefits. By calculating the circuit anomaly coefficients DLZ1 and DLZ2 respectively, the system can evaluate the abnormal conditions of the circuit with and without the path correction value LJX respectively. This dual evaluation mechanism enhances the system's comprehensive monitoring ability of the power line status, enabling the inspection personnel to more accurately judge whether there are potential faults in the line.

[0142] Secondly, the key parameters of the image dataset and the intervention coefficient of the sensor data are introduced into the formula, making the calculation of the anomaly coefficient more detailed and accurate. This approach of comprehensively considering different data sources not only improves the analysis ability of the power equipment status but also can identify and locate problems in a timely manner, thereby reducing the power outage risk caused by faults.

[0143] In addition, the dynamic data marked with timestamps can reflect the changes in the operating state of the equipment at different times, providing real-time trend analysis for the inspection. This dynamic analysis ability helps the operation and maintenance personnel quickly respond to potential problems and enhances the flexibility and intelligence level of the inspection system. Overall, this improvement promotes the efficiency and safety of power line management, meeting the requirements of modern smart grids for automation and precision.

[0144] Embodiment Nine: Please refer to Figure 1 , the second data analysis module 8 includes a first analysis unit 81 and a second analysis unit 82;

[0145] The first analysis unit 81 is used to generate a second comparison result, and the second analysis unit 82 is used to generate a re-comparison result;

[0146] The second comparison result is specifically as follows:

[0147] When DLZ ≤ DYZ, it means that the current path value does not need to intervene in the subsequent calculation. When DLZ > DYZ, it means that the current path value needs to intervene in the subsequent calculation;

[0148] The re-comparison result is specifically as follows:

[0149] When DCZ ≤ DYZ × 10%, it means that the current circuit has a first-level anomaly;

[0150] When DYZ × 10% < DCZ ≤ DYZ × 20%, it means that the current circuit has a second-level anomaly;

[0151] When DCZ > DYZ × 20%, it means that the current circuit has a third-level anomaly;

[0152] The abnormal circuit magnitude value DCZ is obtained by calculation through the following formula:

[0153]

[0154] Where: DLZ is the circuit abnormality coefficient, including DLZ1 and DLZ1, and DYZ is the circuit abnormality threshold.

[0155] In this embodiment: Through the collaborative work of the first analysis unit 81 and the second analysis unit 82 of the second data analysis module 8, the accuracy and flexibility of circuit abnormality recognition are significantly improved. The second comparison result formed by the first analysis unit 81 enables the system to quickly determine whether the circuit abnormality coefficient DLZ exceeds the preset threshold DYZ. When DLZ is less than or equal to DYZ, it means that no further calculation is required, simplifying the decision-making process; when DLZ is greater than DYZ, subsequent analysis needs to be involved to ensure that potential problems can be identified and processed in a timely manner.

[0156] Secondly, the re-comparison result generated by the second analysis unit 82 provides a more detailed judgment of the fault level through hierarchical abnormality assessment. This subdivision mechanism not only helps the operation and maintenance personnel quickly understand the severity of the fault, but also facilitates the adoption of corresponding countermeasures. For example, the determination criteria for level 1 abnormality, level 2 abnormality, and level 3 abnormality enable the system to classify and process faults of different severities, thereby optimizing resource allocation and ensuring that the most urgent problems are processed first.

[0157] This multi-level analysis method improves the intelligence level of the inspection system and enhances the dynamic monitoring ability of the power line status. In addition, the fast judgment and feedback mechanism ensure that the operation and maintenance personnel can take measures in a timely manner when abnormalities occur, reducing the risk of equipment failure and power outage, thereby improving the safety and efficiency of power line inspection.

[0158] This application also includes a method for inspecting power lines by an unmanned aerial vehicle. Please refer to Figure 2 , and the specific steps are as follows:

[0159] S1. During the flight of the unmanned aerial vehicle, use the first data acquisition module 1 to obtain multi-source data for the inspection path and generate a first multi-source data set;

[0160] S2. Use the first data parsing module 2 to preprocess and dimensionlessize the obtained first multi-source data, remove noise and unify the data format, and reorganize the preprocessed data into a first data set, a second data set, and a third data set;

[0161] S3. The first data calculation module 3 integrates and calculates the sorted data to generate a path correction value LJX. The first data analysis module 4 compares the path correction value LJX with a preset path intervention threshold LYZLYZ to generate a first comparison result and determine whether path correction needs to be intervened.

[0162] S4. The second data acquisition module 5 collects image and sensor data during the UAV inspection to generate a second multi-source dataset.

[0163] S5. The second data parsing module 6 preprocesses the second multi-source data, sorts it into an image dataset and a sensor dataset. The second data calculation module 7 integrates the image dataset, the sensor dataset, and the path correction value LJX to calculate a circuit anomaly coefficient DLZ.

[0164] S6. The second data analysis module 8 compares the circuit anomaly coefficient DLZ with a preset circuit anomaly threshold DYZ value to generate a second comparison result and determine whether the current circuit is abnormal. If the first comparison result shows no abnormality, the circuit anomaly coefficient DLZ and the circuit anomaly threshold DYZ value are integrated and calculated to generate a circuit anomaly magnitude value DCZ, and then re-compared to determine the circuit anomaly level.

[0165] S7. The feedback module 9 sends the data collected and calculated during the inspection to the terminal for feedback operations to ensure the traceability and timely response of the inspection results.

[0166] The content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0167] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An unmanned aerial vehicle power line inspection system, characterized in that: It includes a first data acquisition module (1), a first data parsing module (2), a first data calculation module (3), a first data analysis module (4), a second data acquisition module (5), a second data parsing module (6), a second data calculation module (7), a second data analysis module (8), and a feedback module (9); The first data acquisition module (1) is used to obtain multi-source data for the UAV detection path, so as to generate a first multi-source data set; The first data parsing module (2) is used to preprocess the first multi-source data and reorganize the preprocessed first multi-source data, so as to generate a first data set, a second data set, and a third data set; The first data calculation module (3) is used to integrally calculate the first data set, the second data set, and the third data set, so as to generate a path correction value LJX; The first data analysis module (4) is used to compare the path correction value LJX with a preset path intervention threshold LYZ, so as to generate a first comparison result, and determine whether the subsequent system needs to use the path correction value LJX according to the first comparison result; The second data acquisition module (5) is used to collect multi-source data during UAV inspection and organize it into a second multi-source data set; The second data parsing module (6) is used to preprocess the second multi-source data and reorganize it into an image data set and a sensor data set; The second data calculation module (7) is used to integrate the image data set, the sensor data set, and the path correction value LJX, so as to obtain a circuit anomaly coefficient DLZ; The second data analysis module (8) is used to compare the circuit anomaly coefficient DLZ with a preset circuit anomaly threshold DYZ value, so as to generate a second comparison result. According to the second comparison result, it is judged whether the current circuit is abnormal. If the first comparison result shows no abnormality, the circuit anomaly coefficient DLZ and the circuit anomaly threshold DYZ value are integrally calculated to generate a circuit anomaly magnitude value DCZ, and the circuit anomaly magnitude value DCZ is compared with the circuit anomaly threshold DYZ value again to generate a re-comparison result. According to the re-comparison result, the anomaly level of the current circuit is judged; The feedback module (9) is used to send multiple data to the terminal for feedback operations.

2. The unmanned aerial vehicle power line inspection system according to claim 1, characterized in that: The first multi-source data set collected by the first data acquisition module (1) includes the target height, target curvature, target distribution coefficient, obstacle height, obstacle distribution rate, environmental wind speed, environmental humidity, and environmental temperature.

3. The UAV power line inspection system according to claim 2, wherein: The first data parsing module (2) includes a first preprocessing unit (21) and a first reorganization unit (22). The first preprocessing unit (21) is used to preprocess and dimensionless the first multi-source data set, and the first reorganization unit (22) is used to reorganize the preprocessed first multi-source data set into a first data set, a second data set, and a third data set; The first data set includes the target height A, target curvature B, and target distribution coefficient C; The second data set includes the obstacle height D and obstacle distribution rate E; The third data set includes the environmental wind speed, environmental humidity, and environmental temperature; Among them, the environmental wind speed is respectively recorded as F1, F2, F3, ..., Fn according to the time stamp; The environmental humidity is respectively recorded as G1, G2, G3, ..., Gn according to the time stamp; The environmental temperature is respectively recorded as H1, H2, H3, ..., Hn according to the time stamp.

4. The unmanned aerial vehicle power line inspection system according to claim 3, characterized in that: The first data calculation module (3) calculates and obtains the path correction value LJX through the following formula; In the formula, A is the target height, B is the target curvature, C is the target distribution coefficient, D is the obstacle height, and E is the obstacle distribution rate; Fn, Gn, and Hn are the environmental wind speed, environmental humidity, and environmental temperature at the nth time stamp; F1, G1, and H1 are the environmental wind speed, environmental humidity, and environmental temperature at the first time stamp; a1, a2, and a3 are weight values, and the values of A1, a2, and a3 are adjusted and set by the user.

5. The UAV power line inspection system according to claim 4, wherein: The specific first comparison result generated by the first data analysis module (4) is as follows: When LJX ≤ LYZ, it means that the current path correction value LJX does not need to intervene in subsequent calculations. When LJX > LYZ, it means that the current path correction value LJX needs to intervene in subsequent calculations.

6. The UAV power line inspection system according to claim 5, wherein: The second data acquisition module (5) includes an image acquisition unit (51) and a sensor acquisition unit (52); The image acquisition unit (51) is used to acquire the image data of the drone, including resolution, frame rate, viewing angle offset degree, and color depth; The sensor acquisition unit (52) is used to acquire the drone sensor data, including the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value; The resolution, frame rate, viewing angle offset degree, color depth, target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value constitute the second multi-source data.

7. The UAV power line inspection system according to claim 6, characterized in that: The second data analysis module (6) includes a second preprocessing unit (61) and a second sorting unit (62); The second preprocessing unit (61) is used to preprocess and dimensionlessize the second multi-source data set, and the second sorting unit (62) is used to reorganize the processed second multi-source data set into an image data set and a sensor data set; The image data set includes resolution I, frame rate viewing angle offset degree J, and color depth K; The sensor data set includes the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value; Among them, the target temperature value is respectively recorded as L1, L1, L1, ..., L1 according to the time stamp; The measurement distance is respectively recorded as M1, M1, M1, ..., M1 according to the time stamp; The electromagnetic radiation intensity is respectively recorded as O1, O1, O1, ..., O1 according to the time stamp; The vibration value is respectively recorded as P1, P1, P1, ..., P1 according to the time stamp.

8. The UAV power line inspection system according to claim 7, characterized in that: The second data calculation module (7) calculates and obtains two circuit anomaly coefficients DLZ through the following formulas respectively; In the formula: DLZ1 is the circuit anomaly coefficient calculated by adding the path correction value LJX, and DLZ2 is the circuit anomaly coefficient calculated without adding the path correction value LJX; I is the resolution, J is the frame rate viewing angle offset degree, and K is the color depth; PL is the intervention coefficient of the target temperature value, PM is the intervention coefficient of the measurement distance, PO is the intervention coefficient of the magnetic radiation intensity, and PP is the intervention coefficient of the vibration value; Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth timestamp; Ln, Mn, On, and Pn are the target temperature value, measurement distance, electromagnetic radiation intensity, and vibration value at the nth timestamp.

9. The drone power line inspection system according to claim 8, characterized in that: The second data analysis module (8) includes a first analysis unit (81) and a second analysis unit (82); The first analysis unit (81) is used to generate a second comparison result, and the second analysis unit (82) is used to generate a re-comparison result; The specific content of the second comparison result is as follows: When DLZ ≤ DYZ, it means that the current path value does not need to intervene in subsequent calculations. When DLZ > DYZ, it means that the current path value needs to intervene in subsequent calculations; The specific content of the re-comparison result is as follows: When DCZ ≤ 10% × DYZ, it means that the current circuit has a first-level abnormality; When 10% × DYZ < DCZ ≤ 20% × DYZ, it means that the current circuit has a second-level abnormality; When DCZ > 20% × DYZ, it means that the current circuit has a third-level abnormality; The circuit abnormality magnitude value DCZ is obtained by calculating through the following formula: In the formula: DLZ is the circuit abnormality coefficient, including DLZ1 and DLZ1, and DYZ is the circuit abnormality threshold.

10. A method for inspecting power lines by an unmanned aerial vehicle, characterized in that: The specific steps are as follows: S1. During the flight of the drone, use the first data acquisition module (1) to obtain multi-source data for the inspection path and generate a first multi-source data set; S2. Use the first data parsing module (2) to preprocess and dimensionlessize the obtained first multi-source data, remove noise and unify the data format, and reorganize the preprocessed data into a first data set, a second data set, and a third data set; S3. Through the first data calculation module (3), integrate and calculate the sorted data to generate a path correction value LJX (LJX). Use the first data analysis module (4) to compare the path correction value LJX with the preset path intervention threshold LYZ (LYZ) to generate a first comparison result and judge whether path correction is required; S4. During the drone inspection process, use the second data acquisition module (5) to collect image and sensor data and generate a second multi-source data set; S5. Through the second data parsing module (6), preprocess the second multi-source data, organize it into an image data set and a sensor data set, and use the second data calculation module (7) to integrate the image data set, the sensor data set, and the path correction value LJX to calculate the circuit abnormality coefficient DLZ; S6. Through the second data analysis module (8), compare the circuit abnormality coefficient DLZ with the preset circuit abnormality threshold DYZ value to generate a second comparison result and judge whether the current circuit has an abnormality. If the first comparison result shows no abnormality, then integrate and calculate the circuit abnormality coefficient DLZ and the circuit abnormality threshold DYZ value to generate a circuit abnormality magnitude value DCZ and perform a re-comparison to judge the circuit abnormality level; S7. Use the feedback module (9) to send the data collected and calculated during the inspection tour to the terminal for feedback operations, ensuring the traceability and timely response of the inspection results.

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