Anti-collision obstacle-avoiding intelligent power line inspection unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses an anti-collision and obstacle-avoiding intelligent power line inspection unmanned aerial vehicle, the intelligent level and safety of the unmanned aerial vehicle in the power line inspection process are improved through mutual cooperation of a plurality of modules, and through systematic data acquisition, preprocessing, calculation, analysis and execution processes, the power line inspection efficiency of the unmanned aerial vehicle is improved. The efficiency of data processing in the inspection process of the unmanned aerial vehicle is effectively improved, the capability of real-time risk assessment is enhanced, and the unmanned aerial vehicle can perform self-adjustment and avoid obstacles in a complex environment. The innovative technology not only improves the accuracy and safety of routing inspection, but also effectively reduces the demand of human intervention, remarkably improves the working efficiency, and can more accurately perform risk assessment, identify potential collision risks and adjust the flight path in time by separately sorting the environmental factors and the internal state data of the unmanned aerial vehicle. And the intelligent decision-making capability of the unmanned aerial vehicle is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to an intelligent power line inspection UAV capable of preventing collision and avoiding obstacles. Background Art

[0002] As an important innovative tool, intelligent drones are widely used in fields such as agriculture, transportation, and environmental monitoring. In the power industry, the introduction of drones provides a highly effective solution for power line inspections, greatly improving inspection efficiency and safety, and becoming a vital auxiliary tool for power facility maintenance.

[0003] However, current technology still faces several key challenges in practical operation. Existing systems often rely on traditional data processing methods, resulting in inefficient data collection and analysis and an inability to assess flight risks in real time. Furthermore, drones' autonomous obstacle avoidance capabilities are insufficient in complex environments, resulting in potential collision risks not being identified promptly. These deficiencies threaten inspection safety, increase reliance on manual intervention, and reduce overall efficiency.

[0004] Therefore, we propose an intelligent power line inspection drone with anti-collision and obstacle avoidance to solve the above problems. Summary of the Invention

[0005] The present invention aims to provide an intelligent, collision- and obstacle-avoiding drone for power line inspections. This addresses the aforementioned issues, which include existing systems often relying on traditional data processing methods, inefficient data collection and analysis, and an inability to assess flight risks in real time. Furthermore, in complex environments, the drone's autonomous obstacle avoidance capabilities are insufficient, resulting in potential collision risks not being identified in a timely manner. These deficiencies threaten inspection safety, increase reliance on manual intervention, and reduce overall efficiency.

[0006] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent power line inspection drone with anti-collision and obstacle avoidance, comprising a drone body, a data acquisition module, a data preprocessing module, a data calculation module, a data analysis module, and an execution module;

[0007] The data acquisition module is used to collect multi-source data during the drone inspection process and integrate it into a first multi-source data set and a second multi-source data set;

[0008] The data preprocessing module is used to preprocess the first multi-source dataset and the second multi-source dataset, and reorganize the preprocessed first multi-source dataset and the second multi-source dataset into an external factor dataset and an internal factor dataset;

[0009] The data calculation module is used to integrate the external factor data set and the internal factor data set into a collision coefficient ZJX and an avoidance coefficient GBX, and integrate the collision coefficient and the avoidance coefficient into a flight path safety factor AQX;

[0010] The data analysis module is used to compare the calculated flight path safety factor AQX with a preset first threshold Y to generate a first comparison result, and determine whether the current drone has a flight risk based on the first comparison result. If the first comparison result shows that there is a risk, the collision coefficient ZJX and the avoidance coefficient GBX are compared with the preset collision damage threshold ZSY and avoidance threshold GBY respectively to generate a second comparison result, and determine whether the collision can be avoided and the extent of the collision damage based on the second comparison result;

[0011] The execution module is used to automatically adjust the flight of the UAV according to the second comparison result, and generate an intervention report and send it to the control terminal.

[0012] Preferably, the data acquisition module includes a sensor group and a recording unit;

[0013] The sensor group is used to collect various parameters of the UAV during the inspection flight, and the recording unit is used to generate the parameters collected by the sensor group into a first multi-source data set and a second multi-source data set;

[0014] The first multi-source data set includes obstacle distance, ambient temperature, wind speed, vertical altitude, flight speed, current altitude, and attitude angle;

[0015] The second multi-source data set includes ground height, obstacle distance, obstacle height, obstacle width, image resolution, sensor response time, and viewing angle offset.

[0016] Preferably, the data preprocessing module includes a processing unit and a sorting unit;

[0017] The processing unit is used to preprocess and dimensionlessly transform the first multi-source dataset and the second multi-source dataset, and the arranging unit is used to arrange the processed first multi-source dataset and the second multi-source dataset into:

[0018] The external factor dataset includes obstacle distance A, obstacle height B, obstacle width C, ambient temperature D, and wind speed E;

[0019] The obstacle distances are recorded as A1, A2, A3, ..., An according to the timestamps;

[0020] The wind speed is recorded as E1, E2, E3, ..., En according to the timestamp;

[0021] The internal factor dataset includes vertical height F, flight speed G, attitude angle H, image resolution I, sensor response time J, and viewing angle deviation K;

[0022] The vertical heights are recorded as F1, F2, F3, ..., Fn according to the timestamp;

[0023] The flight speed is recorded as G1, G2, G3, ..., Gn according to the timestamp;

[0024] The attitude angles are recorded as H1, H2, H3, ..., Hn according to the timestamp;

[0025] The viewing angle offsets are recorded as K1, K2, K3, ..., Kn according to the timestamps.

[0026] Preferably, the data calculation module includes a first calculation unit, a second calculation unit and a third calculation unit;

[0027] The first calculation unit is used to calculate and obtain the collision coefficient ZJX, the second calculation unit is used to calculate and obtain the avoidance coefficient GBX, and the third calculation unit is used to calculate and obtain the flight path safety factor AQX. The specific calculation formulas are as follows:

[0028]

[0029] Where: PA is the obstacle distance change rate, PG is the flight speed change rate, X is the intervention correction value, B is the obstacle height, C is the obstacle width, and D is the ambient temperature;

[0030] Fn, Gn, and Hn are the vertical height, flight speed, and attitude angle at timestamp N, respectively;

[0031] a1, a2, a3, b1, b2, b3, c1, and c2 are weight values, respectively. The values of a1, a2, a3, b1, b2, b3, c1, and c2 are adjusted and set by the user. ln is a logarithmic function.

[0032] Preferably, the obstacle distance change rate PA, the flight speed change rate PG and the intervention correction value X are respectively calculated and obtained by the following formulas:

[0033]

[0034] Where n and m are timestamps, A1, G1, F1, and E1 are the obstacle distance, vertical height, flight speed, and wind speed at the first timestamp, respectively; An, Gn, Fn, and En are the obstacle distance, vertical height, flight speed, and wind speed at the nth timestamp, respectively.

[0035] Preferably, the data analysis module includes a first analysis unit, a second analysis unit, and a third analysis unit;

[0036] The first analysis unit is used to compare the flight safety path coefficient AQX with a preset first threshold Y to generate a first comparison result. The second analysis unit is respectively used to compare the impact coefficient ZJX with a preset impact damage threshold ZSY and compare the avoidance coefficient GBX with a preset avoidance threshold GBY to generate a second comparison result.

[0037] Preferably, the first comparison result is specifically as follows:

[0038] When AQX ≤ Y, it represents that the inspection path of the current drone is risk-free. When AQX > Y, it represents that the inspection path of the current drone has risks;

[0039] When Y < AQJ ≤ Y × 110%, it represents that the inspection path of the current drone has a first-level risk and has a relatively low harm to the drone;

[0040] When Y × 110% < AQJ ≤ Y × 115%, it represents that the inspection path of the current drone has a second-level risk and has a moderate harm to the drone;

[0041] When AQJ > Y × 115%, it represents that the inspection path of the current drone has a third-level risk and has a relatively high harm to the drone;

[0042] Among them, ZJJ is the path risk magnitude coefficient, which is obtained by integrating and calculating the flight path safety coefficient AQX and the preset first threshold Y. The specific calculation formula is as follows;

[0043]

[0044] In the formula: AQX is the flight safety path coefficient, Y is the first threshold, and ln is the logarithmic function.

[0045] Preferably, the second comparison result is specifically as follows:

[0046] When ZJX ≤ ZSY, it represents that the current drone has no impact risk. When ZJX > ZSY, it represents that the current drone has an impact risk;

[0047] When ZSY < ZJJ ≤ ZSY × 110%, it represents that the current drone has a first-level risk impact and has a relatively low harm to the drone;

[0048] When ZSY × 110% < ZJJ ≤ ZSY × 115%, it represents that the current drone has a second-level risk impact and has a moderate harm to the drone;

[0049] When ZJJ > ZSY × 115%, it represents that there is a three - level impact risk in the current inspection path of the UAV, which is highly harmful to the UAV;

[0050] When GBX ≤ GBY, it represents that the current UAV has no avoidance difficulty. When GBX > GBY, it represents that the current UAV has an avoidance difficulty;

[0051] When GBY < GBJ ≤ GBY × 110%, it represents that the current UAV has a first - level avoidance difficulty, and the UAV can perform avoidance actions relatively easily;

[0052] When GBY × 110% < GBJ ≤ GBY × 115%, it represents that the current UAV has a second - level avoidance difficulty, and the UAV can perform avoidance actions;

[0053] When GBJ > GBY × 115%, it represents that the current UAV has a third - level avoidance difficulty, and the UAV is difficult to perform avoidance actions;

[0054] Where ZJJ is the impact magnitude coefficient, which is obtained by integrating the impact coefficient ZJX and the preset impact damage threshold ZSY. GBJ is the avoidance magnitude coefficient, which is obtained by integrating the avoidance coefficient GBX and the preset avoidance threshold GBY.

[0055] Preferably, the impact magnitude coefficient ZJJ and the avoidance magnitude coefficient GBJ are respectively obtained by the following formulas:

[0056]

[0057] In the formula: ZJX is the impact coefficient, GBX is the avoidance coefficient, ZJY is the impact damage threshold ZSY, GBY is the avoidance threshold, and ln is the logarithmic function.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] 1. Through the mutual cooperation between multiple modules, the intelligent level and safety of the UAV in the power line inspection process are improved. Through the systematic data collection, pre - processing, calculation, analysis and execution process, the efficiency of data processing in the UAV inspection process is effectively improved, and the ability of real - time risk assessment is enhanced, enabling the UAV to self - adjust and avoid obstacles in a complex environment. This innovative technology not only improves the accuracy and safety of inspection, but also effectively reduces the need for human intervention and significantly improves work efficiency.

[0060] 2. By separating environmental factors from the drone's internal status data, the system can more accurately assess risks, identify potential collision risks, and adjust the flight path in a timely manner. The organized data provides an accurate data foundation for subsequent calculations and analysis, enhancing the drone's intelligent decision-making capabilities, enabling it to better navigate autonomously and avoid obstacles in complex environments. Clear data sets of external and internal factors make data analysis more efficient, enabling faster extraction of key information, improving data utilization, and helping to optimize inspection strategies.

[0061] 3. The impact coefficient ZJX calculated by the first calculation unit provides the system with an accurate impact risk assessment. Based on multiple parameters, it ensures flight safety in complex environments. The avoidance coefficient GBX calculated by the second calculation unit enables the drone to develop an effective avoidance strategy, dynamically adjusting the flight speed change rate and other environmental factors to improve its ability to avoid obstacles. The flight path safety factor AQX calculated by the third calculation unit comprehensively considers the impact coefficient and avoidance coefficient, making the flight safety assessment more comprehensive and accurate, and supporting the drone's intelligent decision-making. By comprehensively calculating the impact coefficient, avoidance coefficient, and safety factor, the system can provide the drone with more accurate decision-making basis, making it more autonomous and efficient during inspections. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a system flow chart of the present invention.

[0063] In the figure: 1. Data acquisition module; 11. Sensor group; 12. Recording unit; 2. Data preprocessing module; 21. Processing unit; 22. Sorting unit; 3. Data calculation module; 31. First calculation unit; 32. Second calculation unit; 33. Third calculation unit; 4. Data analysis module; 41. First analysis unit; 42. Second analysis unit; 43. Third analysis unit; 5. Execution module. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0065] Example 1: Please refer to Figure 1 , an intelligent power line inspection drone with anti-collision and obstacle avoidance, including a drone body, a data acquisition module 1, a data preprocessing module 2, a data calculation module 3, a data analysis module 4 and an execution module 5;

[0066] The data acquisition module 1 is used to collect multi-source data during the drone inspection process and integrate it into a first multi-source data set and a second multi-source data set;

[0067] The data preprocessing module 2 is used to preprocess the first multi-source dataset and the second multi-source dataset, and reorganize the preprocessed first multi-source dataset and the second multi-source dataset into an external factor dataset and an internal factor dataset;

[0068] The data calculation module 3 is used to integrate the external factor data set and the internal factor data set into a collision coefficient ZJX and an avoidance coefficient GBX, and integrate the collision coefficient and the avoidance coefficient into a flight path safety factor AQX;

[0069] The data analysis module 4 is used to compare the calculated flight path safety factor AQX with a preset first threshold value Y to generate a first comparison result, and determine whether the current drone has a flight risk based on the first comparison result. If the first comparison result shows that there is a risk, the collision coefficient ZJX and the avoidance coefficient GBX are compared with the preset collision damage threshold value ZSY and the avoidance threshold value GBY respectively to generate a second comparison result, and determine whether the collision can be avoided and the extent of the collision damage based on the second comparison result;

[0070] The execution module 5 is used to automatically adjust the flight of the UAV according to the second comparison result, and generate an intervention report and send it to the control terminal.

[0071] In this embodiment, Data Acquisition Module 1 is responsible for collecting multi-source data during drone inspections, including visual data, LiDAR data, and Global Positioning System data. By integrating this data into a first and second multi-source datasets, the module ensures comprehensiveness and diversity. This comprehensive data collection lays a solid foundation for subsequent analysis and processing, enabling drones to more accurately identify surrounding obstacles and powerline locations in complex environments, improving inspection accuracy.

[0072] Data preprocessing module 2 cleans and organizes the first and second multi-source datasets to generate external factor datasets and internal factor datasets. Through filtering and format conversion, this module effectively removes noise and outliers, ensuring data quality. High-quality data preprocessing significantly improves the efficiency of subsequent calculations and analysis, providing reliable data support for safe drone flight and reducing the possibility of misjudgments and misoperations.

[0073] Data calculation module 3 integrates the external and internal factor datasets to calculate the impact coefficient ZJX and the avoidance coefficient GBX, which are ultimately combined into the flight path safety factor AQX. This module quantifies various environmental factors and the drone's status to form a comprehensive safety assessment metric. Through quantitative analysis of the flight path, the system can more accurately determine flight safety, providing data for subsequent risk analysis and enhancing the drone's autonomous decision-making capabilities.

[0074] Data analysis module 4 compares the flight path safety factor AQX with a preset first threshold Y, generating a first comparison result to determine whether the drone faces a flight risk. If a risk is identified, the module further compares the impact coefficient ZJX and avoidance coefficient GBX with the corresponding thresholds to generate a second comparison result. This process effectively enhances the system's intelligence, enabling the drone to conduct risk assessments based on real-time data and respond promptly, ensuring flight safety and reducing the likelihood of collisions.

[0075] Execution Module 5 automatically adjusts the drone's flight path based on the second comparison results and generates an intervention report, which is then sent to the control terminal. By adjusting flight control parameters in real time, the drone can quickly take evasive action upon identifying a risk. The efficient execution of this module not only improves drone safety but also provides real-time feedback to operators, enhancing the system's operability and transparency, thereby improving the overall efficiency of inspection missions.

[0076] The interplay between multiple modules enhances the intelligence and safety of drones during power line inspections. Through a systematic process of data collection, preprocessing, calculation, analysis, and execution, the system effectively improves the efficiency of data processing during drone inspections and enhances real-time risk assessment capabilities, enabling drones to self-adjust and avoid obstacles in complex environments. This innovative technology not only improves the accuracy and safety of inspections, but also effectively reduces the need for human intervention, significantly improving work efficiency.

[0077] Example 2: Please refer to Figure 1 , the data acquisition module 1 includes a sensor group 11 and a recording unit 12;

[0078] The sensor group 11 is used to collect various parameters of the UAV during the inspection flight, and the recording unit 12 is used to generate the parameters collected by the sensor group 11 into a first multi-source data set and a second multi-source data set;

[0079] The first multi-source data set includes obstacle distance, ambient temperature, wind speed, vertical altitude, flight speed, current altitude, and attitude angle;

[0080] The second multi-source data set includes ground height, obstacle distance, obstacle height, obstacle width, image resolution, sensor response time, and viewing angle offset.

[0081] In this embodiment: the sensor group 11 can collect multiple parameters of the drone during the inspection flight in real time, ensure the comprehensiveness of the data, and enable the system to monitor changes in the flight environment in real time, thereby improving the response capability to external conditions and enhancing the adaptability of the drone under different climatic conditions.

[0082] The recording unit 12 generates a first multi-source data set and a second multi-source data set based on the parameters collected by the sensor group 11, providing a reliable data source for subsequent analysis and calculation, improving the convenience of data management, and enabling the drone to dynamically identify and evaluate surrounding obstacles, thereby reducing collision risks, improving flight safety, and enabling the drone to more effectively avoid obstacles during inspections and optimize inspection routes.

[0083] Example 3: Please refer to Figure 1 , the data pre-processing module 2 includes a processing unit 21 and a sorting unit 22;

[0084] The processing unit 21 is used to preprocess and dimensionlessly transform the first multi-source dataset and the second multi-source dataset, and the sorting unit 22 is used to sort the processed first multi-source dataset and the second multi-source dataset into;

[0085] The external factor dataset includes obstacle distance A, obstacle height B, obstacle width C, ambient temperature D, and wind speed E;

[0086] The obstacle distances are recorded as A1, A2, A3, ..., An according to the timestamps;

[0087] The wind speed is recorded as E1, E2, E3, ..., En according to the timestamp;

[0088] The internal factor dataset includes vertical height F, flight speed G, attitude angle H, image resolution I, sensor response time J, and viewing angle deviation K;

[0089] The vertical heights are recorded as F1, F2, F3, ..., Fn according to the timestamp;

[0090] The flight speed is recorded as G1, G2, G3, ..., Gn according to the timestamp;

[0091] The attitude angles are recorded as H1, H2, H3, ..., Hn according to the timestamp;

[0092] The viewing angle offsets are recorded as K1, K2, K3, ..., Kn according to the timestamps.

[0093] In this embodiment, the processing unit 21 preprocesses and dimensionlessly converts the first multi-source dataset and the second multi-source dataset to ensure consistency of data from different sources, making subsequent analysis more comparable and improving the efficiency and accuracy of data processing.

[0094] The sorting unit 22 sorts the processed data into an external factor data set and an internal factor data set, respectively, so as to make the data structure clearer, facilitate subsequent calculation and analysis, and improve the operability of the system.

[0095] The external factor data system can monitor and reflect environmental changes in real time, enhancing the drone's ability to adapt to dynamic environments. The internal factor data facilitates the analysis of the dynamic changes of the drone during the inspection process and supports real-time evaluation and optimization of the flight status.

[0096] By separating and organizing environmental factors from the drone's internal status data, the system can perform risk assessments more accurately, identify potential collision risks, and adjust the flight path in a timely manner. The organized data provides an accurate data basis for subsequent calculations and analysis, enhancing the drone's intelligent decision-making capabilities, enabling it to better navigate autonomously and avoid obstacles in complex environments. Clear data sets of external and internal factors make data analysis more efficient, enabling faster extraction of key information, improving data utilization, and helping to optimize inspection strategies. The systematic data processing and organization process has promoted the intelligent upgrade of drone technology and met the power inspection industry's demand for efficient, safe, and intelligent inspection solutions.

[0097] Example 4: Please refer to Figure 1 , the data calculation module 3 includes a first calculation unit 31, a second calculation unit 32 and a third calculation unit 33;

[0098] The first calculation unit 31 is used to calculate and obtain the collision coefficient ZJX, the second calculation unit 32 is used to calculate and obtain the avoidance coefficient GBX, and the third calculation unit 33 is used to calculate and obtain the flight path safety factor AQX. The specific calculation formulas are as follows:

[0099]

[0100] Where: PA is the obstacle distance change rate, PG is the flight speed change rate, X is the intervention correction value, B is the obstacle height, C is the obstacle width, and D is the ambient temperature;

[0101] Fn and Gn are the vertical height and flight speed at time stamp N respectively;

[0102] a1, a2, a3, b1, b2, b3, c1, and c2 are weight values, respectively. The values of a1, a2, a3, b1, b2, b3, c1, and c2 are adjusted and set by the user. ln is a logarithmic function.

[0103] In this embodiment, the impact coefficient ZJX calculated by the first calculation unit 31 provides the system with an accurate impact risk assessment, which ensures flight safety in complex environments based on multiple parameters.

[0104] The avoidance coefficient GBX calculated by the second calculation unit 32 enables the drone to develop an effective avoidance strategy, dynamically adjusting it using the flight speed change rate and other environmental factors, thereby improving its ability to avoid obstacles. The flight path safety factor AQX calculated by the third calculation unit 33 comprehensively considers the impact coefficient and the avoidance coefficient, making flight safety assessments more comprehensive and accurate, and supporting intelligent decision-making by the drone. By comprehensively calculating the impact coefficient, avoidance coefficient, and safety factor, the system can provide the drone with more accurate decision-making, enabling it to perform inspections more autonomously and efficiently.

[0105] The weight value is adjusted by the user according to the specific application scenario, which enhances the flexibility and adaptability of the system and makes the flight path safety assessment under different environments and mission requirements more accurate.

[0106] Example 5: Please refer to Figure 1 , the obstacle distance change rate PA, the flight speed change rate PG and the intervention correction value X are calculated and obtained by the following formulas respectively;

[0107]

[0108]

[0109] Where n and m are timestamps, A1, G1, F1, and E1 are the obstacle distance, vertical height, flight speed, and wind speed at the first timestamp, respectively; An, Gn, Fn, and En are the obstacle distance, vertical height, flight speed, and wind speed at the nth timestamp, respectively.

[0110] In this embodiment: the obstacle distance change rate PA and the flight speed change rate PG are calculated through timestamp data, which can monitor the dynamic changes of the drone's surrounding environment during the inspection process in real time, thereby improving the system's sensitivity to the environment. By averaging multiple timestamp data, PA and PG provide a comprehensive assessment of the flight status, enabling the drone to more accurately judge the current flight safety situation.

[0111] The calculation of the intervention correction value X takes into account the relationship between the vertical height and the wind speed, making the adjustment of the flight path more scientific and reasonable, which helps to improve the flight stability and safety. The dynamic calculation method based on the time stamp enables the system to flexibly respond to different flight environments and conditions, enhancing the adaptability of the UAV in complex and changeable environments.

[0112] Example 6: Please refer to Figure 1 , the data analysis module 4 includes a first analysis unit 41, a second analysis unit 42 and a third analysis unit 43;

[0113] The first analysis unit 41 is used to compare the flight safety path coefficient AQX with a preset first threshold Y to generate a first comparison result. The second analysis unit 42 is respectively used to compare the impact coefficient ZJX with a preset impact damage threshold ZSY and compare the avoidance coefficient GBX with a preset avoidance threshold GBY to generate a second comparison result.

[0114] The specific first comparison result is as follows:

[0115] When AQX ≤ Y, it means that the inspection path of the current UAV is risk-free. When AQX > Y, it means that the inspection path of the current UAV has risks;

[0116] When Y < AQJ ≤ Y × 110%, it means that the inspection path of the current UAV has a first-level risk, with a relatively low harm to the UAV;

[0117] When Y × 110% < AQJ ≤ Y × 115%, it means that the inspection path of the current UAV has a second-level risk, with a moderate harm to the UAV;

[0118] When AQJ > Y × 115%, it means that the inspection path of the current UAV has a third-level risk, with a relatively high harm to the UAV;

[0119] Among them, ZJJ is the path risk magnitude coefficient, which is obtained by integrating and calculating the flight path safety coefficient AQX and the preset first threshold Y. The specific calculation formula is as follows;

[0120]

[0121] In the formula: AQX is the flight safety path coefficient, Y is the first threshold, and ln is the logarithmic function.

[0122] The specific second comparison result is as follows:

[0123] When ZJX ≤ ZSY, it means that the current UAV has no impact risk. When ZJX > ZSY, it means that the current UAV has an impact risk;

[0124] When ZSY < ZJJ ≤ ZSY × 110%, it represents that the current drone has a first-level risk of impact, and the harm to the drone is relatively low;

[0125] When ZSY × 110% < ZJJ ≤ ZSY × 115%, it represents that the current drone has a second-level risk of impact, and the harm to the drone is moderate;

[0126] When ZJJ > ZSY × 115%, it represents that the inspection path of the current drone has a third-level impact risk, and the harm to the drone is relatively high;

[0127] When GBX ≤ GBY, it represents that the current drone has no difficulty in avoidance. When GBX > GBY, it represents that the current drone has a difficulty in avoidance;

[0128] When GBY < GBJ ≤ GBY × 110%, it represents that the current drone has a first-level difficulty in avoidance, and the drone can perform avoidance actions relatively easily;

[0129] When GBY × 110% < GBJ ≤ GBY × 115%, it represents that the current drone has a second-level difficulty in avoidance, and the drone can perform avoidance actions;

[0130] When GBJ > GBY × 115%, it represents that the current drone has a third-level difficulty in avoidance, and the drone has difficulty performing avoidance actions;

[0131] Among them, ZJJ is the impact magnitude coefficient, which is obtained by integrating the impact coefficient ZJX and the preset impact damage threshold ZSY. GBJ is the avoidance magnitude coefficient, which is obtained by integrating the avoidance coefficient GBX and the preset avoidance threshold GBY.

[0132] The impact magnitude coefficient ZJJ and the avoidance magnitude coefficient GBJ are respectively obtained by the following formulas:

[0133]

[0134] In the formula: ZJX is the impact coefficient, GBX is the avoidance coefficient, ZJY is the impact damage threshold ZSY, GBY is the avoidance threshold, and ln is the logarithmic function.

[0135] In this embodiment: Through the comparative analysis of the first analysis unit 41 and the second analysis unit 42, the data analysis module 4 realizes a comprehensive evaluation of the flight safety path, impact risk and avoidance difficulty, and enhances the safety of the drone during the inspection process.

[0136] By comparing the flight safety path coefficient AQX with the preset first threshold Y in real time, the drone can quickly identify the risk level of the inspection path, so that it can make timely decisions when danger occurs, improving emergency response capabilities. The classification of the magnitude of flight path risk, collision risk and avoidance difficulty enables the drone to take corresponding protective measures when facing different levels of risk, which helps to optimize the inspection strategy.

[0137] By calculating the impact magnitude coefficient ZJJ and the avoidance magnitude coefficient GBJ, the system can provide a more accurate risk assessment, enabling the UAV to have higher decision-making capabilities in complex environments and reduce the possibility of misjudgment. By comparing and analyzing the impact coefficient and the avoidance coefficient, it can effectively prevent potential safety hazards caused by environmental changes or system errors, ensure the reliability of inspection operations and data security. Through comprehensive analysis, it provides the UAV with more intelligent flight control capabilities, enabling it to achieve autonomous avoidance when facing obstacles and dynamic environments, improving flight safety and efficiency. Through real-time monitoring and intelligent decision-making, the system can reduce flight interruptions caused by potential risks, improve the smoothness of the inspection process, and reduce inspection time and costs.

[0138] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0139] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent power line inspection drone with collision and obstacle avoidance, characterized by: The system comprises a drone body, a data acquisition module (1), a data preprocessing module (2), a data calculation module (3), a data analysis module (4) and an execution module (5); The data acquisition module (1) is used to collect multi-source data during the inspection process of the drone and integrate it into a first multi-source data set and a second multi-source data set; The data preprocessing module (2) is used to preprocess the first multi-source data set and the second multi-source data set, and reorganize the preprocessed first multi-source data set and the second multi-source data set into an external factor data set and an internal factor data set; The data calculation module (3) is used to integrate the external factor data set and the internal factor data set into a collision coefficient ZJX and an avoidance coefficient GBX, and integrate the collision coefficient and the avoidance coefficient into a flight path safety factor AQX; The data analysis module (4) is used to compare the calculated flight path safety factor AQX with a preset first threshold value Y to generate a first comparison result, and determine whether the current UAV has a flight risk based on the first comparison result. If the first comparison result shows that there is a risk, the collision coefficient ZJX and the avoidance coefficient GBX are compared with the preset collision damage threshold value ZSY and the avoidance threshold value GBY respectively to generate a second comparison result, and determine whether the collision can be avoided and the degree of collision damage based on the second comparison result; The execution module (5) is used to automatically adjust the flight of the UAV according to the second comparison result, and generate an intervention report and send it to the control terminal.

2. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 1, characterized in that: The data acquisition module (1) comprises a sensor group (11) and a recording unit (12); The sensor group (11) is used to collect various parameters of the unmanned aerial vehicle during inspection flight, and the recording unit (12) is used to generate the parameters collected by the sensor group (11) into a first multi-source data set and a second multi-source data set; The first multi-source data set includes obstacle distance, ambient temperature, wind speed, vertical altitude, flight speed, current altitude, and attitude angle; The second multi-source data set includes ground height, obstacle distance, obstacle height, obstacle width, image resolution, sensor response time, and viewing angle offset.

3. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 2, characterized in that: The data pre-processing module (2) includes a processing unit (21) and a sorting unit (22); The processing unit (21) is used to preprocess and dimensionlessly transform the first multi-source dataset and the second multi-source dataset, and the sorting unit (22) is used to sort the processed first multi-source dataset and the second multi-source dataset into: The external factor dataset includes obstacle distance A, obstacle height B, obstacle width C, ambient temperature D, and wind speed E; The obstacle distances are recorded as A1, A2, A3, ..., An according to the timestamps; The wind speed is recorded as E1, E2, E3, ..., En according to the timestamp; The internal factor dataset includes vertical height F, flight speed G, attitude angle H, image resolution I, sensor response time J, and viewing angle deviation K; The vertical heights are recorded as F1, F2, F3, ..., Fn according to the timestamp; The flight speeds are respectively recorded as G1, G2, G3, ..., Gn according to the timestamps; The attitude angles are respectively recorded as H1, H2, H3, ..., Hn according to the timestamps; The perspective deviation degrees are respectively recorded as K1, K2, K3, ..., Kn according to the timestamps.

4. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 3, characterized in that: The data calculation module (3) includes a first calculation unit (31), a second calculation unit (32), and a third calculation unit (33); The first calculation unit (31) is used to calculate and obtain the impact coefficient ZJX, the second calculation unit (32) is used to calculate and obtain the avoidance coefficient GBX, and the third calculation unit (33) is used to calculate and obtain the flight path safety coefficient AQX. The specific calculation formulas are as follows; In the formula: PA is the obstacle distance change rate, PG is the flight speed change rate, X is the intervention correction value, B is the obstacle height, C is the obstacle width, and D is the environmental temperature; Fn, Gn, and Hn are respectively the vertical height, flight speed, and attitude angle at the N timestamp; a1, a2, a3, b1, b2, b3, c1, and c2 are respectively weight values, and the values of a1, a2, a3, b1, b2, b3, c1, and c2 are adjusted and set by the user. ln is the logarithmic function.

5. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 4, characterized in that: The obstacle distance change rate PA, the flight speed change rate PG, and the intervention correction value X are respectively calculated and obtained through the following formulas; In the formula: n and m are timestamps, A1, G1, F1, and E1 are respectively the obstacle distance, vertical height, flight speed, and wind speed at the first timestamp, and An, Gn, Fn, and En are respectively the obstacle distance, vertical height, flight speed, and wind speed at the nth timestamp.

6. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 5, characterized in that: The data analysis module (4) includes a first analysis unit (41), a second analysis unit (42), and a third analysis unit (43); The first analysis unit (41) is used to compare the flight safety path coefficient AQX with a preset first threshold Y to generate a first comparison result. The second analysis unit (42) is respectively used to compare the impact coefficient ZJX with a preset impact damage threshold ZSY and compare the avoidance coefficient GBX with a preset avoidance threshold GBY to generate a second comparison result.

7. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 6, characterized in that: The specific first comparison result is as follows: When AQX ≤ Y, it means that the inspection path of the current drone is risk-free. When AQX > Y, it means that the inspection path of the current drone is risky; When Y < AQJ ≤ Y × 110%, it means that the inspection path of the current drone has a first-level risk and poses a relatively low hazard to the drone; When Y × 110% < AQJ ≤ Y × 115%, it means that the inspection path of the current drone has a second-level risk and poses a moderate hazard to the drone; When AQJ > Y × 115%, it means that the inspection path of the current drone has a third-level risk and poses a relatively high hazard to the drone; Among them, ZJJ is the path risk magnitude coefficient, which is obtained by integrating and calculating the flight path safety coefficient AQX and a preset first threshold Y. The specific calculation formula is as follows; In the formula: AQX is the flight safety path coefficient, Y is the first threshold, and ln is the logarithmic function.

8. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 7, characterized in that: The specific second comparison results are as follows: When ZJX ≤ ZSY, it means that the current drone has no collision risk. When ZJX > ZSY, it means that the current drone has a collision risk; When ZSY < ZJJ ≤ ZSY × 110%, it means that the current drone has a first-level risk of collision, and the harm to the drone is relatively low; When ZSY × 110% < ZJJ ≤ ZSY × 115%, it means that the current drone has a second-level risk of collision, and the harm to the drone is moderate; When ZJJ > ZSY × 115%, it means that the inspection path of the current drone has a third-level collision risk, and the harm to the drone is relatively high; When GBX ≤ GBY, it means that the current drone has no avoidance difficulty. When GBX > GBY, it means that the current drone has an avoidance difficulty; When GBY < GBJ ≤ GBY × 110%, it means that the current drone has a first-level avoidance difficulty, and the drone can perform avoidance actions relatively easily; When GBY × 110% < GBJ ≤ GBY × 115%, it means that the current drone has a second-level avoidance difficulty, and the drone can perform avoidance actions; When GBJ > GBY × 115%, it means that the current drone has a third-level avoidance difficulty, and it is difficult for the drone to perform avoidance actions; Among them, ZJJ is the collision magnitude coefficient, which is obtained by integrating the collision coefficient ZJX and the preset collision damage threshold ZSY. GBJ is the avoidance magnitude coefficient, which is obtained by integrating the avoidance coefficient GBX and the preset avoidance threshold GBY.

9. The intelligent power line inspection drone with collision and obstacle avoidance according to claim 1, characterized in that: The collision magnitude coefficient ZJJ and the avoidance magnitude coefficient GBJ are respectively obtained by the following formulas: In the formula: ZJX is the collision coefficient, GBX is the avoidance coefficient, ZJY is the collision damage threshold ZSY, GBY is the avoidance threshold, and ln is the logarithmic function.

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