Distribution line unmanned aerial vehicle nest operation coverage capability evaluation method

Abstract

The invention relates to the technical field of distribution line inspection, and discloses a distribution line unmanned aerial vehicle nest operation coverage capability evaluation method, which comprises the following steps: S1, planning a geographic information system and an unmanned aerial vehicle route; s2, a multi-mode positioning system; s3, dynamic communication priority adjustment; s4, optimizing the charging rate and the battery; s5, induction charging technology; s6, optimizing the route of the unmanned aerial vehicle; s7, performing unmanned aerial vehicle nest operation strategy and task allocation; s8, application of machine learning and artificial intelligence technologies in coverage capability evaluation; s9, performing real-time fault detection and early warning; the geographic information system and unmanned aerial vehicle route planning comprises the step of acquiring topographic features, building distribution and other information by using the geographic information system. By means of a geographic information system, route planning, multi-mode positioning, dynamic communication priority adjustment and the like, operation and maintenance cost can be reduced, existing resources and technologies are fully utilized, and efficiency, safety and reliability of the unmanned aerial vehicle in distribution line inspection and maintenance work are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution line inspection, and in particular to a method for evaluating the coverage capability of unmanned aerial vehicle (UAV) nest operations on power distribution lines. Background Art

[0002] A drone nest is a drone guard device used for drone storage, charging and flight scheduling. It is usually deployed to a fixed location for long-term operation. The nest needs to be powered and connected to the Internet. The background system can control the nest to charge the drone, open the door and control the drone to take off, control the drone to fly autonomously, control the drone to land and recover and close the door, and monitor the status and video images of the drone and nest. The distribution line drone nest operation coverage capability refers to the evaluation of the distribution line inspection coverage capability when the nest is deployed in a certain location. This capability is evaluated by the number of towers that can be inspected and the line length.

[0003] At present, the conventional method for evaluating the coverage capability of distribution line machine nests is to draw a circular range with the machine nest as the center and half of the empirical value of the effective range of the drone as the radius, and assume that all distribution lines within the range can be inspected. However, this method does not take into account the influence of factors such as terrain, tower coordinates, and no-fly zones. The evaluation method is not accurate and is not conducive to the economic benefit evaluation of the deployment of machine nests on distribution lines. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for evaluating the coverage capability of UAV nests in distribution lines, which solves the problem that the existing evaluation methods are inaccurate and not conducive to the economic benefit evaluation of the deployment of UAV nests in distribution lines.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for evaluating the coverage capability of a distribution line drone nest operation comprises the following steps:

[0006] S1. Geographic Information System and UAV Route Planning;

[0007] S2, multi-mode positioning system;

[0008] S3, dynamic communication priority adjustment;

[0009] S4, charging rate and battery optimization;

[0010] S5, inductive charging technology;

[0011] S6, drone route optimization;

[0012] S7, UAV nest operation strategy and task allocation;

[0013] S8. Application of machine learning and artificial intelligence technologies in coverage capability assessment;

[0014] S9. Real-time fault detection and early warning.

[0015] Preferably, the geographic information system and UAV route planning include: using the geographic information system to obtain information such as terrain features and building distribution; combining UAV performance parameters to perform UAV route planning; and achieving visual management of the UAV nest operation area to facilitate monitoring and scheduling.

[0016] Preferably, the multi-mode positioning system includes: using positioning systems such as the Global Positioning System and Beidou to provide accurate positioning of the UAV during distribution line operations; combining an inertial navigation system to improve positioning accuracy and reliability; and using a ground-based augmentation system to further improve positioning accuracy.

[0017] Preferably, the dynamic communication priority adjustment includes: adopting a priority adjustment algorithm based on factors such as signal quality, availability and power consumption; dynamically adjusting the priority of the wireless communication link according to parameters such as the distance between the drone and the base station, signal strength and radio spectrum utilization.

[0018] Preferably, the charging rate and battery optimization include: using multi-channel charging technology to balance the charging status of each channel through an intelligent management algorithm; using maximum power point tracking technology to optimize the efficiency of the solar charging system; using an intelligent charge and discharge management system to optimize the charging strategy according to the battery charging characteristics and usage requirements.

[0019] Preferably, the inductive charging technology includes: utilizing the principle of magnetic induction to establish magnetic field coupling between the transmitting end and the receiving end for energy transmission; adopting laser energy transmission technology to transmit energy through a laser beam; and implementing magnetic field anti-interference technology to ensure the charging efficiency and safety of the charging process.

[0020] Preferably, the UAV route optimization includes: a path planning algorithm based on factors such as terrain characteristics, wind speed, and aircraft performance; using an optimization algorithm to evaluate the cost and effectiveness of different routes; and dynamically adjusting the route in combination with real-time sensor data and an onboard computing system.

[0021] Preferably, the drone nest operation strategy and task allocation include: formulating drone nest operation strategy and task allocation based on the drone nest operation coverage capability assessment results; achieving efficient and safe distribution line inspection and maintenance; and verifying the assessment results using simulation models and actual test data.

[0022] Preferably, the application of the machine learning and artificial intelligence technologies in coverage capability assessment includes: using machine learning and artificial intelligence technologies to continuously optimize and adjust the drone nest operation coverage capability assessment method based on historical data and real-time information; and using deep learning methods to identify and analyze drone inspection images.

[0023] Preferably, the real-time fault detection and early warning include: using sensor technology and data analysis methods to perform real-time detection of faults during the operation of the drone nest; combining artificial intelligence technology to perform fault early warning, predict potential risks in advance, and improve operation safety and efficiency.

[0024] The present invention provides a method for evaluating the coverage capability of a distribution line drone nest operation. It has the following beneficial effects:

[0025] The present invention improves the efficiency, safety and reliability of UAVs in distribution line inspection and maintenance operations. Through geographic information system and route planning, multi-mode positioning, dynamic communication priority adjustment, battery charging and optimization, inductive charging technology, route optimization, operation strategy and task allocation, machine learning and artificial intelligence technology, and real-time fault detection and early warning, the method realizes efficient flight, precise positioning, stable communication, fast charging, intelligent scheduling and safe operation of UAVs during operation. The comprehensive application of these technologies and methods helps to reduce operation and maintenance costs while making full use of existing resources and technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Embodiment 1:

[0029] The embodiment of the present invention provides a method for evaluating the coverage capability of a distribution line drone nest operation, comprising the following steps:

[0030] S1. Geographic Information System and UAV Route Planning:

[0031] S101. Use geographic information system to obtain information such as terrain features and building distribution: Geographic Information System (GIS) can collect, store, analyze and display geospatial data. Through GIS, information such as terrain, buildings, waters, roads, vegetation, etc. can be obtained to provide basic data for drone route planning.

[0032] S102. Plan the route of the drone based on the performance parameters of the drone: Based on the performance parameters of the drone (such as speed, range, load, etc.), combined with the geospatial data in GIS, use the route planning algorithm (such as A* algorithm, Dijkstra algorithm, etc.) to plan the best route. This will help improve the inspection efficiency and safety of the drone.

[0033] S103. Realize visual management of drone nest operation area for easy monitoring and scheduling: Through GIS visualization tools, the real-time location, route and nest operation area of ​​drones can be displayed on the map. This helps operation and maintenance personnel to monitor the operation status of drones in real time and perform scheduling and optimization according to actual conditions.

[0034] S2, multi-mode positioning system:

[0035] S201. Use positioning systems such as the Global Positioning System and BeiDou to provide accurate positioning of drones in power distribution line operations: Global Positioning Systems (such as GPS and BeiDou) provide global coverage positioning services for drones. By receiving satellite signals, drones can obtain their location information in real time, thereby achieving accurate positioning in power distribution line operations.

[0036] S202. Combine the inertial navigation system to improve positioning accuracy and reliability; use the ground-based augmentation system to further improve positioning accuracy: The inertial navigation system (INS) calculates the position and speed of the drone in real time by measuring acceleration and angular velocity, and can maintain high-precision positioning when satellite signals are blocked. The ground-based augmentation system (such as RTK) receives satellite signals through ground base stations, calculates and corrects signal errors, and further improves the positioning accuracy of the drone.

[0037] S3. Dynamic communication priority adjustment:

[0038] S301. Use a priority adjustment algorithm based on factors such as signal quality, availability, and power consumption: To ensure the priority and efficiency of critical data transmission, a priority adjustment algorithm based on factors such as signal quality, availability, and power consumption can be used. The algorithm dynamically adjusts the communication priority between the UAV and the ground base station according to the real-time communication status.

[0039] S302. Dynamically adjust the priority of the wireless communication link according to the distance between the UAV and the base station, signal strength, and radio spectrum utilization: When establishing a communication link between the UAV and the ground base station, the communication priority can be dynamically adjusted according to the distance, signal strength, and radio spectrum utilization. This will help improve the quality and stability of communication and ensure the timeliness and accuracy of key data transmission.

[0040] S4, charging rate and battery optimization:

[0041] S401. Use multi-channel charging technology and balance the charging status of each channel through intelligent management algorithm: Multi-channel charging technology can realize charging of multiple batteries at the same time. Through intelligent management algorithm, the charging status of each channel can be balanced to avoid overcharging and over-discharging, and improve battery life.

[0042] S402. Optimize the efficiency of the solar charging system using maximum power point tracking technology: Maximum power point tracking (MPPT) technology can monitor the output power of the solar charging system in real time and adjust system parameters to maximize the output power. This will help improve the efficiency of the solar charging system and extend the endurance of the drone.

[0043] S403. Use intelligent charge and discharge management system to optimize charging strategy according to battery charging characteristics and usage requirements: The intelligent charge and discharge management system can optimize charging strategy according to battery charging characteristics (such as voltage, current, temperature, etc.) and usage requirements (such as UAV operation tasks, expected flight time, etc.). This will help improve battery charging efficiency and service life and reduce the impact of charging time on operating efficiency.

[0044] S5, inductive charging technology:

[0045] S501. Using the principle of magnetic induction, a magnetic field coupling is established between the transmitter and the receiver to transfer energy: Magnetic induction charging technology achieves wireless energy transfer by establishing a magnetic field coupling between the transmitter and the receiver. This charging method does not require physical contact and can improve charging efficiency and safety.

[0046] S502. Using laser power transmission technology to transmit energy through laser beams: Laser power transmission (LPT) technology achieves wireless energy transmission by converting light energy into electrical energy. This technology has the advantages of long transmission distance and high transmission efficiency, but requires precise alignment and a stable communication link.

[0047] S503. Implement magnetic field anti-interference technology to ensure charging efficiency and safety during the charging process: Magnetic field anti-interference technology ensures charging efficiency and safety during the induction charging process by shielding or eliminating external magnetic field interference. This will help improve the endurance of the drone during operation and reduce downtime.

[0048] S6. Drone route optimization:

[0049] S601. Path planning algorithm based on terrain features, wind speed, aircraft performance and other factors: The UAV route optimization algorithm can dynamically adjust the route based on terrain features, wind speed, aircraft performance and other factors. This will help reduce unnecessary flight distances, reduce energy consumption and improve operational efficiency.

[0050] S602. Use optimization algorithms to evaluate the cost and effect of different routes: Through optimization algorithms (such as genetic algorithms, ant colony algorithms, etc.), the cost and effect of different routes can be evaluated, so as to select the best route. This will help improve the efficiency and safety of drone inspection and maintenance operations.

[0051] S603. Dynamically adjust the route based on real-time sensor data and onboard computing systems: The drone can dynamically adjust the route based on real-time sensor data (such as wind speed, wind direction, temperature, etc.) and onboard computing systems (such as route planning algorithms, flight control systems, etc.). This will help achieve efficient operation of drones in complex environments.

[0052] S7. UAV nest operation strategy and task allocation:

[0053] S701. Formulate UAV nest operation strategies and task allocation based on the UAV nest operation coverage capability assessment results: Based on the coverage capability assessment results, efficient and safe operation strategies and task allocation can be formulated. This will help to reasonably allocate UAV resources and achieve optimized scheduling.

[0054] S702. Achieve efficient and safe inspection and maintenance of distribution lines: Through drone nest operation strategies and task allocation, drones can achieve efficient and safe inspection and maintenance of distribution lines. This will help improve inspection efficiency, reduce operation and maintenance costs, and ensure the safe operation of distribution lines.

[0055] S703. Use simulation models and actual test data to verify the evaluation results: By building a simulation model and collecting actual test data, the evaluation results of the drone nest operation coverage capability can be verified. This will help to continuously optimize the evaluation method and improve the operation effect.

[0056] S8. Application of machine learning and artificial intelligence technologies in coverage capability assessment:

[0057] S801. Use machine learning and artificial intelligence technologies to continuously optimize and adjust the coverage capability assessment method for drone nest operations based on historical data and real-time information: Through machine learning and artificial intelligence technologies, the coverage capability assessment method can be continuously optimized and adjusted based on historical data and real-time information. This will help improve the effectiveness and efficiency of drone nest operations.

[0058] S802. Use deep learning methods to identify and analyze drone inspection images: Through deep learning methods (such as convolutional neural networks, recurrent neural networks, etc.), drone inspection images can be identified and analyzed to achieve functions such as fault detection and equipment identification. This will help improve inspection accuracy and reduce missed detection and false detection rates.

[0059] S9, Real-time fault detection and early warning:

[0060] S901. Use sensor technology and data analysis methods to detect faults in the operation of drone nests in real time: Through sensor technology (such as temperature sensors, vibration sensors, etc.) and data analysis methods (such as time series analysis, spectrum analysis, etc.), faults in the operation of drone nests can be detected in real time. This will help to detect faults in a timely manner and reduce the risk of accidents.

[0061] S902. Combine artificial intelligence technology for fault warning, predict potential risks in advance, and improve operation safety and efficiency: By combining artificial intelligence technology (such as machine learning, deep learning, etc.), the fault warning function can be realized. This will help predict potential risks in advance and improve the safety and efficiency of drones in distribution line inspection and maintenance operations.

[0062] Embodiment 2:

[0063] The embodiment of the present invention provides a method for evaluating the coverage capability of a distribution line drone nest operation, comprising the following steps:

[0064] Basic GIS route planning: Use basic information about terrain and buildings to manually plan the drone’s route, without automated planning algorithms.

[0065] Single positioning system: relies only on GPS positioning, with no auxiliary system to improve positioning accuracy.

[0066] Fixed communication link: There is no priority adjustment for the communication link, and there is no alternative plan when communication is interrupted.

[0067] Traditional charging method: using standard wired charging, which takes a long time and has low efficiency.

[0068] Simple route optimization: Route optimization is based on a fixed pattern and has no real-time adjustment capability.

[0069] Manual work strategy and task allocation: Task allocation and scheduling rely on manual experience and judgment.

[0070] No intelligent fault detection: Lack of intelligent fault detection system, slow response and low accuracy.

[0071] Performance Indicators Embodiment 1 Embodiment 2 Task completion time 2 hours 3 hours Battery efficiency 1 charge, lasts 4 hours 2 charges, lasts 2 hours Positioning accuracy ±0.5 m ±5 m Communication interruption times 0 times 2 times Fault detection rate 95％ 75％ Fault warning efficiency 90％ No warning

[0072] Table 1

[0073] In summary, the differences between Example 1 and Example 2 in the evaluation of the coverage capability of the distribution line drone nest operation are shown. Example 1 adopts modern and intelligent technologies and methods, while Example 2 relies more on traditional technologies and manual operations and lacks automation and intelligent support. In Example 1, the application of the multi-mode positioning system provides more accurate positioning information, and the application of technologies such as dynamic communication priority adjustment and inductive charging technology enhances the flexibility and efficiency of the system. In addition, the application of machine learning and artificial intelligence technologies and the establishment of a real-time fault detection and early warning system enable the system to have the ability of continuous optimization and self-learning, thereby improving the safety and reliability of operations. In contrast, Example 2 lags behind in these aspects. For example, relying only on GPS positioning, lacking a communication system with priority adjustment, and using standard wired charging all indicate the limitations of Example 2 in technical application. Based on these differences, it can be concluded that the modern technologies and methods used in Example 1 are more suitable for the evaluation of the coverage capability of the distribution line drone nest operation, and can provide more efficient, safer and more reliable support.

[0074] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the coverage capability of drone nest operations on distribution lines, characterized in that: The following steps are involved: S1. Geographic Information System and UAV Route Planning; S2, multi-mode positioning system; S3, dynamic communication priority adjustment; S4, charging rate and battery optimization; S5, inductive charging technology; S6, drone route optimization; S7, UAV nest operation strategy and task allocation; S8. Application of machine learning and artificial intelligence technologies in coverage capability assessment; S9. Real-time fault detection and early warning.

2. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The geographic information system and UAV route planning include: using the geographic information system to obtain information such as terrain features and building distribution; combining UAV performance parameters to plan UAV routes; and realizing visual management of the UAV nest operation area for easy monitoring and scheduling.

3. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The multi-mode positioning system includes: using global positioning system, Beidou and other positioning systems to provide accurate positioning of drones in distribution line operations; combining inertial navigation system to improve positioning accuracy and reliability; using ground-based augmentation system to further improve positioning accuracy.

4. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The dynamic communication priority adjustment includes: adopting a priority adjustment algorithm based on factors such as signal quality, availability and power consumption; dynamically adjusting the priority of the wireless communication link according to parameters such as the distance between the drone and the base station, signal strength and radio spectrum utilization.

5. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The charging rate and battery optimization include: using multi-channel charging technology to balance the charging status of each channel through an intelligent management algorithm; using maximum power point tracking technology to optimize the efficiency of the solar charging system; using an intelligent charge and discharge management system to optimize the charging strategy according to the battery charging characteristics and usage requirements.

6. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The induction charging technology includes: using the principle of magnetic induction to establish magnetic field coupling between the transmitting end and the receiving end for energy transmission; using laser energy transmission technology to transmit energy through a laser beam; and implementing magnetic field anti-interference technology to ensure charging efficiency and safety during the charging process.

7. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The UAV route optimization includes: a path planning algorithm based on factors such as terrain features, wind speed, and aircraft performance; using an optimization algorithm to evaluate the cost and effectiveness of different routes; and dynamically adjusting the route in combination with real-time sensor data and an onboard computing system.

8. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The drone nest operation strategy and task allocation include: formulating drone nest operation strategy and task allocation according to the drone nest operation coverage capability assessment results; achieving efficient and safe distribution line inspection and maintenance; and verifying the assessment results using simulation models and actual test data.

9. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1 is characterized in that: The application of the machine learning and artificial intelligence technologies in coverage capability assessment includes: using machine learning and artificial intelligence technologies to continuously optimize and adjust the drone nest operation coverage capability assessment method based on historical data and real-time information; and using deep learning methods to identify and analyze drone inspection images.

10. The method for evaluating the coverage capability of distribution line drone nest operations according to claim 1, characterized in that: The real-time fault detection and early warning include: using sensor technology and data analysis methods to perform real-time detection of faults during the operation of the drone nest; combining artificial intelligence technology to perform fault early warning, predict potential risks in advance, and improve operation safety and efficiency.

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