Unmanned aerial vehicle nest collaborative inspection method and system for power transmission line
By constructing a multi-objective optimization model and edge computing nodes to optimize the hibernation, take-off, landing, and power supply parameters of UAV nests, the safety monitoring problem of UAV nests in complex weather conditions and remote environments was solved, and efficient power line inspection was achieved.
Patent Information
- Application Number
- CN202511642051.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2025-12-19
AI Technical Summary
Existing technologies cannot adapt to complex weather conditions and the environmental characteristics of remote areas, resulting in insufficient continuity of power transmission line safety monitoring and frequent equipment failures.
By constructing a multi-objective optimization model, the hibernation and wake-up timing, take-off and landing frequency, and photovoltaic power supply allocation parameters of the UAV nest are dynamically adjusted. Combined with edge computing nodes and digital twin models, the collaborative inspection control commands of the UAV nest are optimized to enhance environmental adaptability.
Stable operation and efficient inspection coverage of UAV nests were achieved under complex weather conditions, improving the continuity of safety monitoring of power transmission lines and the reliability of equipment.
Smart Images
Figure CN121165765A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power transmission line inspection, in particular to a UAV nest cooperative inspection method and system for a power transmission line. BACKGROUND
[0002] Through the autonomous take-off and landing, charging and data backhaul functions of the UAV nest, all-weather uninterrupted inspection is realized. However, the UAV nest cooperative inspection relies on fixed UAV take-off and landing frequency and nest hibernation strategy, which is prone to cause equipment failures such as power supply interruption caused by snow accumulation on photovoltaic panels and UAV take-off deviation in strong wind weather, and is difficult to cope with weak communication in remote areas and equipment failure early warning problems, affecting the continuity of power transmission line safety monitoring.
[0003] In summary, the prior art has the technical problems of fixed hibernation and wake-up timing, take-off and landing frequency and power supply strategy, which cannot adapt to complex weather conditions and remote area environmental characteristics, and the continuity of power transmission line safety monitoring is insufficient. SUMMARY
[0004] The present application provides a UAV nest cooperative inspection method and system for a power transmission line, which aims to solve the technical problems of fixed hibernation and wake-up timing, take-off and landing frequency and power supply strategy in the prior art, which cannot adapt to complex weather conditions and remote area environmental characteristics, and the continuity of power transmission line safety monitoring is insufficient.
[0005] In view of the above problems, the technical scheme of the present application is as follows: The first aspect of the present application provides a UAV nest cooperative inspection method for a power transmission line, wherein the method comprises: acquiring basic technical parameters of a UAV nest and tower deployment environment parameters; based on the basic technical parameters and tower deployment environment parameters, configuring a multi-objective optimization model that meets the inspection requirements of the power transmission line, dynamically adjusting the hibernation and wake-up timing, UAV take-off and landing frequency and photovoltaic power supply distribution parameters; simulating the stability of the UAV nest and the UAV inspection coverage rate under different weather conditions, combining the multi-objective optimization model to generate a UAV nest cooperative scheduling scheme; deploying an edge computing node, and configuring a cooperative inspection control instruction for the power transmission line according to the UAV nest cooperative scheduling scheme.
[0006] Preferably, the hibernation duration of the UAV nest, the UAV inspection route, the charging power of the photovoltaic panel, the battery charging and discharging threshold and the tower platform load are set as decision variables, and the constraint range is set.
[0007] Preferably, a digital twin model of the UAV nest and the tower platform is constructed, and an energy storage module and an environment monitoring module are integrated; a data transmission interface is accessed, and the voltage of the energy storage battery and the position of the UAV are uploaded; based on the voltage of the energy storage battery and the position of the UAV, the power supply efficiency coefficient and the UAV inspection path deviation are dynamically corrected in combination with the digital twin model.
[0008] Preferably, the cooperative inspection control instruction corresponds to a UAV take-off instruction, a UAV route adjustment instruction, a data back transmission instruction, and a photovoltaic controller charge-discharge instruction; based on the edge computing node, a data synchronization mechanism is configured in the data transmission interface using a mixed communication protocol of MQTT / CoAP, and the data synchronization mechanism is used to correct the transmission time delay of the cooperative inspection control instruction.
[0009] Preferably, a low-power storage unit is integrated; during deep hibernation of the UAV nest, the low-power storage unit is used to automatically latch the breakpoint information of the UAV inspection task.
[0010] Preferably, a signal strength detector is arranged at a key position of the UAV nest, and the key position includes a battery compartment; when it is monitored that the communication signal strength in the key position is lower than a signal strength threshold value and the duration exceeds an allowable delay window, signal relay enhancement is triggered.
[0011] Preferably, a device fault mode database of the UAV nest is constructed, the device fault mode database stores photovoltaic panel fault instances, battery bulging instances, and UAV take-off deviation instances; based on the device fault mode database, in combination with real-time operation data, the remaining life of the device is predicted and a maintenance reminder is generated.
[0012] In a second aspect, the application provides a UAV nest cooperative inspection system for a power transmission line, wherein the system comprises: a parameter acquisition module, configured to acquire basic technical parameters of a UAV nest and tower deployment environment parameters; a dynamic adjustment module, configured to configure a multi-objective optimization model in accordance with the power transmission line inspection requirements based on the basic technical parameters and the tower deployment environment parameters, and dynamically adjust hibernation and wake-up timing, UAV take-off frequency, and photovoltaic power distribution parameters; a UAV nest cooperative scheduling scheme generation module, configured to simulate the UAV nest operation stability and the UAV inspection coverage rate under different meteorological conditions, and generate a UAV nest cooperative scheduling scheme in combination with the multi-objective optimization model; and a cooperative inspection control instruction configuration module, configured to deploy an edge computing node, and configure a cooperative inspection control instruction for the power transmission line according to the UAV nest cooperative scheduling scheme.
[0013] To sum up, the one or more technical solutions provided in the application achieve the technical effects of constructing a multi-objective optimization model according to technical parameters of a UAV nest and environmental parameters of a tower, dynamically adjusting hibernation, take-off and landing, and power supply parameters, and optimizing a scheduling scheme through simulation of different meteorological conditions to enhance environmental adaptability. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creative effort on the basis of the provided drawings.
[0015] Figure 1 A flowchart of a UAV nest cooperative inspection method for a power transmission line is provided for the application.
[0016] Figure 2 A structural diagram of a UAV nest cooperative inspection system for a power transmission line is provided for the application. The reference signs are explained as follows: a parameter acquisition module 11, a dynamic adjustment module 12, a UAV nest cooperative scheduling scheme generation module 13, and a cooperative inspection control instruction configuration module 14. DETAILED DESCRIPTION
[0017] The technical solutions in the application will be described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the exemplary embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of protection of the application.
[0018] In one embodiment, the basic technical parameters of the UAV nest include the size, weight, compatibility, protection level, meteorological monitoring capability, power supply mode, communication function, etc. of the UAV nest; and the tower deployment environment parameters include the meteorological conditions, geographic information, and communication signal strength of the area where the tower is located, further, the meteorological conditions of the area where the tower is located include wind speed, wind direction, temperature and humidity, rainfall, and the geographic information includes altitude and topography. Figure 1 The method comprises the following steps: Acquiring basic technical parameters of a UAV nest and tower deployment environment parameters.
[0019] In one embodiment, the basic technical parameters of the UAV nest include the size, weight, compatibility, protection level, meteorological monitoring capability, power supply mode, communication function, etc. of the UAV nest; and the tower deployment environment parameters include the meteorological conditions, geographic information, and communication signal strength of the area where the tower is located, further, the meteorological conditions of the area where the tower is located include wind speed, wind direction, temperature and humidity, rainfall, and the geographic information includes altitude and topography.
[0020] Optionally, the basic technical parameters of the UAV nest and the tower deployment environment parameters are obtained. Specifically, the power supply mode and communication function of the UAV nest determine its applicability and reliability in different environments, and the meteorological conditions and communication signal strength of the tower deployment environment directly affect the take-off and landing safety and data transmission efficiency of the UAV. Preferably, according to the meteorological conditions of the area where the tower is located, the take-off and landing time and flight path of the UAV are predicted and adjusted in advance to avoid dangerous operations in bad weather. At the same time, by understanding the communication signal strength, the data transmission scheme is optimized to ensure the real-time return of inspection data.
[0021] Based on the basic technical parameters and tower deployment environment parameters, a multi-objective optimization model that meets the needs of power line inspection is configured to dynamically adjust the sleep-wake timing, UAV take-off and landing frequency, and photovoltaic power distribution parameters.
[0022] In one embodiment, the multi-objective optimization model is used to optimize multiple objective functions simultaneously, and the main objectives include improving energy utilization, increasing inspection coverage, reducing device failure rate, etc. Dynamic adjustment refers to automatically adjusting system parameters according to real-time data and preset conditions to adapt to different environments and task requirements. Sleep-wake timing refers to controlling the UAV nest to enter a sleep state or wake up at a specific time to save energy and optimize the execution of inspection tasks. UAV take-off and landing frequency refers to the number of take-offs and landings of the UAV per unit time, which affects the inspection efficiency and energy consumption. Photovoltaic power distribution parameters refer to determining how photovoltaic energy is distributed among different devices and tasks to ensure efficient use of energy.
[0023] Optionally, by inputting the basic technical parameters of the UAV nest and the tower deployment environment parameters, the sleep-wake timing, UAV take-off and landing frequency, and photovoltaic power distribution parameters are dynamically adjusted. Preferably, according to the meteorological conditions and geographic information of the area where the tower is located, the take-off and landing time and flight path of the UAV are optimized, and at the same time, by dynamically adjusting the photovoltaic power distribution parameters, the energy supply of the UAV nest and the UAV is ensured to be stable under different environmental conditions.
[0024] The running stability of the UAV nest and the UAV inspection coverage under different meteorological conditions are simulated, and a UAV nest cooperative scheduling scheme is generated in combination with the multi-objective optimization model.
[0025] In one embodiment, the weather condition refers to the working state under different weather conditions, including rain, fog, strong wind, low temperature, high temperature, etc.; the operation stability of the UAV nest refers to whether the operation state of the UAV nest is stable under various weather conditions, including whether the photovoltaic panel is normally powered, whether the UAV nest structure is stable, etc.; the UAV inspection coverage refers to the range of transmission lines that can be covered by the UAV during the inspection process, including the planning and actual execution of the inspection path; the collaborative scheduling scheme is generated based on a multi-objective optimization model and is used to guide the collaborative work plan of the UAV nest and the UAV under different weather conditions.
[0026] Optionally, using weather data and historical records, scenarios under different weather conditions are simulated, such as rain, fog, strong wind, low temperature, etc.; under the simulated weather conditions, the operation stability of the UAV nest is evaluated, including the power supply capacity of the photovoltaic panel and the stability of the UAV nest structure; according to the take-off frequency, flight path and weather conditions of the UAV, the inspection coverage of the UAV is calculated to ensure the comprehensive coverage of the transmission line; combined with the multi-objective optimization model, the operation stability and the inspection coverage are comprehensively considered to generate the optimal collaborative scheduling scheme.
[0027] Preferably, through simulation and evaluation, the operation risks under different weather conditions are predicted in advance, and the scheduling scheme is optimized to improve the environmental adaptability and inspection efficiency of the system. At the same time, the flight path and take-off frequency of the UAV are reasonably planned to ensure that the high-coverage inspection task can still be realized under complex weather conditions, improve the safety and reliability of the inspection, reduce the interruption of the inspection caused by equipment failure, and ensure the safety monitoring continuity of the transmission line.
[0028] Deploy an edge computing node to configure the collaborative inspection control instruction of the transmission line according to the UAV nest collaborative scheduling scheme.
[0029] In one embodiment, the edge computing node refers to a computing node deployed near the data source or user terminal, which is used to process and analyze local data, reduce the dependence on the cloud center, and improve the real-time performance and efficiency of data processing; the collaborative inspection control instruction refers to a specific operation instruction generated according to the UAV nest collaborative scheduling scheme, which is used to guide the take-off, route adjustment, data backhaul, photovoltaic controller charging and discharging, etc. of the UAV.
[0030] Optionally, edge computing nodes are deployed at key locations of the power transmission line, which are equipped with sufficient computing and storage capabilities to process local data and respond quickly; based on the edge computing nodes, a data synchronization mechanism is configured using a communication protocol to ensure real-time transmission and synchronization of data; according to the UAV nest cooperative scheduling scheme, specific cooperative inspection control instructions are generated, including UAV take-off instructions, flight path adjustment instructions, data back transmission instructions, photovoltaic controller charging and discharging instructions, etc., and the cooperative inspection control instructions are dynamically corrected in combination with the real-time data of the edge computing nodes to ensure the accuracy and real-time performance of the instructions.
[0031] Preferably, by deploying edge computing nodes, a closed-loop cooperation of "model optimization-scheduling decision-control execution" is realized to replace the traditional cloud centralized control mode, ensuring real-time response of flight path adjustment, emergency recall, etc. This fast response mechanism not only improves the efficiency and safety of the inspection, but also enhances the overall performance and reliability of the system, thereby significantly improving the response speed and data processing efficiency of the instructions.
[0032] Further, the application provides a multi-objective optimization model that meets the power transmission line inspection requirements based on the basic technical parameters and tower deployment environment parameters, and the method further comprises: The hibernation time of the UAV nest, the UAV inspection flight path, the photovoltaic panel charging power, the battery charging and discharging threshold, and the tower platform load are set as decision variables with a constraint range.
[0033] In one embodiment, the decision variable refers to a parameter in the optimization model that can be adjusted to achieve the optimization goal, including the hibernation time of the UAV nest, the UAV inspection flight path, the photovoltaic panel charging power, the battery charging and discharging threshold, and the tower platform load; the constraint range refers to the limiting condition that the decision variable needs to meet in actual operation, ensuring that the system operates within a safe and feasible range.
[0034] Optionally, the decision variables are defined, including the hibernation time of the UAV nest, the UAV inspection flight path, the photovoltaic panel charging power, the battery charging and discharging threshold, and the tower platform load. Specifically, the hibernation time of the UAV nest refers to the hibernation time of the UAV nest in the non-working state, which affects energy consumption and equipment life; the UAV inspection flight path refers to the flight path of the UAV, which affects the inspection efficiency and coverage; the photovoltaic panel charging power refers to the charging capacity of the photovoltaic panel, which affects the energy supply of the UAV nest; the battery charging and discharging threshold refers to the charging and discharging limit of the battery, which affects the battery life and system stability; and the tower platform load refers to the load capacity of the tower platform, which affects the safety and reliability of the equipment.
[0035] Set the constraint range, specifically, for the length of the UAV nest hibernation, according to the actual demand and equipment characteristics, set a reasonable hibernation time range, ensure that the equipment can fully rest during non-working hours, while ensuring that it can respond quickly when needed; for the UAV inspection route, according to the layout and geographical information of the transmission line, set a reasonable inspection route to ensure that all key areas are covered, while avoiding unnecessary flight paths to improve inspection efficiency; for the photovoltaic panel charging power, according to the rated power and actual working conditions of the photovoltaic panel, set the range of charging power to ensure effective charging under different light conditions; for the battery charge and discharge threshold, according to the specifications and safety requirements of the battery, set the charge and discharge threshold to prolong the battery life and ensure the stable operation of the system; for the tower platform load, according to the design load capacity of the tower platform, set the load range to ensure the safety and reliability of the equipment.
[0036] Further, the method described in the present application also includes: Constructing a digital twin model of the UAV nest and the tower platform, integrating the energy storage module and the environmental monitoring module; accessing the data transmission interface to upload the energy storage battery voltage and the UAV position; based on the energy storage battery voltage and the UAV position, dynamically correcting the power supply efficiency coefficient and the UAV inspection path deviation in combination with the digital twin model.
[0037] In one embodiment, the digital twin model is a virtual model that synchronizes with the physical entity through real-time data, and the digital twin model is used to simulate the operating state of the UAV nest and the tower platform; the energy storage module is used to store and manage the energy module to ensure the energy supply of the UAV nest under different working conditions; the environmental monitoring module is used to monitor the environmental parameters around the UAV nest and the tower platform in real time; the data transmission interface is used to transmit data from the physical device to the digital twin model, ensuring real-time synchronization of data; the power supply efficiency coefficient reflects the power supply efficiency of the energy storage module, which is used to evaluate and optimize energy utilization efficiency; the UAV inspection path deviation refers to the deviation between the actual flight path and the preset path, which is used to evaluate and optimize the flight path of the UAV.
[0038] Optionally, a virtual model of the UAV nest and the tower platform is created, integrating the energy storage module and the environmental monitoring module to monitor and simulate the system operating state in real time; through the data transmission interface, the energy storage battery voltage, the UAV position and other real-time data are uploaded to the digital twin model to ensure real-time synchronization of data; based on the uploaded real-time data, the digital twin model is combined to dynamically correct the power supply efficiency coefficient and the UAV inspection path deviation; further, if the energy storage battery voltage is lower than the set threshold, adjust the power supply strategy to optimize energy utilization; if the UAV position deviates from the preset path, adjust the flight path to ensure the completion of the inspection task.
[0039] Further, the application provides a cooperative inspection control instruction for configuring a power transmission line according to the unmanned aerial vehicle nest cooperative scheduling scheme, and the method comprises the following steps: The cooperative inspection control instruction corresponds to an unmanned aerial vehicle take-off instruction, an unmanned aerial vehicle route adjustment instruction, a data back transmission instruction, and a photovoltaic controller charging and discharging instruction; based on the edge computing node, a data synchronization mechanism is configured on the data transmission interface using an MQTT / CoAP hybrid communication protocol, and the data synchronization mechanism is used to correct the transmission time delay of the cooperative inspection control instruction.
[0040] In one embodiment, the cooperative inspection control instruction is used to guide the specific operation instruction for the cooperative work of the unmanned aerial vehicle and the unmanned aerial vehicle nest, including unmanned aerial vehicle take-off, route adjustment, data back transmission, photovoltaic controller charging and discharging, etc.; the MQTT / CoAP hybrid communication protocol refers to a communication protocol combining MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol), which is used for efficient data transmission in an Internet of Things environment; the data synchronization mechanism refers to a mechanism for ensuring real-time synchronization of data between different devices and systems, which is used to reduce data transmission time delay and improve data consistency; the transmission time delay refers to the time required for data to be transmitted from the sending end to the receiving end, which affects the real-time performance and response speed of the system.
[0041] Optionally, the cooperative inspection control instruction is defined, and specifically, the unmanned aerial vehicle take-off instruction triggers the unmanned aerial vehicle to take off from the unmanned aerial vehicle nest and start the inspection task; the unmanned aerial vehicle route adjustment instruction adjusts the flight path of the unmanned aerial vehicle according to real-time data to ensure that all key areas are covered; the data back transmission instruction controls the unmanned aerial vehicle to back transmit the inspection data to the unmanned aerial vehicle nest or the control center; the photovoltaic controller charging and discharging instruction controls the charging of the photovoltaic panel and the charging and discharging process of the battery to ensure stable energy supply; the MQTT / CoAP hybrid communication protocol combines the efficient message transmission capability of MQTT and the low-power consumption characteristics of CoAP to ensure efficient data transmission between different devices and systems; the edge computing node is used to configure the data synchronization mechanism to ensure the real-time performance and accuracy of the cooperative inspection control instruction; the publish / subscribe model of the MQTT protocol is used to quickly publish and receive control instructions; the lightweight transmission of the CoAP protocol is used to reduce the energy consumption of data transmission.
[0042] The edge computing node monitors the data transmission state in real time, dynamically adjusts the communication parameters, reduces the transmission delay, ensures the consistency and accuracy of data between different devices and systems, and avoids instruction errors or delayed execution caused by transmission delay. Preferably, by using the MQTT / CoAP hybrid communication protocol and data synchronization mechanism, the data transmission delay is significantly reduced, the response speed of the instruction and the overall performance of the system are improved, the real-time response of the flight route adjustment, emergency recall and other instructions is ensured, the efficient transmission and real-time execution of the cooperative inspection control instruction are ensured, the efficiency and safety of the inspection are improved, and the overall performance and reliability of the system are enhanced Further, the method described in the application also includes: An integrated low-power storage unit; during deep sleep of the UAV nest, the low-power storage unit is used for automatic latch processing of breakpoint information of the UAV inspection task.
[0043] In one embodiment, the low-power storage unit refers to a storage device that can still work normally in a low-power state, used to save critical data to ensure that data is not lost during deep sleep of the UAV nest; automatic latch processing refers to automatically saving the state information of the current task to the storage unit so that the task can continue to execute from the breakpoint when resuming work.
[0044] Optionally, the low-power storage unit is integrated in the UAV nest system to ensure that critical data can still be saved in the sleep state; before the UAV nest enters a deep sleep state, the state of the current UAV inspection task is automatically detected, the breakpoint information such as the current inspection position of the task, the completed task part, and the remaining task is saved to the low-power storage unit, and after the UAV nest wakes up from the sleep state, the breakpoint information is read from the low-power storage unit to resume the task and continue to execute, ensuring that the state information of the UAV inspection task is saved during deep sleep of the UAV nest, so that it can seamlessly connect when resuming work, avoiding task interruption and data loss caused by sleep, and improving the reliability and continuity of the system.
[0045] Further, the method described in the application also includes: Signal strength detectors are arranged at key positions of the UAV nest, including the battery compartment; when it is monitored that the communication signal strength in the key position is lower than the signal strength threshold value and the duration exceeds the allowable delay window, signal relay enhancement is triggered.
[0046] In one embodiment, the signal strength detector is used to monitor the strength of the communication signal in real time, ensuring that the signal is within the effective range; the key position refers to the position in the UAV nest where the communication signal strength requirement is higher, such as the battery compartment; the signal strength threshold refers to the preset signal strength threshold, when the actual signal strength is lower than this value, it is considered that the signal strength is insufficient; the allowable delay window refers to the maximum time allowed for the signal strength to be below the threshold, exceeding this time triggers the signal relay enhancement; the signal relay enhancement refers to enhancing the communication signal through the signal relay device to ensure the stability and reliability of the communication.
[0047] Optionally, signal strength detectors are arranged at key positions such as battery compartments in the UAV nest to monitor the strength of the communication signal in real time; the signal strength detector measures the strength of the communication signal periodically through sensor technology and transmits the data to the control center; the data of the signal strength detector is monitored in real time to determine whether the signal strength is below the preset threshold, if the signal strength is below the threshold, the system starts timing and records the duration of insufficient signal strength; when the signal strength is below the threshold and the duration exceeds the allowable delay window, the signal relay enhancement mechanism is triggered, the signal relay device is started to enhance the communication signal, ensuring the stability and reliability of the communication between the UAV and the UAV nest Preferably, by setting the signal strength threshold and the allowable delay window, the signal relay enhancement mechanism can be started in time when the signal strength is insufficient, ensuring the continuity and stability of the communication, for example, in mountainous or remote areas, the signal strength may be weakened due to terrain obstruction, through the signal relay enhancement mechanism, the risk of communication interruption can be effectively reduced, in complex environments such as remote areas and mountains, the communication signal between the UAV and the UAV nest is always within the effective range, avoiding communication interruption caused by insufficient signal strength, ensuring the smooth progress of the UAV inspection task.
[0048] Further, the method described in the present application further comprises: Constructing a device failure mode database of the UAV nest, the device failure mode database stores photovoltaic panel failure instances, battery bulging instances, and UAV take-off and landing deviation instances; based on the device failure mode database, the remaining life of the device is predicted and maintenance reminders are generated in combination with real-time operation data.
[0049] In one embodiment, the device failure mode database refers to a database that stores device failure modes and instances for analyzing and predicting device failures; the photovoltaic panel failure instances refer to records of various failure conditions of photovoltaic panels during operation, such as snow cover, damage, etc.; the battery bulging instances refer to records of bulging phenomena of batteries during use and related data; the UAV take-off and landing deviation instances refer to records of various deviation conditions of UAVs during take-off and landing and related data; the real-time operation data refer to data collected in real time during operation of the device, including temperature, voltage, current, flight parameters, etc.; the predicted device remaining life refers to predicting the remaining service life of the device based on the failure mode database and real-time operation data through data analysis and modeling; and the maintenance reminder refers to generating a maintenance reminder based on the prediction result to prompt the user to maintain or replace the device.
[0050] Optionally, the photovoltaic panel failure instances, battery bulging instances, UAV take-off and landing deviation instances, etc. are collected and sorted, and stored in the device failure mode database, each instance in the device failure mode database including detailed information such as failure type, failure occurrence time, failure cause, and treatment method; the operation data of the UAV nest are collected in real time through sensors and monitoring devices, such as voltage, current, and temperature of the photovoltaic panel, voltage, current, and temperature of the battery, and flight parameters of the UAV; the remaining life of the device is predicted using data analysis and machine learning models in combination with historical data in the device failure mode database and real-time operation data, for example, by analyzing the voltage and current changes of the photovoltaic panel to predict the remaining life of the photovoltaic panel; and by analyzing the charge-discharge curve of the battery to predict the remaining life of the battery.
[0051] According to the prediction result, when the remaining life of the device is lower than a set threshold, a maintenance reminder is generated, the maintenance reminder including information such as device type, predicted remaining life, recommended maintenance time, and maintenance suggestions, and the user is notified through a system interface or a short message, etc. Preferably, by predicting device failures in advance and generating maintenance reminders, the impact of device failures on inspection work is reduced, and the reliability and safety of the system are improved, for example, by analyzing the voltage and current changes of the photovoltaic panel to predict the remaining life of the photovoltaic panel in advance and generating a maintenance reminder when the remaining life is lower than a set threshold.
[0052] In summary, the beneficial effects of the embodiments of the present application are: The basic technical parameters of the unmanned aerial vehicle nest and the tower deployment environment parameters are acquired, a multi-objective optimization model conforming to the power transmission line inspection requirements is configured based on the basic technical parameters and the tower deployment environment parameters, the hibernation wake-up timing, the unmanned aerial vehicle take-off and landing frequency and the photovoltaic power supply distribution parameters are dynamically adjusted, the unmanned aerial vehicle nest operation stability and the unmanned aerial vehicle inspection coverage rate under different meteorological conditions are simulated, the multi-objective optimization model is combined to generate an unmanned aerial vehicle nest cooperative scheduling scheme, and an edge computing node is deployed to configure the cooperative inspection control instruction of the power transmission line according to the unmanned aerial vehicle nest cooperative scheduling scheme. The unmanned aerial vehicle nest cooperative inspection method and system for the power transmission line are provided. The multi-objective optimization model is constructed according to the nest technical parameters and the tower environment parameters, the hibernation, take-off and landing, and power supply parameters are dynamically adjusted, and the environmental adaptability is enhanced by simulating different meteorological conditions to optimize the scheduling scheme.
[0053] In the second embodiment, based on the same inventive concept as the unmanned aerial vehicle nest cooperative inspection method for the power transmission line in the foregoing embodiments, as shown in the accompanying drawings, the present application provides an unmanned aerial vehicle nest cooperative inspection system for the power transmission line, which comprises: Figure 2 The parameter acquisition module 11 is configured to acquire the basic technical parameters of the unmanned aerial vehicle nest and the tower deployment environment parameters.
[0054] The dynamic adjustment module 12 is configured to configure a multi-objective optimization model conforming to the power transmission line inspection requirements based on the basic technical parameters and the tower deployment environment parameters, and dynamically adjust the hibernation wake-up timing, the unmanned aerial vehicle take-off and landing frequency and the photovoltaic power supply distribution parameters.
[0055] The unmanned aerial vehicle nest cooperative scheduling scheme generation module 13 is configured to simulate the unmanned aerial vehicle nest operation stability and the unmanned aerial vehicle inspection coverage rate under different meteorological conditions, combine the multi-objective optimization model, and generate an unmanned aerial vehicle nest cooperative scheduling scheme.
[0056] The cooperative inspection control instruction configuration module 14 is configured to deploy an edge computing node, configure the cooperative inspection control instruction of the power transmission line according to the unmanned aerial vehicle nest cooperative scheduling scheme, and perform the following method.
[0057] Further, the dynamic adjustment module 12 is further configured to perform the following method: The hibernation duration of the unmanned aerial vehicle nest, the unmanned aerial vehicle inspection route, the photovoltaic panel charging power, the battery charging and discharging threshold, and the tower platform load are set as decision variables, and the constraint range is set.
[0058] Further, the dynamic adjustment module 12 is further configured to perform the following method: The digital twin model of the unmanned aerial vehicle nest and the tower platform is constructed, and an energy storage module and an environment monitoring module are integrated; a data transmission interface is accessed, and the voltage of the energy storage battery and the position of the unmanned aerial vehicle are uploaded; based on the voltage of the energy storage battery and the position of the unmanned aerial vehicle, the power supply efficiency coefficient and the deviation of the unmanned aerial vehicle inspection path are dynamically corrected in combination with the digital twin model.
[0059] Further, the cooperative inspection control instruction configuration module 14 is configured to perform the following method: The cooperative inspection control instruction corresponds to an unmanned aerial vehicle take-off instruction, an unmanned aerial vehicle route adjustment instruction, a data back transmission instruction, and a photovoltaic controller charging and discharging instruction; based on the edge computing node, a data synchronization mechanism is configured in the data transmission interface using a mixed communication protocol of MQTT / CoAP, and the data synchronization mechanism is used to correct the transmission time delay of the cooperative inspection control instruction.
[0060] Further, the unmanned aerial vehicle nest cooperative inspection system for the power transmission line is also configured to perform the following method: A low-power storage unit is integrated; during deep hibernation of the unmanned aerial vehicle nest, the low-power storage unit is used to automatically latch the breakpoint information of the unmanned aerial vehicle inspection task.
[0061] Further, the unmanned aerial vehicle nest cooperative inspection system for the power transmission line is also configured to perform the following method: A signal strength detector is arranged at a key position of the unmanned aerial vehicle nest, and the key position includes a battery compartment; when it is monitored that the communication signal strength in the key position is lower than a signal strength threshold value and the duration exceeds an allowable delay window, signal relay enhancement is triggered.
[0062] Further, the unmanned aerial vehicle nest cooperative inspection system for the power transmission line is also configured to perform the following method: A device fault mode database of the unmanned aerial vehicle nest is constructed, the device fault mode database stores photovoltaic panel fault instances, battery bulging instances, and unmanned aerial vehicle take-off and landing deviation instances; based on the device fault mode database, in combination with real-time operation data, the remaining life of the device is predicted and a maintenance reminder is generated.
[0063] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.
[0064] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.
[0065] The specification and drawings are only exemplary and illustrative of the present application and are considered to cover any and all modifications, variations, combinations or equivalents that are within the scope of the present application. Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application cover the modifications and changes as they come within the scope of the application, and that the scope of the application be limited only by the claims.
Claims
1. A method for unmanned aerial vehicle nest cooperative inspection of power transmission lines, characterized in that, The method comprises: acquiring basic technical parameters of the UAV nest and tower deployment environment parameters; based on the basic technical parameters and tower deployment environment parameters, configuring a multi-objective optimization model that meets the needs of power line inspection, dynamically adjusting the hibernation wake-up timing, UAV take-off and landing frequency, and photovoltaic power distribution parameters; simulate the operation stability of the UAV nest and the UAV inspection coverage rate under different meteorological conditions, combine the multi-objective optimization model to generate a UAV nest cooperative scheduling scheme; deploy edge computing nodes, and configure cooperative inspection control instructions for the power line according to the UAV nest cooperative scheduling scheme.
2. The method of claim 1, wherein, Based on the basic technical parameters and tower deployment environment parameters, configure a multi-objective optimization model that meets the needs of power line inspection, the method further comprises: Taking the hibernation length of the UAV nest, the UAV inspection route, the charging power of the photovoltaic panel, the battery charging and discharging threshold, and the tower platform load as decision variables, set the constraint range.
3. The method of claim 2, wherein, The method comprises: constructing a digital twin model of the UAV nest and the tower platform, integrating energy storage modules and environmental monitoring modules; accessing a data transmission interface to upload energy storage battery voltage and UAV position; based on the energy storage battery voltage and UAV position, dynamically correct the power supply efficiency coefficient and UAV inspection path deviation based on the digital twin model.
4. The method of claim 3, wherein, According to the UAV nest cooperative scheduling scheme, configure the cooperative inspection control instructions for the power line, the method comprises: The cooperative inspection control instructions correspond to UAV take-off instructions, UAV route adjustment instructions, data back transmission instructions, and photovoltaic controller charging and discharging instructions; based on the edge computing nodes, use the MQTT / CoAP hybrid communication protocol to configure a data synchronization mechanism on the data transmission interface, and the data synchronization mechanism is used to correct the transmission delay of the cooperative inspection control instructions.
5. The method of claim 1, wherein, The method comprises: integrate a low-power storage unit; during deep hibernation of the UAV nest, automatically latch the UAV inspection task breakpoint information based on the low-power storage unit.
6. The method of claim 5, wherein, The method comprises: arranging signal strength detectors at key positions of the UAV nest, the key positions including the battery compartment; when the communication signal strength in the key position is lower than the signal strength threshold value and the duration exceeds the allowable delay window, trigger signal relay enhancement.
7. The method of claim 6, wherein, The method comprises: constructing a device fault mode database of the UAV nest, the device fault mode database stores photovoltaic panel fault instances, battery bulging instances, and UAV take-off and landing deviation instances; based on the device fault mode database, combine real-time operation data to predict the remaining life of the device and generate a maintenance reminder.
8. An unmanned aerial vehicle nest cooperative inspection system for power transmission lines, characterized in that, A system for implementing the UAV nest cooperative inspection method for power lines of any one of claims 1-7, the system comprises: a parameter acquisition module for acquiring basic technical parameters of the UAV nest and tower deployment environment parameters; a dynamic adjustment module for configuring a multi-objective optimization model that meets the needs of power line inspection based on the basic technical parameters and tower deployment environment parameters, dynamically adjusting the hibernation wake-up timing, UAV take-off and landing frequency, and photovoltaic power distribution parameters; The unmanned aerial vehicle nest cooperative scheduling scheme generation module is configured to simulate the operation stability of the unmanned aerial vehicle nest and the unmanned aerial vehicle inspection coverage under different meteorological conditions, and generate an unmanned aerial vehicle nest cooperative scheduling scheme in combination with the multi-objective optimization model; and the cooperative inspection control instruction configuration module is configured to deploy an edge computing node, and configure a cooperative inspection control instruction of the power transmission line according to the unmanned aerial vehicle nest cooperative scheduling scheme.