An unmanned aerial vehicle for automatically patrolling a photovoltaic power station field

Abstract

The application discloses a power station photovoltaic field area automatic inspection unmanned plane, and relates to the technical field of unmanned plane inspection. The unmanned plane comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power module, a rotor device and a communication module; the power module is used for providing power support for each component of the unmanned plane; the communication module is used for realizing data interaction between the unmanned plane, a ground control station and a photovoltaic field area monitoring device; the meteorological self-adaptive intelligent inspection flight integrated system is carried, the meteorological conditions of the photovoltaic field area can be sensed and analyzed in real time, and the inspection path and the flight attitude are dynamically optimized accordingly; the unmanned plane can preferentially inspect the area most likely to have problems, thereby significantly improving the inspection efficiency and quickly positioning potential fault points; meanwhile, the unmanned plane can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of the inspection data is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle inspection, in particular to an automatic inspection unmanned aerial vehicle for photovoltaic field area of power station. BACKGROUND

[0002] With the growing global demand for renewable energy, the solar photovoltaic power generation industry has ushered in an unprecedented development opportunity. As an important place for solar power generation, the scale of photovoltaic field area of power station is expanding, and the number of photovoltaic equipment is also increasing rapidly. However, the operation status of photovoltaic equipment is directly related to the power generation efficiency and safety of the power station. Therefore, it is crucial to carry out efficient, accurate and safe inspection and maintenance work.

[0003] Traditional inspection methods often rely on manual work, which is not only time-consuming and laborious, but also difficult to ensure the comprehensiveness and accuracy of the inspection. In recent years, with the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicle inspection has gradually become a new trend in the inspection of photovoltaic field area of power station. Unmanned aerial vehicles have shown great potential in the field of photovoltaic inspection due to their advantages of high efficiency, flexibility and low cost. However, despite the many advantages of unmanned aerial vehicle inspection, there are still many problems in actual application, especially in complex weather conditions. First, in terms of inspection path planning, existing unmanned aerial vehicles often lack dynamic adaptability and cannot dynamically adjust the inspection path according to real-time weather information. Since the fault risk of photovoltaic equipment under different weather conditions is different, traditional unmanned aerial vehicles lack precise assessment and consideration of this, resulting in the inability to optimize the inspection path according to the actual situation, greatly reducing the inspection efficiency and making it difficult to quickly locate potential fault points. Second, in terms of flight stability, traditional unmanned aerial vehicles are difficult to effectively maintain stable flight attitude and trajectory in the face of different intensity and direction of wind and other weather factors, which not only affects the shooting accuracy of the shooting equipment, causing inaccurate inspection data, but also may cause flight accidents, posing a serious threat to the safety of the unmanned aerial vehicle itself and surrounding facilities and personnel.

[0004] In view of the above problems, it is necessary to optimize the existing automatic inspection unmanned aerial vehicle for photovoltaic field area of power station by deeply integrating dynamic self-adaptive inspection path planning and adaptive wind-resistant flight capability to realize efficient and accurate inspection operation under complex weather conditions. Therefore, it is of great significance to develop an automatic inspection unmanned aerial vehicle for photovoltaic field area of power station that can comprehensively realize the above characteristics. SUMMARY

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an automatic inspection drone for power plant photovoltaic areas. By deeply integrating dynamic adaptive inspection path planning and adaptive wind-resistant flight capabilities, it achieves high efficiency and accuracy in inspection operations under complex weather conditions. At the same time, by optimizing relevant decisions through energy management, it further extends the drone's endurance and improves the sustainability of inspection operations, providing a more intelligent, efficient and safe solution for the operation and maintenance management of power plant photovoltaic areas.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an automatic inspection drone for photovoltaic power plant areas, the drone comprising the following components: a weather-adaptive intelligent inspection flight system, a power module, a rotor device, and a communication module;

[0007] The power module is used to provide power support for the various components of the drone;

[0008] The communication module is used to enable data interaction between the UAV, the ground control station, and the photovoltaic field monitoring equipment.

[0009] The rotor system is installed around the main body of the fuselage to provide the lift and power required for the drone's flight, and can be adjusted by the weather-adaptive intelligent inspection flight system.

[0010] The weather-adaptive intelligent inspection flight system is communicatively connected to the weather sensor array, rotor device and flight control system. The system includes: a data acquisition and analysis submodule, a comprehensive decision-making submodule and a command execution submodule.

[0011] The data acquisition and analysis submodule uses a meteorological sensor array positioned at an appropriate location on the main body of the drone to collect real-time data covering wind speed, wind direction, temperature, and air pressure. It combines this data with a fault risk assessment model based on the layout of the photovoltaic field and the characteristics of the photovoltaic equipment, as well as the drone's own flight performance parameters, to analyze the impact of the differences in fault risk of photovoltaic equipment in different areas under different meteorological conditions on the inspection path planning. At the same time, based on the meteorological data and its own performance parameters, it uses complex mechanical analysis and flight attitude simulation algorithms to preliminarily calculate the direction and degree of flight attitude adjustment required to maintain stable flight under the current meteorological conditions.

[0012] The comprehensive decision submodule obtains the inspection task requirement related information, and comprehensively considers the analysis results of the influence of the weather conditions on the inspection path and flight attitude provided by the data acquisition and analysis submodule, determines the regional inspection priority by using a priority determination algorithm, automatically promotes the priority of the region with high wind influence and high fault risk, and uses an intelligent algorithm to take the geographical layout of the photovoltaic field, the regional inspection priority and the current weather conditions as input parameters to dynamically optimize the inspection path in real time. Based on the analysis results of the flight attitude adjustment of the data acquisition and analysis submodule, combined with the current weather conditions and the performance parameters of the unmanned aerial vehicle itself, the calculation model is used to obtain the key parameter adjustment amount of the flight attitude and rotor speed required to maintain stable flight. In addition, by obtaining the real-time state information of the unmanned aerial vehicle and the weather conditions, the energy consumption is analyzed and calculated based on the preset energy consumption calculation model, and the energy management optimization strategy is formulated according to different situations.

[0013] The instruction execution submodule is responsible for receiving the inspection path optimization scheme determined by the comprehensive decision submodule, the flight attitude adjustment instruction and the decision result of the energy management optimization strategy, and establishing a stable and efficient communication link with the communication module of the unmanned aerial vehicle and each related execution component to ensure accurate and timely transmission of instructions.

[0014] Further, the data acquisition and analysis submodule analyzes the influence of the fault risk difference of the photovoltaic equipment in each region on the inspection path planning under different weather conditions by combining the fault risk evaluation model set according to the layout of the photovoltaic field and the characteristics of the photovoltaic equipment and the flight performance parameters of the unmanned aerial vehicle itself, and uses the formula to quantitatively evaluate the fault risk of the photovoltaic equipment in each region, wherein R f (i) represents the fault risk value of the i-th regional photovoltaic equipment, a and β are weight coefficients, n represents the number of types of meteorological factors affecting the fault risk of the photovoltaic equipment, W j is the weight of the j-th meteorological factor, F ij represents the actual measurement value of the j-th meteorological factor in the i-th region, S i is the structural strength parameter of the i-th regional photovoltaic equipment, m represents the number of types of photovoltaic equipment self-characteristics factors affecting the fault risk of the photovoltaic equipment, V k is the weight of the k-th photovoltaic equipment self-characteristics factor, G ik represents the actual value of the k-th photovoltaic equipment self-characteristics factor in the i-th region, T i is the performance parameter of the i-th regional photovoltaic equipment.

[0015] Further, the data acquisition and analysis submodule preliminarily calculates the flight attitude adjustment direction and degree required for maintaining stable flight under the current weather condition by complex mechanical analysis and flight attitude simulation algorithm according to the weather data and the performance parameters of the unmanned aerial vehicle, and the calculation formula is: Wherein, θ a represents the flight attitude adjustment angle required for the unmanned aerial vehicle to maintain stable flight under the current weather condition, γ and δ are weight coefficients for balancing the influence degree of different factors on the flight attitude adjustment estimation, p represents the number of weather factor types affecting the flight attitude adjustment, X1 is the weight of the first weather factor, H il represents the actual measurement value of the lth weather factor in the ith region, Z i is the spatial characteristic parameter of the ith region, r represents the number of unmanned aerial vehicle self-characteristic factor types affecting the flight attitude adjustment, Y q is the weight of the qth unmanned aerial vehicle self-characteristic factor, I iq represents the actual value of the qth unmanned aerial vehicle self-characteristic factor in the ith region, U i is the flight condition parameter of the ith region.

[0016] Further, the comprehensive decision submodule determines the inspection priority of each region by using a priority determination algorithm, and the calculation formula is: P pri (i) = μ × R f (i) + v × E eff (i) + ω × P adj (i), wherein P pri (i) represents the inspection priority value of the ith region, the higher the value, the higher the degree of priority of the region under the current weather condition and the overall inspection demand, μ, v and ω are weight coefficients for balancing the influence degree of different factors on the determination of the inspection region priority, R f (i) represents the fault risk value of the photovoltaic equipment in the ith region, which comprehensively reflects the fault possibility of the photovoltaic equipment in the region under the current weather condition based on weather factors and photovoltaic equipment self-characteristic factors, E eff (i) is the inspection efficiency influence value of the ith region, which is obtained by comprehensively evaluating the influence of various weather factors on the inspection efficiency, and represents the influence degree of the weather condition factors of the region on the overall inspection efficiency, P adj (i) is the necessity degree value of the inspection path adjustment of the ith region, which reflects the necessity degree of adjusting the inspection path of the region under the current weather condition and the state of the photovoltaic equipment.

[0017] Further, in the comprehensive decision submodule, E eff (i) is the inspection efficiency influence value of the ith region, and the calculation formula is: Where η is the weighting coefficient, N represents the total number of areas divided into photovoltaic field zones, and F ij F represents the actual measured value of the j-th meteorological factor within the i-th region, which is obtained in real time by the meteorological sensor array. 0j P represents the baseline value of the j-th meteorological factor. adj (i) is the necessity value for adjusting the inspection path of the i-th area, and its calculation formula is: Among them, P adj The higher the value of (i), the greater the necessity to adjust the inspection path for the area under the current meteorological conditions and photovoltaic equipment status. ∈ and ζ are weighting coefficients used to balance the influence of meteorological factors and photovoltaic equipment characteristics on the assessment of the necessity of inspection path adjustment. n: represents the number of meteorological factors affecting inspection path adjustment, covering wind speed, wind direction, temperature, and air pressure. W j Let be the weight of the j-th meteorological factor.

[0018] Furthermore, the integrated decision-making submodule employs an intelligent algorithm that uses the geographical layout of the photovoltaic field, the inspection priority of each area, and current weather conditions as input parameters to dynamically optimize the inspection path in real time. Specifically, let G(V, E) represent the photovoltaic field map, where V is the set of vertices representing the various areas of the photovoltaic field, and E is the set of edges representing the reachability relationships between areas. Let S be the starting point, i.e., the takeoff point of the UAV or the end point of the previous inspection, and T be the target point, i.e., the end point specified for this inspection task or a certain area that needs to be inspected. Let P = {S} be the currently determined inspection path, initially containing only the starting point, C visited (v) represents the number of times vertex v has been visited. Initially, the number of visits for all vertices is 0. In each iteration, starting from the last vertex u of the current path P, the cost function C(u, v) from u to all unvisited vertices v is calculated. The formula is: C(u, v) = α1 × D(u, v) + α2 × P pri (v)+α3×E eff (v), α1, α2, and α3 are weighting coefficients used to balance the importance of distance, inspection priority, and inspection efficiency in path selection. D(u, v) represents the distance from vertex u to vertex v. pri (v) represents the inspection priority value of the v-th area, E eff (v) represents the impact of the inspection efficiency of the v-th region. The vertex v with the smallest cost function C(u,v) is selected. min Add it to the current path P, that is, P = P∪{v min}, and update vertex v min The number of visits C visited (vmin ) = C visited (v cin )+1, when the target point T is added to the path P or the maximum number of iterations is reached, the iteration process ends, and the path P obtained at this time is the optimized inspection path.

[0019] Further, the comprehensive decision submodule obtains the key parameter adjustment amount of the flight attitude and the rotor speed required to maintain stable flight based on the analysis result of the flight attitude adjustment of the data acquisition and analysis submodule, in combination with the performance parameters of the unmanned aerial vehicle itself and the current meteorological conditions, and by using a calculation model, the calculation formula of the flight attitude parameter adjustment amount is: The calculation formula of the rotor speed parameter adjustment amount is: Δθ f represents the change amount of the flight attitude adjustment angle of the unmanned aerial vehicle calculated accurately, Δr s represents the rotor speed adjustment amount calculated accurately, φ and ρ are weight coefficients, p represents the number of meteorological factor types affecting the flight attitude adjustment, X l is the weight of the first meteorological factor, H il represents the actual measurement value of the first meteorological factor in the i-th region, H 0l represents the reference value of the first meteorological factor, n represents the number of meteorological factor types affecting the rotor speed adjustment, W j is the weight of the j-th meteorological factor, F ij represents the actual measurement value of the j-th meteorological factor in the i-th region, F 0j represents the reference value of the j-th meteorological factor, Z i is the spatial characteristic parameter of the i-th region, S i is the structural strength parameter of the photovoltaic equipment in the i-th region.

[0020] Further, the comprehensive decision submodule analyzes and calculates the inspection energy consumption based on a preset energy consumption calculation model, and according to different situations, uses corresponding strategies to formulate an algorithm to formulate an energy management optimization strategy for optimizing the inspection path, obtains the inspection priority value P pri (i) of each region, and the distance D i from the starting point to the i-th region calculated according to the geographical layout of the photovoltaic field and the flight characteristics of the unmanned aerial vehicle, simultaneously obtains the preset weight coefficient β2, and uses a strategy formula to calculate the optimized inspection path energy consumption value Substitute the obtained parameters into the formula, and by comprehensively considering the inspection priority of each region and the distance factor from the starting point to each region, calculate the optimized inspection path energy consumption value E path，opt , so as to optimize the inspection path.

[0021] Compared with the prior art, the power station photovoltaic field area automatic inspection unmanned aerial vehicle has the following beneficial effects:

[0022] Firstly, the unmanned aerial vehicle can real-time perceive and analyze the meteorological conditions of the photovoltaic field area, and dynamically optimize the inspection path and flight attitude according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect the area most likely to have problems, thereby significantly improving the inspection efficiency and quickly locating the potential fault point.

[0023] Secondly, the unmanned aerial vehicle can analyze and calculate the energy consumption according to the current flight state, meteorological conditions and inspection task requirements, and develop corresponding optimization strategies, so that the unmanned aerial vehicle can reduce the energy consumption by appropriately reducing the rotor speed when the wind speed is low, and reasonably plan the inspection path to reduce the total energy consumption when it is necessary to preferentially inspect the high-risk area and the energy consumption is high, thereby effectively prolonging the endurance time of the unmanned aerial vehicle and improving the sustainability of the inspection operation.

[0024] Other advantages, objects and features of the present application will be set forth in part in the following specification, and in part will become apparent to those skilled in the art upon examination of the following specification, or can be learned from practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0026] Figure 1 It is a structural schematic diagram of an automatic inspection unmanned aerial vehicle for a power station photovoltaic field area.

[0027] Figure 2 It is a structural schematic diagram of a meteorological adaptive intelligent inspection flight system of an automatic inspection unmanned aerial vehicle for a power station photovoltaic field area.

[0028] Figure 3 It is a work flow chart of an automatic inspection unmanned aerial vehicle for a power station photovoltaic field area. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0030] Embodiment one

[0031] This embodiment describes in detail the specific application of a power station photovoltaic field area automatic inspection unmanned aerial vehicle in large ground photovoltaic power station inspection. In the inspection process, the inspection strategy can be flexibly adjusted according to real-time meteorological conditions and photovoltaic equipment state, efficient and accurate inspection operation is realized, potential faults and abnormal conditions of photovoltaic equipment are found in time, and the endurance time of the unmanned aerial vehicle is prolonged through energy management optimization strategy, and the sustainability of the inspection operation is improved.

[0032] Before inspection, the main body of the unmanned aerial vehicle is comprehensively inspected to ensure that the internal flight control system, data processing unit, power module and communication module are all working normally, especially the power module is detected for power to ensure that the power is sufficient and the power supply is stable, which can meet the needs of the whole inspection task. The inspection coverage range is set as the whole photovoltaic power station field area, the key inspection areas include the photovoltaic component area near the air outlet position, the area with recent maintenance records and the area with abnormal fluctuation of power generation efficiency, and the inspection time interval is set as once a week. Each inspection needs to be carried out in the period with good light condition and relatively stable meteorological condition in the daytime.

[0033] When the unmanned aerial vehicle starts the inspection flight in the photovoltaic power station field area, the meteorological sensor array collects the meteorological information of the surrounding environment in real time. After the data acquisition and analysis submodule receives the real-time meteorological data collected by the meteorological sensor array, the real-time meteorological data are analyzed in combination with the pre-set photovoltaic equipment fault risk assessment model and the flight performance parameters of the unmanned aerial vehicle: the fault risk assessment formula is used to calculate the fault risk value of each area photovoltaic equipment, and the flight attitude adjustment direction and degree are preliminarily estimated according to the formula θ a =γ× wherein γ is the flight attitude adjustment angle, and R is the fault risk value of the photovoltaic equipment in the area. f (i) is relatively high, which may be caused by the relatively large wind speed and the long service life of the photovoltaic equipment in the area, resulting in the increase of the calculated risk value. The flight attitude adjustment angle required to maintain stable flight under the current meteorological condition is preliminarily estimated, for example, according to the collected wind speed, wind direction and spatial characteristic parameters of the area, it is estimated that the unmanned aerial vehicle body may need to tilt a certain angle to the northwest to maintain stable flight. The inspection path adjustment necessity assessment formula The necessity degree of adjusting the inspection path of each region is evaluated. If the wind speed of a region changes greatly compared with the benchmark wind speed, and the characteristics of the photovoltaic device also change to a certain extent, the necessity degree value of adjusting the inspection path of the region is calculated to be higher, indicating that the inspection path of the region needs to be adjusted.

[0034] The comprehensive decision sub-module comprehensively considers the inspection task requirements based on the analysis results of the data acquisition and analysis sub-module, and makes the following decisions: according to the inspection region priority determination formula P pri (i) = μ × R f (i) + V × E eff (i) + ω × P adj (i), the inspection priority of each region is determined. For the regions with high fault risk value, large flight attitude adjustment demand and high necessity degree of inspection path adjustment, the inspection priority is significantly improved. The inspection path optimization formula is used for inspection path optimization. In the initialization, a graph G(V, E) representing the field area of the photovoltaic power station is constructed, where V includes each photovoltaic component region, key monitoring point, etc. as a vertex, E represents the reachable relationship between regions, the starting point is set as the takeoff point of the unmanned aerial vehicle, and the target point is a certain one in the key inspection region specified in this inspection task (such as a region with recent maintenance records). In the iteration process, starting from the last vertex of the current path, the cost function C(u, v) = α1 × D(u, v) + α2 × P pri (v) + α3 × E eff (v) of all unvisited vertices is calculated, the vertex with the minimum cost function value is selected to join the current path, until the target point is joined to the path or the preset maximum iteration number is reached, the optimized inspection path is obtained, and the flight attitude and rotor speed adjustment amount accurate calculation formula And The adjustment amount of the flight attitude and rotor speed required to maintain stable flight, such as the body should be inclined to the northwest by a specific angle value, and the rotor speed needs to be increased to a specific speed value to increase the lift to resist the influence of the wind, is accurately calculated. For the energy management optimization strategy, according to the inspection path planning, the strategy formula for reducing energy consumption is calculated. By optimizing the inspection path, high-priority regions are preferentially inspected, unnecessary detours are reduced, and the total energy consumption is further reduced.

[0035] The instruction execution submodule accurately sends the inspection path optimization scheme, the flight attitude adjustment instruction and the energy management optimization strategy determined by the comprehensive decision submodule to the relevant execution components respectively: for the inspection path optimization scheme, through the close cooperation of the communication module and the automatic driving function of the unmanned aerial vehicle, the unmanned aerial vehicle is guided to fly according to the optimized path, the automatic driving function of the unmanned aerial vehicle accurately and accurately performs the inspection operation according to the new inspection path according to the received path information, for the flight attitude adjustment instruction, the execution mechanism such as the rotor device and the rudder of the unmanned aerial vehicle is sent, so that the fuselage is inclined to the northwest according to the calculated angle, the flight attitude is adjusted, the rotor device receives the instruction, adjusts the motor speed, increases the rotor speed to the specific speed value calculated, thereby increases the lift, maintains the stable flight of the unmanned aerial vehicle in the strong wind environment, and can accurately fly according to the optimized inspection path, ensures that the shooting accuracy of the shooting equipment is not affected, for the energy management optimization strategy, the control system adjusts the flight attitude according to the instruction to reduce the energy consumption, the rotor device reduces the rotor speed according to the instruction to reduce the energy consumption, so as to realize the corresponding operation according to the energy management optimization strategy, so as to achieve the purpose of reducing the energy consumption and prolonging the endurance time.

[0036] While the unmanned aerial vehicle maintains stable flight, the shooting equipment (such as high-definition camera, infrared thermal imager, etc.) carried by the unmanned aerial vehicle shoots and collects data of the photovoltaic components, supports, wiring, etc. of the photovoltaic power station according to the predetermined inspection route and shooting parameters, for example, the high-definition camera shoots the surface of the photovoltaic component at a frame rate of 10 frames per second, and the infrared thermal imager detects the temperature distribution of the component. The collected data is transmitted to the ground control station in real time through the communication module, and the staff of the ground control station can view, analyze and store the data in real time, so as to timely discover the abnormal conditions of the photovoltaic power station and take corresponding measures, for example, if it is found that there is obvious shadow or abnormal temperature rise on the surface of a photovoltaic component, personnel can be arranged for further inspection and maintenance in a timely manner.

[0037] Embodiment Two

[0038] This embodiment describes in detail the specific application of the power station photovoltaic field area automatic inspection unmanned aerial vehicle in the distributed roof photovoltaic system inspection. In the inspection process, the inspection strategy can be flexibly adjusted according to the real-time meteorological conditions and the state of the photovoltaic equipment, the efficient and accurate inspection operation is realized, the potential faults and abnormal conditions of the photovoltaic equipment are timely discovered, and the endurance time of the unmanned aerial vehicle is prolonged through the energy management optimization strategy, thereby improving the sustainability of the inspection operation.

[0039] As in Embodiment One, each component of the unmanned aerial vehicle is comprehensively checked to ensure that each module in the main body of the fuselage works normally and the power module has sufficient power.

[0040] The inspection coverage range is set as all registered distributed rooftop photovoltaic systems in the city area, the key inspection areas include photovoltaic systems installed on old building rooftops, photovoltaic systems with recent power generation efficiency decline, and photovoltaic systems located in the vicinity of high-rise buildings which may be affected by shading, the inspection time interval is set as once a month, each inspection can be performed in the off-peak period of weekdays to reduce the impact on normal city activities, and the influence of weather conditions also needs to be considered.

[0041] After the UAV starts the inspection flight in the area where the distributed rooftop photovoltaic system is located, the weather sensor array collects real-time weather information, the data acquisition and analysis submodule receives the real-time weather data collected by the weather sensor array, analyzes the pre-set photovoltaic equipment failure risk assessment model and the flight performance parameters of the UAV itself, calculates the failure risk value of the photovoltaic equipment in each area by using the failure risk assessment formula, assumes that in a certain rooftop photovoltaic system area, the calculated failure risk value of the photovoltaic equipment in this area is relatively high, which may be because the temperature fluctuation in this area is large and the photovoltaic equipment is produced by a certain manufacturer, and its performance stability under temperature change is relatively poor, resulting in an increase in the risk value calculated according to the formula, the flight attitude adjustment angle required to maintain stable flight under the current weather conditions is preliminarily estimated according to the flight attitude adjustment direction and degree preliminary estimation formula, for example, according to the collected wind speed, wind direction and space characteristic parameters of the area, it is estimated that the UAV fuselage may need to be slightly tilted to a certain angle in the southwest direction to maintain stable flight, the necessity of adjusting the inspection path of each area is evaluated by the inspection path adjustment necessity evaluation formula, if the temperature in a certain area changes greatly compared with the reference temperature, and the characteristics of the photovoltaic equipment also change to a certain extent, the calculated necessity degree value of adjusting the inspection path in this area is high, indicating that the inspection path in this area needs to be adjusted.

[0042] The comprehensive decision submodule makes the following decisions based on the analysis results of the data acquisition and analysis submodule and considering the inspection task requirements, determines the inspection priority of each area according to the inspection area priority determination formula, the inspection priority of the area with high failure risk value, large flight attitude adjustment requirement and high inspection path adjustment necessity degree is significantly improved, the inspection path optimization formula is used to optimize the inspection path, a graph G(V, E) representing the area where the distributed rooftop photovoltaic system is located is constructed, V contains each rooftop photovoltaic system, key monitoring points, etc. as vertices, E represents the reachable relationship between areas, the starting point is set as the UAV takeoff point, and the target point is set as a certain one in the key inspection area of this inspection task (such as a photovoltaic system with recent power generation efficiency decline), in the iteration process, the cost function C(u, v) = α1 × D(u, v) + α2 × P pπi(v) + a3 x E eff (v), the vertex with the minimum cost function value is selected to join the current path, until the target point is added to the path or the preset maximum number of iterations is reached, to obtain an optimized inspection path, and the flight attitude and rotor speed adjustment amount are accurately calculated according to the flight attitude and rotor speed adjustment amount accurate calculation formula and The adjustment amount of the key parameters required to maintain stable flight, such as flight attitude and rotor speed, is accurately calculated, for example, it is calculated that the fuselage should be tilted to the southwest by a specific angle value, and the rotor speed needs to be adjusted appropriately (may be slightly increased or decreased, depending on the calculation result) to maintain stable flight, and according to the energy management optimization strategy, the strategy formula for reducing energy consumption is calculated according to the inspection path planning The optimized inspection path is discussed, the high-priority area is preferentially inspected, unnecessary detours are reduced, and the total energy consumption is further reduced.

[0043] The instruction execution submodule accurately sends the inspection path optimization scheme determined by the comprehensive decision submodule, the flight attitude adjustment instruction, and the energy management optimization strategy to the relevant execution components: for the inspection path optimization scheme, through the communication module and the automatic driving function of the unmanned aerial vehicle, the unmanned aerial vehicle is guided to fly according to the optimized path, the automatic driving function of the unmanned aerial vehicle accurately follows the new inspection path according to the received path information, combined with its own flight control logic and algorithm, to ensure efficient and comprehensive coverage of each key inspection area and other roof photovoltaic system areas that need to be inspected, for the flight attitude adjustment instruction, it is sent to the rotor device and the steering mechanism of the unmanned aerial vehicle, etc. The execution mechanism controls the fuselage to tilt to the southwest according to the calculated angle, realizes the accurate adjustment of the flight attitude, the rotor device receives the instruction according to the rotor speed adjustment amount information contained in the instruction, adjusts the speed control module of the motor itself, adjusts the rotor speed to the appropriate value, so as to maintain the stable flight of the unmanned aerial vehicle in the environment with variable wind direction, and ensure the shooting accuracy of the shooting equipment, so as to clearly and accurately obtain the state information of each roof photovoltaic system, for the energy management optimization strategy, it is sent to the rotor device, and the control system adjusts the flight attitude according to the instruction to reduce energy consumption, for example, in the case of large temperature fluctuations and variable wind direction, the flight attitude is adjusted to reduce air resistance, thereby reducing energy consumption, and the rotor device reduces the rotor speed according to the instruction to reduce energy consumption, and when it is determined that the rotor speed is appropriately reduced under the current wind speed, temperature and other meteorological conditions without affecting the flight stability, it operates according to the calculated optimized rotor speed, so as to realize the corresponding operation according to the energy management optimization strategy, achieve the purpose of reducing energy consumption and prolonging the endurance time, and ensure that the unmanned aerial vehicle can maintain sufficient power support during the entire inspection task.

[0044] While the UAV keeps stable flight, the shooting device (such as a high-definition camera, an infrared thermal imager, etc.) carried by the UAV shoots and collects data of the photovoltaic components, supports, wiring, etc. of the distributed rooftop photovoltaic system according to a predetermined inspection route and shooting parameters. For example, the high-definition camera shoots the surface of the photovoltaic components at a frame rate of 8 frames per second, focuses on whether the components are damaged, whether the surface is clean, etc., and the infrared thermal imager detects the temperature distribution of the components to check whether there is an abnormal phenomenon such as local overheating, etc. The collected data is transmitted to the ground control station in real time through the communication module.

[0045] The staff of the ground control station can view, analyze and store the data in real time, so as to discover abnormal conditions of the distributed rooftop photovoltaic system in time and take corresponding measures. For example, if it is found that the surface of a component of a rooftop photovoltaic system has obvious stains affecting the power generation efficiency, or the temperature of the components in a certain area is obviously higher than that in other normal areas, the staff can timely inform the relevant maintenance personnel to go to the site for cleaning or further inspection and repair, so as to ensure the normal operation of the distributed rooftop photovoltaic system.

[0046] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the involved claims.

Claims

1. An automated inspection drone for photovoltaic power plant areas, characterized in that, The drone comprises the following components: a weather-adaptive intelligent inspection flight system, a power module, a rotor assembly, and a communication module; The power module is used to provide power to the various components of the drone; The communication module is used to enable data interaction between the UAV, the ground control station, and the photovoltaic field monitoring equipment. The rotor system is installed around the fuselage to provide the lift and power required for the UAV to fly, and can be adjusted by the weather-adaptive intelligent inspection flight system. The meteorological adaptive intelligent inspection flight system is communicatively connected to the meteorological sensor array, rotor device and flight control system. The system includes: a data acquisition and analysis submodule, a comprehensive decision-making submodule and a command execution submodule. The data acquisition and analysis submodule uses a meteorological sensor array positioned at an appropriate location on the main body of the drone to collect real-time data covering wind speed, wind direction, temperature, and air pressure. It combines this data with a fault risk assessment model based on the layout of the photovoltaic field and the characteristics of the photovoltaic equipment, as well as the drone's own flight performance parameters, to analyze the impact of the differences in fault risk of photovoltaic equipment in different areas under different meteorological conditions on the inspection path planning. At the same time, based on the meteorological data and its own performance parameters, it uses complex mechanical analysis and flight attitude simulation algorithms to preliminarily calculate the direction and degree of flight attitude adjustment required to maintain stable flight under the current meteorological conditions. The integrated decision-making submodule acquires relevant information about the inspection task requirements and integrates it with the analysis results of the impact of meteorological conditions on the inspection path and flight attitude provided by the data acquisition and analysis submodule. It then uses a priority determination algorithm to determine the inspection priority of each area, automatically increasing the priority of areas heavily affected by wind and with high potential for faults. Furthermore, an intelligent algorithm is employed, using the geographical layout of the photovoltaic field, the inspection priorities of each area, and current meteorological conditions as input parameters, to dynamically optimize the inspection path in real time. Specifically, it sets... This represents a photovoltaic field area map, in which... It is a set of vertices, representing the various regions of the photovoltaic field. Let be a set of edges, representing the reachability relationships between regions. The starting point is either the drone's takeoff point or the end point of the previous inspection. Let the target point be the end point specified in this inspection task or a specific area that requires focused inspection. The inspection path is currently determined, initially containing only the starting point. As vertex The visited count is initially set to 0 for all vertices. In each iteration, the visited count is calculated from the current path. The last vertex Starting from, calculate from To all unvisited vertices Cost function The calculation formula is as follows: , , and These are weighting coefficients used to balance the importance of factors such as distance, inspection priority, and inspection efficiency in path selection. Indicates from vertex To the top distance, Indicates the first Inspection priority values ​​for each area It is the first The impact of inspection efficiency on each region, and the selection of the cost function. The vertex with the smallest value Add it to the current path In, that is and update the vertices. Number of visits When the target point Added to path The replacement process ends when the maximum number of iterations is reached, and the resulting path is... This is the optimized inspection path. Based on the analysis results of the data acquisition and analysis submodule regarding flight attitude adjustment, combined with the UAV's own performance parameters and current weather conditions, the calculation model is used to derive the key parameter adjustment amounts of flight attitude and rotor speed required to maintain stable flight. In addition, by acquiring the UAV's real-time status information and weather conditions, the inspection energy consumption is analyzed and calculated based on the preset energy consumption calculation model, and energy management optimization strategies for optimizing the inspection path are formulated according to different situations using corresponding strategies and algorithms. The instruction execution submodule is responsible for receiving the inspection path optimization plan, flight attitude adjustment instructions, and energy management optimization strategy decision results determined by the comprehensive decision-making submodule, and establishing a stable and efficient communication link with the UAV's communication module and related execution components to ensure accurate and timely transmission of instructions.

2. The automatic inspection drone for power plant photovoltaic areas according to claim 1, characterized in that, The data acquisition and analysis submodule, combining a fault risk assessment model based on the photovoltaic field layout and photovoltaic equipment characteristics with the UAV's own flight performance parameters, analyzes the impact of differences in photovoltaic equipment fault risk in different areas under different weather conditions on inspection path planning, using formulas... A quantitative assessment of the failure risk of photovoltaic equipment in various regions was conducted, including... Indicates the first Failure risk value of photovoltaic equipment in each region , These are the weighting coefficients. This indicates the number of meteorological factors that affect the risk of photovoltaic equipment failure. For the first The weight of various meteorological factors Indicates the first The first region Actual measured values ​​of various meteorological factors For the first Structural strength parameters of photovoltaic equipment in each region This indicates the number and types of inherent characteristics of photovoltaic (PV) equipment that affect the risk of PV equipment failure. For the first The weighting of inherent characteristics of various photovoltaic devices. Indicates the first The first region The actual values ​​of the inherent characteristics of the photovoltaic equipment. For the first Performance parameters of photovoltaic equipment in each region.

3. The automatic inspection drone for power plant photovoltaic areas according to claim 1, characterized in that, The data acquisition and analysis submodule, based on meteorological data and its own performance parameters, uses complex mechanical analysis and flight attitude simulation algorithms to preliminarily calculate the direction and degree of flight attitude adjustment required to maintain stable flight under current meteorological conditions. The calculation formula is as follows: ,in, This indicates the flight attitude adjustment angle required for the drone to maintain stable flight under current weather conditions. and These are weighting coefficients used to balance the influence of different factors on the estimation of flight attitude adjustment. This indicates the number of different meteorological factors that affect flight attitude adjustment. For the first The weight of various meteorological factors Indicates the first Within the region, the first Actual measured values ​​of various meteorological factors For the first Spatial characteristic parameters of each region This indicates the number of types of inherent characteristics of the UAV that affect its flight attitude adjustment. For the first The weighting of the inherent characteristics of the drone. Indicates the first Within the region, the first The actual values ​​of the inherent characteristics of the drone. For the first Flight condition parameters for each region.

4. The automatic inspection drone for power plant photovoltaic field as described in claim 1, characterized in that, The integrated decision-making submodule uses a priority determination algorithm to determine the inspection priority of each area. The calculation formula is as follows: ,in, Indicates the first The inspection priority value for each area indicates that, given the current weather conditions and overall inspection needs, that area should be prioritized for inspection. , and These are weighting coefficients used to balance the influence of different factors on the determination of inspection area priorities. Indicates the first The failure risk value of photovoltaic equipment in a region comprehensively reflects the likelihood of failure of photovoltaic equipment in that region under current meteorological conditions, based on both meteorological factors and the inherent characteristics of the photovoltaic equipment itself. It is the first The impact value on inspection efficiency for each region is obtained through a comprehensive assessment of the impact of various meteorological factors on inspection efficiency within that region. It represents the degree to which meteorological conditions in that region affect the overall inspection efficiency. It is the first The necessity value for adjusting the inspection route in each area reflects the degree of necessity for adjusting the inspection route in that area under the current meteorological conditions and the status of the photovoltaic equipment.

5. The automatic inspection drone for power plant photovoltaic field according to claim 2, characterized in that, In the comprehensive decision-making submodule It is the first The formula for calculating the impact of inspection efficiency on a given area is as follows: ,in, These are the weighting coefficients. This indicates the total number of areas divided into photovoltaic power plant zones. Indicates the first Within the region, the first The actual measured values ​​of various meteorological factors are obtained in real time by an array of meteorological sensors. Indicates the first The baseline values ​​for various meteorological factors, It is the first The formula for calculating the necessity of adjusting the inspection route for each area is as follows: ,in, The higher the value, the greater the necessity to adjust the inspection path for this area under the current weather conditions and photovoltaic equipment status. and These are weighting coefficients used to balance the impact of meteorological factors and the inherent characteristics of photovoltaic equipment on the assessment of the necessity of adjusting the inspection route. For the first The weight of various meteorological factors.

6. The automatic inspection drone for power plant photovoltaic field according to claim 1, characterized in that, The integrated decision-making submodule, based on the analysis results of the data acquisition and analysis submodule regarding flight attitude adjustment, and combined with the UAV's own performance parameters and current weather conditions, uses a calculation model to derive the key parameter adjustments for flight attitude and rotor speed required to maintain stable flight. The calculation formula for the flight attitude parameter adjustments is as follows: The formula for calculating the rotor speed parameter adjustment is: , This represents the amount of change in the drone's flight attitude adjustment angle, calculated precisely. This represents the precisely calculated rotor speed adjustment. and These are the weighting coefficients. This indicates the number of different meteorological factors that affect flight attitude adjustment. For the first The weight of various meteorological factors Indicates the first Within the region, the first Actual measured values ​​of various meteorological factors Indicates the first The baseline values ​​for various meteorological factors, This indicates the number of different meteorological factors that affect rotor speed adjustment. For the first The weight of various meteorological factors Indicates the first Within the region, the first Actual measured values ​​of various meteorological factors Indicates the first The baseline values ​​for various meteorological factors, For the first Spatial characteristic parameters of each region For the first Structural strength parameters of photovoltaic equipment in each region.

7. The automatic inspection drone for power plant photovoltaic field as described in claim 5, characterized in that, The integrated decision-making submodule analyzes and calculates the inspection energy consumption based on a preset energy consumption calculation model, and formulates energy management optimization strategies for optimizing inspection paths according to different situations using corresponding strategies and algorithms, and obtains the inspection priority value for each area. And the distance from the starting point to the [missing information] calculated based on the geographical layout of the photovoltaic field and the flight characteristics of the drone. Distance between regions At the same time, obtain the pre-set weighting coefficients. The optimized inspection path energy consumption value is calculated using the strategy formula. Substituting the obtained parameters into the formula, and taking into account the inspection priority of each area and the distance from the starting point to each area, the energy consumption value of the optimized inspection path is calculated. This is to optimize the inspection route.

Images