A big data-based photovoltaic inspection method and system

Abstract

The present application relates to a kind of photovoltaic inspection method and system based on big data, it is related to the field of photovoltaic management, it includes: step 100: collection meteorological data;Step 101: meteorological factor is retrieved from the meteorological data;Step 102: in response to the meteorological factor retrieval rated interval, and meteorological intensity is retrieved from meteorological data based on the meteorological factor;Step 103: determine failure probability in conjunction with the meteorological intensity and rated interval;Step 104: when the failure probability is greater than the preset inspection threshold, determine inspection area based on the failure probability;Step 105: according to the inspection area generates and sends photovoltaic inspection instruction.The present application has the effect of improving the stability of photovoltaic operation, can timely patrol the photovoltaic equipment working under the changing weather conditions.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic management, and in particular to a photovoltaic inspection method and system based on big data. Background Technology

[0002] Photovoltaic inspection is a regular or irregular inspection, maintenance, troubleshooting and data monitoring of all equipment and supporting facilities in a photovoltaic power station, including photovoltaic modules, inverters, brackets, cables, combiner boxes and so on.

[0003] In existing technologies, photovoltaic power plants are relatively large in scale, and it is generally necessary to regularly inspect the operation of each photovoltaic device in order to troubleshoot faults by combining manual labor and intelligent equipment such as drones. When using intelligent equipment such as drones to assist in inspections, staff usually need to pre-set the inspection route and time.

[0004] Photovoltaic equipment is susceptible to malfunctions due to weather conditions. Inspecting according to pre-set routes and times is difficult to match the working status of photovoltaic equipment under changing weather conditions, which makes it difficult for intelligent devices such as drones to detect malfunctions in a timely manner. Summary of the Invention

[0005] To improve the stability of photovoltaic operation and enable timely inspection of photovoltaic equipment operating under varying weather conditions, this invention provides a photovoltaic inspection method and system based on big data.

[0006] Firstly, the present invention provides a photovoltaic inspection method based on big data, employing the following technical solution: A photovoltaic inspection method based on big data includes: Step 100: Collect meteorological data; Step 101: Retrieve meteorological factors from the meteorological data; Step 102: In response to the meteorological factor, retrieve the rated interval and retrieve the meteorological intensity from the meteorological data based on the meteorological factor; Step 103: Determine the probability of failure by combining the meteorological intensity and the rated range; Step 104: When the fault probability is greater than the preset inspection threshold, determine the inspection area based on the fault probability; Step 105: Generate and send photovoltaic inspection instructions based on the inspection area.

[0007] By adopting the above technical solution, using meteorological data as the core data source, key meteorological factors are accurately retrieved, and the failure probability of the photovoltaic system is quantified by combining the rated range and meteorological intensity. In this way, when the failure probability is too high, intelligent equipment such as drones are dispatched to conduct fixed-point inspections, effectively reducing the amount of ineffective inspections and ensuring the stability of photovoltaic operation.

[0008] Optionally, the method for determining the failure probability includes: Step 106: Determine the deviation ratio by combining the meteorological intensity and the rated range; Step 107: Determine the fault coefficient based on the meteorological factors and the deviation ratio, and determine the weighting coefficient based on the meteorological factors; Step 108: Determine the degree of failure by combining the fault coefficient and the weighting coefficient; Step 109: Determine the failure probability based on the severity of the failure.

[0009] By adopting the above technical solution, the calculation logic of fault probability is optimized. First, the deviation ratio between meteorological intensity and rated interval is calculated. Then, the fault coefficient and weight coefficient are determined by combining meteorological factors. In this way, the impact difference of different meteorological factors is fully considered through weighted calculation, thereby improving the calculation accuracy of fault probability and avoiding inspection redundancy or missed inspection due to misjudgment of a single meteorological parameter. This provides accurate data support for subsequent inspection decisions.

[0010] Optionally, the method for determining the failure probability further includes: Step 110: Determine the direction of influence based on the meteorological factors; Step 111: Determine the point of influence concentration in response to the direction of influence, and determine the attenuation coefficient based on the meteorological factors; Step 112: Determine the intensity distribution map by combining the aforementioned impact concentration points, meteorological intensity, and attenuation coefficient; Step 113: Read the attenuation intensity from the intensity distribution map; Step 114: Update the deviation ratio in response to the attenuation intensity.

[0011] By adopting the above technical solution, strong winds, sandstorms and other meteorological conditions are easily obstructed when they come into contact with photovoltaic equipment, which leads to a greater impact on the photovoltaic equipment located at the meteorological contact surface. By selecting an appropriate attenuation coefficient according to the obstruction of photovoltaic equipment to different meteorological conditions, a meteorological intensity distribution map is generated and the attenuation intensity is extracted. The failure probability is then corrected according to the attenuation intensity, thereby correcting the defect of ignoring the differences in meteorological distribution in traditional calculations and reducing inspection deviations.

[0012] Optionally, it also includes an inspection planning method, which includes: Step 200: When the fault probability is greater than the preset inspection threshold, determine the photovoltaic location based on the inspection area; Step 201: Determine the inspection route based on the location of the photovoltaic system, and determine the fault factor based on the fault probability. Step 202: Determine the fault characteristics in response to the fault factors; Step 203: Determine the location of the fault based on the fault characteristics; Step 204: Determine the deviation distance based on the described characteristic orientation; Step 205: Determine the characteristic route by combining the deviation distance and the inspection route; Step 206: Update the photovoltaic inspection command in response to the characteristic route.

[0013] By adopting the above technical solution, the location of photovoltaics in the inspection area can be accurately located and a basic inspection route can be planned. According to the possible types of failures of photovoltaic equipment, the characteristic location of the fault characteristics on the photovoltaic equipment can be located. In this way, the inspection route can be optimized by combining the characteristic location to generate a characteristic route that fits the fault characteristics, thereby improving the inspection efficiency and ensuring that the inspection process is more targeted.

[0014] Optionally, the inspection planning method further includes: Step 207: When the fault probability is greater than the preset inspection threshold, retrieve the fault intensity according to the fault factor, and determine the flight threshold according to the fault factor; Step 208: If the fault intensity is less than the flight threshold, determine the inspection duration based on the characteristic route, and determine the predicted intensity from the meteorological data based on the fault factor; Step 209: When the predicted intensity is not less than the flight threshold, determine the prediction duration in response to the predicted intensity; Step 210: Determine the flight duration based on the predicted duration; Step 211: If the inspection duration is not greater than the flight duration, send the photovoltaic inspection command.

[0015] By adopting the above technical solutions, when photovoltaic equipment may malfunction, the current weather conditions can be checked to see if intelligent equipment such as drones are permitted to be used. This allows for a full consideration of the impact of weather conditions on inspection operations, and proactive avoidance of inspection safety risks and equipment damage risks caused by excessive weather intensity, thus ensuring the safety and feasibility of inspection operations.

[0016] Optionally, the inspection planning method further includes: Step 212: If the inspection duration is not greater than the flight duration, determine the rate of change based on the fault intensity; Step 213: Determine the buffer amplitude by combining the rate of change and the fault factor; Step 214: Determine the buffer threshold based on the buffer amplitude and flight threshold; Step 215: When the fault intensity is greater than the buffer threshold, determine the flight position based on the characteristic route; Step 216: Generate a return route based on the flight position; Step 217: In response to the generation of the return route, send the inspection return command.

[0017] By adopting the above technical solution, the changes in meteorological conditions are analyzed to obtain the average rate of change of meteorological conditions. Then, an appropriate buffer threshold is selected according to the rate of change. When the meteorological conditions exceed the buffer threshold during the inspection process, the flight position is quickly located, the return route is planned, and the return command is sent. This avoids damage to intelligent equipment such as drones due to deteriorating meteorological conditions and improves the emergency response capability of photovoltaic inspection.

[0018] Optionally, it also includes an inspection return method, wherein the inspection return method includes: Step 300: When the fault intensity is greater than the buffer threshold, determine the return direction based on the return route and retrieve the actual wind direction from the meteorological data; Step 301: Determine the angle coefficient by combining the return direction and the actual wind direction, and retrieve the actual wind force from the meteorological data; Step 302: Determine the drag coefficient based on the angle coefficient and the actual wind force, and determine the return distance based on the return route; Step 303: Determine the return time by combining the aforementioned obstacle coefficient and return distance; Step 304: In response to the return time, generate and display inspection return information.

[0019] By adopting the above technical solution, when intelligent devices such as drones return to base, the current actual wind force and direction are extracted, thereby calculating the wind resistance coefficient for drone flight and predicting the return time required for the drone. This provides staff with a clear return reference, improving return efficiency and safety.

[0020] Optionally, the inspection return method further includes: Step 305: When the obstruction coefficient is greater than the preset delay threshold, determine the photovoltaic angle based on the flight position; Step 306: Generate a photovoltaic distribution by combining the photovoltaic location and photovoltaic angle; Step 307: Generate wind direction distribution and wind force coefficient in response to the actual wind direction and photovoltaic distribution; Step 308: Determine the coefficient distribution by combining the photovoltaic distribution and wind coefficient; Step 309: Determine the downwind route based on the coefficient distribution; Step 310: Update the return route in response to the tailwind route.

[0021] By adopting the above technical solution, when wind conditions significantly hinder the drone's return flight, the photovoltaic angle of the photovoltaic panel at the drone's location can be retrieved. This allows for the prediction of the actual wind obstruction at various locations due to the photovoltaic panel's position. The route with the least obstruction can then be selected as the tailwind route. This utilizes photovoltaic equipment to shorten the return flight time, reduce energy consumption, and further improve the efficiency and safety of the return flight.

[0022] Optionally, the inspection return method further includes: Step 311: When the obstruction coefficient is greater than the preset delay threshold, compare the tailwind route and the return route to determine the overlapping position; Step 312: Determine the reference distance by combining the overlapping position and the return route; Step 313: Determine the reference duration based on the reference distance and the obstruction coefficient, and determine the tailwind distance by combining the deviation distance and the tailwind route; Step 314: Determine the tailwind duration based on the tailwind distance and coefficient distribution; Step 315: Update the return route in response to the base duration and tailwind duration.

[0023] By adopting the above technical solution, the tailwind route is generally more tortuous, resulting in a longer tailwind route than the original return route. By comparing the tailwind route and the original return route and extracting the intersection of the routes as the overlapping position, the reference time and tailwind time required for the UAV to fly between adjacent overlapping positions according to the tailwind route and the original return route are calculated. The shorter of the two is then selected as the actual return route, which maximizes the return efficiency, reduces inspection costs, and improves the intelligent management and control of the entire photovoltaic inspection process.

[0024] Secondly, this application provides a photovoltaic inspection system based on big data, which adopts the following technical solution: A photovoltaic inspection system based on big data includes: The data acquisition module is used to collect meteorological data. A memory is used to store the program for any of the above-mentioned big data-based photovoltaic inspection methods; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0025] By adopting the above technical solution, using meteorological data as the core data source, key meteorological factors are accurately retrieved, and the failure probability of the photovoltaic system is quantified by combining the rated range and meteorological intensity. In this way, when the failure probability is too high, intelligent equipment such as drones are dispatched to conduct fixed-point inspections, effectively reducing the amount of ineffective inspections and ensuring the stability of photovoltaic operation.

[0026] In summary, this application includes at least one of the following beneficial technical effects: Using meteorological data as the core data source, key meteorological factors are accurately retrieved, and the failure probability of the photovoltaic system is quantified by combining the rated range and meteorological intensity. In this way, when the failure probability is too high, intelligent equipment such as drones are dispatched to conduct fixed-point inspections, effectively reducing the amount of ineffective inspections and ensuring the stability of photovoltaic operation. The calculation logic of fault probability is optimized. First, the deviation ratio is calculated by comparing the meteorological intensity with the rated interval. Then, the fault coefficient and weight coefficient are determined by combining meteorological factors. By weighted calculation, the influence differences of different meteorological factors are fully considered, thereby improving the calculation accuracy of fault probability and avoiding inspection redundancy or missed inspections caused by misjudgment of a single meteorological parameter. This provides accurate data support for subsequent inspection decisions. Strong winds, sandstorms, and other weather conditions can easily obstruct photovoltaic equipment when they come into contact with it, resulting in a significant impact on the equipment located at the weather contact surface. By selecting an appropriate attenuation coefficient according to the different weather conditions affecting the photovoltaic equipment, a weather intensity distribution map can be generated and the attenuation intensity can be extracted. The failure probability can then be corrected based on the attenuation intensity, thereby correcting the shortcomings of traditional calculations that ignore the differences in weather distribution and reducing inspection deviations. Attached Figure Description

[0027] Figure 1 This is a flowchart of a photovoltaic inspection method based on big data; Figure 2 This is a flowchart of the inspection planning method; Figure 3 This is a flowchart of the inspection and return process. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] This application discloses a photovoltaic inspection method based on big data. (Refer to...) Figure 1 A photovoltaic inspection method based on big data includes: Step 100: Collect meteorological data.

[0030] Meteorological data refers to a collection of various meteorological data that affect the operating status of photovoltaic equipment and may cause equipment failure. These include parameters such as light intensity, wind speed, wind direction, rainfall, ambient temperature, ambient humidity, hail intensity, and dust concentration. Meteorological data can be collected synchronously through meteorological monitoring stations deployed in photovoltaic power stations, meteorological sensors carried by drones, and third-party meteorological big data platforms. The method of collecting meteorological data is selected by the staff according to the scale of the power station, and will not be elaborated here.

[0031] Step 101: Retrieve meteorological factors from the meteorological data.

[0032] Meteorological factors refer to key meteorological types that have a significant impact on the operation of photovoltaic equipment and are directly related to the occurrence of failures, such as scorching sun, strong winds, rainfall, high temperatures, hail, and sandstorms. The methods for retrieving meteorological factors are common knowledge in this field and will not be elaborated here.

[0033] Step 102: In response to the meteorological factor, retrieve the rated interval and retrieve the meteorological intensity from the meteorological data based on the meteorological factor.

[0034] The rated range refers to the reasonable range of values ​​for each meteorological factor when the photovoltaic equipment is operating normally and stably. It is preset by the photovoltaic equipment manufacturer based on the performance of the device (the rated range varies slightly for different models of modules). The rated range corresponding to the meteorological factor can be found in the rated correspondence table, which is a data table that records different meteorological factors and their corresponding rated ranges.

[0035] Meteorological intensity refers to the actual value of various meteorological factors, such as light intensity, wind speed, rainfall, ambient temperature, ambient humidity, hail intensity, and dust concentration. The method for obtaining meteorological intensity is selected by the staff according to the scale of the power station, and will not be elaborated here.

[0036] Step 103: Determine the probability of failure by combining the meteorological intensity and the rated range.

[0037] Failure probability refers to the likelihood that photovoltaic equipment will fail (such as module damage, sudden drop in power generation efficiency, short circuit, etc.) under the current weather conditions. The calculation method for failure probability is described below and will not be repeated here.

[0038] Step 104: When the fault probability is greater than the preset inspection threshold, determine the inspection area based on the fault probability.

[0039] The inspection threshold refers to the critical failure probability value at which photovoltaic (PV) equipment is deemed to have a high risk of failure. It is preset by staff based on the inspection costs and potential losses from failures at the PV power plant, and will not be elaborated upon here. A failure probability greater than the inspection threshold indicates that PV equipment requires inspection. The inspection area refers to the specific area within the PV power plant that needs to be inspected; that is, the area enclosed by PV equipment whose failure probability equals the inspection threshold. The method for determining the inspection area is selected by staff based on the actual situation, and will not be elaborated upon here.

[0040] Step 105: Generate and send photovoltaic inspection instructions based on the inspection area.

[0041] Photovoltaic inspection instructions refer to control signals / instruction information that control drones (such as drones or inspection robots) or notify inspection personnel to go to designated inspection areas to carry out inspection operations. Photovoltaic inspection instructions are generated by the big data inspection platform and sent to the drone terminal or the inspection personnel's mobile APP via industrial Ethernet. The generation and transmission methods of photovoltaic inspection instructions are common knowledge in this field and will not be elaborated here.

[0042] Using meteorological data as the core data source, key meteorological factors are accurately retrieved, and the failure probability of the photovoltaic system is quantified by combining the rated range and meteorological intensity. When the failure probability is too high, intelligent equipment such as drones are dispatched for fixed-point inspections, which effectively reduces the amount of ineffective inspections and ensures the stability of photovoltaic operation.

[0043] Methods for determining the probability of failure include: Step 106: Determine the deviation ratio by combining the meteorological intensity and the rated range.

[0044] Deviation ratio is a numerical value used to accurately reflect the degree of abnormality in meteorological intensity. The larger the deviation ratio, the more the meteorological intensity deviates from the normal range, and the higher the risk of component failure. It can be calculated using the formula: Deviation ratio = (Meteorological intensity - Midpoint of rated range) / Midpoint of rated range.

[0045] Step 107: Determine the fault coefficient based on the meteorological factors and the deviation ratio, and determine the weighting coefficient based on the meteorological factors.

[0046] The fault coefficient is a numerical value that reflects the degree of impact of the abnormality of meteorological factors on the failure of photovoltaic modules. The larger the deviation ratio, the larger the fault coefficient. The fault coefficient corresponding to the meteorological factor and the deviation ratio can be found in the fault correspondence table. The fault correspondence table is a data table that records different meteorological factors and deviation ratios and their corresponding fault coefficients.

[0047] The weighting coefficient refers to the numerical value that reflects the protection level of photovoltaic equipment against meteorological factors. The protective equipment used in photovoltaic equipment set up in different regions is different. For example, photovoltaic power stations located in desert areas have weak protection against rainwater. The better the protection of photovoltaic equipment against meteorological factors, the lower the weighting coefficient. The weighting coefficient corresponding to the meteorological factor can be found in the weighting correspondence table, which is a data table that records different meteorological factors and their corresponding weighting coefficients.

[0048] Step 108: Determine the degree of fault by combining the fault coefficient and the weighting coefficient.

[0049] The degree of failure refers to the quantification of the severity of a photovoltaic device's potential failure under current weather conditions. It can be calculated by summing the product of the failure coefficients of each weather factor and their corresponding weighting coefficients.

[0050] Step 109: Determine the failure probability based on the severity of the failure.

[0051] The greater the degree of failure, the greater the probability of failure. The probability of failure corresponding to the degree of failure can be found in the probability correspondence table, which is a data table that records different degrees of failure and their corresponding probabilities.

[0052] The calculation logic for fault probability is optimized by first calculating the deviation ratio between meteorological intensity and rated interval, and then determining the fault coefficient and weight coefficient by combining meteorological factors. This weighted calculation fully considers the differences in the impact of different meteorological factors, thereby improving the accuracy of fault probability calculation and avoiding inspection redundancy or missed inspections caused by misjudgment of a single meteorological parameter. This provides accurate data support for subsequent inspection decisions.

[0053] Methods for determining failure probability also include: Step 110: Determine the direction of influence based on the meteorological factors.

[0054] The direction of influence refers to the distribution of the degree of influence of meteorological factors on photovoltaic equipment. For example, uniform exposure to sunlight affects photovoltaic equipment, while strong winds affect it in a specific direction. The direction of influence of meteorological factors can be found in the direction correspondence table, which is a data table that records different meteorological factors and their corresponding directions of influence.

[0055] Step 111: Determine the point of influence concentration in response to the direction of influence, and determine the attenuation coefficient based on the meteorological factors.

[0056] The point of concentrated impact refers to the geographical coordinate point where meteorological factors have the most concentrated impact on the photovoltaic power station. For example, if the direction of impact is from east to west, then the point of concentrated impact is the east side of the photovoltaic power station. The method for determining the point of concentrated impact is selected by the staff based on the actual situation, and will not be elaborated here.

[0057] The attenuation coefficient refers to the quantitative value of how the intensity of a meteorological factor decreases with increasing distance after it is blocked by a photovoltaic device. For example, when a strong wind comes into contact with a photovoltaic device, the wind speed is easily reduced due to the obstruction of the photovoltaic device. The attenuation coefficient corresponding to the meteorological factor can be found in the attenuation correspondence table, which is a data table that records different meteorological factors and their corresponding attenuation coefficients.

[0058] Step 112: Determine the intensity distribution map by combining the influence concentration point, meteorological intensity, and attenuation coefficient.

[0059] An intensity distribution map is a spatial distribution map of the actual meteorological intensity after meteorological factors are blocked by photovoltaic equipment. It is a map that gradually decreases from the point of concentrated influence according to the attenuation coefficient. The method for determining the intensity distribution map is common knowledge in the field and will not be elaborated here.

[0060] Step 113: Read the attenuation intensity from the intensity distribution map.

[0061] Attenuation intensity refers to the actual meteorological intensity experienced by the photovoltaic equipment, extracted from the intensity distribution map. The method for extracting attenuation intensity is selected by the staff based on the actual situation and will not be elaborated here.

[0062] Step 114: Update the deviation ratio in response to the attenuation intensity.

[0063] Strong winds, sandstorms, and other weather conditions can easily obstruct photovoltaic equipment when they come into contact with it, resulting in a significant impact on the equipment located at the weather contact surface. By selecting an appropriate attenuation coefficient according to the different weather conditions affecting the photovoltaic equipment, a weather intensity distribution map can be generated and the attenuation intensity can be extracted. The failure probability can then be corrected based on the attenuation intensity, thereby correcting the shortcomings of traditional calculations that ignore the differences in weather distribution and reducing inspection deviations.

[0064] Reference Figure 2 Inspection planning methods include: Step 200: When the fault probability is greater than the preset inspection threshold, determine the photovoltaic location based on the inspection area.

[0065] Photovoltaic location refers to the specific geographical coordinates of all photovoltaic devices within the inspection area. The photovoltaic location can be found in the photovoltaic record table, which is a data table that records the locations of all photovoltaic devices.

[0066] Step 201: Determine the inspection route based on the location of the photovoltaic system, and determine the fault factor based on the fault probability.

[0067] The inspection route refers to the path taken by the drone from its starting position, passing through all photovoltaic locations in sequence to complete the inspection, and then returning to its starting position. The method for determining the inspection route is selected by the staff based on the actual situation, and will not be elaborated here. Fault factors refer to the core meteorological factors that cause the probability of faults in the current inspection area to exceed the standard. The three meteorological factors with the largest product of the fault coefficient and the corresponding weight coefficient can be selected as fault factors. The selection of fault factors is determined by the staff based on the actual situation, and will not be elaborated here.

[0068] Step 202: Determine the fault characteristics in response to the fault factors.

[0069] Fault characteristics refer to the specific fault manifestations that fault factors may cause in photovoltaic modules. They are used to identify the key points of inspection during the inspection process and avoid blind inspections. The fault characteristics corresponding to the fault factors can be found from the characteristic correspondence table, which is a data table that records different fault factors and their corresponding fault characteristics.

[0070] Step 203: Determine the location of the feature based on the fault characteristics.

[0071] The characteristic location refers to the specific location on the photovoltaic equipment where the corresponding fault characteristics are most likely to occur. The characteristic location corresponding to the fault characteristics can be found from the location correspondence table, which is a data table that records different fault characteristics and their corresponding characteristic locations.

[0072] Step 204: Determine the deviation distance based on the described characteristic orientation.

[0073] Deviation distance refers to the minimum distance that the characteristic orientation deviates from the inspection route, which is the length of the perpendicular segment of the inspection route drawn from the characteristic orientation. The method for determining the deviation distance is selected by the staff according to the actual situation, and will not be elaborated here.

[0074] Step 205: Determine the characteristic route by combining the deviation distance and the inspection route.

[0075] The characteristic route is the inspection route optimized and adjusted based on the original inspection route and the deviation distance. This ensures that the inspection points can accurately cover the characteristic locations of all components, improving the targeting of the inspection. The method for determining the characteristic route is as follows: if the deviation distance is ≤0.5m, the original inspection route is used; if the deviation distance is >0.5m, the coordinates of the inspection points are adjusted so that the deviation distance is ≤0.5m, and then the route is replanned. The method for planning the characteristic route is selected by the staff according to the actual situation, and will not be elaborated here.

[0076] Step 206: Update the photovoltaic inspection command in response to the characteristic route.

[0077] Accurately locate the photovoltaic (PV) locations within the inspection area and plan a basic inspection route. Based on the possible types of PV equipment failures, pinpoint the characteristic locations of the fault features on the PV equipment. Then, combine these characteristic locations to optimize the inspection route and generate a feature route that matches the fault features, thereby improving inspection efficiency and ensuring that the inspection process is more targeted.

[0078] Inspection planning methods also include: Step 207: When the fault probability is greater than the preset inspection threshold, retrieve the fault intensity according to the fault factor, and determine the flight threshold according to the fault factor.

[0079] Fault intensity refers to the meteorological intensity value corresponding to the fault factor. The method for retrieving fault intensity is selected by the staff according to the actual situation, and will not be elaborated here.

[0080] Flight threshold refers to the maximum weather intensity threshold at which a drone can fly safely. Flight thresholds can be found in the parameter correspondence table. The number of flight thresholds corresponds to the number of fault factors. The parameter correspondence table is a data table that records different flight thresholds.

[0081] Step 208: If the fault intensity is less than the flight threshold, determine the inspection duration based on the characteristic route, and determine the predicted intensity from the meteorological data based on the fault factor.

[0082] Fault intensity less than the flight threshold means the drone can fly under the current weather conditions. Inspection time refers to the minimum time required for the drone to fly along the characteristic route. The length of the characteristic route can be read as the route length, and then the inspection time corresponding to the route length can be retrieved from the time correspondence table. The time correspondence table is a data table that records different route lengths and their corresponding inspection times.

[0083] Predicted intensity refers to the expected meteorological intensity of different fault factors within a future period (consistent with the inspection duration). Predicted intensity can be retrieved from a third-party meteorological big data platform. The method for retrieving predicted intensity is selected by the staff based on the actual situation and will not be elaborated here.

[0084] Step 209: When the prediction intensity is not less than the flight threshold, determine the prediction duration in response to the prediction intensity.

[0085] A prediction intensity of not less than the flight threshold means that the drone will be unable to continue flying after a certain period of time. The prediction duration is the maximum duration during which the prediction intensity of each fault factor is not less than the flight threshold. The method for determining the prediction duration is selected by the staff based on the actual situation, and will not be elaborated here.

[0086] Step 210: Determine the flight duration based on the predicted duration.

[0087] Flight duration refers to the maximum duration for which a drone can fly stably, which is the minimum predicted duration. The method for determining flight duration is selected by the staff based on the actual situation, and will not be elaborated here.

[0088] Step 211: If the inspection duration is not greater than the flight duration, send the photovoltaic inspection command.

[0089] If the inspection time is no greater than the flight time, it means that the UAV inspection operation can be completed safely. At this time, the safety requirements are met, and the updated photovoltaic inspection command is sent to start the inspection operation.

[0090] When photovoltaic equipment may malfunction, the current weather conditions are checked to determine whether the use of intelligent equipment such as drones is permitted. This allows for a full consideration of the impact of weather conditions on inspection operations, proactively mitigating safety risks and equipment damage risks caused by excessive weather intensity, and ensuring the safety and feasibility of inspection operations.

[0091] Inspection planning methods also include: Step 212: If the inspection duration is not greater than the flight duration, determine the rate of change based on the fault intensity.

[0092] The rate of change refers to the amount of change in fault intensity per unit time. It can be calculated using the formula: Rate of change = (Current fault intensity - Fault intensity of the previous cycle) / Time interval, which will not be elaborated here.

[0093] Step 213: Determine the buffer magnitude by combining the rate of change and the fault factor.

[0094] The buffer magnitude refers to the flight threshold buffer amount set to cope with rapid changes in fault intensity. The greater the rate of change, the larger the buffer magnitude is used to cope with sudden changes in weather conditions. The buffer magnitude corresponding to the rate of change and fault factor can be found in the magnitude correspondence table. The magnitude correspondence table is a data table that records different rates of change and fault factors and their corresponding buffer magnitudes.

[0095] Step 214: Determine the buffer threshold based on the buffer amplitude and flight threshold.

[0096] The buffer threshold refers to the flight threshold after correction according to the buffer amplitude. The difference between the flight threshold and the buffer amplitude can be calculated as the buffer threshold. The calculation method of the buffer threshold is selected by the staff according to the actual situation, and will not be elaborated here.

[0097] Step 215: When the fault intensity is greater than the buffer threshold, determine the flight position based on the characteristic route.

[0098] A fault intensity greater than the buffer threshold indicates that weather conditions have deteriorated during the inspection process. Continuing to perform the inspection under these conditions could easily lead to damage to the drone. The flight position refers to the real-time location of the drone as it follows the characteristic route. The method for determining the flight position is selected by the staff based on the actual situation and will not be elaborated here.

[0099] Step 216: Generate a return route based on the flight location.

[0100] The return route refers to the shortest path for the drone to return from its flight position to a preset docking point (such as the roof of the photovoltaic power station inspection control room). The method for generating the return route is selected by the staff according to the actual situation, and will not be elaborated here.

[0101] Step 217: In response to the generation of the return route, send the inspection return command.

[0102] The inspection return command is a control signal that controls the drone to immediately stop the inspection operation and return to the docking point along the return route. The inspection return command is generated by the big data inspection platform and sent to the drone terminal via wireless communication. The generation and transmission methods of the inspection return command are common knowledge to those in the field and will not be elaborated here.

[0103] By analyzing changes in meteorological conditions to obtain the average rate of change, an appropriate buffer threshold can be selected based on the rate of change. Then, when meteorological conditions exceed the buffer threshold during the inspection process, the flight position can be quickly located, a return route can be planned, and a return command can be sent. This prevents damage to intelligent equipment such as drones due to deteriorating weather conditions and improves the emergency response capability of photovoltaic inspections.

[0104] Reference Figure 3 The inspection and return methods include: Step 300: When the fault intensity is greater than the buffer threshold, determine the return direction according to the return route and retrieve the actual wind direction from the meteorological data.

[0105] The return direction refers to the angle value of the drone's flight along the return route. The return direction can be determined by calculating the coordinates of the flight position and the docking point. The method for determining the return direction is selected by the staff according to the actual situation, and will not be elaborated here.

[0106] Actual wind direction refers to the actual direction of the meteorological wind at the current moment. It can be retrieved from meteorological data. The method for retrieving the actual wind direction is selected by the staff according to the actual situation, and will not be elaborated here.

[0107] Step 301: Determine the angle coefficient by combining the return direction and the actual wind direction, and retrieve the actual wind force from the meteorological data.

[0108] The angle coefficient is a quantitative value of the degree of obstruction caused by the actual wind direction to the return process. The smaller the angle between the return direction and the actual wind direction, the smaller the angle coefficient. The angle between the return direction and the actual wind direction can be calculated as the obstruction angle, and then the angle coefficient corresponding to the obstruction angle can be found in the angle coefficient table. The angle coefficient table is a data table that records different obstruction angles and their corresponding angle coefficients.

[0109] Actual wind force refers to the actual wind speed value at the current moment. Actual wind force can be retrieved from meteorological data. The method for retrieving actual wind force is selected by the staff according to the actual situation, and will not be elaborated here.

[0110] Step 302: Determine the obstruction coefficient based on the angle coefficient and the actual wind force, and determine the return distance based on the return route.

[0111] The drag coefficient is a quantitative value of the overall degree of obstruction caused by the actual wind force during the return process. The larger the angle coefficient, the greater the actual wind force, and the greater the drag coefficient. When the drag coefficient is 1, it means that the wind force has no effect on the drone. The drag coefficient corresponding to the angle coefficient and the actual wind force can be found in the drag coefficient table. The drag coefficient table is a data table that records different angle coefficients and actual wind forces and their corresponding drag coefficients.

[0112] The return distance refers to the total length of the return route. The method for determining the return distance is selected by the staff based on the actual situation, and will not be elaborated here.

[0113] Step 303: Determine the return time by combining the aforementioned obstacle coefficient and return distance.

[0114] Return time refers to the estimated time required for a drone to complete its return journey along the return route. The greater the obstacle coefficient and the greater the return distance, the longer the return time. The return time corresponding to the obstacle coefficient and return distance can be found in the time correspondence table. The time correspondence table is a data table that records different obstacle coefficients and return distances and their corresponding return times.

[0115] Step 304: In response to the return time, generate and display inspection return information.

[0116] Inspection return information refers to visualized information including parameters such as return route, return time, actual wind direction, actual wind force, and obstruction coefficient. It is used to display the return progress and conditions for inspection personnel to monitor in real time. The inspection return information is generated by the big data inspection platform and displayed on the screen of the inspection control terminal, and is also pushed to the inspection personnel's mobile APP at the same time. The generation and display methods of inspection return information will not be elaborated here.

[0117] When drones and other intelligent devices return to base, the current actual wind force and direction are extracted to calculate the wind resistance coefficient for drone flight. This allows for the prediction of the return time required for the drone, providing staff with a clear reference for return and improving return efficiency and safety.

[0118] Inspection return methods also include: Step 305: When the obstruction coefficient is greater than the preset delay threshold, determine the photovoltaic angle based on the flight position.

[0119] The delay threshold refers to the minimum resistance coefficient that significantly hinders the drone's return, potentially causing excessively long return times. This threshold is selected by staff based on actual conditions and will not be elaborated upon here. A resistance coefficient greater than the delay threshold indicates a need to reduce the impact of wind on the drone. The photovoltaic angle refers to the installation tilt angle of the photovoltaic panels surrounding the flight location. This angle can be retrieved from the photovoltaic power station component parameter database. The database is used to filter the installation angles of surrounding components based on the flight location. The photovoltaic power station component parameter database records the photovoltaic angles and locations of different photovoltaic panels.

[0120] Step 306: Generate photovoltaic distribution by combining the photovoltaic location and photovoltaic angle.

[0121] Photovoltaic distribution refers to the spatial distribution of photovoltaic devices around the drone. The photovoltaic distribution demonstrates the wind-blocking effect of the photovoltaic devices. The method for generating the photovoltaic distribution is selected by the staff based on the actual situation, and will not be elaborated here.

[0122] Step 307: Generate wind direction distribution and wind force coefficient in response to the actual wind direction and photovoltaic distribution.

[0123] Wind direction distribution refers to the flow trajectory of actual wind within the photovoltaic distribution area. It is used to analyze the shading and guiding effects of the photovoltaic array on the wind direction. The wind direction distribution is generated through fluid dynamics simulation, combining the actual wind direction and the layout of the photovoltaic components. The wind force coefficient refers to the resistance coefficient of actual wind force after passing through the photovoltaic array. The wind force coefficient is calculated based on the number of photovoltaic components and the angle of the channel formed by the photovoltaic components. It can be expressed by the formula: Wind force coefficient = Resistance coefficient * Channel coefficient * Attenuation coefficient ^ Number of photovoltaic components. The channel coefficient is the angle coefficient corresponding to the angle between the channel formed by the photovoltaic components and the actual wind direction. The channel coefficient can be found in the angle coefficient table. The generation methods of wind direction distribution and wind force coefficient are selected by the staff according to the actual situation and will not be elaborated here.

[0124] Step 308: Determine the coefficient distribution by combining the photovoltaic distribution and wind coefficient.

[0125] The coefficient distribution refers to the spatial distribution of the wind force coefficient. The method for generating the coefficient distribution is selected by the staff based on the actual situation, and will not be elaborated here.

[0126] Step 309: Determine the downwind route based on the coefficient distribution.

[0127] A tailwind route is the return route with the least wind resistance (lowest wind coefficient) selected from the coefficient distribution. The method for determining the tailwind route is chosen by the staff based on the actual situation, and will not be elaborated here.

[0128] Step 310: Update the return route in response to the tailwind route.

[0129] When wind conditions significantly hinder the drone's return flight, the angle of the photovoltaic panels at the drone's location is retrieved. This, combined with the location of the photovoltaic panels, allows for the prediction of the actual wind obstruction at various points. The route with the least obstruction is then selected as the tailwind route. This utilizes photovoltaic equipment to shorten the return flight time, reduce energy consumption, and further improve the efficiency and safety of the return flight.

[0130] Inspection return methods also include: Step 311: When the obstruction coefficient is greater than the preset delay threshold, the overlapping position is determined by comparing the tailwind route and the return route.

[0131] The overlapping position refers to the coordinate point where the downwind route and the return route overlap. The method for determining the overlapping position is selected by the staff based on the actual situation, and will not be elaborated here.

[0132] Step 312: Determine the reference distance by combining the overlapping position and the return route.

[0133] The baseline distance refers to the length of the return route between any two adjacent overlapping positions. The method for determining the baseline distance is selected by the staff based on the actual situation, and will not be elaborated here.

[0134] Step 313: Determine the reference duration based on the reference distance and the obstruction coefficient, and determine the tailwind distance by combining the deviation distance and the tailwind route.

[0135] The reference time refers to the time required for the drone to fly along the return route. The reference time corresponding to the reference distance and the obstacle coefficient can be found in the time correspondence table.

[0136] The tailwind distance refers to the length of the tailwind route between any two adjacent overlapping locations. The method for determining the tailwind distance is selected by the staff based on the actual situation, and will not be elaborated here.

[0137] Step 314: Determine the tailwind duration by combining the tailwind distance and coefficient distribution.

[0138] Tailwind duration refers to the time required for a drone to fly along a tailwind route. The base duration corresponding to the tailwind distance and tailwind coefficient can be found in the duration correspondence table. The average wind force coefficient at each point on the tailwind route can be read from the coefficient distribution as the tailwind coefficient.

[0139] Step 315: Update the return route in response to the base duration and tailwind duration.

[0140] The tailwind route is generally more winding, resulting in a longer tailwind route than the original return route. By comparing the tailwind route and the original return route and extracting the intersection of the routes as the overlapping position, the baseline time and tailwind time required for the drone to fly between adjacent overlapping positions along the tailwind route and the original return route are calculated. The shorter of the two is then selected as the actual return route, maximizing return efficiency, reducing inspection costs, and improving the intelligent management of the entire photovoltaic inspection process.

[0141] Based on the same inventive concept, embodiments of the present invention provide a photovoltaic inspection system based on big data, comprising: The data acquisition module is used to collect meteorological data. A memory is used to store the program for any of the above-mentioned big data-based photovoltaic inspection methods; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0143] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A photovoltaic inspection method based on big data, characterized in that, include: Step 100: Collect meteorological data; Step 101: Retrieve meteorological factors from the meteorological data; Step 102: In response to the meteorological factor, retrieve the rated interval and retrieve the meteorological intensity from the meteorological data based on the meteorological factor; Step 103: Determine the probability of failure by combining the meteorological intensity and the rated range; Step 104: When the fault probability is greater than the preset inspection threshold, determine the inspection area based on the fault probability; Step 105: Generate and send photovoltaic inspection instructions based on the inspection area.

2. The photovoltaic inspection method based on big data according to claim 1, characterized in that, The method for determining the failure probability includes: Step 106: Determine the deviation ratio by combining the meteorological intensity and the rated range; Step 107: Determine the fault coefficient based on the meteorological factors and the deviation ratio, and determine the weighting coefficient based on the meteorological factors; Step 108: Determine the degree of failure by combining the fault coefficient and the weighting coefficient; Step 109: Determine the failure probability based on the severity of the failure.

3. The photovoltaic inspection method based on big data according to claim 2, characterized in that, The method for determining the failure probability also includes: Step 110: Determine the direction of influence based on the meteorological factors; Step 111: Determine the point of influence concentration in response to the direction of influence, and determine the attenuation coefficient based on the meteorological factors; Step 112: Determine the intensity distribution map by combining the aforementioned impact concentration points, meteorological intensity, and attenuation coefficient; Step 113: Read the attenuation intensity from the intensity distribution map; Step 114: Update the deviation ratio in response to the attenuation intensity.

4. The photovoltaic inspection method based on big data according to claim 3, characterized in that, It also includes an inspection planning method, which includes: Step 200: When the fault probability is greater than the preset inspection threshold, determine the photovoltaic location based on the inspection area; Step 201: Determine the inspection route based on the location of the photovoltaic system, and determine the fault factor based on the fault probability. Step 202: Determine the fault characteristics in response to the fault factors; Step 203: Determine the location of the fault based on the fault characteristics; Step 204: Determine the deviation distance based on the described characteristic orientation; Step 205: Determine the characteristic route by combining the deviation distance and the inspection route; Step 206: Update the photovoltaic inspection command in response to the characteristic route.

5. The photovoltaic inspection method based on big data according to claim 4, characterized in that, The inspection planning method also includes: Step 207: When the fault probability is greater than the preset inspection threshold, retrieve the fault intensity according to the fault factor, and determine the flight threshold according to the fault factor; Step 208: If the fault intensity is less than the flight threshold, determine the inspection duration based on the characteristic route, and determine the predicted intensity from the meteorological data based on the fault factor; Step 209: When the predicted intensity is not less than the flight threshold, determine the prediction duration in response to the predicted intensity; Step 210: Determine the flight duration based on the predicted duration; Step 211: If the inspection duration is not greater than the flight duration, send the photovoltaic inspection command.

6. The photovoltaic inspection method based on big data according to claim 5, characterized in that, The inspection planning method also includes: Step 212: If the inspection duration is not greater than the flight duration, determine the rate of change based on the fault intensity; Step 213: Determine the buffer amplitude by combining the rate of change and the fault factor; Step 214: Determine the buffer threshold based on the buffer amplitude and flight threshold; Step 215: When the fault intensity is greater than the buffer threshold, determine the flight position based on the characteristic route; Step 216: Generate a return route based on the flight position; Step 217: In response to the generation of the return route, send the inspection return command.

7. The photovoltaic inspection method based on big data according to claim 6, characterized in that, It also includes a patrol return method, which includes: Step 300: When the fault intensity is greater than the buffer threshold, determine the return direction based on the return route and retrieve the actual wind direction from the meteorological data; Step 301: Determine the angle coefficient by combining the return direction and the actual wind direction, and retrieve the actual wind force from the meteorological data; Step 302: Determine the drag coefficient based on the angle coefficient and the actual wind force, and determine the return distance based on the return route; Step 303: Determine the return time by combining the aforementioned obstacle coefficient and return distance; Step 304: In response to the return time, generate and display inspection return information.

8. The photovoltaic inspection method based on big data according to claim 7, characterized in that, The inspection return method also includes: Step 305: When the obstruction coefficient is greater than the preset delay threshold, determine the photovoltaic angle based on the flight position; Step 306: Generate a photovoltaic distribution by combining the photovoltaic location and photovoltaic angle; Step 307: Generate wind direction distribution and wind force coefficient in response to the actual wind direction and photovoltaic distribution; Step 308: Determine the coefficient distribution by combining the photovoltaic distribution and wind coefficient; Step 309: Determine the downwind route based on the coefficient distribution; Step 310: Update the return route in response to the tailwind route.

9. A photovoltaic inspection method based on big data according to claim 8, characterized in that, The inspection return method also includes: Step 311: When the obstruction coefficient is greater than the preset delay threshold, compare the tailwind route and the return route to determine the overlapping position; Step 312: Determine the reference distance by combining the overlapping position and the return route; Step 313: Determine the reference duration based on the reference distance and the obstruction coefficient, and determine the tailwind distance by combining the deviation distance and the tailwind route; Step 314: Determine the tailwind duration based on the tailwind distance and coefficient distribution; Step 315: Update the return route in response to the base duration and tailwind duration.

10. A photovoltaic inspection system based on big data, characterized in that, include: The data acquisition module is used to collect meteorological data. A memory for storing a program for a photovoltaic inspection method based on big data as described in any one of claims 1 to 9; The processor is the unit of memory that allows programs to be loaded and executed by the processor.

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