A configuration generation method and device for a drone cleaning system and a drone cleaning system

Abstract

The present application discloses a configuration generation method and device of a drone cleaning system, and a drone cleaning system, which relates to the field of photovoltaic operation and maintenance technology, including: converting the injection parameters of the drone cleaning device into model simulation data according to the injection network model; obtaining fitting parameters, fitting the model simulation data and generating fitting result data, wherein the fitting parameters include the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device; generating the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device; generating the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraint conditions. The configuration parameters of the drone cleaning system can be obtained by using the corresponding simulation results when the interval distance range between the drone cleaning device and the photovoltaic module is greater than the current interval distance range between the drone cleaning device and the photovoltaic module.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic operation and maintenance technology, and in particular to a configuration generation method and device for an unmanned aerial vehicle cleaning system, and an unmanned aerial vehicle cleaning system. Background Art

[0002] The accumulation of pollutants on the surface of photovoltaic modules will reduce the transmittance of the modules and cause hot spot effects, which will have a significant negative impact on the power generation performance of photovoltaic power stations. Therefore, photovoltaic modules need to be cleaned regularly. Traditional manual cleaning methods are difficult to ensure cleaning efficiency, cleaning effects and personnel safety for scenes where the module brackets are high and fluctuating, such as mountain photovoltaics, pastoral photovoltaics, water power stations, and carport photovoltaics. In addition, photovoltaic cleaning robots can also be used to clean the surface of photovoltaic modules, but they consume a lot of electricity during use, have high initial investment and operating costs, and are difficult to move on unevenly installed photovoltaic arrays, making them difficult to promote. In addition, the pressure of the robot's own weight on the photovoltaic modules and the vibration during operation may damage the surface of the modules, resulting in reduced cleaning efficiency and shortened photovoltaic module life.

[0003] At present, drone cleaning is gradually developing and being applied in the fields of building curtain wall cleaning and photovoltaic module cleaning. However, due to the different basic cleaning conditions such as the location of each photovoltaic power station, water and electricity costs, module length, and the timeliness of cleaning completion, the selection of different drone cleaning devices has a great impact on the cleaning cost of the power station. It is very necessary to customize drone cleaning devices to match the application requirements of different power stations. In addition, the computational fluid dynamics simulation results of the fan-shaped nozzles used for cleaning are concentrated within a spray distance of 0.5 meters. In the process of drone cleaning photovoltaic modules, in order to avoid accidental collisions between the modules and the drone, the drone cleaning device should be separated from the photovoltaic modules by 1-1.5 meters. Therefore, the fluid dynamics simulation results corresponding to the spray conditions of the fan-shaped nozzles within the range of 1-1.5 meters are very important for the optimization of the configuration parameters of the drone cleaning system.

[0004] From the above, it can be seen that how to use the corresponding simulation results when the interval distance range between the UAV cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the UAV cleaning device and the photovoltaic component to be cleaned to determine the configuration parameters to configure the UAV cleaning system is a problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a configuration generation method and device for a drone cleaning system, and a drone cleaning system, which can determine configuration parameters by using the simulation results corresponding to the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned when the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned, so as to configure the drone cleaning system. The specific scheme is as follows:

[0006] In a first aspect, the present application provides a configuration generation method for a drone cleaning system, comprising:

[0007] The spray parameters of the UAV cleaning device are converted into model simulation data according to the spray grid model;

[0008] Acquire fitting parameters, fit the model simulation data and generate fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device;

[0009] generating an operating constraint condition of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint condition of the drone cleaning device; the time constraint condition of the drone cleaning device is the flight time of the drone;

[0010] The configuration parameters of the UAV cleaning system are generated according to the fitting result data and the operating constraints.

[0011] Optionally, converting the injection parameters of the drone cleaning device into model simulation data according to the injection grid model includes:

[0012] Using a structured grid to perform grid simulation on the jet flow domain of the UAV cleaning device to obtain a first jet grid model corresponding to the structured grid;

[0013] Inputting the injection parameter into the first injection grid model to obtain first injection parameter simulation data output by the first injection grid model;

[0014] Using an unstructured grid to perform grid simulation on the jet flow domain of the UAV cleaning device to obtain a second jet grid model corresponding to the unstructured grid;

[0015] Inputting the injection parameter into the second injection grid model to obtain second injection parameter simulation data output by the second injection grid model;

[0016] Calculating the similarity between the first injection parameter simulation data and preset reference simulation data to obtain a first similarity;

[0017] Calculating the similarity between the second injection parameter simulation data and the preset reference simulation data to obtain a second similarity;

[0018] A target similarity having a larger value between the first similarity and the second similarity is determined, and injection parameter simulation data corresponding to the target similarity is determined as model simulation data.

[0019] Optionally, the acquiring of fitting parameters, fitting the model simulation data and generating fitting result data includes:

[0020] Acquiring fitting parameters, and fitting the model simulation data based on the fitting parameters to obtain a fitting formula;

[0021] Determining fitting result data of the UAV cleaning device using the fitting formula and based on benchmark parameters; the benchmark parameters include cleaning information of the target power station and operating condition parameters of the UAV cleaning device;

[0022] Among them, the fitting formulas include the nozzle outlet flow velocity fitting formula, the nozzle flow fitting formula, the injection angle and the groove half angle fitting formula, the nozzle cleaning coverage distance fitting formula and the cross-sectional flow field maximum impact pressure fitting formula.

[0023] Optionally, generating the operating constraint condition of the drone cleaning device according to the operating condition parameter of the drone cleaning device and the time constraint condition of the drone cleaning device includes:

[0024] Determine the time constraint of the UAV cleaning device based on the cleaning information of the target power station; the cleaning information includes the nozzle cleaning distance value, the forward speed value of the UAV, the cleaning component area value, the spray box capacity value, the one-way distance value of the UAV charging and replacing, the average flight speed value of the UAV charging and replacing, the one-way distance value of the UAV replenishing the cleaning agent, the UAV endurance time value and the average flight speed value of the UAV replenishing the cleaning agent;

[0025] According to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device, the cleaning completion time constraint conditions, cleaning cost constraint conditions, cleaning coverage distance constraint conditions and recoil force constraint conditions of the drone cleaning device are generated.

[0026] Optionally, generating configuration parameters of the UAV cleaning system according to the fitting result data and the operating constraints includes:

[0027] Based on the fitting result data and the operating constraints, generate the purchasing parameters corresponding to the fan-shaped nozzle, the capacity value of the spray tank storing the cleaning liquid, the working pressure value and flow value of the water pump in the UAV cleaning device, and the airborne weight value of the UAV;

[0028] The optional parameters include nozzle inlet pressure value, nozzle aperture value and spray angle value.

[0029] In a second aspect, the present application provides a configuration generation device for a drone cleaning system, comprising:

[0030] A simulation data acquisition module, used to convert the injection parameters of the UAV cleaning device into model simulation data according to the injection grid model;

[0031] A fitting result generation module, used for obtaining fitting parameters, fitting the model simulation data and generating fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device;

[0032] A constraint condition generating module, used for generating an operating constraint condition of the drone cleaning device according to an operating condition parameter of the drone cleaning device and a time constraint condition of the drone cleaning device; the time constraint condition of the drone cleaning device is the flight time of the drone;

[0033] A configuration parameter generation module is used to generate configuration parameters of the UAV cleaning system according to the fitting result data and the operation constraint conditions.

[0034] Optionally, the fitting result generating module includes:

[0035] A fitting formula determination unit, used to obtain fitting parameters, and fit the model simulation data based on the fitting parameters to obtain a fitting formula;

[0036] A fitting result determination unit is used to determine the fitting result data of the UAV cleaning device using the fitting formula and based on benchmark parameters; the benchmark parameters include the cleaning information of the target power station and the operating condition parameters of the UAV cleaning device; wherein the fitting formula includes a nozzle outlet flow velocity fitting formula, a nozzle flow rate fitting formula, a jet angle and a groove half angle fitting formula, a nozzle cleaning coverage distance fitting formula and a cross-sectional flow field maximum impact pressure fitting formula.

[0037] Optionally, the constraint condition generating module includes:

[0038] A first constraint condition generating unit is used to determine the time constraint condition of the UAV cleaning device based on the cleaning information of the target power station; the cleaning information includes a nozzle cleaning distance value, a UAV forward speed value, a cleaning component area value, a spray box capacity value, a one-way distance value for UAV charging and battery replacement, an average flight speed value for UAV charging and battery replacement, a one-way distance value for UAV replenishment of cleaning agent, a UAV endurance time value, and an average flight speed value for UAV replenishment of cleaning agent;

[0039] The second constraint condition generating unit is used to generate the cleaning completion time constraint condition, cleaning cost constraint condition, cleaning coverage distance constraint condition and recoil force constraint condition of the UAV cleaning device according to the operating condition parameters of the UAV cleaning device and the time constraint condition of the UAV cleaning device.

[0040] In a third aspect, the present application provides a drone cleaning system, comprising:

[0041] UAV cleaning device;

[0042] Memory, used to store computer programs;

[0043] A processor is communicatively connected to the drone cleaning device and is used to execute the computer program to implement the configuration generation method of the drone cleaning system.

[0044] Optionally, the components of the drone cleaning device include a drone, and at least one of a power supply device, a motor, a water pump, a pressure gauge, a spray box, a fan-shaped nozzle, or a pressure regulating valve;

[0045] The water pump is used to pump the cleaning liquid into the spray box, the pressure gauge is used to measure and display the water pressure of the water pump, the spray box is connected with the fan-shaped nozzle through a preset pipeline, so as to spray the cleaning liquid in the spray box onto the surface of the object to be cleaned through the preset pipeline and the fan-shaped nozzle in sequence; the pressure regulating valve is used to adjust the water pressure in the water pump;

[0046] In addition, the processor automatically collects relevant information parameters of each of the components inside the processor through the drone cleaning device.

[0047] As can be seen from the above, before the configuration generation of the drone cleaning system is performed, the present application needs to convert the injection parameters of the drone cleaning device into model simulation data according to the injection grid model; obtain fitting parameters, fit the model simulation data and generate fitting result data, wherein the fitting parameters include the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device; generate the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device; the time constraint conditions of the drone cleaning device are the endurance time of the drone; generate the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraint conditions. It can be seen from this that the present application converts the injection parameters of the drone cleaning device into model simulation data according to the injection network model; then obtains the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device, fits the model simulation data and generates fitting result data; then generates the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device; finally, generates the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraint conditions. In this way, based on the corresponding simulation results when the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned, the configuration parameters corresponding to the drone cleaning device can be obtained, so as to configure the drone cleaning device using the configuration parameters, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0049] Figure 1 A flow chart of a configuration generation method for a drone cleaning system disclosed in this application;

[0050] Figure 2 A flowchart of obtaining configuration parameters of a specific UAV cleaning system disclosed in this application;

[0051] Figure 3 This is a schematic diagram of the structure of a configuration generation device for a drone cleaning system disclosed in this application;

[0052] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The accumulation of pollutants on the surface of photovoltaic modules will reduce the transmittance of the modules and cause hot spot effects, which will have a significant negative impact on the power generation performance of photovoltaic power stations. Therefore, it is necessary to clean photovoltaic modules regularly. At present, in actual application scenarios, photovoltaic cleaning robots are used to clean the surface of photovoltaic modules. In the process of drones cleaning photovoltaic modules, in order to avoid accidental collisions between the modules and the drones, the distance between the drones and the photovoltaic modules should be 1-1.5 meters. Therefore, the fluid mechanics simulation results corresponding to the spraying conditions of the fan-shaped nozzles within the range of 1-1.5 meters are very important for the optimization of the configuration parameters of the drone cleaning system. To this end, the present application provides a configuration generation method for a drone cleaning system, which can obtain the configuration parameters corresponding to the drone cleaning device based on the corresponding simulation results when the interval distance range between the drone cleaning device and the photovoltaic module to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic module to be cleaned.

[0055] See also Figure 1 As shown, an embodiment of the present invention discloses a configuration generation method for a drone cleaning system, comprising:

[0056] Step S11: converting the injection parameters of the UAV cleaning device into model simulation data according to the injection grid model.

[0057] In this embodiment, before converting the injection parameters of the UAV cleaning device into model simulation data according to the injection grid model, the embodiment of the present application uses ICEM (The Integrated Computer Engineering, i.e., a pre-processing software) and establishes a first injection grid model and a second injection grid model corresponding to the fan-shaped nozzle respectively based on a preset structured grid rule and a preset unstructured grid rule, wherein the first injection grid model and the second injection grid model are both three-dimensional grid models. That is, a structured grid is used to perform grid simulation on the injection flow domain of the UAV cleaning device to obtain a first injection grid model corresponding to the structured grid; then, the injection parameters are input into the first injection grid model to obtain the first injection parameter simulation data output by the first injection grid model. Correspondingly, an unstructured grid is used to perform grid simulation on the injection flow domain of the UAV cleaning device to obtain a second injection grid model corresponding to the unstructured grid; then, the injection parameters are input into the second injection grid model to obtain the second injection parameter simulation data output by the second injection grid model, wherein the injection parameters are the nozzle inlet pressure value, nozzle aperture value, and injection angle value corresponding to the fan-shaped nozzle.

[0058] It is worth mentioning that after obtaining the first injection parameter simulation data corresponding to the first injection grid model and the second injection parameter simulation data corresponding to the second injection grid model, it is necessary to calculate the similarity between the first injection parameter simulation data and the second injection parameter simulation data and the preset benchmark simulation data, respectively, so as to determine the model simulation data based on the similarity. Specifically, the similarity between the first injection parameter simulation data and the preset benchmark simulation data is calculated to obtain the first similarity; the similarity between the second injection parameter simulation data and the preset benchmark simulation data is calculated to obtain the second similarity; the target similarity with the larger value between the first similarity and the second similarity is determined, and the injection parameter simulation data corresponding to the target similarity is determined as the model simulation data. That is, if the first similarity is not less than the second similarity, the first injection parameter simulation data corresponding to the first similarity is set as the model simulation data, otherwise, the second injection parameter simulation data corresponding to the second similarity is set as the model simulation data.

[0059] In a specific implementation, after obtaining the first injection parameter simulation data and the second injection parameter simulation data, the first injection parameter simulation data and the second injection parameter simulation data need to be compared and verified with the preset benchmark simulation data for the injection distance within 0.5 meters described in the published paper, so as to select the model simulation data with high similarity from the first injection parameter simulation data and the second injection parameter simulation data. It is worth mentioning that the lower limit of the injection distance range of the fan-shaped cleaning liquid nozzle corresponding to the first injection parameter simulation data and the second injection parameter simulation data is greater than the upper limit of the injection distance range of the fan-shaped cleaning liquid nozzle corresponding to the preset benchmark simulation data.

[0060] It is worth mentioning that the above model simulation data are simulation data such as nozzle outlet flow rate, nozzle flow rate, injection angle, flow field impact pressure and nozzle cleaning coverage distance output by the fan nozzle under different nozzle inlet pressure values, nozzle aperture values ​​and injection angle values. Specifically, the various benchmark parameters of the UAV cleaning device are respectively input into the first injection grid model and the second injection grid model to obtain the first injection parameter simulation data and the second injection parameter simulation data output by the first injection grid model and the second injection grid model respectively, which can include: inputting the nozzle inlet pressure value, nozzle aperture value and injection angle value corresponding to the fan-shaped cleaning liquid nozzle on the UAV cleaning device into the first injection grid model and the second injection grid model respectively, so that the first injection grid model and the second injection grid model output the first injection parameter simulation data and the second injection parameter simulation data based on the nozzle inlet pressure value, nozzle aperture value and injection angle value respectively; wherein the first injection parameter simulation data and the second injection parameter simulation data both include the nozzle outlet flow rate, nozzle flow rate, injection angle, impact pressure of the nozzle injection flow field on the photovoltaic module and the nozzle cleaning coverage distance corresponding to the fan-shaped cleaning liquid nozzle.

[0061] In this embodiment, the components of the drone cleaning device include a drone, a power supply device, a motor, a water pump, a pressure gauge, a spray box, a fan-shaped nozzle or a pressure regulating valve. It is worth mentioning that the power supply device adopts two independent power supply modes for flight and cleaning; when the drone cleaning system is used to clean the components, the drone cleaning device uses the independent power supply on the drone for power supply, and the power supply equipped on the drone itself is only used to ensure the flight of the drone, thereby ensuring that the drone can operate safely during flight and cleaning operations.

[0062] Among them, the water pump is used to pump the cleaning liquid into the spray box, the pressure gauge is used to measure and display the water pressure of the water pump, and the spray box establishes a water connection with the fan-shaped nozzle through a preset pipe, so as to spray the cleaning liquid in the spray box onto the surface of the object to be cleaned through the preset pipe and the fan-shaped nozzle in turn; the pressure regulating valve is used to adjust the water pressure in the water pump; and the drone cleaning device automatically collects relevant information parameters of each component inside itself, and uploads the automatically collected relevant information parameters to the server.

[0063] It is worth mentioning that when the UAV cleaning device is flushing the photovoltaic module, the embodiment of the present application uses the cleaning fluid stored in the spray box to impact the pollutants on the surface of the module, and relies on the impact pressure to peel off the pollutants. Among them, the fan-shaped nozzle makes the spray flow field fan-shaped through the V-shaped groove, thereby forming a wider cleaning coverage distance than the conventional nozzle. Different fan-shaped nozzles and water pump selections will affect the flow field distribution, velocity distribution and impact pressure distribution of the jet during cleaning. That is, the greater the impact pressure, the more effectively it can remove dirt and sediment on the surface of the equipment, thereby improving the cleanliness and operating efficiency of the equipment, but it also means that higher equipment costs and loads, faster cleaning fluid consumption, and more round trips of the UAV are required.

[0064] In addition, after the first injection grid model and the second injection grid model are established, the grid quality of the first injection grid model and the second injection grid model needs to be judged. If the quality of the first injection grid model and the second injection grid model meets the preset quality condition, subsequent operations can be performed; if the quality of the first injection grid model and the second injection grid model does not meet the preset quality condition, the model parameters corresponding to the first injection grid model and the second injection grid model are optimized until the model quality corresponding to the first injection grid model and the second injection grid model meets the preset quality condition. That is, after the first injection grid model and the second injection grid model are obtained, it can also include: judging whether the model quality of the first injection grid model meets the preset quality condition; if the model quality of the first injection grid model does not meet the preset quality condition, adjusting the model parameters corresponding to the first injection grid model until the model quality of the first injection grid model meets the preset quality condition. Correspondingly, judging whether the model quality of the second injection grid model meets the preset quality condition; if the model quality of the second injection grid model does not meet the preset quality condition, adjusting the model parameters corresponding to the second injection grid model until the model quality of the second injection grid model meets the preset quality condition.

[0065] In this embodiment, since the grid division density will affect the grid model's processing time for data, and there will be a certain degree of influence between the number of grids and the obtained simulation results, after obtaining the first injection grid model and the second injection grid model that meet the preset quality conditions, the first injection grid model and the second injection grid model need to be subjected to a grid independence test. It is worth mentioning that only when the first injection grid model and the second injection grid model pass the grid independence test can the embodiment of the present application perform subsequent operations.

[0066] Specifically, it is determined whether there is any irrelevance between the grid division method corresponding to the first grid model and the first injection parameter simulation data output by the first grid model. If there is no irrelevance between the grid division method corresponding to the first grid model and the first injection parameter simulation data output by the first grid model, the grid division method corresponding to the first grid model is adjusted until there is any irrelevance between the grid division method corresponding to the adjusted first grid model and the first injection parameter simulation data output by the first grid model. Correspondingly, it is determined whether there is any irrelevance between the grid division method corresponding to the second grid model and the second injection parameter simulation data output by the second grid model. If there is no irrelevance between the grid division method corresponding to the second grid model and the second injection parameter simulation data output by the second grid model, the grid division method corresponding to the second grid model is adjusted until there is any irrelevance between the grid division method corresponding to the adjusted second grid model and the second injection parameter simulation data output by the second grid model.

[0067] Step S12, obtaining fitting parameters, fitting the model simulation data and generating fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device.

[0068] In this embodiment, it is necessary to obtain fitting parameters, and use the fitting parameters to fit the model simulation data to generate fitting result data. Among them, the feeding parameters of the pressure source are the working pressure value and flow value provided by the water pump, and the operating condition parameters of the drone cleaning device are the equipment parameters of the drone cleaning device and the spray box capacity value and battery power value in the drone cleaning device. Specifically, obtaining fitting parameters, fitting the model simulation data and generating fitting result data can include: fitting the target model simulation data based on the fitting parameters to obtain a fitting formula; using the fitting formula and based on the benchmark parameters to determine the fitting result data of the drone cleaning device; the benchmark parameters include the cleaning information of the target power station and the operating condition parameters of the drone cleaning device; wherein the fitting formula includes the nozzle outlet flow rate fitting formula, the nozzle flow rate fitting formula, the injection angle and the slot half angle fitting formula, the nozzle cleaning coverage distance fitting formula and the cross-sectional flow field maximum impact pressure fitting formula.

[0069] Among them, the embodiment of the present application uses the nozzle outlet flow rate fitting formula and determines the nozzle outlet flow rate fitting result of the drone cleaning device based on the nozzle inlet pressure value, nozzle aperture value and injection angle value corresponding to the fan-shaped nozzle. In the first specific implementation, the nozzle outlet flow rate fitting formula is a fitting formula for calculating the nozzle outlet flow rate based on the nozzle inlet pressure, nozzle aperture, and slot half angle, and the expression of the formula is as follows:

[0070] v max =27.06+12.78×d×sinα+9.659×P-1.917×(d×sinα) 2 +0.1028×P×d×sinα-0.1512×P 2 ;

[0071] Among them, v max is the nozzle outlet flow rate; α is the slot half angle; P is the nozzle inlet pressure, provided by the water pump; d is the nozzle aperture.

[0072] In the second specific implementation, the nozzle flow fitting formula is used and the nozzle inlet pressure and nozzle aperture corresponding to the fan-shaped nozzle are used to determine the nozzle flow fitting result of the drone cleaning device. In the second specific implementation, the nozzle flow fitting formula is fitted using polynomial fitting and empirical formula coefficients to obtain and output three nozzle flow fitting formulas, wherein the three nozzle flow fitting formulas are all based on the nozzle inlet pressure, nozzle aperture, and outlet flow rate to determine the nozzle flow, and then the average flow rate obtained based on the three nozzle flow calculation methods is used as the nozzle flow, and the three nozzle flow fitting formulas and nozzle flow determination formulas are as follows:

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] Where P is the nozzle inlet pressure, provided by the water pump; is the density of the sprayed cleaning liquid, which is water or a mixture of water and cleaning agent; N is the number of nozzles, and Q1, Q2, and Q3 are all nozzle flow rates.

[0078] In a third specific implementation, the fitting formula of the spray angle and the groove half angle is used to determine the spray angle fitting result of the UAV cleaning device based on the groove half angle corresponding to the fan-shaped nozzle. In the third specific implementation, the fitting formula of the spray angle and the groove half angle is as follows:

[0079] ;

[0080] In the fourth specific implementation, the nozzle cleaning coverage distance fitting formula is used to determine the nozzle cleaning coverage distance fitting result of the drone cleaning device based on the nozzle outlet flow rate, the spray angle, and the number of nozzles corresponding to the fan-shaped nozzle. In the fourth specific implementation, the nozzle cleaning coverage distance fitting formula is as follows:

[0081] ;

[0082] Where, φ is the spray angle; L is the nozzle cleaning distance.

[0083] In a fifth specific implementation, the maximum impact pressure fitting formula of the cross-sectional flow field is used to determine the maximum impact pressure fitting result of the cross-sectional flow field based on the spray distance corresponding to the fan-shaped nozzle, the nozzle inlet pressure corresponding to the water pump, and the nozzle flow rate. In the fifth specific implementation, the maximum impact pressure fitting formula of the cross-sectional flow field is as follows:

[0084] ;

[0085] Among them, z is the spray distance; Q is the nozzle flow rate, which is provided by the water pump; β is the angle between the nozzle axis and the normal direction of the nozzle impact surface.

[0086] It is worth mentioning that in this embodiment, since the model simulation data is only applicable to the working conditions of drone cleaning photovoltaic modules, in a specific implementation method, the application range of the above-mentioned several fitting formulas is: the inlet pressure range is 0.15-2MPa, the nozzle aperture range is 0.66mm-4mm, the groove half angle range is 30°-60°, and the spray distance z is 1-1.5 meters.

[0087] Step S13: generating the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device.

[0088] It is worth mentioning that before generating the operating constraints of the drone cleaning device based on the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device, the time constraints of the drone cleaning device need to be determined. That is, the time constraints of the drone cleaning device are determined based on the cleaning information of the target power station; wherein the cleaning information includes the nozzle cleaning distance value, the forward speed value of the drone, the area value of the cleaning component, the capacity value of the spray box, the one-way distance value of the drone charging and battery replacement, the average flight speed value of the drone charging and battery replacement, the one-way distance value of the drone replenishing the cleaning agent, the drone endurance time value, and the average flight speed value of the drone replenishing the cleaning agent.

[0089] In this embodiment, it is necessary to obtain the cleaning information of the photovoltaic power station; wherein the cleaning information includes the cleaning component area value A p , Component cleaning length L p , Estimated cleaning completion time t p and cleaning cost limit C p . Among them, the component cleaning length is determined by the component model in the photovoltaic power station, and is the long side or short side of the component covered by the jet flow field corresponding to the fan-shaped nozzle, and is perpendicular to the cleaning direction of the drone. In addition, the embodiment of the present application needs to calculate the flight time of the drone based on the cleaning information of the target power station, that is, the time constraint condition of the drone cleaning device is the flight time of the drone, wherein the calculation formula of the time constraint condition of the drone cleaning device is as follows:

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] Among them, t F Indicates the flight time of the drone; Represents the battery weight ratio of the drone, that is, the battery weight G B and takeoff weight G F The ratio of the thrust to speed required for the UAV to fly; G F Indicates takeoff weight, which is the empty weight in G v , Cleaning device weight G L , Battery weight G B The sum of the capacity density, which is the ratio of the battery capacity E to the battery weight G B ; P is the average power of the UAV; G pump is the weight of the water pump. It is worth mentioning that the water pump selection depends on the inlet pressure and flow rate required by the fan nozzle. This item is the selection optimization item; Q tank is the capacity value of the spray box. The selection of the spray box depends on the total cleaning time of the drone and the cleaning completion time of the drone cleaning device. This item is the selection optimization item; G sub The weight of the auxiliary equipment refers to the remaining equipment in the UAV cleaning device except the UAV, water pump and spray box.

[0096] In this embodiment, after obtaining the time constraints of the drone cleaning device, the embodiment of the present application also needs to generate the operating constraints of the drone cleaning device based on the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device. Specifically, generating the operating constraints based on the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device may include: generating the cleaning completion time constraints, cleaning cost constraints, cleaning coverage distance constraints and recoil force constraints of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device.

[0097] Among them, the cleaning completion time constraint condition of the drone cleaning device is that the cleaning completion time spent by the drone cleaning device on the photovoltaic module should not exceed the expected cleaning completion time, and the expression formula is as follows:

[0098] ;

[0099] Among them, t is the cleaning completion time of the UAV cleaning device, t p The estimated time for cleaning to be completed.

[0100] Furthermore, the calculation formula for the cleaning completion time of the drone cleaning device is as follows:

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] Among them, v c A is the cleaning forward speed; p is the cleaning component area value; t c The total cleaning time for the drone; t e The total round trip time for charging and replacing the battery for the drone; t l The total round trip time for replenishing the cleaning agent for the drone, L tank The one-way distance value for replenishing cleaning agent for the drone; V tank The average flight speed value for replenishing cleaning agent for the drone; L bat The one-way distance value for charging and replacing the battery of the drone; V bat The average flight speed for charging and replacing the battery of the drone.

[0106] Among them, the cleaning cost constraint condition is that the cost spent by the drone cleaning device when cleaning the photovoltaic modules should not exceed the cleaning cost limit, which is expressed as follows:

[0107] ;

[0108] It is worth mentioning that the cost of drone cleaning is composed of staff salary expenses, energy consumption costs such as water and electricity, equipment maintenance costs and equipment depreciation costs. The calculation formula for the cost of drone cleaning is as follows:

[0109] ;

[0110] ;

[0111] ;

[0112] ;

[0113] Where C is the total cost of drone cleaning; C E Energy consumption fee for water, electricity, etc.; C P The daily salary cost of a single staff member; M C is the equipment maintenance fee; s V is the equipment depreciation fee; d is the original value of the cleaning device (yuan); is the maintenance cost rate of the cleaning device (%); n d The number of times the cleaning device can be used (times); is the estimated residual value rate of the cleaning device (%); t F represents the flight time of the drone; t is the cleaning completion time of the drone cleaning device; E(t) is the electricity price; W(t) is the water price.

[0114] The nozzle cleaning coverage distance constraint condition is that when the drone cleaning device cleans the photovoltaic module, the fan-shaped nozzle cleaning coverage distance L should not be greater than the module cleaning length L P , and the expression formula is as follows:

[0115] ;

[0116] The recoil force constraint of the cleaning device is that the recoil force of the cleaning device should not exceed the maximum recoil force that the drone can withstand, so as to ensure the stable flight attitude of the drone and maintain dynamic balance. The expression formula is as follows:

[0117] ;

[0118] Where, F is the recoil force of the cleaning device, F max is the maximum recoil force that the drone can withstand; it is worth mentioning that the formula for determining F is as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] Among them, F1 is the recoil force of the cleaning device determined based on the inlet flow rate and the inlet pressure, and F2 is the recoil force of the cleaning device determined based on the fan nozzle aperture and the inlet pressure.

[0123] Step S14: generating configuration parameters of the UAV cleaning system according to the fitting result data and the operating constraints.

[0124] In this embodiment, the target configuration parameters corresponding to the UAV cleaning device need to be determined based on the model simulation data and the corresponding operating constraints, so as to configure the UAV cleaning device using the target configuration parameters. Specifically, generating the configuration parameters of the UAV cleaning system based on the fitting result data and the operating constraints may include: generating the optional parameters corresponding to the fan-shaped nozzle, the capacity value of the spray box storing the cleaning liquid, the working pressure value and flow value of the water pump in the UAV cleaning device, and the onboard weight value of the UAV based on the fitting result data and the operating constraints; wherein the optional parameters include the nozzle inlet pressure value, the nozzle aperture value and the spray angle value.

[0125] In this embodiment, after obtaining several fitting formulas and several preset constraints, the embodiment of the present application can obtain the target configuration parameters corresponding to the drone cleaning device, that is, the optimal configuration of the drone cleaning device. Among them, the target configuration parameters include the purchase parameters, the capacity value of the spray box storing the cleaning liquid, the working pressure value and flow value of the water pump in the drone cleaning device. It is worth mentioning that the purchase parameters of the fan nozzle are the nozzle inlet pressure value, the nozzle aperture value and the spray angle value, and the weight of the cleaning device is used to select the drone load.

[0126] As can be seen from the above, before the configuration generation of the drone cleaning system is performed in the embodiment of the present application, it is necessary to convert the injection parameters of the drone cleaning device into model simulation data according to the injection network model; obtain fitting parameters, fit the model simulation data and generate fitting result data, wherein the fitting parameters include the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device; generate the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device; generate the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraint conditions. In this way, the configuration parameters corresponding to the drone cleaning device can be obtained based on the corresponding simulation results when the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned.

[0127] For further information, see Figure 2 As shown, an embodiment of the present invention discloses a flow chart of obtaining configuration parameters of a drone cleaning device, including:

[0128] In this embodiment, the flow chart of obtaining the configuration parameters of the UAV cleaning device is as follows: Figure 2 As shown:

[0129] First, by utilizing ICEM and based on preset structured grid rules and preset unstructured grid rules, a first injection grid model and a second injection grid model corresponding to the injection flow domain of the drone cleaning device are respectively established. In a specific embodiment, the injection range corresponding to the injection flow domain of the drone cleaning device is 0 to 1.5 meters.

[0130] Secondly, after the first injection mesh model and the second injection mesh model are established, it is necessary to judge the mesh quality of the first injection mesh model and the second injection mesh model. If the quality of the first injection mesh model and the second injection mesh model meets the preset quality conditions, subsequent operations can be performed; if the quality of the first injection mesh model and the second injection mesh model does not meet the preset quality conditions, the model parameters corresponding to the first injection mesh model and the second injection mesh model are optimized until the model quality corresponding to the first injection mesh model and the second injection mesh model meets the preset quality conditions.

[0131] Then, the initial conditions, boundary conditions and preset control parameters are respectively input into the first injection grid model and the second injection grid model using the Fluent solver (i.e., a general computational fluid dynamics software) to initially configure the first injection grid model and the second injection grid model. It is worth mentioning that because the grid division density will affect the grid model's processing time for data, and there will be a certain degree of influence between the number of grids and the obtained simulation results, it is necessary to perform a grid independence test on the first injection grid model and the second injection grid model. Only when the first injection grid model and the second injection grid model pass the grid independence test can the embodiment of the present application perform subsequent operations.

[0132] Subsequently, each reference parameter is input into the first injection grid model and the second injection grid model, respectively, so as to obtain the first injection parameter simulation data and the second injection parameter simulation data outputted by the first injection grid model and the second injection grid model, respectively. It is worth mentioning that the above injection parameter simulation data are simulation data such as nozzle outlet flow rate, nozzle flow rate, injection angle, flow field impact pressure and nozzle cleaning coverage distance outputted by the fan-shaped nozzle under different nozzle inlet pressure values, nozzle aperture values ​​and injection angle values.

[0133] Furthermore, after obtaining the first injection parameter simulation data and the second injection parameter simulation data, it is necessary to compare and verify the first injection parameter simulation data and the second injection parameter simulation data with the preset benchmark simulation data described in the published paper. In a specific embodiment, the preset benchmark simulation data is the simulation data corresponding to the injection distance range within 0.5 meters in the published paper, and a similarity calculation is performed to obtain the first similarity and the second similarity corresponding to the first injection parameter simulation data and the second injection parameter simulation data respectively, so as to select the model simulation data with high accuracy from the first injection parameter simulation data and the second injection parameter simulation data based on the obtained first similarity and the second similarity.

[0134] Subsequently, the embodiment of the present application needs to perform formula fitting based on the model simulation data, the equipment parameters of the UAV cleaning device and the cleaning information of the target power station to establish several fitting formulas. Among them, the several fitting formulas include the nozzle outlet flow rate fitting formula, the nozzle flow rate fitting formula, the injection angle and the slot half angle fitting formula, the nozzle cleaning coverage distance fitting formula and the cross-sectional flow field maximum impact pressure fitting formula.

[0135] Then, several constraints are used to constrain the configuration parameters of the UAV cleaning device; among them, the operation constraints include cleaning completion time constraints, cleaning cost constraints, nozzle cleaning coverage distance constraints and cleaning device recoil constraints.

[0136] Finally, after obtaining several fitting formulas and several operating constraints, the embodiment of the present application can obtain the configuration parameters of the drone cleaning system, and use the target configuration parameters to perform corresponding configuration operations on the drone cleaning system. Among them, the target configuration parameters include the fan nozzle purchase parameters, the capacity value of the spray box storing the cleaning liquid, and the working pressure value and flow value of the water pump in the drone cleaning device. It is worth mentioning that the fan nozzle purchase parameters are the nozzle inlet pressure value, the nozzle aperture value and the spray angle value, and the weight of the cleaning device is used to select the drone load.

[0137] As can be seen from the above, before the configuration generation of the drone cleaning system is performed in the embodiment of the present application, it is necessary to convert the injection parameters of the drone cleaning device into model simulation data according to the injection grid model; then obtain the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device, fit the model simulation data and generate the fitting result data; then generate the operating constraint conditions of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device; finally, generate the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraint conditions. In this way, when the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned, the drone cleaning system can be configured using the simulation results corresponding to the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned being greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned.

[0138] Accordingly, see Figure 3 As shown, the present application also provides a configuration generation device for a drone cleaning system, comprising:

[0139] A simulation data acquisition module 11 is used to convert the injection parameters of the UAV cleaning device into model simulation data according to the injection network model;

[0140] A fitting result generating module 12, used for acquiring fitting parameters, fitting the model simulation data and generating fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device;

[0141] The constraint condition generating module 13 is used to generate the operating constraint condition of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint condition of the drone cleaning device; the time constraint condition of the drone cleaning device is the flight time of the drone;

[0142] The configuration parameter generating module 14 is used to generate the configuration parameters of the UAV cleaning system according to the fitting result data and the operation constraint conditions.

[0143] As can be seen from the above, before the configuration generation of the drone cleaning system is performed, the embodiment of the present application needs to convert the injection parameters of the drone cleaning device into model simulation data according to the injection grid model; obtain fitting parameters, fit the model simulation data and generate fitting result data, wherein the fitting parameters include the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device; generate the operating constraints of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device; generate the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraints. It can be seen from this that the present application converts the injection parameters of the drone cleaning device into model simulation data according to the injection grid model; then obtains the cleaning information of the target power station, the feeding parameters of the pressure source and the operating condition parameters of the drone cleaning device, fits the model simulation data and generates fitting result data; then generates the operating constraints of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraints of the drone cleaning device; finally generates the configuration parameters of the drone cleaning system according to the fitting result data and the operating constraints. In this way, based on the corresponding simulation results when the interval distance range between the drone cleaning device and the photovoltaic component to be cleaned is greater than the current interval distance range between the drone cleaning device and the photovoltaic component to be cleaned, the configuration parameters corresponding to the drone cleaning system can be obtained, so as to configure the drone cleaning system using the configuration parameters.

[0144] In some specific implementations, the simulation data acquisition module 11 may specifically include:

[0145] A first grid model determination unit, configured to use a structured grid to perform grid simulation on a jet flow domain of the UAV cleaning device to obtain a first jet grid model corresponding to the structured grid;

[0146] A first simulation data acquisition unit, configured to input the injection parameter into the first injection grid model to obtain first injection parameter simulation data output by the first injection grid model;

[0147] A second grid model determination unit is used to perform grid simulation on the spray flow domain of the UAV cleaning device using an unstructured grid to obtain a second spray grid model corresponding to the unstructured grid;

[0148] A second simulation data acquisition unit, configured to input the injection parameter into the second injection grid model to obtain second injection parameter simulation data output by the second injection grid model;

[0149] A first similarity calculation unit, used for calculating the similarity between the first injection parameter simulation data and preset reference simulation data to obtain a first similarity;

[0150] A second similarity calculation unit, used for calculating the similarity between the second injection parameter simulation data and the preset reference simulation data to obtain a second similarity;

[0151] The model simulation data determining unit is used to determine a target similarity with a larger value between the first similarity and the second similarity, and determine the injection parameter simulation data corresponding to the target similarity as the model simulation data.

[0152] In some specific implementations, the fitting result generating module 12 may specifically include:

[0153] A fitting formula determination unit, used for acquiring fitting parameters, and fitting the model simulation data based on the fitting parameters to obtain a fitting formula;

[0154] A fitting result data determination unit is used to determine the fitting result data of the UAV cleaning device using the fitting formula and based on benchmark parameters; the benchmark parameters include the cleaning information of the target power station and the operating condition parameters of the UAV cleaning device; wherein the fitting formula includes a nozzle outlet flow velocity fitting formula, a nozzle flow rate fitting formula, a jet angle and a groove half angle fitting formula, a nozzle cleaning coverage distance fitting formula and a cross-sectional flow field maximum impact pressure fitting formula.

[0155] In some specific implementations, the constraint condition generating module 13 may specifically include:

[0156] A time constraint determination unit, used to determine the time constraint of the UAV cleaning device based on the cleaning information of the target power station; the cleaning information includes a nozzle cleaning distance value, a UAV forward speed value, a cleaning component area value, a spray box capacity value, a one-way distance value for UAV charging and battery replacement, an average flight speed value for UAV charging and battery replacement, a one-way distance value for UAV replenishment of cleaning agent, a UAV endurance time value, and an average flight speed value for UAV replenishment of cleaning agent;

[0157] The constraint condition generation subunit is used to generate the cleaning completion time constraint condition, cleaning cost constraint condition, cleaning coverage distance constraint condition and recoil force constraint condition of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint condition of the drone cleaning device.

[0158] In some specific implementations, the configuration parameter generating module 14 may specifically include:

[0159] A configuration parameter generation subunit is used to generate optional parameters corresponding to the fan-shaped nozzle, the capacity value of the spray tank storing the cleaning liquid, the working pressure value and flow value of the water pump in the UAV cleaning device, and the onboard weight value of the UAV based on the fitting result data and the operating constraints; wherein the optional parameters include the nozzle inlet pressure value, the nozzle aperture value and the spray angle value.

[0160] Furthermore, the embodiment of the present application also discloses a drone cleaning system, wherein the drone cleaning system includes a drone cleaning device, a memory and a processor. The drone cleaning device is communicatively connected to the processor, and the components in the drone cleaning device include a drone, and at least one of a power supply device, a motor, a water pump, a pressure gauge, a spray box, a fan-shaped nozzle or a pressure regulating valve; wherein the water pump is used to pump the cleaning liquid into the spray box, the pressure gauge is used to measure and display the water pressure of the water pump, and the spray box is connected to the fan-shaped nozzle through a preset pipe to establish a water channel connection with the fan-shaped nozzle, so as to spray the cleaning liquid in the spray box to the surface of the object to be cleaned through the preset pipe and the fan-shaped nozzle in turn; the pressure regulating valve is used to adjust the water pressure in the water pump; and the processor automatically collects the relevant information parameters of each of the components inside itself through the drone cleaning device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the figure cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the configuration generation method of the drone cleaning system disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0161] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0162] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0163] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the configuration generation method of the drone cleaning system executed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0164] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the configuration generation method of the drone cleaning system disclosed above is implemented. The specific steps of the method can be referred to the corresponding contents disclosed in the above embodiments, and will not be repeated here.

[0165] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0166] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0167] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0168] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0169] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A configuration generation method for a drone cleaning system, characterized in that: include: The spray parameters of the UAV cleaning device are converted into model simulation data according to the spray grid model; Acquire fitting parameters, fit the model simulation data and generate fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device; generating an operating constraint condition of the drone cleaning device according to the operating condition parameters of the drone cleaning device and the time constraint condition of the drone cleaning device; the time constraint condition of the drone cleaning device is the flight time of the drone; The configuration parameters of the UAV cleaning system are generated according to the fitting result data and the operating constraints.

2. The configuration generation method of the drone cleaning system according to claim 1, characterized in that: The method of converting the spray parameters of the UAV cleaning device into model simulation data according to the spray grid model includes: Using a structured grid to perform grid simulation on the jet flow domain of the UAV cleaning device to obtain a first jet grid model corresponding to the structured grid; Inputting the injection parameter into the first injection grid model to obtain first injection parameter simulation data output by the first injection grid model; Using an unstructured grid to perform grid simulation on the jet flow domain of the UAV cleaning device to obtain a second jet grid model corresponding to the unstructured grid; Inputting the injection parameter into the second injection grid model to obtain second injection parameter simulation data output by the second injection grid model; Calculating the similarity between the first injection parameter simulation data and preset reference simulation data to obtain a first similarity; Calculating the similarity between the second injection parameter simulation data and the preset reference simulation data to obtain a second similarity; A target similarity having a larger value between the first similarity and the second similarity is determined, and injection parameter simulation data corresponding to the target similarity is determined as model simulation data.

3. The configuration generation method of the drone cleaning system according to claim 1, characterized in that: The obtaining of fitting parameters, fitting the model simulation data and generating fitting result data comprises: Acquiring fitting parameters, and fitting the model simulation data based on the fitting parameters to obtain a fitting formula; Determining fitting result data of the UAV cleaning device using the fitting formula and based on benchmark parameters; the benchmark parameters include cleaning information of the target power station and operating condition parameters of the UAV cleaning device; Among them, the fitting formulas include the nozzle outlet flow velocity fitting formula, the nozzle flow fitting formula, the injection angle and the groove half angle fitting formula, the nozzle cleaning coverage distance fitting formula and the cross-sectional flow field maximum impact pressure fitting formula.

4. The configuration generation method of the drone cleaning system according to claim 1, characterized in that: The step of generating the operating constraint condition of the drone cleaning device according to the operating condition parameter of the drone cleaning device and the time constraint condition of the drone cleaning device comprises: Determine the time constraint of the UAV cleaning device based on the cleaning information of the target power station; the cleaning information includes the nozzle cleaning distance value, the forward speed value of the UAV, the cleaning component area value, the spray box capacity value, the one-way distance value of the UAV charging and replacing, the average flight speed value of the UAV charging and replacing, the one-way distance value of the UAV replenishing the cleaning agent, the UAV endurance time value and the average flight speed value of the UAV replenishing the cleaning agent; According to the operating condition parameters of the drone cleaning device and the time constraint conditions of the drone cleaning device, the cleaning completion time constraint conditions, cleaning cost constraint conditions, cleaning coverage distance constraint conditions and recoil force constraint conditions of the drone cleaning device are generated.

5. The configuration generation method of the drone cleaning system according to claim 1, characterized in that: The generating of the configuration parameters of the UAV cleaning system according to the fitting result data and the operation constraint conditions includes: Based on the fitting result data and the operating constraints, generate the purchasing parameters corresponding to the fan-shaped nozzle, the capacity value of the spray tank storing the cleaning liquid, the working pressure value and flow value of the water pump in the UAV cleaning device, and the airborne weight value of the UAV; The optional parameters include nozzle inlet pressure value, nozzle aperture value and spray angle value.

6. A configuration generation device for a drone cleaning system, characterized in that: include: A simulation data acquisition module, used to convert the injection parameters of the UAV cleaning device into model simulation data according to the injection grid model; A fitting result generation module, used for obtaining fitting parameters, fitting the model simulation data and generating fitting result data, wherein the fitting parameters include cleaning information of the target power station, feeding parameters of the pressure source and operating condition parameters of the UAV cleaning device; A constraint condition generating module, used for generating an operating constraint condition of the drone cleaning device according to an operating condition parameter of the drone cleaning device and a time constraint condition of the drone cleaning device; the time constraint condition of the drone cleaning device is the flight time of the drone; A configuration parameter generation module is used to generate configuration parameters of the UAV cleaning system according to the fitting result data and the operation constraint conditions.

7. The configuration generating device of the drone cleaning system according to claim 6, characterized in that: The fitting result generating module comprises: A fitting formula determination unit, used to obtain fitting parameters, and fit the model simulation data based on the fitting parameters to obtain a fitting formula; A fitting result determination unit is used to determine the fitting result data of the UAV cleaning device using the fitting formula and based on benchmark parameters; the benchmark parameters include the cleaning information of the target power station and the operating condition parameters of the UAV cleaning device; wherein the fitting formula includes a nozzle outlet flow velocity fitting formula, a nozzle flow rate fitting formula, a jet angle and a groove half angle fitting formula, a nozzle cleaning coverage distance fitting formula and a cross-sectional flow field maximum impact pressure fitting formula.

8. The configuration generating device of the drone cleaning system according to claim 6, characterized in that: The constraint condition generating module comprises: A first constraint condition generating unit is used to determine the time constraint condition of the UAV cleaning device based on the cleaning information of the target power station; the cleaning information includes a nozzle cleaning distance value, a UAV forward speed value, a cleaning component area value, a spray box capacity value, a one-way distance value for UAV charging and battery replacement, an average flight speed value for UAV charging and battery replacement, a one-way distance value for UAV replenishment of cleaning agent, a UAV endurance time value, and an average flight speed value for UAV replenishment of cleaning agent; The second constraint condition generating unit is used to generate the cleaning completion time constraint condition, cleaning cost constraint condition, cleaning coverage distance constraint condition and recoil force constraint condition of the UAV cleaning device according to the operating condition parameters of the UAV cleaning device and the time constraint condition of the UAV cleaning device.

9. A drone cleaning system, characterized in that: include: UAV cleaning device; Memory, used to store computer programs; A processor is communicatively connected to the drone cleaning device, and is used to execute the computer program to implement the steps of the configuration generation method of the drone cleaning system as described in any one of claims 1 to 5.

10. The drone cleaning system according to claim 9, characterized in that: The components of the drone cleaning device include a drone, and at least one of a power supply device, a motor, a water pump, a pressure gauge, a spray box, a fan-shaped nozzle, or a pressure regulating valve; The water pump is used to pump the cleaning liquid into the spray box, the pressure gauge is used to measure and display the water pressure of the water pump, the spray box is connected with the fan-shaped nozzle through a preset pipeline, so as to spray the cleaning liquid in the spray box onto the surface of the object to be cleaned through the preset pipeline and the fan-shaped nozzle in sequence; the pressure regulating valve is used to adjust the water pressure in the water pump; In addition, the processor automatically collects relevant information parameters of each of the components inside the processor through the drone cleaning device.

Images