Intelligent photovoltaic self-cleaning method and device

Abstract

The invention discloses an intelligent photovoltaic self-cleaning method and device, and relates to the technical field of photovoltaic modules, and the method comprises the steps: automatically calculating the weekly generating capacity through a photovoltaic control platform, obtaining an image monitored by a camera, constructing a prediction model, judging the smudginess degree of each photovoltaic module, and calculating an optimal cleaning path; starting a first cleaning operation according to the optimal cleaning path, evaluating the cleaning effect, and judging whether a second cleaning operation is performed or not; and resetting the cleaning operation according to the result of the second cleaning operation to complete the intelligent photovoltaic self-cleaning operation. Through automatic data acquisition, prediction modeling and cleaning path optimization, the efficient and accurate cleaning process is realized, the smudginess degree is intelligently judged, automatic cleaning is executed, the effect is dynamically evaluated, whether secondary cleaning is needed or not is determined, the cleaning efficiency and quality are improved, resource utilization is optimized, and the overall performance and reliability of the photovoltaic system are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of photovoltaic components, and in particular to an intelligent photovoltaic self-cleaning method and device. Background Art

[0002] There are three main ways to clean photovoltaic modules: manual cleaning, semi-automatic cleaning, and fully automatic cleaning. Manual cleaning relies on manpower to scrub and spray water, which has the problems of high labor costs, low work efficiency, and safety risks. Semi-automatic cleaning requires manual driving or operation of water spraying equipment, which has strict requirements on the spacing and size of photovoltaic modules, and is easy to cause inconvenience in operation. Although fully automatic cleaning entrusts the entire cleaning process to the equipment, it is difficult to be widely used at present due to the complexity of the equipment and difficulty in operation.

[0003] In the prior art, photovoltaic cleaning equipment mostly focuses on structural optimization, such as structural modification of the cleaning brush, improvement of the nozzle and water spraying method, and automatic control of the cleaning brush according to the degree of dirtiness of the photovoltaic module to determine whether the cleaning requirements are met. However, there is no corresponding program in the existing patents for auxiliary operation.

[0004] If the surface of photovoltaic modules is not cleaned for a long time, it will affect the light transmittance, thereby reducing the power generation effect. The current operation and maintenance of distributed photovoltaic power stations mainly rely on manual scrubbing, which has a long cleaning cycle and high manpower and transportation costs. In order to solve these problems, the present invention proposes an intelligent photovoltaic self-cleaning method and device, which can accurately locate the photovoltaic modules that need to be cleaned through remote program calculation and camera comparison, and perform intelligent partition cleaning, which greatly saves cleaning costs, water resources and electricity resources. Summary of the invention

[0005] In view of the problems existing in the existing photovoltaic component cleaning method, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to achieve efficient self-cleaning of photovoltaic modules through intelligent means, thereby reducing maintenance costs, improving power generation efficiency, and saving water and electricity resources.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an intelligent photovoltaic self-cleaning method, which includes automatically calculating weekly power generation through a photovoltaic control platform and acquiring images monitored by a camera; constructing a prediction model based on the weekly power generation and the images; judging the degree of dirtiness of each photovoltaic component according to the prediction results of the prediction model, and calculating the optimal cleaning path; starting a first cleaning operation according to the optimal cleaning path, and evaluating the cleaning effect; judging whether to perform a second cleaning operation based on the evaluation result of the cleaning effect; resetting the cleaning operation according to the result of the second cleaning operation to complete the intelligent photovoltaic self-cleaning operation.

[0009] As a preferred solution of the intelligent photovoltaic self-cleaning method of the present invention, the construction process of the prediction model includes the following steps: collecting power generation data, image data and meteorological information of photovoltaic modules; extracting feature information from image data through computer vision algorithm to quantify the degree of dirtiness; based on power generation data, extracted feature information and meteorological information, using support vector machine algorithm to establish a prediction model and output the expected power generation efficiency η of each photovoltaic module pred ; The expected power generation efficiency η pred The specific formula is as follows:

[0010]

[0011] Among them, η pred is the expected power generation efficiency, E w is the weekly power generation, E w is the weekly power generation, E d is the daily power generation, W is the meteorological factor, T is the temperature, and S is the dirtiness score. The specific formula is as follows:

[0012] S=∑(w i *A i ) / A total ;

[0013] Among them, w i is the dirtiness weight of component i, A i is the dirty area of ​​component i, A total is the total area.

[0014] As a preferred solution of the intelligent photovoltaic self-cleaning method of the present invention, it also includes: for component i, if the dirtiness score S is greater than the dirtiness threshold S threshold , it is marked as a component that needs to be cleaned, and a list of components that need to be cleaned is created, recording the component ID and the corresponding dirtiness score; if the dirtiness score S is less than or equal to the dirtiness threshold S threshold, then record the ID and current dirtiness score of this component in the cleaning component list, and continue to monitor the dirtiness of this component; sort the components to be cleaned according to the priority score, and divide the sorted component list into several cleaning groups; use genetic algorithms to define the starting point and end point of several cleaning groups, and convert the component positions into nodes in the graph, calculate the distance matrix between nodes, and generate the optimal cleaning path; store the optimal cleaning path in the system database and transmit it to the cleaning operation; the specific formula of the priority score is as follows:

[0015] P=ω1S i +ω2L i +ω3T i ;

[0016] Among them, P is the priority score, ω1, ω2 and ω3 are weight coefficients, S i is the dirtiness score of component i, L i is the location factor of component i, T i is the time since component i was last cleaned.

[0017] The expected power generation efficiency includes: pred Less than the first cleaning threshold η threshold When the expected power generation efficiency η pred Greater than or equal to the first cleaning threshold η threshold When the power generation efficiency of the component is low, the ID and the current expected power generation efficiency of the component are recorded in the list of normally operating components, and the power generation efficiency of the component continues to be monitored.

[0018] As a preferred solution of the intelligent photovoltaic self-cleaning method described in the present invention, the cleaning operation includes a first cleaning operation and a second cleaning operation; the first cleaning operation includes the following steps: starting the cleaning operation according to the optimal cleaning path; checking whether the liquid level of the tank level gauge and the power supply of each electrical device are normal, and whether the local switches are all closed; if the liquid level of the tank level gauge and the power supply of each electrical device are normal, and the local switches are all closed, then starting the cleaning operation; turning on the cleaning in the running operation panel, and checking whether there is an alarm on the panel; if there is an alarm on the panel, immediately stopping all cleaning operation preparation processes and sending an alarm to the operator Notification; if there is no alarm on the panel, open the pump inlet and outlet solenoid valves, and at the same time open and check that the booster pump outlet pressure is normal; check that the cleaning permission light at the control station is on, start the cleaning procedure, and start all the photovoltaic module inlet solenoid valves in sequence; at the same time, start this group of motors, drive the transmission device in the cleaning brush to rotate at the set speed, and the cleaning brush begins to brush the photovoltaic modules at a uniform speed; carry out the cleaning process of the entire group of photovoltaic modules in this area in this order; take the image of the photovoltaic module after the first cleaning operation is completed, transmit it to the prediction model to evaluate the cleaning effect, and use the camera to check the dirtiness and water accumulation of the photovoltaic modules.

[0019] As a preferred embodiment of the intelligent photovoltaic self-cleaning method described in the present invention, the evaluation of the cleaning effect includes: when it is checked that the dirt on the photovoltaic module is still there or the water in the photovoltaic module has not been discharged, a second cleaning operation is performed; when the photovoltaic power generation after cleaning is less than the preset photovoltaic power generation, a second cleaning operation is performed; if a second cleaning operation is performed, the operator is asked whether to confirm the execution of the second cleaning operation; if the operator vetoes the second cleaning operation, the decision is recorded and the cleaning process is terminated; if the operator confirms the execution of the second cleaning operation, the second cleaning operation is performed, the photovoltaic module image is taken again and transmitted to the prediction model for evaluation; when the photovoltaic power generation after cleaning is greater than or equal to the preset photovoltaic power generation, it is determined that the cleaning effect is good and there is no need to perform a second cleaning operation.

[0020] As a preferred solution of the intelligent photovoltaic self-cleaning method described in the present invention, the second cleaning operation includes the following steps: restarting the cleaning program, and opening the cleaning operation again on the running operation panel, checking whether there is an alarm on the panel and whether the power supply status of the tank liquid level equipment is normal; turning on the booster pump and related solenoid valves, clicking the cleaning operation, starting the cleaning program, and cleaning the photovoltaic components that need to be cleaned specifically; monitoring the cleaning effect in real time through the camera, checking whether the photovoltaic components have been thoroughly cleaned, and confirming that the accumulated water has been completely drained, and resetting the cleaning operation.

[0021] As a preferred solution of the intelligent photovoltaic self-cleaning method described in the present invention, the reset includes the following steps: after completing all cleaning operations, closing the solenoid valves of all photovoltaic modules in sequence and stopping the booster pump; at the same time, controlling the cleaning brush motor to start and return the cleaning brush to the stop position; confirming that the cleaning brush is in the correct position through the Hall limiter and stopping the cleaning brush motor; after all mechanical parts are reset, check whether all indicator lights on the control panel have returned to the standby state; if the control panel indicates normal, perform a safety check, send a notification of the completion of the cleaning operation to the operator, and continue to monitor the power generation efficiency of the photovoltaic module.

[0022] In a second aspect, an embodiment of the present invention provides an intelligent photovoltaic self-cleaning device, which includes: an acquisition module, which is used to automatically calculate the weekly power generation through a photovoltaic control platform and obtain images monitored by a camera; a construction module, which constructs a prediction model based on the weekly power generation and the image; a calculation module, which is used to determine the degree of dirtiness of each photovoltaic component according to the prediction results of the prediction model, and calculate the optimal cleaning path; a cleaning module, which is used to start a first cleaning operation according to the optimal cleaning path and evaluate the cleaning effect; an evaluation module, which determines whether to perform a second cleaning operation based on the evaluation results of the cleaning effect; and a control module, which is used to reset the cleaning operation according to the result of the second cleaning operation to complete the intelligent photovoltaic self-cleaning operation.

[0023] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the intelligent photovoltaic self-cleaning method as described in the first aspect of the present invention are implemented.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the intelligent photovoltaic self-cleaning method as described in the first aspect of the present invention are implemented.

[0025] The beneficial effects of the present invention are as follows: the present invention realizes an efficient and accurate cleaning process through automatic data collection, predictive modeling and optimization of cleaning paths, intelligently judges the degree of dirtiness, performs automated cleaning, and dynamically evaluates the effects to determine whether secondary cleaning is needed, thereby improving cleaning efficiency and quality, optimizing resource utilization, and significantly improving the overall performance and reliability of the photovoltaic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0027] Figure 1 This is a flow chart of the intelligent photovoltaic self-cleaning method of Example 1.

[0028] Figure 2 This is a schematic diagram of the photovoltaic panel cleaning process of the intelligent photovoltaic self-cleaning method in Example 1.

[0029] Figure 3 This is an electronic equipment control diagram for the intelligent photovoltaic self-cleaning method of Example 2.

[0030] Figure 4 This is a computer equipment diagram of the intelligent photovoltaic self-cleaning method of Example 3. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1

[0035] Reference Figure 1-2 , which is the first embodiment of the present invention, provides an intelligent photovoltaic self-cleaning method, comprising:

[0036] S1: Automatically calculate weekly power generation through the photovoltaic control platform and obtain images monitored by the camera.

[0037] S2: constructing a prediction model based on the weekly power generation and the image.

[0038] Specifically, the process of building the prediction model includes the following steps: collecting the power generation data, image data and meteorological information of the photovoltaic modules; the power generation data is recorded hourly and the weekly power generation is calculated; the meteorological data includes temperature, humidity, wind speed and sunshine intensity; all data are standardized, outliers are removed, and feature scaling is performed to make data of different dimensions comparable.

[0039] Furthermore, feature information is extracted from the image data through computer vision algorithms to quantify the degree of dirtiness. Feature information includes edge detection, color analysis, and texture analysis; edge detection is used to identify the outline of dust and stains, color analysis is used to distinguish different types of pollutants, and texture analysis is used to assess the degree of contamination. Based on the power generation data, feature information, and meteorological information, a prediction model is established using the support vector machine algorithm to output the expected power generation efficiency η of each photovoltaic module. pred .

[0040] Furthermore, the expected power generation efficiency η predThe specific formula is as follows:

[0041]

[0042] Among them, η pred is the expected power generation efficiency, E w is the weekly power generation, E w is the weekly power generation, E d is the daily power generation, W is the meteorological factor, T is the temperature, and S is the dirtiness score. The specific formula is as follows:

[0043] S=∑(w i *A i ) / A total ;

[0044] Among them, w i is the dirtiness weight of component i, A i is the dirty area of ​​component i, A total is the total area.

[0045] S3: According to the prediction results of the prediction model, the degree of dirtiness of each photovoltaic module is determined, and the optimal cleaning path is calculated.

[0046] Specifically, for component i, if the dirtiness score S is greater than the dirtiness threshold S threshold , it is marked as a component that needs to be cleaned, and a list of components that need to be cleaned is created, recording the component ID and the corresponding dirtiness score; if the dirtiness score S is less than or equal to the dirtiness threshold S threshold , then record the ID and current dirtiness score of this component in the cleaning component list, and continue to monitor the dirtiness of this component; sort the components to be cleaned according to the priority scores, and divide the sorted component list into several cleaning groups; use genetic algorithms to define the starting and end points of several cleaning groups, and convert the component positions into nodes in the graph, calculate the distance matrix between the nodes, and generate the optimal cleaning path; store the optimal cleaning path in the system database and transmit it to the cleaning operation.

[0047] It should be noted that the dirt threshold S threshold It is a criterion used to determine whether a single PV module needs to be cleaned. When the dirtiness score of a module exceeds this threshold, the system will mark the module as needing cleaning and add it to the list of modules that need cleaning.

[0048] Furthermore, the specific formula for the priority score is as follows:

[0049] P=ω1S i +ω2L i +ω3T i ;

[0050] Among them, P is the priority score, ω1, ω2 and ω3 are weight coefficients, S i is the dirtiness score of component i, L i is the location factor of component i, T i is the time since component i was last cleaned.

[0051] Furthermore, the expected power generation efficiency includes, when the expected power generation efficiency η pred Less than the first cleaning threshold η threshold When the expected power generation efficiency η pred Greater than or equal to the first cleaning threshold η threshold When the power generation efficiency of the component is low, the ID and the current expected power generation efficiency of the component are recorded in the list of normally operating components, and the power generation efficiency of the component continues to be monitored.

[0052] It should be noted that the first cleaning threshold η threshold It is usually based on the normal operating parameters and historical data of the photovoltaic system, and intervenes in time when the overall power generation efficiency begins to decline to ensure that the photovoltaic system remains in the best working condition.

[0053] S4: According to the optimal cleaning path, the first cleaning operation is started and the cleaning effect is evaluated.

[0054] Specifically, the cleaning operation includes a first cleaning operation and a second cleaning operation; the first cleaning operation includes the following steps: starting the cleaning operation according to the optimal cleaning path; checking whether the liquid level of the tank level gauge and the power supply of each electrical equipment are normal, and whether all local switches are turned off.

[0055] Furthermore, if the liquid level of the tank level gauge and the power supply of each electrical equipment are normal, and the local switches are closed, start the cleaning operation; turn on the cleaning in the running operation panel and check whether there is an alarm on the panel; if there is an alarm on the panel, immediately stop all cleaning operation preparation processes and send an alarm notification to the operator; if there is no alarm on the panel, open the pump inlet and outlet solenoid valves, and at the same time open and check that the booster pump outlet pressure is normal.

[0056] Furthermore, check that the cleaning permission light at the control station is on, start the cleaning procedure, and start all the PV module inlet solenoid valves in sequence; at the same time, start this group of motors to drive the transmission device inside the cleaning brush to rotate at the set speed, and the cleaning brush begins to brush the PV modules at a uniform speed; carry out the cleaning process of the entire group of PV modules in this area in this order; take an image of the PV module after the first cleaning operation is completed, transmit it to the prediction model to evaluate the cleaning effect, and use the camera to check the dirtiness and water accumulation of the PV modules.

[0057] Specifically, the evaluation of the cleaning effect includes: when it is checked that the dirt on the photovoltaic module is still there or the water in the photovoltaic module has not been discharged, a second cleaning operation is performed; when the photovoltaic power generation after cleaning is less than the preset photovoltaic power generation, a second cleaning operation is performed; if a second cleaning operation is performed, the operator is asked whether to confirm the execution of the second cleaning operation; if the operator vetoes the second cleaning operation, the decision is recorded and the cleaning process is ended; if the operator confirms the execution of the second cleaning operation, the second cleaning operation is performed, and the image of the photovoltaic module is taken again and transmitted to the prediction model for evaluation.

[0058] Furthermore, when the photovoltaic power generation after cleaning is greater than or equal to the preset photovoltaic power generation, it is determined that the cleaning effect is good and there is no need to perform a second cleaning operation.

[0059] S5: Based on the evaluation result of the cleaning effect, it is determined whether to perform a second cleaning operation.

[0060] Furthermore, the second cleaning operation includes the following steps: restarting the cleaning program, and opening the cleaning operation again on the running operation panel, checking whether there are any alarms on the panel and whether the power supply status of the tank liquid level equipment is normal; turning on the booster pump and related solenoid valves, clicking the cleaning operation, starting the cleaning program, and cleaning the photovoltaic components that need to be cleaned specifically; monitoring the cleaning effect in real time through the camera, checking whether the photovoltaic components have been thoroughly cleaned, and confirming that the accumulated water has been completely drained, and resetting the cleaning operation.

[0061] S6: According to the result of the second cleaning operation, the cleaning operation is reset to complete the intelligent photovoltaic self-cleaning operation.

[0062] Specifically, the reset includes the following steps: after completing all cleaning operations, close the solenoid valves of all photovoltaic modules in sequence and stop the booster pump; at the same time, control the cleaning brush motor to start and return the cleaning brush to the stop position; confirm that the cleaning brush is in the correct position through the Hall limiter and stop the cleaning brush motor; after all mechanical parts are reset, check whether all indicator lights on the control panel have returned to standby mode.

[0063] Furthermore, if the control panel indicates normal, a safety check is performed, and a notification of the completion of the cleaning operation is sent to the operator, and the power generation efficiency of the photovoltaic module continues to be monitored.

[0064] In summary, the present invention realizes an efficient and accurate cleaning process through automatic data collection, predictive modeling and optimization of cleaning paths, intelligently judges the degree of dirtiness, performs automated cleaning, and dynamically evaluates the effects to determine whether secondary cleaning is needed, thereby improving cleaning efficiency and quality, optimizing resource utilization, and significantly improving the overall performance and reliability of the photovoltaic system.

[0065] Example 2

[0066] Reference Figure 3 , is an embodiment of the present invention, which provides an intelligent photovoltaic self-cleaning device, including: an electric booster pump, a solenoid valve, a motor and a lower computer, wherein the lower computer is mainly composed of a control chip, a communication unit and a power supply; an acquisition module, used to automatically calculate the weekly power generation through a photovoltaic control platform and obtain the image monitored by the camera; a construction module, based on the weekly power generation and the image, to build a prediction model; a calculation module, used to judge the degree of dirtiness of each photovoltaic component according to the prediction result of the prediction model, and calculate the optimal cleaning path; a cleaning module, used to start the first cleaning operation according to the optimal cleaning path, and evaluate the cleaning effect; an evaluation module, based on the evaluation result of the evaluation of the cleaning effect, and judge whether to perform a second cleaning operation; a control module, used to reset the cleaning operation according to the result of the second cleaning operation, and complete the intelligent photovoltaic self-cleaning operation.

[0067] Example 3

[0068] Reference Figure 4 , is an embodiment of the present invention, which is different from the previous embodiment in that:

[0069] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0070] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0071] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0072] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0073] Example 4

[0074] Referring to Table 1, which is the second embodiment of the present invention, this embodiment provides an intelligent photovoltaic self-cleaning method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0075] Specifically, as shown in Table 1, by comparing the test data, the intelligent photovoltaic self-cleaning method of the present invention shows obvious advantages in multiple key indicators, including Group A: manual cleaning, Group B: fixed-cycle automatic cleaning, Group C: simple intelligent cleaning, and Group D: the method of the present invention; in terms of average daily power generation, Group D reached 1750kWh, which is 12.2% higher than the traditional manual cleaning method, 8.0% higher than the fixed-cycle automatic cleaning, and 4.2% higher than the simple intelligent cleaning. This significant increase in power generation directly reflects the excellent performance of the method of the present invention in maintaining the cleanliness of photovoltaic panels and improving power generation efficiency.

[0076] Table 1 Comparison between the present invention and the prior art

[0077] parameter Group A Group B Group C Group D Average daily power generation (kWh) 1560 1620 1680 1750 Cleaning frequency (times / month) 1 4 3 2.5 Water consumption per cleaning (L / ㎡) 3.5 2.8 2.5 2.0 Cleaning labor cost (yuan / month) 2000 500 400 200 Equipment failure rate (%) 0 2.5 2.0 0.5 Average cleanliness rating (0-100) 85 88 92 97

[0078] Furthermore, in terms of cleaning frequency, Group D (2.5 times / month) is lower than Group B (4 times / month) and Group C (3 times / month), but higher than Group A (1 time / month); this shows that the method of the present invention can more accurately judge the cleaning needs and avoid the problems of excessive cleaning and insufficient cleaning. At the same time, Group D has the lowest water consumption for a single cleaning, which is only 2.0L / ㎡, saving 42.9% of water compared to traditional manual methods, reflecting the significant advantages of this method in water conservation. In terms of cleaning labor costs, Group D only needs 200 yuan per month, which is significantly lower than other groups, especially saving 90% of labor costs compared to manual cleaning. This not only reduces operation and maintenance expenses, but also reduces the complexity of personnel management.

[0079] Furthermore, the equipment failure rate is an important indicator for measuring system reliability. The failure rate of Group D is only 0.5%, which is much lower than other automated solutions (Group B 2.5%, Group C 2.0%), and close to the reliability of manual cleaning. This shows that the method of the present invention not only achieves a high degree of automation, but also maintains extremely high system stability. The average cleanliness score directly reflects the cleaning effect. Group D scored as high as 97 points, far exceeding other solutions. This excellent result is due to the precise dirt detection, flexible cleaning strategy and all-round cleaning design without dead ends of the present invention.

[0080] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent photovoltaic self-cleaning method, characterized in that: include, Automatically calculate weekly power generation through the photovoltaic control platform and obtain images monitored by cameras; constructing a prediction model based on the weekly power generation and the image; According to the prediction results of the prediction model, the degree of dirtiness of each photovoltaic module is determined, and the optimal cleaning path is calculated; According to the optimal cleaning path, start the first cleaning operation and evaluate the cleaning effect; Based on the evaluation result of the cleaning effect, determining whether to perform a second cleaning operation; According to the result of the second cleaning operation, the cleaning operation is reset to complete the intelligent photovoltaic self-cleaning operation.

2. The intelligent photovoltaic self-cleaning method according to claim 1, characterized in that: The process of constructing the prediction model includes the following steps: Collecting power generation data, image data and meteorological information of photovoltaic modules; Extract feature information from image data through computer vision algorithms to quantify the degree of dirtiness; Based on power generation data, feature information and meteorological information, a prediction model is established using the support vector machine algorithm to output the expected power generation efficiency η of each photovoltaic module. pred ; The expected power generation efficiency η pred The specific formula is as follows: Among them, η pred is the expected power generation efficiency, E w is the weekly power generation, E w is the weekly power generation, E d is the daily power generation, W is the meteorological factor, T is the temperature, and S is the dirtiness score. The specific formula is as follows: S=∑(w i *A i ) / A total ; Among them, w i is the dirtiness weight of component i, A i is the dirty area of ​​component i, A total is the total area.

3. The intelligent photovoltaic self-cleaning method according to claim 2, characterized in that: Also includes, For component i, if the dirtiness score S is greater than the dirtiness threshold S threshold , it is marked as a component that needs to be cleaned, and a list of components that need to be cleaned is created, recording the component ID and the corresponding dirtiness score; If the dirtiness score S is less than or equal to the dirtiness threshold S threshold , then record the ID and current dirtiness score of this component in the cleaning component list, and continue to monitor the dirtiness of this component; Sort the components to be cleaned according to the priority scores, and divide the sorted component list into several cleaning groups; Using genetic algorithm to define the starting and ending points of several cleaning groups, and convert the component positions into nodes in the graph, calculate the distance matrix between nodes, and generate the optimal cleaning path; The optimal cleaning path is stored in the system database and transmitted to the cleaning operation; The specific formula of the priority score is as follows: P=ω1S i +ω2L i +ω3T i ; Among them, P is the priority score, ω1, ω2 and ω3 are weight coefficients, S i is the dirtiness score of component i, L i is the location factor of component i, T i is the time since the last cleaning of component i; The expected power generation efficiency includes: When the expected power generation efficiency η pred Less than the first cleaning threshold η threshold When the cleaning operation is required; When the expected power generation efficiency η pred Greater than or equal to the first cleaning threshold η threshold When the power generation efficiency of the component is low, the ID and the current expected power generation efficiency of the component are recorded in the list of normally operating components, and the power generation efficiency of the component continues to be monitored.

4. The intelligent photovoltaic self-cleaning method according to claim 3, characterized in that: The cleaning operation includes a first cleaning operation and a second cleaning operation; the first cleaning operation includes the following steps: Start the cleaning operation according to the optimal cleaning path; Check whether the liquid level of the tank level gauge and the power supply of each electrical equipment are normal, and whether all local switches are turned off; If the tank level gauge and the power supply of each electrical equipment are normal, and the local switches are all turned off, start the cleaning operation; Open the cleaning function in the operation panel and check whether there is any alarm on the panel; If there is an alarm on the panel, all cleaning operation preparation processes will be stopped immediately and an alarm notification will be sent to the operator; If there is no alarm on the panel, open the pump inlet and outlet solenoid valves, and at the same time open and check that the booster pump outlet pressure is normal; Check that the cleaning permission light at the control station is on, start the cleaning procedure, and start all the photovoltaic module inlet solenoid valves in sequence; At the same time, the motor group is started to drive the transmission device in the cleaning brush to rotate at a set speed, and the cleaning brush begins to brush the photovoltaic module at a uniform speed; Carry out the cleaning process of the entire group of photovoltaic modules in this area in this order; The images of the PV panels after the first cleaning operation are taken and transmitted to the prediction model to evaluate the cleaning effect. The dirtiness and water accumulation of the PV panels are checked through the camera.

5. The intelligent photovoltaic self-cleaning method according to claim 4, characterized in that: The evaluation of cleaning effect comprises: If the dirt on the photovoltaic module is still there or the water on the photovoltaic module has not been drained, perform a second cleaning operation; When the photovoltaic power generation after cleaning is less than the preset photovoltaic power generation, a second cleaning operation is performed; If a second cleaning operation is to be performed, the operator is asked whether to confirm the execution of the second cleaning operation; If the operator vetoes the second cleaning operation, the decision is recorded and the cleaning process ends; If the operator confirms to perform the second cleaning operation, the second cleaning operation is performed, and the PV module image is taken again and transmitted to the prediction model for evaluation; When the photovoltaic power generation after cleaning is greater than or equal to the preset photovoltaic power generation, it is determined that the cleaning effect is good and there is no need to perform a second cleaning operation.

6. The intelligent photovoltaic self-cleaning method according to claim 5, characterized in that: The second cleaning operation comprises the following steps: Restart the cleaning program and open the cleaning operation again on the operation panel, check whether there is any alarm on the panel and whether the power supply status of the tank liquid level equipment is normal; Turn on the booster pump and related solenoid valves, click the cleaning operation, start the cleaning program, and clean the photovoltaic components that need to be cleaned specifically; The cleaning effect is monitored in real time by the camera to check whether the PV panels have been thoroughly cleaned, and to confirm that the accumulated water has been completely drained, and to reset the cleaning operation.

7. The intelligent photovoltaic self-cleaning method according to claim 6, characterized in that: The resetting comprises the following steps: After completing all cleaning operations, close the solenoid valves of all PV panels in sequence and stop the booster pump; At the same time, the cleaning brush motor is controlled to start, so that the cleaning brush returns to the stop position; Confirm the cleaning brush is in the correct position through the Hall limiter and stop the cleaning brush motor; After all mechanical parts have been reset, check whether all indicators on the control panel have returned to standby mode; If the control panel indicates normal, a safety check is performed, and a notification is sent to the operator that the cleaning operation is complete, and the power generation efficiency of the PV panels continues to be monitored.

8. A device using an intelligent photovoltaic self-cleaning method as claimed in any one of claims 1 to 7, characterized in that: include: The acquisition module is used to automatically calculate the weekly power generation through the photovoltaic control platform and obtain the images monitored by the camera; A construction module, based on the weekly power generation and the image, constructs a prediction model; A calculation module, used to determine the degree of dirtiness of each photovoltaic module and calculate the optimal cleaning path according to the prediction results of the prediction model; A cleaning module, used to start the first cleaning operation according to the optimal cleaning path and evaluate the cleaning effect; An evaluation module, based on the evaluation result of the cleaning effect, determines whether to perform a second cleaning operation; The control module is used to reset the cleaning operation according to the result of the second cleaning operation to complete the intelligent photovoltaic self-cleaning operation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent photovoltaic self-cleaning method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent photovoltaic self-cleaning method according to any one of claims 1 to 7 are implemented.

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