A photovoltaic power station operation management method and system based on data analysis

Abstract

The present invention provides a photovoltaic power station operation management method and system based on data analysis, which belongs to the field of operation management technology, and specifically includes: determining an effective cleaning process cycle in a cleaning process cycle according to a deviation of a power generation waveform, determining a change in the historical power generation of the photovoltaic power station under different focused light intensities according to analysis results of historical power generation data under different effective cleaning process cycles, determining a reference cleaning process cycle under different focused light intensities based on the change, determining time period distribution data of different focused light intensities within a preset time period in the future through prediction results of weather data, and determining a cleaning process cycle of photovoltaic components of the photovoltaic power station in combination with the reference cleaning process cycle under different focused light intensities, thereby improving the power generation efficiency of the photovoltaic power station.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation management, and in particular relates to a photovoltaic power station operation management method and system based on data analysis. Background Art

[0002] By building a photovoltaic operation and management platform, we can access the equipment of photovoltaic power stations, monitor the operation of photovoltaic power stations online, and ensure safe operation. Based on data analysis, we can monitor the efficiency of photovoltaic power generation, issue alarms in time for abnormalities, and maximize the benefits of photovoltaic power generation.

[0003] In the existing technical solution, the invention patent application CN202311545987.0 "A three-dimensional visualization system for photovoltaic power stations" realizes comprehensive monitoring and management of photovoltaic power stations through technical means such as automated data collection, intelligent video analysis and big data analysis, so that operation managers can obtain accurate data information in a timely manner. However, at the same time, there are the following technical problems:

[0004] During the operation of photovoltaic power stations, dust accumulation is inevitable. Therefore, if dust cleaning recommendations cannot be output based on the power generation data of the photovoltaic power station, the power generation efficiency of the photovoltaic power station cannot be improved.

[0005] In response to the above technical problems, the present invention provides a photovoltaic power station operation management method and system based on data analysis. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a photovoltaic power station operation management method based on data analysis is provided.

[0008] A photovoltaic power station operation management method based on data analysis, specifically comprising:

[0009] S1 determines the time period distribution data under different light intensities based on the analysis results of the historical light data of the area where the photovoltaic power station is located, and determines the light intensity of interest among the light intensities according to the time period distribution data under different light intensities;

[0010] S2: determining the deviation of the power generation waveform of the photovoltaic components of the photovoltaic power station under different cleaning treatment cycles based on the light intensity of interest, and determining the effective cleaning treatment cycle in the cleaning treatment cycle according to the deviation of the power generation waveform;

[0011] S3 determines the change of the historical power generation of the photovoltaic power station under different light intensities of interest according to the analysis results of the historical power generation data under different effective cleaning treatment cycles, and determines the reference cleaning treatment cycle under different light intensities of interest based on the change;

[0012] S4 determines the time period distribution data of different light intensities of interest within a preset time period in the future through the prediction results of weather data, and determines the cleaning process cycle of the photovoltaic components of the photovoltaic power station in combination with the reference cleaning process cycle under different light intensities of interest.

[0013] The beneficial effects of the present invention are:

[0014] The effective cleaning process cycle in the cleaning process cycle is determined according to the deviation of the power generation waveform, thereby realizing the determination of the effective cleaning process cycle according to the deviation of the power generation waveform under different light intensities of interest, avoiding the occurrence of technical problems such as the power generation quality of the photovoltaic power station not meeting the requirements due to the untimely cleaning of the photovoltaic panels of the photovoltaic power station. At the same time, through the determination of the effective cleaning process cycle, the efficiency of determining the reference cleaning process cycle is further improved.

[0015] The cleaning process cycle of the photovoltaic components of the photovoltaic power station is determined based on the time period distribution data of different focused light intensities within a preset time period in the future and the reference cleaning process cycles under different focused light intensities. Dynamic adjustment of the cleaning process cycle of the photovoltaic components of the photovoltaic power station is achieved based on the time period distribution of focused light intensity, avoiding the emergence of technical problems such as unreliable power quality and electricity quantity of the photovoltaic power station caused by adopting a unified cleaning process cycle, and improving the reliability of the operation of the photovoltaic power station.

[0016] A further technical solution is that the historical illumination data is determined based on analysis results of historical monitoring data of the photovoltaic power station.

[0017] A further technical solution is that the time period distribution data under the light intensity includes the distribution of time periods corresponding to different light intensities on different dates.

[0018] A further technical solution is that the method for determining the intensity of the light of interest is:

[0019] Based on the time period distribution data, determine the time period corresponding to the light intensity and use it as the matching time period;

[0020] Determine the date on which the matching period exists according to the period distribution data of the matching period, and use it as the matching date;

[0021] Whether the light intensity is the light intensity of interest is determined based on the proportion of the number of matching dates.

[0022] A further technical solution is that when the proportion of the number of matching dates of the light intensity is greater than the proportion of a preset number, the light intensity is determined to be the light intensity of interest.

[0023] A further technical solution is that the time period distribution data includes distribution data of time periods corresponding to the light intensity of interest on different dates within a preset time period.

[0024] A further technical solution is that the method for determining the cleaning cycle of the photovoltaic components of the photovoltaic power station is:

[0025] The time period distribution data of different light intensity of interest are used to calculate the time period distribution quantity of the light intensity of interest, and the quantity proportions of the time period distribution quantities of different light intensity of interest are calculated according to the different time period distribution quantities of the light intensity of interest;

[0026] The weight coefficients of different focused light intensities are determined based on the proportion of the time period distribution of the focused light intensities, and the cleaning cycle of the photovoltaic components of the photovoltaic power station is determined in combination with the reference cleaning cycle under different focused light intensities.

[0027] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned photovoltaic power station operation management method based on data analysis when running the computer program.

[0028] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0031] Figure 1 It is a flow chart of a photovoltaic power station operation management method based on data analysis;

[0032] Figure 2 is a flow chart of a method focusing on determination of light intensity;

[0033] Figure 3 is a flow chart of a method for determining an effective cleaning process cycle;

[0034] Figure 4 is a flow chart of a method for determining a reference cleaning process cycle with regard to light intensity;

[0035] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0037] During the operation of photovoltaic power stations, dust accumulation is inevitable. Therefore, it is necessary to output dust cleaning suggestions based on the power generation data of the photovoltaic power station, so as to improve the power generation efficiency of the photovoltaic power station while reducing the difficulty of inspection and processing.

[0038] Focus on light intensity: Based on the time period distribution data, determine the time period corresponding to the light intensity, and use it as the matching time period. According to the time period distribution data of the matching time period, determine the date when the matching time period exists, and use it as the matching date. When the proportion of matching dates is greater than 0.5, the light intensity is determined to be the focus light intensity.

[0039] Effective cleaning process cycle: Based on the power generation data of different photovoltaic panels of the photovoltaic power station under the cleaning process cycle, the power generation data of the photovoltaic panels under different light intensities of interest are determined, and the power generation data are used to determine the photovoltaic panels with deviations in the power generation waveform under the cleaning process cycle, and they are used as waveform deviation panels. When the proportion of waveform deviation panels under different light intensities of interest in the number of photovoltaic panels in the photovoltaic power station is less than 0.1, it is determined that the cleaning process cycle is an effective cleaning process cycle.

[0040] Reference cleaning process cycle: Utilize the change data of power generation of the photovoltaic power station under the effective cleaning process cycle to determine the change of power generation under different monitoring times, and determine the number of monitoring times falling within the preset change range based on the change and the preset change range. When the proportion of monitoring times falling within the preset change range in the monitoring times is less than 0.2, the effective cleaning process cycle is determined to be the reference cleaning process cycle.

[0041] The cleaning processing cycle of photovoltaic components of photovoltaic power stations: determine the number of time period distributions of concerned light intensities based on the time period distribution data of different concerned light intensities, and make different proportions of time period distributions of concerned light intensities according to different time period distributions of concerned light intensities, determine different weight coefficients of concerned light intensities based on the proportions of the time period distributions of concerned light intensities, and take the reference cleaning processing cycle under the concerned light intensity with the largest weight coefficient as the cleaning processing cycle of photovoltaic components of photovoltaic power stations.

[0042] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a photovoltaic power station operation management method based on data analysis is provided, which specifically includes:

[0043] S1 determines the time period distribution data under different light intensities based on the analysis results of the historical light data of the area where the photovoltaic power station is located, and determines the light intensity of interest among the light intensities according to the time period distribution data under different light intensities;

[0044] Furthermore, the historical illumination data is determined based on analysis results of historical monitoring data of the photovoltaic power station.

[0045] Specifically, the time period distribution data under the light intensity includes the distribution of time periods corresponding to different light intensities on different dates.

[0046] It should be noted that if Figure 2 As shown, the method for determining the intensity of the light concerned is:

[0047] Based on the time period distribution data, determine the time period corresponding to the light intensity and use it as the matching time period;

[0048] Determine the date on which the matching period exists according to the period distribution data of the matching period, and use it as the matching date;

[0049] Whether the light intensity is the light intensity of interest is determined based on the proportion of the number of matching dates.

[0050] Further, when the proportion of the number of matching dates of the light intensity is greater than a preset proportion, the light intensity is determined to be the focus light intensity.

[0051] It should also be noted that the method for determining the intensity of the light of interest is:

[0052] Based on the time period distribution data, determine the time period corresponding to the light intensity and use it as the matching time period;

[0053] Whether the light intensity is a light intensity of interest is determined based on the number of matching time periods.

[0054] Optionally, the method for determining the intensity of the light of interest is:

[0055] S11 determines the time period corresponding to the light intensity based on the time period distribution data, and uses it as the matching time period;

[0056] S12: determining the date on which the matching period exists according to the period distribution data of the matching period, and using it as the matching date, and determining the matching coefficients of different matching dates and the light intensity according to the proportion of the number of matching periods of different matching dates;

[0057] S13 determines a comprehensive matching coefficient based on the proportion of the number of matching dates and the matching coefficients of different matching dates and the light intensity, and determines whether the light intensity is the light intensity of interest through the comprehensive matching coefficient.

[0058] S2: determining the deviation of the power generation waveform of the photovoltaic components of the photovoltaic power station under different cleaning treatment cycles based on the light intensity of interest, and determining the effective cleaning treatment cycle in the cleaning treatment cycle according to the deviation of the power generation waveform;

[0059] Furthermore, the deviation of the power generation waveform is determined based on the waveform deviation between the power generation waveform and the sine wave.

[0060] Specifically, it should be noted that Figure 3 As shown, the method for determining the effective cleaning process cycle is:

[0061] Based on the power generation data of different photovoltaic panels of the photovoltaic power station under the cleaning process cycle, the power generation data of the photovoltaic panels under different light intensities of interest are determined, and the photovoltaic panels having deviations in power generation waveforms under the cleaning process cycle are determined using the power generation data as waveform deviation panels;

[0062] Determining waveform deviation coefficients under different light intensities of interest according to the proportion of waveform deviation panels under different light intensities of interest in the number of photovoltaic panels in the photovoltaic power station;

[0063] An effective cleaning process cycle in the cleaning process cycle is determined by using waveform deviation coefficients under different light intensities of interest.

[0064] Furthermore, when there is a waveform deviation coefficient in the cleaning process cycle that does not meet the required light intensity, it is determined that the cleaning process cycle does not belong to a valid cleaning process cycle.

[0065] It should also be noted that the method for determining the effective cleaning process cycle is:

[0066] Based on the power generation data of different photovoltaic panels of the photovoltaic power station under the cleaning process cycle, the power generation data of the photovoltaic panels under different light intensities of interest are determined, and the photovoltaic panels having deviations in power generation waveforms under the cleaning process cycle are determined using the power generation data as waveform deviation panels;

[0067] The total number of the cleaning process cycles is determined according to the number of waveform deviation panels under different light intensities of interest, and the effective cleaning process cycles in the cleaning process cycles are determined according to the total number.

[0068] Optionally, the method for determining the effective cleaning process cycle is:

[0069] Based on the power generation data of different photovoltaic panels of the photovoltaic power station under the cleaning process cycle, the power generation data of the photovoltaic panels under different light intensities of interest are determined, and the power generation data are used to determine the photovoltaic panels with deviations in the power generation waveform under the cleaning process cycle, and the panels are used as waveform deviation panels. When the number of light intensities of interest of the panels with waveform deviations does not meet the requirements, it is determined that the cleaning process cycle does not belong to a valid cleaning process cycle;

[0070] When the number of concerned light intensities of the waveform deviation panel meets the requirement:

[0071] When the number of panels with waveform deviations does not meet the required light intensity, it is determined that the cleaning process cycle does not belong to a valid cleaning process cycle;

[0072] When there is no waveform deviation and the number of panels does not meet the required light intensity of interest:

[0073] The total number of waveform deviation panels is calculated based on the number of waveform deviation panels with different light intensity of interest, and when the total number of waveform deviation panels does not meet the requirement, it is determined that the cleaning process cycle does not belong to a valid cleaning process cycle;

[0074] When the total number of waveform deviation panels meets the requirement:

[0075] Determining waveform deviation coefficients under different light intensities of interest according to the number of waveform deviation panels under different light intensities of interest and the waveform deviation amounts of different waveform deviation panels, and determining that the cleaning process cycle does not belong to a valid cleaning process cycle when there is a waveform deviation coefficient that does not meet the required light intensity of interest;

[0076] When there is no concerned light intensity where the waveform deviation coefficient does not meet the requirements:

[0077] The comprehensive deviation coefficient under the cleaning process cycle is determined by the waveform deviation coefficient under different light intensities of interest, and the effective cleaning process cycle in the cleaning process cycle is determined by the comprehensive deviation coefficient.

[0078] Furthermore, the effective cleaning process cycle is a cleaning process cycle in which the comprehensive deviation coefficient is less than a preset deviation coefficient.

[0079] It should be noted that the comprehensive deviation coefficient is determined according to the average value of the waveform deviation coefficients under different light intensities of interest.

[0080] S3 determines the change of the historical power generation of the photovoltaic power station under different light intensities of interest according to the analysis results of the historical power generation data under different effective cleaning treatment cycles, and determines the reference cleaning treatment cycle under different light intensities of interest based on the change;

[0081] Furthermore, the change in the historical power generation of the photovoltaic power station is determined based on the change in the historical power generation on a recent date.

[0082] Specifically, Figure 4 As shown, the method for determining the reference cleaning process cycle under the light intensity of interest is:

[0083] Based on the change of the historical power generation of the photovoltaic power station under the light intensity of interest, determining the change data of the power generation of the photovoltaic power station under the effective cleaning process cycle;

[0084] Determine the variation of power generation under different monitoring times by using the variation data of power generation of the photovoltaic power station under the effective cleaning treatment cycle, and determine the monitoring times falling within the preset variation interval according to the variation and the preset variation interval;

[0085] Whether the effective cleaning process cycle is a reference cleaning process cycle is determined according to the proportion of the monitoring times falling within the preset variation interval in the monitoring times.

[0086] Further, when, in the effective cleaning process cycle, the proportion of the number of monitoring times falling within the preset variation interval in the number of monitoring times is greater than the proportion of the preset number of monitoring times, the effective cleaning process cycle is determined to be a reference cleaning process cycle.

[0087] It can be understood that the monitoring times are determined according to the monitoring data of the photovoltaic power station in the most recent preset time period and according to a preset times threshold.

[0088] S4 determines the time period distribution data of different light intensities of interest within a preset time period in the future through the prediction results of weather data, and determines the cleaning process cycle of the photovoltaic components of the photovoltaic power station in combination with the reference cleaning process cycle under different light intensities of interest.

[0089] Furthermore, the time period distribution data includes distribution data of time periods corresponding to the light intensity of interest on different dates within a preset time period.

[0090] Specifically, the method for determining the cleaning cycle of the photovoltaic components of the photovoltaic power station is:

[0091] The time period distribution data of different light intensity of interest are used to calculate the time period distribution quantity of the light intensity of interest, and the quantity proportions of the time period distribution quantities of different light intensity of interest are calculated according to the different time period distribution quantities of the light intensity of interest;

[0092] The weight coefficients of different focused light intensities are determined based on the proportion of the time period distribution of the focused light intensities, and the cleaning cycle of the photovoltaic components of the photovoltaic power station is determined in combination with the reference cleaning cycle under different focused light intensities.

[0093] The reference cleaning process cycle corresponding to the light intensity of interest with the largest weight coefficient is used as the cleaning process cycle of the photovoltaic module.

[0094] Second, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned photovoltaic power station operation management method based on data analysis when running the computer program.

[0095] Optionally, the above step S11 includes steps S111-S112, which are specifically:

[0096] S111 determines the time period corresponding to the light intensity based on the time period distribution data, and uses it as the matching time period. When the number of the matching time periods is within the preset time period number interval, the process proceeds to step S112. When the number of the matching time periods is not within the preset time period number interval, it is determined that the light intensity does not belong to the light intensity of interest.

[0097] S112: When the number of the matching time periods is greater than the preset number of matching time periods, it is determined that the light intensity belongs to the focus light intensity; when the number of the matching time periods is not greater than the preset number of matching time periods, the process proceeds to step S12.

[0098] Optionally, the above step S12 includes steps S121-S124, specifically:

[0099] S121 determines the date of the matching period according to the period distribution data of the matching period, and uses it as the matching date. When the number of matching dates is less than the preset number of matching dates, it is determined that the light intensity does not belong to the attention light intensity. When the number of matching dates is not less than the preset number of matching dates, it goes to step S122.

[0100] S122 determines the matching coefficients of different matching dates and the light intensity according to the proportion of the number of matching time periods of different matching dates. When there is a matching date with a matching coefficient greater than a preset matching coefficient, the process proceeds to step S123. When there is no matching date with a matching coefficient greater than the preset matching coefficient, the process proceeds to step S13.

[0101] S123: taking the matching date whose matching coefficient is greater than the preset matching coefficient as the screening matching date, and when the number of the screening matching dates is greater than the preset number of screening dates, determining that the light intensity belongs to the concerned light intensity, and when the number of the screening matching dates is not greater than the preset number of screening dates, proceeding to step S124;

[0102] S124 determines the screening matching coefficient by using the matching coefficient of different screening matching dates and the light intensity. When the screening matching coefficient is greater than the preset coefficient threshold, it is determined that the light intensity belongs to the light intensity of concern. When the screening matching coefficient is not greater than the preset coefficient threshold, proceed to step S13.

[0103] Optionally, the method for determining the reference cleaning process cycle under the light intensity of interest is:

[0104] Based on the change of the historical power generation of the photovoltaic power station under the light intensity of interest, determining the change data of the power generation of the photovoltaic power station under the effective cleaning process cycle;

[0105] Using the variation data of the power generation of the photovoltaic power station under the effective cleaning process cycle, the variation of the power generation under different monitoring times is determined, and when there is a monitoring number with a variation greater than a preset variation, it is determined that the effective cleaning process cycle does not belong to the reference cleaning process cycle;

[0106] When there is no monitoring number with a change amount greater than the preset change amount:

[0107] Obtaining the variation under different monitoring times, and when the variation under different monitoring times is not within the preset variation range, determining that the effective cleaning process cycle does not belong to the reference cleaning process cycle;

[0108] When there is a monitoring number of changes within the preset change range:

[0109] The monitoring times are divided into times inside the interval and times outside the interval according to whether the variation is within the preset variation interval, and the proportion of times outside the interval in the monitoring times is obtained. When the proportion of times outside the interval in the monitoring times is greater than the proportion of preset times:

[0110] It is determined that the effective cleaning process cycle does not belong to the reference cleaning process cycle;

[0111] When the proportion of the number of times outside the interval in the number of monitoring times is not greater than the proportion of the preset number of times:

[0112] The variation deviation under different monitoring external times is calculated based on the variation under different monitoring external times and the deviation between the adjacent endpoints of the preset variation interval. When there is no monitoring external times whose variation deviation does not meet the requirement, it is determined that the effective cleaning process cycle belongs to the reference cleaning process cycle.

[0113] When there is a change deviation that does not meet the required external monitoring times:

[0114] The number of external monitoring times when the variation deviation does not meet the requirement is taken as the variation deviation number, and when the variation deviation number does not meet the requirement, it is determined that the effective cleaning process cycle belongs to the reference cleaning process cycle;

[0115] When the number of variation deviations meets the requirements:

[0116] Determine a comprehensive deviation coefficient according to the variation deviation under different external monitoring times and the proportion of external monitoring times in the monitoring times, and when the comprehensive deviation coefficient does not meet the requirements, determine that the effective cleaning process cycle does not belong to the reference cleaning process cycle;

[0117] When the comprehensive deviation coefficient meets the requirements:

[0118] A cleaning requirement coefficient is calculated based on the proportion of the number of times within the interval in the monitoring number and the comprehensive deviation coefficient, and the cleaning requirement coefficient is used to determine whether the effective cleaning process cycle is a reference cleaning process cycle.

[0119] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0120] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A photovoltaic power station operation management method based on data analysis, characterized in that: Specifically include: Based on the analysis results of the historical light data of the area where the photovoltaic power station is located, the time period distribution data under different light intensities are determined, and the light intensity of interest among the light intensities is determined according to the time period distribution data under different light intensities; Based on the light intensity of interest, determining the deviation of the power generation waveform of the photovoltaic components of the photovoltaic power station under different cleaning treatment cycles, and determining the effective cleaning treatment cycle in the cleaning treatment cycle according to the deviation of the power generation waveform; Determine the change of the historical power generation of the photovoltaic power station under different light intensities of interest according to the analysis results of the historical power generation data under different effective cleaning treatment cycles, and determine the reference cleaning treatment cycle under different light intensities of interest based on the change; Determine the time distribution data of different light intensities of interest within a preset time period in the future through the prediction results of weather data, and determine the cleaning process cycle of the photovoltaic components of the photovoltaic power station in combination with the reference cleaning process cycle under different light intensities of interest; The method for determining the light intensity is as follows: Based on the time period distribution data, determine the time period corresponding to the light intensity and use it as the matching time period; Determine the date on which the matching period exists according to the period distribution data of the matching period, and use it as the matching date; Determining whether the light intensity is a light intensity of interest based on a proportion of the number of matching dates; The method for determining the effective cleaning process cycle is: Based on the power generation data of different photovoltaic panels of the photovoltaic power station under the cleaning process cycle, the power generation data of the photovoltaic panels under different light intensities of interest are determined, and the photovoltaic panels having deviations in power generation waveforms under the cleaning process cycle are determined using the power generation data as waveform deviation panels; Determining waveform deviation coefficients under different light intensities of interest according to the proportion of waveform deviation panels under different light intensities of interest in the number of photovoltaic panels in the photovoltaic power station; Determining an effective cleaning process cycle in the cleaning process cycle by using waveform deviation coefficients under different light intensities of interest; The method for determining the reference cleaning process cycle under the light intensity of interest is: Based on the historical power generation change of the photovoltaic power station under the light intensity of interest, determining the power generation change data of the photovoltaic power station under the effective cleaning process cycle; Determine the variation of power generation under different monitoring times by using the variation data of power generation of the photovoltaic power station under the effective cleaning treatment cycle, and determine the monitoring times falling within the preset variation range according to the variation and the preset variation range; Determining whether the effective cleaning process cycle is a reference cleaning process cycle according to the proportion of the monitoring times falling within the preset variation interval in the monitoring times; The method for determining the cleaning cycle of the photovoltaic components of the photovoltaic power station is: The time period distribution data of different light intensity of interest are used to calculate the time period distribution quantity of the light intensity of interest, and the quantity proportions of the time period distribution quantities of different light intensity of interest are calculated according to the different time period distribution quantities of the light intensity of interest; The weight coefficients of different focused light intensities are determined based on the proportion of the time period distribution of the focused light intensities, and the cleaning cycle of the photovoltaic components of the photovoltaic power station is determined in combination with the reference cleaning cycle under different focused light intensities.

2. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: The historical illumination data is determined based on analysis results of historical monitoring data of the photovoltaic power station.

3. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: The time period distribution data under the light intensity includes the distribution of time periods corresponding to different light intensities on different dates.

4. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: When the proportion of the number of matching dates of the light intensity is greater than the preset proportion, the light intensity is determined to be the light intensity of interest.

5. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: The deviation of the power generation waveform is determined according to the waveform deviation between the power generation waveform and the sine wave.

6. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: The change in the historical power generation of the photovoltaic power station is determined based on the change in the historical power generation on a recent date.

7. The photovoltaic power station operation management method based on data analysis according to claim 1, characterized in that: When, in the effective cleaning process cycle, the proportion of the monitoring times falling within the preset variation interval in the monitoring times is greater than the preset monitoring times, the effective cleaning process cycle is determined to be a reference cleaning process cycle.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, a photovoltaic power station operation management method based on data analysis as described in any one of claims 1-7 is executed.

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