Photovoltaic information processing method and system

Abstract

The invention discloses a photovoltaic information processing method and system, and the method comprises the steps: obtaining the distribution information of equipment, and constructing an information storage matrix according to the distribution information of the equipment; updating the power information of each unit in the information storage matrix according to a statistical period; polling and detecting whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of the adjacent units by taking a statistical period as granularity, and the deviation condition is that a deviation greater than a first proportion exists; judging the equipment corresponding to the unit meeting the deviation condition as target equipment with a suspected fault, and marking, tracking and monitoring the target equipment; and continuously carrying out data tracking of a plurality of statistical cycles on the target equipment, and when the target equipment presents a trend that the deviation is increased or reduced along with time compared with the power data of the equipment in the adjacent unit, judging that the fault type of the equipment is a rotating mechanism fault.

Description

Technical Field

[0001] The present application relates to photovoltaic management technology, and in particular to a photovoltaic information processing method and system. Background Art

[0002] With the development of clean energy technology, photovoltaics constitute an important part of the supply of clean energy. Generally, photovoltaic facilities need to be distributed on a large scale to generate benefits. However, since they are usually distributed in areas with low development levels, considerable human resources are required to maintain the area. Now, most equipment can turn according to the sunlight, which can produce greater power output than fixed photovoltaic panels. If the steering mechanism fails, the efficiency of the equipment will be greatly reduced. Some solutions can use sensors to detect the failure of most rotating mechanisms. However, sensors need to be installed on each photovoltaic panel, which increases costs. In addition, some failure modes, such as mechanism detachment, cannot be detected by detecting the motor sensor status. In addition, this type of fault is not very urgent, and even maintenance requires a certain period of time. Therefore, the inventor believes that a solution with lower cost can be proposed, which can distinguish the fault type as a steering mechanism failure. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a photovoltaic information processing method and system, which can distinguish the type of rotation mechanism fault through power information without adding sensors.

[0004] On the one hand, an embodiment of the present application provides a photovoltaic information processing method, including:

[0005] Acquire distribution information of the device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix;

[0006] Updating the power information of each unit in the information storage matrix according to the statistical period;

[0007] According to the statistical cycle, polling is performed to detect whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units, and the deviation condition is that there is a deviation greater than a first ratio;

[0008] The equipment corresponding to the unit that meets the deviation condition is determined as a target equipment with suspected fault, and the target equipment is marked and tracked and monitored;

[0009] The target device is continuously tracked for multiple statistical cycles. When the power data of the target device, compared with the power data of the devices in the adjacent units, shows a trend of increasing or decreasing deviation over time, the fault type of the device is determined to be a rotating mechanism fault.

[0010] In some embodiments, the deviation condition further includes the deviation being less than a second ratio, and the second ratio being greater than the first ratio.

[0011] In some embodiments, when the power data of the target device shows a trend of increasing or decreasing deviation over time compared with the power data of the devices in the adjacent units, it is determined that the fault type of the device is a rotation mechanism fault, specifically:

[0012] When the power data of the target device compared with the power data of the device in the adjacent unit shows a trend of the deviation increasing or decreasing over time in at least three statistical cycles, the fault type of the device is determined to be a rotating mechanism fault.

[0013] In some embodiments, the following steps are also included: according to the deviation ratio of power data collected in multiple different statistical periods, the angle deviation corresponding to each statistical period is estimated by looking up an empirical table, and according to the theoretical angle of normal equipment around the target device in each statistical period, the position where the target device stays after the failure is estimated; the empirical table records the power deviation ratio of different angle deviations relative to the optimal position.

[0014] In some embodiments, in the process of estimating whether a target device is stuck after a failure, the process includes:

[0015] When the theoretical moving trajectory of the target device is determined, the theoretical position of the normal device in the statistical period is calculated, and the angle deviation obtained by looking up the empirical table based on the deviation ratio of the statistical period is determined to determine the estimated position of the target device on the theoretical moving trajectory in the statistical period. The average position of the estimated positions obtained in more than two statistical periods is calculated as the position where the target device stays after the failure.

[0016] In some embodiments, the power generation loss in the future period is estimated based on the location where the target equipment is stranded after a failure, the revenue loss is estimated based on the power generation loss of all target equipment and the power generation loss caused by other failure types, and whether advance maintenance is needed is determined based on the revenue loss and the cost of one-time maintenance.

[0017] In some embodiments, judging whether to perform maintenance in advance based on the cost of one maintenance specifically includes:

[0018] Determine the cost of a single maintenance, and divide the single maintenance cost by the maintenance interval to get the cost incurred per day in advance;

[0019] The revenue loss generated in the next few days is calculated. When the revenue loss is greater than a preset multiple of the cost of advance maintenance, a maintenance work order is generated, and the preset multiple is greater than 1.

[0020] In some embodiments, the following steps are also included: when the deviation ratio of the target device relative to the average value of the power data of adjacent devices in multiple statistical periods remains basically unchanged, the device is judged to be partially faulty or locally contaminated, wherein the deviation ratio remains basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

[0021] On the other hand, an embodiment of the present application provides a photovoltaic information processing system, including: a memory for storing a program; and a processor for loading the program to execute the photovoltaic information processing method.

[0022] On the other hand, an embodiment of the present application provides a photovoltaic information processing system, including:

[0023] An acquisition module, used to acquire distribution information of a device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix;

[0024] An updating module, used for updating the power information of each unit in the information storage matrix according to a statistical period;

[0025] A polling module, used for polling and detecting whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units according to a statistical period as a granularity, wherein the deviation condition is that there is a deviation greater than a first ratio;

[0026] A monitoring module is used to determine the device corresponding to the unit that meets the deviation condition as a target device with suspected failure, and mark and track the target device;

[0027] The determination module is used to continuously track the data of the target device for multiple statistical cycles. When the power data of the target device shows a trend of increasing or decreasing deviation over time compared with the power data of the devices in the adjacent units, the fault type of the device is determined to be a rotating mechanism fault.

[0028] Beneficial effects: The embodiment of the present application acquires distribution information of a device, constructs an information storage matrix according to the distribution information of the device, and stores power information of the device through the units in the information storage matrix; constructs an information storage matrix, and maps the distribution information of the device through the power information of the units in the information storage matrix, thereby simplifying orderly operations while ensuring a certain degree of accuracy; then updates the power information of each unit in the information storage matrix according to a statistical period; and polls and detects whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units according to a statistical period as a granularity, wherein the deviation condition is that there is a deviation greater than a first ratio; By finding a large proportion of deviations, abnormal devices are identified, and then the devices corresponding to the units that meet the deviation conditions are determined as target devices with suspected faults, and the target devices are marked and tracked; by continuously tracking the data of the target devices for multiple statistical cycles, when the power data of the target device shows a trend of expanding or shrinking deviations over time compared to the power data of the devices in the adjacent units, the fault type of the device is determined to be a rotating mechanism fault. Through this solution, in the absence of sensors specifically for rotating structure faults, devices with rotating structure faults can be distinguished from power data, thereby providing more data support for the system, facilitating maintenance personnel to perform targeted maintenance, and increasing maintenance efficiency. At the same time, analysis can be performed on existing data without the need for additional hardware, making it easy to deploy and implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0030] Figure 1 is a flow chart of a method provided by an embodiment of the present application;

[0031] Figure 2 It is a system module block diagram provided by an embodiment of the present application;

[0032] Figure 3 This is another system module block diagram provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described through the implementation method with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0034] Reference Figure 1 The embodiment of the present application discloses a photovoltaic information processing method. It is understandable that photovoltaics are usually deployed in batches in places with sufficient sunlight. Due to the cost of land, the deployment location is relatively remote, and there is a certain cost for inspection and maintenance. Common faults include but are not limited to line faults, partial / whole unit faults of photovoltaic panels, rotation mechanism faults, panel contamination, etc. This method mainly analyzes the rotation mechanism fault from the fault type through the existing collected power data, and provides more information for maintenance personnel.

[0035] Methods include:

[0036] S1. Obtain distribution information of a device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix.

[0037] Usually, related devices are usually placed in a planned manner according to the area, usually forming a certain array form. Although the actual distribution method does not strictly form a certain N*M matrix, this solution only needs to consider the proximity between devices. Therefore, mapping the distribution of devices in a matrix way can facilitate calculations without having to query the distance relationship between devices every time. It can be understood that in this step, according to the adjacent relationship of the devices, it is mapped into an A*B size information storage matrix.

[0038] Usually, most of the areas basically arrange the devices in a certain array, so the mapping difficulty is relatively low. Just poll the position of each device, find out the adjacent devices of each device that has been mapped to the matrix, and map them to the matrix units around it according to its adjacent devices. In some embodiments, there are drawings that have been built, in which the distribution is marked, and these drawings can be mapped to the matrix after being processed graphically. Of course, even if there are only the coordinate positions of the devices, they can be mapped based on the distance relationship between the devices. First, the first device placed in the matrix is ​​selected, and the coordinate system is established based on this as the origin. Usually, the first device selects the device in the corner, and then starts mapping according to the device closest to it, and fills the matrix in turn. In fact, as long as the matrix can satisfy the relative position relationship between the devices, it does not require the distance between each. The principle used is that within a certain distance, the energy received by different devices in normal state is theoretically the same. Therefore, anomalies can be found by comparing the differences between the device and the surrounding devices.

[0039] S2. Update the power information of each unit in the information storage matrix according to the statistical period.

[0040] It is understandable that in each statistical period, the power generated by each device will be output, and the power can be an average value over a period of time. It is understandable that power information will be interfered by many factors, including: weather reasons (such as clouds, etc.), seasonal reasons (light intensity in the season), the receiving angle of the device, and device pollution and device functional integrity. The statistical period can be one hour, half an hour, or 15 minutes, etc. It is understandable that the average output power in the statistical period is usually used as the data filled in the unit of the information storage matrix.

[0041] S3. Using a statistical cycle as a granularity, poll and detect whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units, wherein the deviation condition is the existence of a deviation greater than a first ratio.

[0042] It is understandable that the position of the device in the matrix actually reflects the positional relationship between it and the surrounding devices. Therefore, for its adjacent units, since the positions are basically close, the influencing factors that can be encountered will be similar. Therefore, by comparing the power difference between the device and the surrounding devices, anomalies can be found. At the same time, this method maps the device to the matrix in advance, which can simplify the algorithm. When polling, you only need to query the data of eight units (or several units) around the current unit. Even if the matrix only roughly simulates the distribution position, as long as the distance between adjacent units of the matrix is ​​not too large, it will not affect the implementation of the solution.

[0043] S4. Determine the device corresponding to the unit that meets the deviation condition as a target device with suspected failure, and mark and track the target device.

[0044] Usually, when a device has a steering mechanism failure at a certain position, or is contaminated, or has a partial failure, the power difference between it and the surrounding normal devices will be reflected in a certain statistical period. In the case of contamination or partial failure, the characteristics reflected in the power difference are different from those of the steering mechanism failure. In the case of contamination or failure, a relatively obvious mutation will occur. At the same time, since failures and contamination generally do not change over time, their reduction ratio relative to surrounding equipment will be relatively fixed. For steering mechanism failures, since the equipment cannot rotate, it will produce a relatively obvious power change after a period of time. Therefore, the type of failure can be distinguished from it.

[0045] S5. Continuously track the data of the target device for multiple statistical cycles. When the power data of the target device shows a trend of increasing or decreasing deviation over time compared with the power data of the devices in the adjacent units, it is determined that the fault type of the device is a rotating mechanism fault.

[0046] It is understandable that for the type of rotating mechanism fault discovered by output power, it is necessary to observe the change of power data in multiple statistical cycles compared with the deviation of surrounding equipment. Since the output of the equipment is affected by sunlight, it only works during the day. When the fault occurs early, such as 9 o'clock, the power deviation will gradually occur after the fault occurs on the same day, and the power deviation will tend to expand over time. Because as time goes by, the faulty equipment does not rotate, so the angle deviation with the light will increase over time, which will be reflected in the output power and the difference with the surrounding normal equipment will be widened. If the fault occurs at a later time, such as near night, its position at the time of the fault is before and after the sun sets, and its power change may need to be observed the next day. At this time, since the angle between the light and the equipment is getting closer and closer to the optimal position, the power difference tends to decrease. But the initial difference will be more obvious. Therefore, through the above method, equipment with rotating mechanism faults can be distinguished from equipment with other fault types.

[0047] In some embodiments, the deviation condition also includes a deviation less than a second ratio, and the second ratio is greater than the first ratio. Usually, with tracking and without tracking, the power output deviation can reach 25% to 30%, that is, there will be an obvious deviation in power output between the best position and the worse position, but the deviation still has an upper limit, which is different from the extremely large deviation. Therefore, if a certain deviation ratio is exceeded, it means that other types of faults may have occurred. Therefore, in this scheme, only when the deviation ratio is set to be less than the second ratio will it be determined that it may be a fault of the rotating mechanism. In general, the way to calculate the deviation of two values ​​can be (larger value - smaller value) / larger value.

[0048] In some embodiments, the following steps are also included: when the deviation ratio of the target device relative to the average value of the power data of the adjacent devices in multiple statistical periods remains basically unchanged, the device is determined to be partially faulty or locally contaminated, wherein the deviation ratio remains basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

[0049] It is understandable that since there are many factors that affect the output power, the normal output power of the equipment will also fluctuate to a certain extent. Therefore, these deviations are usually within a certain threshold range. Therefore, when the deviation ratio in two statistical cycles is less than a certain threshold, it is determined that the deviation ratio remains unchanged. For example, the absolute value of the difference in the deviation ratio is set to be less than 2% and is considered to remain unchanged.

[0050] In some embodiments, when the power data of the target device shows a trend of increasing or decreasing deviation over time compared to the power data of the devices in the adjacent units, it is determined that the fault type of the device is a rotation mechanism fault, specifically:

[0051] When the target device shows a trend of deviation expansion or contraction over time compared to the power data of the device in the adjacent unit in at least three statistical cycles, the fault type of the device is determined to be a rotating mechanism fault. It can be understood that three statistical cycles are generally required to determine whether there is a trend of shrinking or expanding. Of course, due to the uncertainty of environmental changes, when the deviation changes over time, it is not necessarily completely monotonous. There may be individual different change points. For example, in five cycles, one cycle shows a situation where the deviation shrinks or remains flat, while the remaining four statistical cycles show a trend of monotonous expansion. For this case, the judgment conditions can be relaxed. As long as Ns cycles show a trend of deviation expansion or contraction over time in N cycles, it is determined to meet the conditions. N and s can be set based on experience. Usually s takes a smaller number, such as 1 cycle or 2 cycles, and Ns needs to be at least greater than 3. Users can determine the relevant judgment settings according to the size of the statistical cycle.

[0052] In some embodiments, the following steps are also included: according to the ratio of the deviation of the power data collected in multiple different statistical periods, the angle deviation corresponding to each statistical period is estimated by looking up an empirical table, and according to the theoretical angle of the normal equipment around the target device in each statistical period, the position where the target device stays after the failure is estimated.

[0053] In the process of estimating the target equipment remaining after a failure, include:

[0054] When the theoretical moving trajectory of the target device is determined, the theoretical position of the normal device in the statistical period is calculated, and the angle deviation obtained by looking up the empirical table based on the deviation ratio of the statistical period is determined to determine the estimated position of the target device on the theoretical moving trajectory in the statistical period. The average position of the estimated positions obtained in more than two statistical periods is calculated as the position where the target device stays after the failure.

[0055] It is understandable that, whether it is a single-axis or multi-axis steering mechanism, its motion trajectory is related to the direction of sunlight movement, so the trajectory is generally determined. Therefore, when the trajectory is determined, the center point of the equipment panel is used as the equipment position, because the equipment is turning around a point, so the position of the point will move on a certain trajectory of a moving surface. In different time periods of each day, there is an optimal position for the equipment, and if the faulty equipment has a certain angle difference with the position, it will be reflected in the loss of power output. Through experience summary, the loss caused by different angle differences can be determined. Therefore, the angle deviation can be reversed based on the proportion of power deviation by looking up the table. When the trajectory is determined, the approximate position of the faulty equipment can be determined. By averaging the approximate positions estimated by multiple cycles, the position where the faulty equipment is stranded can be obtained.

[0056] In some embodiments, based on the location where the target device is stranded after a failure, the power generation loss in the future is estimated, the revenue loss is estimated based on the power generation loss of all target devices and the power generation loss caused by other types of failures, and whether early maintenance is needed is determined based on the revenue loss and the cost of one maintenance. In general, since the maintenance area is large, maintenance is usually performed on an area at intervals. Usually, maintenance is performed on a fixed number of days, for example, once a week. Since the manpower traffic light cost of each maintenance increases as the maintenance time shortens. Therefore, it is necessary to consider whether there are many faulty devices to determine whether early maintenance is cost-effective.

[0057] In some embodiments, judging whether to perform maintenance in advance based on the cost of one maintenance specifically includes:

[0058] Determine the cost of a single maintenance, and divide the single maintenance cost by the maintenance interval to get the cost incurred per day in advance;

[0059] The revenue loss generated in the next few days is calculated. When the revenue loss is greater than a preset multiple of the cost of advance maintenance, a maintenance work order is generated, and the preset multiple is greater than 1.

[0060] It is understandable that the cost of each maintenance per day can be determined by the cost of a single maintenance and the maintenance interval. Then the cost incurred each day in advance can be calculated. If the additional expenditure incurred by advance maintenance is smaller than the loss of income caused by equipment failure, it means that advance maintenance can be performed. Usually, a certain coefficient can be set, for example, it is stipulated that advance maintenance is performed only when the loss of income is greater than 1.2 times the cost of advance maintenance. In this way, a certain redundancy value can be set to avoid excessive maintenance.

[0061] Reference Figure 2, a photovoltaic information processing system, comprising:

[0062] Memory, used to store programs;

[0063] A processor is used to load the program to execute the photovoltaic information processing method.

[0064] The system can communicate with peripherals through the communication interface and perform further processing.

[0065] Reference Figure 3 , a photovoltaic information processing system, comprising:

[0066] An acquisition module, used to acquire distribution information of a device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix;

[0067] An updating module, used for updating the power information of each unit in the information storage matrix according to a statistical period;

[0068] A polling module, used for polling and detecting whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units according to a statistical period as a granularity, wherein the deviation condition is that there is a deviation greater than a first ratio;

[0069] A monitoring module is used to determine the device corresponding to the unit that meets the deviation condition as a target device with suspected failure, and mark and track the target device;

[0070] The determination module is used to continuously track the data of the target device for multiple statistical cycles. When the power data of the target device shows a trend of increasing or decreasing deviation over time compared with the power data of the devices in the adjacent units, the fault type of the device is determined to be a rotating mechanism fault.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0072] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A photovoltaic information processing method, characterized in that: include: Acquire distribution information of the device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix; Updating the power information of each unit in the information storage matrix according to the statistical period; According to the statistical cycle, polling is performed to detect whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units, and the deviation condition is that there is a deviation ratio greater than a first ratio; The equipment corresponding to the unit that meets the deviation condition is determined as a target equipment with suspected fault, and the target equipment is marked and tracked and monitored; The target device is continuously tracked for multiple statistical cycles. When the power data of the target device shows a trend of increasing or decreasing in deviation ratio over time compared with the power data of the devices in the adjacent units, the fault type of the device is determined to be a rotating mechanism fault.

2. The method according to claim 1, characterized in that The deviation condition also includes the deviation being less than a second ratio, the second ratio being greater than the first ratio.

3. The method according to claim 1, characterized in that When the target device shows a trend of increasing or decreasing deviation over time compared to the power data of the device in the adjacent unit, the fault type of the device is determined to be a rotation mechanism fault, specifically: When the power data of the target device is compared with the power data of the device in the adjacent unit, and the deviation ratio shows a trend of increasing or decreasing over time in at least three statistical cycles, the fault type of the device is determined to be a rotating mechanism fault.

4. The method according to claim 1, characterized in that: The following steps are also included: According to the deviation ratio of power data collected in multiple different statistical periods, the angle deviation corresponding to each statistical period is estimated by looking up the empirical table, and according to the theoretical angle of normal equipment around the target equipment in each statistical period, the position where the target equipment stays after the failure is estimated; the empirical table records the power deviation ratio of different angle deviations relative to the optimal position.

5. The method according to claim 4, characterized in that In the process of estimating the target equipment remaining after a failure, include: When the theoretical moving trajectory of the target device is determined, the theoretical position of the normal device in the statistical period is calculated, and the angle deviation obtained by looking up the empirical table based on the deviation ratio of the statistical period is determined to determine the estimated position of the target device on the theoretical moving trajectory in the statistical period. The average position of the estimated positions obtained in more than two statistical periods is calculated as the position where the target device stays after the failure.

6. The method according to claim 1, characterized in that Based on the location where the target equipment is stranded after the failure, the power generation loss in the future is estimated. The revenue loss is estimated based on the power generation loss of all target equipment and the power generation loss caused by other failure types. Based on the revenue loss and the cost of one-time maintenance, it is determined whether early maintenance is needed.

7. The method according to claim 1, characterized in that The determining whether to perform advance maintenance in combination with the cost of one maintenance specifically includes: Determine the cost of a single maintenance, and divide the single maintenance cost by the maintenance interval to get the cost incurred per day in advance; The revenue loss generated in the next few days is calculated. When the revenue loss is greater than a preset multiple of the cost of advance maintenance, a maintenance work order is generated, and the preset multiple is greater than 1.

8. The method according to claim 1, characterized in that The method also includes the following steps: when the deviation ratio of the target device relative to the average value of the power data of the adjacent devices in multiple statistical periods remains basically unchanged, the device is judged to be partially faulty or locally contaminated, wherein the deviation ratio remains basically unchanged means that the absolute value of the difference between the deviation ratios of two adjacent statistical periods is less than a third ratio.

9. A photovoltaic information processing system, characterized in that: include: Memory, used to store programs; A processor, used to load the program to execute the photovoltaic information processing method according to any one of claims 1 to 8.

10. A photovoltaic information processing system, characterized in that: include: An acquisition module, used to acquire distribution information of a device, construct an information storage matrix according to the distribution information of the device, and store power information of the device through units in the information storage matrix; An updating module, used for updating the power information of each unit in the information storage matrix according to a statistical period; A polling module, configured to poll and detect, according to a statistical period, whether the power data of each unit in the information storage matrix meets a deviation condition compared with the average power data of adjacent units, wherein the deviation condition is that there is a deviation greater than a first ratio; A monitoring module is used to determine the device corresponding to the unit that meets the deviation condition as a target device with suspected failure, and mark and track the target device; The determination module is used to continuously track the data of the target device for multiple statistical cycles. When the power data of the target device shows a trend of increasing or decreasing deviation over time compared with the power data of the devices in the adjacent units, the fault type of the device is determined to be a rotating mechanism fault.

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