Photovoltaic equipment early warning method, system and equipment and storage medium

Abstract

The invention provides a photovoltaic equipment early warning method, system and equipment and a medium, and belongs to the technical field of photovoltaic equipment monitoring. Index data of photovoltaic equipment are collected in real time, data preprocessing is carried out, an index analysis data set is obtained, and on the basis of the same index, a pre-warning result is obtained; taking an index corresponding to the index analysis data which exceeds a preset normal data range and lasts for a preset time period in the index analysis data set as a target index, and determining an equipment fault type corresponding to the target index based on a pre-created complexity framework model of the photovoltaic equipment so as to determine a target equipment fault type; according to the target index corresponding to the fault type of the target equipment and the preset weight value of the target index, the early warning level of the fault type of the target equipment is determined based on the preset early warning level table, and the fault type of the photovoltaic equipment is rapidly and accurately positioned; and each type of equipment fault is subjected to multi-index comprehensive analysis, so that the accuracy, stability and reliability of evaluating the health condition of the photovoltaic equipment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic equipment monitoring, and in particular relates to a photovoltaic equipment early warning method, system, equipment and storage medium. Background Art

[0002] With the rapid development of science and technology, the structure and internal systems of photovoltaic equipment are becoming increasingly complex, and the uncertainty in its operation is also increasing.

[0003] Traditional photovoltaic equipment monitoring methods often rely on a single indicator or fixed threshold judgment, which is difficult to fully and accurately reflect the actual health status of the equipment. Especially in complex and changeable operating environments, the monitoring of a single indicator often has lags and incompleteness, and cannot timely warn of potential faults, affecting the overall performance and safety of the photovoltaic system. Summary of the invention

[0004] Based on the above status of existing photovoltaic equipment monitoring, the present invention provides a photovoltaic equipment early warning method, system, equipment and medium to overcome at least one technical problem existing in the prior art.

[0005] To achieve the above object, the present invention provides a photovoltaic equipment early warning method, comprising:

[0006] According to a preset indicator data collection table, the indicator data of the photovoltaic equipment is collected in real time to obtain an indicator data set of the photovoltaic equipment; wherein the indicator data set includes an indicator and indicator data corresponding to the indicator;

[0007] Performing data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data, thereby obtaining an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators;

[0008] Based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is used as the target indicator;

[0009] Based on a pre-created complexity framework model of photovoltaic equipment, determine the equipment failure type that matches the target indicator, and use the equipment failure type that matches the target indicator as the target equipment failure type; wherein the complexity framework model includes an equipment failure type and at least two indicators that match the equipment failure type;

[0010] Determine the warning level of the target device failure type based on a preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator;

[0011] According to the warning level, warning information of the photovoltaic device is generated; wherein the warning information includes a target device failure type and a warning level.

[0012] In order to solve the above problems, the present invention further provides a photovoltaic equipment early warning system, which is used to apply the photovoltaic equipment early warning method as described above to perform photovoltaic equipment early warning; the system comprises:

[0013] A data collection module, used to collect the index data of the photovoltaic equipment in real time according to a preset index data collection table, and obtain an index data set of the photovoltaic equipment; wherein the index data set includes an index and index data corresponding to the index;

[0014] A preprocessing module, used for performing data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators;

[0015] An analysis and judgment module, for taking, based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds a preset normal data range and lasts for a preset time period as a target indicator;

[0016] A fault type locating module is used to determine the equipment fault type corresponding to the target indicator based on a pre-created complexity framework model of photovoltaic equipment, and use the equipment fault type corresponding to the target indicator as the target equipment fault type; wherein the complexity framework model includes an equipment fault type and at least two indicators corresponding to the equipment fault type;

[0017] An early warning level determination module, configured to determine the early warning level of the target device failure type based on a preset early warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator;

[0018] The warning information generation module is used to generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment failure type and the warning level.

[0019] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0020] at least one processor; and,

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps in the photovoltaic device early warning method as described above.

[0023] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. When the at least one instruction is executed by a processor in an electronic device, the above photovoltaic device early warning method is implemented.

[0024] The photovoltaic equipment early warning method, system, equipment and medium provided by the present invention collect the indicator data of the photovoltaic equipment in real time according to a preset indicator data collection table to obtain an indicator data set including multiple indicator data; then preprocess the indicator data set to obtain an indicator analysis data set, and then based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is used as the target indicator, and the equipment fault type corresponding to the target indicator is determined based on a pre-created complexity framework model of the photovoltaic equipment to determine the target equipment fault type; then, according to the number of target indicators corresponding to the target equipment fault type and the preset weight value of the target indicator, the warning level of the target equipment fault type is determined based on a preset warning level table; finally, warning information including the target equipment fault type and the warning level is generated to quickly and accurately locate the fault type of the photovoltaic equipment; each type of equipment fault does not rely on the threshold judgment of a single indicator, and through multi-indicator comprehensive analysis and quantitative management of the complexity of each equipment fault type through the complexity framework model, each type of equipment fault can be comprehensively analyzed by multiple indicators, thereby improving the accuracy, stability and reliability of evaluating the health status of photovoltaic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of a process flow of a photovoltaic equipment early warning method provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of a module of a photovoltaic equipment early warning system provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of the internal structure of an electronic device for implementing a photovoltaic device early warning method provided by an embodiment of the present invention.

[0029] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0030] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0031] Based on the problems existing in the above-mentioned prior art, the present invention mainly provides a photovoltaic equipment early warning method, system, equipment and medium, whose main purpose is to solve the problem in the prior art that it is difficult to fully and accurately reflect the actual health status of the equipment by relying on the judgment of a single indicator or a fixed threshold; especially in a complex and changeable operating environment, the monitoring of a single indicator often has lags and incompleteness, and is unable to timely warn of potential faults, affecting the overall performance and safety of the photovoltaic system.

[0032] Figure 1 This is a flow chart of a photovoltaic equipment early warning method provided by an embodiment of the present invention. The method can be executed by a system, which can be implemented by software and / or hardware.

[0033] Figure 1 The early warning method of photovoltaic equipment is described in general. Figure 1 As shown, in this embodiment, the photovoltaic equipment early warning method includes steps S110 to S160.

[0034] Step S110: According to a preset indicator data collection table, the indicator data of the photovoltaic equipment is collected in real time to obtain an indicator data set of the photovoltaic equipment; wherein the indicator data set includes an indicator and indicator data corresponding to the indicator.

[0035] Specifically, based on actual work experience or experimental tests, multiple key indicators that affect each type of equipment failure of photovoltaic equipment are determined, thereby obtaining a preset indicator data collection table. The preset indicator data collection table includes multiple key indicators, and according to the multiple key indicators recorded in the preset indicator data collection table, the indicator data corresponding to each indicator is collected in real time. Among them, the indicators are preferably, but not limited to, current, voltage, temperature, light intensity, etc.

[0036] As an optional embodiment of the present invention, according to a preset index data collection table, the index data of the photovoltaic equipment is collected in real time to obtain the index data set of the photovoltaic equipment, including:

[0037] By installing intelligent sensing devices or sensor devices on photovoltaic equipment, the indicator data corresponding to the indicators in the preset indicator data collection table can be obtained in real time;

[0038] A corresponding relationship is established between the indicator and the indicator data corresponding to the indicator, and an indicator data set of the photovoltaic equipment is obtained.

[0039] Specifically, according to the indicators in the preset indicator data collection table, an intelligent sensing device or a sensor device is installed on the photovoltaic equipment corresponding to each indicator to facilitate real-time acquisition of the indicator data corresponding to the indicator. For example, if the indicator is temperature, a temperature sensor can be installed on the photovoltaic equipment to obtain the temperature data of the photovoltaic equipment in real time. After establishing a corresponding relationship between the indicator and the indicator data, an indicator data set of the photovoltaic equipment is formed.

[0040] Step S120: preprocess the indicator data in the indicator data set to improve the data quality of the indicator data and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators.

[0041] Specifically, in order to improve data quality and the accuracy of subsequent data analysis, each indicator data in the indicator data set is preprocessed to obtain an indicator analysis data set. The indicator analysis data set includes indicators and preprocessed indicator analysis data corresponding to the indicators.

[0042] As an optional embodiment of the present invention, data preprocessing is performed on the indicator data in the indicator data set to improve the data quality of the indicator data, and the indicator analysis data set obtained includes:

[0043] Denoising the indicator data in the indicator data set to obtain a denoised indicator data set;

[0044] Performing filtering processing on the denoised index data in the denoised index data set to obtain a filtered index data set;

[0045] The filtering index data in the filtering index data set is standardized to obtain an index analysis data set.

[0046] Specifically, each indicator data in the indicator data set is subjected to denoising, filtering and standardization processing in turn, so as to obtain high-quality indicator analysis data. Multiple indicator analysis data constitute the indicator analysis data set.

[0047] Step S130: Based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is taken as the target indicator.

[0048] Specifically, for each identical indicator, the indicator analysis data in the indicator analysis data set is compared and analyzed with the preset normal data range corresponding to the indicator. When the indicator analysis data exceeds the preset normal data range and lasts for a preset time period, the indicator is used as the target indicator. Among them, the preset normal data ranges corresponding to different indicators are the same or different, and the preset time periods corresponding to different indicators are the same or different. The indicator analysis data exceeds the preset normal data range means that the indicator analysis data is greater than the maximum value in the preset normal data range or less than the minimum value in the preset normal data range.

[0049] As an optional embodiment of the present invention, based on the same indicator, taking the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period as the target indicator includes:

[0050] Each indicator in the indicator analysis data set is treated as a pending indicator;

[0051] The indicator analysis data corresponding to the pending indicator is compared with the preset normal data range corresponding to the pending indicator. If the indicator analysis data corresponding to the pending indicator exceeds the preset normal data range corresponding to the pending indicator and lasts for a preset time period, the pending indicator will be used as the target indicator; wherein each pending indicator corresponds to a preset normal data range and a preset time period.

[0052] Specifically, each indicator in the indicator analysis data set is used as a pending indicator separately, and the indicator analysis data corresponding to the pending indicator is compared with the preset normal data range corresponding to the pending indicator. If the indicator analysis data corresponding to the pending indicator exceeds the preset normal data range corresponding to the pending indicator and lasts for a preset time period, it means that the pending indicator meets the abnormal standard, and the pending indicator is used as the target indicator; if the indicator analysis data corresponding to the pending indicator does not exceed the preset normal data range corresponding to the pending indicator or the time of exceeding the preset normal data range does not reach the preset time period, it means that the pending indicator is not abnormal and can be ignored. Among them, the preset normal data range and the preset time period can be obtained based on actual experience or historical data analysis.

[0053] Step S140, based on the pre-created complexity framework model of photovoltaic equipment, determine the equipment failure type corresponding to the target indicator, and use the equipment failure type corresponding to the target indicator as the target equipment failure type; wherein the complexity framework model includes the equipment failure type and at least two indicators corresponding to the equipment failure type.

[0054] Specifically, there are many types of photovoltaic equipment failures. Based on actual experience or historical data analysis, multiple indicators corresponding to each equipment failure type are determined. That is, when each equipment failure type occurs, it may be affected by multiple indicators, thereby quantifying the complexity and uncertainty of equipment failures. When there is a target indicator (abnormal indicator) in the indicators of each type of equipment failure, it means that there is a potential problem with the equipment failure type and an early warning is required. Therefore, it is used as the target equipment failure type.

[0055] As an optional embodiment of the present invention, based on the pre-created complexity framework model of photovoltaic equipment, determining the equipment failure type corresponding to the target indicator, and taking the equipment failure type corresponding to the target indicator as the target equipment failure type includes:

[0056] Matching at least two indicators for different types of equipment failures of photovoltaic equipment to obtain a complexity framework model of photovoltaic equipment;

[0057] According to the complexity framework model, the equipment failure type corresponding to the target indicator is used as the target equipment failure type; wherein the target indicator corresponds to one or more target equipment failure types, and the target equipment failure type corresponds to one or more target indicators.

[0058] Specifically, at least two indicators are matched for different types of equipment failures of photovoltaic equipment, thereby obtaining a complexity framework model of photovoltaic equipment, and then according to the complexity framework model, the equipment failure type corresponding to the target indicator is used as the target equipment failure type. The target indicator corresponds to one or more target equipment failure types, and the target equipment failure type corresponds to one or more target indicators. For example, the equipment failure types include three categories, namely, the first type of failure, the second type of failure, and the third type of failure; the target indicators include temperature, voltage, and current. Then the temperature corresponds to the first type of failure and the second type of failure at the same time, and the voltage and current correspond to the third type of failure.

[0059] Step S150: Determine the warning level of the target device failure type based on a preset warning level table according to the target indicator that matches the target device failure type and the preset weight value of the target indicator.

[0060] Specifically, count the target indicators corresponding to each target device failure type, obtain the preset weight value of each target indicator, calculate the sum of the preset weight values ​​of the target indicators corresponding to each target device failure type, and determine the warning level of the target device failure type according to the preset warning level table based on the sum of the weight values.

[0061] As an optional embodiment of the present invention, according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator, determining the warning level of the target device failure type based on the preset warning level table includes:

[0062] Count the target indicators corresponding to the target device failure type and obtain the preset weight value of each target indicator;

[0063] Calculate the sum of the preset weight values ​​of the target indicators that match the target equipment failure type as the warning level judgment value;

[0064] The warning level judgment value is compared with a preset warning level table to determine the warning level of the target device failure type; wherein the preset warning level table includes warning levels and ranges of warning level judgment values ​​corresponding to the warning levels.

[0065] Specifically, statistics are collected for the target indicators of each target device fault type, and the preset weight value of each target indicator is obtained. The sum of the preset weight values ​​of the target indicators corresponding to the target device fault type is calculated as the warning level judgment value, and the warning level judgment value is compared with the preset warning level table to determine the warning level of the target device fault type. For example, a target device fault type corresponds to three target indicators, namely temperature, current and voltage. The preset weight value corresponding to the temperature is 1, the preset weight value corresponding to the current is 2, and the preset weight value corresponding to the voltage is 3. Then the warning level judgment value of the target device fault type is 1+2+3=6. In the preset warning level table, the warning level judgment value corresponding to the first level (primary) warning level is in the range of 1-3; the warning level judgment value corresponding to the second level (intermediate) warning level is in the range of 4-6; the warning level judgment value corresponding to the third level (advanced) warning level is in the range of 7-10; then the warning level of the target device fault type is the second level (intermediate).

[0066] Step S160: Generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment fault type and the warning level.

[0067] Specifically, warning information of photovoltaic equipment is generated according to the warning level, and the warning information includes the equipment failure type and the corresponding warning level, so as to accurately locate the specific equipment failure, and different processing prompts are given based on different warning levels.

[0068] As an optional embodiment of the present invention, the warning information is expressed in the form of voice or text or a combination of voice and text.

[0069] Specifically, the warning information can be given in the form of voice, text, or warning lights, or a combination of voice, text, and warning lights.

[0070] like Figure 2As shown, the present invention provides a photovoltaic equipment early warning system 200, which is used to apply the photovoltaic equipment early warning method as described above to perform photovoltaic equipment early warning; the present invention can be installed in an electronic device. According to the functions implemented, the photovoltaic equipment early warning system 200 may include: a data acquisition module 210, a preprocessing module 220, an analysis and judgment module 230, a fault type positioning module 240, an early warning level determination module 250 and an early warning information generation module 260. The unit of the present invention can also be called a module, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0071] In this embodiment, the functions of each module / unit are as follows:

[0072] The data collection module 210 is used to collect the index data of the photovoltaic equipment in real time according to the preset index data collection table to obtain the index data set of the photovoltaic equipment; wherein the index data set includes the index and the index data corresponding to the index;

[0073] The preprocessing module 220 is used to perform data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators;

[0074] The analysis and judgment module 230 is used to take the indicator corresponding to the indicator analysis data that exceeds the preset normal data range and lasts for a preset time period in the indicator analysis data set as the target indicator based on the same indicator;

[0075] The fault type location module 240 is used to determine the equipment fault type corresponding to the target indicator based on the pre-created complexity framework model of the photovoltaic equipment, and use the equipment fault type corresponding to the target indicator as the target equipment fault type; wherein the complexity framework model includes the equipment fault type and at least two indicators corresponding to the equipment fault type;

[0076] The warning level determination module 250 is used to determine the warning level of the target device failure type based on the preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator;

[0077] The warning information generation module 260 is used to generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment fault type and the warning level.

[0078] The photovoltaic equipment early warning system 200 of the present invention collects the indicator data of the photovoltaic equipment in real time according to a preset indicator data collection table to obtain an indicator data set including multiple indicator data; then preprocesses the indicator data set to obtain an indicator analysis data set, and then based on the same indicator, takes the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period as the target indicator, and determines the equipment fault type corresponding to the target indicator based on the complexity framework model of the photovoltaic equipment created in advance to determine the target equipment fault type; then, according to the number of target indicators corresponding to the target equipment fault type and the preset weight value of the target indicator, determines the warning level of the target equipment fault type based on the preset warning level table; finally, generates warning information including the target equipment fault type and the warning level to quickly and accurately locate the fault type of the photovoltaic equipment; each type of equipment fault does not rely on the threshold judgment of a single indicator, and through multi-indicator comprehensive analysis and quantitative management of the complexity of each equipment fault type through the complexity framework model, each type of equipment fault can be subjected to multi-indicator comprehensive analysis, thereby improving the accuracy, stability and reliability of evaluating the health status of photovoltaic equipment.

[0079] like Figure 3 As shown, the present invention provides an electronic device 3 for a photovoltaic device early warning method.

[0080] The electronic device 3 may include a processor 30, a memory 31 and a bus, and may also include a computer program stored in the memory 31 and executable on the processor 30, such as a photovoltaic device early warning program 32. The memory 31 may also include both an internal storage unit of the photovoltaic device early warning system and an external storage device. The memory 31 may be used not only to store application software and various data, such as the code of the photovoltaic device early warning program, but also to temporarily store data that has been output or is to be output.

[0081] Wherein, the memory 31 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 31 may be an internal storage unit of the electronic device 3 in some embodiments, such as a mobile hard disk of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 31 may not only be used to store application software and various types of data installed in the electronic device 3, such as photovoltaic device early warning method code, etc., but also be used to temporarily store data that has been output or is to be output.

[0082] The processor 30 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 30 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as photovoltaic equipment early warning programs, etc.) stored in the memory 31, and calls data stored in the memory 31 to execute various functions of the electronic device 3 and process data.

[0083] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 31 and at least one processor 30, etc.

[0084] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 3, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0085] For example, although not shown, the electronic device 3 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 30 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0086] Furthermore, the electronic device 3 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 3 and other electronic devices.

[0087] Optionally, the electronic device 3 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 3 and to display a visual user interface.

[0088] It should be understood that the embodiments are for illustration purposes only and the scope of the invention application is not limited by this structure.

[0089] The photovoltaic equipment early warning program 32 stored in the memory 31 in the electronic device 3 is a combination of multiple instructions. When running in the processor 30, it can achieve:

[0090] Step S110: According to a preset indicator data collection table, the indicator data of the photovoltaic equipment is collected in real time to obtain an indicator data set of the photovoltaic equipment; wherein the indicator data set includes an indicator and indicator data corresponding to the indicator;

[0091] Step S120: preprocess the indicator data in the indicator data set to improve the data quality of the indicator data, and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators;

[0092] Step S130: Based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is used as the target indicator;

[0093] Step S140: based on the pre-created complexity framework model of photovoltaic equipment, determine the equipment failure type that matches the target indicator, and use the equipment failure type that matches the target indicator as the target equipment failure type; wherein the complexity framework model includes the equipment failure type and at least two indicators that match the equipment failure type;

[0094] Step S150, determining the warning level of the target device failure type based on a preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator;

[0095] Step S160: Generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment fault type and the warning level.

[0096] Specifically, the specific implementation method of the processor 30 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0097] Furthermore, if the module / unit integrated in the electronic device 3 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0098] An embodiment of the present invention further provides a computer-readable storage medium, which may be non-volatile or volatile, and stores a computer program, which, when executed by a processor, implements:

[0099] Step S110: According to a preset indicator data collection table, the indicator data of the photovoltaic equipment is collected in real time to obtain an indicator data set of the photovoltaic equipment; wherein the indicator data set includes an indicator and indicator data corresponding to the indicator;

[0100] Step S120: preprocess the indicator data in the indicator data set to improve the data quality of the indicator data, and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators;

[0101] Step S130: Based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is used as the target indicator;

[0102] Step S140: based on the pre-created complexity framework model of photovoltaic equipment, determine the equipment failure type that matches the target indicator, and use the equipment failure type that matches the target indicator as the target equipment failure type; wherein the complexity framework model includes the equipment failure type and at least two indicators that match the equipment failure type;

[0103] Step S150, determining the warning level of the target device failure type based on a preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator;

[0104] Step S160: Generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment fault type and the warning level.

[0105] Specifically, the specific implementation method when the computer program is executed by the processor can refer to the description of the relevant steps in the photovoltaic equipment early warning method in the embodiment, which will not be repeated here.

[0106] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0107] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0109] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0110] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0111] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. Secondary words are used to indicate names, but not to indicate any particular order.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution 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 solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A photovoltaic equipment early warning method, characterized in that: The steps include: According to a preset indicator data collection table, the indicator data of the photovoltaic equipment is collected in real time to obtain an indicator data set of the photovoltaic equipment; wherein the indicator data set includes an indicator and indicator data corresponding to the indicator; Performing data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data, thereby obtaining an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators; Based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period is used as the target indicator; Based on a pre-created complexity framework model of photovoltaic equipment, determine the equipment failure type that matches the target indicator, and use the equipment failure type that matches the target indicator as the target equipment failure type; wherein the complexity framework model includes an equipment failure type and at least two indicators that match the equipment failure type; Determine the warning level of the target device failure type based on a preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator; According to the warning level, warning information of the photovoltaic equipment is generated; wherein the warning information includes the target equipment failure type and the warning level.

2. The photovoltaic equipment early warning method according to claim 1, characterized in that: The step of collecting the index data of the photovoltaic equipment in real time according to the preset index data collection table to obtain the index data set of the photovoltaic equipment includes: By installing an intelligent sensing device or a sensor device on the photovoltaic device, according to the indicators in the preset indicator data collection table, the indicator data corresponding to the indicator is obtained in real time; A corresponding relationship is established between the indicator and the indicator data corresponding to the indicator to obtain an indicator data set of the photovoltaic device.

3. The photovoltaic equipment early warning method according to claim 1, characterized in that: The performing data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data to obtain the indicator analysis data set includes: Performing denoising processing on the indicator data in the indicator data set to obtain a denoised indicator data set; Performing filtering processing on the denoised index data in the denoised index data set to obtain a filtered index data set; The filtering index data in the filtering index data set is standardized to obtain an index analysis data set.

4. The photovoltaic equipment early warning method according to claim 1, characterized in that: Based on the same indicator, taking the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds the preset normal data range and lasts for a preset time period as the target indicator includes: Taking each indicator in the indicator analysis data set as a pending indicator separately; The indicator analysis data corresponding to the pending indicator is compared with the preset normal data range corresponding to the pending indicator. If the indicator analysis data corresponding to the pending indicator exceeds the preset normal data range corresponding to the pending indicator and lasts for a preset time period, the pending indicator is used as the target indicator; wherein each of the pending indicators corresponds to a preset normal data range and a preset time period.

5. The photovoltaic equipment early warning method according to claim 1, characterized in that: The method of determining the equipment failure type corresponding to the target indicator based on the pre-created complexity framework model of the photovoltaic equipment, and using the equipment failure type corresponding to the target indicator as the target equipment failure type includes: Matching at least two indicators for different types of equipment failures of the photovoltaic equipment to obtain a complexity framework model of the photovoltaic equipment; According to the complexity framework model, the equipment failure type matched by the target indicator is used as the target equipment failure type; wherein the target indicator corresponds to one or more target equipment failure types, and the target equipment failure type corresponds to one or more target indicators.

6. The photovoltaic equipment early warning method according to claim 1, characterized in that: The step of determining the warning level of the target device failure type based on a preset warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator comprises: Counting target indicators corresponding to the target device failure type, and obtaining a preset weight value of each target indicator; Calculate the sum of the preset weight values ​​of the target indicators that match the target device failure type as the warning level judgment value; The warning level judgment value is compared with a preset warning level table to determine the warning level of the target device failure type; wherein the preset warning level table includes warning levels and ranges of warning level judgment values ​​corresponding to the warning levels.

7. The photovoltaic equipment early warning method according to claim 1, characterized in that: The warning information may be in the form of voice or text or a combination of voice and text.

8. A photovoltaic equipment early warning system, characterized in that: Used to apply the photovoltaic equipment early warning method as described in any one of claims 1 to 7 to perform photovoltaic equipment early warning; the system comprises: A data collection module, used to collect the index data of the photovoltaic equipment in real time according to a preset index data collection table, and obtain an index data set of the photovoltaic equipment; wherein the index data set includes an index and index data corresponding to the index; A preprocessing module, used for performing data preprocessing on the indicator data in the indicator data set to improve the data quality of the indicator data and obtain an indicator analysis data set; wherein the indicator analysis data set includes indicators and indicator analysis data corresponding to the indicators; An analysis and judgment module, for taking, based on the same indicator, the indicator corresponding to the indicator analysis data in the indicator analysis data set that exceeds a preset normal data range and lasts for a preset time period as a target indicator; A fault type locating module is used to determine the equipment fault type corresponding to the target indicator based on a pre-created complexity framework model of photovoltaic equipment, and use the equipment fault type corresponding to the target indicator as the target equipment fault type; wherein the complexity framework model includes an equipment fault type and at least two indicators corresponding to the equipment fault type; An early warning level determination module, configured to determine the early warning level of the target device failure type based on a preset early warning level table according to the target indicator corresponding to the target device failure type and the preset weight value of the target indicator; The warning information generation module is used to generate warning information of the photovoltaic equipment according to the warning level; wherein the warning information includes the target equipment failure type and the warning level.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps in the photovoltaic device early warning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing at least one instruction, characterized in that: When the at least one instruction is executed by a processor in the electronic device, the photovoltaic device early warning method as described in any one of claims 1 to 7 is implemented.

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