Photovoltaic equipment networking control method and system

Abstract

The invention discloses a photovoltaic equipment networking control method and system, and relates to the field of photovoltaic equipment networking control, and the system comprises an environment data monitoring module, an operation data acquisition module, a data preliminary analysis module, a prediction data model building module and a networking control module. The environment data model and the equipment operation data set corresponding to the photovoltaic equipment are established, intelligent analysis is performed according to the historical environment data model and the historical operation data set corresponding to the photovoltaic equipment, and the power generation prediction data model corresponding to the photovoltaic equipment is established, so that the judgment on the power generation stability of the photovoltaic equipment is effectively improved; and power grid stability and power supply quality can be guaranteed.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic equipment networking control, and in particular to a photovoltaic equipment networking control method and system. Background Art

[0002] Photovoltaic equipment network control is a core technology for addressing PV power generation volatility, improving grid-connected performance, and optimizing resources. With the increasing penetration of distributed energy resources and the development of smart grids, network control is shifting from "single-device management" to "system-level collaboration." Combined with technologies like digital twins and AI prediction, it will become a key enabler for photovoltaic industry upgrades and the construction of new power systems.

[0003] Existing photovoltaic power generation technologies are significantly affected by environmental factors such as light, temperature, and cloud cover. Existing control strategies are slow to respond to rapidly changing weather conditions, making it difficult to assess the stability of photovoltaic power generation in real time, which can easily lead to reduced system efficiency or equipment damage. With existing technologies, a single device failure in large-scale networking may trigger a chain reaction, but fault location and isolation are slow, and there is a lack of effective pre-diagnosis and control methods, which in turn affects grid stability and easily causes power failures. Summary of the Invention

[0004] The object of the present invention is to provide a photovoltaic equipment networking control method and system to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a photovoltaic equipment networking control system, comprising: Environmental data monitoring module: used to monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Operation data acquisition module: used to acquire the equipment operation status corresponding to the photovoltaic equipment and obtain the equipment operation data set corresponding to the photovoltaic equipment; Data preliminary analysis module: used to obtain and perform preliminary analysis on the historical environmental data model and environmental forecast data corresponding to the photovoltaic equipment, and obtain the environmental data comparison set corresponding to the photovoltaic equipment; Prediction data model building module: used to obtain the historical operation data set corresponding to the photovoltaic equipment and compare and analyze it with the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Network control module: used to analyze the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment to obtain the corresponding network control results of the photovoltaic equipment.

[0006] In the preferred embodiment of this solution, the specific implementation of the environmental data monitoring module is as follows: The real-time light intensity of the photovoltaic equipment area is obtained by using a preset light sensor in the photovoltaic equipment area, and the real-time external temperature and wind speed corresponding to the photovoltaic equipment area are obtained by using a weather station around the photovoltaic equipment. The temperature of the photovoltaic device backboard is measured in real time by a temperature sensor attached to the photovoltaic device backboard, and recorded as the real-time device temperature of the photovoltaic device; An environmental data model corresponding to the photovoltaic equipment is established through the real-time light intensity, real-time external temperature, real-time wind speed and real-time equipment temperature of the area where the photovoltaic equipment is located, and the model establishment time point corresponding to the environmental data model is obtained.

[0007] In a preferred embodiment of this solution, the specific execution method of running the data acquisition module is as follows: The DC voltage and DC current corresponding to the output of the photovoltaic equipment are obtained through the intelligent combiner box built into the photovoltaic equipment; Acquiring AC power parameters corresponding to the inverter, wherein the AC power parameters include the AC output voltage and output current corresponding to the inverter; The DC voltage and DC current outputted by the photovoltaic device and the AC parameters corresponding to the inverter are recorded as the device operation data set corresponding to the photovoltaic device.

[0008] In the preferred embodiment of this solution, the specific implementation method of the data preliminary analysis module is as follows: Establishing a data extraction relationship between the data preliminary analysis module and the database, extracting the environmental prediction model corresponding to each environmental data model stored in the database, and filtering and obtaining the environmental prediction model corresponding to the photovoltaic equipment based on the environmental data model corresponding to the photovoltaic equipment, wherein the environmental prediction model includes the effective comparison time of the environmental prediction and the length of each standard interval; Obtain a historical environmental data model corresponding to the photovoltaic equipment, extract data from the historical environmental data model corresponding to the photovoltaic equipment through the predicted effective comparison duration in the environmental prediction model corresponding to the photovoltaic equipment, and obtain a valid historical environmental data model corresponding to the photovoltaic equipment; divide the valid historical environmental data model corresponding to the photovoltaic equipment by each standard interval duration to obtain each sub-valid historical environmental data model corresponding to the photovoltaic equipment and the time period corresponding to each sub-valid historical environmental data model; According to the real-time light intensity, real-time external temperature, real-time wind speed and real-time device temperature of the photovoltaic equipment in the area where each sub-valid historical environmental data model corresponds to, establish the light intensity change curve, external temperature change curve, wind speed change curve and device temperature change curve corresponding to each sub-valid historical environmental data model; Obtain the curve change rate and slope of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to the curve; calculate the average value and standard deviation of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to each sub-valid historical environment data model based on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve; perform difference calculation on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve and the average value corresponding to the curve , obtain the difference between the actual value of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve and the corresponding average value of the curve, record it as the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve, calculate the ratio of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve, and obtain the ratio multiple of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve; Extract the deviation ratio multiple intervals corresponding to the curve change rates of various curves stored in the database, and obtain the deviation ratio multiple intervals corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model according to the curve change rates of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve. Compare the ratio multiples of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model with the deviation ratio multiple intervals corresponding to the curve. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained and connected. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained, and the effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are recorded as the environmental data comparison set corresponding to the photovoltaic equipment.

[0009] In the preferred embodiment of this solution, the specific implementation method of the prediction data model building module is as follows: Obtaining the device operation log corresponding to the photovoltaic device, and filtering and obtaining the historical operation data set corresponding to each sub-valid historical environment data model according to the time period corresponding to each sub-valid historical environment data model, wherein the historical operation data set includes the average DC voltage and average DC current corresponding to the output of the photovoltaic device, and the average AC output voltage and average output current corresponding to the inverter; By analyzing the historical operation data set, the DC power corresponding to the photovoltaic equipment, the AC power output of the inverter, and the inverter conversion efficiency of the historical operation data set are obtained; According to the time period sequence corresponding to each sub-valid historical environmental data model, the DC power corresponding to the photovoltaic equipment corresponding to the historical operation data set, the AC power corresponding to the inverter output and the inverter conversion efficiency are used to establish a DC power curve, an AC power curve and an inverter conversion efficiency curve. The DC power curve, the AC power curve and the inverter conversion efficiency curve are recorded as the power generation prediction data model corresponding to the photovoltaic equipment.

[0010] In the preferred embodiment of this solution, the specific implementation method of the prediction data model building module is as follows: Obtaining the device operation log corresponding to the photovoltaic device, and filtering and obtaining the historical operation data set corresponding to each sub-valid historical environment data model according to the time period corresponding to each sub-valid historical environment data model, wherein the historical operation data set includes the average DC voltage and average DC current corresponding to the output of the photovoltaic device, and the average AC output voltage and average output current corresponding to the inverter; By analyzing the historical operation data set, the DC power corresponding to the photovoltaic equipment, the AC power output of the inverter, and the inverter conversion efficiency of the historical operation data set are obtained; According to the time period sequence corresponding to each sub-valid historical environmental data model, the DC power corresponding to the photovoltaic equipment corresponding to the historical operation data set, the AC power corresponding to the inverter output and the inverter conversion efficiency are used to establish a DC power curve, an AC power curve and an inverter conversion efficiency curve. The DC power curve, the AC power curve and the inverter conversion efficiency curve are recorded as the power generation prediction data model corresponding to the photovoltaic equipment.

[0011] To achieve the above object, the present invention further provides the following technical solution: a photovoltaic device networking control method, comprising the following steps: Monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Acquire the device operation status corresponding to the photovoltaic device to obtain the device operation data set corresponding to the photovoltaic device; Obtain and perform preliminary analysis on the historical environmental data model and environmental forecast data corresponding to the photovoltaic equipment to obtain a comparison set of environmental data corresponding to the photovoltaic equipment; Acquire the historical operation data set corresponding to the photovoltaic equipment and compare and analyze it with the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Based on the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment, the network control results corresponding to the photovoltaic equipment are obtained.

[0012] Compared with the prior art, the present invention has the following beneficial effects: By establishing environmental data models and equipment operation data sets corresponding to photovoltaic equipment, and conducting intelligent analysis based on the historical environmental data models and historical operation data sets corresponding to photovoltaic equipment, a power generation prediction data model corresponding to photovoltaic equipment is established, which effectively improves the judgment of the power generation stability of photovoltaic equipment and is conducive to ensuring the stability of the power grid and the quality of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of module connections according to an embodiment of the present invention.

[0015] Figure 2 This is a schematic diagram of the connection steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 , the present invention provides a photovoltaic equipment networking control system, the system includes an environmental data monitoring module, an operation data acquisition module, a data preliminary analysis module, a prediction data model establishment module and a networking control module; The environmental data monitoring module is used to monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Furthermore, the specific implementation of the environmental data monitoring module is as follows: The real-time light intensity of the photovoltaic equipment area is obtained by using a preset light sensor in the photovoltaic equipment area, and the real-time external temperature and wind speed corresponding to the photovoltaic equipment area are obtained by using a weather station around the photovoltaic equipment. The temperature of the photovoltaic device backboard is measured in real time by a temperature sensor attached to the photovoltaic device backboard, and recorded as the real-time device temperature of the photovoltaic device; An environmental data model corresponding to the photovoltaic equipment is established through the real-time light intensity, real-time external temperature, real-time wind speed and real-time equipment temperature of the area where the photovoltaic equipment is located, and the model establishment time point corresponding to the environmental data model is obtained.

[0018] The operation data acquisition module is used to acquire the device operation status corresponding to the photovoltaic device and obtain the device operation data set corresponding to the photovoltaic device; Furthermore, the specific execution method of running the data acquisition module is as follows: The DC voltage and DC current corresponding to the output of the photovoltaic equipment are obtained through the intelligent combiner box built into the photovoltaic equipment; Acquiring AC power parameters corresponding to the inverter, wherein the AC power parameters include the AC output voltage and output current corresponding to the inverter; The DC voltage and DC current outputted by the photovoltaic device and the AC parameters corresponding to the inverter are recorded as the device operation data set corresponding to the photovoltaic device.

[0019] The data preliminary analysis module is used to obtain and perform preliminary analysis on the historical environmental data model and environmental prediction data corresponding to the photovoltaic equipment to obtain a comparison set of environmental data corresponding to the photovoltaic equipment; Furthermore, the specific execution method of the data preliminary analysis module is as follows: Establishing a data extraction relationship between the data preliminary analysis module and the database, extracting the environmental prediction model corresponding to each environmental data model stored in the database, and filtering and obtaining the environmental prediction model corresponding to the photovoltaic equipment based on the environmental data model corresponding to the photovoltaic equipment, wherein the environmental prediction model includes the effective comparison time of the environmental prediction and the length of each standard interval; Obtain a historical environmental data model corresponding to the photovoltaic equipment, extract data from the historical environmental data model corresponding to the photovoltaic equipment through the predicted effective comparison duration in the environmental prediction model corresponding to the photovoltaic equipment, and obtain a valid historical environmental data model corresponding to the photovoltaic equipment; divide the valid historical environmental data model corresponding to the photovoltaic equipment by each standard interval duration to obtain each sub-valid historical environmental data model corresponding to the photovoltaic equipment and the time period corresponding to each sub-valid historical environmental data model; According to the real-time light intensity, real-time external temperature, real-time wind speed and real-time device temperature of the photovoltaic equipment in the area where each sub-valid historical environmental data model corresponds to, establish the light intensity change curve, external temperature change curve, wind speed change curve and device temperature change curve corresponding to each sub-valid historical environmental data model; Obtain the curve change rate and slope of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to the curve; calculate the average value and standard deviation of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to each sub-valid historical environment data model based on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve; perform difference calculation on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve and the average value corresponding to the curve , obtain the difference between the actual value of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve and the corresponding average value of the curve, record it as the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve, calculate the ratio of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve, and obtain the ratio multiple of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve; Extract the deviation ratio multiple intervals corresponding to the curve change rates of various curves stored in the database, and obtain the deviation ratio multiple intervals corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model according to the curve change rates of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve. Compare the ratio multiples of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model with the deviation ratio multiple intervals corresponding to the curve. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained and connected. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained, and the effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are recorded as the environmental data comparison set corresponding to the photovoltaic equipment.

[0020] The prediction data model building module is used to obtain the historical operation data set corresponding to the photovoltaic equipment and compare and analyze the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Furthermore, the specific execution method of the prediction data model building module is as follows: Obtaining the device operation log corresponding to the photovoltaic device, and filtering and obtaining the historical operation data set corresponding to each sub-valid historical environment data model according to the time period corresponding to each sub-valid historical environment data model, wherein the historical operation data set includes the average DC voltage and average DC current corresponding to the output of the photovoltaic device, and the average AC output voltage and average output current corresponding to the inverter; By analyzing the historical operation data set, the DC power corresponding to the photovoltaic equipment, the AC power output of the inverter, and the inverter conversion efficiency of the historical operation data set are obtained; According to the time period sequence corresponding to each sub-valid historical environmental data model, the DC power corresponding to the photovoltaic equipment corresponding to the historical operation data set, the AC power corresponding to the inverter output and the inverter conversion efficiency are used to establish a DC power curve, an AC power curve and an inverter conversion efficiency curve. The DC power curve, the AC power curve and the inverter conversion efficiency curve are recorded as the power generation prediction data model corresponding to the photovoltaic equipment.

[0021] The networking control module is used to analyze the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment to obtain the networking control results corresponding to the photovoltaic equipment.

[0022] Furthermore, the specific implementation of the networking control module is as follows: The environmental data model corresponding to the photovoltaic equipment is data-connected with the effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve, and the effective equipment temperature change curve corresponding to each sub-effective historical environmental data model, and the light intensity change curve, the external temperature change curve, the wind speed change curve, and the equipment temperature change curve after data connection are obtained. The data change rate of each type of data in the environmental data model and the data at the tail end of the corresponding curve are recorded as the environmental comparison model; Match the environmental comparison model with the effective light intensity change curve, effective external temperature change curve, effective wind speed change curve, and effective equipment temperature change curve corresponding to each sub-effective historical environmental data model to obtain each time point that matches the environmental comparison model, and record each time point that matches the environmental comparison model as each comparison time point; By comparing each comparison time point with the power generation prediction data model corresponding to the photovoltaic equipment, the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the historical operation data set corresponding to the effective historical environment data model at each comparison time point are obtained. The DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the historical operation data set corresponding to the effective historical environment data model at each comparison time point are statistically analyzed to obtain the DC power change rate interval, AC power change rate interval, and inverter conversion efficiency change rate interval corresponding to the comparison time point; Perform data analysis on the tail data of the DC power curve, AC power curve, and inverter conversion efficiency curve corresponding to the equipment operation data set of the photovoltaic equipment and the power generation prediction data model to obtain the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the equipment operation data set of the photovoltaic equipment; The DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the equipment operation data set are analyzed with the DC power change rate interval, AC power change rate interval, and inverter conversion efficiency change rate interval corresponding to the comparison time point to obtain the electrical parameter stability coefficient corresponding to the photovoltaic equipment; The electrical parameter stability coefficient corresponding to the photovoltaic equipment is compared and analyzed with the preset electrical parameter stability coefficient threshold. If the electrical parameter stability coefficient corresponding to the photovoltaic equipment is less than or equal to the preset electrical parameter stability coefficient threshold, it means that the power generation condition and equipment condition of the photovoltaic equipment are stable. If the electrical parameter stability coefficient corresponding to the photovoltaic equipment is greater than the preset electrical parameter stability coefficient threshold, it means that the power generation condition and equipment condition of the photovoltaic equipment are unstable, and the photovoltaic equipment is networked and controlled.

[0023] The electrical parameter stability coefficient corresponding to the photovoltaic device is obtained as follows: Obtaining the evaluation weights of power generation stability corresponding to the DC power change rate, AC power change rate, and inverter conversion efficiency change rate stored in the database; Match the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the equipment operation data set with the DC power change rate interval, AC power change rate interval, and inverter conversion efficiency change rate interval corresponding to the comparison time point. If the DC power change rate is within the DC power change rate interval, it is recorded as DC compliance and the compliance value is recorded as 0. If the DC power change rate is not within the DC power change rate interval, the calculation formula is: compliance value = ┃the middle value of the DC power change rate interval - DC power change rate ┃ / half the length of the DC power change rate interval; Similarly, obtain the DC compliance value, AC compliance value, and inverter conversion efficiency compliance value. For example, mark the DC compliance value, AC compliance value, and inverter conversion efficiency compliance value as A, B, and C, respectively. The electrical parameter stability coefficient = A×a+B×b+C×c, where a, b, and c represent the evaluation weights of the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the power generation stability, respectively.

[0024] See also Figure 2 To achieve the above-mentioned purpose, the present invention further provides the following technical solution: a photovoltaic equipment networking control method, comprising the following steps: Monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Acquire the device operation status corresponding to the photovoltaic device to obtain the device operation data set corresponding to the photovoltaic device; Obtain and perform preliminary analysis on the historical environmental data model and environmental forecast data corresponding to the photovoltaic equipment to obtain a comparison set of environmental data corresponding to the photovoltaic equipment; Acquire the historical operation data set corresponding to the photovoltaic equipment and compare and analyze it with the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Based on the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment, the network control results corresponding to the photovoltaic equipment are obtained.

[0025] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A photovoltaic equipment networking control system, characterized by: include: Environmental data monitoring module: used to monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Operation data acquisition module: used to acquire the equipment operation status corresponding to the photovoltaic equipment and obtain the equipment operation data set corresponding to the photovoltaic equipment; Data preliminary analysis module: used to obtain and perform preliminary analysis on the historical environmental data model and environmental forecast data corresponding to the photovoltaic equipment, and obtain the environmental data comparison set corresponding to the photovoltaic equipment; Prediction data model building module: used to obtain the historical operation data set corresponding to the photovoltaic equipment and compare and analyze it with the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Network control module: used to analyze the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment to obtain the corresponding network control results of the photovoltaic equipment.

2. A photovoltaic equipment networking control system according to claim 1, characterized in that: The specific implementation of the environmental data monitoring module is as follows: The real-time light intensity of the photovoltaic equipment area is obtained by using a preset light sensor in the photovoltaic equipment area, and the real-time external temperature and wind speed corresponding to the photovoltaic equipment area are obtained by using a weather station around the photovoltaic equipment. The temperature of the photovoltaic device backboard is measured in real time by a temperature sensor attached to the photovoltaic device backboard, and recorded as the real-time device temperature of the photovoltaic device; An environmental data model corresponding to the photovoltaic equipment is established through the real-time light intensity, real-time external temperature, real-time wind speed and real-time equipment temperature of the area where the photovoltaic equipment is located, and the model establishment time point corresponding to the environmental data model is obtained.

3. The photovoltaic equipment networking control system according to claim 1, characterized in that: The specific execution method of the operation data acquisition module is as follows: The DC voltage and DC current corresponding to the output of the photovoltaic equipment are obtained through the intelligent combiner box built into the photovoltaic equipment; Acquiring AC power parameters corresponding to the inverter, wherein the AC power parameters include the AC output voltage and output current corresponding to the inverter; The DC voltage and DC current outputted by the photovoltaic device and the AC parameters corresponding to the inverter are recorded as the device operation data set corresponding to the photovoltaic device.

4. A photovoltaic equipment networking control system according to claim 3, characterized in that: The specific execution method of the data preliminary analysis module is as follows: Establishing a data extraction relationship between the data preliminary analysis module and the database, extracting the environmental prediction model corresponding to each environmental data model stored in the database, and filtering and obtaining the environmental prediction model corresponding to the photovoltaic equipment based on the environmental data model corresponding to the photovoltaic equipment, wherein the environmental prediction model includes the effective comparison time of the environmental prediction and the length of each standard interval; Obtain a historical environmental data model corresponding to the photovoltaic equipment, extract data from the historical environmental data model corresponding to the photovoltaic equipment through the predicted effective comparison duration in the environmental prediction model corresponding to the photovoltaic equipment, and obtain a valid historical environmental data model corresponding to the photovoltaic equipment; divide the valid historical environmental data model corresponding to the photovoltaic equipment by each standard interval duration to obtain each sub-valid historical environmental data model corresponding to the photovoltaic equipment and the time period corresponding to each sub-valid historical environmental data model; According to the real-time light intensity, real-time external temperature, real-time wind speed and real-time device temperature of the photovoltaic equipment in the area where each sub-valid historical environmental data model corresponds to, establish the light intensity change curve, external temperature change curve, wind speed change curve and device temperature change curve corresponding to each sub-valid historical environmental data model; Obtain the curve change rate and slope of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to the curve; calculate the average value and standard deviation of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve corresponding to each sub-valid historical environment data model based on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve; perform difference calculation on the actual value of each point on the curve corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve and the average value corresponding to the curve , obtain the difference between the actual value of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve and the corresponding average value of the curve, record it as the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve, calculate the ratio of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve, and obtain the ratio multiple of the difference of each point on the corresponding curves of the light intensity change curve, the external temperature change curve, the wind speed change curve and the equipment temperature change curve to the standard deviation of the curve; Extract the deviation ratio multiple intervals corresponding to the curve change rates of various curves stored in the database, and obtain the deviation ratio multiple intervals corresponding to the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model according to the curve change rates of the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve. Compare the ratio multiples of each point on the light intensity change curve, external temperature change curve, wind speed change curve and equipment temperature change curve of each sub-valid historical environment data model with the deviation ratio multiple intervals corresponding to the curve. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained and connected. The effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are obtained, and the effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve and the effective equipment temperature change curve corresponding to each sub-valid historical environmental data model are recorded as the environmental data comparison set corresponding to the photovoltaic equipment.

5. A photovoltaic equipment networking control system according to claim 4, characterized in that: The specific implementation of the prediction data model building module is as follows: Obtaining the device operation log corresponding to the photovoltaic device, and filtering and obtaining the historical operation data set corresponding to each sub-valid historical environment data model according to the time period corresponding to each sub-valid historical environment data model, wherein the historical operation data set includes the average DC voltage and average DC current corresponding to the output of the photovoltaic device, and the average AC output voltage and average output current corresponding to the inverter; By analyzing the historical operation data set, the DC power corresponding to the photovoltaic equipment, the AC power output of the inverter, and the inverter conversion efficiency of the historical operation data set are obtained; According to the time period sequence corresponding to each sub-valid historical environmental data model, the DC power corresponding to the photovoltaic equipment corresponding to the historical operation data set, the AC power corresponding to the inverter output and the inverter conversion efficiency are used to establish a DC power curve, an AC power curve and an inverter conversion efficiency curve. The DC power curve, the AC power curve and the inverter conversion efficiency curve are recorded as the power generation prediction data model corresponding to the photovoltaic equipment.

6. A photovoltaic equipment networking control system according to claim 4, characterized in that: The specific implementation of the networking control module is as follows: The environmental data model corresponding to the photovoltaic equipment is data-connected with the effective light intensity change curve, the effective external temperature change curve, the effective wind speed change curve, and the effective equipment temperature change curve corresponding to each sub-effective historical environmental data model, and the light intensity change curve, the external temperature change curve, the wind speed change curve, and the equipment temperature change curve after data connection are obtained. The data change rate of each type of data in the environmental data model and the data at the tail end of the corresponding curve are recorded as the environmental comparison model; Match the environmental comparison model with the effective light intensity change curve, effective external temperature change curve, effective wind speed change curve, and effective equipment temperature change curve corresponding to each sub-effective historical environmental data model to obtain each time point that matches the environmental comparison model, and record each time point that matches the environmental comparison model as each comparison time point; By comparing each comparison time point with the power generation prediction data model corresponding to the photovoltaic equipment, the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the historical operation data set corresponding to the effective historical environment data model at each comparison time point are obtained. The DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the historical operation data set corresponding to the effective historical environment data model at each comparison time point are statistically analyzed to obtain the DC power change rate interval, AC power change rate interval, and inverter conversion efficiency change rate interval corresponding to the comparison time point; Perform data analysis on the tail data of the DC power curve, AC power curve, and inverter conversion efficiency curve corresponding to the equipment operation data set of the photovoltaic equipment and the power generation prediction data model to obtain the DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the equipment operation data set of the photovoltaic equipment; The DC power change rate, AC power change rate, and inverter conversion efficiency change rate corresponding to the equipment operation data set are analyzed with the DC power change rate interval, AC power change rate interval, and inverter conversion efficiency change rate interval corresponding to the comparison time point to obtain the electrical parameter stability coefficient corresponding to the photovoltaic equipment; The electrical parameter stability coefficient corresponding to the photovoltaic equipment is compared and analyzed with the preset electrical parameter stability coefficient threshold. If the electrical parameter stability coefficient corresponding to the photovoltaic equipment is less than or equal to the preset electrical parameter stability coefficient threshold, it means that the power generation condition and equipment condition of the photovoltaic equipment are stable. If the electrical parameter stability coefficient corresponding to the photovoltaic equipment is greater than the preset electrical parameter stability coefficient threshold, it means that the power generation condition and equipment condition of the photovoltaic equipment are unstable, and the photovoltaic equipment is networked and controlled.

7. A photovoltaic equipment networking control method, applied to a photovoltaic equipment networking control system according to any one of claims 1 to 6, characterized in that: include: Monitor the environment of the area where the photovoltaic equipment is located and obtain the environmental data model corresponding to the photovoltaic equipment; Acquire the device operation status corresponding to the photovoltaic device to obtain the device operation data set corresponding to the photovoltaic device; Obtain and perform preliminary analysis on the historical environmental data model and environmental forecast data corresponding to the photovoltaic equipment to obtain a comparison set of environmental data corresponding to the photovoltaic equipment; Acquire the historical operation data set corresponding to the photovoltaic equipment and compare and analyze it with the environmental data comparison set corresponding to the photovoltaic equipment to obtain the power generation prediction data model corresponding to the photovoltaic equipment; Based on the environmental data model, equipment operation data set and power generation prediction data model corresponding to the photovoltaic equipment, the network control results corresponding to the photovoltaic equipment are obtained.

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