CN114706732A - Method and device for monitoring operation data of photovoltaic power station - Google Patents
Method and device for monitoring operation data of photovoltaic power station Download PDFInfo
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Abstract
The invention discloses a method and a device for monitoring operation data of a photovoltaic power station, relates to the technical field of data processing, and mainly aims to solve the problem that a third-party enterprise has low effectiveness in monitoring the operation data of the photovoltaic power station. The method comprises the following steps: acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data; determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station; and when a third party executes business operation, predicting the operation data based on the operation monitoring model which is trained by the model, and obtaining the monitoring result of the operation data.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for monitoring operation data of a photovoltaic power station.
Background
The photovoltaic power station refers to a photovoltaic power station system for directly converting solar energy into electric energy, and the photovoltaic power station generally used widely is a distributed photovoltaic power station, that is, a photovoltaic module for generating electricity is installed on a roof of a power consumer. Because the power stations of photovoltaic power station enterprises are dispersed and have wide distribution range, the photovoltaic power station enterprises are difficult to master actual operation data of the photovoltaic power station as third-party enterprises having cooperative relations with the photovoltaic power station enterprises, and accurate assessment can not be made on the operation conditions of the photovoltaic power station.
At present, the existing monitoring of the operation data of the photovoltaic power station is generally based on manual inspection of each photovoltaic power station or collection of the operation data of the photovoltaic power station provided by a photovoltaic power station enterprise, so that the operation state of the photovoltaic power station is monitored. However, the method is high in labor cost and low in monitoring accuracy and timeliness, and is not beneficial to accurately and timely finding the operation condition of the photovoltaic power station, so that the monitoring requirement of a third-party enterprise cannot be met, and the effectiveness of performing business processing on the third-party enterprise by using the operation data obtained by monitoring is reduced.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for monitoring operation data of a photovoltaic power station, and mainly aims to solve the problem of low effectiveness of performing third-party enterprise service processing by using operation data obtained through monitoring.
According to one aspect of the invention, a method for monitoring operation data of a photovoltaic power station is provided, which comprises the following steps:
acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data;
determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station;
when a third party executes business operation, the operation data is predicted based on an operation monitoring model which is trained by the model, and a monitoring result of the operation data is obtained, wherein the operation monitoring model is obtained by configuring model weight based on resource distribution information of the business resource data and completing training.
Further, the determining the power generation efficiency and the transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the power generation amount data and the service resource data comprises:
analyzing the grid-connected state information of the photovoltaic sub-power station in the equipment information;
performing power supply serial-parallel connection division on the generated energy data according to the grid connection state information, and classifying the business resource data corresponding to the divided generated energy data to obtain the business resource types of the generated energy data corresponding to different power supply serial-parallel connection classifications;
and calculating the power generation efficiency based on the generated energy data corresponding to the power supply serial-parallel classification, and calculating transaction data based on the service resource data corresponding to the service resource type.
Further, the performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station comprises:
determining a data curve fitting function based on the power supply serial-parallel classification and the service resource type, wherein the data curve fitting function is used for fitting the power generation efficiency and/or the transaction data;
if the third party is a virtual resource type, fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data comprising power generation efficiency operation data and transaction operation data;
and if the third party is the entity resource type, integrally fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data.
Further, before the operation monitoring model based on the model training is used for predicting the operation data and obtaining the monitoring result of the operation data, the method further comprises the following steps:
acquiring operation training sample data, and constructing a prediction network matched with the service resource type, wherein the prediction network is a single-input or multi-input at least three-layer classification prediction network;
determining resource allocation information matched with the service resource data based on a preset service resource allocation corresponding relation, wherein the resource allocation information is used for representing service resource allocation capacity, and the preset service resource allocation corresponding relation records resource allocation information corresponding to distribution intervals of different service resource data;
and configuring model weights of the prediction network based on the resource allocation information, and performing model training on the prediction model with the configured model weights through the operation training sample data to obtain the operation monitoring model.
Further, before the power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and the service resource data matched with the power generation capacity data are acquired, the method further comprises the following steps:
establishing data communication connection with a third party server, and distributing service communication interfaces for different third parties;
and after determining the resource type of the third party, calling a service communication interface matched with the resource type, and executing the service operation through the service communication interface, wherein the resource type comprises a virtual resource type and an entity resource type.
Further, the method further comprises:
calculating an operation monitoring coefficient of the operation data based on the monitoring result, wherein the operation monitoring coefficient is used for representing extreme value floating intervals of different operation data under different monitoring requirements, and the monitoring result comprises normal operation state, abnormal operation state and high risk of operation state;
and if the operation monitoring coefficient in the monitoring result is larger than a preset monitoring threshold value, sending alarm information to a third party server so that the third party can adjust the generated energy and the input resource data.
Further, the third party includes at least one of a financial data enterprise party, an entity industrial enterprise party, and a scientific and technological product enterprise party, and the method further includes:
and distributing terminal acquisition permission for the third party, wherein the acquisition permission is used for representing permission of different third party terminals for acquiring different photovoltaic sub-power stations.
According to another aspect of the invention, there is provided a monitoring device for operation data of a photovoltaic power plant, comprising:
the acquisition module is used for acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data;
the fitting module is used for determining the power generation efficiency and the transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station;
a prediction module: the operation monitoring model is used for predicting the operation data based on the operation monitoring model which is trained based on the model when a third party executes the business operation, so as to obtain the monitoring result of the operation data, and the operation monitoring model is obtained by configuring model weight based on the resource distribution information of the business resource data and completing the training.
Further, the fitting module includes:
the analysis unit is used for analyzing the grid-connected state information of the photovoltaic sub-power station in the equipment information;
the classification unit is used for carrying out power supply serial-parallel connection division on the generated energy data according to the grid-connected state information and classifying the service resource data corresponding to the divided generated energy data to obtain the service resource types of the generated energy data corresponding to different power supply serial-parallel connection classifications;
and the calculating unit is used for calculating the power generation efficiency based on the power generation amount data corresponding to the power supply serial-parallel classification and calculating the transaction data based on the service resource data corresponding to the service resource type.
Further, the fitting module further comprises:
the first fitting unit is used for determining a data curve fitting function based on the power supply serial-parallel classification and the service resource type, and the data curve fitting function is used for fitting the power generation efficiency and/or the transaction data;
the second fitting unit is used for respectively fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data comprising power generation efficiency operation data and transaction operation data if the third party is a virtual resource type;
and the third fitting unit is used for performing overall fitting on the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data if the third party is the entity resource type.
Further, the apparatus further comprises:
the training module is used for acquiring operation training sample data and constructing a prediction network matched with the service resource type, wherein the prediction network is a single-input or multi-input at least three-layer classification prediction network;
the distribution module is used for determining resource distribution information matched with the service resource data based on a preset service resource distribution corresponding relation, the resource distribution information is used for representing service resource distribution capacity, and the preset service resource distribution corresponding relation records resource distribution information corresponding to distribution intervals of different service resource data;
and the training module is used for configuring the model weight of the prediction network based on the resource allocation information and performing model training on the prediction model after the model weight is configured through the operation training sample data to obtain the operation monitoring model.
Further, the apparatus further comprises:
the distribution module is used for establishing data communication connection with a third-party server and distributing service communication interfaces for different third parties;
and the execution module is used for calling a service communication interface matched with the resource type after the resource type of the third party is determined, and executing the service operation through the service communication interface, wherein the resource type comprises a virtual resource type and an entity resource type.
Further, the apparatus further comprises:
the calculation module is used for calculating an operation monitoring coefficient of the operation data based on the monitoring result, the operation monitoring coefficient is used for representing the extreme value floating interval of different operation data under different monitoring requirements, and the monitoring result comprises normal operation state, abnormal operation state and high risk operation state;
and the alarm module is used for sending alarm information to a third party server side if the operation monitoring coefficient in the monitoring result is greater than a preset monitoring threshold value, so that the third party can adjust the generated energy and the input resource data.
Further, the apparatus further comprises:
the distribution module is further used for distributing terminal acquisition permission for the third party, and the acquisition permission is used for representing permission of different third party terminals for acquiring different photovoltaic sub-power stations.
According to a further aspect of the present invention, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction causes a processor to execute operations corresponding to the monitoring method for the photovoltaic power plant operation data.
According to still another aspect of the present invention, there is provided a computer apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the monitoring method of the photovoltaic power station operation data.
By the technical scheme, the technical scheme provided by the embodiment of the invention at least has the following advantages:
the invention provides a method and a device for monitoring operation data of a photovoltaic power station. Compared with the prior art, the embodiment of the invention acquires the power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and the service resource data matched with the power generation capacity data; determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station; when a third party executes business operation, the operation data is predicted based on the operation monitoring model which is trained by the model, and the monitoring result of the operation data is obtained, so that the labor cost is greatly reduced, the timeliness and the accuracy of the operation data of the photovoltaic power station are improved, and the effectiveness of performing business processing of the third party enterprise by using the operation data of the photovoltaic power station obtained through monitoring is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a method for monitoring operating data of a photovoltaic power plant according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method for monitoring operational data of a photovoltaic power plant according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for monitoring operational data of a photovoltaic power plant according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating a monitoring apparatus for monitoring operating data of a photovoltaic power plant according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a method for monitoring operation data of a photovoltaic power station, which comprises the following steps of:
101. and acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data.
In the embodiment of the invention, in order to ensure the reliability of the generated energy data, a third-party enterprise configures the data acquisition terminal and can be non-contact data acquisition equipment for acquiring the display data of each photovoltaic sub-power station electric meter. For example, the frequency of data acquisition may be customized according to the requirements of the user, such as once every 1 hour, once every 1 natural day, and the like, and the embodiment of the present invention is not limited specifically. The generated energy data collected by the third-party terminal comprises electric energy directly provided for an enterprise or an individual under a house with a photovoltaic sub-power station on the roof, and electric energy provided for an enterprise sharing generated energy with the enterprise under the house or input into a national power grid in a serial or parallel mode. In order to better analyze the operation data in combination with the service information, service resource data also needs to be acquired. The business resource data is used for representing data for investing in the photovoltaic sub-power station or resource investment, and the data comprises but is not limited to capital investment, manpower investment, power station equipment investment, photovoltaic power station installation site investment and the like.
It should be noted that, the terminal device for collecting the generated energy data is configured by a third-party enterprise, so that the accuracy and objectivity of the data can be effectively guaranteed, and the accuracy and timeliness of monitoring of the operation data are improved. In addition, non-contact data acquisition equipment with lower cost is adopted, generated energy data are acquired from an ammeter end, the problems that photovoltaic power station operation equipment is interfered by the installation of third-party terminal equipment and power station equipment damage responsibility division is unclear in the installation process of the third-party terminal equipment can be avoided, the cost of operation data monitoring is effectively reduced, and the acceptance of third-party enterprises and photovoltaic power station enterprises is improved.
102. And determining the power generation efficiency and the transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station.
In the embodiment of the invention, the equipment information of the photovoltaic sub-power station comprises the equipment installed capacity of the photovoltaic sub-power station, the theoretical generated energy of the current photovoltaic sub-power station can be determined according to the installed capacity, and the ratio of the actual generated energy to the theoretical generated energy in the acquired generated energy data is the integral generating efficiency of the photovoltaic sub-power station. And calculating the generated energy data of the photovoltaic sub-power station supported by different service resources according to the service resource data matched with the generated energy data to obtain transaction data. For example, if the power generation amount of a photovoltaic sub-power station raised by financing of a certain bank is a and the power supply price is b, the transaction data is the product of a and b. After the generating efficiency and the transaction data are obtained, data fitting is carried out based on the generating efficiency and the transaction data of a certain photovoltaic sub-power station in a period of time, at least one fitting curve of the photovoltaic sub-power station can be obtained, and the fitting curve is used for representing the operation data of the photovoltaic sub-power station.
It should be noted that the operation data in the form of a curve is obtained by fitting the power generation efficiency and the transaction data, and the change condition and the overall change trend of the operation data can be more accurately reflected, so that the accuracy and the intuitiveness of the monitoring of the operation data of the photovoltaic sub-power station are improved. In addition, the power generation efficiency for fitting and the time period corresponding to the transaction data can be customized according to the actual requirements of the user. For example, if a third-party enterprise needs to obtain the running state data of the photovoltaic sub-power station equipment, or the failure rate of equipment running, etc., a shorter time period can be set; if the third-party enterprise needs to acquire the long-term operation trend of the photovoltaic sub-power station, a longer time period can be set. Based on different data analysis demands, the fitted data volume is set, the monitoring demands of different third-party enterprises on the operation data can be met, and therefore the flexibility of operation data monitoring is improved.
103. And when a third party executes business operation, predicting the operation data based on the operation monitoring model which is trained by the model, and obtaining the monitoring result of the operation data.
In the embodiment of the invention, in order to guarantee the rights and interests of third-party enterprises, when the third-party enterprises execute business operations of the photovoltaic sub-power station, such as capital investment, photovoltaic equipment investment and the like, the operation conditions of the photovoltaic sub-power station are evaluated based on the operation data of the photovoltaic sub-power station, specifically, the operation monitoring model is used for predicting the operation data so as to obtain the monitoring results of the operation data, the operation monitoring model is obtained by configuring the model weight for the resource distribution information based on the business resource data and completing training, the monitoring results of the operation data of the photovoltaic sub-power station can be accurately predicted, reference and guidance are provided for the third-party enterprises to the business operations of the photovoltaic sub-power station, and the effectiveness of the third-party enterprises to the business operations of the photovoltaic sub-power station is guaranteed.
For further explanation and limitation, in an embodiment of the present invention, as shown in fig. 2, the step 102 of determining the power generation efficiency and the transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the power generation amount data and the business resource data includes:
201. and analyzing the grid-connected state information of the photovoltaic sub-power station in the equipment information.
202. And performing power supply serial-parallel connection division on the generated energy data according to the grid connection state information, and classifying the business resource data corresponding to the divided generated energy data to obtain the business resource types of the generated energy data corresponding to different power supply serial-parallel connection classifications.
203. And calculating the power generation efficiency based on the generated energy data corresponding to the power supply serial-parallel classification, and calculating transaction data based on the service resource data corresponding to the service resource type.
In the embodiment of the invention, the equipment information comprises specification information such as installed capacity of the power station, the type of the inverter and the like, and also comprises grid-connected state information and power supply user information, wherein the grid-connected state information comprises state information used for representing whether the photovoltaic sub-power station is connected with the grid or not, state information used for representing whether grid-connected conditions exist or not, and the like. The power supply user information is used for representing the type of a power supply user, such as a heavy industry enterprise, a breeding enterprise, a common resident and the like. Because in the actual power supply application scene, often there is the situation that single photovoltaic sub-power plant can't satisfy power supply user power consumption demand, need through the mode of merging into power supply network with each photovoltaic sub-power plant series connection or parallelly connected, reach high current or high voltage to satisfy different power supply user's power consumption demand. For example, industrial and mining enterprises and chemical enterprises use equipment that requires high current, and two photovoltaic sub-power stations need to be operated in parallel to support normal operation of the equipment. For different power supply user types and different power supply voltage grades, the electricity price of unit generated energy is greatly different. Therefore, it is necessary to divide the power supply series-parallel connection of the generated energy data based on whether the grid-connected state of each photovoltaic sub-power station is series connection or parallel connection. For example, when the photovoltaic sub-power stations are connected in series and incorporated into the power supply grid, the generated power is classified as series power supply, and when the photovoltaic sub-power stations are connected in parallel and incorporated into the power supply grid, the generated power is classified as series power supply and parallel power supply.
Furthermore, the generated energy and the electricity price of the photovoltaic sub-power station corresponding to different service resource types are different from the transaction mode, and a third-party enterprise investing resources into the photovoltaic power station exists, and meanwhile, the third-party enterprise supplies power to the photovoltaic power station. For example, as a photovoltaic panel supplier of the photovoltaic sub-power station, which invests in photovoltaic panel resources, and at the same time, for a power supply user of the photovoltaic sub-power station, the power generation amount provided by the photovoltaic sub-power station to the power supply user is converted into the number of photovoltaic panels of a photovoltaic panel manufacturer for trading. Therefore, according to different power supply prices and transaction modes, service resource types are set, for example, an entity service resource type for performing physical resource investment on the photovoltaic power station and a virtual resource type for performing fund investment on the photovoltaic power station. And presetting a mapping relation between different service resource types and different power supply series-parallel connection classifications, and further matching the generated energy data corresponding to each series-parallel connection classification with the service resource types according to the preset mapping relation to obtain the service resource type of the generated energy data corresponding to each power supply series-parallel connection type. Further, according to all the installed capacity and power generation amount data of the sub-power stations corresponding to each series-parallel classification, power generation efficiency corresponding to each series-parallel classification is calculated, and transaction data are calculated according to the service resource data and the power generation amount data corresponding to the service resource types.
It should be noted that the generated energy data is divided according to the grid-connected state information, the generated energy data of different power supply grid-connected classifications can be obtained, the distribution situation and the development trend of the generated energy data of different power supply grid-connected types can be obtained, each power supply series-parallel classification is further divided according to the type of the service resource, the generated energy data of different power supply prices and different transaction modes can be accurately classified, the calculation of the transaction data is more accurate, the accuracy of statistics and analysis of the generated energy data is greatly improved, and meanwhile, the accuracy of the transaction data is improved.
For further explanation and limitation, in an embodiment of the present invention, as shown in fig. 2, the step 102 of performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station includes:
204. and determining a data curve fitting function based on the power supply serial-parallel classification and the service resource type.
205. And if the third party is the virtual resource type, fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data comprising power generation efficiency operation data and transaction operation data.
206. And if the third party is the entity resource type, integrally fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data.
In the embodiment of the invention, according to the type of the resource which is put into the photovoltaic power station by the third-party enterprise, the third party can be divided into an entity resource type and a virtual resource type. The entity resource type is an enterprise which takes entity resources such as photovoltaic power station equipment and the like as input; the virtual resource type is the enterprise invested in the virtual form of fund or software products. Because the third-party enterprises of different resource types pay different attention to the operation data, for example, financial investment enterprises often pay attention to the power generation efficiency operation data and the transaction operation data at the same time, aiming at enterprises of virtual resource types, a quadratic polynomial fitting regression equation fitting function is needed to be used for respectively fitting the power generation efficiency and the transaction data to obtain a power generation efficiency operation data curve and a transaction operation data curve; however, enterprises of entity resource types only invested in photovoltaic equipment often pay more attention to the overall operation condition of the photovoltaic power station, and therefore, for enterprises of entity resource types, a cubic polynomial is required to be used for fitting a regression equation fitting function to the power generation efficiency and the transaction data, so that a curve of the overall operation data is obtained.
It should be noted that, based on the actual requirements of third-party enterprises of different resource types, the power generation efficiency and the transaction data are fitted by adopting different data fitting modes, so as to obtain power generation efficiency operation data, transaction operation data and operation data representing the overall operation state. The data reference requirements of different third-party enterprises can be met in a more targeted manner, and therefore the operation data monitoring experience of the third-party enterprises is improved.
For further explanation and limitation, in an embodiment of the present invention, as shown in fig. 3, before the step 103 of predicting the operation data based on the operation monitoring model that has been trained, and obtaining the monitoring result of the operation data, the method further includes:
301. and acquiring the operating training sample data, and constructing a prediction network matched with the service resource type.
302. And determining resource allocation information matched with the service resource data based on a preset service resource allocation corresponding relation.
303. And configuring the model weight of the prediction network based on the resource allocation information, and performing model training on the prediction model after the model weight is configured through the operation training sample data to obtain the operation monitoring model.
In the embodiment of the invention, in order to predict the monitoring result, corresponding prediction networks are constructed aiming at different service resource types. The operation data corresponding to different service resource types can be one or more groups. For example, the operation data corresponding to the virtual resource type includes two sets of data, i.e., power generation efficiency operation data and transaction operation data, and the entity resource type corresponds to one set of integrated operation data. Thus, the prediction network may be single input or multiple input. And because the service resource types at least comprise virtual resource types and entity resource types, each service resource type corresponds to one layer of prediction network, and different service resource types need to be superposed in the last layer of prediction network according to the weight, the operation monitoring model needs to be at least three layers of classification prediction networks. Based on the above requirements, the prediction network may be a Long Short-term Memory neural network (LSTM). Of course, the service resource type may also be a further subdivision type based on the virtual resource type and the entity resource type. For example, the virtual resource types may be further classified according to the amount of resources put into the service resource data and the power supply price.
In the embodiment of the invention, the influence degree of resource investment of different service resource types on the operation of the photovoltaic sub-power station is different. For example, capital investment may support the operation of a larger number of photovoltaic sub-plants, while the number of photovoltaic sub-plants that a photovoltaic plant investment may support is quite limited. Therefore, it is necessary to set a service resource allocation correspondence according to the distribution interval of different service resource data, and match resource allocation information of different service resource types according to the service resource allocation correspondence, that is, match different weights for different service resource types. The distribution interval division mode of the service resource data and the specific weight in the resource allocation information can be customized according to the actual service requirement. For example, the distribution section of the amount of invested funds in the business resource data is divided, the section with the amount of invested funds being 50 ten thousand lower is divided into one section, and the corresponding resource allocation information, that is, the weight is set to 0.2, the section with the amount of invested funds being 50 to 200 ten thousand (including 50 ten thousand and 200 ten thousand) is divided into one section, and the corresponding resource allocation information, that is, the weight is set to 0.3, the section with the amount of invested funds being more than 200 ten thousand is divided into one section, and the corresponding resource allocation information, that is, the weight is set to 0.5. Further, configuring corresponding weights for the prediction network layer corresponding to the service resource types based on the weights of the different service resource types, and performing model training on the prediction model after the model weights are configured by using operation training sample data to obtain the operation monitoring model.
It should be noted that, the prediction network is constructed according to different service resource types, and the prediction network layer is configured with corresponding weights according to the operation influence degree and importance degree of the different service resource types on the photovoltaic power station, so that the monitoring result can better conform to the actual operation condition of the actual photovoltaic power station, and the prediction accuracy of the operation monitoring model is effectively improved.
In an embodiment of the present invention, for further explanation and limitation, before the acquiring, in step 101, the power generation data collected by the third party terminal from the at least one photovoltaic sub-power station and the service resource data matched with the power generation data, the method further includes:
and establishing data communication connection with a third party server, and distributing service communication interfaces for different third parties.
And after the resource type of the third party is determined, calling a service communication interface matched with the resource type, and executing the service operation through the service communication interface.
In the embodiment of the invention, the information security of the third-party enterprise is ensured. And establishing data communication connection with the third-party server in a mode of distributing corresponding communication interfaces to different third-party enterprise servers, and determining the resource type corresponding to the third-party server. The resource types include a virtual resource type and an entity resource type. When third-party enterprises of different resource types put resources into one or more photovoltaic sub-power stations, corresponding business operations can be triggered through different data communication interfaces, for example, when a financial enterprise injects funds into the photovoltaic sub-power stations, the business operations of fund transfer can be carried out through communication interfaces configured by bank account numbers; and the photovoltaic equipment resource investor can perform the business operation invested by the photovoltaic equipment through the communication interface configured by the product ordering system. Through the mode of establishing the data communication interface corresponding to the resource type with the third-party enterprises with different resource types, the data communication interface can be accurately matched with the service operation type of the third-party enterprises, redundant transfer communication is not needed, and therefore the information safety and confidentiality of the third-party enterprises are effectively improved.
In an embodiment of the present invention, for further explanation and limitation, the method further comprises:
calculating an operation monitoring coefficient of the operation data based on the monitoring result, wherein the operation monitoring coefficient is used for representing extreme value floating intervals of different operation data under different monitoring requirements, and the monitoring result comprises normal operation state, abnormal operation state and high risk of operation state;
and if the operation monitoring coefficient in the monitoring result is larger than a preset monitoring threshold value, sending alarm information to a third party server so that the third party can adjust the generated energy and the input resource data.
In the embodiment of the invention, in order to further improve the monitoring effectiveness, operation monitoring coefficients of different operation data under different monitoring requirements are calculated based on monitoring results, specifically, the power generation efficiency is distributed in a floating interval of a maximum value and a minimum value, the power generation efficiency takes the minimum value when the operation state is normal, the power generation efficiency takes the maximum value when the operation state is high-risk, the power generation efficiency takes an average value when the operation state is abnormal, and corresponding weight coefficients are set for the operation data corresponding to different monitoring requirements. For example, in the monitoring demand in which the power generation efficiency is more focused, the weight coefficient of the power generation efficiency is 0.7, and the weight coefficient of the transaction data is 0.3; under the monitoring requirement of paying more attention to the transaction data, the weight coefficient of the power generation efficiency is 0.3, and the weight coefficient of the transaction data is 0.7. And multiplying each operation data determined based on the monitoring result by the corresponding weight coefficient, and adding the product results of each operation data to obtain the operation monitoring coefficient. For example, in a monitoring demand in which power generation efficiency is more focused, the power generation efficiency is 0.8, and the transaction data is 2, and the operation monitoring coefficient is 0.8 × 0.7+2 × 0.3 — 1.16. And further, comparing and judging by using a preset monitoring threshold value and an operation monitoring coefficient, and when the operation monitoring coefficient is larger than the preset monitoring threshold value, generating corresponding alarm information and sending the alarm information to a third-party server, so that the third party can adjust the resource input quantity or the quantity of the input photovoltaic sub-power stations according to the alarm information. The weight coefficient of the operating data and the setting of the preset monitoring threshold value may be customized according to an actual application scenario, and embodiments of the present invention are not particularly limited.
It should be noted that by calculating the operation monitoring coefficient, determining the operation monitoring coefficient by using a preset monitoring threshold value, and determining whether to generate the early warning information according to the determination result, the operation monitoring coefficient can be timely and effectively notified to the third-party enterprise when the operation data monitoring result is abnormal, so that the safety and effectiveness of enterprise investment operation are ensured.
In an embodiment of the present invention, for further explanation and limitation, the third party includes at least one of a financial data enterprise party, a physical industrial enterprise party, and a scientific and technological product enterprise party, and the method further includes:
and distributing terminal acquisition permission for the third party, wherein the acquisition permission is used for representing permission of different third party terminals for acquiring different photovoltaic sub-power stations.
In the embodiment of the present invention, the third party at least includes one of a financial data enterprise, that is, an enterprise that invests the photovoltaic power station in the form of fund, such as a bank and a financial enterprise, an entity industrial enterprise, that is, an enterprise that invests the photovoltaic power station in the form of resources such as a photovoltaic device or a power station installation site, such as a photovoltaic panel manufacturer and an inverter manufacturer, and a scientific and technical product enterprise, that is, an enterprise that invests the photovoltaic power station in the form of software resources such as a power station operation platform and an APP, such as a software company. And distributing corresponding terminal data acquisition permission to the target photovoltaic sub-power station according to the investment of the third-party enterprise. For example, when an investment bank invests in the photovoltaic sub-power station No. 100-110, the terminal equipment data acquisition permission of the photovoltaic sub-power station No. 100-110 is distributed to the investment bank. Thereby guaranteeing the rights and interests of the third-party enterprises. Meanwhile, different data acquisition time can be allocated to each third-party enterprise, so that data communication pressure is relieved, and data communication stability is guaranteed.
The invention provides a method for monitoring operation data of a photovoltaic power station. The embodiment of the invention acquires the generated energy data acquired by a third-party terminal from at least one photovoltaic sub-power station and the service resource data matched with the generated energy data; determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station; when a third party executes business operation, the operation data is predicted based on the operation monitoring model which is trained by the model, and the monitoring result of the operation data is obtained, so that the labor cost is greatly reduced, the timeliness and the accuracy of the operation data of the photovoltaic power station are improved, and the effectiveness of performing business processing on third-party enterprises by using the operation data of the photovoltaic power station obtained through monitoring is improved.
Further, as an implementation of the method shown in fig. 1, an embodiment of the present invention provides a device for monitoring operating data of a photovoltaic power plant, as shown in fig. 4, where the device includes:
the acquisition module 41 is configured to acquire power generation amount data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation amount data;
the fitting module 42 is configured to determine power generation efficiency and transaction data of the photovoltaic sub-power station based on the device information of the photovoltaic sub-power station, the generated energy data and the service resource data, and perform data curve fitting on the power generation efficiency and the transaction data to obtain operation data of the photovoltaic sub-power station;
the prediction module 43: the operation monitoring model is used for predicting the operation data based on the operation monitoring model which is trained based on the model when a third party executes the business operation to obtain the monitoring result of the operation data, and the operation monitoring model is obtained by configuring model weight based on the resource distribution information of the business resource data and completing the training.
Further, the fitting module includes:
the analysis unit is used for analyzing the grid-connected state information of the photovoltaic sub-power station in the equipment information;
the classification unit is used for carrying out power supply serial-parallel connection division on the generated energy data according to the grid-connected state information and classifying the business resource data corresponding to the divided generated energy data to obtain the business resource types of the generated energy data corresponding to different power supply serial-parallel connection classifications;
and the calculating unit is used for calculating the power generation efficiency based on the power generation amount data corresponding to the power supply serial-parallel classification and calculating the transaction data based on the service resource data corresponding to the service resource type.
Further, the fitting module further comprises:
the first fitting unit is used for determining a data curve fitting function based on the power supply serial-parallel classification and the service resource type, and the data curve fitting function is used for fitting the power generation efficiency and/or the transaction data;
the second fitting unit is used for respectively fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data comprising power generation efficiency operation data and transaction operation data if the third party is a virtual resource type;
and the third fitting unit is used for integrally fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data if a third party is an entity resource type.
Further, the apparatus further comprises:
the training module is used for acquiring the operating training sample data and constructing a prediction network matched with the service resource type, wherein the prediction network is a single-input or multi-input at least three-layer classification prediction network;
the distribution module is used for determining resource distribution information matched with the service resource data based on a preset service resource distribution corresponding relation, the resource distribution information is used for representing service resource distribution capacity, and the preset service resource distribution corresponding relation records resource distribution information corresponding to distribution intervals of different service resource data;
and the training module is used for configuring the model weight of the prediction network based on the resource allocation information and performing model training on the prediction model after the model weight is configured through the operation training sample data to obtain the operation monitoring model.
Further, the apparatus further comprises:
the distribution module is used for establishing data communication connection with a third-party server and distributing service communication interfaces for different third parties;
and the execution module is used for calling a service communication interface matched with the resource type after the resource type of the third party is determined, and executing the service operation through the service communication interface, wherein the resource type comprises a virtual resource type and an entity resource type.
Further, the apparatus further comprises:
the calculation module is used for calculating an operation monitoring coefficient of the operation data based on the monitoring result, the operation monitoring coefficient is used for representing the extreme value floating interval of different operation data under different monitoring requirements, and the monitoring result comprises normal operation state, abnormal operation state and high risk operation state;
and the alarm module is used for sending alarm information to a third party server side if the operation monitoring coefficient in the monitoring result is greater than a preset monitoring threshold value, so that the third party can adjust the generated energy and the input resource data.
Further, the apparatus further comprises:
the distribution module is further used for distributing terminal acquisition permission for the third party, and the acquisition permission is used for representing permission of different third party terminals for acquiring different photovoltaic sub-power stations.
The invention provides a monitoring device for operation data of a photovoltaic power station. The embodiment of the invention acquires the generated energy data acquired by a third-party terminal from at least one photovoltaic sub-power station and the service resource data matched with the generated energy data; determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station; when a third party executes business operation, the operation data is predicted based on the operation monitoring model which is trained by the model, and the monitoring result of the operation data is obtained, so that the labor cost is greatly reduced, the timeliness and the accuracy of the operation data of the photovoltaic power station are improved, and the effectiveness of performing business processing of the third party enterprise by using the operation data of the photovoltaic power station obtained through monitoring is improved.
According to an embodiment of the present invention, a computer storage medium is provided, where at least one executable instruction is stored, and the computer executable instruction can execute the data query method in any of the above method embodiments.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computer device.
As shown in fig. 5, the computer apparatus may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508.
Wherein: the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508.
A communication interface 504 for communicating with network elements of other devices, such as clients or other servers.
The processor 502 is configured to execute the program 510, and may specifically execute relevant steps in the monitoring embodiment of the photovoltaic power plant operation data.
In particular, program 510 may include program code that includes computer operating instructions.
The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computer device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may specifically be used to cause the processor 502 to perform the following operations:
acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data;
determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station;
when a third party executes business operation, the operation data is predicted based on an operation monitoring model which is trained by the model, and a monitoring result of the operation data is obtained, wherein the operation monitoring model is obtained by configuring model weight based on resource distribution information of the business resource data and completing training.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for monitoring operation data of a photovoltaic power station is characterized by comprising the following steps:
acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data;
determining the power generation efficiency and transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station;
when a third party executes business operation, the operation data is predicted based on an operation monitoring model which is trained by the model, and a monitoring result of the operation data is obtained, wherein the operation monitoring model is obtained by configuring model weight based on resource distribution information of the business resource data and completing training.
2. The method of claim 1, wherein the determining the power generation efficiency, the trading data of the photovoltaic sub-power plant based on the equipment information of the photovoltaic sub-power plant, the power generation capacity data, and the business resource data comprises:
analyzing the grid-connected state information of the photovoltaic sub-power station in the equipment information;
performing power supply serial-parallel connection division on the generated energy data according to the grid connection state information, and classifying the business resource data corresponding to the divided generated energy data to obtain the business resource types of the generated energy data corresponding to different power supply serial-parallel connection classifications;
and calculating the power generation efficiency based on the generated energy data corresponding to the power supply serial-parallel classification, and calculating transaction data based on the service resource data corresponding to the service resource type.
3. The method of claim 1, wherein the performing a data curve fit on the power generation efficiency and the transaction data to obtain the operational data of the photovoltaic sub-power plant comprises:
determining a data curve fitting function based on the power supply serial-parallel classification and the service resource type, wherein the data curve fitting function is used for fitting the power generation efficiency and/or the transaction data;
if the third party is a virtual resource type, fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data comprising power generation efficiency operation data and transaction operation data;
and if the third party is the entity resource type, integrally fitting the power generation efficiency and the transaction data according to the data curve fitting function to obtain operation data.
4. The method of claim 1, wherein before the operation monitoring model based on the completed model training predicts the operation data and obtains the monitoring result of the operation data, the method further comprises:
acquiring operation training sample data, and constructing a prediction network matched with the service resource type, wherein the prediction network is a single-input or multi-input at least three-layer classification prediction network;
determining resource allocation information matched with the service resource data based on a preset service resource allocation corresponding relation, wherein the resource allocation information is used for representing service resource allocation capacity, and the preset service resource allocation corresponding relation records resource allocation information corresponding to distribution intervals of different service resource data;
and configuring model weights of the prediction network based on the resource allocation information, and performing model training on the prediction model with the configured model weights through the operation training sample data to obtain the operation monitoring model.
5. The method of claim 1, wherein before the obtaining of the power generation data collected from the third party terminal from the at least one photovoltaic sub-power station and the service resource data matched with the power generation data, the method further comprises:
establishing data communication connection with a third party server, and distributing service communication interfaces for different third parties;
and after determining the resource type of the third party, calling a service communication interface matched with the resource type, and executing the service operation through the service communication interface, wherein the resource type comprises a virtual resource type and an entity resource type.
6. The method of claim 1, further comprising:
calculating an operation monitoring coefficient of the operation data based on the monitoring result, wherein the operation monitoring coefficient is used for representing extreme value floating intervals of different operation data under different monitoring requirements, and the monitoring result comprises normal operation state, abnormal operation state and high risk of operation state;
and if the operation monitoring coefficient in the monitoring result is larger than a preset monitoring threshold value, sending alarm information to a third party server so that the third party can adjust the generated energy and the input resource data.
7. The method of any one of claims 1-6, wherein the third party comprises at least one of a financial data enterprise party, a physical industrial enterprise party, and a scientific and technological product enterprise party, and the method further comprises:
and distributing terminal acquisition permission for the third party, wherein the acquisition permission is used for representing permission of different third party terminals for acquiring different photovoltaic sub-power stations.
8. A monitoring device of photovoltaic power plant operational data, characterized in that includes:
the acquisition module is used for acquiring power generation capacity data acquired by a third-party terminal from at least one photovoltaic sub-power station and service resource data matched with the power generation capacity data;
the fitting module is used for determining the power generation efficiency and the transaction data of the photovoltaic sub-power station based on the equipment information of the photovoltaic sub-power station, the generated energy data and the service resource data, and performing data curve fitting on the power generation efficiency and the transaction data to obtain the operation data of the photovoltaic sub-power station;
a prediction module: the operation monitoring model is used for predicting the operation data based on the operation monitoring model which is trained based on the model when a third party executes the business operation, so as to obtain the monitoring result of the operation data, and the operation monitoring model is obtained by configuring model weight based on the resource distribution information of the business resource data and completing the training.
9. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the method of monitoring photovoltaic power plant operational data as claimed in any one of claims 1 to 7.
10. A computer device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the monitoring method of the photovoltaic power plant operation data in any one of claims 1-7.
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