Ground photovoltaic power station virtual simulation training system based on real-time operation data

Abstract

The present invention discloses a ground photovoltaic power station virtual simulation training system based on real-time operation data, relates to the field of virtual simulation technology, and improves the accuracy of ground photovoltaic power station simulation. The present invention sets associated environmental deviation parameters and associated operation deviation parameters between each operation data and environmental data, and establishes a virtual simulation training network according to the equipment distribution map of the ground photovoltaic power station. According to the photovoltaic operation and maintenance events to be executed, a real-time simulation trend decision tree is traversed in the virtual simulation training network, and the real-time environmental data and real-time operation data of the corresponding photovoltaic operation and maintenance equipment are obtained. The real-time environmental data is input into the real-time simulation trend decision tree for simulation simulation. According to the simulation, the operation data labels expected to be carried by multiple operation data of each photovoltaic operation and maintenance equipment are simulated, and the operation data labels expected to be carried by each operation data are compared with the corresponding real-time operation data. According to the comparison result, it is judged whether the simulation is successful.

Description

Technical Field

[0001] The present invention relates to the field of virtual simulation technology, and in particular to a ground photovoltaic power station virtual simulation training system based on real-time operation data. Background Art

[0002] In the operation and maintenance of modern power systems, ground-mounted photovoltaic power plants have attracted widespread attention due to their environmental and sustainability considerations. However, the operation and maintenance of photovoltaic power plants presents numerous challenges, such as equipment fault detection, performance optimization, and environmental adaptability. Traditional operation and maintenance methods often rely on manual inspections and empirical judgment, resulting in low efficiency and high costs.

[0003] Existing simulation systems have shortcomings in simulating the operation and environmental impact of photovoltaic power stations. They are unable to accurately predict the operating status and potential failures of equipment, and lack in-depth analysis and utilization of historical data, making it difficult to achieve intelligent operation and maintenance decision support. To this end, a ground photovoltaic power station virtual simulation training system based on real-time operation data is provided. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a ground photovoltaic power station virtual simulation training system based on real-time operation data.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A ground photovoltaic power station virtual simulation training system based on real-time operation data includes a cloud computing platform, wherein the cloud computing platform is communicatively connected to a simulation data acquisition module, a virtual simulation attribute analysis module, and an operation simulation training module;

[0007] The simulation data acquisition module is used to set multiple sensors on each power equipment in the ground photovoltaic power station and set a number of photovoltaic operation and maintenance events, thereby collecting historical operation records and historical environmental data sets after multiple cycles of the ground photovoltaic power station;

[0008] The virtual simulation attribute analysis module is used to establish a simulation trend decision tree based on the historical operation records of each photovoltaic operation and maintenance event, and to establish normal change intervals and multiple abnormal change intervals based on the historical operation records and historical environmental data sets;

[0009] According to the normal change interval and multiple abnormal change intervals, the historical operation records and each historical data in the historical environment data set are marked with level abnormal environment data and level abnormal operation data, thereby obtaining the associated environmental deviation parameters between each level abnormal operation data mark and each level abnormal environment data mark, as well as the associated operation deviation parameters between each level abnormal operation data;

[0010] The operation simulation training module is used to obtain the equipment distribution map of the ground photovoltaic power station and the photovoltaic operation and maintenance events to be executed, and establish a virtual simulation training network based on the equipment distribution map of the ground photovoltaic power station. According to the photovoltaic operation and maintenance events to be executed, a real-time simulation trend decision tree is traversed in the virtual simulation training network, and real-time environmental data and real-time operation data of the corresponding photovoltaic operation and maintenance equipment are obtained;

[0011] The real-time environmental data is input into the real-time simulation trend decision tree for simulation. According to the associated environmental deviation parameters and real-time environmental data, multiple operating data of each photovoltaic operation and maintenance equipment are simulated, and the operating data annotations expected are compared with the corresponding real-time operating data. The success of the simulation is judged based on the comparison results.

[0012] Furthermore, the process of collecting the historical operation records and historical environment data sets includes:

[0013] The staff uploads the equipment distribution map of the ground photovoltaic power station to the simulation data acquisition module. The equipment distribution map includes the name, spatial location, and interaction direction of each photovoltaic operation and maintenance equipment in the ground photovoltaic power station.

[0014] Each photovoltaic operation and maintenance device in the ground photovoltaic power station is numbered and equipped with multiple sensors, and the sensors on each photovoltaic operation and maintenance device are divided into environmental sensors and operation sensors;

[0015] Set up several photovoltaic operation and maintenance events, and then simulate the data acquisition module to collect each photovoltaic operation and maintenance event through sensors, and perform historical operation records and historical environmental data sets after m cycles of execution, and perform the number of executions of each historical operation record and historical environmental data set and the event name of the photovoltaic operation and maintenance event, where m is a natural number greater than 0.

[0016] Furthermore, the photovoltaic operation and maintenance event includes the event name, the type of photovoltaic operation and maintenance equipment corresponding to the event center equipment and the event-related equipment, the event duration, and the energy trend diagram;

[0017] The historical operation record includes several items of historical operation data corresponding to each event center device and event-related device;

[0018] The historical environment data set includes several items of historical environment data related to each event center device and event association device.

[0019] Furthermore, the process of establishing the simulation trend decision tree includes:

[0020] Establish several simulation device nodes according to the number of photovoltaic operation and maintenance equipment in the ground photovoltaic power station, and mark each simulation device node with the corresponding photovoltaic operation and maintenance equipment number and name;

[0021] According to the energy trend diagram of each photovoltaic operation and maintenance event, the corresponding simulation device nodes are retrieved and connected in sequence to obtain m identical simulation trend decision trees. According to the types of photovoltaic operation and maintenance equipment corresponding to the event center device and event-related equipment, the simulation device nodes in the simulation trend decision trees are classified into simulation center device nodes and simulation related device nodes.

[0022] Furthermore, the process of establishing the normal change interval and multiple abnormal change intervals includes:

[0023] Establish a multi-dimensional coordinate system and set several time nodes, and divide the entire historical change curve into several historical change curve segments through the time nodes;

[0024] Normal distribution is performed on historical change segments corresponding to the same photovoltaic operation and maintenance equipment, with the same data type and at the same time node. Based on the normal distribution results, normal operating change intervals and multiple abnormal operating change intervals of various operating data of each photovoltaic operation and maintenance equipment under each photovoltaic operation and maintenance event are obtained.

[0025] Input the historical operation records corresponding to each photovoltaic operation and maintenance event and the historical change curves in the historical environment data set into each simulation device node in the corresponding simulation trend decision tree;

[0026] According to the normal operation change interval and abnormal operation change interval of various operation data, each historical operation data is marked with abnormal operation data and normal operation data respectively;

[0027] Selecting several simulation trend decision trees from the m simulation trend decision trees corresponding to the same photovoltaic operation and maintenance event, in which all historical operation data of all simulation device nodes are marked with normal operation data, and recording the selected simulation trend decision trees as normal simulation trend decision trees;

[0028] The normal operating change intervals and abnormal operating change intervals of various operating data are obtained by the process of obtaining the normal operating change intervals and abnormal operating change intervals of various environmental data, and then normal environmental data labels and abnormal environmental data labels are set for the historical environmental data carried by each simulation device node.

[0029] Furthermore, the process of setting the associated environment deviation parameter and the associated operation deviation parameter includes:

[0030] Starting from the simulation center device node, determine whether all historical operation data are labeled with normal operation data. If they are all labeled with normal operation data, then determine whether all historical operation data of the next simulation-related device node are labeled with normal operation data according to the direction of the arrow of the directed connection line connected to the simulation center device node;

[0031] When historical operation data with abnormal level operation data annotations appear on the same type of photovoltaic operation and maintenance equipment according to the judgment results, the corresponding historical environmental data with abnormal level environment data annotations appear;

[0032] Count the occurrence probabilities of each historical environmental data with abnormal environmental data labels, and set a probability threshold. The historical environmental data with abnormal environmental data labels whose occurrence probability is greater than or equal to the probability threshold are recorded as abnormal associated environmental data of the corresponding historical operating data of the corresponding type of photovoltaic operation and maintenance equipment. Otherwise, ignore the corresponding historical environmental data with abnormal environmental data labels.

[0033] Obtain the anomaly data annotations of different levels, and the probability of simultaneous occurrence of various abnormal associated environment data in the abnormal environment data annotations of different levels, and record the simultaneous occurrence probability between the abnormal operation data annotations of each level and the abnormal environment data annotations of each level as the associated environment deviation parameter;

[0034] By adopting the process of obtaining the associated environmental deviation parameters, according to the connection status between each simulation device node in the simulation trend decision tree, the probability of simultaneous occurrence of various historical operation data between adjacent sequential simulation device nodes when various levels of abnormal operation data are marked is obtained, and the simultaneous occurrence probability is recorded as the associated operation deviation parameter of the corresponding type of historical operation data when the corresponding level of abnormal operation data is marked.

[0035] Furthermore, the virtual simulation training network is composed of a plurality of simulation device nodes. A corresponding real-time simulation trend decision tree is traversed in the virtual simulation training network. A plurality of real-time environmental data of each photovoltaic operation and maintenance device in the ground photovoltaic power station is obtained from the simulation data acquisition module, and each real-time environmental data is input into the corresponding simulation device node.

[0036] Determine the numerical range of each real-time environmental data, and set a normal environmental data label or a graded abnormal environmental data label for each real-time environmental data according to the determination result; obtain the associated environmental deviation parameters corresponding to each grade of abnormal operating data label for the operating data with the type of real-time environmental data as the abnormal associated environmental data according to the determination result;

[0037] Normal parameter intervals and multiple abnormal parameter intervals are set for various types of operating data, and the sum of the associated environmental deviation parameters of various types of operating data is counted. According to the parameter interval in which the sum of the associated environmental deviation parameters is located, the corresponding operating data is marked with abnormal operating data or normal operating data.

[0038] Furthermore, the simulation process of the photovoltaic operation and maintenance event includes:

[0039] Based on the real-time environmental data of the photovoltaic operation and maintenance equipment corresponding to the simulation center device node, the simulation outputs the expected abnormal operation data labeling or normal operation data labeling of each operation data of the simulation center device node. Then, based on the trend between the simulation device nodes in the real-time simulation trend decision tree, the simulation outputs the expected abnormal operation data labeling or normal operation data labeling of each operation data of the next event-related device node.

[0040] According to the trend between the simulation device nodes in the real-time simulation trend decision tree, each event-related device node or event center device node is sequentially set with a level of abnormal operation data label or normal operation data label until all simulation device nodes in the real-time simulation trend decision tree are marked with data. The corresponding photovoltaic operation and maintenance event simulation is judged to be completed.

[0041] Furthermore, the process of determining whether the simulation is successful includes:

[0042] Set a total deviation threshold to determine whether several real-time operating data items of each photovoltaic operation and maintenance equipment are within the numerical range corresponding to the abnormal operation data label or normal operation data label obtained from the photovoltaic operation and maintenance event simulation results, and count the number of real-time operating data items that are not within the corresponding numerical range, which is recorded as the total simulation deviation number;

[0043] If the total number of simulation deviations is less than the total number of deviations threshold, the photovoltaic operation and maintenance event simulation is judged to be successful; otherwise, the photovoltaic operation and maintenance event simulation is judged to be unsuccessful.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention sets level abnormal environment data labels and level abnormal operation data labels for historical operation records and each historical data in the historical environment data set according to normal change intervals and multiple abnormal change intervals, and then obtains the associated environmental deviation parameters between each level abnormal operation data label and each level abnormal environment data label, as well as the associated operation deviation parameters between each level abnormal operation data, thereby improving the in-depth analysis and utilization of historical data and providing a data basis for subsequent simulation of photovoltaic operation and maintenance equipment.

[0046] 2. The present invention performs simulation by inputting real-time environmental data into a real-time simulation trend decision tree, and simulates multiple operating data of each photovoltaic operation and maintenance equipment based on the simulation, and predicts the operating data annotations. The operating data annotations expected to be carried by each operating data are compared with the corresponding real-time operating data, and whether the simulation is successful is judged based on the comparison results, thereby improving the accuracy of the simulation operation results of the photovoltaic power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0048] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0049] like Figure 1 As shown, a ground photovoltaic power station virtual simulation training system based on real-time operation data includes a cloud computing platform, which is communicatively connected to a simulation data acquisition module, a virtual simulation attribute analysis module, and an operation simulation training module;

[0050] The simulation data acquisition module is used to set multiple sensors on each power equipment in the ground photovoltaic power station and set a number of photovoltaic operation and maintenance events, thereby collecting historical operation records and historical environmental data sets after multiple cycles of the ground photovoltaic power station;

[0051] The virtual simulation attribute analysis module is used to establish a simulation trend decision tree based on the historical operation records of each photovoltaic operation and maintenance event, and to establish normal change intervals and multiple abnormal change intervals based on the historical operation records and historical environmental data sets;

[0052] According to the normal change interval and multiple abnormal change intervals, the historical operation records and each historical data in the historical environment data set are marked with level abnormal environment data and level abnormal operation data, thereby obtaining the associated environmental deviation parameters between each level abnormal operation data mark and each level abnormal environment data mark, as well as the associated operation deviation parameters between each level abnormal operation data;

[0053] The operation simulation training module is used to obtain the equipment distribution map of the ground photovoltaic power station and the photovoltaic operation and maintenance events to be executed, and establish a virtual simulation training network based on the equipment distribution map of the ground photovoltaic power station. According to the photovoltaic operation and maintenance events to be executed, a real-time simulation trend decision tree is traversed in the virtual simulation training network, and real-time environmental data and real-time operation data of the corresponding photovoltaic operation and maintenance equipment are obtained;

[0054] The real-time environmental data is input into the real-time simulation trend decision tree for simulation. According to the associated environmental deviation parameters and real-time environmental data, multiple operating data of each photovoltaic operation and maintenance equipment are simulated, and the operating data annotations expected are compared with the corresponding real-time operating data. The success of the simulation is judged based on the comparison results.

[0055] Further, the working principle of the present invention is described below by way of examples:

[0056] The staff uploads the equipment distribution map of the ground photovoltaic power station to the simulation data acquisition module. The equipment distribution map includes the name, spatial location, and interaction direction of each photovoltaic operation and maintenance equipment in the ground photovoltaic power station.

[0057] The photovoltaic operation and maintenance equipment includes photovoltaic modules, transformers, inverters, etc.

[0058] The simulation data acquisition module assigns numbers and multiple sensors to each photovoltaic operation and maintenance device in the ground photovoltaic power station according to the device distribution map, and divides the sensors of each photovoltaic operation and maintenance device into environmental sensors and operation sensors. The environmental sensors are used to collect environmental data around each photovoltaic operation and maintenance device, such as external temperature and external humidity.

[0059] The operation sensor is used to collect various data under the operating status of photovoltaic operation and maintenance equipment, such as voltage, power, internal temperature value, etc.;

[0060] The numbers can be a1, a2, a3, ..., a n ,The types of sensors include temperature sensors, voltage sensors, power sensors, etc., n is a natural number greater than 0;

[0061] It should be noted that the types of sensors carried by various photovoltaic operation and maintenance equipment are not exactly the same. For example, photovoltaic modules are equipped with temperature sensors and current sensors, while transformers are equipped with temperature sensors and voltage sensors.

[0062] Set up several photovoltaic operation and maintenance events, and then the simulation data acquisition module collects each photovoltaic operation and maintenance event through sensors, and respectively performs historical operation records and historical environmental data sets after m cycles of execution, and performs the number of executions of each historical operation record and historical environmental data set as well as the event name of the photovoltaic operation and maintenance event, where m is a natural number greater than 0;

[0063] The photovoltaic operation and maintenance event includes the event name, the type of photovoltaic operation and maintenance equipment corresponding to the event center device and the event-related equipment, the event duration, and the energy trend diagram;

[0064] The historical operation records include several items of historical operation data corresponding to each event center device and event-related device, such as historical voltage change curves, historical power change curves, etc.;

[0065] The historical environment data set includes several items of historical environment data related to each event center device and event-related device, such as historical external temperature change curve, historical external humidity change curve, etc.;

[0066] It should be noted that the historical operation records and various change curves in the historical environmental data set are marked with the numbers of the corresponding photovoltaic operation and maintenance equipment, and each cycle execution is completed when the photovoltaic operation and maintenance equipment has no hardware defects.

[0067] Furthermore, the simulation data acquisition module sends all historical operation records and historical environment data sets to the virtual simulation attribute analysis module;

[0068] The virtual simulation attribute parsing module establishes n simulation device nodes according to the number of photovoltaic operation and maintenance equipment in the ground photovoltaic power station, and labels each simulation device node with the corresponding photovoltaic operation and maintenance equipment number and name;

[0069] According to the energy trend diagram of each photovoltaic operation and maintenance event, the corresponding simulation device nodes are retrieved and connected in sequence to obtain m identical simulation trend decision trees. According to the types of photovoltaic operation and maintenance equipment corresponding to the event center device and the event-related equipment, the simulation device nodes in the simulation trend decision tree are classified into simulation center device nodes and simulation-related equipment nodes. It should be noted that each simulation center device node and simulation-related equipment node in the simulation trend decision tree is provided with a directed connection line, and the direction of the arrow of the directed connection line indicates the direction of energy flow;

[0070] Establish a multi-dimensional coordinate system and map various historical change curves in the historical operation records with the event name of the same photovoltaic operation and maintenance event into the same multi-dimensional coordinate system;

[0071] Since the collection time of each historical change curve is the same, each historical change curve has a common time dimension. Therefore, a coordinate axis is set on the multidimensional coordinate system as the common time axis, and several time nodes are set on the common time axis. The entire historical change curve is divided into several historical change curve segments by the time nodes.

[0072] The historical change segments corresponding to the same photovoltaic operation and maintenance equipment, the same data type and the same time node are normally distributed, and multiple value intervals are divided on the normal distribution graph. The historical change segments in each value interval are counted, and then the value interval with the largest number of historical change segments is selected as the normal change interval segment, and the remaining value intervals are recorded as abnormal change interval segments;

[0073] Connect each normal change interval segment and abnormal change interval segment in chronological order, and then obtain the normal operation change interval and multiple abnormal operation change intervals of various operating data of each photovoltaic operation and maintenance equipment under each photovoltaic operation and maintenance event. It should be noted that according to the interval order between each numerical interval and the numerical interval corresponding to the normal change interval segment, the numerical interval to the right of the numerical interval corresponding to the normal change interval segment is divided into a first-level abnormal change interval segment, a second-level abnormal change interval segment, etc., among which the first-level abnormal change interval segment has the lightest abnormality, and the subsequent abnormalities are successively superimposed;

[0074] Input the historical operation records corresponding to each photovoltaic operation and maintenance event and the historical change curves in the historical environment data set into each simulation device node in the corresponding simulation trend decision tree;

[0075] According to the normal operation change interval and abnormal operation change interval of various operation data, each historical operation data is respectively marked with abnormal operation data and normal operation data. The abnormal operation data marking is divided into level 1 abnormal operation data marking and level 2 abnormal operation data marking.

[0076] Selecting several simulation trend decision trees from the m simulation trend decision trees corresponding to the same photovoltaic operation and maintenance event, in which all historical operation data of all simulation device nodes are marked with normal operation data, and recording the selected simulation trend decision trees as normal simulation trend decision trees;

[0077] By adopting the process of obtaining the normal operation variation interval and the graded abnormal operation variation interval of various operation data, the historical environmental data of each simulation device node in each normal simulation trend decision tree is mapped to a multi-dimensional coordinate system, thereby obtaining the normal operation variation interval and the abnormal operation variation interval of various environmental data;

[0078] According to the normal operation change interval and abnormal operation change interval of various historical environmental data, normal environmental data labels and graded abnormal environmental data labels are set for the historical environmental data carried by each simulation device node.

[0079] Furthermore, for each simulation trend decision tree of any photovoltaic operation and maintenance event, starting from the simulation center device node, it is determined whether all historical operation data are labeled with normal operation data. If it is determined that all are labeled with normal operation data, then according to the direction of the arrow of the directed connection line connecting the simulation center device node, it is determined whether all historical operation data of the next simulation associated device node are labeled with normal operation data.

[0080] When any historical operation data is marked with abnormal operation data, the historical operation records and historical environmental data sets corresponding to all photovoltaic operation and maintenance events are used to obtain the historical environmental data corresponding to the abnormal environment data when the historical operation data of the same type of photovoltaic operation and maintenance equipment appears with abnormal operation data;

[0081] Count the occurrence probabilities of each historical environmental data with abnormal environmental data labels, and set a probability threshold. The historical environmental data with abnormal environmental data labels whose occurrence probability is greater than or equal to the probability threshold are recorded as abnormal associated environmental data of the corresponding historical operating data of the corresponding type of photovoltaic operation and maintenance equipment. Otherwise, ignore the corresponding historical environmental data with abnormal environmental data labels.

[0082] For any type of historical operating data of photovoltaic operation and maintenance equipment, obtain the probability of simultaneous occurrence of the data under different levels of abnormal operating data annotation and each abnormal associated environmental data under different levels of abnormal environmental data annotation, and record the simultaneous occurrence probability between each level of abnormal operating data annotation and each level of abnormal environmental data annotation as the associated environmental deviation parameter;

[0083] By adopting the process of obtaining the associated environmental deviation parameter, according to the connection status between each simulation device node in the simulation trend decision tree, the probability of simultaneous occurrence of various historical operation data between adjacent sequential simulation device nodes when abnormal operation data of various levels are marked is obtained, and the simultaneous occurrence probability is recorded as the associated operation deviation parameter of the corresponding type of historical operation data when abnormal operation data of the corresponding level is marked;

[0084] For example, for adjacent simulation device nodes α and β, for historical operation data A in simulation device node α and historical operation data B in simulation device node β, the probability of simultaneous occurrence of historical operation data A and historical operation data B when annotating abnormal operation data of any level is θ, and the associated operation deviation parameter of historical operation data A and historical operation data B when annotating abnormal operation data of the corresponding level is θ;

[0085] Each associated operation deviation parameter is marked in the normal simulation trend decision tree of the corresponding photovoltaic operation and maintenance event, and then the virtual simulation attribute analysis module sends all the normal simulation trend decision trees to the operation simulation training module.

[0086] Furthermore, the operation simulation training module establishes a virtual simulation training network according to the equipment distribution diagram of the ground photovoltaic power station;

[0087] The virtual simulation training network is composed of n simulation device nodes, and the spatial distribution of each simulation device node in the virtual simulation training network is the same as the spatial distribution of actual photovoltaic operation and maintenance equipment in a ground photovoltaic power station;

[0088] The staff uploads the photovoltaic operation and maintenance events to be executed to the operation simulation training module. Then, every time the operation simulation training module receives a photovoltaic operation and maintenance event, it traverses the corresponding real-time simulation trend decision tree in the virtual simulation training network based on the photovoltaic operation and maintenance equipment types corresponding to the event center equipment and event-related equipment in the photovoltaic operation and maintenance event, as well as the energy trend diagram, and matches the corresponding normal simulation trend decision tree based on the photovoltaic operation and maintenance event.

[0089] Acquire several real-time environmental data of each photovoltaic operation and maintenance equipment in the ground photovoltaic power station from the simulation data acquisition module, and input each real-time environmental data into the corresponding simulation device node;

[0090] Determine the numerical range of each real-time environmental data, and set a normal environmental data label or a graded abnormal environmental data label for each real-time environmental data according to the determination result;

[0091] If the real-time environment data contains normal environment data, no operation will be performed;

[0092] If the real-time environment data is annotated with level abnormal environment data, then according to the level of the level abnormal environment data annotation, the associated environment deviation parameters corresponding to the abnormal operation data annotations of each level are obtained for the operation data with the real-time environment data of this type as the abnormal associated environment data;

[0093] For each type of operating data, a normal parameter interval and multiple abnormal parameter intervals are set. The abnormal parameter intervals are divided into a first-level abnormal parameter interval, a second-level abnormal parameter interval, and so on. The extreme values of adjacent abnormal parameter intervals are closed to each other. For example, if the normal parameter interval is [0, a], the first-level abnormal parameter interval is (a, b), then the second-level abnormal parameter interval is [b, c), and so on.

[0094] The sum of the associated environmental deviation parameters of various types of operating data is counted, and according to the parameter range in which the sum of the associated environmental deviation parameters is located, the corresponding operating data is marked with abnormal operating data or normal operating data.

[0095] Furthermore, first, based on the real-time environmental data of the photovoltaic operation and maintenance equipment corresponding to the simulation center device node, the abnormal operation data labeling or normal operation data labeling expected to be carried by each operation data of the simulation center device node is simulated, and then based on the trend between the simulation device nodes in the real-time simulation trend decision tree, the abnormal operation data labeling or normal operation data labeling expected to be carried by each operation data of the next event-related device node is simulated;

[0096] The simulation process of data annotation of the event-related device node includes:

[0097] First, based on the real-time environmental data of the photovoltaic operation and maintenance equipment corresponding to the event-related device node, the sum of the associated environmental deviation parameters of each type of operating data is obtained. Then, based on the real-time simulation trend decision tree, it is determined whether the previous event-related device node or the event center device node has operating data with abnormal operating data levels.

[0098] If it does not exist, no operation is performed; if it exists, the associated operation deviation parameters between the corresponding simulation device nodes and the corresponding level abnormal operation data labels are obtained, the associated operation deviation parameters are added to the sum of the associated environment deviation parameters, and the parameter interval in which the sum of the associated environment deviation parameters is located is determined, and the level abnormal operation data label or normal operation data label is set for the event-related device node according to the judgment result;

[0099] According to the trend between the simulated device nodes in the real-time simulation trend decision tree, set the abnormal operation data label or normal operation data label for each event-related device node or event center device node in turn, until all the simulated device nodes in the real-time simulation trend decision tree are marked with data, the corresponding photovoltaic operation and maintenance event simulation is judged to be completed;

[0100] Execute photovoltaic operation and maintenance events uploaded by staff at ground-based photovoltaic power stations, obtain several real-time operating data items corresponding to each photovoltaic operation and maintenance equipment, and set a total deviation threshold;

[0101] Determine whether several real-time operating data of each photovoltaic operation and maintenance equipment are within the numerical range corresponding to the abnormal operation data label or normal operation data label obtained from the photovoltaic operation and maintenance event simulation results, and count the number of real-time operation data that are not within the corresponding numerical range, and record it as the total simulation deviation number;

[0102] If the total number of simulation deviations is less than the total deviation number threshold, the photovoltaic operation and maintenance event simulation is judged to be successful; otherwise, the photovoltaic operation and maintenance event simulation is judged to be unsuccessful. Then, according to the real-time operation data and real-time environmental data of each photovoltaic operation and maintenance equipment during the execution of the corresponding photovoltaic operation and maintenance event, the corresponding historical operation records and historical environmental data sets are generated, and the historical operation records and historical environmental data sets are sent to the virtual simulation attribute parsing module, and then the various associated environmental deviation parameters and associated operation deviation parameters are corrected.

[0103] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A ground photovoltaic power station virtual simulation training system based on real-time operation data, including a cloud computing platform, is characterized by: The cloud computing platform is in communication with the virtual simulation attribute analysis module and the operation simulation training module; The virtual simulation attribute analysis module is used to establish a simulation trend decision tree based on the historical operation data of each photovoltaic operation and maintenance event, and to establish normal change intervals and multiple abnormal change intervals based on the historical operation data and historical environmental data sets; According to the normal change interval and multiple abnormal change intervals, each data in the historical operation data and the historical environment data set is marked with a level of abnormal environment data and a level of abnormal operation data, thereby obtaining the associated environmental deviation parameters between each level of abnormal operation data and each level of abnormal environment data, including: Starting from the simulation center device node of the simulation trend decision tree, determine whether all historical operation data are labeled with normal operation data. If it is determined that all are labeled with normal operation data, determine whether all historical operation data of the next simulation associated device node are labeled with normal operation data. Otherwise, when obtaining historical operation data with abnormal level operation data annotation for the same type of photovoltaic operation and maintenance equipment, the corresponding historical environmental data with abnormal level environment data annotation is obtained; Count the occurrence probabilities of each historical environmental data annotated with abnormal environmental data, set a probability threshold, and record the historical environmental data annotated with graded abnormal environmental data with an occurrence probability greater than or equal to the probability threshold as abnormal associated environmental data of the corresponding historical operating data of the corresponding type of photovoltaic operation and maintenance equipment; For any type of historical operating data of a photovoltaic operation and maintenance device, obtain the probability of simultaneous occurrence of the data with each abnormally associated environmental data under different levels of abnormal operating data annotation, and record it as the associated environmental deviation parameter; The operation simulation training module is used to establish a virtual simulation training network based on the equipment distribution map of the ground photovoltaic power station, traverse a real-time simulation trend decision tree in the virtual simulation training network according to the photovoltaic operation and maintenance events to be executed, and obtain real-time environmental data and real-time operation data of the corresponding photovoltaic operation and maintenance equipment; Input real-time environmental data into the real-time simulation trend decision tree, simulate multiple operating data of each photovoltaic operation and maintenance equipment based on the associated environmental deviation parameters and real-time environmental data, and predict the operating data annotations. Compare each predicted operating data annotation with the corresponding real-time operating data, and judge whether the simulation is successful based on the comparison results, including: Set normal environment data labels or level abnormal environment data labels for each real-time environment data, and then obtain the associated environment deviation parameters corresponding to the abnormal operation data labels of each level with the operation data of each real-time environment data as the abnormal associated environment data; Normal parameter intervals and multiple abnormal parameter intervals are set for various types of operating data, and the sum of the associated environmental deviation parameters of various types of operating data is counted. According to the parameter interval in which the sum of the associated environmental deviation parameters is located, a graded abnormal operating data label or a normal operating data label is set for each operating data.

2. The ground photovoltaic power station virtual simulation training system based on real-time operation data according to claim 1 is characterized in that: The equipment distribution map includes the name, spatial location, and interaction direction of each photovoltaic operation and maintenance equipment in the ground photovoltaic power station. Several photovoltaic operation and maintenance events are set, and historical operation data and historical environmental data sets are collected through sensors after each photovoltaic operation and maintenance event is executed m times in a cycle, where m is a natural number greater than 0.

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