Photovoltaic energy storage efficiency management method based on virtual reality

Abstract

The invention discloses a photovoltaic energy storage efficiency management method based on virtual reality, relates to the technical field of photovoltaic energy storage monitoring, and improves the accuracy of fault prediction. According to the invention, through setting a plurality of abnormal constraint factors, a standard voltage change difference curve between different abnormal constraint factor combinations and the same power generation node or interaction node is obtained, and an energy transmission request is set and input to a photovoltaic equipment node network. The photovoltaic equipment node network distributes energy transmission equipment according to the energy transmission request, collects various real-time state data of the distributed energy transmission equipment, judges whether the corresponding energy transmission equipment is abnormal or not according to the normal data distribution interval, calls a standard voltage change difference curve according to the abnormal data type, and transmits the abnormal data to the photovoltaic equipment node network; and according to the abnormal constraint factor combination corresponding to the standard voltage change difference curve and the same power generation node or interaction node, the predicted abnormal condition of the energy transmission equipment after the abnormal energy transmission equipment exists is judged.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage monitoring, and in particular to a photovoltaic energy storage efficiency management method based on virtual reality. Background Art

[0002] As the global energy crisis and environmental problems become increasingly serious, solar energy as a renewable energy source has received more and more attention. Photovoltaic power stations convert solar energy into electrical energy through solar panels, which not only has environmental advantages but also can significantly reduce energy costs.

[0003] The existing photovoltaic energy storage operation and maintenance technology has the following defects:

[0004] Incomplete data collection: Existing systems can only collect operating data of some equipment and cannot fully cover all equipment and energy transmission paths in a photovoltaic power station.

[0005] Insufficient fault prediction capabilities: The existing system lacks an effective fault prediction mechanism and is unable to detect potential equipment failures in advance, resulting in low operation and maintenance efficiency.

[0006] Inaccurate anomaly detection: Existing systems have problems with false positives and false negatives in anomaly detection and are unable to accurately determine the abnormal conditions of equipment.

[0007] Therefore, how to improve the accuracy of fault prediction while achieving synchronous supervision of associated equipment in the entire energy storage process is a difficulty in the existing technology. For this purpose, a photovoltaic energy storage efficiency management method based on virtual reality is provided. Summary of the invention

[0008] In order to solve the above technical problems, the purpose of the present invention is to provide a photovoltaic energy storage efficiency management method based on virtual reality.

[0009] In order to achieve the above object, the present invention provides the following technical solutions:

[0010] A photovoltaic energy storage efficiency management method based on virtual reality includes the following steps:

[0011] Step S1: installing roadside units for each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, generating energy interaction events each time a photovoltaic power generation equipment starts power generation operation, and collecting corresponding historical operation and maintenance data sets;

[0012] Step S2: Establish a photovoltaic device node network according to the spatial location distribution of each photovoltaic power generation device and energy transmission device in the photovoltaic power station, and traverse the energy flow tree corresponding to each energy interaction event in the photovoltaic device node network, and obtain the normal data distribution interval of each photovoltaic power generation device and energy transmission device according to the historical operation and maintenance data set;

[0013] Step S3: setting abnormal annotations for each data in the historical operation and maintenance data set according to the normal data distribution interval, and setting several abnormal constraint factors, so as to obtain standard voltage change difference curves between different abnormal constraint factor combinations and the same power generation node or interactive node;

[0014] Step S4, setting an energy transmission request and inputting it into the photovoltaic device node network, and then the photovoltaic device node network allocates energy transmission equipment according to the energy transmission request, collects various real-time status data of the allocated energy transmission equipment, and determines whether the corresponding energy transmission equipment has an abnormality according to the normal data distribution interval, and retrieves the standard voltage change difference curve according to the type of data with the abnormality, and determines the abnormal condition expected to occur in the next energy transmission equipment after the abnormal energy transmission equipment according to the abnormal constraint factor combination corresponding to the standard voltage change difference curve and the same power generation node or interaction node.

[0015] Furthermore, the generation process of the historical operation and maintenance data set includes:

[0016] Each roadside unit is numbered, and whenever a photovoltaic power generation device starts power generation operation, an energy interaction event is generated between the photovoltaic power generation device and the roadside unit where the energy transmission device associated with the photovoltaic power generation device is installed;

[0017] Then, the roadside unit associated with the energy interaction event collects historical status data within the corresponding event data collection period through various sensors;

[0018] When the photovoltaic power generation equipment stops generating electricity, the corresponding energy interaction event ends, and all historical status data collected during the energy interaction event are integrated into a historical operation and maintenance data set.

[0019] Furthermore, the process of establishing the energy flow tree includes:

[0020] According to the spatial distribution of each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, a number of power generation nodes and interaction nodes are established, and each power generation node and interaction node are connected in sequence to obtain a photovoltaic device node network, and each power generation node and interaction node is marked with a corresponding number;

[0021] According to the photovoltaic power generation equipment and energy transmission equipment corresponding to each historical operation and maintenance data set, the corresponding energy flow tree is traversed in the photovoltaic equipment node network.

[0022] Furthermore, the process of obtaining the normal data distribution intervals of each photovoltaic power generation equipment and energy transmission equipment includes:

[0023] The historical operation and maintenance data sets corresponding to the same energy flow tree are integrated and established as a two-dimensional coordinate system, and all historical status data except historical image data are extracted from each historical operation and maintenance data set;

[0024] Then, the historical status data of the same data type in different historical operation and maintenance data sets are mapped onto the same two-dimensional coordinate system, a number of time coordinate points are set on the x-axis of the two-dimensional coordinate system, the historical status data are mapped and divided into a number of data coordinate points by the time coordinate points, and a data density detection frame is set, the width of the data density detection frame is equal to the length between the time coordinate points, and the height of the data density detection frame is less than its width;

[0025] Use the data density detection box to start from the lowest data coordinate point between each time coordinate point, and select the data coordinate points upwards in sequence until the highest data coordinate point;

[0026] The statistical density detection box counts the number of data coordinate points selected in each frame, and then selects the positions of the number of data coordinate points to generate normal data distribution segments between corresponding time coordinate points. The normal data distribution segments between each time coordinate point are connected in chronological order, and then the normal data distribution intervals of the historical status data of each data type are obtained under the corresponding energy flow tree.

[0027] Furthermore, the process of setting abnormal annotations for each data in the historical operation and maintenance data set according to the normal data distribution interval includes:

[0028] Establishing a 3D image model of each photovoltaic power generation equipment and energy transmission equipment based on each historical image data in the historical operation and maintenance data set, and splitting each 3D image model of the equipment into a number of model blocks of the same size;

[0029] Overlapping and fusing the model blocks corresponding to the same position of the same photovoltaic power generation equipment or energy transmission equipment, and then sequentially splicing the overlapped and fused model blocks to obtain a three-dimensional image model of a standard device of the photovoltaic power generation equipment and the energy transmission equipment under normal conditions;

[0030] The three-dimensional image model of each standard device is replaced with the corresponding power generation node and the interaction node in the photovoltaic device node network, and a number of state detection points are set for each three-dimensional image model of the standard device according to the number of data types of the historical state data other than the historical image data, and the normal data distribution interval of each data type is placed in the corresponding state detection point;

[0031] According to the historical operation and maintenance data set corresponding to each energy flow tree, each historical state data except the historical image data in the historical operation and maintenance data set is compared with the normal data distribution interval in the corresponding state detection point. If the historical state data is within the corresponding normal data distribution interval, no operation is performed;

[0032] If the historical status data is not within the corresponding normal data distribution range, an abnormal mark is set for the historical status data.

[0033] Furthermore, the process of setting the abnormal constraint factors includes:

[0034] According to the connection sequence of power generation nodes and interactive nodes in the energy flow tree, the data in each historical operation and maintenance data set corresponding to the energy flow tree are mapped to the state detection point in the photovoltaic device node network. It should be noted that for historical image data, a three-dimensional image model of the device is established based on the historical image data, and the three-dimensional image model of the device is overlapped and mapped with the corresponding standard device three-dimensional image model;

[0035] The state detection points of the historical state data with abnormal annotations are retained, and abnormal constraint factors are generated according to the data types corresponding to the retained state detection points.

[0036] Furthermore, the process of setting the standard voltage change difference curve includes:

[0037] Then, the characteristic energy flow tree corresponding to each historical operation and maintenance data set is obtained. The historical voltage change curve is used as the detection indicator. Starting from the power generation node, the historical voltage change difference curve between the corresponding power generation nodes or interaction nodes is statistically analyzed when multiple abnormal constraint factors appear simultaneously.

[0038] The historical voltage change difference curves between the same abnormal constraint factor combination and the same power generation node or interactive node are mapped in the same two-dimensional coordinate system. The process of generating a normal data distribution interval is adopted to establish the standard voltage change difference curves corresponding to the same abnormal constraint factor combination and the same power generation node or interactive node according to each historical voltage change difference curve.

[0039] Furthermore, the process of allocating energy transmission equipment according to the energy transmission request includes:

[0040] Obtaining each energy transmission device and the rated voltage value and the rated energy storage capacity of the energy transmission device through the Internet, wherein the energy transmission request includes the number of the photovoltaic power generation device, the expected transmission voltage value and the expected energy transmission capacity;

[0041] According to the number of the photovoltaic power generation equipment in the energy transmission request, starting from the standard equipment three-dimensional image model corresponding to the photovoltaic equipment node network, it is judged in turn whether the real-time voltage value or real-time energy storage capacity of the energy transmission equipment corresponding to each standard equipment three-dimensional image model is less than the rated voltage value or rated energy storage capacity, and an energy transmission task or energy storage task is generated according to the judgment result.

[0042] Furthermore, the process of determining whether the energy transmission equipment is abnormal includes:

[0043] Send each energy transmission task or energy storage task to the corresponding energy transmission device, and traverse the real-time energy flow tree on the photovoltaic device node network, and match the energy flow tree according to the photovoltaic power generation equipment and energy transmission equipment associated with the real-time energy flow tree;

[0044] During the execution of the energy transmission task or the energy storage task, several types of real-time status data are collected from the roadside units associated with the photovoltaic power generation equipment and the energy transmission equipment;

[0045] Input the real-time status data into the corresponding energy flow tree, and determine whether each real-time status data is in the corresponding normal data distribution interval. If not, generate real-time abnormal constraint factors according to the data type of the real-time status data, and map them in the real-time energy flow tree, and perform maintenance on the corresponding photovoltaic power generation equipment or energy transmission equipment according to the abnormal type corresponding to the real-time abnormal constraint factors, otherwise do not perform any operation;

[0046] At the same time, according to the type of real-time abnormal constraint factors and the number of the energy transmission equipment connected to the corresponding photovoltaic power generation equipment or energy transmission equipment, the standard voltage change difference curves between all the same power generation nodes or interaction nodes with the same type of abnormal constraint factors are retrieved;

[0047] Obtaining the proportional overlap between the energy transmission request value curve and the standard voltage change difference curve;

[0048] Set the ratio overlap threshold. If the ratio overlap threshold is less than or equal to the ratio overlap, no operation will be performed.

[0049] Otherwise, the abnormal constraint factor combination of the next energy transmission equipment under the corresponding combination is retained, and the abnormal constraint factor combination with the largest proportion overlap among the retained abnormal constraint factor combinations is selected as the expected abnormal constraint factor combination;

[0050] The expected abnormal condition of the next energy transmission equipment is determined based on the expected abnormal constraint factor combination, and then the corresponding energy transmission equipment is maintained according to the corresponding abnormal type in the expected abnormal constraint factor combination.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. By installing roadside units, the present invention can comprehensively collect data on all equipment and energy transmission paths in a photovoltaic power station to ensure the comprehensiveness and accuracy of operation and maintenance management. At the same time, by establishing a photovoltaic equipment node network and an energy flow tree, combined with historical operation and maintenance data sets, in-depth analysis of complex fault modes can be achieved.

[0053] 2. The present invention can accurately judge the abnormal condition of the equipment and reduce false alarms and missed alarms by setting abnormal annotations and standard voltage change difference curves of different abnormal constraint factors. It can also predict the abnormal condition of subsequent equipment based on the data of known abnormal equipment and the standard voltage change difference curve, take maintenance measures in advance, and improve operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention.

[0055] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0056] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0057] like Figure 1 As shown, a photovoltaic energy storage efficiency management method based on virtual reality includes the following steps:

[0058] Step S1: installing roadside units for each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, generating energy interaction events each time a photovoltaic power generation equipment starts power generation operation, and collecting corresponding historical operation and maintenance data sets;

[0059] Step S2: Establish a photovoltaic device node network according to the spatial location distribution of each photovoltaic power generation device and energy transmission device in the photovoltaic power station, and traverse the energy flow tree corresponding to each energy interaction event in the photovoltaic device node network, and obtain the normal data distribution interval of each photovoltaic power generation device and energy transmission device according to the historical operation and maintenance data set;

[0060] Step S3: setting abnormal annotations for each data in the historical operation and maintenance data set according to the normal data distribution interval, and setting several abnormal constraint factors, so as to obtain standard voltage change difference curves between different abnormal constraint factor combinations and the same power generation node or interactive node;

[0061] Step S4, setting an energy transmission request and inputting it into the photovoltaic device node network, and then the photovoltaic device node network allocates energy transmission equipment according to the energy transmission request, collects various real-time status data of the allocated energy transmission equipment, and determines whether the corresponding energy transmission equipment has an abnormality according to the normal data distribution interval, and retrieves the standard voltage change difference curve according to the type of data with the abnormality, and determines the abnormal condition expected to occur in the next energy transmission equipment after the abnormal energy transmission equipment according to the abnormal constraint factor combination corresponding to the standard voltage change difference curve and the same power generation node or interaction node.

[0062] The step S1 is implemented by the following process:

[0063] Roadside units are installed for each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, and a cloud management platform is set up to communicate with each roadside unit, and each roadside unit is numbered a1, a2, a3, ..., a n , b1, b2, b3, ..., b m , where n and m are natural numbers greater than 0, a n and b m They represent the nth and mth photovoltaic power generation equipment and energy transmission equipment respectively. It should be noted that the models of the photovoltaic power generation equipment are the same;

[0064] The roadside unit is composed of a wireless communication device, a camera, a temperature sensor, a humidity sensor, a voltage sensor, a power sensor and other sensors;

[0065] Whenever a photovoltaic power generation device starts power generation operation, the photovoltaic power generation device and the roadside unit installed by the energy transmission device associated with the photovoltaic power generation device generate an energy interaction event;

[0066] Then, the roadside unit associated with the energy interaction event collects historical status data within the corresponding event data collection period through various sensors, and the historical status data is, for example, historical image data, historical voltage change curve, historical temperature change curve, etc.;

[0067] When the photovoltaic power generation equipment stops generating electricity, the corresponding energy interaction event ends, and all historical status data collected during the energy interaction event are sent to the cloud management platform. The cloud management platform sets a number for each historical status data according to the number of the roadside unit, and integrates the historical status data with the same number into a subset of historical electricity consumption data, and then integrates the historical electricity consumption data subsets generated by the same energy interaction event into a historical operation and maintenance data set.

[0068] The step S2 is implemented by the following process:

[0069] According to the spatial distribution of each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, n power generation nodes and m interaction nodes are established, and each power generation node and interaction node are connected in sequence to obtain a photovoltaic device node network, and each power generation node and interaction node are marked with a corresponding number;

[0070] According to the photovoltaic power generation equipment and energy transmission equipment corresponding to each historical operation and maintenance data set, a corresponding energy flow tree is traversed in the photovoltaic equipment node network, and the connection lines between each power generation node and the interaction node in the energy flow tree are provided with arrows to indicate the direction of energy flow;

[0071] The historical operation and maintenance data sets corresponding to the same energy flow tree are integrated and established as a two-dimensional coordinate system, and all historical status data except historical image data are extracted from each historical operation and maintenance data set;

[0072] Then, the historical status data of the same data type in different historical operation and maintenance data sets are mapped onto the same two-dimensional coordinate system, a number of time coordinate points are set on the x-axis of the two-dimensional coordinate system, the historical status data are mapped and divided into a number of data coordinate points by the time coordinate points, and a data density detection frame is set, the width of the data density detection frame is equal to the length between the time coordinate points, and the height of the data density detection frame is less than its width;

[0073] Use the data density detection box to start from the lowest data coordinate point between each time coordinate point, and select the data coordinate points upwards in sequence until the highest data coordinate point;

[0074] The statistical density detection box counts the number of data coordinate points selected in each frame, and then selects the positions of the number of data coordinate points to generate normal data distribution segments between corresponding time coordinate points. The normal data distribution segments between each time coordinate point are connected in chronological order, and then the normal data distribution intervals of the historical status data of each data type are obtained under the corresponding energy flow tree.

[0075] The step S3 is implemented by the following process:

[0076] Establishing a 3D image model of each photovoltaic power generation equipment and energy transmission equipment based on each historical image data in the historical operation and maintenance data set, and splitting each 3D image model of the equipment into a number of model blocks of the same size;

[0077] Overlapping and fusing the model blocks corresponding to the same position of the same photovoltaic power generation equipment or energy transmission equipment, and then sequentially splicing the overlapped and fused model blocks to obtain a three-dimensional image model of a standard device of the photovoltaic power generation equipment and the energy transmission equipment under normal conditions;

[0078] The three-dimensional image model of each standard device is replaced with the corresponding power generation node and the interaction node in the photovoltaic device node network, and a number of state detection points are set for each three-dimensional image model of the standard device according to the number of data types of the historical state data other than the historical image data, and the normal data distribution interval of each data type is placed in the corresponding state detection point;

[0079] According to the historical operation and maintenance data set corresponding to each energy flow tree, each historical state data except the historical image data in the historical operation and maintenance data set is compared with the normal data distribution interval in the corresponding state detection point. If the historical state data is within the corresponding normal data distribution interval, no operation is performed;

[0080] If the historical status data is not within the corresponding normal data distribution range, an abnormal mark is set for the historical status data.

[0081] Furthermore, according to the connection sequence of power generation nodes and interactive nodes in the energy flow tree, the data in each historical operation and maintenance data set corresponding to the energy flow tree are mapped to the state detection point in the photovoltaic device node network. It should be noted that for historical image data, a three-dimensional image model of the device is established based on the historical image data, and the three-dimensional image model of the device is overlapped and mapped with the corresponding standard device three-dimensional image model;

[0082] The state detection points of the historical state data with abnormal annotations are retained, and abnormal constraint factors are generated according to the data types corresponding to the retained state detection points, such as abnormal temperature factors, abnormal appearance missing factors, etc.;

[0083] Then, the characteristic energy flow tree corresponding to each historical operation and maintenance data set is obtained. The historical voltage change curve is used as the detection indicator. Starting from the power generation node, the historical voltage change difference curve between the corresponding power generation nodes or interaction nodes is statistically analyzed when multiple abnormal constraint factors appear simultaneously.

[0084] The historical voltage change difference curves between the same abnormal constraint factor combination and the same power generation node or interactive node are mapped in the same two-dimensional coordinate system. The process of generating a normal data distribution interval is adopted to establish the standard voltage change difference curves corresponding to the same abnormal constraint factor combination and the same power generation node or interactive node according to each historical voltage change difference curve.

[0085] Further, step S4 is implemented by the following process:

[0086] The cloud management platform obtains each energy transmission device and the rated voltage value and rated energy storage capacity of the energy transmission device through the Internet;

[0087] Before each photovoltaic power generation device performs energy transmission, the roadside unit associated with the corresponding photovoltaic power generation device generates an energy transmission request to the cloud management platform, wherein the energy transmission request includes the number of the photovoltaic power generation device, the expected transmission voltage value, and the expected energy transmission amount;

[0088] Then, the cloud management platform allocates energy transmission equipment according to the estimated transmission voltage value and the estimated energy transmission amount in the energy transmission request. The allocation process of the energy transmission equipment includes:

[0089] According to the number of the photovoltaic power generation equipment in the energy transmission request, starting from the standard equipment three-dimensional image model corresponding to the photovoltaic equipment node network, it is judged in turn whether the real-time voltage value or real-time energy storage capacity of the energy transmission equipment corresponding to each standard equipment three-dimensional image model is less than the rated voltage value or rated energy storage capacity. If it is equal, the corresponding energy transmission equipment is ignored. If it is less, the energy transmission task or energy storage task is allocated according to the difference between its rated voltage value and the real-time voltage value, or the rated energy storage capacity and the real-time energy storage capacity. The energy transmission task or energy storage task includes the allocated voltage value or allocated storage capacity, the allocated energy transmission equipment number and the energy transmission equipment number expected to be connected thereto;

[0090] If the sum of the allocated voltage value and the allocated storage amount in each energy transmission task or energy storage task is equal to the estimated transmission voltage value and the estimated energy transmission amount, then it is determined that the energy transmission request can be executed, otherwise it is determined that the energy transmission request cannot be executed at present;

[0091] Further, according to the energy transmission equipment number assigned in each energy transmission task or energy storage task, each energy transmission task or energy storage task is sent to the corresponding energy transmission equipment, and the corresponding real-time energy flow tree is traversed on the photovoltaic equipment node network, and at the same time, the corresponding energy flow tree is matched according to the photovoltaic power generation equipment and energy transmission equipment associated in the real-time energy flow tree;

[0092] During the execution of energy transmission tasks or energy storage tasks, the roadside units associated with the corresponding photovoltaic power generation equipment and energy transmission equipment collect several types of real-time status data, and send all the real-time status data to the cloud management platform synchronously;

[0093] The cloud management platform inputs the real-time status data into the corresponding energy flow tree, and determines whether each real-time status data is in the corresponding normal data distribution interval. If not, it generates real-time abnormal constraint factors according to the data type of the real-time status data, and maps them in the real-time energy flow tree, and maintains the corresponding photovoltaic power generation equipment or energy transmission equipment according to the abnormal type corresponding to the real-time abnormal constraint factors, otherwise no operation is performed;

[0094] At the same time, according to the type of real-time abnormal constraint factors and the number of the energy transmission equipment connected to the corresponding photovoltaic power generation equipment or energy transmission equipment, the standard voltage change difference curves between all the same power generation nodes or interaction nodes with the same type of abnormal constraint factors are retrieved;

[0095] Obtaining the proportional overlap between the energy transmission request value curve and the standard voltage change difference curve;

[0096] Set the ratio overlap threshold. If the ratio overlap threshold is less than or equal to the ratio overlap, no operation will be performed.

[0097] Otherwise, the abnormal constraint factor combination of the next energy transmission equipment under the corresponding combination is retained, and the abnormal constraint factor combination with the largest proportion overlap among the retained abnormal constraint factor combinations is selected as the expected abnormal constraint factor combination;

[0098] The abnormal condition expected to occur in the next energy transmission equipment is determined based on the expected abnormal constraint factor combination, and then the corresponding energy transmission equipment is maintained according to the corresponding abnormal type in the expected abnormal constraint factor combination, and the above energy transmission equipment pre-maintenance operation is repeated until the corresponding energy transmission request is completed.

[0099] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A photovoltaic energy storage efficiency management method based on virtual reality, characterized in that: The following steps are involved: Step S1: installing roadside units for each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, generating energy interaction events each time a photovoltaic power generation equipment starts power generation operation, and collecting corresponding historical operation and maintenance data sets; Step S2: Establish a photovoltaic device node network according to the spatial location distribution of each photovoltaic power generation device and energy transmission device in the photovoltaic power station, and traverse the energy flow tree corresponding to each energy interaction event in the photovoltaic device node network, and obtain the normal data distribution interval of each photovoltaic power generation device and energy transmission device according to the historical operation and maintenance data set; Step S3: setting abnormal annotations for each data in the historical operation and maintenance data set according to the normal data distribution interval, and setting several abnormal constraint factors, so as to obtain standard voltage change difference curves between different abnormal constraint factor combinations and the same power generation node or interactive node; Step S4, setting an energy transmission request and inputting it into the photovoltaic device node network, and then the photovoltaic device node network allocates energy transmission equipment according to the energy transmission request, collects various real-time status data of the allocated energy transmission equipment, and determines whether the corresponding energy transmission equipment has an abnormality according to the normal data distribution interval, and retrieves the standard voltage change difference curve according to the type of data with the abnormality, and determines the abnormal condition expected to occur in the next energy transmission equipment after the abnormal energy transmission equipment according to the abnormal constraint factor combination corresponding to the standard voltage change difference curve and the same power generation node or interaction node.

2. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 1, characterized in that: The generation process of the historical operation and maintenance data set includes: Each roadside unit is numbered, and whenever a photovoltaic power generation device starts power generation operation, an energy interaction event is generated between the photovoltaic power generation device and the roadside unit where the energy transmission device associated with the photovoltaic power generation device is installed; Then, the roadside unit associated with the energy interaction event collects the historical status data within the corresponding event data collection period through various sensors. When the photovoltaic power generation equipment stops the power generation operation, the corresponding energy interaction event ends, and all the historical status data collected during the energy interaction event are integrated into a historical operation and maintenance data set.

3. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 2, characterized in that: The process of establishing the energy flow tree includes: According to the spatial distribution of each photovoltaic power generation equipment and energy transmission equipment in the photovoltaic power station, a number of power generation nodes and interaction nodes are established, and each power generation node and interaction node are connected in sequence to obtain a photovoltaic equipment node network. At the same time, each power generation node and interaction node is marked with a corresponding number. According to the photovoltaic power generation equipment and energy transmission equipment corresponding to each historical operation and maintenance data set, the corresponding energy flow tree is traversed in the photovoltaic equipment node network.

4. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 3, characterized in that: The process of obtaining the normal data distribution intervals of each photovoltaic power generation equipment and energy transmission equipment includes: A two-dimensional coordinate system is established, and the historical status data of the same data type in different historical operation and maintenance data sets are mapped onto the two-dimensional coordinate system. Several time coordinate points are set, and the historical status data is divided into several data coordinate points through the time coordinate points, and a data density detection frame is set; Use the data density detection box to start from the lowest data coordinate point between each time coordinate point, and select the data coordinate points upwards in sequence until the highest data coordinate point; The statistical density detection box counts the number of data coordinate points selected in each frame, selects the positions of the number of data coordinate points to generate normal data distribution segments between corresponding time coordinate points, and connects the normal data distribution segments between each time coordinate point in chronological order to obtain the normal data distribution intervals of the historical status data of each data type under the corresponding energy flow tree.

5. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 4, characterized in that: The process of setting abnormal annotations for each data in the historical operation and maintenance data set according to the normal data distribution range includes: Establish a 3D image model of each photovoltaic power generation equipment and energy transmission equipment based on the historical operation and maintenance data set, split each 3D image model of the equipment into several model blocks of the same size, and overlap and fuse the model blocks corresponding to the same position of the same photovoltaic power generation equipment or energy transmission equipment to obtain a standard equipment 3D image model; The three-dimensional image model of each standard device replaces the corresponding power generation node and interaction node in the photovoltaic device node network. According to the number of data types of the historical image data, a number of status detection points are set for each three-dimensional image model of the standard device, and the normal data distribution interval of each data type is placed in the corresponding status detection point. At the same time, it is judged whether each historical status data is within the corresponding normal data distribution interval, and abnormal annotations are set for the historical status data according to the judgment results.

6. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 5, characterized in that: The process of setting the abnormal constraint factors includes: According to the connection order of power generation nodes and interactive nodes in the energy flow tree, the data in each historical operation and maintenance data set corresponding to the energy flow tree are mapped to the status detection points in the photovoltaic equipment node network, the status detection points of historical status data with abnormal annotations are retained, and abnormal constraint factors are generated according to the data type corresponding to the retained status detection points.

7. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 6, characterized in that: The process of setting the standard voltage variation difference curve includes: When multiple abnormal constraint factors appear simultaneously, statistics are collected from the power generation node, and the historical voltage change difference curves between the corresponding power generation nodes or interaction nodes are obtained; The historical voltage change difference curves between the same abnormal constraint factor combination and the same power generation node or interactive node are mapped in a two-dimensional coordinate system. The process of generating a normal data distribution interval is adopted to establish the standard voltage change difference curves between the same abnormal constraint factor combination and the same power generation node or interactive node according to each historical voltage change difference curve.

8. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 7, characterized in that: The process of allocating energy transfer equipment based on an energy transfer request includes: Obtaining each energy transmission device and the rated voltage value and the rated energy storage capacity of the energy transmission device through the Internet, wherein the energy transmission request includes the number of the photovoltaic power generation device, the expected transmission voltage value and the expected energy transmission capacity; Starting from the standard equipment three-dimensional image model corresponding to the photovoltaic device node network, it is determined in turn whether the real-time voltage value or real-time energy storage capacity of the energy transmission equipment corresponding to each standard equipment three-dimensional image model is less than the rated voltage value or rated energy storage capacity, and an energy transmission task or energy storage task is generated according to the judgment result.

9. A photovoltaic energy storage efficiency management method based on virtual reality according to claim 8, characterized in that: The process of determining whether there is an abnormality in the energy transmission equipment includes: Send each energy transmission task or energy storage task to the corresponding energy transmission device, and traverse the real-time energy flow tree on the photovoltaic device node network, and match the energy flow tree according to the photovoltaic power generation equipment and energy transmission equipment associated with the real-time energy flow tree; During the execution of the energy transmission task or the energy storage task, several types of real-time status data are collected from the roadside units associated with the photovoltaic power generation equipment and the energy transmission equipment; Input the real-time status data into the corresponding energy flow tree, and determine whether each real-time status data is in the corresponding normal data distribution interval. If not, generate real-time abnormal constraint factors according to the data type of the real-time status data, otherwise do not do anything; Retrieve the standard voltage change difference curve between the same generation nodes or interaction nodes with the same type of abnormal constraint factors, obtain the proportional overlap between the energy transmission request value curve and the standard voltage change difference curve, set the proportional overlap threshold, and select the expected abnormal constraint factor combination according to the relationship between the proportional overlap threshold and the proportional overlap; The expected abnormal condition of the next energy transmission equipment is determined based on the expected abnormal constraint factor combination, and then the corresponding energy transmission equipment is maintained according to the corresponding abnormal type in the expected abnormal constraint factor combination.

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