Photovoltaic power station management method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a photovoltaic power station management method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a power station management micro-service of a service aggregation platform, and comprises the steps: determining to-be-identified equipment in a suspected abnormal photovoltaic power station under the condition of detecting that the suspected abnormal photovoltaic power station exists in a target region; acquiring equipment data of the to-be-identified equipment, calling a fault identification model according to the equipment data, and performing fault identification on the to-be-identified equipment to obtain a fault identification result; and under the condition that the fault identification result represents that a fault occurs, operation and maintenance data is generated based on the fault identification result and the equipment information of the to-be-identified equipment, and the operation and maintenance data is sent to an order dispatching micro-service of the service aggregation platform, so that the order dispatching micro-service is indicated to determine target operation and maintenance personnel of the to-be-identified equipment according to the operation and maintenance data, and the to-be-identified equipment is identified according to the target operation and maintenance personnel. The photovoltaic power station can be effectively managed, so that the operation safety of the photovoltaic power station is improved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic technology, and in particular to a photovoltaic power station management method, device, computer equipment, computer-readable storage medium and computer program product. Background Art

[0002] With the continuous progress and development of photovoltaic technology, photovoltaic power stations have been widely used as an important facility for converting solar energy into electrical energy. Among them, photovoltaic power stations are photovoltaic power generation systems that are connected to the power grid and transmit electricity to the power grid. Photovoltaic power generation is a technology that uses the photovoltaic effect of the semiconductor interface to directly convert light energy into electrical energy. After the solar cells are connected in series and packaged for protection, they can form a large-area solar cell module, and then combined with power controllers and other components to form a photovoltaic power generation device, which has the advantages of less geographical restrictions, safety and reliability, no noise, low pollution, and no need to consume fuel.

[0003] However, with the large-scale construction of photovoltaic power stations, safety and quality issues have gradually become the primary issue for the long-term stable power generation of photovoltaic power stations. Therefore, it is urgent to effectively manage photovoltaic power stations to ensure the safety of photovoltaic power station operation. Summary of the invention

[0004] Based on this, it is necessary to provide a photovoltaic power station management method, device, computer equipment, computer readable storage medium and computer program product that can effectively manage photovoltaic power stations to improve the safety of photovoltaic power station operation in response to the above technical problems.

[0005] In a first aspect, the present application provides a photovoltaic power station management method, which is applied to a power station management microservice of a service aggregation platform, including:

[0006] In the case where a photovoltaic power station suspected of being abnormal is detected in the target area, determining a device to be identified in the photovoltaic power station suspected of being abnormal;

[0007] Acquire device data of the device to be identified, and call a fault identification model according to the device data to perform fault identification on the device to be identified to obtain a fault identification result;

[0008] When the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel of the equipment to be identified based on the operation and maintenance data.

[0009] In a second aspect, the present application also provides a photovoltaic power station management device, including:

[0010] A device determination module is used to determine the device to be identified in the photovoltaic power station suspected of being abnormal when a photovoltaic power station suspected of being abnormal is detected in the target area;

[0011] A fault identification module is used to obtain device data of the device to be identified, and according to the device data, call a fault identification model to perform fault identification on the device to be identified to obtain a fault identification result;

[0012] A data sending module is used to generate operation and maintenance data based on the fault identification result and the equipment information of the equipment to be identified when the fault identification result indicates the occurrence of a fault, and send the operation and maintenance data to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel of the equipment to be identified based on the operation and maintenance data.

[0013] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0014] In the case where a photovoltaic power station suspected of being abnormal is detected in the target area, determining a device to be identified in the photovoltaic power station suspected of being abnormal;

[0015] Acquire device data of the device to be identified, and call a fault identification model according to the device data to perform fault identification on the device to be identified to obtain a fault identification result;

[0016] When the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0017] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0018] In the case where a photovoltaic power station suspected of being abnormal is detected in the target area, determining a device to be identified in the photovoltaic power station suspected of being abnormal;

[0019] Acquire device data of the device to be identified, and call a fault identification model according to the device data to perform fault identification on the device to be identified to obtain a fault identification result;

[0020] When the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0021] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0022] In the case where a photovoltaic power station suspected of being abnormal is detected in the target area, determining a device to be identified in the photovoltaic power station suspected of being abnormal;

[0023] Acquire device data of the device to be identified, and call a fault identification model according to the device data to perform fault identification on the device to be identified to obtain a fault identification result;

[0024] When the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0025] The photovoltaic power station management method, device, computer equipment, computer readable storage medium and computer program product described above first detect whether there is a suspected abnormal photovoltaic power station in the target area through the power station management microservice of the service aggregation platform. When the suspected abnormal photovoltaic power station is detected in the target area, the device to be identified is determined from the photovoltaic power station with the suspected abnormality. There is no need to identify each device in each photovoltaic power station in the target area one by one, avoiding the identification of normal photovoltaic power stations and ensuring the accuracy of power station management. Then, the power station management microservice obtains the device data of the device to be identified, and calls the fault identification model according to the device data to perform fault identification on the device to be identified to verify whether there is a misjudgment of the suspected abnormal photovoltaic power station and obtain the fault identification result; when the fault identification result indicates that a fault has occurred, it means that the suspected abnormal photovoltaic power station is not a misjudgment and there is an abnormal device. Therefore, based on the fault identification result and the device information of the device to be identified, the operation and maintenance data is generated in time, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform. In this way, the dispatching microservice can quickly determine the target operation and maintenance personnel of the device to be identified according to the operation and maintenance data. During the whole process, the power station management microservice can realize the detection operation of photovoltaic power stations suspected of abnormalities, the determination operation of equipment to be identified, and the fault identification operation, ensuring the accurate identification of photovoltaic power stations with real faulty equipment, so that the dispatching microservice can promptly determine the target operation and maintenance personnel and perform operation and maintenance in time, thereby realizing effective management of photovoltaic power stations and improving the safety of photovoltaic power station operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 An application environment diagram of a photovoltaic power station management method in an embodiment;

[0028] Figure 2 A schematic diagram of a process of managing a photovoltaic power station in one embodiment;

[0029] Figure 3 is a schematic diagram of a fault identification process in one embodiment;

[0030] Figure 4 is a schematic diagram of a photovoltaic power station management system in one embodiment;

[0031] Figure 5 is a structural block diagram of a photovoltaic power station management device in an embodiment;

[0032] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] The photovoltaic power station management method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, a service aggregation platform is deployed on the server 104. Among them, the photovoltaic power station management method provided in the embodiment of the present application can be executed by the power station management microservice of the service aggregation platform in the server 104, and can also be executed collaboratively by the terminal 102 and the power station management microservice of the service aggregation platform in the server 104.

[0035] In one embodiment, when the power station management microservice of the service aggregation platform in the server 104 detects the existence of a photovoltaic power station with suspected abnormality in the target area, it determines the equipment to be identified in the photovoltaic power station with suspected abnormality; the power station management microservice obtains the equipment data of the equipment to be identified, and calls the fault identification model based on the equipment data to perform fault identification on the equipment to be identified to obtain a fault identification result; when the fault identification result indicates that a fault has occurred, the power station management microservice generates operation and maintenance data based on the fault identification result and the equipment information of the equipment to be identified, and sends the operation and maintenance data to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0036] The terminal 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, etc. The server 104 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0037] In an exemplary embodiment, Figure 2 As shown, a photovoltaic power station management method is provided, and the method is applied to Figure 1 Taking the power station management microservice of the service aggregation platform in the server 104 as an example, the method includes the following steps 202 to 206. Among them:

[0038] Step 202: when a suspected abnormal photovoltaic power station is detected in the target area, a device to be identified in the suspected abnormal photovoltaic power station is determined.

[0039] Among them, the target area is the area managed by the service aggregation platform in the server. For example, different areas are managed by different service aggregation platforms. For example, there are areas 1-3, area 1 is managed by service aggregation platform 1, area 2 is managed by service aggregation platform 2, and area 3 is managed by service aggregation platform 3. A photovoltaic power station is a power generation system that uses solar energy and adopts special materials such as crystalline silicon panels, inverters and other electronic components. Each photovoltaic power station contains multiple devices, such as photovoltaic modules, inverters, etc. A suspected abnormal photovoltaic power station refers to a photovoltaic power station where there may be faulty equipment. It can be understood that for any photovoltaic power station, if there is at least one device in the photovoltaic power station that fails, the photovoltaic power station is an abnormal photovoltaic power station.

[0040] Optionally, when the power station management microservice of the service aggregation platform detects a suspected abnormal photovoltaic power station in the target area, it locates the device to be identified from multiple devices of the suspected abnormal photovoltaic power station.

[0041] Exemplarily, for a photovoltaic power station suspected of being abnormal, the power station management microservice sequentially regards each device in the photovoltaic power station suspected of being abnormal as a device to be identified, and sequentially executes the following steps 204-206. Exemplarily, for each device in the photovoltaic power station suspected of being abnormal, the power station management microservice determines whether the device is a device to be identified based on at least one of the power station data of the photovoltaic power station suspected of being abnormal and the device data of the device. Exemplarily, the power station management microservice selects the device with the highest identification priority from the devices that have not been identified for faults as the device to be identified based on the identification priority of each device in the photovoltaic power station suspected of being abnormal.

[0042] Among them, the service aggregation platform can be understood as a platform that aggregates multiple microservices in a photovoltaic power station. Microservices include at least a power station management microservice and a dispatching microservice. The power station management microservice is used for fault identification and operation and maintenance management of photovoltaic power stations. The power station management microservice includes a data acquisition unit, an operation and maintenance unit, a report generation unit, etc. Among them, the operation and maintenance unit performs this step. The dispatching microservice is used to automatically dispatch orders to the faulty equipment to be identified.

[0043] Step 204, obtaining device data of the device to be identified, and calling a fault identification model according to the device data, performing fault identification on the device to be identified, and obtaining a fault identification result.

[0044] The device data of the device to be identified includes data of at least one data type, for example, temperature data of temperature type, vibration data of vibration type, power data of current type, etc. The fault identification model is a trained neural network model, which is used to identify faults of the device. Fault identification is used to identify whether the device to be identified has a fault. The fault identification result indicates whether the device to be identified has a fault. If so, the fault identification result also includes the fault type.

[0045] Optionally, the power station management microservice of the service aggregation platform obtains the device data of the device to be identified in the historical operating period and calls the fault identification model. The power station management microservice inputs the device data into the fault identification model, performs fault identification on the device to be identified, and outputs the fault identification result. Among them, the historical operating period is the operating period closest to the current time.

[0046] Exemplarily, the data acquisition unit in the power station management microservice obtains the power station operation data of the photovoltaic power station suspected of abnormality. The power station operation data includes the operation data and environmental data of the power station. The power station operation data is collected from the equipment such as voltage and current sensors, power meters, cameras, temperature and humidity sensors deployed on the photovoltaic power station site, and from photovoltaic components and the environment where the photovoltaic power station is located. After obtaining the power station operation data, the data acquisition unit cleans the data to obtain the equipment data. The data acquisition unit sends the obtained equipment data to the operation and maintenance unit in the power station management microservice, and the operation and maintenance unit returns to the above-mentioned call fault identification model step to continue execution.

[0047] Step 206, when the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0048] The device information includes at least one of the device type of the device to be identified, the device's historical operation and maintenance information, etc. The operation and maintenance data is used to query the target operation and maintenance personnel that match the device to be identified.

[0049] Optionally, when the power station management microservice determines that the fault identification result indicates a fault, it obtains the fault type from the fault identification result, generates operation and maintenance data according to the fault type and equipment type, and sends the operation and maintenance data to the dispatching microservice of the service aggregation platform. The dispatching microservice selects the target operation and maintenance personnel from multiple candidate operation and maintenance personnel based on the obtained operation and maintenance data.

[0050] Exemplarily, after obtaining the operation and maintenance data, the dispatching microservice selects the target operation and maintenance personnel from at least one candidate operation and maintenance personnel in the target area. Exemplarily, after traversing each candidate operation and maintenance personnel in the target area, if the dispatching microservice cannot find the target operation and maintenance personnel that matches the operation and maintenance data, it will search for other operation and maintenance personnel that match the operation and maintenance data and are closest to the target area from other areas, and use the other searched operation and maintenance personnel as the target operation and maintenance personnel.

[0051] In the above photovoltaic power station management method, the power station management microservice of the service aggregation platform first detects whether there is a suspected abnormal photovoltaic power station in the target area. When a suspected abnormal photovoltaic power station is detected in the target area, the device to be identified is determined from the photovoltaic power station with the suspected abnormality. There is no need to identify each device in each photovoltaic power station in the target area one by one, which avoids the identification of normal photovoltaic power stations and ensures the accuracy of power station management. Then, the power station management microservice obtains the device data of the device to be identified, and calls the fault identification model according to the device data to perform fault identification on the device to be identified to verify whether there is a misjudgment of the suspected abnormal photovoltaic power station and obtain the fault identification result; when the fault identification result indicates that a fault has occurred, it means that the suspected abnormal photovoltaic power station is not a misjudgment and there is an abnormal device. Therefore, based on the fault identification result and the device information of the device to be identified, the operation and maintenance data is generated in time, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform. In this way, the dispatching microservice can quickly determine the target operation and maintenance personnel of the device to be identified according to the operation and maintenance data. During the whole process, the power station management microservice can realize the detection operation of photovoltaic power stations suspected of abnormalities, the determination operation of equipment to be identified, and the fault identification operation, ensuring the accurate identification of photovoltaic power stations with real faulty equipment, so that the dispatching microservice can promptly determine the target operation and maintenance personnel and perform operation and maintenance in time, thereby realizing effective management of photovoltaic power stations and improving the safety of photovoltaic power station operation.

[0052] In one embodiment, the method also includes: for each photovoltaic power station in the target area, obtaining the expected sound field data corresponding to the photovoltaic power station, and collecting the sound field data to be detected of the photovoltaic power station within a preset time period; comparing the expected sound field data of the photovoltaic power station with the sound field data to be detected to obtain a comparison result; based on the comparison result, determining whether the photovoltaic power station is a suspected abnormal photovoltaic power station.

[0053] The sound field data is data describing the distribution and characteristics of sound in a specific space, such as the intensity, directionality, reflection, etc. of the sound. The expected sound field data refers to the sound field data of a photovoltaic power station without abnormalities.

[0054] Optionally, after the power station management microservice determines that the detection cycle of the target area has been reached at the current moment, for each photovoltaic power station in the target area, the power station management microservice obtains the expected sound field data corresponding to the photovoltaic power station from the expected sound field data of multiple photovoltaic power stations in the target area that are preset. The power station management microservice collects the sound field data to be detected of the photovoltaic power station within a preset time period, and compares the sound field data to be detected with the expected sound field data to see if they are consistent. If they are consistent, the power station management microservice determines that the photovoltaic power station is not a suspected abnormal photovoltaic power station (that is, a normal photovoltaic power station). If they are inconsistent, the power station management microservice determines that the photovoltaic power station is a suspected abnormal photovoltaic power station.

[0055] Exemplarily, for each photovoltaic power station in the target area, after the power station management microservice obtains the expected sound field data corresponding to the photovoltaic power station, it obtains the expected device sound field data of each device in the photovoltaic power station from the expected sound field data, and obtains the device sound field data of each device to be detected from the sound field data to be detected. For each device in the photovoltaic power station, the corresponding expected device sound field data and the device sound field data to be detected are compared to see if they are consistent, and the device comparison result is obtained. If there is at least one device comparison result that is inconsistent, a suspected abnormal photovoltaic power station is determined; if each device comparison result is consistent, it is determined not to be a suspected abnormal photovoltaic power station.

[0056] In this embodiment, before determining the device to be identified, based on the sound field data to be detected of each photovoltaic power station in the target area, photovoltaic power stations suspected of being abnormal can be quickly and preliminarily screened out from multiple photovoltaic power stations in the target area. In this way, fault judgment and operation and maintenance can be performed on the photovoltaic power stations suspected of being abnormal later, avoiding direct fault judgment on each photovoltaic power station in the target area, thereby ensuring the efficiency of photovoltaic power station management.

[0057] In one embodiment, determining a device to be identified in a photovoltaic power station suspected of being abnormal includes: identifying an area to be identified in the photovoltaic power station suspected of being abnormal according to the sound field data to be detected, and treating each device in the area to be identified as a device to be identified.

[0058] Exemplarily, abnormal sound field data is determined from the sound field data to be detected, the location information where the abnormal sound field data appears is determined, and based on the location information, the area to be identified in the photovoltaic power station suspected of abnormality is determined, and each device in the area to be identified is used as a device to be identified. The abnormal sound field data refers to the part of the sound field data to be detected that is inconsistent with the expected sound field data.

[0059] In this way, the area to be identified can be located from the photovoltaic power station suspected of being abnormal based on the sound field data to be detected, thereby determining the device to be identified.

[0060] In another embodiment, determining the device to be identified in a photovoltaic power station suspected of being abnormal includes: after obtaining the device comparison result of each device in the photovoltaic power station suspected of being abnormal, taking the device corresponding to the inconsistent device comparison result as the device to be identified, thereby enabling the device to be identified more quickly.

[0061] In one embodiment, the fault identification model determination step includes: based on the device attributes of the device to be identified, selecting at least one candidate model that matches the device attributes from a plurality of trained candidate models, and using the selected at least one candidate model as the fault identification model.

[0062] Each trained candidate model is a neural network model, and the candidate model is used to identify the fault of the device. The device attribute of the device to be identified reflects the sensitive environment type of the device to be identified, that is, reflects which environment is more sensitive. For example, the sensitive environment type can be a temperature sensitive type (characterizing that the device is sensitive to temperature), a vibration sensitive type (characterizing that the device is sensitive to vibration), and so on.

[0063] Exemplarily, the power station management microservice determines that the device attributes of the device to be identified indicate that the device is sensitive to temperature and humidity, and then obtains a candidate model for fault identification based on temperature and a candidate model for fault identification based on humidity as fault identification models.

[0064] Exemplarily, the device attribute of the device to be identified reflects the sensitivity of the device to different sensitive environment types. Therefore, at least one sensitive environment type with a sensitivity greater than a sensitivity threshold is screened out from the sensitive environment types of the device to be identified, and the candidate model matched by each of the at least one screened sensitive environment type is used as the fault identification model.

[0065] The training steps of the above-mentioned multiple candidate models can be trained by ensemble learning, and the ensemble methods can include: Bagging (Bootstrap Aggregating, bagging method), Boosting (boosting method), Stacking (stacking method), and Blending (mixing method).

[0066] Bagging: Perform multiple bootstrap sampling on the original data set to generate multiple different training data sets, train a base model on each training data set, and combine the prediction results of each base model by voting or averaging. If the voting method is used for prediction, in order to improve the accuracy, the voting weight can also be optimized by introducing an attention mechanism, for example , Represents the final recognition result. is the voting weight of model i, is the recognition result of model i, updated using the attention mechanism , , is a learnable parameter, It is a bias term, which is updated in real time as the training progresses. Boosting: Train multiple models in sequence, and each new model is trained on the residual of the previous model. The weight of each model is adjusted according to its performance to optimize the overall performance. Stacking: The prediction results of multiple different models are used as new features and input into one or more meta-models (meta-learner) for training. Among them, the meta-model can be any type of machine learning algorithm. Blending: Similar to Stacking, but different methods are usually used to combine the predictions of the base model. In addition, you can also combine the various integration techniques mentioned above, first use the Boosting method to train multiple models, and then use the Bagging method to integrate these models to train multiple candidate models. This method is called Hybrid Methods. In this way, integrated learning can provide better generalization ability than a single model and reduce the risk of overfitting.

[0067] In this embodiment, a candidate model matching the device attributes is screened out from multiple candidate models according to the device attributes of the device to be identified, thereby enabling more reasonable, comprehensive and effective fault identification of the device to be identified, thereby improving the reliability and accuracy of fault identification.

[0068] In one embodiment, when there are multiple fault identification models, the fault identification model is called according to the device data, and fault identification is performed on the device to be identified to obtain a fault identification result, including: for each fault identification model, determining the data type that matches the fault identification model, and obtaining data corresponding to the data type from the device data; for each fault identification model, inputting the data corresponding to the data type into the fault identification model to obtain a model output result corresponding to the fault identification model; and determining the fault identification result according to the model output results of each fault identification model.

[0069] Among them, in the case where there are multiple fault identification models, each of the multiple fault identification models has a different identification dimension (the sensitive environment type mentioned above), and each model input is the data of the corresponding identification dimension. For example, there are temperature-based fault identification models and vibration-based fault identification models. Among them, the temperature-based fault identification model determines whether the device to be identified has a fault based on the temperature data of the device to be identified. The vibration-based fault identification model determines whether the device to be identified has a fault based on the vibration data of the device to be identified.

[0070] like Figure 3As shown, it is a schematic diagram of a fault identification process in an embodiment. For the device to be identified, there are N sensitive data types, and each sensitive data type has a fault identification model of a corresponding dimension, for example, a fault identification model based on dimension 1, ..., a fault identification model based on dimension N, after inputting the corresponding data types into the corresponding fault identification model, the corresponding model output results are obtained, such as obtaining model output results 1, ..., model output results N.

[0071] Exemplarily, when each model output result indicates that there is no fault, it is determined that there is no fault in the device to be identified. When there is at least one model output result indicating a fault, it is determined that there is a fault, and according to the corresponding data type, the corresponding fault type is queried, and it is determined that the fault identification result indicates that there is a fault of the fault type corresponding to the data type. For example, a mapping relationship between the data type of the device data and the fault type is obtained in advance, and thus, for each model output result indicating a fault, the corresponding fault type is determined according to the mapping relationship.

[0072] In one embodiment, the method further includes: for each device to be identified in the suspected abnormal photovoltaic power station, if the fault identification result of at least one device to be identified indicates the presence of a fault, then it is determined that the photovoltaic power station is abnormal; if the fault identification result of each device to be identified indicates the absence of a fault, then it is determined that the photovoltaic power station is not abnormal.

[0073] In this embodiment, when there are multiple fault identification models, the multiple fault identification models are used to comprehensively and accurately identify the device to be identified from multiple dimensions, thereby improving the accuracy and effectiveness of fault identification.

[0074] In one embodiment, the method further includes: after selecting at least one sensitive environment type whose sensitivity is greater than a sensitivity threshold from the sensitive environment types of the device to be identified according to the device attributes of the device to be identified, determining the data type corresponding to the at least one sensitive environment type, obtaining data of at least one data type from the device data, and inputting the data of at least one data type into a fault identification model to obtain a fault identification result of the device to be identified. In other words, through a fault identification model, the data of at least one data type can be directly integrated to comprehensively identify the device fault and ensure the accuracy of fault identification.

[0075] In one embodiment, the target operation and maintenance personnel for the equipment to be identified is determined based on the operation and maintenance data, including: determining the operation and maintenance specifications based on the operation and maintenance data through a dispatching microservice, and determining at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications; querying the target operation and maintenance personnel from at least one candidate operation and maintenance personnel based on the operation and maintenance status of each candidate operation and maintenance personnel through the dispatching microservice.

[0076] The operation and maintenance specification refers to the operation and maintenance rules required for the operation and maintenance of the device to be identified, which can be understood as the processing method or operation and maintenance method that matches the current fault type of the device to be identified. The operation and maintenance status can be the current idle state of the candidate operation and maintenance personnel (idle state or busy state), or the distance state of the candidate operation and maintenance personnel from the device to be identified in the target area (long distance state or close distance state).

[0077] Exemplarily, after obtaining the operation and maintenance data, the dispatching microservice generates an operation and maintenance specification that matches the current device to be identified based on the fault type and device information of the fault identification result in the operation and maintenance data. The dispatching microservice filters out at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications from the list of operation and maintenance personnel in the target area. The dispatching microservice filters out the target operation and maintenance personnel from at least one candidate operation and maintenance personnel based on at least one of the idle state and distance state of each candidate operation and maintenance personnel, with non-busy and close distance as the screening targets. For example, candidate operation and maintenance personnel 1 is currently in an idle state and a close distance state; candidate operation and maintenance personnel 2 is currently in a busy state and a close distance state, and candidate operation and maintenance personnel 3 is currently in an idle state and a long distance state, then candidate operation and maintenance personnel 1 is selected as the target operation and maintenance personnel.

[0078] Exemplarily, candidate operation and maintenance personnel in an idle state are screened out from at least one candidate operation and maintenance personnel, and then those in a close state are screened out from the candidate operation and maintenance personnel in an idle state. Exemplarily, if there is no candidate operation and maintenance personnel in an idle state among at least one candidate operation and maintenance personnel, the neighboring area closest to the target area is obtained, and neighboring operation and maintenance personnel who meet the operation and maintenance specifications and are idle in the neighboring area are checked, and the neighboring operation and maintenance personnel closest to the device to be identified are found from the neighboring operation and maintenance personnel, and the found neighboring operation and maintenance personnel are used as the target operation and maintenance personnel.

[0079] In this embodiment, the operation and maintenance specifications are determined according to the operation and maintenance data through the dispatching microservice, thereby querying at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications, and then selecting a suitable target operation and maintenance personnel according to the operation and maintenance status of the candidate operation and maintenance personnel to achieve precise operation and maintenance.

[0080] In a specific embodiment, Figure 4 FIG. 1 is a schematic diagram of a photovoltaic power station management system in one embodiment. Figure 4 It can be seen that the photovoltaic power station management system includes a service aggregation platform, a management function block, an interaction and display function block, and a service function block.

[0081] Among them, the interaction and display function block is installed on the client's mobile terminal (mobile phone, tablet computer login) or the backend management center. The interaction and display function block is used by users or managers to view various service data of the power station. Specifically, the interaction and display function block also includes a digital twin unit, which closely associates various project information of the photovoltaic power station with the digital twin model through unified coding, so that customers and managers can obtain the required information through human-computer interaction with the three-dimensional model. The three-dimensional model and the structure tree are directly connected through coding, and the attributes are no longer attached to the three-dimensional model, realizing the separation of digital and analog. Through the design plug-in, the three-dimensional model is rendered using the digital twin graphics engine, and the three-dimensional model is displayed in the form of a picture stream using a virtual desktop, realizing the lightweight conversion of the digital twin model source file. Furthermore, through the data import standard interface, the Unity three-dimensional plug-in (a plug-in for a three-dimensional game engine) is used to reprogram the three-dimensional model and then import it into the digital twin system. The system is loaded into the IE browser through the model loading method of different regions and perspectives and the color gradient display method.

[0082] Among them, the management function block is installed in the background management center and is used by managers to manage services, including customer management, microservice management, message management, etc., and then output customer information, microservice information, notification information, device information, etc. Specifically, customer management includes unified application, authorization, change, and deletion of administrator accounts and operator accounts for the basic resources and microservices involved, including basic environment and application account management. The basic environment and applications include various cloud resources, operating systems, databases, middleware, and external auxiliary applications (such as resource detection tools). The basic environment and application accounts include: administrator accounts and operator accounts. The administrator account is an account for unified account management of the basic environment and applications (such as cloud resources). The administrator account can be assigned an operator account, but does not perform daily monitoring of the basic environment and applications. In principle, each basic environment and application has only one administrator account. The operator account is the account used to carry out daily operation and maintenance, indicator monitoring, etc. for the basic environment and applications (such as cloud resources). In principle, each basic environment and application can set up multiple operator accounts according to permission requirements. In principle, administrator accounts are not allowed to be applied for except for special reasons. Operator accounts can submit account permission applications to relevant management departments at any time according to operation and maintenance needs, and apply according to the application process. Administrator accounts and operator accounts must be set with a validity period, and it is strictly forbidden to create accounts with a permanent validity period. Business application account management: Apply for and manage the accounts of the Internet product system itself.

[0083] Among them, the service aggregation platform embeds power station management microservices, dispatching microservices, acceptance microservices, and grid-connected management microservices into the service aggregation platform through the iframe tag in html (Hypertext Markup Language) technology. It can be understood that the iframe tag is a web page embedding technology that can embed a web page into another web page, and the iframe tag specifies an inline frame. The iframe element will create an inline frame (i.e., an inline frame) containing another document. An inline frame is used to embed another document in the current html document. The service aggregation platform is the hardware entity corresponding to BFF. BFF (Backends for Frontends) is also called the aggregation layer or adaptation layer. It mainly undertakes an adaptation role: adapting the complex internal microservices into a friendly and unified API (Application Programming Interface) for various different user experiences (wireless / Web (network) / H5 (fifth generation hypertext markup language) / third party, etc.).

[0084] Among them, the service function block includes a tag acquisition unit, a communication unit and a synchronization unit. The tag acquisition unit is used to obtain the iframe tag of each microservice on the service aggregation platform, and register and manage it based on the iframe tag of each microservice. The communication unit is used to determine its communication management mechanism according to the business function definition of each microservice; wherein, the communication management mechanism includes the communication between each microservice and other microservices, and the communication between each microservice and the service aggregation platform. Specifically, it is necessary to first determine the architecture design data format and business data format of each microservice, and then determine the communication method of each microservice with the service aggregation platform according to the architecture design data format, and determine the communication method of each microservice with other microservices according to the business data format. Of course, in order to achieve data isolation between each microservice and other microservices and reduce the mutual influence between microservices, each microservice and other microservices communicate indirectly through the service aggregation platform.

[0085] The following example uses the service aggregation platform as the implementation subject. Before the explanation, the microservices involved are introduced. The service aggregation platform involves power station management microservices, grid-connection management microservices, dispatching microservices, and acceptance microservices. The descriptions of power station management microservices and dispatching microservices are as mentioned above. The acceptance microservice is used to conduct autonomous acceptance of the dispatching operation, including evaluating the operation actions of the operation and maintenance dispatching orders with reference to the built-in operation specifications. If any illegal operations occur, they will be broadcasted and recorded to the corresponding operation and maintenance personnel and management personnel; combined with the real-time operation and environmental data of the photovoltaic power station, the operation results of the operation and maintenance dispatching orders are evaluated. If the fault is not completely eliminated, it should also be broadcasted and recorded to the corresponding operation and maintenance personnel and management personnel in a timely manner. The grid-connection management microservice includes managing the grid-connection approval process of power stations, including application submission, approval flow, and review.

[0086] The following are the specific steps for managing a photovoltaic power station:

[0087] For each photovoltaic power station in the target area, the operation and maintenance unit of the power station management microservice obtains the expected sound field data corresponding to the photovoltaic power station, and the operation and maintenance unit obtains the sound field data to be detected of the photovoltaic power station collected by the data collection unit within a preset period of time; compares the expected sound field data of the photovoltaic power station with the sound field data to be detected to obtain a comparison result; and determines whether the photovoltaic power station is a suspected abnormal photovoltaic power station based on the comparison result. When the comparison result shows that the expected sound field data is inconsistent with the sound field data to be detected, the photovoltaic power station is determined to be a suspected abnormal photovoltaic power station.

[0088] Next, when the operation and maintenance unit detects that there is a suspected abnormal photovoltaic power station in the target area, the area to be identified in the suspected abnormal photovoltaic power station is identified according to the sound field data to be detected, and each device in the area to be identified is used as a device to be identified.

[0089] The operation and maintenance unit obtains the equipment data of the equipment to be identified. The operation and maintenance unit selects at least one candidate model that matches the equipment attributes from multiple trained candidate models according to the equipment attributes of the equipment to be identified, and uses the at least one selected candidate model as a fault identification model. In the case where there are multiple fault identification models, for each fault identification model, the data type that matches the fault identification model is determined, and data corresponding to the data type is obtained from the equipment data; for each fault identification model, the data corresponding to the data type is input into the fault identification model to obtain the model output result corresponding to the fault identification model; and the fault identification result is determined according to the model output results of each fault identification model.

[0090] Then, when the fault identification result indicates that a fault has occurred, the operation and maintenance unit generates operation and maintenance data based on the fault identification result and the equipment information of the equipment to be identified, and sends the operation and maintenance data to the dispatching microservice of the service aggregation platform. The dispatching microservice determines the operation and maintenance specifications based on the operation and maintenance data, and determines at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications; the dispatching microservice queries the target operation and maintenance personnel from at least one candidate operation and maintenance personnel according to the operation and maintenance status of each candidate operation and maintenance personnel, and sends the personnel identification of the target operation and maintenance personnel to the report generation unit of the power station management microservice.

[0091] The report generation unit in the power plant management microservice obtains the operation and maintenance status of the target operation and maintenance personnel on the identified equipment in real time, generates a report based on the fault identification results, the personnel identification of the target operation and maintenance personnel, and the operation and maintenance status, and sends it to the interaction and display function block for display.

[0092] It should be noted that in the above process, the interaction and display function block connects the three-dimensional model and the structure tree through coding, and the attributes are no longer attached to the three-dimensional model, thus realizing the separation of digital and analog. The microservices related to the photovoltaic power station are embedded in the service aggregation platform, which provides a unified interface and interface for customers to use and managers to manage the photovoltaic power station. Under the microservice architecture, each microservice function is relatively independent and runs in its own process. It can be independently tested, deployed, and run to ensure strong real-time performance and high security.

[0093] In this embodiment, the power station management microservice of the service aggregation platform first detects whether there is a suspected abnormal photovoltaic power station in the target area. When a suspected abnormal photovoltaic power station is detected in the target area, the device to be identified is determined from the photovoltaic power station with suspected abnormality. There is no need to identify each device in each photovoltaic power station in the target area one by one, avoiding the identification of normal photovoltaic power stations and ensuring the accuracy of power station management. Then, the power station management microservice obtains the device data of the device to be identified, and calls the fault identification model based on the device data to perform fault identification on the device to be identified to verify whether there is a misjudgment of the suspected abnormal photovoltaic power station and obtain the fault identification result; when the fault identification result indicates that a fault has occurred, it means that the suspected abnormal photovoltaic power station is not a misjudgment and there is an abnormal device. Therefore, based on the fault identification result and the device information of the device to be identified, the operation and maintenance data is generated in time, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform. In this way, the dispatching microservice can quickly determine the target operation and maintenance personnel of the device to be identified based on the operation and maintenance data. During the whole process, the power station management microservice can realize the detection operation of photovoltaic power stations suspected of abnormalities, the determination operation of equipment to be identified, and the fault identification operation, ensuring the accurate identification of photovoltaic power stations with real faulty equipment, so that the dispatching microservice can promptly determine the target operation and maintenance personnel and perform operation and maintenance in time, thereby realizing effective management of photovoltaic power stations and improving the safety of photovoltaic power station operation.

[0094] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0095] Based on the same inventive concept, the embodiment of the present application also provides a photovoltaic power station management device for implementing the photovoltaic power station management method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more photovoltaic power station management device embodiments provided below can refer to the limitations of the photovoltaic power station management method above, and will not be repeated here.

[0096] In an exemplary embodiment, Figure 5 As shown, a photovoltaic power station management device 500 is provided, including: a device determination module 502, a fault identification module 504 and a data sending module 506, wherein:

[0097] The device determination module 502 is used to determine the device to be identified in the photovoltaic power station suspected of being abnormal when a photovoltaic power station suspected of being abnormal is detected in the target area;

[0098] The fault identification module 504 is used to obtain the device data of the device to be identified, and call the fault identification model according to the device data to perform fault identification on the device to be identified to obtain the fault identification result;

[0099] The data sending module 506 is used to generate operation and maintenance data based on the fault identification result and the equipment information of the equipment to be identified when the fault identification result indicates the occurrence of a fault, and send the operation and maintenance data to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel for the equipment to be identified based on the operation and maintenance data.

[0100] In one embodiment, the device determination module 502 is also used to obtain the expected sound field data corresponding to each photovoltaic power station in the target area, and collect the sound field data to be detected of the photovoltaic power station within a preset time period; compare the expected sound field data of the photovoltaic power station with the sound field data to be detected to obtain a comparison result; and determine whether the photovoltaic power station is a suspected abnormal photovoltaic power station based on the comparison result.

[0101] In one embodiment, the device determination module 502 is further configured to identify a region to be identified in the photovoltaic power station suspected of being abnormal according to the sound field data to be detected, and to use each device in the region to be identified as a device to be identified.

[0102] In one embodiment, the device further includes a model determination module for selecting at least one candidate model that matches the device attributes from a plurality of trained candidate models according to the device attributes of the device to be identified, and using the selected at least one candidate model as a fault identification model.

[0103] In one embodiment, when there are multiple fault identification models, the fault identification module 504 is used to determine the data type that matches the fault identification model for each fault identification model, and obtain data corresponding to the data type from the device data; for each fault identification model, input the data corresponding to the data type into the fault identification model to obtain the model output result corresponding to the fault identification model; and determine the fault identification result based on the model output results of each fault identification model.

[0104] In one embodiment, the data sending module 506 is also used to determine the operation and maintenance specifications based on the operation and maintenance data through the dispatch microservice, and determine at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications; through the dispatch microservice, according to the operation and maintenance status of each candidate operation and maintenance personnel, query the target operation and maintenance personnel from at least one candidate operation and maintenance personnel.

[0105] Each module in the above photovoltaic power station management device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0106] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a photovoltaic power station management method is implemented.

[0107] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0108] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0109] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0110] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0112] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0113] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0114] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A photovoltaic power station management method, characterized in that: A power station management microservice applied to a service aggregation platform, the method comprising: In the case where a photovoltaic power station suspected of being abnormal is detected in the target area, determining a device to be identified in the photovoltaic power station suspected of being abnormal; Acquire device data of the device to be identified, and call a fault identification model according to the device data to perform fault identification on the device to be identified to obtain a fault identification result; When the fault identification result indicates that a fault has occurred, operation and maintenance data is generated based on the fault identification result and the equipment information of the equipment to be identified, and the operation and maintenance data is sent to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel of the equipment to be identified based on the operation and maintenance data.

2. The method according to claim 1, characterized in that: The method further comprises: For each photovoltaic power station in the target area, obtaining expected sound field data corresponding to the photovoltaic power station, and collecting sound field data to be detected of the photovoltaic power station within a preset time period; Comparing the expected sound field data of the photovoltaic power station with the sound field data to be detected to obtain a comparison result; According to the comparison result, it is determined whether the photovoltaic power station is a suspected abnormal photovoltaic power station.

3. The method according to claim 2, characterized in that The step of determining a device to be identified in a photovoltaic power station that is suspected to be abnormal includes: According to the sound field data to be detected, an area to be identified in the photovoltaic power station suspected of being abnormal is identified, and each device in the area to be identified is used as a device to be identified.

4. The method according to claim 1, characterized in that The fault identification model determination step comprises: According to the device attributes of the device to be identified, at least one candidate model matching the device attributes is screened out from a plurality of trained candidate models, and the screened out at least one candidate model is used as a fault identification model.

5. The method according to claim 1, characterized in that In the case where there are multiple fault identification models, calling the fault identification model according to the device data, performing fault identification on the device to be identified, and obtaining a fault identification result includes: For each fault identification model, determining a data type matching the fault identification model, and acquiring data corresponding to the data type from the device data; For each fault identification model, inputting data corresponding to the data type into the fault identification model to obtain a model output result corresponding to the fault identification model; The fault identification result is determined according to the model output results of each fault identification model.

6. The method according to claim 1, characterized in that The determining a target operation and maintenance personnel of the device to be identified according to the operation and maintenance data includes: Determining operation and maintenance specifications according to the operation and maintenance data through the dispatching microservice, and determining at least one candidate operation and maintenance personnel who meets the operation and maintenance specifications; The dispatching microservice queries a target operation and maintenance personnel from at least one candidate operation and maintenance personnel according to the operation and maintenance status of each candidate operation and maintenance personnel.

7. A photovoltaic power station management device, characterized in that: The device comprises: A device determination module is used to determine the device to be identified in the photovoltaic power station suspected of being abnormal when a photovoltaic power station suspected of being abnormal is detected in the target area; A fault identification module is used to obtain device data of the device to be identified, and according to the device data, call a fault identification model to perform fault identification on the device to be identified to obtain a fault identification result; A data sending module is used to generate operation and maintenance data based on the fault identification result and the equipment information of the equipment to be identified when the fault identification result indicates the occurrence of a fault, and send the operation and maintenance data to the dispatching microservice of the service aggregation platform to instruct the dispatching microservice to determine the target operation and maintenance personnel of the equipment to be identified based on the operation and maintenance data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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