Interactive photovoltaic power station automatic management method and system

Abstract

The invention relates to an interactive photovoltaic power station automatic management method and system, and the work of the system is based on the abnormality detection of a photovoltaic power station, forms a linguistic inquiry instruction when an abnormality occurs, sends the inquiry instruction to a user, and enables the user to confirm the state related to the abnormality. According to the method, abnormal false alarms caused by the environment can be eliminated through reciprocating effective interaction with a user according to the conditions such as the environment, manual power utilization adjustment and external damage of equipment, so that the processing efficiency of the abnormal state is improved, the investment of manpower and material resources is saved, the operation and maintenance cost is reduced, and the user experience can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic control technology, in particular to the field of photovoltaic power station management technology, and specifically to an interactive photovoltaic power station automation management method and system. Background Art

[0002] Photovoltaic power station refers to a photovoltaic power generation system that uses solar energy and special materials such as crystalline silicon panels, inverters and other electronic components to connect to the power grid and transmit electricity to the grid. Photovoltaic power stations are generally composed of solar cell arrays, battery packs, battery management systems BMS, charge and discharge controllers, inverters, AC distribution cabinets, solar tracking control systems and other equipment. During the operation of the photovoltaic power station, its various working parameters, such as power parameters, ground impedance, power generation efficiency, power consumption and other data will be fed back to the cloud, and remote management will be carried out through cloud data storage and analysis. The cloud and the equipment end together constitute the management system of the photovoltaic power station. The photovoltaic power station management system is an operation and maintenance system applied to photovoltaic power stations. It plays a very important role in maintaining the normal operation of photovoltaic power stations. When abnormalities occur in photovoltaic power stations, it can actively give alarms or draw users' attention.

[0003] However, it is inevitable that changes in the external environment or the user's personal electricity usage habits will lead to abnormal false alarms. The photovoltaic power station management method in the existing technology lacks effective interaction with users, which makes it impossible to solve these false alarms in a timely and effective manner. Instead, it uses cumbersome background steps to troubleshoot the abnormalities, which is inefficient and wastes resources. For example, the battery management system BMS reports abnormal battery temperature rise. After reporting, the photovoltaic power station management system analyzes the cause of the temperature rise, including inspections of various points in the circuit, internal inspections of the battery, etc., but this may also be caused by the temperature rise of the external environment of the battery. If the user's self-inspection and information exchange can be introduced, it will undoubtedly be quickly checked.

[0004] Therefore, developing a method and system that can face and interact with users to achieve automated photovoltaic management is a technical problem that needs to be solved urgently. Summary of the invention

[0005] A main purpose of the present invention is to overcome at least one of the above-mentioned defects, and to provide an interactive photovoltaic power station automation management method that can automatically eliminate abnormalities, and can also specifically provide a guided fault solution.

[0006] A main purpose of the present invention is to overcome at least one of the above-mentioned drawbacks, and to provide an interactive photovoltaic power station automation management system that can automatically eliminate abnormalities.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] The present invention provides an interactive photovoltaic power station automation management method, comprising the following processing steps:

[0009] S1: Detect the status of the photovoltaic power station and send the detection information to the cloud;

[0010] S2: confirming whether there is an abnormality according to the detection information, outputting an abnormal instruction when an abnormality exists, and parsing and processing the abnormal instruction to form an inquiry instruction;

[0011] S3: Displaying the inquiry instruction to the user and asking the user to give a reply;

[0012] S4: confirming whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action according to the execution instruction to eliminate the abnormality or self-checks and reports the abnormality.

[0013] According to one embodiment of the present invention, the abnormal instruction is parsed, and directional data matching the abnormal instruction is retrieved from a photovoltaic standard database to form an inquiry instruction.

[0014] According to one embodiment of the present invention, in the process of retrieving the directional data matching the abnormal instruction from the photovoltaic standard database and forming the query instruction,

[0015] The retrieved directional data is read and processed through a large language model to form the query instruction.

[0016] According to one embodiment of the present invention, the large language model is a ChatGPT model.

[0017] According to one embodiment of the present invention, in the process of the large language model reading and processing the directional data to form the query instruction,

[0018] The directional data is sorted to form a plurality of interactive data, and the plurality of interactive data constitute the inquiry instruction. When the inquiry instruction is displayed to the user, the interaction with the user is performed in sequence according to the plurality of interactive data.

[0019] According to one of the embodiments of the present invention, in the process of sequentially interacting with the user according to the plurality of pieces of interaction data, the interaction data is converged according to the response information fed back by the user.

[0020] According to one embodiment of the present invention, the directional data matched with the abnormal instruction is an environmental factor or a human factor causing the abnormal instruction to occur.

[0021] According to one embodiment of the present invention, whether there is an abnormality is confirmed based on the detection information, and when an abnormality exists, an abnormal instruction is outputted. In the process of parsing the abnormal instruction to form an inquiry instruction, an environmental factor matching the occurrence of the abnormal instruction is retrieved from a photovoltaic standard database to form a first inquiry instruction based on natural language;

[0022] The first inquiry instruction performs information convergence after interacting with the user to reply information, and retrieves a second inquiry instruction matching the data after information convergence from the photovoltaic standard database, and repeats this step until the cause of the abnormality is determined;

[0023] The execution instruction is formed according to the determined cause of the abnormality, and the photovoltaic power station takes action to eliminate the abnormality or self-checks and reports the abnormality according to the execution instruction.

[0024] According to one embodiment of the present invention, the first inquiry instruction performs information convergence after interacting with the user to reply information, and gradually converges from environmental factors to non-environmental factors.

[0025] According to one embodiment of the present invention, basic parameters of a detection model corresponding to the photovoltaic power station are updated according to the user's reply information, and anomalies of the photovoltaic power station are analyzed according to the updated detection model.

[0026] According to one embodiment of the present invention, in step S4, in the process of confirming the sending of the execution instruction to the photovoltaic power station according to the reply information,

[0027] The abnormal instruction is confirmed through the reply information, and an execution strategy is formed by searching and calling in the photovoltaic standard database and issuing the execution instruction. After receiving the execution instruction, the photovoltaic power station performs corresponding actions to resolve the abnormality.

[0028] According to one embodiment of the present invention, the photovoltaic standard database includes API call documents, product descriptions, and photovoltaic industry technical information.

[0029] According to one embodiment of the present invention, the query instruction is an output instruction based on natural language.

[0030] According to one embodiment of the present invention, the abnormal instruction includes a fault instruction, a power abnormal instruction, a power consumption abnormal instruction, and a BMS alarm instruction.

[0031] According to one embodiment of the present invention, step S2 and step S4 are completed in the cloud, on an APP side or at a photovoltaic power station.

[0032] According to one implementation manner of the present invention, step S3 is completed on the APP side.

[0033] According to one embodiment of the present invention, the reply information input by the user through the APP is voice or text.

[0034] In particular, the present invention also provides an interactive photovoltaic power station automation management system, which includes:

[0035] An equipment monitoring unit performs status detection on the photovoltaic power station and records detection information, and the detection information is sent to the abnormality analysis unit;

[0036] An analysis management unit receives the detection information, confirms whether there is an abnormality according to the detection information, outputs an abnormal instruction when an abnormality exists, parses and processes the abnormal instruction to form an inquiry instruction, and sends the inquiry instruction to the APP end;

[0037] An APP end is provided at the user's place, and is used to receive the inquiry instruction and display it to the user. The user inputs the reply information through the APP end, and the reply information is sent to the analysis management unit;

[0038] The analysis management unit determines whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action to eliminate abnormalities or self-checks and reports abnormalities according to the execution instruction.

[0039] According to one embodiment of the present invention, the analysis management unit includes:

[0040] An input preprocessing module is used to receive the abnormal instruction and capture the features of the abnormal instruction to form effective feature data;

[0041] A context comprehensive processing module, connected to the input preprocessing module, receives the valid feature data, and retrieves and calls data matching the valid feature data to form a comprehensive input instruction;

[0042] The large language model is connected to the input preprocessing module, receives the comprehensive input instruction and processes it to form a query instruction or an execution instruction.

[0043] According to one embodiment of the present invention, the analysis management unit further includes a memory storage module, wherein the memory storage module is used to store photovoltaic standard data and real-time interaction data, and the memory storage module is connected to the context comprehensive processing module.

[0044] According to one embodiment of the present invention, the memory storage module includes a permanent storage submodule and a flash memory submodule, the permanent storage submodule is used to store photovoltaic standard data, and the flash memory submodule is used to store real-time interactive data.

[0045] According to one embodiment of the present invention, the memory storage module includes a storage management submodule, which is connected to the large language model and is used to receive the feedback query instructions and the response information and update them in the real-time interaction data.

[0046] According to one embodiment of the present invention, the analysis and management unit also includes an output parsing module, which receives the execution instruction and parses the execution instruction to form a control instruction. The control instruction is in a machine language format after formatting and parsing. The photovoltaic power station receives the control instruction and executes it to eliminate abnormalities or self-check and report abnormalities.

[0047] According to one embodiment of the present invention, the analysis and management unit is configured in the cloud, an APP terminal or a photovoltaic power station.

[0048] In particular, the present invention also provides a computing device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0049] The present invention also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method described above is implemented. The computer-readable storage medium is preferably a non-volatile readable storage medium.

[0050] Compared with the prior art, the advantages and beneficial effects of the interactive photovoltaic power station automation management system and method of the patent application of the present invention are:

[0051] The interactive photovoltaic power station automation management method of the present application is based on the abnormality detection of the photovoltaic power station. When an abnormality occurs, a language-based inquiry instruction is generated, and the inquiry instruction is sent to the user for the user to confirm the status related to the abnormality, such as the status of the environment, manual power adjustment, external damage to the equipment, etc. Through effective reciprocating interaction with the user, abnormal false alarms caused by the environment are eliminated, thereby improving the efficiency of handling abnormal conditions, saving manpower and material resources, reducing operation and maintenance costs, and effectively improving user experience.

[0052] The analysis body of the interactive photovoltaic power station automation management system of the present application is set on the cloud or APP side, which simplifies the user input on the interactive side. The user only needs to respond to the inquiry command through voice or text input, which facilitates the information interaction between the user and the analysis and processing unit, reduces the technical threshold requirements for users to answer questions, and enables users to be more proactive in cooperating with exception handling, which not only improves the user experience, but also enables better management of photovoltaic power stations.

[0053] Furthermore, this application innovatively introduces a large language model (such as the ChatGPT model). The input side of the large language model uses the retrieved text-based exception instructions, and the output side is a natural language-based query instruction or execution instruction. From the front-end input to the back-end processing output, it is all based on natural language processing, which is more in line with the needs of traditional users. In the final specific execution, the execution instruction based on natural language is converted into a format (converted into a control instruction) and then used for specific execution, which reduces the difficulty of processing this photovoltaic information, can better meet user needs, improve processing efficiency, and realize the automation of exception processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0055] Figure 1 is a schematic diagram of the workflow of an interactive photovoltaic power station automation management method according to an embodiment of the present invention;

[0056] Figure 2 is a structural block diagram of an interactive photovoltaic power station automation management system according to an embodiment of the present invention;

[0057] Figure 3 is a structural diagram of a computing device according to another embodiment of the present application;

[0058] Figure 4 is a structural diagram of a computer-readable storage medium according to another embodiment of the present application;

[0059] Figure 5 It is a structural block diagram of an interactive photovoltaic power station automation management system according to another embodiment of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0062] Embodiment 1:

[0063] This embodiment describes an interactive photovoltaic power station automation management method, including the following processing steps:

[0064] S1: Detect the status of the photovoltaic power station and send the detection information to the cloud;

[0065] S2: confirming whether there is an abnormality according to the detection information, outputting an abnormal instruction when there is an abnormality, retrieving directional data matching the abnormal instruction from the photovoltaic standard database, reading and processing the retrieved directional data through a large language model, and forming an inquiry instruction;

[0066] S3: Displaying the inquiry instruction to the user and asking the user to give a reply;

[0067] S4: confirming whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action according to the execution instruction to eliminate the abnormality or self-checks and reports the abnormality.

[0068] In one embodiment, the large language model reads and processes the directional data to form the query instruction, sorts the directional data to form a plurality of interactive data, the plurality of interactive data constitute the query instruction, and interacts with the user in sequence according to the plurality of interactive data when displaying the query instruction to the user. In the process of interacting with the user in sequence according to the plurality of interactive data, the interactive data is converged according to the reply information fed back by the user.

[0069] In one implementation, the directional data matched with the abnormal instruction is an environmental factor or a human factor causing the abnormal instruction to occur.

[0070] In one embodiment, whether there is an abnormality is confirmed based on the detection information, and when an abnormality exists, an abnormal instruction is output. In the process of parsing the abnormal instruction to form an inquiry instruction, the environmental factors that match the occurrence of the abnormal instruction are retrieved from the photovoltaic standard database to form a first inquiry instruction based on natural language. The first inquiry instruction described here may be composed of several instructions, and each instruction is correlated and hierarchical, and can gradually converge according to the user's subsequent reply information.

[0071] According to the abnormality or fault information of the photovoltaic power station, the inquiry command is displayed to the user, and the user can reply in the form of text, voice, picture, etc. The instruction data after the interaction is converged according to the user's reply information, and the second inquiry command after convergence is sent to the user, and then the interaction with the user continues. This step is repeated until the cause of the abnormality is determined.

[0072] In the process of forming the inquiry instruction, first check the possibility of environmental factors such as environmental changes and artificial power adjustment. If the environmental factors are abnormal and may cause false fault alarms, the inquiry instruction will be converged within the scope of external environmental factors, such as asking the user "surrounding temperature" and "whether the power grid is under maintenance". If the environmental factors are normal or will not cause false fault alarms, the inquiry instruction will gradually converge to the scope of non-environmental factors, and the user will reply to the device information such as the device wiring status and the health status of the device shell through the inquiry instruction. It can be seen that through real-time and effective interaction with the user, the environmental status can be learned relatively quickly, and then the environmental factors that cause the abnormality can be effectively eliminated. If it is not caused by environmental factors, it can also be effectively eliminated, so that the first inquiry instruction converges information after interacting with the user to reply information, and gradually converges from environmental factors to non-environmental factors.

[0073] It can be understood that the first query instruction converges information after interacting with the user to reply information, retrieves the second query instruction matching the data after information convergence from the photovoltaic standard database, and repeats this step until the cause of the abnormality is determined. The execution instruction is formed according to the determined cause of the abnormality, and the photovoltaic power station takes action to eliminate the abnormality or self-checks and reports the abnormality according to the execution instruction.

[0074] According to the interactive information replied by the user, after confirming the specific cause of the abnormality, on the one hand, an execution strategy is formed and the execution instruction is issued. After receiving the execution instruction, the photovoltaic power station takes corresponding actions to release the fault abnormality instruction or start self-inspection; on the other hand, the large language model continues to interact with the user to obtain the label of the abnormal scene, and according to the label of the abnormal alarm, the fault detection detection model is switched or the model is self-learned. The switched or trained updated model is used for abnormality detection, which improves the accuracy of fault detection and reduces false alarms.

[0075] When troubleshooting anomalies in a photovoltaic power station, the prior art will focus on establishing relevant detection models to achieve rapid judgment of the anomalies. The interactive photovoltaic automation management method of the embodiment of the present application can update the basic parameters of the detection model corresponding to the photovoltaic power station according to the user's aforementioned reply information, and analyze the anomalies of the photovoltaic power station according to the updated detection model.

[0076] like Figure 5 As shown, after the abnormality detection is performed in this embodiment and the photovoltaic power station is detected to be abnormal, the abnormal instruction can be analyzed and parsed to obtain the visible factors that cause the abnormality rather than the factors of the internal operation failure of the equipment, that is, environmental factors, such as terminal virtual connection, artificial modification of the line, etc. According to these environmental factors, a number of natural language-based inquiry instructions are formed, such as "Is the local power line being artificially reconnected?", "Please confirm whether the *th group string connection terminal is virtual or detached?", "Please upload a photo of the terminal connection," "How far is the length of the solar panel from the equipment?", etc., and the above instructions can be regarded as inquiry instructions.

[0077] The system interacts with users through APP or other terminals, and displays inquiry instructions to troubleshoot external environmental factors. For example, if the user answers "No" to the question "Is the power line being artificially reconnected locally?" (the first inquiry instruction), the inquiry instruction will be narrowed to non-environmental factors, and the second inquiry instruction will be issued to the customer, "Please confirm whether the *th group of string wiring terminals are loose or detached." If the user answers "Yes," the system will continue to issue the inquiry instruction "Please upload photos of the terminal connections" to the user, and so on, until the cause of the abnormality is finally confirmed.

[0078] After confirming the cause of the abnormality through the user's reply to the inquiry command, the large language model forms an execution strategy and issues the execution command. After parsing the execution command, the photovoltaic power station executes the corresponding operation.

[0079] Furthermore, after sending a query command to the user such as "How far is the solar panel from the device?", the environmental data label of the user's photovoltaic power station is obtained through the user's reply information, and the device anomaly detection model is switched or self-learned according to the data update of the environmental label. The updated detection model is used for anomaly detection again. This model is switched or trained according to the user's on-site data label, which can better fit the actual working scenario of the photovoltaic power station and has a higher detection accuracy.

[0080] In one embodiment, in the process of confirming the issuance of an execution instruction to the photovoltaic power station based on the reply information, the abnormal instruction is confirmed through the reply information, and an execution strategy is formed and the execution instruction is issued in combination with retrieval and calling in the photovoltaic standard database. After receiving the execution instruction, the photovoltaic power station performs corresponding actions to resolve the abnormality.

[0081] In one embodiment, the large language model is a ChatGPT model.

[0082] In one implementation, the photovoltaic standards database includes API call documents, product descriptions, and photovoltaic industry technical information.

[0083] In one implementation, the inquiry instruction is an output instruction based on natural language, and the abnormal instruction includes a fault instruction, a power abnormality instruction, a power consumption abnormality instruction, and a BMS alarm instruction.

[0084] In one implementation, step S2 and step S4 are completed on the cloud, APP side or photovoltaic power station, and step S3 is completed on the APP side. For the specific analysis and output process of the query instruction, it is preferably carried out on the cloud with powerful processing capabilities, but it can also be carried out on the APP side or photovoltaic power station, but the required analysis and processing capabilities are larger, which can be adjusted according to the user's usage requirements and the data processing performance of each end. Even the functional modules in the analysis management unit are reasonably divided and arranged in one of the three of the APP side, the cloud side or the photovoltaic power station. In other words, the functional modules of the comprehensive APP side, the cloud side or the photovoltaic power station are combined to realize the processing of the analysis management unit, which is also technically feasible.

[0085] In one embodiment, the reply information input by the user through the APP is voice or text. The user's input is simplified on the interactive side. The user only needs to reply to the inquiry instruction through voice or text input, which is convenient for the information interaction between the user and the analysis and processing unit, and reduces the technical threshold requirements for the user to reply to the question, so that the user can be more proactive in cooperating with the exception handling, which can not only improve the user experience, but also better realize the management of the photovoltaic power station.

[0086] Taking the processing of battery temperature rise abnormality in the background technology as an example, after receiving the abnormal instruction, it is retrieved from the database, and the visible factors causing the abnormality rather than the factors of internal equipment operation failure may include external environment temperature, foreign objects around the battery that cause temperature rise, reflective irradiation of the battery, etc., so the large language model intends to issue multiple inquiry instructions "Is the local temperature rising?", "Are there any foreign objects around the battery?", and send the inquiry instructions to the user's APP end and wake up the APP end. If the user gives a reply of "yes" to "Is the local temperature rising?", the user will continue to issue an inquiry instruction "How many degrees has it risen?". If the temperature has indeed risen suddenly, it is judged that the battery temperature has risen due to the environment, and the inquiry is terminated. However, if the user's reply is "no", another inquiry instruction "Is there any foreign object around the battery?" is issued, and then according to the user's reply information "yes" or "no", the next inquiry instruction is issued in a targeted manner. For example, if there is a foreign object, the user is guided to eliminate the foreign object and eliminate the abnormality from the environment. This cannot be conveniently achieved through interaction in the prior art. Furthermore, if the cause of the rapid heating of the battery cannot be determined after eliminating all environmental anomalies, the comprehensive self-inspection of the photovoltaic power station or manual intervention is started, and an execution instruction can be issued to shut down certain circuits for further analysis and confirmation of the cause of the anomaly. However, there are many implementation methods for how to troubleshoot anomalies through equipment self-inspection in the prior art, but it does not involve the protection scope of this protection point for interactively eliminating environmental factors in this application. Therefore, the equipment self-inspection to troubleshoot anomalies will not be elaborated.

[0087] Taking "reduced power generation of solar panels" as an example, the environmental factors that lead to this situation may be "obstructions around the solar panels" or "broken solar panels". Through the analysis of the anomaly, it can be specific to the problem of a certain solar panel, and the inquiry instruction can be directly specific, "Please check whether there is any obstruction around the solar panel with number XX" or "Please check whether the solar panel with number XX is damaged". According to the reply information fed back by the user, the cause of the anomaly can be determined and the anomaly can be resolved or solved.

[0088] It can be seen that through the interaction between the APP and the user, information can be obtained from the user in reverse, and the abnormal solution can be supplemented. This can facilitate the method to accurately give suggestions to the user and formulate a solution more accurately.

[0089] In addition, based on the detection information, it is possible to learn and record the user's electricity usage habits, so the abnormality may also be due to sudden changes in electricity usage. For this working condition, it is impossible to determine whether this is a temporary change in electricity usage in the short term (such as a decrease in electricity usage due to a business trip by the user) or a long-term change in electricity usage (such as the introduction of new electrical appliances in the home). Therefore, an inquiry instruction can be issued to the user to determine the cause of the change in electricity usage. After the specific cause is determined, the situation can be recorded. For example, if the user only uses electricity temporarily, then the abnormal alarm can be directly canceled. If it is a long-term electricity usage, the load usage forecast can be adjusted by issuing execution instructions, the electricity usage habit record can be updated, and the common battery storage capacity at the photovoltaic power station can be increased.

[0090] From the above specific cases, it can be seen that the interactive photovoltaic power station automation management method of this embodiment is based on the abnormality detection of the photovoltaic power station. When an abnormality occurs, a language-based inquiry instruction is formed, and the inquiry instruction is sent to the user for the user to confirm the status related to the abnormality, such as the environment, manual power adjustment, external damage to the equipment, etc. Through effective reciprocating interaction with the user, abnormal false alarms caused by the environment are eliminated, thereby improving the efficiency of handling abnormal conditions, saving manpower and material resources, reducing operation and maintenance costs, and effectively improving user experience.

[0091] Embodiment 2:

[0092] This embodiment provides an interactive photovoltaic power station automation management system. Figure 2 As shown, it includes:

[0093] An equipment monitoring unit performs status detection on the photovoltaic power station and records detection information, and the detection information is sent to the abnormality analysis unit;

[0094] An analysis management unit receives the detection information, confirms whether there is an abnormality according to the detection information, outputs an abnormal instruction when an abnormality exists, parses the abnormal instruction to form an inquiry instruction, and sends the inquiry instruction to the APP end;

[0095] An APP end is provided at the user's place, and is used to receive the inquiry instruction and display it to the user. The user inputs the reply information through the APP end, and the reply information is sent to the analysis management unit;

[0096] The analysis management unit determines whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action to eliminate abnormalities or self-checks and reports abnormalities according to the execution instruction.

[0097] In one embodiment, the analysis management unit includes:

[0098] An input preprocessing module is used to receive the abnormal instruction and capture the features of the abnormal instruction to form effective feature data;

[0099] A context comprehensive processing module, connected to the input preprocessing module, receives the valid feature data, and retrieves and calls data matching the valid feature data to form a comprehensive input instruction;

[0100] The large language model is connected to the input preprocessing module, receives the comprehensive input instruction and processes it to form a query instruction or an execution instruction.

[0101] In this embodiment, the large language model used is the popular ChatGPT model. The ChatGPT model is a natural language processing tool driven by artificial intelligence technology. It can communicate by understanding and learning human language, and can also interact according to the context of the chat. Therefore, the context comprehensive processing module of this embodiment outputs the comprehensive input instruction to the non-word single word model to form an output instruction based on natural language. This ChatGPT model can be trained using an open source algorithm to obtain a model or deployed after fine-tuning using an open source model. Taking the opening of a site as an example, fine-tune can convert the process of opening a site into a series of conversations, and then use these conversations as training data to fine-tune the large language model to make it more suitable for the site opening scenario.

[0102] In one embodiment, the analysis management unit further includes a memory storage module, wherein the memory storage module is used to store photovoltaic standard data and real-time interaction data, and the memory storage module is connected to the context comprehensive processing module.

[0103] Specifically, the memory storage module includes a permanent storage submodule and a flash memory submodule, the permanent storage submodule is used to store photovoltaic standard data, the flash memory submodule is used to store real-time interaction data, and the memory storage module includes a storage management submodule, which is connected to the large language model and is used to receive the feedback query instructions and the reply information and update them in the real-time interaction data.

[0104] In one embodiment, the analysis management unit also includes an output parsing module, which receives the execution instruction and parses the execution instruction to form a control instruction. The control instruction is in a machine language format after formatting and parsing. The photovoltaic power station receives the control instruction and executes it to eliminate abnormalities or self-check and report abnormalities.

[0105] In one implementation, the analysis management unit is configured in the cloud, the APP end, or the photovoltaic power station. In this embodiment, the analysis management unit is preferably set in the cloud, and the analysis management unit can also be configured in the terminal or the photovoltaic power station, which can be adjusted according to the user's usage requirements and the data processing performance of each end. Even the functional modules in the analysis management unit are reasonably split and arranged in one of the APP end, the cloud end, or the photovoltaic power station. In other words, the functional modules of the integrated APP end, the cloud end, or the photovoltaic power station are combined to realize the processing of the analysis management unit, which is also technically feasible.

[0106] The analysis body of the interactive photovoltaic power station automation management system of this embodiment is set on the cloud or APP side, which simplifies the user input on the interactive side (APP side). The user only needs to respond to the inquiry command through voice or text input, which facilitates the information interaction between the user and the analysis and processing unit, reduces the technical threshold requirements for users to answer questions, and enables users to be more proactive in cooperating with exception handling, which not only improves the user experience, but also enables better management of photovoltaic power stations.

[0107] In addition, this application innovatively introduces a large language model (such as the ChatGPT model). The input side of the large language model uses the retrieved text-based exception instructions, and the output side is based on natural language query instructions or execution instructions. From the front-end input to the back-end processing output, it is all based on natural language processing, which is more in line with the needs of traditional users. In the final specific execution, the execution instruction format based on natural language is converted (converted into control instructions) and then used for specific execution, which reduces the difficulty of processing this photovoltaic information, can better meet user needs, improve processing efficiency, and realize the automation of exception processing.

[0108] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application.

[0109] The present application also provides a computing device, referring to Figure 3 The computing device includes a memory 1120, a processor 1110, and a computer program stored in the memory 1120 and executable by the processor 1110. The computer program is stored in a space 1130 for program code in the memory 1120. When the computer program is executed by the processor 1110, it is implemented to execute any one of the method steps 1131 according to the present invention.

[0110] The present application also provides a computer-readable storage medium. Figure 4The computer-readable storage medium comprises a storage unit for program code, the storage unit being provided with a program 1131 ′ for executing the method steps according to the present invention, the program being executed by a processor.

[0111] The embodiment of the present application further provides a computer program product including instructions. When the computer program product is run on a computer, the computer is caused to execute the method steps according to the present invention.

[0112] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer loads and executes the computer program instructions, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0113] The professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0114] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing a processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof.

[0115] In particular, the present invention also provides a computing device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein the processor implements the method as described above when executing the computer program.

[0116] The present invention also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method described above is implemented. The computer-readable storage medium is preferably a non-volatile readable storage medium.

[0117] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with this technology to understand the contents of the present invention and implement them accordingly. They cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. An interactive photovoltaic power station automation management method, characterized in that: The processing steps include: S1: Detect the status of the photovoltaic power station and send the detection information to the cloud; S2: confirming whether there is an abnormality according to the detection information, outputting an abnormal instruction when an abnormality exists, and parsing and processing the abnormal instruction to form an inquiry instruction; S3: Displaying the inquiry instruction to the user and asking the user to give a reply; S4: confirming whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action according to the execution instruction to eliminate the abnormality or self-checks and reports the abnormality.

2. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: The abnormal instruction is parsed and processed, and directional data matching the abnormal instruction is retrieved from a photovoltaic standard database to form an inquiry instruction.

3. The interactive photovoltaic power station automation management method according to claim 2, characterized in that: In the process of retrieving the directional data matching the abnormal instruction from the photovoltaic standard database and forming an inquiry instruction, The retrieved directional data is read and processed through a large language model to form the query instruction.

4. The interactive photovoltaic power station automation management method according to claim 3, characterized in that: In the process of the large language model reading and processing the directional data to form the query instruction, The directional data is sorted to form a plurality of interactive data, and the plurality of interactive data constitute the inquiry instruction. When the inquiry instruction is displayed to the user, the interaction with the user is performed in sequence according to the plurality of interactive data.

5. The interactive photovoltaic power station automation management method according to claim 3, characterized in that: In the process of sequentially interacting with the user according to the plurality of pieces of interaction data, the interaction data is converged according to the response information fed back by the user.

6. The interactive photovoltaic power station automation management method according to claim 3, characterized in that: The large language model is the ChatGPT model.

7. The interactive photovoltaic power station automation management method according to claim 2, characterized in that: The directional data matched with the abnormal instruction is the environmental factor or human factor causing the abnormal instruction to occur.

8. The interactive photovoltaic power station automation management method according to claims 1 to 7, characterized in that: Determine whether there is an abnormality according to the detection information, output an abnormal instruction when an abnormality exists, and in the process of parsing the abnormal instruction to form an inquiry instruction, retrieve environmental factors matching the abnormal instruction from the photovoltaic standard database to form a first inquiry instruction based on natural language; The first inquiry instruction performs information convergence after interacting with the user to reply information, and retrieves a second inquiry instruction matching the data after information convergence from the photovoltaic standard database, and repeats this step until the cause of the abnormality is determined; The execution instruction is formed according to the determined cause of the abnormality, and the photovoltaic power station takes action to eliminate the abnormality or self-checks and reports the abnormality according to the execution instruction.

9. The interactive photovoltaic power station automation management method according to claim 8, characterized in that: The first inquiry instruction performs information convergence after interacting with the user in reply information, and gradually converges from environmental factors to non-environmental factors.

10. The interactive photovoltaic power station automation management method according to claim 8, characterized in that: The basic parameters of the detection model corresponding to the photovoltaic power station are updated according to the user's reply information, and the abnormality of the photovoltaic power station is analyzed according to the updated detection model.

11. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: In step S4, in the process of confirming the sending of the execution instruction to the photovoltaic power station according to the reply information, The abnormal instruction is confirmed through the reply information, and an execution strategy is formed by searching and calling in the photovoltaic standard database and issuing the execution instruction. After receiving the execution instruction, the photovoltaic power station performs corresponding actions to resolve the abnormality.

12. The interactive photovoltaic power station automation management method according to any one of claims 2 to 11, characterized in that: The photovoltaic standard database includes API call documents, product descriptions, and photovoltaic industry technical information.

13. The interactive photovoltaic power station automation management method according to any one of claims 1 to 11, characterized in that: The query instruction is an output instruction based on natural language.

14. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: The abnormal instructions include fault instructions, power abnormal instructions, power consumption abnormal instructions, and BMS alarm instructions.

15. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: Step S2 and step S4 are completed in the cloud, APP or photovoltaic power station.

16. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: Step S3 is completed on the APP side.

17. The interactive photovoltaic power station automation management method according to claim 1, characterized in that: The reply information input by the user through the APP is voice or text.

18. An interactive photovoltaic power station automation management system, characterized in that: include: The equipment monitoring unit detects the status of the photovoltaic power station and records the detection information, and the detection information is sent to the analysis and management unit; An analysis management unit receives the detection information, confirms whether there is an abnormality according to the detection information, outputs an abnormal instruction when an abnormality exists, parses and processes the abnormal instruction to form an inquiry instruction, and sends the inquiry instruction to the APP end; An APP end is provided at the user's place, and is used to receive the inquiry instruction and display it to the user. The user inputs the reply information through the APP end, and the reply information is sent to the analysis management unit; The analysis management unit determines whether to continue to issue an inquiry instruction to the user or to issue an execution instruction to the photovoltaic power station according to the reply information, and the photovoltaic power station takes action to eliminate abnormalities or self-checks and reports abnormalities according to the execution instruction.

19. The interactive photovoltaic power station automation management system according to claim 18, characterized in that: The analysis management unit comprises: An input preprocessing module is used to receive the abnormal instruction and capture the features of the abnormal instruction to form effective feature data; A context comprehensive processing module, connected to the input preprocessing module, receives the valid feature data, and retrieves and calls data matching the valid feature data to form a comprehensive input instruction; The large language model is connected to the input preprocessing module, receives the comprehensive input instruction and processes it to form a query instruction or an execution instruction.

20. The interactive photovoltaic power station automation management system according to claim 19, characterized in that: The analysis management unit also includes a memory storage module, which is used to store photovoltaic standard data and real-time interactive data. The memory storage module is connected to the context comprehensive processing module.

21. The interactive photovoltaic power station automation management system according to claim 20, characterized in that: The memory storage module includes a permanent storage submodule and a flash memory submodule. The permanent storage submodule is used to store photovoltaic standard data, and the flash memory submodule is used to store real-time interactive data.

22. The interactive photovoltaic power station automation management system according to claim 21, characterized in that: The memory storage module includes a storage management submodule, which is connected to the large language model and is used to receive the feedback query instructions and the response information and update them in the real-time interaction data.

23. The interactive photovoltaic power station automation management system according to claim 18, characterized in that: The analysis management unit also includes an output parsing module, which receives the execution instruction and parses the execution instruction to form a control instruction. The control instruction is in a machine language format after formatting and parsing. The photovoltaic power station receives and executes the control instruction to eliminate abnormalities or self-check and report abnormalities.

24. The interactive photovoltaic power station automation management system according to any one of claims 18 to 23, characterized in that: The analysis and management unit is configured in the cloud, APP or photovoltaic power station.

25. A computing device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 17 is implemented.

26. A computer-readable storage medium having a computer program stored therein, wherein the computer program implements the method according to any one of claims 1 to 17 when executed by a processor.

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