AI large model-based energy efficiency analysis and visualization method, system and medium

Abstract

The invention provides an energy efficiency analysis and visualization method and system based on an AI large model and a medium, and the method comprises the steps: obtaining user information, logging in a human-computer interaction interface based on the user information, and verifying the user information; analyzing an access permission matched with the user based on the verification result; selecting an input window based on a human-computer interaction interface, and inputting a natural language question; the method comprises the steps of preprocessing a natural language question to obtain a preprocessed natural language question, obtaining a preorder question, synchronously sending the preorder question and the preprocessed natural language question to an AI agent to generate input data, and loading a typical question case and a data view based on the input data; outputting an analysis result based on the AI large model, and performing visual display; aI analysis and visual display are carried out through the large language model, a divergent and personalized data analysis function can be provided for a user, and the labor cost is saved.

Description

Technical Field

[0001] The present application relates to the field of energy efficiency analysis technology, and more specifically, to an energy efficiency analysis and visualization method, system, and medium based on an AI large model. Background Art

[0002] Currently, most energy management systems use a fixed program structure for data analysis and visualization, using pre-made reports and curves for display. Users can only query existing functional modules in a pre-made format, and the output parameters are also fixed. When existing functional templates cannot meet user needs, developers are required to develop solutions based on the increased needs, which results in a long development cycle and high costs. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide an energy efficiency analysis and visualization method, system and medium based on an AI large model. By performing AI analysis and visualization through a large language model, users can be provided with more divergent and personalized data analysis functions, saving labor costs.

[0004] The present application also provides an energy efficiency analysis and visualization method based on an AI large model, including: Obtain user information, log in to the human-computer interaction interface based on the user information, verify the user information, and obtain the verification result; Analyzing the access rights matched by the user based on the verification result, wherein the access rights include the accessible data range and the executed command type; Select an input window based on a human-computer interaction interface, input a question in the form of natural language based on the input window, and generate a natural language question; Preprocess the natural language question to obtain the preprocessed natural language question, obtain the preceding question, and send the preceding question and the preprocessed natural language question to the AI ​​agent simultaneously; The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model; Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

[0005] Optionally, in the energy efficiency analysis and visualization method based on the AI ​​large model described in the embodiment of the present application, obtaining user information, logging into the human-computer interaction interface based on the user information, and verifying the user information to obtain the verification result specifically include: Obtain user information and analyze the username and password entered by the user based on the user information; Log in to the human-computer interaction interface based on the entered username and password, and obtain the login status; Verify user information based on login status; If the login status is successful, the user information verification is passed, and the components and interface distribution information of the human-computer interaction interface are obtained; If the login status is failed, analyze the failure reasons, which may include incorrect username, incorrect password, or network error; Get the verification result based on the login status.

[0006] Optionally, in the energy efficiency analysis and visualization method based on the AI ​​large model described in the embodiment of the present application, the access rights matched by the user are analyzed based on the verification result. The access rights include the accessible data range and the executed command type, specifically including: Analyze the user's functions and work content based on the verification results of the user who successfully logged in; The scope of data obtained and the type of commands executed based on the user's functions and work content; Analyze user access rights based on data scope and the types of commands executed.

[0007] Optionally, in the energy efficiency analysis and visualization method based on the AI ​​large model described in the embodiment of the present application, an input window is selected based on the human-computer interaction interface, and a question is input in the form of natural language based on the input window to generate a natural language question, specifically including: Obtain the human-computer interaction interface and analyze multiple functional areas within the human-computer interaction interface, including the title bar, function button area, input window, and output window; Based on the cursor selection input window, a text box pops up and natural language is entered in the text box; Analyze whether the input natural language meets user needs; If the user's needs are met, a natural language question is generated based on the cursor clicking the OK button; If it does not meet user needs, the natural language will be edited, changed or deleted.

[0008] Optionally, in the energy efficiency analysis and visualization method based on the AI ​​large model described in the embodiment of the present application, the natural language question is preprocessed to obtain the preprocessed natural language question, specifically including: Obtain natural language questions, perform text cleaning on the natural language questions, and remove special characters, punctuation marks, and redundant whitespace characters; Extract the stems of the natural language questions after the text is cleaned and analyze the semantic information; Analyze whether the semantic information is correct; If it is correct, the preprocessed natural language question is obtained, and the natural language question is classified into text according to the voice information to obtain text data of multiple categories; If there is an error, adjust the punctuation or sentence position.

[0009] Optionally, in the energy efficiency analysis and visualization method based on the AI ​​big model described in the embodiment of the present application, the AI ​​agent obtains the preceding question and the preprocessed natural language question, generates input data, loads typical problem use cases and data views based on the input data, and constructs the AI ​​big model, specifically including: Record the original questions raised by the user based on the human-computer interaction interface to obtain the previous questions; Combine the preceding question with the preprocessed natural language question to obtain input data; Collect typical problem use cases based on historical records, user feedback, and industry standard questions, and classify and label them; Build multiple data views based on different user needs and analysis angles; Use input data, typical problem cases, and data views as training data to train the selected model and obtain a large AI model.

[0010] In a second aspect, an embodiment of the present application provides an energy efficiency analysis and visualization system based on an AI large model, the system comprising: a memory and a processor, the memory comprising a program for an energy efficiency analysis and visualization method based on an AI large model, the program for an energy efficiency analysis and visualization method based on an AI large model, when executed by the processor, implementing the following steps: Obtain user information, log in to the human-computer interaction interface based on the user information, verify the user information, and obtain the verification result; Analyzing the access rights matched by the user based on the verification result, wherein the access rights include the accessible data range and the executed command type; Select an input window based on a human-computer interaction interface, input a question in the form of natural language based on the input window, and generate a natural language question; Preprocess the natural language question to obtain the preprocessed natural language question, obtain the preceding question, and send the preceding question and the preprocessed natural language question to the AI ​​agent simultaneously; The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model; Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

[0011] Optionally, in the energy efficiency analysis and visualization system based on the AI ​​large model described in the embodiment of the present application, obtaining user information, logging into the human-computer interaction interface based on the user information, and verifying the user information to obtain the verification result specifically include: Obtain user information and analyze the username and password entered by the user based on the user information; Log in to the human-computer interaction interface based on the entered username and password, and obtain the login status; Verify user information based on login status; If the login status is successful, the user information verification is passed, and the components and interface distribution information of the human-computer interaction interface are obtained; If the login status is failed, analyze the failure reasons, which may include incorrect username, incorrect password, or network error; Get the verification result based on the login status.

[0012] Optionally, in the AI ​​large model-based energy efficiency analysis and visualization system described in the embodiment of the present application, the access rights matched by the user are analyzed based on the verification result. The access rights include the accessible data range and the executed command type, specifically including: Analyze the user's functions and work content based on the verification results of the user who successfully logged in; The scope of data obtained and the type of commands executed based on the user's functions and work content; Analyze user access rights based on data scope and the types of commands executed.

[0013] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which includes an energy efficiency analysis and visualization method program based on an AI big model. When the energy efficiency analysis and visualization method program based on an AI big model is executed by a processor, the steps of the energy efficiency analysis and visualization method based on an AI big model as described in any one of the above items are implemented.

[0014] As can be seen from the above, the embodiment of the present application provides an energy efficiency analysis and visualization method, system and medium based on an AI large model, which obtains user information, logs in to the human-computer interaction interface based on the user information, and verifies the user information to obtain a verification result; analyzes the user's matching access rights based on the verification result, and the access rights include the accessible data range and the type of command to be executed; selects an input window based on the human-computer interaction interface, inputs questions in natural language based on the input window, and generates natural language questions; preprocesses the natural language questions to obtain preprocessed natural language questions, obtains predecessor questions, and sends the predecessor questions and the preprocessed natural language questions to the AI ​​agent synchronously; the AI ​​agent obtains the predecessor questions and the preprocessed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and constructs an AI large model; analyzes the preprocessed natural language questions based on the AI ​​large model, outputs the analysis results, and visualizes the analysis results in a set manner; AI analysis and visualization through a large language model can provide users with more divergent and personalized data analysis functions, saving labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of the energy efficiency analysis and visualization method based on the AI ​​large model provided in an embodiment of the present application; Figure 2 A flowchart of a user information verification method for an energy efficiency analysis and visualization method based on an AI large model provided in an embodiment of the present application; Figure 3 A flowchart of the access rights analysis method for the energy efficiency analysis and visualization method based on the AI ​​large model provided in the embodiment of the present application; Figure 4 A block diagram of the energy efficiency analysis and visualization system based on the AI ​​big model provided in the embodiment of this application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0018] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0019] Please refer to Figure 1 , Figure 1 This is a flowchart of an energy efficiency analysis and visualization method based on an AI large model in some embodiments of the present application. The energy efficiency analysis and visualization method based on the AI ​​large model is used in a terminal device and includes the following steps: S101, obtaining user information, logging into the human-computer interaction interface based on the user information, and verifying the user information to obtain a verification result; S102, analyzing the access rights matched by the user based on the verification result, where the access rights include the accessible data range and the type of command to be executed; S103, selecting an input window based on the human-computer interaction interface, inputting a question in natural language form through the input window, and generating a natural language question; S104, preprocessing the natural language question to obtain a preprocessed natural language question, obtaining a preceding question, and synchronously sending the preceding question and the preprocessed natural language question to the AI ​​agent; S105: The AI ​​agent obtains the preceding questions and preprocessed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model. S106: Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

[0020] It should be noted that the AI ​​agent loads typical problem use cases and data views based on the input content. The system's relational database and business model are loaded in the data view. The AI ​​agent uses a large language model based on the previous input to output SQL statements through pre-made prompt words. The data query module of the AI ​​agent executes the SQL statements output by the large model. The AI ​​agent returns the reasoning process and SQL statement execution results, summary and other introspective outputs. The background processing system formats the results returned by the AI ​​agent and returns them to the human-computer interaction system, and records them in the historical question library of the current session user. The human-computer interaction system displays the formatted data returned by the background processing system in the form of text, tables, curves, etc.

[0021] Furthermore, the AI ​​agent implementation steps are as follows: 1. Task analysis phase: The large model is called with the input parameters of the original input statement, the current conversation history, the data view (data table structure), and the necessary business background knowledge. The large model performs the following processing: If the input statement is a general question, the historical records are analyzed and matched with relevant knowledge base files, combined with the capabilities of the large model itself, to output the answer. If the input statement is not a general question, the system analyzes the historical records and, based on the built-in business information and input statement, splits the database query steps step by step and specifies the relevant database tables. The input statement is then reconstructed and output as task information. 2. Database query statement generation stage: Call the big model and input the task information, data view (data table structure), necessary business background knowledge, and use case library generated in step 1. The big model generates and outputs SQL statements based on the given database table structure and relevant business knowledge, combined with the use case library. 3. Database query stage: The agent connects to the database, executes database query statements to obtain data and outputs it; 4. Visual graphics code generation stage: Call the big model and input the task information generated in step 1 and the data queried in step 3. The big model selects the appropriate visualization type, generates the JS code for the visualization, and outputs it.

[0022] Please refer to Figure 2 , Figure 2 This is a flowchart of a user information verification method for an energy efficiency analysis and visualization method based on an AI large model in some embodiments of the present application. According to an embodiment of the present invention, obtaining user information, logging into a human-computer interaction interface based on the user information, and verifying the user information to obtain a verification result specifically include: S201, obtaining user information, and analyzing the user name and password entered by the user based on the user information; S202, logging into the human-computer interaction interface based on the input user name and password, and obtaining the login status; S203, verifying user information based on login status; S204, if the login status is successful, the user information verification is passed, and the component elements and interface distribution information of the human-computer interaction interface are obtained; S205, if the login status is failed, analyzing the failure reason, which may include incorrect username, incorrect password, or network error; S206: Obtain a verification result based on the login status.

[0023] It should be noted that by analyzing the user's login status to determine whether the username and password are correct, the user information is verified to improve user login security.

[0024] Please refer to Figure 3 , Figure 3 This is a flowchart of an access rights analysis method for an AI-based large-scale model-based energy efficiency analysis and visualization method in some embodiments of the present application. According to an embodiment of the present invention, the user's matching access rights are analyzed based on the verification results. The access rights include the accessible data range and the type of command that can be executed, specifically including: S301, analyzing the user's function and work content based on the verification result for the user who successfully logged in; S302, obtaining data scope and executed command type based on the user's function and work content; S303: Analyze the user's access rights based on the data range and the executed command type.

[0025] It should be noted that the access rights that match the user are obtained by analyzing the user's functions and work content, analyzing the user's historical access data, and analyzing the corresponding access rights based on the historical access data.

[0026] According to an embodiment of the present invention, an input window is selected based on a human-computer interaction interface, and a question is input in a natural language form through the input window to generate a natural language question, specifically including: Obtain the human-computer interaction interface and analyze multiple functional areas within the human-computer interaction interface, including the title bar, function button area, input window, and output window; Based on the cursor selection input window, a text box pops up and natural language is entered in the text box; Analyze whether the input natural language meets user needs; If the user's needs are met, a natural language question is generated based on the cursor clicking the OK button; If it does not meet user needs, the natural language will be edited, changed or deleted.

[0027] It should be noted that on the human-computer interaction interface, users can see a selection of input windows. These input windows may be text boxes specifically designed for entering natural language questions, or they may be input areas corresponding to different functional modules. Users select the window for entering questions by clicking the mouse or other interactive methods (such as touching the screen). Once the user selects an input window, it will receive focus, typically indicated by a blinking cursor within the window, prompting the user to begin entering content.

[0028] According to an embodiment of the present invention, preprocessing a natural language question to obtain a preprocessed natural language question specifically includes: Obtain natural language questions, perform text cleaning on the natural language questions, and remove special characters, punctuation marks, and redundant whitespace characters; Extract the stems of the natural language questions after the text is cleaned and analyze the semantic information; Analyze whether the semantic information is correct; If it is correct, the preprocessed natural language question is obtained, and the natural language question is classified into text according to the voice information to obtain text data of multiple categories; If there is an error, adjust the punctuation or sentence position.

[0029] It should be noted that there may be special characters or continuous whitespace characters (such as spaces, tabs, and newlines) in the text. Replacing special characters with common characters and replacing redundant whitespace characters with single spaces can make the text more standardized and easier to process.

[0030] According to an embodiment of the present invention, an AI agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI big model, specifically including: Record the original questions raised by the user based on the human-computer interaction interface to obtain the previous questions; Combine the preceding question with the preprocessed natural language question to obtain input data; Collect typical problem use cases based on historical records, user feedback, and industry standard questions, and classify and label them; for example, classify the questions by topic, difficulty level, field, etc., and add corresponding labels to each question; Build multiple data views based on different user needs and analysis angles, display the distribution of issues by time dimension, and display the proportion of different types of issues by issue type; Use input data, typical problem cases, and data views as training data to train the selected model and obtain a large AI model.

[0031] It should be noted that the appropriate AI large model architecture should be selected based on the specific task requirements and data characteristics. For example, for natural language processing tasks, models with a Transformer architecture, such as BERT and GPT, can be selected.

[0032] If you are processing structured data, you can choose a deep learning model (such as a multi-layer perceptron, convolutional neural network, etc.) or a traditional machine learning model (such as a decision tree, random forest, etc.).

[0033] During the training process, it is necessary to set appropriate hyperparameters (such as learning rate, batch size, number of training rounds, etc.) and use the validation set to evaluate the performance of the model and perform model tuning.

[0034] Please refer to Figure 4 , Figure 4 This is a block diagram of an energy efficiency analysis and visualization system based on an AI large model in some embodiments of the present application. In a second aspect, embodiments of the present application provide an energy efficiency analysis and visualization system based on an AI large model, the system comprising: a memory and a processor, the memory including a program for an energy efficiency analysis and visualization method based on an AI large model, and when the program for an energy efficiency analysis and visualization method based on an AI large model is executed by the processor, the following steps are implemented: Obtain user information, log in to the human-computer interaction interface based on the user information, verify the user information, and obtain the verification result; Analyze the user's matching access rights based on the verification results. Access rights include the accessible data scope and the type of commands that can be executed. Select an input window based on a human-computer interaction interface, input a question in the form of natural language based on the input window, and generate a natural language question; Preprocess the natural language question to obtain the preprocessed natural language question, obtain the preceding question, and send the preceding question and the preprocessed natural language question to the AI ​​agent simultaneously; The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model; Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

[0035] It should be noted that the AI ​​agent loads typical problem use cases and data views based on the input content. The system's relational database and business model are loaded in the data view. The AI ​​agent uses a large language model based on the previous input to output SQL statements through pre-made prompt words. The data query module of the AI ​​agent executes the SQL statements output by the large model. The AI ​​agent returns the reasoning process and SQL statement execution results, summary and other introspective outputs. The background processing system formats the results returned by the AI ​​agent and returns them to the human-computer interaction system, and records them in the historical question library of the current session user. The human-computer interaction system displays the formatted data returned by the background processing system in the form of text, tables, curves, etc.

[0036] Specifically, the energy efficiency analysis and visualization system includes a background processing system, which implements the following functions: 1. Authority authentication and management: This system is embedded in the original energy management system and adopts the login method and authority management method of the original system. The user enters a username and password, and the backend compares the password in the database with the username and password entered. If they match, the login is successful, otherwise the login fails. The system administrator assigns and revokes access rights to the system according to the account; 2. Historical data: After the user logs in successfully, the system displays a list of all the user's historical sessions; Users can click on a conversation to view the historical message records of the current conversation; Users can delete conversations; 3. Input request and processing: The user needs to establish a session first and then make an input request. The background will establish a new session with the agent and store the session unique identifier in the local database.

[0037] This system implements three typical scenarios: intelligent question-and-answer (Q&A), data reporting, and data visualization. To inform the agent of the scenario type, we concatenate "General Question:" (for intelligent Q&A), "Table Display" (for data reporting), and "Visual Display" (for data visualization) before the user enters the request. Furthermore, user data permission constraint fields and values ​​are added to the end of the question. The backend then sends the preprocessed statement to the agent.

[0038] 4. Output processing and return: After invoking the agent, it returns the call results using SSE (Server Push Events). After receiving the streaming data, the backend stores the relevant data (for example, visualization code is stored in the option field) in the JSON format, based on the different nodes executed by the agent (the returned results carry the node data). After receiving all the returned results, they are first saved to the local database and then returned to the frontend.

[0039] According to an embodiment of the present invention, obtaining user information, logging into a human-computer interaction interface based on the user information, and verifying the user information to obtain a verification result specifically include: Obtain user information and analyze the username and password entered by the user based on the user information; Log in to the human-computer interaction interface based on the entered username and password, and obtain the login status; Verify user information based on login status; If the login status is successful, the user information verification is passed, and the components and interface distribution information of the human-computer interaction interface are obtained; If the login status is failed, analyze the failure reasons, which may include incorrect username, incorrect password, or network error; Get the verification result based on the login status.

[0040] It should be noted that by analyzing the user's login status to determine whether the username and password are correct, the user information is verified to improve user login security.

[0041] According to an embodiment of the present invention, the access rights matched by the user are analyzed based on the verification results. The access rights include the accessible data range and the type of command that can be executed, specifically including: Analyze the user's functions and work content based on the verification results of the user who successfully logged in; The scope of data obtained and the type of commands executed based on the user's functions and work content; Analyze user access rights based on data scope and the types of commands executed.

[0042] It should be noted that the access rights that match the user are obtained by analyzing the user's functions and work content, analyzing the user's historical access data, and analyzing the corresponding access rights based on the historical access data.

[0043] According to an embodiment of the present invention, an input window is selected based on a human-computer interaction interface, and a question is input in a natural language form through the input window to generate a natural language question, specifically including: Obtain the human-computer interaction interface and analyze multiple functional areas within the human-computer interaction interface, including the title bar, function button area, input window, and output window; Based on the cursor selection input window, a text box pops up and natural language is entered in the text box; Analyze whether the input natural language meets user needs; If the user's needs are met, a natural language question is generated based on the cursor clicking the OK button; If it does not meet user needs, the natural language will be edited, changed or deleted.

[0044] It should be noted that on the human-computer interaction interface, users can see a selection of input windows. These input windows may be text boxes specifically designed for entering natural language questions, or they may be input areas corresponding to different functional modules. Users select the window for entering questions by clicking the mouse or other interactive methods (such as touching the screen). Once the user selects an input window, it will receive focus, typically indicated by a blinking cursor within the window, prompting the user to begin entering content.

[0045] According to an embodiment of the present invention, preprocessing a natural language question to obtain a preprocessed natural language question specifically includes: Obtain natural language questions, perform text cleaning on the natural language questions, and remove special characters, punctuation marks, and redundant whitespace characters; Extract the stems of the natural language questions after the text is cleaned and analyze the semantic information; Analyze whether the semantic information is correct; If it is correct, the preprocessed natural language question is obtained, and the natural language question is classified into text according to the voice information to obtain text data of multiple categories; If there is an error, adjust the punctuation or sentence position.

[0046] It should be noted that there may be special characters or continuous whitespace characters (such as spaces, tabs, and newlines) in the text. Replacing special characters with common characters and replacing redundant whitespace characters with single spaces can make the text more standardized and easier to process.

[0047] According to an embodiment of the present invention, an AI agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI big model, specifically including: Record the original questions raised by the user based on the human-computer interaction interface to obtain the previous questions; Combine the preceding question with the preprocessed natural language question to obtain input data; Collect typical problem use cases based on historical records, user feedback, and industry standard questions, and classify and label them; for example, classify the questions by topic, difficulty level, field, etc., and add corresponding labels to each question; Build multiple data views based on different user needs and analysis angles, display the distribution of issues by time dimension, and display the proportion of different types of issues by issue type; Use input data, typical problem cases, and data views as training data to train the selected model and obtain a large AI model.

[0048] It should be noted that the appropriate AI large model architecture should be selected based on the specific task requirements and data characteristics. For example, for natural language processing tasks, models with a Transformer architecture, such as BERT and GPT, can be selected.

[0049] If you are processing structured data, you can choose a deep learning model (such as a multi-layer perceptron, convolutional neural network, etc.) or a traditional machine learning model (such as a decision tree, random forest, etc.).

[0050] During the training process, it is necessary to set appropriate hyperparameters (such as learning rate, batch size, number of training rounds, etc.) and use the validation set to evaluate the performance of the model and perform model tuning.

[0051] The third aspect of the present invention provides a computer-readable storage medium, which includes an energy efficiency analysis and visualization method program based on an AI large model. When the energy efficiency analysis and visualization method program based on an AI large model is executed by a processor, the steps of the energy efficiency analysis and visualization method based on an AI large model as described above are implemented.

[0052] The present invention discloses an energy efficiency analysis and visualization method, system and medium based on an AI large model. The method obtains user information, logs in to a human-computer interaction interface based on the user information, and verifies the user information to obtain a verification result; analyzes the user's matching access rights based on the verification result, and the access rights include the accessible data range and the executed command type; selects an input window based on the human-computer interaction interface, inputs a question in natural language based on the input window, and generates a natural language question; pre-processes the natural language question to obtain a pre-processed natural language question, obtains a preceding question, and synchronously sends the preceding question and the pre-processed natural language question to an AI agent; the AI ​​agent obtains the preceding question and the pre-processed natural language question, generates input data, loads typical problem use cases and data views based on the input data, and constructs an AI large model; analyzes the pre-processed natural language question based on the AI ​​large model, outputs the analysis results, and visualizes the analysis results in a set manner; performing AI analysis and visualization through a large language model can provide users with more divergent and personalized data analysis functions, saving labor costs.

[0053] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0054] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0055] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0056] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0057] Alternatively, if the integrated units described above are implemented as software modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. An energy efficiency analysis and visualization method based on AI large model, characterized by: include: Obtain user information, log in to the human-computer interaction interface based on the user information, verify the user information, and obtain the verification result; Analyzing the access rights matched by the user based on the verification result, wherein the access rights include the accessible data range and the executed command type; Select an input window based on a human-computer interaction interface, input a question in the form of natural language based on the input window, and generate a natural language question; Preprocess the natural language question to obtain the preprocessed natural language question, obtain the preceding question, and send the preceding question and the preprocessed natural language question to the AI ​​agent simultaneously; The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model; Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

2. The energy efficiency analysis and visualization method based on AI large model according to claim 1 is characterized in that: Obtain user information, log in to the human-computer interaction interface based on the user information, and verify the user information to obtain the verification results, including: Obtain user information and analyze the username and password entered by the user based on the user information; Log in to the human-computer interaction interface based on the entered username and password, and obtain the login status; Verify user information based on login status; If the login status is successful, the user information verification is passed, and the components and interface distribution information of the human-computer interaction interface are obtained; If the login status is failed, analyze the failure reasons, which may include incorrect username, incorrect password, or network error; Get the verification result based on the login status.

3. The energy efficiency analysis and visualization method based on AI large model according to claim 2 is characterized in that: Analyze the user's matching access rights based on the verification results. The access rights include the accessible data range and the type of commands that can be executed, specifically: Analyze the user's functions and work content based on the verification results of the user who successfully logged in; The scope of data obtained and the type of commands executed based on the user's functions and work content; Analyze user access rights based on data scope and the types of commands executed.

4. The energy efficiency analysis and visualization method based on AI large model according to claim 3 is characterized in that: Select an input window based on the human-computer interaction interface, input questions in natural language based on the input window, and generate natural language questions, specifically including: Obtain the human-computer interaction interface and analyze multiple functional areas within the human-computer interaction interface, including the title bar, function button area, input window, and output window; Based on the cursor selection input window, a text box pops up and natural language is entered in the text box; Analyze whether the input natural language meets user needs; If the user's needs are met, a natural language question is generated based on the cursor clicking the OK button; If it does not meet user needs, the natural language will be edited, changed or deleted.

5. The energy efficiency analysis and visualization method based on AI large model according to claim 4 is characterized in that: Preprocess the natural language question to obtain the preprocessed natural language question, which specifically includes: Obtain natural language questions, perform text cleaning on the natural language questions, and remove special characters, punctuation marks, and redundant whitespace characters; Extract the stems of the natural language questions after the text is cleaned and analyze the semantic information; Analyze whether the semantic information is correct; If it is correct, the preprocessed natural language question is obtained, and the natural language question is classified into text according to the voice information to obtain text data of multiple categories; If there is an error, adjust the punctuation or sentence position.

6. The energy efficiency analysis and visualization method based on AI large model according to claim 5 is characterized in that: The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI model. Specifically, it includes: Record the original questions raised by the user based on the human-computer interaction interface to obtain the previous questions; Combine the preceding question with the preprocessed natural language question to obtain input data; Collect typical problem use cases based on historical records, user feedback, and industry standard questions, and classify and label them; Build multiple data views based on different user needs and analysis angles; Use input data, typical problem cases, and data views as training data to train the selected model and obtain a large AI model.

7. An energy efficiency analysis and visualization system based on AI large model, characterized by: The system includes: a memory and a processor, wherein the memory includes a program for an energy efficiency analysis and visualization method based on an AI large model, and when the program for an energy efficiency analysis and visualization method based on an AI large model is executed by the processor, the following steps are implemented: Obtain user information, log in to the human-computer interaction interface based on the user information, verify the user information, and obtain the verification result; Analyzing the access rights matched by the user based on the verification result, wherein the access rights include the accessible data range and the executed command type; Select an input window based on a human-computer interaction interface, input a question in the form of natural language based on the input window, and generate a natural language question; Preprocess the natural language question to obtain the preprocessed natural language question, obtain the preceding question, and send the preceding question and the preprocessed natural language question to the AI ​​agent simultaneously; The AI ​​agent obtains pre-order questions and pre-processed natural language questions, generates input data, loads typical problem use cases and data views based on the input data, and builds an AI large model; Analyze the pre-processed natural language questions based on the AI ​​big model, output the analysis results, and visualize the analysis results according to the set method.

8. The energy efficiency analysis and visualization system based on AI big model according to claim 7 is characterized in that: Obtain user information, log in to the human-computer interaction interface based on the user information, and verify the user information to obtain the verification results, including: Obtain user information and analyze the username and password entered by the user based on the user information; Log in to the human-computer interaction interface based on the entered username and password, and obtain the login status; Verify user information based on login status; If the login status is successful, the user information verification is passed, and the components and interface distribution information of the human-computer interaction interface are obtained; If the login status is failed, analyze the failure reasons, which may include incorrect username, incorrect password, or network error; Get the verification result based on the login status.

9. The energy efficiency analysis and visualization system based on AI large model according to claim 8 is characterized in that: Analyze the user's matching access rights based on the verification results. The access rights include the accessible data range and the type of commands that can be executed, specifically: Analyze the user's functions and work content based on the verification results of the user who successfully logged in; The scope of data obtained and the type of commands executed based on the user's functions and work content; Analyze user access rights based on data scope and the types of commands executed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes an energy efficiency analysis and visualization method program based on an AI large model. When the energy efficiency analysis and visualization method program based on an AI large model is executed by a processor, the steps of the energy efficiency analysis and visualization method based on an AI large model as described in any one of claims 1 to 6 are implemented.

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