Electricity price policy knowledge base construction system and method based on artificial intelligence

Abstract

The invention discloses an electricity price policy knowledge base system based on artificial intelligence, and the system comprises an electricity price information collection and arrangement module which is used for automatically collecting and arranging electricity price and electricity charge policy information from the Internet and forming policy text data; the knowledge base architecture establishing module is used for designing and establishing a graph knowledge base to store the incidence relation between the policy texts; the data processing and analyzing module is used for deeply analyzing the policy text by utilizing a natural language processing technology, extracting key information, converting the key information into structured data and storing the structured data in the graph knowledge base; the user interaction module is used for establishing user interaction data employees and providing a natural language query function; and the policy analysis model module is used for collecting and analyzing historical data and real-time data, training a model capable of identifying and classifying data related to an electricity price and electricity charge policy to obtain a policy change trend, and predicting electricity price change. The invention further discloses a corresponding method. According to the invention, the timeliness and accuracy of the obtained policy information can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity price and electricity charge policy knowledge base, and in particular to an artificial intelligence-based electricity price policy knowledge base construction system and method. Background Art

[0002] With the acceleration of the electricity marketization process, electricity price policies are becoming increasingly diversified. The changes in various electricity prices and electricity fee standards have put forward higher requirements for the operation and management of power companies. Traditional electricity price and electricity fee policy management methods often have problems such as scattered information, low retrieval efficiency, and inaccurate policy interpretation. Therefore, establishing a comprehensive and systematic electricity price and electricity fee policy knowledge base can effectively integrate and store information related to electricity price policies and provide reliable decision-making support for enterprises. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a system and method for constructing an electricity price policy knowledge base based on artificial intelligence, conduct in-depth analysis of electricity price and electricity fee policy texts through natural language processing technology, extract key information, and use artificial intelligence technology to train a model that can identify electricity price and electricity fee policy change patterns, thereby ensuring the timeliness and accuracy of policy information.

[0004] In order to solve the above technical problems, as one aspect of the present invention, an electricity price policy knowledge base system based on artificial intelligence is provided, which includes:

[0005] The electricity price information collection and sorting module is used to automatically collect and sort electricity price and electricity fee policy information from the Internet and form policy text data. The electricity price and electricity fee policy information includes: national-level electricity price policies, local implementation details, industry standards and historical electricity price data;

[0006] A knowledge base architecture building module is used to design and build a graphical knowledge base to store the associations between policy texts;

[0007] A data processing and analysis module, for using natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base;

[0008] The user interaction module is used to establish a user interaction data staff and provide a natural language query function for users to conduct interactive queries;

[0009] The policy analysis model module is used to collect and analyze historical data and real-time data, train a model that can identify and classify data related to electricity price and electricity fee policies, and divide the structured policy text data in the database into panels to obtain policy change trends and predict electricity price changes for query by the user interactive digital employee module.

[0010] Optionally, the electricity price information collection and collation module further includes an OCR technology recognition module for identifying the content of electricity price policy documents.

[0011] Optionally, the data processing and analysis module further includes a structured processing unit for converting the policy text into structured data and storing the structured data in a knowledge base, wherein the structured data includes: the effective date, scope of application and specific terms of the policy.

[0012] Optionally, the user interaction module also provides multiple search methods, such as keyword search, category search, full-text search, etc., to meet the search needs of different users.

[0013] Optionally, the policy analysis model module further includes:

[0014] The sentiment analysis submodule is used to perform sentiment analysis on policy texts and evaluate the impact of policies on public sentiment.

[0015] As another aspect of the present invention, a method for constructing an electricity price and electricity fee policy knowledge base based on artificial intelligence technology is also provided, which comprises the following steps:

[0016] Automatically collect and organize electricity price and electricity fee policy information from the Internet and form policy text data, wherein the electricity price and electricity fee policy information includes: national-level electricity price policy, local implementation details, industry standards and historical electricity price data;

[0017] Design and build a graphical knowledge base and store the relationships between policy texts;

[0018] Using natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base;

[0019] Establish a user interactive digital employee and provide natural language query function for users to conduct interactive queries;

[0020] Collect and analyze historical data and real-time data, and train a model that can identify and classify data related to electricity price and electricity fee policies. This model is used to partition the structured policy text data in the database, obtain policy change trends, and predict electricity price changes for query by the user interactive digital employee module.

[0021] Optionally, the step of automatically collecting and collating electricity price and electricity fee policy information from the Internet and forming policy text data also includes a sub-step of using OCR technology to identify the content of the electricity price policy document.

[0022] Optionally, the step of using natural language processing technology to deeply analyze the policy text also includes a sub-step of converting the policy text into structured data and storing it in a knowledge base. The structured data includes: the policy's effective date, scope of application, and specific terms.

[0023] Optionally, the step of constructing an electricity price and electricity tariff policy analysis model also includes a sub-step of performing sentiment analysis on the policy text to evaluate the impact of the policy on public sentiment.

[0024] Optionally, in the step of establishing a user-interactive digital employee, a variety of search methods are also provided, including keyword search, classification search, and full-text search, to meet the search needs of different users.

[0025] The implementation of the embodiments of the present invention has the following beneficial effects:

[0026] The present invention provides an artificial intelligence-based electricity price policy knowledge base construction system and method, which can quickly obtain the required information through electricity price policy information data integration and data analysis, greatly reducing the time cost of information acquisition. With quantitative data support, enterprises can better evaluate the feasibility and potential impact of various plans when making decisions, thereby improving the accuracy and effectiveness of decision-making. Effectively use artificial intelligence technology to develop an electricity price and electricity fee policy knowledge base system, continuously iterate and update the knowledge base content, and ensure the timeliness and accuracy of policy information. It can achieve more scientific and efficient electricity price management in a dynamically changing environment, thereby optimizing resource allocation and improving economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For a person skilled in the art, other drawings obtained based on these drawings still belong to the scope of the present invention without creative labor.

[0028] Figure 1 A schematic diagram of the structure of an embodiment of an artificial intelligence-based electricity price policy knowledge base construction system provided by the present invention;

[0029] Figure 2 A schematic diagram of the main process of an embodiment of a method for constructing an electricity price policy knowledge base based on artificial intelligence provided by the present invention;

[0030] Figure 3 A more detailed flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0031] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0032] like Figure 1 As shown, an artificial intelligence-based electricity price policy knowledge base system provided by the present invention is shown, which includes:

[0033] The electricity price information collection and sorting module 10 is used to automatically collect and sort electricity price and electricity fee policy information from the Internet and form policy text data. The electricity price and electricity fee policy information includes: national-level electricity price policies, local implementation details, industry standards and historical electricity price data;

[0034] In a specific example, the collection and collation from the Internet may be to automatically obtain electricity price and electricity fee policy information containing corresponding keywords from a specific website using a crawler technology;

[0035] In addition, the electricity price information collection and sorting module also includes an OCR technology recognition module for identifying the content of the electricity price policy document;

[0036] Through the work of this module, we can collect national electricity price policies, local implementation rules, industry standards and historical electricity price data, etc. The collation of historical data ensures the timeliness and continuity of the data, and ensures that the collected data is comprehensive and accurate, so as to facilitate subsequent analysis and prediction.

[0037] The knowledge base architecture establishment module 11 is used to design and establish a graphical knowledge base to store the relationship between policy texts; using this module, according to the characteristics of electricity price and electricity fee policies, it is necessary to take into account the diversity and complexity of policy information, and use a graph database to store the relationship between policies to ensure that various types of data can be flexibly stored and retrieved.

[0038] The data processing and analysis module 12 is used to use natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base;

[0039] In a specific example, the data processing and analysis module 12 also includes a structured processing unit for converting the policy text into structured data and storing it in a knowledge base. The structured data includes: the effective date, scope of application, and specific terms of the policy. This information will be converted into structured data and stored in the knowledge base to provide a basis for subsequent retrieval and analysis.

[0040] The user interaction module 13 is used to establish a user interaction data staff and provide a natural language query function for users to perform interactive queries;

[0041] In a specific example, the user interaction module also provides a variety of search methods, such as keyword search, classification search, full-text search, etc., to meet the search needs of different users. It is understandable that in the embodiment of the present invention, users can enter questions in natural language, allowing users to easily perform query operations. At the same time, an intelligent query algorithm is developed to improve the accuracy and efficiency of retrieval through natural language understanding technology to ensure that users can quickly obtain the required information. At the same time, a user feedback function is provided to continuously optimize and adjust the algorithm of the artificial intelligence tool to improve its accuracy and response speed.

[0042] The policy analysis model module 14 is used to collect and analyze historical data and real-time data, train a model that can identify and classify data related to electricity price and electricity fee policies, and is used to divide the structured policy text data in the database into panels, obtain policy change trends, and predict electricity price changes for query by the user interactive digital employee module.

[0043] In a specific example, the policy analysis model module 14 further includes: a sentiment analysis submodule, which is used to perform sentiment analysis on the policy text and evaluate the impact of the policy on public sentiment.

[0044] It is understandable that the policy analysis model module 14 collects a large amount of historical data and real-time data, trains data that can identify and classify electricity price and electricity fee policies, and establishes a model that identifies policy change patterns. Through the "knowledge base + thinking chain" dual-line parallel technical route to optimize the model, analyze policy change trends, predict electricity price changes, etc., predict the future trend of electricity prices under the influence of specific policies, and help users make more informed economic decisions. Conduct sentiment analysis on policy texts to evaluate the impact of policies on public sentiment and provide valuable reference for policy makers.

[0045] In another example, the electricity price information collection and sorting module 10 further includes:

[0046] Historical electricity price and electricity fee configuration data collection submodule: This submodule is responsible for extracting electricity price and electricity fee configuration data from the historical database, including but not limited to historical changes in electricity prices, historical adjustments to electricity fee calculation methods, etc. These data are crucial for understanding the historical evolution and trend analysis of electricity price policies; it can process and integrate electricity price data from different time points and different regions, and provide comprehensive historical background information for the knowledge base; by analyzing historical data, this submodule supports the study of long-term trends in electricity price policy changes and provides decision support for policymakers and enterprises;

[0047] National electricity price policy document import submodule: used to import and integrate electricity price policy documents issued at the national level, including the full text content, implementation scope, effective time, approval number and other key information of the policy documents; it can automatically identify and classify policy documents to ensure the accuracy and timeliness of policy information, and provide full-text retrieval and key information summary functions of policy documents; this submodule also supports tracking the update and abolition of policy documents to keep the policy information in the knowledge base up to date;

[0048] The submodule for obtaining electricity price information related to market-based transactions is used to collect electricity price information in market-based transactions, such as agent power purchase prices, electricity market transaction data, etc.; it can monitor changes in the electricity market in real time, provide dynamic data on market-based electricity prices, and provide data support for electricity transactions and electricity price forecasts; by analyzing market-based transaction data, this submodule helps to reveal the impact of market supply and demand relationships on electricity prices and provide decision-making basis for market participants.

[0049] It can be understood that in the embodiment of the present invention, historical electricity price and fee configuration data, electricity price and fee types, and industry classifications are collected by connecting the business system; national electricity price policy documents and local electricity price policy documents are imported, including implementation scope, time, approval number and other information; and market-based transaction-related electricity price information is obtained, such as agency electricity purchase prices.

[0050] Through OCR technology, electricity price policy documents are identified and structured, and business system data is organized and graph databases are used. According to the characteristics of electricity price and electricity fee policies, the diversity and complexity of policy information must be taken into account, and graph databases are used to store the relationship between policies to ensure that various types of data can be flexibly stored and retrieved.

[0051] Using natural language processing technology, we can deeply analyze the policy text, extract key information, and convert the policy text into structured data, such as the effective date, scope of application, specific terms, etc. This information will be converted into structured data and stored in the knowledge base to provide a basis for subsequent retrieval and analysis.

[0052] Through artificial intelligence technology, an electricity price and electricity fee policy analysis model is established. By collecting a large amount of historical data and real-time data, data related to electricity price and electricity fee policies are trained to identify and classify them, and a model that identifies policy change patterns is established. Through the "knowledge base + thinking chain" dual-line parallel technical route to optimize the model, analyze policy change trends, predict electricity price changes, etc., predict the future trend of electricity prices under the influence of specific policies, and help users make more informed economic decisions. Conduct sentiment analysis on policy texts to evaluate the impact of policies on public sentiment and provide valuable reference for policymakers.

[0053] Build an electricity price and electricity charge knowledge base model, use knowledge modeling technology (such as ontology modeling) to build a conceptual model of electricity price and electricity charge knowledge, clarify the relationship between different concepts (such as the relationship between electricity price and electricity charge calculation method, the relationship between electricity price policy and electricity billing rules, etc.), and provide multiple retrieval methods, such as keyword retrieval, classification retrieval, full-text retrieval, etc., to meet the retrieval needs of different users.

[0054] Through digital employee technology, friendly user interaction is established. Users can enter questions in natural language, allowing users to easily perform query operations. At the same time, intelligent query algorithms are developed to improve the accuracy and efficiency of retrieval through natural language understanding technology, ensuring that users can quickly obtain the information they need. At the same time, user feedback functions are provided to continuously optimize and adjust the algorithms of artificial intelligence tools to improve their accuracy and response speed.

[0055] like Figure 2 As shown, a main flow diagram of an embodiment of a method for constructing an electricity price and electricity fee policy knowledge base based on artificial intelligence technology provided by the present invention is shown; Figure 3 As shown, in this embodiment, the method includes the following steps:

[0056] Step S10, automatically collecting and collating electricity price and electricity fee policy information from the Internet and forming policy text data, wherein the electricity price and electricity fee policy information includes: national-level electricity price policy, local implementation details, industry standards and historical electricity price data;

[0057] Specifically, this step also includes a sub-step of using OCR technology to identify the content of the electricity price policy document.

[0058] Step S11, designing and establishing a graphical knowledge base, and storing the association relationship between policy texts;

[0059] Step S12, using natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base;

[0060] Specifically, this step also includes a sub-step of converting the policy text into structured data and storing it in a knowledge base. The structured data includes: the effective date, scope of application and specific terms of the policy.

[0061] Step S13, establishing a user interactive digital employee, providing a natural language query function for users to perform interactive queries;

[0062] Specifically, in this step, a variety of search methods are also provided, including keyword search, category search and full-text search, to meet the search needs of different users.

[0063] Step S14, collect and analyze historical data and real-time data, train a model that can identify and classify data related to electricity price and electricity fee policies, and use it to divide the structured policy text data in the database into panels, obtain policy change trends, and predict electricity price changes for users to query through the interactive digital employee module.

[0064] Specifically, in this step, a sub-step of sentiment analysis of the policy text is also included to evaluate the impact of the policy on public sentiment.

[0065] For more details, please refer to and combine the above Figure 1 The description is not repeated here.

[0066] The implementation of the embodiments of the present invention has the following beneficial effects:

[0067] The present invention provides an artificial intelligence-based electricity price policy knowledge base construction system and method, which can quickly obtain the required information through electricity price policy information data integration and data analysis, greatly reducing the time cost of information acquisition. With quantitative data support, enterprises can better evaluate the feasibility and potential impact of various plans when making decisions, thereby improving the accuracy and effectiveness of decision-making. Effectively use artificial intelligence technology to develop an electricity price and electricity fee policy knowledge base system, continuously iterate and update the knowledge base content, and ensure the timeliness and accuracy of policy information. It can achieve more scientific and efficient electricity price management in a dynamically changing environment, thereby optimizing resource allocation and improving economic benefits.

[0068] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0069] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. An artificial intelligence-based electricity price policy knowledge base system, characterized in that: include: The electricity price information collection and sorting module is used to automatically collect and sort electricity price and electricity fee policy information from the Internet and form policy text data. The electricity price and electricity fee policy information includes: national-level electricity price policies, local implementation details, industry standards and historical electricity price data; A knowledge base architecture building module is used to design and build a graphical knowledge base to store the associations between policy texts; A data processing and analysis module, for using natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base; The user interaction module is used to establish a user interaction data staff and provide a natural language query function for users to conduct interactive queries; The policy analysis model module is used to collect and analyze historical data and real-time data, train a model that can identify and classify data related to electricity price and electricity fee policies, and divide the structured policy text data in the database into panels to obtain policy change trends and predict electricity price changes for query by the user interactive digital employee module.

2. The system according to claim 1, characterized in that The electricity price information collection and sorting module also includes an OCR technology recognition module for identifying the content of electricity price policy documents.

3. The system according to claim 2, characterized in that The data processing and analysis module also includes a structured processing unit for converting the policy text into structured data and storing it in a knowledge base. The structured data includes: the effective date, scope of application and specific terms of the policy.

4. The system of claim 3, wherein: The user interaction module also provides a variety of search methods, such as keyword search, category search, full-text search, etc., to meet the search needs of different users.

5. The system according to claim 4, characterized in that The policy analysis model module further includes: The sentiment analysis submodule is used to perform sentiment analysis on policy texts and evaluate the impact of policies on public sentiment.

6. A method for constructing an electricity price and electricity fee policy knowledge base based on artificial intelligence technology, characterized in that: The following steps are involved: Automatically collect and organize electricity price and electricity fee policy information from the Internet and form policy text data, wherein the electricity price and electricity fee policy information includes: national-level electricity price policy, local implementation details, industry standards and historical electricity price data; Design and build a graphical knowledge base and store the relationships between policy texts; Using natural language processing technology to deeply analyze the policy text, extract key information, and convert the policy text into structured data and store it in the graphical knowledge base; Establish a user interactive digital employee and provide natural language query function for users to conduct interactive queries; Collect and analyze historical data and real-time data, and train a model that can identify and classify data related to electricity price and electricity fee policies. This model is used to partition the structured policy text data in the database, obtain policy change trends, and predict electricity price changes for query by the user interactive digital employee module.

7. The method according to claim 6, characterized in that The step of automatically collecting and collating electricity price and electricity fee policy information from the Internet and forming policy text data also includes a sub-step of using OCR technology to identify the content of the electricity price policy document.

8. The method according to claim 7, characterized in that The step of using natural language processing technology to deeply analyze the policy text also includes a sub-step of converting the policy text into structured data and storing it in a knowledge base. The structured data includes: the effective date, scope of application and specific terms of the policy.

9. The method according to claim 8, characterized in that The step of building an electricity price and tariff policy analysis model also includes a sub-step of sentiment analysis of the policy text to evaluate the impact of the policy on public sentiment.

10. The method according to claim 9, characterized in that In the step of establishing a user-interactive digital employee, a variety of search methods are also provided, including keyword search, classification search and full-text search to meet the search needs of different users.

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