Energy science and technology intelligence brain construction method integrating machine intelligence and expert intelligence

Abstract

The invention provides an energy science and technology intelligence brain construction method fusing machine intelligence and expert intelligence, and the method is characterized in that the method comprises the following steps: obtaining energy science and technology data; processing and analyzing the energy science and technology data based on a preset machine intelligence algorithm model to obtain energy science and technology analysis data; acquiring expert analysis information based on the energy science and technology analysis data; constructing an energy science and technology knowledge graph based on the energy science and technology analysis data and the expert analysis information; and obtaining and outputting energy science and technology intelligence information based on the energy science and technology knowledge graph. The corresponding energy science and technology intelligence information is output for the user, so that intelligent analysis related to energy science and technology is carried out, and decision-making related to energy science and technology is assisted.

Description

Technical Field

[0001] This application relates to the technical field of energy science and technology, and particularly to a method for constructing an energy science and technology intelligence brain that integrates machine intelligence and expert wisdom. Background Art

[0002] Currently, new technologies related to energy are emerging continuously. With the support of advanced digital technologies, the technology innovation cycle continues to shorten, the achievement transformation accelerates continuously, the amount of relevant technical information increases rapidly, and it increasingly shows a trend of complexity, variability, and fragmentation.

[0003] With the rise of artificial intelligence large model technology, data analysis, intelligent question answering, and intelligent services in various scenarios based on massive data, large models, and generative models will set off a research and application boom. However, in the field of energy science and technology, there is still a lack of a systematic and intelligent service platform to carry out intelligent analysis and research related to energy science and technology, and output intelligence information to assist decision-making. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related technologies to some extent.

[0005] To this end, the first object of this application is to propose a method for constructing an energy science and technology intelligence brain that integrates machine intelligence and expert wisdom to achieve.

[0006] The second object of this application is to propose an apparatus for constructing an energy science and technology intelligence brain that integrates machine intelligence and expert wisdom.

[0007] The third object of this application is to propose an electronic device.

[0008] The fourth object of this application is to propose a computer-readable storage medium.

[0009] The fifth object of this application is to propose a computer program product.

[0010] To achieve the above object, the first aspect embodiment of this application proposes a method for constructing an energy science and technology intelligence brain that integrates machine intelligence and expert wisdom, including the following steps:

[0011] Obtain energy science and technology data;

[0012] Process and analyze the energy science and technology data based on a preset machine intelligence algorithm model to obtain energy science and technology analysis data;

[0013] Obtain expert analysis information based on the energy science and technology analysis data;

[0014] Construct an energy science and technology knowledge graph based on the energy science and technology analysis data and the expert analysis information;

[0015] Obtain and output energy technology intelligence information based on the energy technology knowledge graph.

[0016] Further, process and analyze the energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data, including:

[0017] Process the energy technology data to obtain model analysis data that is convenient for analysis by the preset machine intelligence algorithm model;

[0018] Input the model analysis data into the machine intelligence algorithm model for analysis to obtain energy technology analysis data, where the energy technology analysis data includes entity data and relationship data.

[0019] Further, construct an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information, including:

[0020] Construct an initial energy technology knowledge graph based on the energy technology analysis data;

[0021] Annotate and organize the initial energy technology knowledge graph based on the expert analysis information to obtain an energy technology knowledge graph.

[0022] Further, the energy technology knowledge graph includes knowledge nodes and intelligence information associated with the knowledge nodes. Obtaining and outputting energy technology intelligence information based on the energy technology knowledge graph includes:

[0023] Respond to the query information and determine the target knowledge node in the energy technology knowledge graph based on the query information;

[0024] Obtain the target intelligence information corresponding to the target knowledge node based on the target knowledge node as the energy technology intelligence information;

[0025] Output the energy technology intelligence information based on a preset output module.

[0026] Further, outputting the energy technology intelligence information based on a preset output module includes:

[0027] Process the energy technology intelligence information based on a language large model to obtain an output briefing, where the information processing includes information expansion, information translation, and information condensation;

[0028] Output the output briefing through a preset output module.

[0029] Further, the output module includes at least any one of a fixed output form, a mobile output form, and a digital human output form.

[0030] To achieve the above object, an embodiment of the second aspect of the present application provides an energy technology intelligence brain construction device that integrates machine intelligence and expert wisdom, including a data acquisition module, a data analysis module, and a data output module:

[0031] The data acquisition module is used to acquire energy technology data;

[0032] The data analysis module is used to process and analyze the energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data, obtain expert analysis information based on the energy technology analysis data, and construct an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information;

[0033] The data output module is used to obtain and output energy technology intelligence information based on the energy technology knowledge graph.

[0034] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0035] The memory stores computer execution instructions;

[0036] The processor executes the computer execution instructions stored in the memory to implement the above method.

[0037] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the above method.

[0038] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above method.

[0039] The energy technology intelligence brain construction method provided by the present application analyzes energy technology data by combining machine intelligence and expert wisdom, provides energy technology intelligence information for users to carry out intelligent analysis related to energy technology, and outputs intelligence information to assist in decision-making related to energy technology, which is conducive to the high-quality innovative development of the energy technology industry.

[0040] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, where:

[0042] Figure 1 It is a schematic flowchart of a method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application;

[0043] Figure 2 It is a schematic flowchart of steps S201 - S202 in a method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application.

[0044] Figure 3 It is a schematic flowchart of steps S301 - S303 in a method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application.

[0045] Figure 4 It is a schematic flowchart of steps S401 - S402 in a method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application.

[0046] Figure 5 It is a schematic diagram of modules of an apparatus for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application.

[0047] Description of reference numerals:

[0048] 1. Data acquisition module; 2. Data analysis module; 3. Data output module. Detailed implementation manners

[0049] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0050] The following describes an apparatus and method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom according to an embodiment of the present application with reference to the accompanying drawings.

[0051] Figure 1 It is a schematic flowchart of a method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom provided by an embodiment of the present application.

[0052] As Figure 1 shown, the method for constructing an energy technology intelligence brain that integrates machine intelligence and expert wisdom includes the following steps:

[0053] S101. Obtain energy technology data.

[0054] In this embodiment, energy technology data can be obtained by relying on the big data platform of the group company and various databases connected to the big data platform, including energy Internet project-related data, patent information database, paper information database, enterprise industrial and commercial information database, listed enterprise research report database and other databases. Energy technology data mainly includes energy patents, papers, projects, achievements, institutions, talents, information, books, reports, etc. For example, various data in the energy industry, such as energy production, consumption, price and other information, as well as various data in the field of science and technology, such as technological innovation trends, policies and regulations, etc.

[0055] Specifically, this embodiment builds a solid foundation based on the use of invisible privacy computing technology to access major and cooperative databases and systems. It connects and accesses major domestic and foreign cooperative databases and systems, realizes the integration of domestic and foreign data, and builds a shared data and information base for the intelligent analysis of the science and technology intelligence brain, such as energy Internet project-related data, patent information database, paper information database, enterprise industrial and commercial information database, listed company research report database, etc. For sensitive data, use invisible data encryption technology to ensure data security.

[0056] S102. Process and analyze energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data.

[0057] In this embodiment, energy technology data is processed and analyzed according to the machine intelligence algorithm model, and can be analyzed through energy industry chain panoramic analysis, energy technology / innovation chain panoramic analysis, energy technology analysis, energy organization analysis, energy talent analysis, energy industry information analysis, intelligent application development / access and other algorithm models.

[0058] Specifically, refer to Figure 2 , based on the preset machine intelligence algorithm model, the energy technology data is processed and analyzed to obtain energy technology analysis data, which may include:

[0059] S201. Process the energy technology data to obtain model analysis data that is convenient for analysis by a preset machine intelligence algorithm model;

[0060] S202, input the model analysis data into the machine intelligence algorithm model for analysis to obtain energy technology analysis data, which includes entity data and relationship data.

[0061] Specifically, this embodiment performs data processing, i.e., preprocessing, such as data cleaning, deduplication, and standardization, on energy technology data to ensure data quality and accuracy, thereby obtaining model analysis data that is convenient for machine intelligence algorithm models to analyze. ​

[0062] Subsequently, the processed model analysis data will be input into the machine intelligence algorithm model to identify patterns and trends in the data, obtaining energy technology analysis data.

[0063] In this embodiment, the energy technology analysis data includes entity data and relationship data. In the preprocessed data, it is necessary to use natural language processing technology to identify entity data, such as energy technologies, equipment, enterprises, people, etc., and extract the relationship data between entities. This process can be achieved through algorithm models such as text mining and natural language processing.

[0064] Furthermore, machine learning algorithms are used to perform knowledge representation learning on the identified entities and relationships, converting the text information into a structured knowledge representation form. This may include word vector representation, graph structure representation of knowledge graphs, etc., for facilitating subsequent knowledge graph construction.

[0065] S103. Obtain expert analysis information based on the energy technology analysis data.

[0066] After obtaining the energy technology analysis data, the energy technology analysis data is interpreted and evaluated through expert wisdom to obtain expert analysis information.

[0067] In this embodiment, the strength of experts and talents is fully mobilized, and a mode of expert construction, crowdsourcing supplementation, and process review is adopted to interpret and evaluate the energy technology analysis data. Among them, the construction of knowledge nodes of the energy technology analysis data is jointly built by an authoritative expert group. The knowledge nodes can be content such as industrial chains, technology chains, and innovation chains, and are confirmed through expert review. For marginal details or knowledge nodes that need to be updated over time, a user crowdsourcing mode is adopted for supplementation. Users can put forward supplementary information, and after process approval, the correct information is officially added to the knowledge nodes, and spiritual or material incentives are given to the personnel who compile the crowdsourcing. At the same time, expert review and correction are carried out on the knowledge nodes automatically constructed by the machine, and the results are iteratively used to correct the models and rules of machine learning.

[0068] S104. Construct an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information.

[0069] After obtaining the energy technology analysis data, an initial energy technology knowledge graph is constructed according to the energy technology analysis data, and the initial energy technology knowledge graph is labeled and sorted through the expert analysis information to obtain the energy technology knowledge graph.

[0070] In this embodiment, an energy technology knowledge graph can be constructed based on the entity data and relationship data identified from energy technology analysis data, as well as the representation forms obtained through knowledge representation learning. The energy technology knowledge graph can be a directed graph or an undirected graph, where nodes represent entities and edges represent the relationships between entities. The constructed knowledge graph is stored in memory or on disk, and a suitable query language or interface is designed to enable users to conveniently query and obtain the required knowledge.

[0071] Meanwhile, according to the expert analysis information, by using the professional knowledge and experience in the field provided by expert wisdom, the output of machine intelligence is interpreted and evaluated, so as to annotate and organize the energy technology knowledge graph to obtain a more perfect and reasonable energy technology knowledge graph.

[0072] This embodiment takes the panoramic view of the energy industry chain and technology chain as the starting point, and extracts and outputs data from a large amount of big data based on the accurate data annotated by experts and machine learning (including large models) at the knowledge nodes such as industrial points and technology points in the constructed panoramic view, and converges to form the scientific and technological information related to this point, that is, intelligence information.

[0073] The intelligence information is multiple intelligence contents for the knowledge node, and mainly includes the following contents:

[0074] Relevant data information of scientific and technological databases such as the main patents, papers, projects, achievements, etc. of this knowledge node;

[0075] The main technical analysis of this knowledge node, such as the bibliometrics + big data analysis results based on patents, papers, reports, etc.;

[0076] The main / leading institutions of this knowledge node and their institutional information portraits; including the main / leading talent experts of this point and their talent information portraits;

[0077] The main or latest industry information of this knowledge node, such as policy information, scientific and technological information, major news event information, market information, etc.

[0078] The energy technology intelligence brain system is organically connected through the knowledge graph, so as to provide relatively comprehensive energy technology intelligence information for users.

[0079] S105. Obtain and output energy technology intelligence information based on the energy technology knowledge graph.

[0080] In this embodiment, through the constructed energy technology knowledge graph, energy technology intelligence information can be automatically generated according to the user's query information.

[0081] Refer to Figure 3 , obtaining and outputting energy technology intelligence information based on the energy technology knowledge graph includes:

[0082] S301. In response to the query information, determine the target knowledge node in the energy science and technology knowledge graph based on the query information;

[0083] S302. Obtain the target intelligence information corresponding to the target knowledge node based on the target knowledge node as the energy science and technology intelligence information;

[0084] S303. Output the energy science and technology intelligence information based on a preset output module.

[0085] Through the query information of the user on the system, link to the energy science and technology knowledge graph provided in this embodiment, search for the intelligence information of relevant knowledge nodes in the knowledge graph according to the query information as the energy science and technology intelligence information, and output the energy science and technology intelligence information through the output model.

[0086] Specifically, referring to Figure 4 , outputting the energy science and technology intelligence information based on a preset output module includes:

[0087] S401. Process the energy science and technology intelligence information based on a language large model to obtain an output briefing. The information processing includes information expansion, information translation, and information condensation;

[0088] S402. Output the output briefing through a preset output module.

[0089] In this embodiment, the output module processes the energy science and technology intelligence information through a language large model to obtain an information briefing convenient for users to view. The language large model is used in the first aspect to expand information correlation points, such as expanding the thesaurus of related keywords, expanding industrial nodes, expanding technical nodes, expanding similar enterprises, expanding talent associated collaborators, expanding information associated with information, etc. In the second aspect, the system translation is optimized through the language large model to achieve a more accurate translation effect. In the third aspect, the information is condensed through the language large model, such as generating an overview of large-scale information (papers, information, reports, technologies, etc.). The information output is optimized through the language large model, and a relatively high-quality output briefing is output based on the information template, simplifying the user's work for further processing by the user.

[0090] Furthermore, input and output buffers and monitors are also set up at the front end and the back end of the above-mentioned large speech model. This is to avoid the uncontrollability of the large language model, improve the quality of input information, and improve the quality of output information. The input buffer at the front end of the large language model consists of artificial experts or machine model systems, with the goal of screening and filtering the input information to improve the targeted input information or the quality of the input information. The filtering mechanism and execution are carried out by artificial experts, or by machine algorithms, or by both. After filtering and screening the input information, the input information is then input into the large language model. At the same time, the detector is responsible for real-time monitoring of the input information situation of the large language model, which is used for subsequent analysis of the impact of the input information on the large model. Similarly, the input buffer and detector are used at the output end of the large language model to improve the output quality of the large language model through filtering and screening, etc. And according to the needs of different users, different input and output buffers can be trained to form personalized and precise input and output information services.

[0091] The output form of the output module can adopt various methods such as fixed output form, mobile output form, digital human, etc. Fixed types include traditional computer PC terminals, smart home terminals such as smart speakers, enterprise display large screens, public place display facilities, etc. Mobile types include mobile phones, smart watches, smart glasses, smart wearables, etc. To increase the vividness and effect of the output, the digital human display method can also be adopted, configuring visualized outputs with different appearances, different expressions, and different voices, and the image output of the digital human can be combined with smart output terminals such as smart speakers and smart watches for output to enhance the effect of information output.

[0092] So far, the construction of the energy science and technology knowledge graph in this embodiment has been completed. Based on this, by combining data acquisition and data output, an energy science and technology intelligence brain can be constructed to meet the relevant needs of users through this platform.

[0093] To implement the above embodiment, the present application also proposes an energy science and technology intelligence brain construction device that integrates machine intelligence and expert wisdom.

[0094] Figure 5 It is a schematic structural diagram of an energy science and technology intelligence brain construction device provided by an embodiment of the present application.

[0095] As Figure 5As shown in the figure, the device includes a data acquisition module 1, a data analysis module 2, and a data output module 3. The data acquisition module 1 is used to acquire energy technology data; the data analysis module 2 is used to process and analyze the energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data, and obtain expert analysis information based on the energy technology analysis data, and construct an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information; the data output module 3 is used to acquire and output energy technology intelligence information based on the energy technology knowledge graph.

[0096] It should be noted that the foregoing explanation of the embodiment of the method for constructing an energy technology intelligence brain integrating machine intelligence and expert wisdom also applies to the device for constructing an energy technology intelligence brain integrating machine intelligence and expert wisdom in this embodiment, and will not be elaborated here.

[0097] To implement the above embodiment, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiment.

[0098] To implement the above embodiment, the present application also proposes a computer-readable storage medium storing computer execution instructions, and the computer execution instructions are used to implement the method provided in the foregoing embodiment when executed by a processor.

[0099] To implement the above embodiment, the present application also proposes a computer program product including a computer program, and the computer program implements the method provided in the foregoing embodiment when executed by a processor.

[0100] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0101] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and signing an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0102] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0103] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0104] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0105] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred implementation of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0106] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then storing it in a computer memory.

[0107] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0108] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0109] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0110] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for constructing an energy science and technology intelligence brain that integrates machine intelligence and expert wisdom, characterized in that: The following steps are involved: Access to energy technology data; Processing and analyzing the energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data; Obtain expert analysis information based on the energy technology analysis data; Constructing an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information; Energy technology intelligence information is acquired and output based on the energy technology knowledge graph.

2. The method according to claim 1, characterized in that The energy technology data is processed and analyzed based on a preset machine intelligence algorithm model to obtain energy technology analysis data, including: Processing the energy technology data to obtain model analysis data that is convenient for analysis by a preset machine intelligence algorithm model; The model analysis data is input into the machine intelligence algorithm model for analysis to obtain energy technology analysis data, which includes entity data and relationship data.

3. The method according to claim 2, characterized in that The constructing of an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information includes: Constructing an initial energy technology knowledge graph based on the energy technology analysis data; Based on the expert analysis information, the initial energy technology knowledge graph is annotated and sorted to obtain an energy technology knowledge graph.

4. The method according to claim 3, characterized in that The energy technology knowledge graph includes knowledge nodes and intelligence information associated with the knowledge nodes. The energy technology intelligence information is acquired and output based on the energy technology knowledge graph, including: In response to the query information, determining a target knowledge node in the energy technology knowledge graph based on the query information; Based on the target knowledge node, target intelligence information corresponding to the target knowledge node is acquired as energy technology intelligence information; The energy technology intelligence information is output based on a preset output module.

5. The method according to claim 4, characterized in that The outputting of the energy science and technology intelligence information based on a preset output module includes: Processing the energy science and technology intelligence information based on the language big model to obtain an output briefing, wherein the information processing includes information expansion, information translation and information condensation; The output brief is output through a preset output module.

6. The method according to any one of claims 4 and 5, characterized in that The output module includes at least one of a fixed output form, a mobile output form and a digital human output form.

7. An energy science and technology intelligence brain construction device that integrates machine intelligence and expert wisdom, characterized in that: Including data acquisition module, data analysis module and data output module: The data acquisition module is used to acquire energy technology data; The data analysis module is used to process and analyze the energy technology data based on a preset machine intelligence algorithm model to obtain energy technology analysis data, obtain expert analysis information based on the energy technology analysis data, and construct an energy technology knowledge graph based on the energy technology analysis data and the expert analysis information; The data output module is used to acquire and output energy technology intelligence information based on the energy technology knowledge graph.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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