Power question query system based on multi-wisdom fusion

Abstract

The invention relates to the related field of power systems, in particular to a power question query system based on fusion of multiple wisdom bodies, the system comprises a user interface, a data processing module and a business customization output module, the user interface comprises a data input unit and a data display unit, and the data processing module comprises a data storage unit and a wisdom body module. The service customization output module comprises a customization processing engine and output middleware, the output middleware is further connected with the data processing module, and the problems that in the prior art, a large model is low in query accuracy, insufficient in service correlation and lack of customization output capacity can be solved. The core idea of the system is that a complex electric power query task is effectively disassembled into a series of smaller and easier-to-manage sub-tasks, and each sub-task is independently responsible for a special wisdom body so as to improve the accuracy and efficiency of overall query.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a power data query system based on multi-intelligent body fusion. Background Art

[0002] In the current power system, data query is a crucial link, which involves multiple aspects such as power production, transmission, distribution and consumption. Traditional data query methods mainly rely on manual operation and traditional database query technology. The manual query method requires operators to enter query instructions one by one according to the specific needs of users and retrieve data through the database. This process is not only time-consuming and labor-intensive, but also extremely inefficient when processing a large number of query requests. Operators need to spend a lot of time on data screening, sorting and analysis, which not only increases the workload but also prolongs the query response time.

[0003] In modern power systems, with the development of big data and artificial intelligence technologies, some attempts have begun to use large language models to directly query power data. This method attempts to use large models to directly understand the query text information output by the user, directly generate the query SQL statement, and then use the SQL statement to automatically query the database and return the results directly to the user. However, although large models have shown powerful data processing capabilities in some areas, they still face a series of challenges and problems in the specific application of power data query:

[0004] 1. Weak accuracy: Large models usually require a large amount of data for training to identify and predict patterns. However, in the field of power data query, due to the complexity and diversity of data, large models often find it difficult to achieve ideal accuracy. Especially when faced with specific or non-standardized query requests, the model's prediction and query results may be far from the actual needs.

[0005] 2. Low business relevance: Data query in the power industry often requires an in-depth understanding of business processes and operations. However, existing large models may lack in-depth learning of the specific business logic of the power industry, resulting in low relevance of query results to actual business needs and unable to meet the complex query needs of professional users.

[0006] 3. Inability to customize output: Although large models can handle a wide range of query tasks, they usually provide standardized outputs and lack customization capabilities. In the power industry, users may need to obtain customized data reports or views based on specific business scenarios or analysis purposes, and existing large models are difficult to flexibly adjust to meet such needs. Summary of the invention

[0007] The purpose of the present invention is to provide a highly immersive human-computer interactive virtual driving vehicle testing system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] An electric power data query system based on multi-intelligent body fusion includes a user interface, a data processing module, and a business customization output module. The user interface includes a data input unit and a data display unit. The user inputs the query request to be performed through the data input unit, and the data input by the user is transmitted to the data processing module. The data display unit displays the data.

[0010] The data processing module includes a data storage unit and an intelligent body module. The data storage unit is responsible for storing and managing power data and is a database. In order to facilitate querying the database, SQL statements need to be input, and the SQL statements are generated by the intelligent body module. The intelligent body module mainly includes four intelligent bodies. The data input unit communicates with the first intelligent body. After the user inputs data from the data input unit, the first intelligent body will deeply understand the user's query intention and needs;

[0011] The second intelligent body analyzes the data input by the user from the data input unit, and the second intelligent body uses an advanced intention recognition algorithm to analyze the query intention in the data;

[0012] The third intelligent body extracts key information from the data input by the user from the data input unit, and the key information includes a time range, a specific parameter or a data field;

[0013] The fourth intelligent body will obtain the data query voice generated by the first intelligent body, the query intent analyzed by the second intelligent body, and the key information extracted by the third intelligent body. The fourth intelligent body will generate corresponding SQL query statements based on these data. The fourth intelligent body has the ability to convert natural language queries into database query language.

[0014] The data inputted by the user from the data input unit is converted into an SQL query statement through the above four intelligent bodies, and the SQL query statement is inputted into the data storage unit, and the corresponding SQL query result is generated in the data storage unit;

[0015] The business customized output module includes a customized processing engine and an output middleware. The customized processing engine will obtain the results of the SQL query from the data processing module. The customized processing engine will perform secondary processing on the results according to business needs, and finally generate customized query results. The query results are in a format that is easy to understand and operate, specifically charts, reports, and specific data views. The output middleware obtains the customized query results generated by the customized processing engine and processes the customized query results.

[0016] Preferably, the intelligent body module includes a memory module and an execution module, and all four of the intelligent bodies include a memory module and an execution module. The memory module is responsible for storing memory information of previous queries, and the execution module guides the large model to perform targeted task output through prompt templates based on the memory information and specific requirements of the current task.

[0017] Preferably, the intelligent body module also includes a fine-tuning model library, through which professional knowledge and data of the power industry are introduced, including training the model with industry terminology, concepts and business processes to ensure that the generated query results are highly relevant to the power business.

[0018] Preferably, the output middleware communicates with the data display unit, the output middleware transmits the customized query result to the data display unit, and the data display unit displays the data to show the customized query result to the user.

[0019] Preferably, the SQL query statement generated by the fourth intelligent body is input into the output middleware before being input into the data storage unit, and the output middleware inputs the SQL query statement into the data display unit. When the generated SQL query statement is correct, the user will return the unchanged SQL query statement to the data processing module through the output middleware again without any operation, and input the SQL query statement into the data storage unit;

[0020] When the generated SQL query statement is incorrect, the user modifies it at the data display unit and then returns the modified SQL query statement to the data processing module through the output middleware, and inputs the new SQL query statement into the data storage unit.

[0021] Preferably, when the data processing module needs additional information when processing the data collected by the data input unit, the user inputs the additional information from the data input unit, and the additional information will be directly given to the second intelligent entity. The second intelligent entity will parse the additional information to obtain the query intent and pass the query intent to the third intelligent entity, so as to facilitate the third intelligent entity to extract key information.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The power data query system based on multi-agent fusion of the present invention adopts a task decomposition strategy to subdivide complex query tasks into multiple small tasks, each of which corresponds to a more specific query target and data set. This strategy reduces the complexity of a single task, allowing the model to understand and process each subtask more accurately, thereby improving the accuracy of the query as a whole.

[0024] 2. The power data query system based on multi-intelligence fusion of the present invention introduces the knowledge of industry experts to fine-tune the model so that it can better understand and adapt to the business needs of the power industry. By training the model to identify specific terms, concepts and business processes in the power industry, the present invention can generate query results that are highly relevant to the power business and meet the specific needs of professional users.

[0025] 3. The power data query system based on multi-intelligence fusion of the present invention introduces special processing logic and processes to conduct secondary analysis and adjustment on the initial output results of the large model. In this way, the system can generate customized query results according to the specific business needs of users, such as specific data report formats, visual displays or analysis suggestions, thereby providing more personalized and business-oriented services. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0027] Figure 2 is a system flow chart of the present invention;

[0028] Figure 3 This is a flow chart of the power query task of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be understood that in the embodiments of the present invention, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0031] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0032] See also Figure 1 The present invention provides a technical solution: a power data query system based on multi-intelligent body fusion, including a user interface, a data processing module, and a business customized output module. The user interface includes a data input unit and a data display unit. The user inputs the query request to be performed through the data input unit, and the data input by the user is transmitted to the data processing module. The data display unit displays the data;

[0033] The data processing module includes a data storage unit and an intelligent body module. The data storage unit is responsible for storing and managing power data. It is a database. In order to facilitate the query of the database, SQL statements need to be input. SQL statements are generated by the intelligent body module. The intelligent body module mainly includes four intelligent bodies. The user interface communicates with the first intelligent body. After the user inputs data from the user interface, the first intelligent body will deeply understand the user's query intention and needs. The first intelligent body has advanced natural language understanding capabilities, which enables the first intelligent body to extract key information from the data collected by the data input unit and convert it into data query language;

[0034] The second intelligent body analyzes the data input by the user from the data input unit. The second intelligent body uses advanced intent recognition algorithms to analyze the query intent in the data to ensure an accurate grasp of the deep purpose of the user's query;

[0035] The third intelligent body extracts key information from the data input by the user from the data input unit. The key information includes time range, specific parameters or data fields, which are essential for building accurate queries.

[0036] The fourth intelligent body will obtain the data query voice generated by the first intelligent body, the query intent analyzed by the second intelligent body, and the key information extracted by the third intelligent body. The fourth intelligent body will generate corresponding SQL query statements based on these data. The fourth intelligent body has the ability to convert natural language queries into database query language to ensure the accuracy and execution efficiency of query statements.

[0037] The data inputted by the user from the user interface is converted into SQL query statements through the above four intelligent bodies, and the SQL query statements are inputted into the data storage unit, and the corresponding SQL query results are generated in the data storage unit;

[0038] The business customized output module includes a customized processing engine and output middleware. The customized processing engine will obtain the results of the SQL query from the data processing module. The customized processing engine will perform secondary processing on the results according to business needs and finally generate customized query results. The query results are in a format that is easy to understand and operate, specifically charts, reports, and specific data views. The output middleware obtains the customized query results generated by the customized processing engine and processes the customized query results.

[0039] Furthermore, the intelligent body module includes a memory module and an execution module. All four intelligent bodies include a memory module and an execution module. The memory module is responsible for storing the memory information of previous queries, and the execution module guides the large model to perform targeted task output through prompt templates based on the memory information and the specific requirements of the current task.

[0040] Furthermore, the intelligent body module also includes a fine-tuning model library, which introduces professional knowledge and data from the power industry, including training the model on industry terminology, concepts and business processes to ensure that the generated query results are highly relevant to the power business.

[0041] Furthermore, the output middleware communicates with the data display unit, and the output middleware transmits the customized query result to the data display unit, and the data display unit displays the data and presents the customized query result to the user.

[0042] See also Figure 2 ,Furthermore, before the SQL query statement generated by the fourth intelligent body is input into the output middleware, the output middleware inputs the SQL query statement into the data display unit, and when the generated SQL query statement is correct, the user will return the unchanged SQL query statement to the data processing module through the output middleware again without any operation, and input the SQL query statement into the data storage unit;

[0043] When the generated SQL query statement is incorrect, the user modifies it at the data display unit and then returns the modified SQL query statement to the data processing module through the output middleware, and inputs the new SQL query statement into the data storage unit.

[0044] See also Figure 3Furthermore, when the data processing module needs additional information when processing the data collected by the data input unit, the user inputs the additional information from the data input unit, and the additional information will be directly given to the second intelligent body. The second intelligent body will parse the additional information to obtain the query intent and pass the query intent to the third intelligent body, so that the third intelligent body can extract key information.

[0045] In the embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0046] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0047] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0048] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0049] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0050] It is worth noting that, although the foregoing content has described the spirit and principle of the invention with reference to several specific embodiments, it should be understood that the invention is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined, and such division is only for the convenience of expression. The invention is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the appended claims.

Claims

1. An electric power data query system based on multi-intelligent fusion, including a user interface, a data processing module, and a business customization output module, characterized in that: The user interface includes a data input unit and a data display unit. The user inputs the query request to be performed through the data input unit, and the data input by the user is transmitted to the data processing module. The data display unit displays the data. The data processing module includes a data storage unit and an intelligent body module. The data storage unit is responsible for storing and managing power data and is a database. In order to facilitate querying the database, SQL statements need to be input, and SQL statements are generated by the intelligent body module. The intelligent body module mainly includes four intelligent bodies. The data input unit communicates with the first intelligent body. After the user inputs data from the data input unit, the first intelligent body will deeply understand the user's query intention and needs. The first intelligent body has advanced natural language understanding capabilities, which enables the first intelligent body to extract key information from the data collected by the data input unit and convert it into data query language; The second intelligent body analyzes the data input by the user from the data input unit, and the second intelligent body uses an advanced intention recognition algorithm to analyze the query intention in the data; The third intelligent body extracts key information from the data input by the user from the data input unit, and the key information includes a time range, a specific parameter or a data field; The fourth intelligent body will obtain the data query voice generated by the first intelligent body, the query intent analyzed by the second intelligent body, and the key information extracted by the third intelligent body. The fourth intelligent body will generate corresponding SQL query statements based on these data. The fourth intelligent body has the ability to convert natural language queries into database query language. The data inputted by the user from the user interface is converted into SQL query statements through the above four intelligent bodies, and the SQL query statements are inputted into the data storage unit, and the corresponding SQL query results are generated in the data storage unit; The business customized output module includes a customized processing engine and an output middleware. The customized processing engine will obtain the results of the SQL query from the data processing module. The customized processing engine will perform secondary processing on the results according to business needs, and finally generate customized query results. The query results are in a format that is easy to understand and operate, specifically charts, reports, and specific data views. The output middleware obtains the customized query results generated by the customized processing engine and processes the customized query results.

2. According to claim 1, a power data query system based on multi-intelligent fusion is characterized by: The intelligent agent module includes a memory module and an execution module. All four intelligent agents include a memory module and an execution module. The memory module is responsible for storing the memory information of previous queries, and the execution module guides the large model to perform targeted task output through prompt templates based on the memory information and the specific requirements of the current task.

3. According to claim 1, a power data query system based on multi-intelligent fusion is characterized by: The smart body module also includes a fine-tuning model library, which introduces professional knowledge and data from the power industry, including training the model on industry terms, concepts and business processes to ensure that the generated query results are highly relevant to the power business.

4. The power data query system based on multi-intelligent fusion according to claim 1 is characterized by: The output middleware communicates with the data display unit, and the output middleware transmits the customized query result to the data display unit, and the data display unit displays the data and presents the customized query result to the user.

5. The power data query system based on multi-intelligent fusion according to claim 1 is characterized by: The SQL query statement generated by the fourth intelligent body is input into the output middleware before being input into the data storage unit. The output middleware inputs the SQL query statement into the data display unit. When the generated SQL query statement is correct, the user does not perform any operation and the SQL query statement without modification is returned to the data processing module through the output middleware again, and the SQL query statement is input into the data storage unit. When the generated SQL query statement is incorrect, the user modifies it at the data display unit and then returns the modified SQL query statement to the data processing module through the output middleware, and inputs the new SQL query statement into the data storage unit.

6. The power data query system based on multi-intelligent fusion according to claim 1 is characterized by: When the data processing module processes the data collected by the data input unit and needs additional information, the user inputs the additional information from the data input unit, and the additional information will be directly given to the second intelligent entity. The second intelligent entity will parse the additional information to obtain the query intent and pass the query intent to the third intelligent entity, so that the third intelligent entity can extract key information.

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