Information optimization and analysis method based on multi-agent collaboration

Abstract

The invention provides an information optimization and analysis method based on multi-agent collaboration. The method comprises the following steps: collecting data information; identifying and processing the data information according to a processing prompt word of the processor agent, and outputting a first processing result; identifying and processing the first processing result according to a supervision prompt word of the supervisor agent, and outputting evaluation information and suggestion information; the processor intelligent agent re-identifies and processes the data information according to the processing prompt word, the evaluation information and the suggestion information so as to output a second processing result; the first processing result and the second processing result are identified and processed according to the judge intelligent agent, and an optimization value is output; and judging whether the optimization value exceeds an optimization threshold, if not, defining the second processing result as the first processing result, and operating the supervisor agent, and if yes, outputting the second processing result. The method can efficiently and accurately perform association and convergence analysis on potential important information of the Internet, and intelligently discover and analyze the important information.

Description

Technical Field

[0001] The present invention relates to the field of data recognition technology, and in particular to an information optimization and analysis method based on multi-agent collaboration. Background Art

[0002] There is a huge amount of data on the Internet. The data sources are diverse and the amount of data is huge. There is a lot of specific information in the data. It is not important information in itself, but when combined with other information for association and aggregation analysis, the results may become important information. This type of characteristic information is potentially important information. Due to the massive amount of information on the Internet, manual identification of potentially important information is inefficient. There is no effective way to intelligently and efficiently determine whether different types and massive data contain potentially important information. There is no effective way to intelligently and efficiently associate and aggregate potentially important information, and intelligently discover and analyze important information. Summary of the invention

[0003] In view of this, the problem to be solved by the present invention is to provide an information optimization and analysis method based on multi-agent collaboration, which can efficiently and accurately associate and aggregate important information on the Internet, and intelligently discover and analyze important information.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A multi-agent collaborative data optimization method includes collecting data information;

[0006] Running the processor agent, identifying and processing the data information according to the processing prompt word of the processor agent, and outputting a first processing result;

[0007] Running the supervisor agent, identifying and processing the first processing result according to the supervisory prompt word of the supervisor agent, and outputting evaluation information and suggestion information;

[0008] Optimizing the first processing result, the processor agent re-identifies and processes the data information according to the processing prompt word, the evaluation information and the suggestion information to output a second processing result;

[0009] Running the referee agent, identifying and processing the first processing result and the second processing result according to the referee agent, and outputting an optimized value;

[0010] Determine whether the optimization value exceeds the optimization threshold. If not, define the second processing result as the first processing result and run the supervisor agent. If yes, output the second processing result.

[0011] Furthermore, running the referee agent includes: determining the semantic similarity between the first processing result and the second processing result through a semantic similarity calculation algorithm, and the semantic similarity is positively correlated with the optimization value.

[0012] An information analysis method based on multi-agent collaboration, comprising: collecting Internet data;

[0013] Build a knowledge system. The large language model can identify and call data related to important information in the knowledge base to build a knowledge system.

[0014] Running the discriminator agent, retrieving first knowledge data related to the characteristics of the Internet data in the knowledge base, the discriminator agent calls the knowledge system and the first knowledge data, and then recognizes and processes the Internet data according to the discriminant prompt words to output a discriminant result, and optimizes the discriminant result through a multi-agent collaborative data processing method;

[0015] Run the identifier agent to retrieve the second knowledge data related to the discrimination result characteristics in the knowledge base. The identifier agent calls the knowledge system and the second knowledge data, and then recognizes and processes the discrimination result according to the recognition prompt word to output the recognition result, and optimizes the recognition result through the multi-agent collaborative data processing method.

[0016] Furthermore, the knowledge base includes a graph library, and the construction process thereof includes:

[0017] Run the extractor agent to retrieve the third knowledge data related to the discrimination result characteristics in the knowledge base, the extractor agent calls the knowledge system and the third knowledge data, and then recognizes and processes the discrimination result according to the extraction prompt word to output the extraction result, and optimizes the extraction result through the multi-agent collaborative data processing method;

[0018] Running the parser agent to retrieve the fourth knowledge data related to the extraction result feature in the knowledge base, the parser agent calls the knowledge system and the fourth knowledge data, and then identifies and processes the extraction result according to the parsing prompt word to output the parsing result, and optimizes the parsing result through the multi-agent collaborative data processing method;

[0019] Based on the capabilities of the large language model, the entities and relationships contained in the extraction and parsing results are obtained, the entities and relationships are aligned to generate a graph of important information, and the graphs of several important information are obtained to generate a graph library for intelligent agent calls.

[0020] Furthermore, the knowledge base includes a text library, and the text library includes an analysis report library, and the construction process thereof includes:

[0021] Running the comprehensive analysis agent, retrieving the fifth knowledge data related to the comprehensive analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the analysis result feature in the knowledge base, the comprehensive analysis agent calling the knowledge system and the fifth knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the analysis result according to the comprehensive analysis prompt word to output the comprehensive analysis result, and optimizing the comprehensive analysis result through the multi-agent collaborative data processing method;

[0022] Running the association analysis agent, retrieving the sixth knowledge data related to the association analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the parsing result feature in the knowledge base, the association analysis agent calling the knowledge system and the sixth knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the parsing result according to the association analysis prompt word to output the association analysis result, and optimizing the association analysis result through the multi-agent collaborative data processing method;

[0023] Running the convergent analysis agent, retrieving the seventh knowledge data related to the convergent analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the parsing result feature in the knowledge base, the convergent analysis agent calling the knowledge system and the seventh knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the parsing result according to the convergent analysis prompt word to output the convergent analysis result, and optimizing the convergent analysis result through the multi-agent collaborative data processing method;

[0024] An analysis report library is built based on comprehensive analysis results, correlation analysis results and convergent analysis results for easy calling by intelligent agents.

[0025] Furthermore, the text library includes a knowledge system library, which obtains knowledge systems corresponding to a number of important information to form a knowledge system library for easy invocation by the intelligent agent.

[0026] Furthermore, the discriminator agent, identifier agent, extractor agent and parser agent call a graph library.

[0027] Furthermore, the comprehensive analysis agent, the correlation analysis agent and the convergence analysis agent call the knowledge system library and the analysis report library.

[0028] Furthermore, the knowledge base is retrieved through Graph RAG or RAG for easy calling.

[0029] The advantages and positive effects of the present invention are:

[0030] (1) By making the supervisor agent, referee agent and processor agent work together, the first processing result is optimized multiple times to improve the data processing capability.

[0031] (2) The discriminator, identifier, extractor and parser agents and instruction engineering are constructed through the large language model technology, and the potential graph library is called to intelligently, efficiently and accurately identify the potential important information on the Internet; based on the large language model, the potential important information is identified to construct a knowledge graph and store it in the graph library for easy subsequent identification. The above method can give full play to the semantic understanding ability and knowledge accumulation ability of the large language model, and can accurately discriminate and identify the potential sensitive situations of the data.

[0032] (3) Through the large language model technology, comprehensive analysis, correlation analysis, and convergent analysis agents and instruction projects are constructed, and the analysis report library and knowledge system library are called to realize intelligent, efficient and accurate comprehensive, correlation and convergent analysis of potential sensitive information on the Internet, and intelligent discovery and analysis of important information; based on the large language model, comprehensive, correlation and convergent analysis are identified to construct analysis reports and store them in the analysis report library for easy call during subsequent identification. The above method can give full play to the semantic understanding ability and knowledge accumulation ability of the large language model, and can accurately judge and identify the potential sensitive situations of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0034] Figure 1 It is an overall structural diagram of an information optimization and analysis method based on multi-agent collaboration of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0037] The present invention provides a multi-agent collaborative data processing method, including collecting data information, directly acquiring open source data in the Internet through a data interface, and preliminarily screening the open source data to generate data information.

[0038] The processor agent is run to identify and process data information according to the processing prompt words of the processor agent, and a first processing result is output.

[0039] Based on the data search and processing capabilities of the large speech model, the prompt words are constructed in combination with the processing requirements to generate a processor agent, which recognizes and processes the data information and gives the processing results. An embodiment of the processing requirements is: obtaining important information of a certain scenic spot, which includes the Amway information of a certain scenic spot, the negative review information of a certain scenic spot, the precautions information of a certain scenic spot, and the public security information of a certain scenic spot.

[0040] The process of running the processor agent is as follows: using text to describe the processing requirements to generate processing prompt words, and completing the construction of the processor agent. The large language model recognizes data information based on the processing prompt words and extracts content related to important information of a scenic spot, and then outputs the first processing result in the description format and description method provided by the processing prompt words.

[0041] Run the supervisory agent, identify and process the first processing result according to the supervisory prompt word of the supervisory agent, and output evaluation information and suggestion information.

[0042] The specific process of running the supervisory agent is as follows: generating supervisory prompt words in the form of text description to create the supervisory agent, accessing the first processing result according to the supervisory prompt words, determining the correspondence between the first processing result and the processing prompt words, and providing evaluation information and suggestion information of the first processing result.

[0043] The first processing result is optimized, and the processor agent re-identifies and processes the data information according to the processing prompt words, evaluation information and suggestion information, and outputs the second processing result.

[0044] The process of running the processor agent: the processor agent re-identifies and extracts data information based on the processing prompt words of the first processing result, the evaluation information and suggestion information of the supervisor agent, and re-outputs the second processing result in the description format and description method provided by the processing prompt words.

[0045] The referee agent is run to identify and process the first processing result and the second processing result according to the referee agent, and output an optimized value.

[0046] Since the formats of the first processing result and the second processing result are both set by the processing prompt word, the data structures of the two processing results are the same.

[0047] The process of running the referee intelligent agent: based on the data format of the first processing result or the second processing result, generate referee prompt words in the form of text description to construct the referee intelligent agent, access the text information contained in the first processing result and the second processing result according to the referee prompt words, and use the semantic similarity calculation algorithm to determine the semantic similarity (optimization value) of the two text information. The similarity ranges from 0 to 1. The closer to 1, the more similar the semantics, and the more convergent the optimization result.

[0048] One embodiment of the present application is: setting the optimization threshold to 0.8, determining the semantic similarity between the first processing result and the second processing result through a semantic similarity calculation algorithm, judging whether the optimization value exceeds the optimization threshold based on the similarity, if not, the second processing result is defined as the first processing result, and running the supervisor agent, if yes, outputting the second processing result.

[0049] By having the above-mentioned multiple agents working in collaboration with the processor agent, the first processing result is optimized multiple times until the content before and after optimization is similar, and the optimization is terminated, which can effectively improve the accuracy of the output data of the processor agent.

[0050] An information analysis method based on multi-agent collaboration, such as Figure 1 As shown, it includes collecting Internet data. One embodiment of the present application is: determining an Internet platform that has a relationship with important information, collecting data in the Internet platform and arranging the data to generate Internet data.

[0051] Build a knowledge system, identify and call data related to important information in the knowledge base based on the large language model to build a knowledge system;

[0052] The large language model includes a knowledge base, which includes a graph library that stores data in the form of graphs, a knowledge system library that stores data in the form of data systems, and an analysis report library that stores data in the form of analysis reports.

[0053] Based on the important information, the knowledge base is searched to obtain information related to the important information. All relevant information together forms the knowledge system of the important information.

[0054] Example of knowledge system: Potentially important information related to an object includes the following categories:

[0055] (1)Related to attribute 1.

[0056] (2)Related to attribute 2.

[0057] (3) Relationship 1 is involved.

[0058] (4) Involving relationship 2.

[0059] (5) Situation 1.

[0060] (6) Situation 2.

[0061] Run the discriminator agent to retrieve the first knowledge data related to the characteristics of the Internet data in the knowledge base. The discriminator agent calls the knowledge system and the first knowledge data, and then recognizes and processes the Internet data according to the discriminant prompt words to output the discrimination result, and optimizes the discrimination result through the multi-agent collaborative data processing method.

[0062] The process of running the discriminator agent: (1) Edit the discriminant prompt word template of potential important information and build the discriminator agent; (2) Select the appropriate prompt word template according to the type of Internet data collected; (3) Call the knowledge in the knowledge system as the discrimination rule in the prompt word; (4) Call the Internet data; (5) According to the data characteristics of the Internet data, intelligently retrieve the case data and knowledge related to the data in the knowledge base and call it; (6) Access the discriminator agent, enter the complete prompt word, and return the discrimination result.

[0063] The process of optimizing the discrimination results through the multi-agent collaborative data processing method is as follows: the discrimination result of the potentially important information is taken as the first processing result, the discriminator agent corresponds to the processor agent, and the first processing result is optimized multiple times through mutual assistance between the supervisor agent, the referee agent and the discriminator agent to improve the accuracy of the output result.

[0064] The discriminator agent can obtain valid data related to important information and potential data related to important information from Internet data. The valid data and potential data together constitute the discrimination result.

[0065] Run the recognizer agent to retrieve the second knowledge data related to the discrimination result characteristics in the knowledge base. The recognizer agent calls the knowledge system and the second knowledge data, and then recognizes and processes the discrimination results according to the recognition prompt words to output the recognition results. The recognition results are optimized through the multi-agent collaborative data processing method.

[0066] The process of running the identifier agent: (1) Edit the prompt word template system for potential important information identification and build the identifier agent; (2) Select the corresponding prompt word template based on the data structure of the recognition result; (3) Call the knowledge system in the prompt word as the important information recognition rule; (4) Call the recognition result; (5) According to the data characteristics of the recognition result, intelligently retrieve the case data and knowledge related to the recognition result in the knowledge base and call it; (6) Access the identifier agent, enter the complete prompt word, and return the recognition result.

[0067] The recognition result is then used as the first processing result. The recognizer agent cooperates with the supervisor agent and the referee agent to optimize the recognition result multiple times to improve the accuracy of the output result.

[0068] The discriminator agent can extract a large amount of potential information and effective information related to important information; the identifier agent identifies effective information and potential information and outputs the recognition results of important information. During the operation of the discriminator agent and the identifier agent, combined with the knowledge system related to important information and the graph library in the knowledge base, the effective data and potential data, effective data and effective data, and potential data and potential data can be associated and aggregated, which can extract and analyze data more accurately.

[0069] The knowledge base includes a graph library, and its construction process includes:

[0070] Run the extractor agent to retrieve the third-party knowledge data related to the characteristics of Internet data in the knowledge base. The extractor agent calls the knowledge system and the third-party knowledge data, and then recognizes and processes the Internet according to the extraction prompt words to output the extraction results, and optimizes the extraction results through the multi-agent collaborative data processing method.

[0071] The process of running the extractor agent: (1) Edit the prompt word template system for extracting important information elements and build the element extractor agent; (2) Select the corresponding prompt word template for the Internet data structure; (3) Call the knowledge in the knowledge system as the element extraction rule in the prompt word; (4) Call the judgment result; (5) According to the data characteristics of the judgment result, intelligently retrieve the case data and knowledge related to the data in the knowledge base and call it; (6) Access the element extractor agent, enter the complete prompt word, and return the extraction result.

[0072] The extraction result is then used as the first processing result. The extractor agent collaborates with the supervisor agent and the referee agent to optimize the extraction result multiple times to improve the accuracy of the output result.

[0073] The extractor agent can extract element information related to the important information from the Internet data so as to subsequently generate a map of the important information. One embodiment of the present application is that the extractor agent can extract element information related to the important information from the discrimination result.

[0074] Run the parser agent to retrieve the fourth knowledge data related to the extraction result characteristics in the knowledge base. The parser agent calls the knowledge system and the fourth knowledge data, and then identifies and processes the extraction results according to the parsing prompt words to output the parsing results, and optimizes the parsing results through the multi-agent collaborative data processing method.

[0075] The process of running the parser agent: (1) Edit the prompt word template system for parsing important information elements and build the element parser agent; (2) Select the corresponding prompt word template for the data of the extraction result; (3) Call the knowledge in the knowledge system as the element parsing rule in the prompt word; (4) Call the extraction result; (5) According to the data characteristics of the extraction result, intelligently retrieve the case data and knowledge related to the data in the knowledge base and call it; (6) Access the element parser agent, enter the complete prompt word, and return the parsing result.

[0076] The parsing result is then used as the first processing result. The parser agent collaborates with the supervisor agent and the referee agent to optimize the parsing result multiple times to improve the accuracy of the output result.

[0077] Based on the capabilities of the large language model, the entities and relationships contained in the extraction and parsing results are obtained, and the entities and relationships are aligned to generate a graph of important information. The same method is used to obtain graphs of several important information and store them in the graph library for intelligent agent calls.

[0078] The knowledge base includes a text base, and the text base includes an analysis report base. The construction process includes:

[0079] Run the comprehensive analysis agent to retrieve the fifth knowledge data related to the comprehensive analysis method, discrimination result features, recognition result features, extraction result features and parsing result features in the knowledge base. The comprehensive analysis agent calls the knowledge system and the fifth knowledge data, and then identifies and processes the discrimination results, recognition results, extraction results and parsing results according to the comprehensive analysis prompt words to output the comprehensive analysis results. The comprehensive analysis results are optimized through the multi-agent collaborative data processing method.

[0080] The process of running the comprehensive analysis agent: (1) Construct a prompt word template system for intelligent comprehensive analysis of important information and construct a comprehensive analysis agent; (2) Select the corresponding prompt word template according to the selected categories and analysis methods of the discrimination results, recognition results, extraction results and analysis results; (3) Call the data set containing the discrimination results, recognition results, extraction results and analysis results; (4) According to the comprehensive analysis method and the data characteristics of the data set, intelligently retrieve the case data and knowledge related to the data in the knowledge base and call them; (5) Visit the comprehensive analyst agent, enter the complete prompt word, and return the comprehensive analysis results.

[0081] The comprehensive analysis result is then used as the first processing result. The comprehensive analysis agent cooperates with the supervisor agent and the referee agent to optimize the comprehensive analysis result multiple times to improve the accuracy of the output result.

[0082] Run the association analysis agent to retrieve the sixth knowledge data related to the association analysis method, discrimination result features, recognition result features, extraction result features and parsing result features in the knowledge base. The association analysis agent calls the knowledge system and the sixth knowledge data, and then identifies and processes the discrimination results, recognition results, extraction results and parsing results according to the association analysis prompt words to output the association analysis results, and optimizes the association analysis results through the multi-agent collaborative data processing method.

[0083] The process of running the association analysis agent is as follows: (1) constructing a prompt word template system for intelligent association analysis of important information and constructing an association analyst agent; (2) selecting the corresponding prompt word template for the selected categories and analysis methods of the discrimination results, recognition results, extraction results and parsing results; (3) calling the data set containing the discrimination results, recognition results, extraction results and parsing results; (4) intelligently retrieving case data and knowledge related to the data in the knowledge base and calling them according to the association analysis method and the data characteristics of the data set; (5) retrieving external information related to the element information and key analysis results in the external library and calling them according to the extraction results and parsing results; (6) accessing the association analyst agent, entering the complete prompt word, and returning the association analysis results.

[0084] The association analysis result is then used as the first processing result. The association analysis agent cooperates with the supervisor agent and the referee agent to optimize the association analysis result multiple times to improve the accuracy of the output result.

[0085] Run the convergence analysis agent to retrieve the seventh knowledge data related to the convergence analysis method, discrimination result characteristics, recognition result characteristics, extraction result characteristics and parsing result characteristics in the knowledge base. The convergence analysis agent calls the knowledge system and the seventh knowledge data, and then recognizes and processes the discrimination result, recognition result, extraction result and parsing result according to the convergence analysis prompt word to output the convergence analysis result, and optimizes the convergence analysis result through the multi-agent collaborative data processing method.

[0086] The process of running the convergence analysis agent is as follows: (1) Construct a prompt word template system for intelligent convergence analysis of important information and construct a convergence analysis agent; (2) Select the corresponding prompt word template according to the selected category and analysis method of the discrimination result, recognition result, extraction result or analysis result; (3) Filter the convergence data object according to the convergence conditions; (4) Call the data set consisting of the discrimination result, recognition result, extraction result and analysis result; (5) According to the convergence analysis method and the data characteristics of the data set, intelligently retrieve the case data and knowledge related to the data in the knowledge base and call them; (6) Access the convergence analyst agent, enter the complete prompt word, and return the convergence analysis result.

[0087] The convergent analysis result is then used as the first processing result. The convergent analysis agent cooperates with the supervisor agent and the referee agent to optimize the convergent analysis result multiple times to improve the accuracy of the output result.

[0088] An analysis report library is built based on comprehensive analysis results, correlation analysis results and convergent analysis results for easy calling by intelligent agents.

[0089] The text library also includes a knowledge system library, which obtains the knowledge systems corresponding to several important information to form a knowledge system library for easy calling by the intelligent agent.

[0090] The agent can retrieve the knowledge base through Graph RAG or RAG to obtain data related to important information to build a knowledge system. One embodiment of the present application is that the discriminator agent, the identifier agent, the extractor agent and the parser agent use Graph RAG to retrieve the graph library for invocation. The comprehensive analysis agent, the association analysis agent and the convergence analysis agent use RAG to retrieve the knowledge system library and the analysis report library for invocation.

[0091] Graph example: for an object entity, analyze the attributes of the entity, including attribute 1 and attribute 2; for entity 1 associated with the object entity, analyze the attributes of entity 1, including attribute 1 and attribute 2, and analyze the relationship between entity 1 and the object entity, including relationship 1 and relationship 2; for entity 2 associated with the object entity, analyze the attributes of entity 2, including attribute 1 and attribute 2, and analyze the relationship between entity 2 and the entity object, including relationship 1 and relationship 2.

[0092] Sample analysis report: For a certain object, some Internet data is collected and important information analysis is carried out. The comprehensive analysis results include Question 1 and Question 2; the correlation analysis results include Situation 1 and Situation 2; the convergent analysis results include Situation 1 and Situation 2.

[0093] Parser Agent Task: You are a parser who has certain aspects of cognition and data element parsing for a certain object. Based on the knowledge system and relevant data in the knowledge base, extract the attributes of related entities and the relationships between entities.

[0094] The output of the parser agent: entity 1, parsed attributes, including attribute 1 and attribute 2; entity 2, parsed attributes, including attribute 1 and attribute 2; parsed relationship between entity 1 and entity 2.

[0095] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of this patent.

Claims

1. A multi-agent collaborative data optimization method, characterized in that: include, Collect data information; Running the processor agent, identifying and processing the data information according to the processing prompt word of the processor agent, and outputting a first processing result; Running the supervisor agent, identifying and processing the first processing result according to the supervisory prompt word of the supervisor agent, and outputting evaluation information and suggestion information; Optimizing the first processing result, the processor agent re-identifies and processes the data information according to the processing prompt word, the evaluation information and the suggestion information to output a second processing result; Running the referee agent, identifying and processing the first processing result and the second processing result according to the referee agent, and outputting an optimized value; Determine whether the optimization value exceeds the optimization threshold. If not, the second processing result is defined as the first processing result and the supervisor agent is run. If yes, the second processing result is output.

2. A multi-agent collaborative data optimization method according to claim 1, characterized in that: Running the referee intelligent agent includes: determining the semantic similarity between the first processing result and the second processing result through a semantic similarity calculation algorithm, and the semantic similarity is positively correlated with the optimization value.

3. An information analysis method based on multi-agent collaboration, based on a multi-agent collaborative data optimization method according to any one of claims 1-2, characterized in that: include, Collect Internet data; Build a knowledge system. The large language model can identify and call data related to important information in the knowledge base to build a knowledge system. Running the discriminator agent, retrieving first knowledge data related to the characteristics of the Internet data in the knowledge base, the discriminator agent calls the knowledge system and the first knowledge data, and then recognizes and processes the Internet data according to the discriminant prompt words to output a discriminant result, and optimizes the discriminant result through a multi-agent collaborative data processing method; Run the identifier agent to retrieve the second knowledge data related to the discrimination result characteristics in the knowledge base. The identifier agent calls the knowledge system and the second knowledge data, and then recognizes and processes the discrimination result according to the recognition prompt word to output the recognition result, and optimizes the recognition result through the multi-agent collaborative data processing method.

4. The information analysis method based on multi-agent collaboration according to claim 3 is characterized in that: The knowledge base includes a graph library, and the construction process thereof includes: Run the extractor agent to retrieve the third knowledge data related to the discriminant result features in the knowledge base. The extractor agent calls the knowledge system and the third knowledge data, and then recognizes and processes the Internet data according to the extraction prompt words to output the extraction results, and optimizes the extraction results through the multi-agent collaborative data processing method; Running the parser agent to retrieve the fourth knowledge data related to the extraction result feature in the knowledge base, the parser agent calls the knowledge system and the fourth knowledge data, and then identifies and processes the extraction result according to the parsing prompt word to output the parsing result, and optimizes the parsing result through the multi-agent collaborative data processing method; Based on the capabilities of the large language model, the entities and relationships contained in the extraction and parsing results are obtained, the entities and relationships are aligned to generate a graph of important information, and the graphs of several important information are obtained to generate a graph library for intelligent agent calls.

5. The information analysis method based on multi-agent collaboration according to claim 4 is characterized in that: The knowledge base includes a text base, and the text base includes an analysis report base, and the construction process thereof includes: Running the comprehensive analysis agent, retrieving the fifth knowledge data related to the comprehensive analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the analysis result feature in the knowledge base, the comprehensive analysis agent calling the knowledge system and the fifth knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the analysis result according to the comprehensive analysis prompt word to output the comprehensive analysis result, and optimizing the comprehensive analysis result through the multi-agent collaborative data processing method; Running the association analysis agent, retrieving the sixth knowledge data related to the association analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the parsing result feature in the knowledge base, the association analysis agent calling the knowledge system and the sixth knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the parsing result according to the association analysis prompt word to output the association analysis result, and optimizing the association analysis result through the multi-agent collaborative data processing method; Running the convergent analysis agent, retrieving the seventh knowledge data related to the convergent analysis method, the discrimination result feature, the recognition result feature, the extraction result feature and the parsing result feature in the knowledge base, the convergent analysis agent calling the knowledge system and the seventh knowledge data, and then identifying and processing the discrimination result, the recognition result, the extraction result and the parsing result according to the convergent analysis prompt word to output the convergent analysis result, and optimizing the convergent analysis result through the multi-agent collaborative data processing method; An analysis report library is built based on comprehensive analysis results, correlation analysis results and convergent analysis results for easy calling by intelligent agents.

6. The information analysis method based on multi-agent collaboration according to claim 5 is characterized in that: The text library includes a knowledge system library, which obtains knowledge systems corresponding to several important information to form a knowledge system library for easy calling by the intelligent agent.

7. The information analysis method based on multi-agent collaboration according to claim 5 is characterized in that: The discriminator agent, identifier agent, extractor agent and parser agent call the graph library.

8. The information analysis method based on multi-agent collaboration according to claim 4 is characterized in that: The comprehensive analysis agent, the association analysis agent and the convergence analysis agent call the knowledge system library and the analysis report library.

9. The information analysis method based on multi-agent collaboration according to claim 3 is characterized in that: The knowledge base is retrieved through Graph RAG or RAG for easy calling.

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