A Multi-Source Information Dynamic Fusion Analysis Method Based on Context Protocol

Abstract

This invention discloses a dynamic fusion analysis method for multi-source information based on a context protocol, relating to the fields of artificial intelligence and multi-source information processing. The method includes: creating a preprocessing toolset for multi-source knowledge data and injecting it into a large-scale model analysis framework; the client performing a discovery operation based on the context protocol to obtain the available toolset and constructing a sampling message to send to the large-scale model service; the large-scale model service dynamically selecting tool combinations according to analysis needs and extracting key parameters to return to the client; the client then executing tool calls to obtain knowledge information, and finally, the large-scale model generating analysis results based on this information. This invention, by introducing a context protocol mechanism, achieves dynamic discovery, sampling, and retrieval of multi-source heterogeneous data, solving the problems of fixed processing flows, inability to dynamically access data, and low accuracy of data fusion in existing technologies, significantly improving the flexibility and compatibility of multi-source information analysis.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and multi-source information processing, and specifically to a method for dynamic fusion analysis of multi-source information based on a context protocol. Background Technology

[0002] The statements in this section are provided only as background information in connection with this disclosure and may not constitute prior art.

[0003] In the field of multi-source information processing, multi-source information processing technology based on large language models has become a research hotspot. This type of technology integrates information data from different sources and uses deep learning models for semantic understanding, information extraction, and reasoning, thereby achieving intelligent information processing, analysis, and reasoning. In intelligent question-answering systems, multi-source information fusion and processing schemes based on retrieval enhancement retrieve documents or paragraphs related to the question from multiple knowledge sources, and then combine the retrieved information with large language models to perform semantic understanding and generative answers. However, the above technical solutions have the following two technical drawbacks: 1. Fixed processing flow and lack of dynamic access capability: It relies on a predefined knowledge base structure and a fixed retrieval process, and cannot dynamically access knowledge data according to real-time needs, resulting in insufficient flexibility in information acquisition.

[0004] 2. Limited knowledge fusion capability: Relying on keyword matching or semantic similarity calculation for information fusion makes it difficult to effectively integrate knowledge data from different sources with different structures, resulting in insufficient accuracy and completeness of information fusion. Summary of the Invention

[0005] The purpose of this invention is to address the problems in current multi-source information processing schemes based on large language models, such as fixed processing flow leading to a lack of dynamic access capabilities, and reliance on keyword or semantic matching resulting in limited heterogeneous data fusion capabilities and insufficient accuracy. This invention provides a dynamic fusion analysis method for multi-source information based on a context protocol. This method encapsulates multi-source data into a dynamically invoked toolset based on the context protocol and injects it into a large model analysis framework. Through the interaction between the client and the large model, dynamic discovery, sampling, and invocation of the tools are achieved, thereby solving the technical problems of the inability to dynamically access multi-source knowledge base data and the difficulty in effectively fusing multi-source heterogeneous data.

[0006] The technical solution of the present invention is as follows: A method for dynamic fusion analysis of multi-source information based on context protocols includes: Step S1: By creating a preprocessing tool for multi-source knowledge data, a toolset for acquiring and preprocessing multi-source knowledge data is obtained; Step S2: Inject the created toolset into the large model analysis framework to obtain a large model analysis tool for multi-source knowledge data; Step S3: Based on the input multi-source information analysis requirements, dynamically access the multi-source knowledge data processing tools through the large model analysis tool and obtain knowledge information, and generate analysis results based on the knowledge information and the analysis requirements.

[0007] Further, step S1 specifically includes the following steps: Step S11: Create a tool service based on the context protocol and inject the source data information to which the current tool service belongs; Step S12: For the current knowledge data, create tools for acquiring knowledge data and preprocessing the current knowledge data, and inject the tool service; Step S13: Complete the creation of all processing tools for the current knowledge data and publish the tool services.

[0008] Furthermore, in the step of creating the tool and injecting the tool service, the tool is annotated according to the context protocol. The standard annotation includes: the name of the tool, the parameter name and parameter type required for access, a detailed function description, and a description and type of the return result. In the preprocessing step of the current knowledge data, data processing procedures, including normalization or standardization, are performed according to the characteristics of the current knowledge data.

[0009] Furthermore, step S2 specifically includes the following steps: Step S21: Create a large model analysis framework service based on the RESTful API network protocol, and establish a network connection between the client based on the context protocol and the tool service corresponding to the toolset; Step S22: The client performs a discovery operation based on the context protocol to obtain the available toolset of the tool service.

[0010] Furthermore, in step S3, the process of dynamically accessing multi-source knowledge data processing tools and acquiring knowledge information through the large model analysis tool first includes a sampling step: The client constructs a sampling message structure based on a context protocol and sends a sampling request to the large model analysis framework service. The sampling message structure includes: a large model role message list, a large model name, a large model access address, large model access parameters, and a list of available toolsets. The large model role message list includes assistant role information for instructing large model decomposition tasks and user role information containing the analysis requirements.

[0011] Furthermore, the step of dynamically accessing multi-source knowledge data and acquiring knowledge information through the large model analysis tool, after the sampling step, also includes a tool selection and parameter extraction step: The large model analysis framework service dynamically selects an appropriate tool combination list based on the analysis requirements and available toolset list in the received sampling request, and extracts named entities from the analysis requirements to obtain the request parameters required to access each tool in the tool combination list. The client receives the tool combination list returned by the large model analysis framework service, along with the request parameters.

[0012] Furthermore, after the client receives the tool combination list and the request parameters returned by the large model analysis framework service, the generation of analysis results based on the knowledge information and the analysis requirements specifically includes the following steps: The client executes tool invocation operations sequentially using the request parameters according to the tool combination list to obtain knowledge information from the specified multi-source data; The client constructs a large model analysis message structure based on the acquired knowledge information and the analysis requirements, and sends it to the large model analysis framework service. The large model analysis framework service performs semantic understanding and reasoning based on the knowledge information and analysis requirements in the analysis message structure to generate the analysis results.

[0013] The present invention also proposes an electronic device, comprising: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform the method described above.

[0014] The present invention also proposes a computer-readable storage medium for storing instructions that, when executed, cause the method described above to be implemented.

[0015] The present invention also proposes a computer program product, which implements the above-described method when executed by a processor.

[0016] Compared with existing technologies, the advantages of this invention are: This invention addresses the structural characteristics and interrelationships of multi-source data fusion analysis by introducing a context protocol mechanism, enabling dynamic adaptation of the multi-source information acquisition and processing process. Compared to existing multi-source information fusion schemes that rely on fixed toolsets and processing methods, this invention can dynamically select appropriate toolsets based on user analysis needs, thus acquiring multi-source knowledge data more flexibly. This not only improves the targeting and accuracy of data acquisition but also enhances the system's compatibility and adaptability to different data sources. Furthermore, in the multi-source data processing stage, various preprocessing methods such as data normalization and standardization are introduced, overcoming the limitations of existing technologies that rely solely on keyword matching or semantic similarity retrieval for information fusion. This allows for a more comprehensive mining and extraction of potential information from the data based on the characteristics of the source data, improving the quality of knowledge data, enhancing the depth and breadth of information fusion, and effectively increasing the comprehensiveness and accuracy of data analysis. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is the overall processing flow of the present invention; Figure 2 This is a network architecture diagram of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0020] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0021] Example 1 Please see Figure 1-2 A method for dynamic fusion analysis of multi-source information based on context protocols, comprising: Step S1: By creating a preprocessing tool for multi-source knowledge data, a toolset for acquiring and preprocessing multi-source knowledge data is obtained; Step S2: Inject the created toolset into the large model analysis framework to obtain a large model analysis tool for multi-source knowledge data; Step S3: Based on the input multi-source information analysis requirements, dynamically access the multi-source knowledge data processing tools through the large model analysis tool and obtain knowledge information, and generate analysis results based on the knowledge information and the analysis requirements.

[0022] In this embodiment, specifically, step S1 includes the following steps: Step S11: Create a tool service based on the context protocol and inject the source data information to which the current tool service belongs. It should be noted that when creating the tool, standard comments must be made according to the context protocol. The comments should include: the name of the tool, the names and types of the parameters required for the tool to access, a detailed description of the tool's functions, and a description and type of the tool's return results. When creating the tool, preprocess the acquired source data according to different source data characteristics, including data processing procedures such as normalization and standardization, as well as a detailed description of the source data content. Step S12: For the current knowledge data, create tools for acquiring knowledge data and preprocessing the current knowledge data, and inject the tool service; Step S13: Complete the creation of all processing tools for the current knowledge data, and publish the tool services based on the context protocol.

[0023] In this embodiment, specifically, step S2 includes the following steps: Step S21: Create a large model analysis framework service based on the RESTful API network protocol, and establish a network connection between the client based on the context protocol and the tool service corresponding to the toolset; Step S22: The client performs a discovery operation based on the context protocol to obtain the available toolset of the tool service. It should be noted that the available toolset stores textual content such as the tool's name, access parameters, implemented functions, and return results, which is convenient for large models to understand and select.

[0024] In this embodiment, specifically, in step S3, the process of dynamically accessing multi-source knowledge data processing tools and obtaining knowledge information through the large model analysis tool first includes a sampling step: The client constructs a sampling message structure based on the context protocol and sends a sampling request to the large model analysis framework service; The sampling message structure includes: a large model role message list, a large model name, a large model access address, large model access parameters, and a list of available toolsets; wherein, the large model role message list includes assistant role information for instructing the large model decomposition task and user role information containing the analysis requirements; that is, the content of the assistant role information is the prompt for the large model to understand and decompose the analysis requirements task, and the content of the user role information is the user's analysis requirements.

[0025] In this embodiment, specifically, the step of dynamically accessing multi-source knowledge data and acquiring knowledge information through the large model analysis tool, after the sampling step, further includes a tool selection and parameter extraction step: The large model analysis framework service dynamically selects an appropriate tool combination list based on the analysis requirements and available toolset list in the received sampling request, and extracts named entities from the analysis requirements to obtain the request parameters required to access each tool in the tool combination list. The client receives the tool combination list and the request parameters returned by the large model analysis framework service; that is, based on the tool access requirements, it uses the large model to perform named entity extraction on the user's analysis requirements, and extracts the key parameter content related to tool access in the user's requirements.

[0026] In this embodiment, specifically, after the client receives the tool combination list and the request parameters returned by the large model analysis framework service, the generation of analysis results based on the knowledge information and the analysis requirements specifically includes the following steps: The client executes tool invocation operations sequentially using the request parameters according to the tool combination list to obtain knowledge information from the specified multi-source data; The client constructs a large model analysis message structure based on the acquired knowledge information and the analysis requirements, and sends it to the large model analysis framework service; that is, based on the acquired multi-source data knowledge information, it constructs a large model analysis message structure, including user role information (including user analysis requirements and multi-source knowledge data), large model name, large model access address, and large model access parameters, and sends it to the large model service. The large model analysis framework service performs semantic understanding and reasoning based on the knowledge information and analysis requirements in the analysis message structure to generate the analysis results; that is, the large model performs semantic understanding, knowledge integration, and knowledge reasoning based on the provided user requirements and multi-source knowledge data, and finally generates multi-source knowledge analysis results that meet the user's requirements.

[0027] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the multi-source information dynamic fusion analysis method based on a context protocol provided in the above embodiments of the present invention. In one embodiment, the electronic device can be a server, a terminal device, or other electronic devices. Figure 3 As shown, the electronic device may include: At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 3 The example used is the connection between the processor and memory via a bus. The bus... Figure 3 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 3 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0028] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can perform the multi-source information dynamic fusion analysis method based on a context protocol discussed above. The processor can implement... Figure 3 The functions of each module in the device shown.

[0029] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0030] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0031] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the multi-source information dynamic fusion analysis method based on a context protocol disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0032] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia cards, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), and electrically erasable programmable read-only memory (EPROM). Only memory (EEPROM), magnetic storage, magnetic disks, optical disks, etc. A memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. The memory in embodiments of this invention can also be a circuit or any other device capable of performing storage functions for storing program instructions and / or data.

[0033] By designing and programming the processor, the code corresponding to the multi-source information dynamic fusion analysis method based on the context protocol described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the steps of the method described in the foregoing embodiments during runtime. How to design and program the processor is a technique well known to those skilled in the art, and will not be elaborated here.

[0034] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a multi-source information dynamic fusion analysis method based on a context protocol as described above.

[0035] In some alternative embodiments, the present invention also provides a method for dynamic fusion analysis of multi-source information based on a context protocol, which can also be implemented as a program product including program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in the method for dynamic fusion analysis of multi-source information based on a context protocol according to various exemplary embodiments of the present invention as described above.

[0036] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0037] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs) containing computer-usable program code. The form of a computer program product implemented on ROM, optical memory, etc.

[0038] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0039] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0040] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] In addition, in some embodiments, a computer program product is proposed, which, when executed by a processor, implements the above-described method for dynamic fusion analysis of multi-source information based on a context protocol.

[0044] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0045] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. A method for dynamic fusion analysis of multi-source information based on context protocols, characterized in that, include: Step S1: By creating a preprocessing tool for multi-source knowledge data, a toolset for acquiring and preprocessing multi-source knowledge data is obtained; Step S2: Inject the created toolset into the large model analysis framework to obtain a large model analysis tool for multi-source knowledge data; Step S3: Based on the input multi-source information analysis requirements, dynamically access the multi-source knowledge data processing tools through the large model analysis tool and obtain knowledge information, and generate analysis results based on the knowledge information and the analysis requirements.

2. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 1, characterized in that, Step S1 specifically includes the following steps: Step S11: Create a tool service based on the context protocol and inject the source data information to which the current tool service belongs; Step S12: For the current knowledge data, create tools for acquiring knowledge data and preprocessing the current knowledge data, and inject the tool service; Step S13: Complete the creation of all processing tools for the current knowledge data and publish the tool services.

3. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 2, characterized in that, In the step of creating the tool and injecting the tool service, the tool is marked with standard annotations according to the context protocol. The content of the standard annotations includes: the name of the tool, the name and type of the parameters required for access, a detailed function description, and a description and type of the return result. In the preprocessing step of the current knowledge data, data processing procedures, including normalization or standardization, are performed according to the characteristics of the current knowledge data.

4. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 1, characterized in that, Step S2 specifically includes the following steps: Step S21: Create a large model analysis framework service based on the RESTful API network protocol, and establish a network connection between the client based on the context protocol and the tool service corresponding to the toolset; Step S22: The client performs a discovery operation based on the context protocol to obtain the available toolset of the tool service.

5. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 4, characterized in that, In step S3, the large model analysis tool dynamically accesses the processing tools for multi-source knowledge data and obtains knowledge information, which first includes a sampling step: The client constructs a sampling message structure based on the context protocol and sends a sampling request to the large model analysis framework service; The sampling message structure includes: a large model role message list, a large model name, a large model access address, large model access parameters, and a list of available toolsets; wherein, the large model role message list includes assistant role information for instructing large model decomposition tasks and user role information containing the analysis requirements.

6. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 5, characterized in that, The process of dynamically accessing multi-source knowledge data and acquiring knowledge information through the large model analysis tool, after the sampling step, also includes a tool selection and parameter extraction step: The large model analysis framework service dynamically selects an appropriate tool combination list based on the analysis requirements and available toolset list in the received sampling request, and extracts named entities from the analysis requirements to obtain the request parameters required to access each tool in the tool combination list. The client receives the tool combination list returned by the large model analysis framework service, along with the request parameters.

7. The method for dynamic fusion analysis of multi-source information based on context protocol according to claim 6, characterized in that, After the client receives the tool combination list and the request parameters returned by the large model analysis framework service, the generation of analysis results based on the knowledge information and the analysis requirements specifically includes the following steps: The client executes tool invocation operations sequentially using the request parameters according to the tool combination list to obtain knowledge information from the specified multi-source data; The client constructs a large model analysis message structure based on the acquired knowledge information and the analysis requirements, and sends it to the large model analysis framework service. The large model analysis framework service performs semantic understanding and reasoning based on the knowledge information and analysis requirements in the analysis message structure to generate the analysis results.

8. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which executes the instructions stored in the memory to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1-7 to be implemented.

10. A computer program product, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1-7.

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