Bridge engineering intelligent question and answer method, device and equipment and storage medium

Abstract

The invention discloses a bridge engineering intelligent question answering method, device and equipment and a storage medium, and relates to the technical field of artificial intelligence and natural language process.The bridge engineering intelligent question answering method comprises the steps that a large model conducts natural language processing on an input question, and preprocessing information of the question is obtained, the preprocessing information comprises the bridge engineering field related to the problem; the question is divided into a plurality of sub-tasks according to the preprocessing information, the corresponding sub-tasks are executed through a plurality of agents sharing and transmitting intermediate results so as to complete question answering, and the sub-tasks comprise content retrieval, data structured processing and knowledge fusion and complementation. According to the method, professional and detailed bridge engineering knowledge question-answering services can be provided for users in multiple rounds of interaction, and the intelligent level and response efficiency of a question-answering system in bridge engineering application can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of artificial intelligence and natural language processing, and specifically to an intelligent question-answering method, device, equipment and storage medium for bridge engineering. Background Art

[0002] With the rapid development of artificial intelligence and natural language processing technologies, intelligent question-answering systems have gradually been applied to multiple fields. Especially in highly professional fields such as engineering and medicine, intelligent question-answering systems have become an important tool for the dissemination and application of professional and technical knowledge.

[0003] In the field of bridge engineering, professionals often need to obtain professional knowledge in structural design, construction technology, maintenance and reinforcement, etc. However, bridge engineering knowledge covers a wide range of areas, has complex information, and is highly professional. Existing question-and-answer systems are difficult to efficiently and accurately provide professional answers that meet the technical needs of bridge engineering.

[0004] Traditional intelligent question-answering systems mainly rely on predefined knowledge bases or rule sets, which greatly limit the scope and depth of answers. In addition, they often have difficulty providing effective answers when faced with complex or novel questions. At the same time, although there are question-answering systems based on knowledge graphs or deep learning models, these methods still have shortcomings in terms of professionalism and multi-round interactions. For example, question-answering systems based on knowledge graphs are often limited by the cost of building and the difficulty of maintaining knowledge graphs; and question-answering systems based on a single large model, although they have certain understanding and reasoning capabilities, still find it difficult to provide accurate and comprehensive answers when faced with complex professional fields such as bridge engineering. Summary of the invention

[0005] The present application provides a bridge engineering intelligent question-and-answer method, device, equipment and storage medium, which can provide users with professional and detailed bridge engineering knowledge question-and-answer services in multiple rounds of interaction, and can improve the intelligence level and response efficiency of the question-and-answer system in bridge engineering applications.

[0006] In a first aspect, an embodiment of the present application provides a bridge engineering intelligent question-answering method, the bridge engineering intelligent question-answering method comprising:

[0007] The large model performs natural language processing on the input question to obtain preprocessing information of the question, wherein the preprocessing information includes the bridge engineering field involved in the question;

[0008] The problem is divided into multiple subtasks according to the preprocessing information, and the corresponding subtasks are executed by multiple intelligent agents that share and transmit intermediate results to complete the solution of the problem. The multiple subtasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0009] In conjunction with the first aspect, in one implementation, performing content retrieval by an agent includes:

[0010] Retrieve relevant professional technical information on structural design, construction technology, maintenance and reinforcement from the professional knowledge database of bridges;

[0011] Extracting node and edge relationships from the pre-built structured knowledge network of bridge engineering;

[0012] Obtaining complementary information from unstructured data in the field of bridge engineering.

[0013] In conjunction with the first aspect, in one implementation, performing data structuring processing by an agent includes:

[0014] The retrieved information is cleaned and formatted to remove content not relevant to the field of bridge engineering;

[0015] Convert unstructured data into structured data through natural language processing technology.

[0016] In conjunction with the first aspect, in one implementation, performing knowledge fusion and completion by an agent includes:

[0017] Integrate multi-source information, establish a unified knowledge representation, and remove redundant and conflicting data;

[0018] Use reasoning algorithms to reason and complete incomplete knowledge data to generate complete knowledge links.

[0019] In combination with the first aspect, in one implementation, the method further includes:

[0020] Adjust the task weights of the large model and intelligent agents based on user feedback and optimize the question-answering strategy.

[0021] In combination with the first aspect, in one implementation, the method further includes:

[0022] The cloud records the question and answer data adopted by users and analyzes the question and answer data to strengthen the weight of relevant answers.

[0023] In combination with the first aspect, in one implementation, the method further includes:

[0024] When a user asks a similar or related question recorded in the cloud, the preferred answer adopted in history is directly called, or the recommendation weight of the preferred answer is increased.

[0025] In a second aspect, an embodiment of the present application provides an intelligent question-answering device for bridge engineering, the intelligent question-answering device for bridge engineering comprising:

[0026] A natural language processing module, which is used to perform natural language processing on the input question and obtain pre-processing information of the question, wherein the pre-processing information includes the bridge engineering field involved in the question;

[0027] A multi-agent task processing module divides the problem into multiple sub-tasks according to the pre-processing information, and executes corresponding sub-tasks through multiple agents that share and transmit intermediate results to complete the solution of the problem. The multiple sub-tasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0028] In a third aspect, an embodiment of the present application provides an intelligent question and answer device for bridge engineering, comprising a processor, a memory, and an intelligent question and answer program for bridge engineering stored in the memory and executable by the processor, wherein when the intelligent question and answer program for bridge engineering is executed by the processor, the steps of the above-mentioned intelligent question and answer method for bridge engineering are implemented.

[0029] In a fourth aspect, a computer-readable storage medium stores a bridge engineering intelligent question-answering program, wherein when the bridge engineering intelligent question-answering program is executed by a processor, the steps of the above-mentioned bridge engineering intelligent question-answering method are implemented.

[0030] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0031] The intelligent question-answering method for bridge engineering in the present application performs natural language processing on input questions through a large model to obtain pre-processing information of the question, wherein the pre-processing information includes the field of bridge engineering involved in the question; and divides the question into multiple sub-tasks based on the pre-processing information, and executes corresponding sub-tasks through multiple intelligent agents that share and transmit intermediate results to complete the answer to the question, wherein the multiple sub-tasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0032] Therefore, through the language understanding of the large model and the task allocation of multiple agents, the specialization, response speed and multi-round interaction capabilities of the question-answering system can be effectively improved, providing an intelligent solution for technical exchanges and knowledge dissemination in bridge engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of an embodiment of the intelligent question-answering method for bridge engineering of the present application;

[0034] Figure 2 This is a schematic diagram of multi-agent collaboration in this application;

[0035] Figure 3 This is a structural block diagram of an embodiment of the intelligent question-answering device for bridge engineering of the present application;

[0036] Figure 4 This is a schematic diagram of the hardware structure of the intelligent question-answering device for bridge engineering involved in the embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0038] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0039] In a first aspect, an embodiment of the present application provides an intelligent question-answering method for bridge engineering.

[0040] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the intelligent question-answering method for bridge engineering of this application. Figure 1 As shown in the figure, the intelligent question answering method for bridge engineering includes:

[0041] S1. The large model performs natural language processing on the input question to obtain preprocessing information of the question, wherein the preprocessing information includes the bridge engineering field involved in the question;

[0042] Specifically, the user inputs the question through the interactive interface, and the big model first performs natural language processing on the input question, that is, it performs word segmentation, entity recognition and semantic analysis of the question, determines the bridge engineering field involved in the question (such as structural design, construction technology, inspection and maintenance, etc.), and provides preliminary information analysis for the multi-agent task allocation below according to the type of question.

[0043] The big model question answering engine uses a large-scale pre-trained language model to understand user questions and perform semantic matching and content generation in the knowledge retrieval and reasoning stage. The big model is fine-tuned to adapt to the technical language of bridge engineering to ensure that the answers meet the requirements of professionalism and accuracy.

[0044] S2. Divide the problem into multiple subtasks according to the preprocessing information, and execute corresponding subtasks through multiple intelligent agents that share and transmit intermediate results to complete the solution of the problem. The multiple subtasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0045] In step S2, the problem is divided into several subtasks according to the preprocessing information and assigned to different agents. Each agent is responsible for a specific task submodule: bridge professional knowledge base retrieval, knowledge graph retrieval, external unstructured data retrieval, data structured processing, knowledge fusion and completion. These subtasks are dynamically assigned to professional knowledge base agents, knowledge graph agents, external unstructured data retrieval agents, data structured processing agents, and knowledge fusion and completion agents. This module adjusts the task allocation strategy according to the complexity of the task and the load of the agent to ensure efficient processing. Each agent transmits intermediate results through a shared data interface to ensure the collaborative consistency among multiple agents.

[0046] See also Figure 2 As shown, the above-mentioned intelligent agent is introduced below:

[0047] Professional knowledge base agent: retrieve relevant professional technical information on structural design, construction technology, maintenance and reinforcement from the bridge professional knowledge base.

[0048] Knowledge Graph Agent: Extracts node and edge relationships from the pre-built bridge engineering structured knowledge network to provide concepts, definitions, and relationships related to the problem. The relationship between nodes and edges is a basic concept in graph theory, which is mainly used to describe the elements in the network structure and their connections.

[0049] External unstructured data retrieval agent: obtains supplementary information from papers, technical reports, design specifications, etc. to ensure the comprehensiveness and authority of the system's answers.

[0050] Data structured processing agent: Clean and format the information retrieved from the knowledge base, knowledge graph and external data sources, remove irrelevant content and retain key professional information. Convert unstructured data into structured data through natural language processing techniques (such as entity recognition, relationship extraction and information summarization) to enhance its usability and processing efficiency.

[0051] Knowledge fusion and completion agent: Integrate multi-source information, establish a unified knowledge representation, remove redundant and conflicting data, and ensure the uniqueness and consistency of the answer content. Use reasoning algorithms to reason and complete incomplete knowledge data, generate complete knowledge links, and ensure that the system has logical integrity and coherence in the answering process.

[0052] Preferably, the intelligent question-answering method for bridge engineering in this embodiment also supports multiple rounds of question-answering interactions, and dynamically optimizes the system's reasoning path and answer content after each round of question-answering to ensure that the system's understanding of user needs continues to deepen. In addition, the task weights of the large model and the intelligent agent are adjusted according to user feedback, the question-answering strategy is optimized, and the accuracy of the answer and user satisfaction are improved.

[0053] In addition, some embodiments also use cloud recording to record the question and answer data adopted by users, and analyze these data to strengthen the weight of relevant answers. Each answer adopted by the user will be stored in the cloud memory library for optimizing future questions and answers. The cloud recording method can directly call the preferred answers adopted in the past, or increase the recommendation weight of these answers when the user asks similar or related questions, thereby improving the accuracy and response speed of the questions and answers.

[0054] To summarize, the intelligent question-answering method for bridge engineering in the present application performs natural language processing on the input question through a large model to obtain pre-processing information of the question, wherein the pre-processing information includes the field of bridge engineering involved in the question; and divides the question into multiple sub-tasks according to the pre-processing information, and executes the corresponding sub-tasks through multiple intelligent agents that share and transmit intermediate results to complete the answer to the question, wherein the multiple sub-tasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0055] Therefore, through the language understanding of the large model and the task allocation of multiple agents, the specialization, response speed and multi-round interaction capabilities of the question-answering system can be effectively improved, providing an intelligent solution for technical exchanges and knowledge dissemination in bridge engineering.

[0056] In a second aspect, an embodiment of the present application also provides an intelligent question-and-answer device for bridge engineering.

[0057] In one embodiment, referring to Figure 3 , Figure 3 This is a functional module diagram of an embodiment of the intelligent question-answering device for bridge engineering of this application. Figure 3 As shown, the intelligent question-answering device for bridge engineering includes a natural language processing module and a multi-agent task processing module.

[0058] The natural language processing module is used to perform natural language processing on the input question to obtain pre-processing information of the question, wherein the pre-processing information includes the bridge engineering field involved in the question.

[0059] The multi-agent task processing module divides the problem into multiple sub-tasks according to the pre-processing information, and executes the corresponding sub-tasks through multiple agents that share and transmit intermediate results to complete the solution to the problem. The multiple sub-tasks include content retrieval, data structured processing, and knowledge fusion and completion.

[0060] Furthermore, in one embodiment, the multi-agent task processing module performs content retrieval through agents including:

[0061] Retrieve relevant professional technical information on structural design, construction technology, maintenance and reinforcement from the professional knowledge database of bridges;

[0062] Extracting node and edge relationships from the pre-built bridge engineering structured knowledge network;

[0063] Obtaining complementary information from unstructured data in the field of bridge engineering.

[0064] Furthermore, in one embodiment, the multi-agent task processing module performs data structuring processing through agents, including:

[0065] The retrieved information is cleaned and formatted to remove content not relevant to the field of bridge engineering;

[0066] Convert unstructured data into structured data through natural language processing technology.

[0067] Furthermore, in one embodiment, the multi-agent task processing module performs knowledge fusion and completion through agents, including:

[0068] Integrate multi-source information, establish a unified knowledge representation, and remove redundant and conflicting data;

[0069] Use reasoning algorithms to reason and complete incomplete knowledge data to generate complete knowledge links.

[0070] Furthermore, in one embodiment, a feedback module is also included, and the feedback module is used to:

[0071] Adjust the task weights of the large model and intelligent agents based on user feedback and optimize the question-answering strategy.

[0072] Furthermore, in one embodiment, a cloud memory module is also included, and the cloud memory module is used to:

[0073] The cloud records the question and answer data adopted by users and analyzes the question and answer data to strengthen the weight of relevant answers.

[0074] Furthermore, in one embodiment, the cloud memory module is also used for:

[0075] When a user asks a similar or related question recorded in the cloud, the preferred answer adopted in history is directly called, or the recommendation weight of the preferred answer is increased.

[0076] Among them, the functional implementation of each module in the above-mentioned bridge engineering intelligent question and answer device corresponds to the various steps in the above-mentioned bridge engineering intelligent question and answer method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0077] On the third aspect, an embodiment of the present application provides an intelligent question-answering device for bridge engineering, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0078] Reference Figure 4 , Figure 4 Schematic diagram of the hardware structure of the intelligent question-answering device for bridge engineering involved in the embodiment of the present application. In the embodiment of the present application, the intelligent question-answering device for bridge engineering may include a processor, a memory, a communication interface and a communication bus.

[0079] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0080] The communication interface includes input / output (I / O) interface, physical interface and logical interface, etc., which are used to realize the interconnection of devices inside the bridge engineering intelligent question-answering device, and the interface used to realize the interconnection between the bridge engineering intelligent question-answering device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0081] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0082] The processor may be a general-purpose processor, which may call the intelligent question-answering program for bridge engineering stored in the memory and execute the intelligent question-answering method for bridge engineering provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the intelligent question-answering program for bridge engineering is called may refer to the various embodiments of the intelligent question-answering method for bridge engineering of the present application, which will not be described in detail here.

[0083] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0084] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0085] The readable storage medium of the present application stores a bridge engineering intelligent question and answer program, wherein when the bridge engineering intelligent question and answer program is executed by a processor, the steps of the bridge engineering intelligent question and answer method as described above are implemented.

[0086] Among them, the method implemented when the bridge engineering intelligent question and answer program is executed can refer to the various embodiments of the bridge engineering intelligent question and answer method of this application, and will not be repeated here.

[0087] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0088] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0089] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0090] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0091] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0092] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0093] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

[0094] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A bridge engineering intelligent question-answering method, characterized in that: The bridge engineering intelligent question-answering method comprises: The large model performs natural language processing on the input question to obtain preprocessing information of the question, wherein the preprocessing information includes the bridge engineering field involved in the question; The problem is divided into multiple subtasks according to the preprocessing information, and the corresponding subtasks are executed by multiple intelligent agents that share and transmit intermediate results to complete the solution of the problem. The multiple subtasks include content retrieval, data structured processing, and knowledge fusion and completion.

2. The intelligent question-answering method for bridge engineering according to claim 1, characterized in that: Content retrieval performed by agents includes: Retrieve relevant professional technical information on structural design, construction technology, maintenance and reinforcement from the professional knowledge database of bridges; Extracting node and edge relationships from the pre-built structured knowledge network of bridge engineering; Obtaining complementary information from unstructured data in the field of bridge engineering.

3. The intelligent question-answering method for bridge engineering according to claim 1 or 2, characterized in that: Data structured processing performed by agents includes: The retrieved information is cleaned and formatted to remove content not relevant to the field of bridge engineering; Convert unstructured data into structured data through natural language processing technology.

4. The intelligent question-answering method for bridge engineering as claimed in claim 3, characterized in that: Executing knowledge fusion and completion through intelligent agents includes: Integrate multi-source information, establish a unified knowledge representation, and remove redundant and conflicting data; Use reasoning algorithms to reason and complete incomplete knowledge data to generate complete knowledge links.

5. The intelligent question-answering method for bridge engineering according to claim 1, characterized in that: Also includes: Adjust the task weights of the large model and intelligent agents based on user feedback and optimize the question-answering strategy.

6. The intelligent question-answering method for bridge engineering according to claim 1, characterized in that: Also includes: The cloud records the question and answer data adopted by users and analyzes the question and answer data to strengthen the weight of relevant answers.

7. The intelligent question-answering method for bridge engineering according to claim 6, characterized in that: Also includes: When a user asks a similar or related question recorded in the cloud, the preferred answer adopted in history is directly called, or the recommendation weight of the preferred answer is increased.

8. An intelligent question-answering device for bridge engineering, characterized in that: The intelligent question-answering device for bridge engineering comprises: A natural language processing module, which is used to perform natural language processing on the input question and obtain pre-processing information of the question, wherein the pre-processing information includes the bridge engineering field involved in the question; A multi-agent task processing module divides the problem into multiple sub-tasks according to the pre-processing information, and executes corresponding sub-tasks through multiple agents that share and transmit intermediate results to complete the solution of the problem. The multiple sub-tasks include content retrieval, data structured processing, and knowledge fusion and completion.

9. An intelligent question-answering device for bridge engineering, characterized in that: The bridge engineering intelligent question and answer device includes a processor, a memory, and a bridge engineering intelligent question and answer program stored in the memory and executable by the processor, wherein when the bridge engineering intelligent question and answer program is executed by the processor, the steps of the bridge engineering intelligent question and answer method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a bridge engineering intelligent question-answering program, wherein when the bridge engineering intelligent question-answering program is executed by a processor, the steps of the bridge engineering intelligent question-answering method as described in any one of claims 1 to 7 are implemented.

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