An emergency plan automatic writing system and writing method

Abstract

This application belongs to the field of emergency management and artificial intelligence technology, specifically disclosing an automatic emergency plan writing system and method. The system comprises a six-layer technical collaboration architecture with vertical linkage and collaborative operation from top to bottom: an infrastructure layer, a cloud-native layer, a model layer, an application technology layer, a capability layer, and an application layer. This architecture enables AI-driven intelligent writing of emergency plans throughout the entire process. The infrastructure layer serves as an intelligent computing power base for AI task perception; the cloud-native layer is an elastic service engine driven by emergency scenarios; the model layer is a multimodal collaborative intelligent decision-making hub; the application technology layer is an intelligent technology toolbox and AI capability transformation hub adapted to emergency scenarios; the capability layer is a hub for intelligent service interfaces specific to emergency scenarios; and the application layer is an intelligent application system for the full-scenario implementation of emergency plan writing. This application can effectively improve the writing efficiency, quality, and dynamic adaptability of emergency plans.

Description

Technical Field

[0001] This application belongs to the field of emergency management and artificial intelligence technology, and more specifically, relates to an automatic emergency plan writing system and writing method. Background Technology

[0002] In today's complex and ever-changing social environment, various emergencies occur frequently, including natural disasters, accidents, public health emergencies, and social security incidents. These not only directly threaten the lives and property of the people but also have the potential to severely impact economic development and social order. Emergency management, as a core means of preventing and mitigating major risks and responding promptly to emergencies, can minimize losses and ensure stable social operation by constructing a comprehensive "prevention-preparation-response-recovery" management system. Emergency plan development, as a prerequisite and foundational task in emergency management, is a key support for improving the efficiency and accuracy of emergency response. Emergency plans provide standardized action guidelines for responding to emergencies by pre-analyzing risk scenarios, clarifying responsibilities, standardizing response procedures, and coordinating resource allocation.

[0003] However, current traditional emergency response plan development still suffers from prominent problems such as cumbersome processes, outdated updates, homogenized content, insufficient practicality, and poor dynamic adaptability, making it difficult to adapt to the complexity, suddenness, and multifaceted nature of emergencies in the new era. Traditional plan development relies heavily on the experience of professionals, with a cumbersome and time-consuming process from risk assessment to document writing. Significant differences in experience and knowledge among personnel lead to inconsistent plan quality, making it prone to omissions and inconsistencies. Furthermore, emergencies often change rapidly, and traditional plans struggle to adjust quickly based on real-time information. If the actual situation deviates significantly from the pre-set scenario, the plan's applicability decreases dramatically. In addition, the emergency management field has accumulated a wealth of historical data, including accident cases, risk assessment data, and resource information, but traditional development methods fail to fully mine and utilize this data, resulting in fragmented decision support data and hindering data-driven intelligent decision-making.

[0004] Therefore, improving the efficiency, quality, and dynamic adaptability of emergency response plan development is an urgent problem that needs to be solved. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this application is to provide an automatic emergency plan writing system and method, which can effectively improve the writing efficiency, quality and dynamic adaptability of emergency plans.

[0006] To achieve the above objectives, in the first aspect, this application provides an automatic emergency plan writing system, which includes a six-layer technical collaboration architecture that is vertically linked and collaboratively operated from top to bottom. The layers are, in order, infrastructure layer, cloud-native layer, model layer, application technology layer, capability layer and application layer, which are used to realize the intelligent writing of emergency plans throughout the entire process driven by AI. The infrastructure layer is an intelligent computing power base for AI task perception, used to build a heterogeneous computing power pool for emergency scenarios, and realizes intelligent allocation of hardware resources and elastic computing power supply based on dynamic resource orchestration. The cloud-native layer is an elastic service engine driven by emergency scenarios, used to build microservice clusters based on containerization technology and orchestration frameworks to achieve adaptive emergency business traffic and dynamic scaling. The model layer serves as a multimodal collaborative intelligent decision-making hub, used to aggregate multiple intelligence models and establish a cross-modal collaborative reasoning mechanism. The application technology layer is an intelligent technology toolbox and AI capability transformation hub adapted to emergency scenarios, used to transform the core capabilities of the model layer into tools specifically for emergency scenarios. The capability layer is a central hub for intelligent service interfaces specific to emergency scenarios. It is used to encapsulate the underlying core capabilities in a scenario-based manner and design standardized interfaces, and output callable intelligent service interfaces to the upper layer. The application layer is an intelligent application system for the full-scenario implementation of emergency response preparation. It is used to deeply integrate the capability layer interfaces with actual business operations, and achieve seamless integration between technology and emergency response preparation operations.

[0007] As a further preferred embodiment, the model layer includes a large language model, a visual language model, a speech language model, an OCR model, an intelligent document understanding model, a recall and ranking small model, and a multimodal detection and segmentation model; Among them, the large-scale language model is pre-trained based on a corpus dedicated to the emergency domain and is used for emergency professional text understanding, logical reasoning and content generation. Visual language models are used to construct a bidirectional mapping mechanism between images and text, enabling visual feature parsing and corresponding text association, and completing cross-modal understanding and image generation of the pre-planned text and images. The speech language model is used to establish a real-time speech-to-text conversion channel, supporting voice command interaction and voice broadcast of pre-planned content; The OCR model is trained based on a character library specific to the emergency response field and is used to convert unstructured emergency documents into structured, editable text. The intelligent document understanding model is used to parse the hierarchical structure and semantic logic of emergency documents and automatically extract key information to form knowledge units. The recall and ranking mini-model is used to build an accurate matching intelligent ranking mechanism to provide data filtering for RAG technology; Multimodal detection and segmentation models are used to perform compliance verification, noise filtering, and quality optimization on multimodal input data.

[0008] As a further preferred embodiment, the application technology layer includes an Agent intelligent body, which is an intelligent collaboration hub driven by the emergency process. It is used to decompose the entire process of contingency plan preparation into an ordered chain of sub-tasks based on the emergency task graph, and dynamically schedule RAG knowledge retrieval, fine-tuning model generation, and knowledge graph verification functions to realize the fully automated orchestration of the entire process from demand input to contingency plan output.

[0009] As a further preferred embodiment, the application technology layer includes a RAG retrieval enhancement generation module. The RAG retrieval enhancement generation module adopts an emergency scenario association retrieval mechanism to construct a multimodal knowledge network that includes a legal database, a historical case database, and an equipment ledger database. It also achieves accurate association retrieval based on text similarity and emergency scenario elements, including disaster type, regional characteristics, and enterprise size.

[0010] As a further preferred embodiment, the application technology layer also includes an emergency domain-specific prompt word template library, a dynamic domain adaptation and fine-tuning system, an emergency decision-making thought chain template, an emergency data dynamic perception network, an emergency semantic vector space, a multi-level data cleaning mechanism, and a role-based access control module.

[0011] As a further preferred embodiment, the capability layer includes document generation capability. The document generation capability adopts a template adaptive and content collaboration mechanism, integrates text generation, image generation and formatting functions, automatically matches national standard compliant templates based on user needs, and links with the data cleaning module to ensure the compliance and effectiveness of the content, so as to realize one-click generation and export of complete contingency plan documents.

[0012] As a further preferred embodiment, the application layer includes RAG-type applications, Agent-type applications, OLTP applications, and OLAP-type applications; Among them, RAG-type applications are enterprise emergency knowledge bases, used to realize dynamic association and storage of emergency knowledge, scenario-based retrieval, and conversational question and answer; Agent-type applications include modules for multi-agent collaboration, data analysis, contingency plan comparison, and document generation, which cover the collaborative needs of the entire contingency plan development process. OLTP applications include intelligent customer service and text optimization assistants, which provide real-time interactive and contingency text polishing and compliance enhancement services; OLAP applications include enterprise-level report generation and visualization systems, used to achieve emergency data integration and analysis and overall situation visualization.

[0013] As a further preferred embodiment, the core functions implemented by the system include emergency plan generation, one-click detection, and an emergency plan knowledge base; Among them, the emergency plan generation is used to complete the standardized framework pre-construction and intelligent content writing for comprehensive plans, special plans and on-site disposal plans based on national standards and high-quality cases. It supports the addition, modification, deletion, viewing and related reuse of the plan framework. One-click detection is used to automatically detect the national standard format, content framework, and compilation specifications of the contingency plan, and to verify the compliance and completeness of the content. The emergency response plan knowledge base integrates emergency industry notices, national standards, industry standards, and normative documents, providing search, query, download, and conversational knowledge summaries and Q&A services.

[0014] As a further preferred embodiment, the one-click detection is also used to sequentially perform four-dimensional verification: formal review, compliance analysis, rationality analysis, and operability analysis. The formal review is used to automatically verify the format of the proposal, including the heading level, the completeness of the table of contents, and the format of the attachments; The compliance analysis is used to call upon data from laws, regulations, and standards, and to compare the content of the contingency plan with the regulatory requirements through semantic analysis technology to verify the compliance of the content. The rationality analysis is used to simulate accident scenarios based on the logical reasoning ability of the capability layer and analyze the rationality of the contingency plan process; The operability analysis is used to extract key operational steps for handling similar accidents based on comprehensive contingency plan cases, compare them with the current contingency plan description, and mark operability issues.

[0015] Secondly, this application provides an automatic emergency plan writing method based on any one of the above-mentioned emergency plan automatic writing systems, comprising the following steps: S10: Users log in to the system, enter contingency plan scenario tags, industry attributes and basic compilation information, and trigger the system's intelligent generation function. S20, the system calls the model layer and the application technology layer, and uses RAG to search for and associate relevant regulations, standards and cases. Combined with prompt words and thought chain reasoning, it generates a draft plan adapted to national standards, which supports manual optimization and adjustment. S30 triggers the one-click detection function to conduct four-dimensional verification of form, compliance, rationality, and operability, automatically identify problems and output rectification suggestions; S40 supplements professional knowledge through the system's knowledge base query function, iteratively optimizes the content of the contingency plan, and completes the closed-loop verification of the entire process; The S50 uses the system's collaborative management functions to complete task notifications, permission configurations, process traceability, and data backups, generating final draft plans and supporting one-click export.

[0016] This application has the following beneficial effects: (1) The system leverages the automated generation and intelligent verification capabilities of AI large models to significantly reduce reliance on human experience and substantially improve the efficiency and quality stability of emergency plan preparation. By deeply integrating data resources such as regulations, standards, and historical cases, it can quickly generate a standardized plan framework and core content, shortening the traditional preparation cycle of several weeks to hours. At the same time, relying on preset standardized testing rules and multi-dimensional verification logic, it automatically identifies omissions and unreasonable aspects of the content, reduces quality fluctuations caused by differences in personnel knowledge levels, ensures the consistency and accuracy of the output plan, and meets the core requirements of emergency management for "rapid generation and standardized reliability".

[0017] (2) The system has powerful real-time response and dynamic adjustment capabilities, which can closely follow the evolution of emergencies and significantly improve the scenario adaptability of the contingency plan. Through the real-time data access and inference function of the AI ​​big model, it can be linked to external monitoring data (such as weather warnings, equipment status, on-site real-time feedback, etc.). When the actual situation deviates from the preset scenario, the contingency plan adjustment process is automatically triggered, and key contents such as response strategies and resource allocation plans are quickly updated, realizing the upgrade from "static documents" to "dynamic response guidelines", effectively supporting the emergency management requirements of "real-time monitoring and rapid response".

[0018] (3) The system fully activates the value of massive historical data in the field of emergency management through data integration and intelligent mining technology, and strengthens decision support capabilities. A unified emergency data knowledge base is constructed to integrate multi-source data such as accident cases, risk assessments, and resource information, breaking down data fragmentation barriers; at the same time, by utilizing the data analysis and correlation reasoning capabilities of AI big models, decision support information such as risk evolution patterns and optimal resource allocation paths are mined from historical data, and this information is deeply integrated into the emergency plan preparation process, promoting the shift of emergency decision-making from "experience-oriented" to "data-driven", and significantly improving the scientific nature and effectiveness of decision-making. Attached Figure Description

[0019] Figure 1 This is a diagram of the AI ​​large-scale model fusion architecture of the emergency plan automatic writing system provided in this application embodiment; Figure 2 This is a functional architecture diagram of the emergency plan automatic writing system software provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] To effectively improve the efficiency, quality, and dynamic adaptability of emergency response plan writing, this embodiment provides an automatic emergency response plan writing system, such as... Figure 1 and 2 As shown, the following technical solution is adopted: (1) Layered collaborative innovation architecture design This intelligent emergency response plan development system pioneers a "six-layer technical collaboration architecture," breaking through the functional boundaries of traditional single-architecture systems. Through vertical linkage of hardware, cloud-native technologies, models, application technologies, capabilities, and business processes, it achieves intelligent closed-loop management of the entire AI-driven emergency response plan development process. The collaborative operation logic at each layer focuses on the design of "dynamically adapting to the needs of emergency scenarios," as detailed below: 1) Infrastructure Layer: Intelligent Computing Power Base for AI Task Awareness Abandoning the traditional "static hardware support" model, a heterogeneous computing power pool is constructed for emergency scenarios. Through dynamic resource orchestration of GPUs / TPUs, CPUs, RAM, HDDs, and networks, a foundational support that is "aware of emergency task characteristics" is provided to the upper layers. For multimodal data processing in emergency plan development (such as disaster scene image analysis and historical voice plan retrieval), the system automatically identifies the task's requirements for "computing power type (GPU / CPU), storage scale (HDD / RAM), and network bandwidth," intelligently allocating hardware resources to ensure elastic computing power supply for large-scale emergency data processing and complex model calculations, thus solving the problems of "resource mismatch and performance bottlenecks" in traditional architectures.

[0022] 2) Cloud-native layer: Elastic service engine driven by emergency scenarios Based on Docker containerization technology and the K8S (Kubernetes) orchestration framework, a microservice cluster with "emergency traffic adaptive" capability is created: Docker implements standardized container encapsulation of the "emergency plan preparation toolchain" (modules such as model inference, report generation, and collaborative editing) to ensure application environment consistency; K8S deeply integrates with the characteristics of emergency business traffic (such as a surge in plan preparation requests during sudden disasters), and through intelligent service orchestration and dynamic scaling, it automatically senses the access peak under emergency scenarios and adjusts the number of container instances and resource allocation in seconds, ensuring that the system maintains a highly available and low-latency cloud environment in high-concurrency emergency tasks (such as multi-department collaborative preparation of major disaster plans), solving the pain point of "insufficient ability to cope with sudden traffic" in traditional architectures.

[0023] 3) Model layer: The intelligent decision-making center for multimodal collaboration, constructing the core "intelligent engine" of the system: Based on the architecture diagram's positioning that "the model layer provides intelligent support for upper-layer applications," it aggregates multiple intelligent models and establishes a cross-modal collaborative reasoning mechanism. Through task division and dynamic linkage among models, it forms a "smart engine" adapted to emergency scenarios, with the following specific functions: Large-Scale Language Model (LLM): As the core of text processing, it relies on a domain-specific corpus (historical plans, regulations and standards, typical cases, etc.) to complete domain-adaptive pre-training, possessing professional text understanding, logical reasoning, and generation capabilities in emergency scenarios. In plan development, it not only undertakes basic text creation but can also automatically complete the plan logic based on semantic associations (such as deriving the "response process" based on "disaster type"), providing professional semantic support for plan text generation.

[0024] Visual-Language Model: Breaking through the limitations of a single modality, it constructs a two-way image-text mapping mechanism—capable of parsing visual features (such as abnormal equipment states and process node symbols) in equipment fault diagrams and emergency flowcharts, and associating them with corresponding text descriptions (such as fault handling specifications and node responsibility descriptions), achieving cross-modal understanding of graphics and text. In contingency plan development, it can automatically match corresponding diagrams and generate illustrated descriptions for "equipment fault handling steps," solving the problems of "separation of graphics and text, and inefficient association" in traditional contingency plans.

[0025] Voice-Language Model: Establish a real-time "voice-text" conversion channel to support quick interaction adaptation in emergency scenarios—quickly activating specific compilation functions through voice command recognition (such as "calling historical earthquake contingency plan templates"), or converting the generated contingency plan content into voice broadcasts (such as transmitting key response steps to on-site personnel in real time), expanding the dimensions of human-computer interaction and adapting to the practical needs of "being too busy to operate manually" in emergency situations.

[0026] Image recognition OCR model: For the large amount of unstructured data in the emergency field (scanned regulations, paper-based emergency plans, etc.), it is trained with a domain-specific character library (such as industry-specific terms in power and chemical industries) to improve the recognition accuracy of special format text (such as tables and text next to signatures). It transforms unstructured data into editable and searchable structured text, providing high-quality data input for other models and solving the problem of "difficulty in reusing unstructured data" in traditional emergency plan preparation.

[0027] Intelligent document understanding model: Focusing on the structured parsing of emergency documents, it automatically extracts key information (such as responsible departments, response time limits, and resource allocation nodes) and forms knowledge units by parsing the hierarchical structure (such as "general principles - organizational structure - response process") and semantic logic of emergency documents, providing precise support for plan verification (such as process integrity checks) and knowledge reuse (such as calling mature nodes in historical plans).

[0028] Recall and sorting mini-model: In the information retrieval stage, a "precise matching - intelligent sorting" mechanism is built. Based on user compilation needs, relevant laws and cases are quickly recalled from the knowledge base and sorted according to "relevance - authority - applicability". This provides efficient data filtering for RAG technology, ensuring that the knowledge fragments cited when generating the plan are "precise and prioritized" and improving the relevance of the content.

[0029] Multimodal detection and segmentation model: As a data quality gatekeeper, it performs targeted verification and processing on input multimodal data such as text, images, and voice. It identifies compliance issues in text (such as conflicts with current regulations), interference information in images (such as blurred images), and noise in voice. Through filtering and segmentation, it optimizes data quality, provides reliable input for other models, and ensures the accuracy of contingency plan generation.

[0030] 4) At the application technology layer, an intelligent technology toolkit adapted to emergency scenarios will be built to serve as a hub for AI capability transformation. Based on the architecture diagram's positioning of the "application technology layer connecting the model layer and capability layer," and focusing on "transforming core AI capabilities into tools specifically for emergency scenarios," a technical toolbox adapted to the entire emergency plan development process is formed through scenario-based customization and collaborative orchestration of technical components. Specific innovative designs of the technical modules are as follows: Agent / Intelligent Agent: Constructs an intelligent collaboration hub driven by emergency processes, breaking through the traditional single-task execution mode. For the complex process of emergency plan development (requirements analysis → risk assessment → process design → resource allocation → document generation), it breaks it down into ordered sub-task chains through an emergency task graph, dynamically scheduling RAG to acquire domain knowledge, calling fine-tuning models to generate professional content, and linking knowledge graph verification logic to achieve fully automated orchestration from "requirements input to plan output".

[0031] RAG / Search Enhancement Generation: An innovative "emergency scenario-related retrieval" mechanism addresses the limitations of traditional RAGs in the emergency response field. A multimodal knowledge network is constructed, encompassing a legal database, a historical case database, and an equipment ledger database. During retrieval, it not only bases searches on text similarity but also incorporates emergency scenario elements (such as disaster type, geographical characteristics, and company size) for related searches.

[0032] Prompt / Prompt Engineering: Create a dedicated prompt template library for the emergency response field, enabling automatic conversion from "needs and intent" to "professional prompts." Designing precise and clear prompts to convey the development requirements and constraints (such as compliance with specific regulations and adaptation to industry scenarios) to the large model, guiding the model to output expected contingency plans, process designs, and other content, is a key means of controlling the model's generated results.

[0033] Fine-tuning: Implement a "dynamic domain adaptation" fine-tuning strategy to solve the "knowledge solidification" problem of traditional fine-tuning models. Construct an emergency knowledge incremental update system that automatically triggers incremental model fine-tuning when new regulations are released or typical cases accumulate to a certain scale, and ensures that the model absorbs new knowledge while maintaining its original capabilities through a comparative verification mechanism.

[0034] COT / Thinking Chain: Design an emergency decision-making thinking chain template to simulate the "risk-response-resource" derivation logic of emergency experts. Break down emergency plan development into a thinking chain of risk identification → impact assessment → response plan → resource allocation → division of responsibilities. Each step is anchored by domain knowledge (e.g., linking the risk identification step to the "Emergency Event Classification and Grading Standards"), guiding the model to generate plan content that conforms to emergency response logic.

[0035] Data Acquisition: Constructing a dynamic emergency data sensing network to overcome the limitations of traditional data collection. Addressing the timeliness requirements of emergency data, a multi-channel data collection system is established, encompassing monitoring of official data sources (such as the Ministry of Emergency Management's website and the Meteorological Bureau's API), industry public opinion collection, and enterprise system integration. The knowledge base is updated in real-time through an event-triggered mechanism.

[0036] Data Vectors: An innovative "Emergency Semantic Vector Space" enhances cross-modal knowledge retrieval efficiency. Addressing the characteristics of multimodal data in the emergency response domain, a unified vector representation model incorporating text semantics, image features, and voice sentiment is constructed. Furthermore, an emergency scenario association algorithm enhances the semantic relevance of the vector space. For example, when retrieving a specific accident response plan, not only can the text description be retrieved, but it can also be associated with multimodal resources such as on-site images and expert explanation videos, thereby improving the depth of knowledge reuse through cross-modal vector retrieval.

[0037] Data Cleaning: Develop an emergency data quality assurance engine. In response to the special characteristics of emergency data (such as complex data sources, high timeliness, and inconsistent formats), design a multi-level cleaning mechanism that includes regulatory compliance verification (automatic comparison with the latest regulatory provisions), logical consistency checks (such as conflict detection of response process time nodes), and timeliness assessment (automatic labeling of expired data).

[0038] Access control: Based on the role-based access control mechanism, different user roles are defined (such as compilers, review experts, and administrators), and corresponding data access and function operation permissions are set to ensure system data security (such as sensitive contingency plans and corporate knowledge assets) and standardize user operation processes.

[0039] 5) Capability Layer: A central hub for intelligent service interfaces specifically designed for emergency scenarios, enabling standardized output of AI capabilities. Based on the architecture diagram's positioning of the "capability layer connecting the application technology layer and the application layer," and focusing on "transforming underlying AI capabilities into dedicated emergency response services," this approach encapsulates the core capabilities of the model layer and application technology layer in a scenario-based manner and designs standardized interfaces to output directly callable "intelligent service interfaces" to upper-layer applications. This solves the problems of "poor adaptability and inefficient collaboration" in traditional capability calls. Specific capabilities are as follows: Text generation: Breaking through the general text generation mode, we have built a standardized library of emergency professional expressions. Based on the fine-tuning and optimization of the large model in the field of emergency response, we generate contingency plan texts that conform to industry expression habits. At the same time, we support the generation of professional Q&A such as interpretation of legal provisions and risk warnings, ensuring that the text content is "professional, accurate and logically rigorous".

[0040] Image generation: Combining a visual-language model with emergency graphic standards (such as flowcharts requiring GB / T 15565 standard symbols), visualized images adapted to emergency scenarios are generated. For example, when generating a flood control emergency response flowchart, the standardized color scheme and symbols of "blue warning → yellow warning → orange warning → red warning" are automatically adopted, and the corresponding text descriptions of the handling steps are synchronously associated, realizing "text-image semantic linkage" and solving the problem of traditional image generation "not conforming to emergency standards".

[0041] Audio generation: Emergency scenario voice optimization based on speech-language model, which transforms key content of the plan (such as "personnel evacuation instructions") into clear and urgent speech (adapting to the information transmission needs of emergency scenarios), supports multilingual and multi-voice switching, and can be associated with real-time scenarios to improve the efficiency of information transmission in emergency situations.

[0042] Document generation: An innovative "template adaptive + content collaboration" mechanism integrates text generation (body content), image generation (illustrations), and formatting (compliant with emergency document specifications). It automatically matches the corresponding template based on user needs and ensures the validity of cited laws and regulations and the compatibility of case studies through interface linkage data cleaning, realizing one-click generation from "fragmented content to complete document" and solving the problems of "disorganized format and fragmented content" in traditional document generation.

[0043] Strategy generation: Based on the emergency decision-making logic of the Agent, combined with the risk characteristics of the scenario (such as "heavy rain in mountainous areas may cause landslides"), appropriate emergency strategies are derived.

[0044] Structure Generation: Based on the emergency document structure specifications extracted by the intelligent document understanding model, a "dynamic framework generator" is constructed. It automatically generates a framework that conforms to industry standards according to the type of contingency plan (comprehensive plan / specialized plan / on-site response plan). (For example, a comprehensive plan must include "General Principles - Organizational Structure - Prevention and Early Warning - Emergency Response - Post-Response Response"). It also allows users to flexibly adjust the structural hierarchy according to their company characteristics, ensuring that the contingency plan is "compliant in framework and logically clear."

[0045] 6) Application Layer: Develop an intelligent application system for emergency deployment across all scenarios, achieving seamless integration of "technology and business". Based on the architecture diagram's positioning that "the application layer provides scenario-based functions for end users," and focusing on "the entire emergency preparedness process and the needs of multiple roles," the intelligent service interfaces of the capability layer are deeply integrated with actual business scenarios to form four major categories of applications, solving the problems of "low efficiency, weak collaboration, and difficulty in knowledge reuse" in traditional emergency preparedness: RAG-based applications - Enterprise Knowledge Base: Constructing a dynamic and interconnected network of emergency knowledge, distinct from the "static storage" model of ordinary knowledge bases. Based on RAG technology, it aggregates knowledge such as regulations and standards, historical plans, and industry cases, establishing connections through "scenario tags" (e.g., "Construction - Falls from Heights" and "Power Industry - Substation Fires"), supporting a closed loop of "natural language query → precise knowledge delivery → related case recommendations."

[0046] Agent-based applications – multi-agent, data analysis, contingency plan comparison, and document generation: Driven by agents, an emergency response task collaboration network is formed to achieve multi-task coordination. Multiple agents collaborate to handle complex preparation requirements (such as dividing tasks for risk analysis and process design); data analysis agents mine the value of historical contingency plan data (such as risk distribution and statistics on response effectiveness); contingency plan comparison agents quickly compare differences between different versions and scenarios; and document generation agents, relying on underlying capabilities, automatically produce high-quality contingency plan documents, covering the entire preparation process.

[0047] OLTP Applications - Intelligent Customer Service and Enterprise-Level Text Optimization Assistant: Focusing on real-time interaction and text optimization during the drafting process. Intelligent customer service, based on natural language understanding, answers user questions during the drafting process (such as interpretation of standard clauses and guidance on function operation); the text optimization assistant utilizes large-scale model language optimization capabilities to polish, logically organize, and strengthen the compliance of draft texts, improving the quality and professionalism of the draft content.

[0048] OLAP Applications - Enterprise-Level Report Generation and Visualization System: Addressing the issues of fragmented data and delayed insights in traditional decision-making from a holistic emergency management perspective. The enterprise-level report generation application integrates multi-dimensional emergency data (plan implementation effectiveness, risk trends, resource status, etc.) to generate data analysis reports (such as annual emergency management assessment reports). The visualization system uses charts, dashboards, and other formats to intuitively present key emergency management data (such as risk heat maps and resource distribution maps), assisting management in grasping the overall situation and providing data support for emergency system optimization and resource allocation decisions.

[0049] (2) Core functions and their implementation path: The emergency response plan preparation system uses an AI big data model and follows the "GB / T 29639-2020 Guidelines for the Preparation of Emergency Response Plans for Production Safety Accidents in Production and Business Units" standard to help enterprises quickly and modularly complete the preparation of emergency response plans. Word documents can be generated and exported with one click, and the system can automatically format complete plans based on customizable text, image, and table templates.

[0050] The complex process of traditional contingency plan document writing and compilation, from conception, writing, modification to review, each step relies on the professional capabilities of the compilers, which is time-consuming and labor-intensive. The integrated application of AI big data models provides a complete and mature knowledge base system, truly integrating the enterprise's internal experience knowledge base, ensuring that the intelligently compiled contingency plans are efficient, accurate and traceable, and empowering the production of professional content.

[0051] 1) Emergency plan generation: Contingency plan generation involves pre-constructing specific content based on the requirements of the contingency plan system. For example, the "Comprehensive Contingency Plan" is based on the "Guidelines for the Compilation of Emergency Response Plans for Production Safety Accidents in Production and Business Units" and combines high-quality emergency response plan writing cases to intelligently compile a report of millions of words, including standard templates for specific content requirements such as the general principles of the contingency plan, the emergency organization system and responsibilities, and emergency response requirements.

[0052] It allows users to add, modify, delete, and view different types of contingency plans. It also allows users to pre-build contingency plans by associating them with other contingency plan frameworks in the contingency plan library, generating corresponding contingency plan frameworks for use by the plan developers.

[0053] 2) One-click detection Intelligent detection of contingency plans involves automatically detecting the requirements of the system to which the contingency plan belongs, such as format and style, including the content framework, compilation requirements, and other specific standards related to the text content.

[0054] 3) Emergency Response Plan Knowledge Base The knowledge base is an information repository of professional documents, standards, and norms related to the emergency response industry, including notices, regulations, national standards, and industry standards. It provides functions for querying, searching, and downloading relevant documents, and also allows for professional knowledge summaries and Q&A through a conversational interface.

[0055] This embodiment has the following beneficial effects: (1) The system leverages the automated generation and intelligent verification capabilities of AI large models to significantly reduce reliance on human experience and substantially improve the efficiency and quality stability of emergency plan preparation. By deeply integrating data resources such as regulations, standards, and historical cases, it can quickly generate a standardized plan framework and core content, shortening the traditional preparation cycle of several weeks to hours. At the same time, relying on preset standardized testing rules and multi-dimensional verification logic, it automatically identifies omissions and unreasonable aspects of the content, reduces quality fluctuations caused by differences in personnel knowledge levels, ensures the consistency and accuracy of the output plan, and meets the core requirements of emergency management for "rapid generation and standardized reliability".

[0056] (2) The system has powerful real-time response and dynamic adjustment capabilities, which can closely follow the evolution of emergencies and significantly improve the scenario adaptability of the contingency plan. Through the real-time data access and inference function of the AI ​​big model, it can be linked to external monitoring data (such as weather warnings, equipment status, on-site real-time feedback, etc.). When the actual situation deviates from the preset scenario, the contingency plan adjustment process is automatically triggered, and key contents such as response strategies and resource allocation plans are quickly updated, realizing the upgrade from "static documents" to "dynamic response guidelines", effectively supporting the emergency management requirements of "real-time monitoring and rapid response".

[0057] (3) The system fully activates the value of massive historical data in the field of emergency management through data integration and intelligent mining technology, and strengthens decision support capabilities. A unified emergency data knowledge base is constructed to integrate multi-source data such as accident cases, risk assessments, and resource information, breaking down data fragmentation barriers; at the same time, by utilizing the data analysis and correlation reasoning capabilities of AI big models, decision support information such as risk evolution patterns and optimal resource allocation paths are mined from historical data, and this information is deeply integrated into the emergency plan preparation process, promoting the shift of emergency decision-making from "experience-oriented" to "data-driven", and significantly improving the scientific nature and effectiveness of decision-making.

[0058] The following are the embodiments and benchmark examples provided in this application: 1. Example Develop an "Emergency Response Plan for Seepage Accidents in Pumped Storage Dams": (1) The intelligent generation module is started: The personnel responsible for drafting the plan log in to the system, enter the "Intelligent Generation Module", select the "AI Generation of Plans" function, and enter the basic information: specify it as "Emergency Plan for Seepage Accidents of Pumped Storage Dams in the Hydropower Industry", and associate the industry tag "Hydropower - Pumped Storage Dams - Seepage Accidents".

[0059] The system calls the large language model at the model layer and uses the RAG retrieval enhancement generation technology at the application technology layer to automatically extract knowledge fragments related to seepage accidents in pumped storage dams from the information database query module, such as "comprehensive contingency plan cases (e.g., historical similar dam seepage treatment contingency plans), laws and regulations (e.g., the Water Law of the People's Republic of China and the Regulations on Safety Management of Hydropower Projects), and standards / systems (e.g., dam safety monitoring standards and seepage treatment technical specifications)," as the basis for generation.

[0060] In conjunction with the Prompt prompting project, input constraints into the model: "The structural characteristics of the pumped storage dam should be highlighted, and the impact of leakage risk on the power generation system and the surrounding ecology should be emphasized; the response process should match the collaborative mechanism of the dam operation and maintenance team and the emergency response team; resource allocation should be linked to the company's reserve of leak-sealing materials and drainage equipment."

[0061] The large language model runs the generation logic and quickly outputs the initial draft of the plan, covering core chapters such as "General Principles (purpose of preparation, scope of application), emergency organization system and responsibilities (division of responsibilities between dam management unit and rescue team), emergency response (leakage monitoring and early warning, graded response process), and resource support (material, personnel, and technical resources)". It also generates a preliminary process diagram (based on the image generation capability of the capability layer).

[0062] The drafters switched to the "Online Plan Drafting" function to manually optimize the initial draft: adding personalized content unique to this pumped storage power station, such as "the distribution of dam leakage monitoring points (e.g., the installation location of seepage pressure gauges) and historical leakage handling experience (e.g., the key steps of a successful leak plugging)". The system saves the editing records in real time and supports version rollback.

[0063] (2) Intelligent detection module verification: The compiler triggers the "intelligent detection module" to conduct multi-dimensional detection in sequence: Formal review: The system automatically verifies the format of the contingency plan, including whether the heading level (such as "1 General Provisions" and "1.1 Purpose of Compilation" are standardized), the completeness of the table of contents (whether each chapter is accurately mapped in the table of contents), and the format of attachments (such as whether the format of the leakage monitoring point map and the equipment list table conforms to the system's preset template). It marks formatting issues such as "Section 3.2 heading level is incorrect and should be adjusted to a second-level heading; Attachment 2 equipment list table is missing the 'Equipment Activation Conditions' column" and automatically provides correction suggestions.

[0064] Compliance Analysis: This involves accessing legal regulations, standards, and institutional data from the information database query module and using semantic analysis technology (intelligent document understanding model at the model layer) to compare the emergency response plan content with regulatory requirements. For example, it verifies whether the emergency response activation conditions comply with the provision in the "Emergency Management Measures for Hydropower Projects" that "a Level III response must be activated if the dam leakage exceeds the warning value by 30%" and whether the qualifications of the emergency response team meet the requirements of the "Regulations on the Management of Qualifications of Emergency Response Units for Water Conservancy Projects." It also identifies compliance loopholes such as "the emergency response activation conditions do not cite the latest industry standard (the 2023 edition of the "Safety Monitoring Regulations for Hydropower Dams") and the emergency response team qualification description lacks the requirement for "registration of special operation personnel certificate numbers."

[0065] Reasonableness Analysis: Based on the logical reasoning capabilities of the capability layer, a scenario of seepage accident in a pumped storage dam is simulated to analyze the reasonableness of the emergency response plan. For example, the system deduces whether the time from the monitoring personnel discovering the alarm to the arrival of the rescue team to carry out leak-stopping operations after the seepage reaches the Level III response standard is reasonable (the system, based on historical case data, determines that this time should be controlled within 30 minutes, while the current plan describes it as 45 minutes, which poses a risk of delay) and whether the reserve of leak-stopping materials can meet the needs of sealing the dam's seepage points (based on the dam size and seepage area calculations, the current reserve can only cover 50% of the seepage risk, and additional reserves are needed). The system outputs reasonable rectification suggestions such as "The response process is redundant; it is recommended to optimize the reporting path for monitoring personnel and shorten the assembly time of the rescue team; the material reserves are insufficient, and the procurement plan needs to be adjusted."

[0066] Operability analysis: Based on the comprehensive emergency plan cases in the information database query module, the system extracts key operational steps for handling similar accidents (such as "leak point location needs to be combined with seepage pressure gauge data and manual inspection records for double confirmation"). By comparing with the current emergency plan description, it marks operability issues such as "the leak point location process only mentions manual inspection, lacks the requirement for automatic monitoring data association, and is prone to location deviation in on-site operation".

[0067] Based on the problem list output by the intelligent detection module, the developers optimize the contingency plan one by one, and the system tracks the detection status in real time until all problems are resolved in a closed loop.

[0068] (3) Information database query module assistance: During the compilation and testing process, compilers can supplement their knowledge at any time through the "Information Database Query Module": Perform a "comprehensive contingency plan query" to search for "successful cases of handling seepage accidents in pumped storage dams" within the company or industry, and learn from their "experience in optimizing emergency response processes (such as in one case, shortening response time by 20% by establishing a three-level linkage mechanism of 'monitoring-assessment-disposal') and innovative resource allocation models (such as sharing leak-stopping equipment from surrounding power stations to improve material utilization)" and integrate them into the current contingency plan.

[0069] Conduct "law and regulation search" to accurately locate the responsibility determination and post-investigation requirements for emergency response to dam leakage accidents in the "Emergency Response Law of the People's Republic of China" and the "Regulations on Handling Quality Accidents of Water Conservancy Projects", and improve the content of the "post-incident handling" chapter of the plan (such as clarifying the accident investigation process and responsibility tracing mechanism).

[0070] Perform a "Standards / Regulations Inquiry" to obtain the latest "Technical Specifications for Dam Safety Monitoring" regarding the requirements for leakage monitoring frequency and data thresholds, and update the parameter settings of the "Monitoring and Early Warning" section of the contingency plan (such as adjusting the leakage monitoring frequency from "once every 2 hours" to "once every 1 hour" to align with the new standard).

[0071] (4) Application of the collaboration and management module: Notification and Information Module: The system administrator can use the "Notification and Announcement" function to issue a "Notification on the Start of the Task for the Compilation of Emergency Plans for Pumped Storage Dams", which clarifies the compilation milestones (completion of the first draft, testing and optimization, and final draft review time) and the participating personnel (list of compilation team members and information of review experts). Compilers can log in to the system homepage to view task details and pending items, ensuring information synchronization.

[0072] User module: Developers can view task progress and historical operation records (such as the time of plan generation and the status of problem handling) in the "Personal Center"; system administrators can configure the permissions of developers and review experts through the "User Management" function (such as experts can only view and annotate the plan, and administrators can adjust the development nodes) to ensure an orderly development process.

[0073] System Modules: The system automatically records the entire compilation process log (e.g., "On [Date], compiler A modified the 'Emergency Response Process' section; on [Date], the intelligent detection module detected 3 compliance issues"). Administrators can use the "Log Management" function to trace the compilation process and identify operational risks. The "System Management" function supports regular backup of contingency plan data and updates to system detection rules (e.g., synchronizing the latest regulations and standards to the database) to ensure stable system operation.

[0074] (5) Implementation Results: The emergency response plan was prepared using this intelligent emergency plan preparation system, taking 3 working days (compared to 7-10 working days using the traditional method). The completed plan was verified by the intelligent detection module, achieving a 100% compliance rate in format, a 98% compliance rate in content, an 85% optimization rate in process rationality, and an 80% improvement rate in operability. The plan covers multi-dimensional knowledge (integrating 5 industry standards and 3 successful case experiences), realizing closed-loop management of the entire process of "monitoring-response-handling-recovery," and providing accurate and efficient action guidelines for emergency response to seepage accidents in pumped storage dams.

[0075] 2. Benchmark Example Develop an emergency response plan for seepage accidents in pumped storage dams: (1) Requirements analysis and data collection: A drafting team (3-5 people, including dam operation and maintenance experts, safety management specialists, and document writers) was formed, and a kick-off meeting was held to sort out the drafting requirements for seepage accidents in pumped storage dams: it was clarified that the content to be covered should include risk identification, emergency response, and resource allocation, which took 1 working day.

[0076] Group members divided the work of collecting data: The operation and maintenance experts are responsible for compiling the seepage monitoring data and historical handling experience of the pumped storage dam (which requires reviewing paper ledgers and Excel spreadsheets, taking 2 working days).

[0077] Safety management specialists searched for laws, regulations, and industry standards (logging into government websites and industry databases, manually screening relevant documents such as the "Water Law of the People's Republic of China" and the "Regulations on Safety Management of Hydropower Projects," which took 2 working days).

[0078] The copywriters referenced historical emergency plan templates (downloading the old version of the dam emergency plan from the company's shared folder, which took 0.5 working days).

[0079] (2) Drafting the initial plan: The copywriters compiled information, manually built a preliminary framework (referencing the old template and adjusting chapter titles), and wrote the content chapter by chapter: It took two working days to write the basic chapters such as "General Principles" and "Emergency Organization System".

[0080] When drafting the "Emergency Response Process", due to the lack of a clear reference for the collaboration mechanism, it was necessary to communicate with the operation and maintenance experts multiple times (3 offline meetings, taking 1.5 working days).

[0081] To supplement the "Resource Guarantee" section, we manually compiled the company's material list (data was exported from the warehouse management system and checked one by one, which took one working day).

[0082] After the initial draft was completed, the formatting and layout were manually adjusted using Word (setting heading levels, inserting a table of contents, and formatting charts, which took one working day). The final draft of the contingency plan had problems such as "incoherent chapter logic (e.g., the connection between 'emergency response' and 'resource allocation' is missing) and vague content descriptions (e.g., the time standard for 'as soon as possible' in 'the rescue team arrives as soon as possible' is not clearly defined)".

[0083] (3) Manual inspection and optimization: The team conducted manual testing: Format review: The heading level and table of contents were checked page by page. Problems such as "incorrect heading level in section 3.2 and disordered format of appendix tables" were found. Manual adjustments took 0.5 working days.

[0084] Compliance analysis: The safety management specialist manually compared the emergency plan content with the regulations and standards. Due to the large number of regulations and documents (more than 10 documents), compliance points such as "rescue team qualification registration requirements" were missed. Only 60% of the compliance issues were identified, which took 2 working days.

[0085] Reasonableness and feasibility analysis: Based on their experience, the operation and maintenance experts identified issues such as "redundant response process time and insufficient material reserves." However, due to a lack of data support (no systematic analysis of historical cases), the rectification suggestions were rather vague (e.g., only "shortening response time and increasing material reserves" were proposed, without specifying the direction of optimization), which took 1.5 working days.

[0086] Based on the test results, the development team divided the work and revised the contingency plan. Due to high communication costs (offline document delivery and repeated coordination and revision of content), it was iterated and optimized twice, taking three working days.

[0087] (4) Deficiencies in collaboration and management: Notifications and information delivery rely on emails and offline meetings, which can lead to information delays (such as experts not checking emails in time while on business trips, thus delaying the review process).

[0088] Version control is chaotic. Different rounds of revisions are named "Initial Draft - Revision 1 - Revision 2" and stored in a shared folder, which easily leads to situations such as "editing the wrong version or overwriting the correct content".

[0089] The lack of systematic recording of process logs makes it difficult to trace the compilation process later (such as the reason for modifying a certain content), which is not conducive to the accumulation of experience.

[0090] (5) Implementation Results: The traditional manual compilation method took 14 working days to complete the emergency plan for seepage accidents in pumped storage dams (4.67 times the time taken by the example). After manual inspection, the completed plan still had 3 formatting issues, 5 compliance loopholes, 4 rationality defects, and 3 insufficient operability. These need to be further discovered and corrected in subsequent actual emergency drills. The plan has poor quality stability and is difficult to quickly adapt to the complex emergency management needs of pumped storage dams.

[0091] The intelligent emergency plan preparation system provided in this embodiment has the following advantages compared to the traditional manual preparation method: (1) Advantages in knowledge utilization and drafting efficiency: In terms of knowledge utilization, the system, with the help of the information database query module and RAG technology, can automatically capture multi-source knowledge such as 5 industry standards and 3 successful cases, and build a comprehensive and scenario-appropriate knowledge network, so that the contingency plan can fully absorb mature industry experience and regulatory requirements. Traditional manual drafting is limited by retrieval efficiency and integration capabilities, and can only collect a small amount of incomplete knowledge, which can easily make the content of the contingency plan one-sided. In terms of drafting efficiency, the system relies on the intelligent generation module, and it only takes a few hours from the input of requirements to the generation of the first draft. With the addition of detection and optimization, the total cycle is compressed to 3 working days. Traditional manual drafting goes through a cumbersome process, relies on repeated communication and adjustment, and takes up to 14 working days. The system improves efficiency by more than 78% through AI automation and intelligent collaboration, saving time for rapid response to emergencies.

[0092] (2) Advantages in quality control and scenario adaptation: In terms of quality control, the system's intelligent detection module performs automated verification throughout the entire process from four dimensions: "form, compliance, rationality, and operability," achieving a 100% format compliance rate and a 98% content compliance rate. Traditional manual detection relies on experience, is easily influenced by subjectivity, struggles to avoid format issues, has a low rate of compliance vulnerability identification, and provides vague rectification suggestions, making it difficult to guarantee the stability of the contingency plan's quality. In scenario adaptation, facing dynamically evolving emergency scenarios, the system can use the intelligent generation module to associate real-time data and combine it with the intelligent detection module for dynamic verification, allowing for timely adjustments to the contingency plan. Traditional static contingency plans are difficult to adapt to complex situations, and their practicality is easily affected. The system achieves a "static-dynamic" transition, fitting the needs of actual combat. In multi-role collaborative scenarios, the system's user and notification modules enable online task allocation, progress synchronization, and opinion annotation, ensuring efficient collaboration. Traditional models rely on offline communication, resulting in information lag, version confusion, and high collaboration costs.

[0093] (3) Long-term value creation advantages: In terms of long-term value, the system automatically accumulates and compiles knowledge through information database and log management functions, forming enterprise-specific emergency knowledge assets to facilitate subsequent reuse and create a knowledge closed loop. In the traditional model, knowledge is scattered and lacks inheritance, and new employees need to repeatedly "learn from mistakes" in the process, making it difficult to transform knowledge into organizational capabilities. In terms of technological iteration, the system adopts a layered and decoupled architecture, with the model layer flexibly integrating new algorithms and the application layer expanding functions as needed. The traditional model is limited by manpower and experience, making it difficult to integrate new technologies and new needs, and is easily eliminated due to lag. The system has long-term iteration capabilities, continuously upgrading along with industry development, providing strong support for enterprises to build a modern emergency management system.

[0094] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An automatic emergency plan writing system, characterized in that, It includes a six-layer technical collaboration architecture that operates vertically from top to bottom, consisting of an infrastructure layer, a cloud-native layer, a model layer, an application technology layer, a capability layer, and an application layer, which is used to realize the intelligent compilation of AI-driven emergency response plans throughout the entire process. The infrastructure layer is an intelligent computing power base for AI task perception, used to build a heterogeneous computing power pool for emergency scenarios, and realizes intelligent allocation of hardware resources and elastic computing power supply based on dynamic resource orchestration. The cloud-native layer is an elastic service engine driven by emergency scenarios, used to build microservice clusters based on containerization technology and orchestration frameworks to achieve adaptive emergency business traffic and dynamic scaling. The model layer serves as a multimodal collaborative intelligent decision-making hub, used to aggregate multiple intelligence models and establish a cross-modal collaborative reasoning mechanism. The application technology layer is an intelligent technology toolbox and AI capability transformation hub adapted to emergency scenarios, used to transform the core capabilities of the model layer into tools specifically for emergency scenarios. The capability layer is a central hub for intelligent service interfaces specific to emergency scenarios. It is used to encapsulate the underlying core capabilities in a scenario-based manner and design standardized interfaces, and output callable intelligent service interfaces to the upper layer. The application layer is an intelligent application system for the full-scenario implementation of emergency response preparation. It is used to deeply integrate the capability layer interfaces with actual business operations, and achieve seamless integration between technology and emergency response preparation operations.

2. The emergency response plan automatic writing system as described in claim 1, characterized in that, The model layer includes a large language model, a visual language model, a speech language model, an OCR model, an intelligent document understanding model, a recall and ranking small model, and a multimodal detection and segmentation model; Among them, the large-scale language model is pre-trained based on a corpus dedicated to the emergency domain and is used for emergency professional text understanding, logical reasoning and content generation. Visual language models are used to construct a bidirectional mapping mechanism between images and text, enabling visual feature parsing and corresponding text association, and completing cross-modal understanding and image generation of the pre-planned text and images. The speech language model is used to establish a real-time speech-to-text conversion channel, supporting voice command interaction and voice broadcast of pre-planned content; The OCR model is trained based on a character library specific to the emergency response field and is used to convert unstructured emergency documents into structured, editable text. The intelligent document understanding model is used to parse the hierarchical structure and semantic logic of emergency documents and automatically extract key information to form knowledge units. The recall and ranking mini-model is used to build an accurate matching intelligent ranking mechanism to provide data filtering for RAG technology; Multimodal detection and segmentation models are used to perform compliance verification, noise filtering, and quality optimization on multimodal input data.

3. The emergency response plan automatic writing system as described in claim 1, characterized in that, The application technology layer includes an Agent, which is an intelligent collaboration hub driven by the emergency process. It is used to break down the entire process of contingency plan preparation into an ordered chain of sub-tasks based on the emergency task graph, and dynamically schedule RAG knowledge retrieval, fine-tuning model generation, and knowledge graph verification functions to achieve fully automated orchestration from demand input to contingency plan output.

4. The emergency response plan automatic writing system as described in claim 1, characterized in that, The application technology layer includes a RAG retrieval enhancement generation module. The RAG retrieval enhancement generation module adopts an emergency scenario association retrieval mechanism to construct a multimodal knowledge network that includes a legal database, a historical case database, and an equipment ledger database. It achieves accurate association retrieval based on text similarity and emergency scenario elements including disaster type, regional characteristics, and enterprise size.

5. The emergency response plan automatic writing system as described in claim 1, characterized in that, The application technology layer also includes an emergency domain-specific prompt word template library, a dynamic domain adaptation and fine-tuning system, an emergency decision-making thought chain template, an emergency data dynamic perception network, an emergency semantic vector space, a multi-level data cleaning mechanism, and a role-based access control module.

6. The emergency response plan automatic writing system as described in claim 1, characterized in that, The capability layer includes document generation capability, which adopts a template adaptive and content collaboration mechanism, integrates text generation, image generation and formatting functions, automatically matches national standard compliant templates based on user needs, and links with the data cleaning module to ensure the compliance and effectiveness of content, realizing one-click generation and export of complete contingency plan documents.

7. The emergency response plan automatic writing system as described in claim 1, characterized in that, The application layer includes RAG-type applications, Agent-type applications, OLTP-type applications, and OLAP-type applications; Among them, RAG-type applications are enterprise emergency knowledge bases, used to realize dynamic association and storage of emergency knowledge, scenario-based retrieval, and conversational question and answer; Agent-type applications include modules for multi-agent collaboration, data analysis, contingency plan comparison, and document generation, which cover the collaborative needs of the entire contingency plan development process. OLTP applications include intelligent customer service and text optimization assistants, which provide real-time interactive and contingency text polishing and compliance enhancement services; OLAP applications include enterprise-level report generation and visualization systems, used to achieve emergency data integration and analysis and overall situation visualization.

8. The emergency response plan automatic writing system as described in claim 1, characterized in that, The core functions implemented by the system include emergency plan generation, one-click detection, and an emergency plan knowledge base; Among them, the emergency plan generation is used to complete the standardized framework pre-construction and intelligent content writing for comprehensive plans, special plans and on-site disposal plans based on national standards and high-quality cases. It supports the addition, modification, deletion, viewing and related reuse of the plan framework. One-click detection is used to automatically detect the national standard format, content framework, and compilation specifications of the contingency plan, and to verify the compliance and completeness of the content. The emergency response plan knowledge base integrates emergency industry notices, national standards, industry standards, and normative documents, providing search, query, download, and conversational knowledge summaries and Q&A services.

9. The emergency response plan automatic writing system as described in claim 8, characterized in that, The one-click detection is also used to sequentially perform four-dimensional verification: formal review, compliance analysis, rationality analysis, and operability analysis. The formal review is used to automatically verify the format of the proposal, including the heading level, the completeness of the table of contents, and the format of the attachments; The compliance analysis is used to call upon data from laws, regulations, and standards, and to compare the content of the contingency plan with the regulatory requirements through semantic analysis technology to verify the compliance of the content. The rationality analysis is used to simulate accident scenarios based on the logical reasoning ability of the capability layer and analyze the rationality of the contingency plan process; The operability analysis is used to extract key operational steps for handling similar accidents based on comprehensive contingency plan cases, compare them with the current contingency plan description, and mark operability issues.

10. An automatic emergency plan writing method based on the automatic emergency plan writing system according to any one of claims 1 to 9, characterized in that, Includes the following steps: S10: Users log in to the system, enter contingency plan scenario tags, industry attributes and basic compilation information, and trigger the system's intelligent generation function. S20, the system calls the model layer and the application technology layer, and uses RAG to search for and associate relevant regulations, standards and cases. Combined with prompt words and thought chain reasoning, it generates a draft plan adapted to national standards, which supports manual optimization and adjustment. S30 triggers the one-click detection function to conduct four-dimensional verification of form, compliance, rationality, and operability, automatically identify problems and output rectification suggestions; S40 supplements professional knowledge through the system's knowledge base query function, iteratively optimizes the content of the contingency plan, and completes the closed-loop verification of the entire process; The S50 uses the system's collaborative management functions to complete task notifications, permission configurations, process traceability, and data backups, generating final draft plans and supporting one-click export.

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