Hidden danger identification and emergency decision-making system based on large model analysis

Abstract

The invention discloses a hidden danger identification and emergency decision-making system based on large model analysis, which relates to the technical field of hidden danger identification and emergency decision-making and comprises a knowledge base and query module, an automatic report generation module, an intelligent auxiliary module, an emergency decision-making module and a hidden danger internal report and management module. According to the invention, intelligent information interaction and knowledge retrieval are realized through a large model, and the efficiency and experience of acquiring emergency knowledge by a user are improved. The automatic report generation and intelligent auxiliary functions enable the emergency response to be faster and more scientific, and the accuracy of emergency decision making is improved. And the detection and processing module realizes intelligent detection and efficient law enforcement of hidden dangers, and the supervision intensity is enhanced. The hidden danger internal reporting and management module simplifies the hidden danger reporting process and enhances the management and tracking of the hidden danger. On the whole, the system comprehensively improves the intelligent level of hidden danger identification and emergency management, can effectively reduce disaster loss, and has important social value and wide application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of hidden danger identification and emergency decision-making, and in particular to a hidden danger identification and emergency decision-making system based on large model analysis. Background Art

[0002] In the current field of hazard identification and emergency management, traditional systems suffer from numerous shortcomings. For one thing, information retrieval and interactive functions are limited, making it difficult to meet users' complex query needs. Furthermore, the monotonous information presentation format results in a poor user experience. Furthermore, during emergency response, report generation relies on manual operations, which is inefficient and inaccurate, failing to provide comprehensive information for timely decision-making. Regarding risk early warning, early warning models are not intelligent enough, making it difficult to dynamically optimize based on actual conditions. Hazard detection relies primarily on manual inspection, which is unable to quickly and accurately identify potential risks. Hazard reporting and management processes are cumbersome and lack effective tracking and data analysis. With the increasing diversification and complexity of disaster risks, there is an urgent need for a more intelligent, efficient, and comprehensive hazard identification system to enhance the capabilities and level of emergency management. Summary of the Invention

[0003] The purpose of this invention is to provide a hidden danger identification and emergency decision-making system based on large model analysis. By introducing a large model and integrating multi-module functions, it can realize the full-process intelligent management of disaster hidden dangers, improve the efficiency and accuracy of emergency response, and reduce disaster losses.

[0004] The present invention provides a hidden danger identification and emergency decision-making system based on large model analysis, which includes: a knowledge base and query module, an automatic report generation module, an intelligent auxiliary module, an emergency decision-making module, and a hidden danger internal reporting and management module;

[0005] The knowledge base and query module are used to implement complex semantic understanding, reasoning retrieval and multi-round interaction based on a large model, and provide a user interface for multimedia display;

[0006] The automatic reporting module is used to monitor emergency events in real time, automatically collect information, perform data integration analysis and trend forecasting based on the collected information, and generate quick-view reports and emergency operation procedures;

[0007] The intelligent assistance module is used for risk identification and assessment, disaster warning optimization, situation analysis and emergency decision-making assistance;

[0008] The emergency decision-making module is used to collect enterprise information through large models, use AI image recognition and remote video transmission to detect hidden dangers and make decisions, and automatically generate decision-making reports and evidence lists;

[0009] The hidden danger internal reporting and management module is used to support the collection and reporting, review and classification, tracking and processing, and statistical analysis of hidden danger information on mobile terminals.

[0010] Optionally, the knowledge base and query module include basic information on natural disasters and man-made disasters, emergency plans at all levels, emergency response measures, historical event cases, emergency resource information, and training resources.

[0011] Optionally, the automatic report generation module uses statistical and machine learning methods to conduct in-depth analysis of the integrated multi-source data, predict the development trend of events based on the analysis results, and intuitively display them in the form of charts and maps.

[0012] Optionally, the intelligent auxiliary module is specifically used to build a disaster warning model, optimize the model and release strategy by evaluating the warning effect, conduct situational analysis based on multiple factors of emergency events, and match the optimal emergency plan.

[0013] Optionally, the emergency decision-making module integrates AI image recognition technology to perform real-time analysis of captured images, combines natural language processing technology to convert the recognition results into structured text to generate a decision report, and automatically generates a list containing multimedia evidence.

[0014] Compared with the existing technology, the present invention has the following advantages: the hidden danger identification and emergency decision-making system realizes intelligent information interaction and knowledge retrieval through a large model, which improves the efficiency and experience of users in obtaining emergency knowledge. The automatic generation of reports and intelligent auxiliary functions make emergency response faster and more scientific, and improve the accuracy of emergency decision-making. The detection and processing module realizes intelligent detection of hidden dangers and efficient law enforcement, and enhances supervision. The hidden danger internal reporting and management module simplifies the hidden danger reporting process and strengthens the management and tracking of hidden dangers. Overall, this system has comprehensively improved the intelligence level of hidden danger identification and emergency management, can effectively reduce disaster losses, and has important social value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall structural diagram of the present invention. DETAILED DESCRIPTION

[0016] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. However, the protection scope of the present invention is not limited to the following embodiment.

[0017] like Figure 1 As shown, this embodiment provides a technical solution: a hidden danger identification and emergency decision-making system based on large model analysis, including a knowledge base and query module, an automatic report generation module, an intelligent assistance module, an emergency decision-making module, and a hidden danger internal reporting and management module;

[0018] The knowledge base and query module is used to achieve complex semantic understanding, reasoning retrieval and multi-round interaction based on large models, and provides a user interface for multimedia display.

[0019] Among them, the knowledge base and query module include basic information on natural disasters and man-made disasters, emergency plans at all levels, emergency response measures, historical event cases, emergency resource information, and training resources.

[0020] Exemplarily, the data in the knowledge base may include various types, such as:

[0021] 1. Basic Data: This includes specialized knowledge in various fields, such as emergency management, production safety, public health, and environmental protection, such as laws, regulations, standards, and operational procedures. Basic Information Database: This includes geographic location, topography, meteorological conditions, and socioeconomic data, which provide background information and environmental support for emergency response.

[0022] 2. Historical emergency incident cases: Detailed records of the occurrence, development, response, and outcomes of various emergency incidents, including successful experiences and lessons learned. Emergency drill data: Data from drills conducted by simulating real-world emergency scenarios, including drill plans, process records, and evaluation reports, helps understand emergency response processes and standard operating procedures.

[0023] 3. Expert Knowledge Base: Organizes the expertise, experience, judgment, and best practices of industry experts to form a systematic knowledge base. Human-computer interaction records: Collects conversations, feedback, and corrections during human-computer interaction between experts and models to continuously optimize the content of the knowledge base.

[0024] 4. Environmental monitoring data: Real-time monitoring data such as air quality, water quality, and seismic waves provide real-time environmental information for emergency response. Public safety monitoring data: Real-time data from systems such as video surveillance, facial recognition, and vehicle recognition monitor public safety incidents and abnormal behavior.

[0025] 5. Emergency resource dispatch data: This includes emergency material reserves, emergency team distribution, and emergency facilities and equipment information, providing resource dispatch support for emergency response. Communication and command data: This includes data from emergency communication systems and command and dispatch platforms, ensuring smooth information flow and effective command during the emergency response process.

[0026] 6. Risk Assessment Model and Algorithm: A risk assessment model built based on historical data and expert knowledge provides a quantitative assessment of potential risks. Early Warning Information Data Source: In addition to real-time monitoring data, this integrates public opinion information from social media, news websites, and other channels to promptly identify and issue early warnings of potential risks.

[0027] For example, the knowledge base and query module can achieve complex semantic understanding and context association through large models, and can accurately grasp user questions. By using the reasoning ability of the large model, relevant information can be quickly retrieved from the huge knowledge base and accurate answers can be generated. It supports multiple rounds of dialogue and in-depth interaction, making it more convenient and efficient for users to obtain information. At the same time, the module provides a friendly interactive interface and uses rich multimedia display forms such as pictures and videos to present emergency knowledge to users in a vivid and intuitive way, greatly improving the user experience. The content it includes is extensive and comprehensive, covering basic information on various disasters, emergency plans at all levels, laws and regulations, emergency response measures, historical event cases, emergency resource information, and training resources, providing all-round knowledge support for emergency management.

[0028] In addition, for the knowledge base and query module, various disaster basic information, emergency plans, laws and regulations and other data can be collected from professional databases, government platforms, academic literature and other channels, and imported into the module after cleaning, classification and structuring.

[0029] The automatic reporting module is used to monitor emergency events in real time, automatically collect information, conduct data integration analysis and trend forecasting based on the collected information, and generate quick-view reports and emergency operation procedures.

[0030] Among them, the automatic report generation module uses statistical and machine learning methods to conduct in-depth analysis of integrated multi-source data, predict the development trend of events based on the analysis results, and display them intuitively in the form of charts and maps.

[0031] For example, the automatic reporting module can have the ability to monitor emergency events in real time. Once an event is detected, it can automatically collect basic information about the event and quickly generate a quick overview report, allowing relevant personnel to understand the event overview at the first time. Based on the collected information and in accordance with the preset emergency plan, detailed emergency operation steps are automatically generated to provide clear guidance for emergency response. The module can also automatically integrate multi-source data, use statistical and machine learning methods for in-depth analysis, predict the development trend of the event, and display the analysis results in the form of intuitive charts, maps, etc. In addition, after the emergency response is completed, the results of the response are automatically summarized, successful experiences are extracted, and existing problems are identified, providing valuable reference for subsequent emergency response.

[0032] Exemplary data collected by emergency time monitoring may include:

[0033] 1. Historical emergency case studies: This includes historical examples of various natural disasters, accidents, disasters, and public health incidents. These cases should include detailed information on the cause, development process, response measures, and losses, so that the model can learn the characteristics and response methods of different types of emergency events. Emergency drill data: This includes drill data simulated from real-world emergency scenarios, including drill plans, process records, and evaluation reports. This data helps the model understand emergency response processes and standard operating procedures.

[0034] 2. Environmental monitoring data: Real-time monitoring data such as air quality, water quality, and seismic waves are crucial for predicting and assessing potential environmental risks. Public safety monitoring data: This includes real-time data from systems such as video surveillance, facial recognition, and vehicle identification, used to monitor public safety incidents and unusual behavior. Social media and public opinion data: By capturing real-time information from social media, news websites, and other channels, we understand public reactions and attitudes to emergency events and provide public opinion analysis for the report.

[0035] 3. Emergency Supply Stock Information: This includes the stockpile quantity, type, distribution location, and allocation process for various emergency supplies. This data is crucial for the emergency resource allocation recommendations in the report. Emergency Team Information: This includes the personnel composition, professional skills, and contact information of the emergency rescue team to ensure that the report accurately reflects the emergency response capabilities. Emergency Facility and Equipment Information: This includes information on the location, size, and function of emergency facilities such as shelters, temporary hospitals, and fire stations, as well as the performance parameters and usage of emergency vehicles and equipment.

[0036] 4. Relevant Laws and Regulations: These include laws and regulations related to emergency management, such as the Work Safety Law, Fire Protection Law, and Environmental Protection Law, to provide a basis for compliance assessment in the report. Industry Standards and Regulations: These include emergency management standards and operating procedures across various industries to ensure that the recommendations in the report meet industry requirements.

[0037] 5. Expert Knowledge Base: Organizes the expertise, experience, and best practices of industry experts to form a knowledge base for the model to learn from and draw upon. Human-Computer Interaction Recording: Collects conversations, feedback, and corrections from expert interactions with the model to continuously optimize the model's reporting capabilities.

[0038] 6. Standard report templates: Provides various types of emergency report templates, including accident investigation reports, risk assessment reports, emergency plans, etc., to ensure that the generated reports meet industry standards and specifications. Custom template function: Allows users to customize report templates according to actual needs, improving the flexibility and personalization of reports.

[0039] Intelligent assistance module, used for risk identification and assessment, disaster warning optimization, situation analysis and emergency decision-making assistance.

[0040] Among them, the intelligent assistance module is specifically used to build a disaster warning model, optimize the model and release strategy by evaluating the warning effect, conduct situational analysis based on multiple factors of emergency events, and match the optimal emergency plan.

[0041] The intelligent assistance module is primarily responsible for risk identification and assessment, accurately identifying various risk sources and scientifically assessing the likelihood, impact, and potential consequences of risk occurrence. Based on historical data and expert knowledge, it constructs a disaster warning model, issuing warning information promptly before a disaster occurs. By evaluating the effectiveness of warnings, it continuously optimizes the warning model and issuance strategy to improve the accuracy and timeliness of warnings. When an emergency occurs, it conducts a situational analysis based on factors such as the type, scale, and environment of the event, matching the most appropriate emergency plan from a library of plans, and providing decision-makers with intuitive data presentations and authoritative expert advice. The module also monitors the emergency situation in real time, automatically issuing warnings when preset thresholds are reached, updating the event status and response plan in real time, and providing a historical record playback function to facilitate post-event analysis and summary. Furthermore, it supports voice interaction, facilitating user operation in complex environments, and can answer questions from on-site personnel based on natural language processing technology.

[0042] The data involved in the intelligent assistance module may include:

[0043] 1. Geographic information data: This includes topography, landforms, geological structure, water system distribution, and transportation networks. This data is crucial for disaster prediction and emergency resource dispatch. Socioeconomic data: This includes population distribution, industrial layout, and economic development levels. This data helps assess the scope and extent of the socioeconomic impact of emergency events. Laws, regulations, and standards: Relevant laws, regulations, policy documents, industry standards, and operating procedures provide the model with a basis for compliance assessment and decision-making.

[0044] 2. Environmental monitoring data: Real-time monitoring data on environmental parameters such as air quality, water quality, noise, and radiation helps to promptly detect and warn of environmental pollution incidents. Public safety monitoring data: Data from systems such as video surveillance, facial recognition, and license plate recognition is used to monitor public safety incidents and unusual behavior. Social media and public opinion data: By capturing real-time information from social media, news websites, and other channels, we can understand public reactions and attitudes to emergency events, providing a basis for public opinion analysis and guidance.

[0045] 3. Historical emergency case studies: This includes detailed case studies of various natural disasters, accidents, public health incidents, and social security incidents, documenting the course of events, response measures, and losses. This provides the model with learning examples and decision-making references. Emergency drill data: This records the process, results, and evaluation reports of emergency drills to assess the feasibility and effectiveness of emergency plans and optimize emergency response processes. Statistical data: This includes historical emergency event types, frequencies, and loss statistics, helping the model identify patterns and trends in emergency events.

[0046] 4. Expert Knowledge Base: Organizes the expertise, experience, and best practices of industry experts to form a knowledge base for the model to learn from and draw upon. Knowledge Graph: Constructs a knowledge graph for the emergency response field, providing a structured representation of knowledge elements such as entities, relationships, and attributes to enhance the model's understanding and reasoning capabilities. Human-Computer Interaction Recording: Collects conversations, feedback, and corrections from expert interactions with the model for continuous optimization and improvement.

[0047] 5. Emergency Supply Reserve Information: This includes the reserve quantity, type, distribution location, and allocation process of various emergency supplies to support emergency resource dispatch. Emergency Team Information: This includes the personnel composition, professional skills, and contact information of the emergency rescue team to ensure rapid mobilization of rescue forces in the event of an emergency. Emergency Facility and Equipment Information: This includes the location, size, and function of emergency facilities such as shelters, temporary hospitals, and fire stations, as well as the performance parameters and usage of emergency vehicles and equipment.

[0048] The emergency decision-making module is used to collect enterprise information through large models, use AI image recognition and remote video transmission to realize hidden danger detection and decision-making, and automatically generate decision-making reports and evidence lists.

[0049] Among them, the emergency decision-making module integrates AI image recognition technology to perform real-time analysis of captured images, combines natural language processing technology to convert the recognition results into structured text to generate a decision report, and automatically generates a list containing multimedia evidence.

[0050] For example, detailed information about enterprises or places is collected through large models as basic data for decision-making inspections. The one-click photo function is combined with AI image recognition technology to quickly and accurately identify potential safety hazards or violations on site. Remote real-time video transmission is supported, allowing experts and senior supervisors to view the on-site situation in real time and provide remote support to on-site personnel. For discovered violations, they are identified and punished in accordance with relevant laws and regulations, and a detailed law enforcement report is automatically generated. The report content includes a description of the hidden dangers, the content of the violation, location information, etc. The recognition results are converted into structured text using natural language processing technology, and a report is generated based on a preset template. At the same time, a list of evidence containing pictures, videos, timestamps, etc. is automatically generated to ensure the integrity and validity of the evidence.

[0051] In order to improve the accuracy of emergency decision-making, the data used may include:

[0052] 1. Basic Company Information: This includes the company name, registered address, legal representative, contact number, email address, business scope, company size (e.g., number of employees, annual turnover), industry, etc. Safety Production Permit Information: Information on all licenses and certificates held by the company, such as safety production licenses and special operations operator licenses, as well as the validity period and issuing authority. Risk Assessment and Hazard Investigation Data: The company's regularly conducted safety risk assessment reports, hazard investigation records, and rectification status, including hazard descriptions, rectification measures, rectification progress, and rectification results.

[0053] 2. Emergency management personnel information: including the name, title, contact information, professional background, training status, etc. of emergency management department staff. Enterprise safety manager and employee information: the personal information and contact information of the enterprise safety manager, as well as the basic information of the enterprise employees (such as name, position, responsibilities, etc.) and safety training records.

[0054] 3. Special equipment information: A list of special equipment owned by the enterprise, including equipment name, model, specifications, manufacturer, production date, usage status (in use, out of service, scrapped, etc.), inspection and testing status (next inspection date, inspection results, etc.). Transport equipment: Location information, form and route information of transport equipment such as hazardous chemicals vehicles.

[0055] 4. Geographic location information: geographic location coordinates of the enterprise or relevant area, surrounding environment (such as topography, landforms, meteorological conditions, etc.), traffic conditions, etc. Environmental monitoring data: data on environmental factors that may affect emergency response, such as air quality, water quality monitoring data, geological disaster warning information, etc.

[0056] 5. Work safety laws and regulations: These include national, local, and industry-issued laws, regulations, and policy documents related to work safety. Standards and specifications: These include national, industry, and local standards related to emergency management, which guide emergency management efforts.

[0057] 6. Historical accident cases: Collect and analyze historical accident cases, extract information such as accident causes, handling processes, lessons learned, etc., and provide reference for emergency decision-making.

[0058] The hidden danger internal reporting and management module is used to support the collection and reporting, review and classification, tracking and processing, and statistical analysis of hidden danger information on mobile terminals.

[0059] The hidden danger internal reporting and management module allows users to conveniently collect hidden danger information via mobile devices, including photos, videos, text, or voice descriptions, and automatically captures geographic location information. Users can submit reports with a single click and view real-time processing progress and feedback. Platform administrators review and categorize reports, continuously track confirmed hidden dangers, and record the handling process and results. Statistical analysis of hidden danger reporting data provides strong data support for emergency management and decision-making.

[0060] It is understood that to better address the technical issues of the present invention, a high-performance server cluster can be deployed at the hardware level to meet the needs of large-scale model operations and massive data storage. High-resolution cameras, high-speed network equipment, and other equipment can be configured to ensure image acquisition and transmission for the detection and processing modules and the hidden danger reporting and management modules. On the software side, a deep learning framework environment suitable for large-scale model operation, such as TensorFlow or PyTorch, can be built to establish stable data interaction interfaces and communication protocols between modules.

[0061] For the knowledge base and query modules, various disaster basic information, emergency plans, laws and regulations and other data are collected from professional databases, government platforms, academic literature and other channels, and then imported into the system after cleaning, classification and structured processing.

[0062] Automatic report generation modules, intelligent auxiliary modules, etc. also collect historical data, real-time monitoring data, etc. from corresponding data sources, unify data format standards, and store them in the system database to provide data support for large model training and system operation.

[0063] Large model training and optimization: The integrated data is divided into training, validation, and test sets according to a specific ratio. Based on different functional requirements, such as hidden danger identification, report generation, and risk assessment, training objectives and optimization algorithms are set to train the large model. For example, when training the hidden danger identification function, the backpropagation algorithm is used to adjust model parameters with the goal of improving AI image recognition accuracy. In report generation training, natural language processing parameters are optimized to generate reports that meet standards and actual needs. Through continuous training and validation, the performance of the large model is optimized.

[0064] The present invention realizes intelligent information interaction and knowledge retrieval through a large model, improving the efficiency and experience of users in acquiring emergency knowledge. Automatic report generation and intelligent auxiliary functions make emergency response faster and more scientific, and improve the accuracy of emergency decision-making. The detection and processing module realizes intelligent detection of hidden dangers and efficient law enforcement, and enhances supervision. The hidden danger internal reporting and management module simplifies the hidden danger reporting process and strengthens the management and tracking of hidden dangers. Overall, this system has comprehensively improved the intelligence level of hidden danger identification and emergency management, can effectively reduce disaster losses, and has important social value and broad application prospects.

[0065] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0066] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0067] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A hidden danger identification and emergency decision-making system based on large model analysis, characterized by: It includes knowledge base and query module, automatic report generation module, intelligent assistance module, emergency decision-making module and hidden danger internal reporting and management module; The knowledge base and query module are used to implement complex semantic understanding, reasoning retrieval and multi-round interaction based on a large model, and provide a user interface for multimedia display; The automatic reporting module is used to monitor emergency events in real time, automatically collect information, perform data integration analysis and trend forecasting based on the collected information, and generate quick-view reports and emergency operation procedures; The intelligent assistance module is used for risk identification and assessment, disaster warning optimization, situation analysis and emergency decision-making assistance; The emergency decision-making module is used to collect enterprise information through large models, use AI image recognition and remote video transmission to detect hidden dangers and make decisions, and automatically generate decision-making reports and evidence lists; The hidden danger internal reporting and management module is used to support the collection and reporting, review and classification, tracking and processing, and statistical analysis of hidden danger information on mobile terminals.

2. The hidden danger identification and emergency decision-making system based on large model analysis according to claim 1 is characterized in that: The knowledge base and query module include basic information on natural disasters and man-made disasters, emergency plans at all levels, emergency response measures, historical event cases, emergency resource information, and training resources.

3. The hidden danger identification and emergency decision-making system based on large model analysis according to claim 1 is characterized in that: The automatic report generation module uses statistical and machine learning methods to conduct in-depth analysis of integrated multi-source data, predict event development trends based on the analysis results, and intuitively display them in the form of charts and maps.

4. The hidden danger identification and emergency decision-making system based on large model analysis according to claim 1 is characterized in that: The intelligent auxiliary module is specifically used to build a disaster warning model, optimize the model and release strategy by evaluating the warning effect, conduct situational analysis based on multiple factors of emergency events, and match the optimal emergency plan.

5. The hidden danger identification and emergency decision-making system based on large model analysis according to claim 1 is characterized in that: The emergency decision-making module integrates AI image recognition technology to perform real-time analysis of captured images, and combines natural language processing technology to convert the recognition results into structured text to generate a decision report, and automatically generates a list containing multimedia evidence.

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