Emergency command system and method based on audio and video conference consultation

Abstract

The invention discloses an emergency command system and method based on audio and video conference consultation, and relates to the technical field of emergency command. The system performs standardization processing on multi-source initial information through an event receiving interface and a preprocessing module to generate a preliminary event file; the intelligent converged communication module establishes a converged communication network according to the data to realize access and standardization of heterogeneous audio and video streams; the multi-source information fusion and visualization processing module generates a dynamic situation map layer; the artificial intelligence auxiliary decision-making module performs real-time analysis to enhance situation awareness and generate auxiliary decision-making suggestions; and the instruction distribution module realizes instruction issuing and closed-loop management. According to the invention, the problems of information isolated island, non-visual situation awareness and insufficient decision support in the existing emergency command system are solved, real-time collaboration of cross-department information, visual mastering of global situation and intelligent assistance of scientific decision are realized, and the efficiency and accuracy of emergency response are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of emergency command technology, specifically to an emergency command system and method based on audio and video conferencing. Background Technology

[0002] Currently, emergency command systems play a crucial role in responding to public emergencies, but their technological foundation still faces numerous challenges. Many existing systems rely on traditional communication methods, with different departments and field forces often using their own independent communication equipment and information systems, such as dedicated wireless intercom systems and public telephone networks. The lack of effective interconnection protocols and standards between these systems leads to poor flow of voice, video, and data information between different networks and platforms during emergency response, creating severe information silos. Real-time, seamless information sharing between field personnel's equipment, command vehicles, drones, and the rear command center is difficult, hindering the command center's ability to quickly acquire and integrate firsthand information from multiple sources, thus affecting the timeliness of command decisions.

[0003] At the information processing and situational awareness level, existing systems typically display various aggregated information in a simple, side-by-side manner. For example, multiple video feeds and data tables are displayed on a split-screen display on a command center. This presentation method forces commanders to manually connect different information sources and piece together the overall situation in their minds. Due to the lack of automated capabilities for spatial fusion and intelligent analysis of multi-source information, commanders struggle to quickly and intuitively grasp the dynamic full picture of an event's development. This non-intuitive situational awareness method can easily lead to misjudgments or delays in the crucial emergency decision-making process where every second counts.

[0004] Furthermore, existing emergency command systems generally lack sufficient decision support. The decision-making process relies heavily on the personal experience and on-site judgment of commanders and experts. The system itself lacks the ability to deeply mine and intelligently analyze massive amounts of real-time information and cannot effectively utilize historical case databases and emergency plan knowledge bases to provide structured auxiliary suggestions for commanders. This makes the decision-making process highly subjective, and it is difficult to guarantee the scientific nature and accuracy of decisions when facing complex and rapidly evolving emergencies. Summary of the Invention

[0005] The purpose of this invention is to provide an emergency command system and method based on audio and video conferencing. Through an event receiving interface and a preprocessing module, multi-source initial information is standardized to generate a preliminary event archive. An intelligent fusion communication module establishes a fusion communication network to achieve the access and standardization of heterogeneous audio and video streams. A multi-source information fusion and visualization processing module generates a dynamic situational awareness layer. An artificial intelligence-assisted decision-making module performs real-time analysis to enhance situational awareness and generate auxiliary decision-making suggestions. An instruction distribution module implements instruction issuance and closed-loop management, achieving real-time collaboration of cross-departmental information, intuitive grasp of the overall situation, and intelligent assistance for scientific decision-making. This significantly improves the efficiency and accuracy of emergency response and solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An emergency command system based on audio and video conferencing, characterized in that it includes: The event receiving interface is used to receive initial information about emergency events from different channels; A preprocessing module, connected to the event receiving interface, is used to standardize the initial information to generate a preliminary event file containing the core elements of the event. The standardization process includes speech recognition to text, parsing sensor alarm fields, and extracting text keywords. The intelligent converged communication module is connected to the preprocessing module and is used to retrieve the communication plan according to the preliminary event file and initiate concurrent communication connections through the multi-protocol access gateway to establish a converged communication network. The converged communication network is used to access and perform real-time transcoding and protocol adaptation of heterogeneous audio and video streams. The multi-source information fusion and visualization processing module is connected to the intelligent fusion communication module. It is used to generate a dynamic situation layer by fusing location information and external real-time data with an electronic map as the base map, and to realize the joint operation binding of video footage and geographic icons. An AI-assisted decision-making module is connected to the intelligent fusion communication module and the multi-source information fusion and visualization processing module. It is used to analyze audio and video streams in parallel to extract key situational elements to enhance the situational layer, and generate auxiliary decision-making suggestions based on knowledge base matching. The instruction distribution module, connected to the AI-assisted decision-making module and the intelligent fusion communication module, is used to send composite task packages to designated terminals and realize closed-loop management based on the returned data.

[0007] Preferably, the intelligent converged communication module includes an intelligent routing unit, which continuously monitors the network quality of the communication channel and dynamically adjusts the transmission strategy according to a preset priority strategy to ensure that the transmission channel for critical command voice is given priority.

[0008] Preferably, when the multi-source information fusion and visualization processing module generates a dynamic situational layer, it automatically associates and loads preset data layers around the incident area, and orderly overlays important infrastructure, emergency resource locations, population density and real-time dynamic data on the electronic map base.

[0009] Preferably, the parallel analysis of audio and video streams by the AI-assisted decision-making module includes: The first-line pipeline performs real-time speech recognition and natural language understanding on the conference voice, extracts key entities, commands and status information and associates them with timestamps and personnel roles; The second pipeline performs computer vision analysis on selected video streams to identify specific targets or situations and convert them into vector graphics or heat maps with geographic coordinates. The above analysis results are automatically injected into the dynamic situation layer to achieve intelligent enhancement.

[0010] Preferably, when the AI-assisted decision-making module generates auxiliary decision-making suggestions, it matches the current event type and key elements with the emergency plan knowledge base and historical case database, and generates structured suggestions on evacuation range or rescue routes through a rule engine and prediction model, and marks them on the dynamic situation layer.

[0011] Preferably, the composite task package issued by the instruction distribution module includes text, map screenshots, and voice instructions; the closed-loop management refers to the use of new data transmitted back from the front-end terminal to update the dynamic situation layer in real time, and the use of the artificial intelligence-assisted decision-making module to perform progress analysis and risk warning, so as to assist the commanders in dynamically adjusting instructions.

[0012] An emergency command method based on audio and video conferencing, implemented using an emergency command system, is characterized by the following steps: Information is received through the event receiving interface, and a preliminary event file is generated by the preprocessing module; The intelligent converged communication module establishes a converged communication network based on the archives, and aggregates and standardizes multiple audio and video streams; The multi-source information fusion and visualization processing module generates dynamic situational awareness layers based on electronic maps; The AI-assisted decision-making module analyzes audio and video content in real time to enhance the situational awareness layer and generate auxiliary decision-making suggestions; The instruction distribution module will generate composite task packages from the decisions and issue them out, and perform closed-loop management and post-event analysis based on the returned data.

[0013] Preferably, the post-event analysis includes the AI-assisted decision-making module automatically organizing the full timeline data of the event, generating an emergency response assessment report, and storing the effective model of this response as a new case in the knowledge base to optimize the analysis model.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves full-process management from the occurrence of an event to its resolution by seamlessly connecting multiple links such as event information reception, intelligent fusion communication, multi-source information visualization, artificial intelligence deep analysis, and auxiliary decision generation.

[0015] 2. This invention integrates heterogeneous information from different sources such as telephones, sensors, video surveillance, and individual soldier equipment into a unified converged communication network through unified access and transcoding technology. This ensures that the command center can grasp the situation on the ground in real time and comprehensively, realize cross-departmental and cross-level information collaboration and sharing, and solve the problem of information silos.

[0016] 3. This invention deeply integrates voice, video, and location data with electronic maps, and utilizes artificial intelligence technology to automatically identify key elements, generating a dynamically evolving, visualized situational awareness map. This allows commanders to grasp the overall situation and development trend of an event at a glance, without having to switch between multiple isolated information screens or manually compare data. This significantly reduces cognitive load, accelerates decision-making, and enhances the intuitiveness and accuracy of situational awareness.

[0017] 4. This invention automatically generates structured decision-making references such as resource allocation suggestions and evacuation scope delineation by analyzing on-site information in real time and combining historical cases and contingency plan databases. This frees commanders from the heavy workload of information screening and analysis, allowing them to focus more on key decisions. As a result, the scientific nature, accuracy and response efficiency of emergency command are improved, and the shortcomings of insufficient decision support are effectively made up for. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To address the issues of information silos, unintuitive situational awareness, and insufficient decision support in existing emergency command systems, please refer to [link / reference]. Figure 1-2 This embodiment provides the following technical solution: When an emergency occurs and is reported to the command center, the operation process of the emergency command system based on audio and video conferencing proposed in this invention is immediately activated.

[0021] I. Intelligent Converged Communication Phase First, the initial information of the event, which may be a voice recording of an emergency call, an abnormal data alarm from a monitoring sensor, or a text report from social media or a grassroots informant, is captured by the system's event receiving interface and sent to a preprocessing module.

[0022] The preprocessing module performs initial standardization on the initial information, including but not limited to: performing speech recognition on audio alarm recordings and converting them into structured text records; parsing sensor alarm information to extract key fields such as time, location, type, and value; and segmenting and extracting keywords from the text report. After preprocessing, the system generates a machine-readable preliminary event file for the event, containing core elements such as event type, possible location, and reporting time. The generation of the preliminary digital file automatically triggers subsequent resource mobilization and information aggregation processes.

[0023] The establishment of the preliminary incident file activates the system's intelligent converged communication module. Based on the nature and location of the incident inferred from the preliminary incident file, the intelligent converged communication module automatically retrieves pre-set communication plans from the contingency plan or rule base. The communication plans define a list of personnel, teams, equipment, and experts that need to be immediately contacted and integrated into the command system in such incidents. Then, the intelligent converged communication module initiates a large-scale concurrent communication connection simultaneously through its integrated multiple communication gateways. These connection requests are delivered to various units simultaneously through public telephone networks, mobile communication networks, dedicated wireless communication networks, satellite communication links, and even Internet instant messaging protocols. That is, the individual equipment worn by the first responders on the scene, the mobile terminals of the command vehicles rushing to the scene, the desktop video conferencing terminals of experts, the fixed surveillance cameras around the incident area, the callable drone control stations, and the dedicated intercom systems belonging to different departments all receive the system's invitation to join or connection instructions at the same time.

[0024] Upon receiving the system's invitation to join or connection command, each of the aforementioned units responds immediately, automatically establishing a converged communication network spanning different networks and devices. Video streams from individual soldier helmet cameras, drone aerial footage, expert computer video streams, voice signals from intercom systems, and voice from vehicle-mounted radios—these media streams, varying in format, protocol, and bitrate—are all transcoded and protocol-adapted in real-time by the intelligent converged communication module's access gateway upon access, uniformly encapsulating them into a standardized streaming media format within the system. Simultaneously, the intelligent routing module within the intelligent converged communication module continuously monitors the network quality of all communication channels and dynamically adjusts transmission strategies based on preset priority policies. For example, when an unstable high-definition video backhaul network is detected, its bitrate can be automatically reduced or switched to a backup link, while ensuring that the transmission channel for critical command voice messages is always given priority.

[0025] At this point, an event-centric converged communication network is ready, and first-hand audio and video data from the field and instructions from the backend begin to interact within the converged communication network.

[0026] II. Multi-source information visualization stage The various information streams gathered by the converged communication network are simultaneously sent to the system's multi-source information fusion and visualization processing module. The video components of all standardized audio and video streams are decoded, segmented, and synthesized, ready for flexible layout and display on the command screen. Meanwhile, all audio components, along with the voice in the communication, are processed and the spatial location information contained in each information stream is extracted as a spatial reference for information fusion.

[0027] The multi-source information fusion and visualization module uses a high-precision electronic map as a base map to create a dynamic situational layer specific to the current event. The various spatial location information extracted above is precisely marked on the base map. Simultaneously, it automatically associates and loads pre-set data layers surrounding the incident area, overlaying the location information of important infrastructure and emergency resources, as well as population density and terrain information, onto the base map. Furthermore, real-time wind direction and speed data from meteorological departments and road congestion information from transportation departments—any real-time data obtainable through the event receiving interface—are also systematically overlaid on the base map as transparent dynamic layers. In addition, the video stream transmitted from the scene is not only played in a small side window but also linked to corresponding geographic location icons on the map via video linking. When command personnel click on a geographic location icon, they can retrieve the associated real-time video feed on the display interface, facilitating switching between overall situational and micro-level scenarios.

[0028] III. Deep Analysis Stage of Artificial Intelligence While information is spatially fused and visualized, the AI-assisted decision-making module processes multiple information pipelines in parallel.

[0029] The first pipeline is voice intelligence processing. All voice signals from call channels and consultations are converted into text in real time and continuously. The converted text stream is not simply recorded, but is immediately sent to a natural language understanding model for analysis. The model identifies key entities, commands, requests, and states in the dialogue, such as "east side of the warehouse," "chlorine leak," "three people trapped," "leak plugging tools needed," and "rescue team has arrived." These extracted key information fragments are timestamped in real time and associated with the role and unit of the person who made the voice.

[0030] The second pipeline is video intelligent analysis. The system selects the most critical video streams, calls upon computer vision models for real-time analysis, and converts the analysis results into vector graphics or heat maps with geographic coordinates in real time. For example, by selecting aerial views from drones or monitoring of key locations, the system can identify the direction and area of ​​flame spread in a fire scene, or in a hazardous chemical leak scene, it can analyze the diffusion cloud of leaked gas through thermal imaging, and use computer vision models to analyze the flame front or gas cloud outline, which is then converted into vector graphics or heat maps with geographic coordinates.

[0031] Subsequently, the semantic and visual analysis results processed by the AI-assisted decision-making module are automatically fed back and injected into the previously constructed dynamic situation layer. For example, the flame front is automatically drawn as a constantly changing combustion area polygon, or the gas cloud diffusion path is automatically drawn as a directional arrow. This process realizes the intelligent enhancement of the dynamic situation layer. Commanders no longer need to piece together information from noisy voices and cluttered videos, because important situation elements have been extracted and intuitively marked on the map, greatly saving the analysis time of commanders and improving the speed and accuracy of command.

[0032] IV. Decision Support Generation Stage The AI-assisted decision-making module matches and compares the current event type and key elements with the emergency response plan knowledge base and historical case database. Through a built-in rule engine and predictive model, it quickly generates a series of structured auxiliary suggestions, which are displayed on one side of the command interface and marked in the corresponding position on the dynamic situation layer for commanders' reference. For example, these suggestions might include: recommending the area of ​​residential areas requiring evacuation based on the leaked material and wind direction, and displaying it on a map; calculating the optimal route for rescue forces to enter the area based on the fire location and traffic conditions, and marking it on a map.

[0033] V. Consultation, Analysis, and Command Generation Stage Commanders, experts, and responsible parties conduct analysis and decision-making within an audio-visual conferencing environment formed by a converged communication network. The entire consultation process, including each speaker's viewpoint, the consensus reached, and any existing disagreements, continues to be recorded and analyzed in real time by the AI-assisted decision-making module, automatically summarizing key meeting minutes and a to-do list. When the commander makes a final decision, they can directly mark and manipulate the data on a visual map, and the instruction distribution module can send a composite task package containing text, map screenshots, and voice commands to the mobile terminals of personnel at the front lines with a single click.

[0034] Once the command is issued, the personnel at the front lines who receive it will see not only a text description of the task on their mobile terminals, but also clear guidance with an overlaid task map and annotations. During the task execution, their new locations, statuses, and transmitted live footage are again aggregated through the converged communication network. This data is then processed by a multi-source information fusion and visualization module to update the dynamic situational awareness layer, and analyzed in real time by an AI-assisted decision-making module to assess task progress and identify new risks. If an anomaly is detected, an immediate warning is issued and corrective suggestions are provided to assist command personnel in making dynamic adjustments and issuing subsequent commands.

[0035] VI. Post-event analysis phase Once the incident is effectively controlled and the response is nearing its end, the system enters the post-incident analysis phase. During this phase, the AI-assisted decision-making module automatically compiles key nodes, all decision-making instructions, resource consumption, consultation records, and situation evolution diagrams from the entire timeline of the incident, generating an emergency response assessment report. This report fully recreates the incident's development and command process. More importantly, the effective patterns, key decision points, and results formed during this response, after processing, can be stored as new case samples in the emergency knowledge base. This allows for continuous optimization of the AI ​​analysis model and the reasoning capabilities of the decision-making module, enabling the system to learn and improve its effectiveness in real-world scenarios.

[0036] How it works: First, event information is captured by the system's event receiving interface through multiple channels and preprocessed, including speech recognition, text parsing, and keyword extraction, to generate a standardized preliminary event file. This file contains core elements such as event type and location, and automatically triggers subsequent resource mobilization and information aggregation.

[0037] The system enters the intelligent converged communication phase. Based on the preliminary event file, it retrieves pre-set communication plans and simultaneously contacts relevant personnel and equipment through multiple integrated communication networks, including on-site individual soldier equipment, command vehicles, surveillance cameras, and drones. Upon response from these devices, the system establishes a converged communication network, performing real-time transcoding and protocol adaptation of heterogeneous audio and video streams to ensure stable transmission. Simultaneously, the intelligent routing module dynamically optimizes network quality, prioritizing critical commands.

[0038] In the multi-source information visualization phase, the audio and video streams converged by the integrated communication network are sent to the multi-source information fusion and visualization processing module. This module uses a high-precision electronic map as a base map to create a dynamic situational awareness layer, spatially overlaying extracted location information, real-time data, and video footage. The video conferencing function binds video to map icons, allowing commanders to switch between macro-level and micro-level situations.

[0039] The deep analysis phase of artificial intelligence processes speech and video streams in parallel. Speech signals are transcribed into text in real time, and key entities and commands are extracted by a natural language understanding model. Video streams are analyzed by a computer vision model, identifying features such as flame spread or gas diffusion, and converted into vector graphics on a map. The analysis results are automatically injected into a dynamic situational awareness layer, achieving intelligent situational awareness enhancement.

[0040] During the decision-making support phase, the system matches event types with the emergency response plan knowledge base, generates structured suggestions through a rule engine and predictive model, and marks them on a map. In the consultation, analysis, and command generation phase, commanders make decisions through audio-visual conferencing, and the command distribution module sends composite task packages to frontline mobile terminals with a single click. During execution, newly transmitted data updates the situation in real time, and the system monitors progress and issues early warnings.

[0041] In the post-event analysis phase, the system automatically organizes the entire timeline of event data, generates an emergency response assessment report, stores effective patterns in a knowledge base, optimizes the artificial intelligence model, and enables self-learning. The entire working principle improves the accuracy and efficiency of emergency command through a closed-loop process.

[0042] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0043] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. An emergency command system based on audio-video conference consultation, characterized in that, The application relates to an emergency event management system, comprising: an event receiving interface for receiving initial information of an emergency event from different channels; a preprocessing module connected to the event receiving interface for standardizing the initial information to generate a preliminary event file containing event core elements; an intelligent fusion communication module connected to the preprocessing module for calling a communication plan according to the preliminary event file and initiating a concurrent communication connection to establish a fusion communication network for accessing and standardizing heterogeneous audio and video streams from different devices and networks; a multi-source information fusion and visualization processing module connected to the intelligent fusion communication module for generating and updating a dynamic situation layer by fusing spatial position information in the heterogeneous audio and video streams and external real-time data based on an electronic map as a base map; an artificial intelligence assisted decision-making module connected to the intelligent fusion communication module and the multi-source information fusion and visualization processing module for performing real-time intelligent analysis on the audio and video streams, extracting key situation elements to enhance the dynamic situation layer, and generating assisted decision-making suggestions based on a knowledge base; an instruction distribution module connected to the artificial intelligence assisted decision-making module and the intelligent fusion communication module for distributing a composite task package containing decision-making instructions to designated terminals.

2. The emergency command system based on audio-video conference meeting according to claim 1, characterized in that, The standardization processing of the preprocessing module on the initial information comprises: voice recognition and conversion of audio alarm recordings into structured text, analysis of sensor alarm information to extract key fields, and word segmentation and keyword extraction of text reports.

3. The emergency command system based on audio-video conference meeting according to claim 1, characterized in that, The intelligent fusion communication module specifically performs: According to the event nature and location of the preliminary event file, a list of personnel, teams and equipment to be contacted is called from a plan library; Concurrent connection requests are initiated to individual devices, command vehicle mobile terminals, video conference terminals, monitoring cameras and unmanned aerial vehicle control stations through public telephone networks, mobile communication networks, wireless communication networks, satellite communication links and Internet protocols; Real-time transcoding and protocol adaptation are performed on the accessed heterogeneous audio and video streams, and transmission strategies are dynamically adjusted based on network quality monitoring results to ensure the transmission priority of key instructions.

4. The emergency command system based on audio-video conference meeting according to claim 1, characterized in that, The process of generating a dynamic situation layer by the multi-source information fusion and visualization processing module comprises: Position information extracted from the audio and video streams is marked on the electronic map, and important infrastructure layers, emergency resource location layers, population density layers and real-time dynamic data layers around the incident area are automatically loaded and superimposed.

5. The emergency command system based on audio-video conference meeting according to claim 4, characterized in that, The multi-source information fusion and visualization processing module also implements a video joint logistics function, binds the returned video stream with a geographical position icon on the electronic map, so that clicking the geographical position icon can call the associated real-time video screen.

6. The emergency command system based on audio-video conference meeting according to claim 1, characterized in that, The real-time intelligent analysis of the artificial intelligence assisted decision-making module comprises a first pipeline and a second pipeline processed in parallel: The first pipeline is used for real-time voice recognition and natural language understanding of consultation voice signals to extract key entities, commands and state information, and associate them with the sender role and time stamp; The second pipeline is used to perform computer vision analysis on selected key video streams to identify specific targets or situations, and convert the analysis results into vector graphics or heat maps with geographic coordinates; Wherein, the analysis results of the first pipeline and the second pipeline are automatically fed back and injected into the dynamic situation layer for intelligent enhancement.

7. The emergency command system based on audio-video conference meeting according to claim 6, characterized in that, The artificial intelligence assisted decision making module generates assisted decision suggestions including: Matching the current event type and extracted key elements with the emergency plan knowledge base and historical case library, generating structured suggestions on evacuation range, rescue route or resource allocation through rule engine and prediction model, and marking on the dynamic situation layer.

8. The emergency command system based on audio-video conference meeting according to claim 1, characterized in that, The composite task package issued by the instruction distribution module contains text instructions, map screenshots and voice instructions; The system also supports closed-loop management of instruction execution process, and the new data returned by the front terminal is used to update the dynamic situation layer in real time, and the progress analysis and risk warning are carried out by the artificial intelligence assisted decision making module, so as to assist the dynamic adjustment of instructions.

9. An emergency command method based on audio and video conference consultation, implemented based on the emergency command system based on audio and video conference consultation according to any one of claims 1-8, characterized in that, The method comprises the following steps: Step one, receiving the initial information of emergency event through the event receiving interface; Step two, standardizing the initial information through the preprocessing module to generate the preliminary event file; Step three, establishing the fusion communication network according to the preliminary event file through the intelligent fusion communication module, and converging and standardizing the multi-party audio and video streams; Step four, fusing multi-source information based on electronic map through the multi-source information fusion and visualization processing module, generating and displaying the dynamic situation layer; Step five, real-time analysis of audio and video content through the artificial intelligence assisted decision making module, extraction of key information to enhance the situation layer, and generation of assisted decision suggestions; Step six, the command decision is formed into a composite task package and issued to the front terminal through the instruction distribution module, and the situation is updated based on the data returned by the front terminal, and closed-loop management and post analysis are carried out, evaluation report is generated and knowledge base is optimized.

10. The emergency command method based on audio-video conference meeting according to claim 9, characterized in that, The post analysis includes: the artificial intelligence assisted decision making module automatically sorts out the key nodes, decision instructions, resource consumption and situation evolution diagram of the whole time line of the event, generates the emergency disposal evaluation report, and stores the effective mode formed in this disposal as a new case sample into the emergency knowledge base.

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