Distributed architecture and collaborative decision-making method of intelligent emergency command and dispatch system

Abstract

The invention discloses a distributed architecture of an intelligent emergency command and dispatch system and a collaborative decision-making method. A distributed architecture comprising a basic resource layer, a support service layer and an application layer is constructed, and functions of multi-type alarm access, accurate alarm positioning, intelligent power dispatching and the like are realized by using multiple technologies. And establishing a collaborative decision-making mechanism which covers problem modeling, communication protocol design, negotiation mechanism development, dynamic adjustment and decision-making execution. The system is deployed after integration testing and security testing, personnel training, daily hardware maintenance, software monitoring, data backup and security component updating are carried out. The emergency commanding and dispatching efficiency, accuracy and collaboration are improved, the method adapts to complex emergencies, and public safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field related to smart emergency command and dispatch systems, and specifically to a distributed architecture and collaborative decision-making method for smart emergency command and dispatch systems. Background Art

[0002] With the development of social economy and the acceleration of urbanization, the frequency and complexity of various emergencies are increasing, which puts forward higher requirements for public safety and emergency management. As the core platform for responding to emergencies, the performance and efficiency of the emergency command and dispatch system are directly related to the success or failure of rescue work and the safety of people's lives and property.

[0003] Traditional emergency command and dispatch systems have many limitations in terms of architecture design and functional implementation. In terms of architecture, centralized or relatively simple distributed architectures are often used. The coupling between the various levels of the system is high, and the flexibility and scalability are poor, making it difficult to adapt to large-scale, complex and changeable emergency scenarios. For example, when an emergency involves multiple regions or departments, information transmission and collaborative work are prone to delays and obstacles, making it impossible to achieve fast and effective resource allocation and command decisions.

[0004] In terms of functions, the traditional system has a single alarm access method, which mainly relies on telephone alarms and lacks effective integration of new alarm methods, resulting in untimely and incomplete alarm information. The accuracy of alarm positioning is low, mainly relying on manual inquiries and simple geographic information queries, making it difficult to quickly and accurately determine the location of the accident, affecting the rapid arrival of rescue forces. Force dispatch is mostly based on experience judgment and lacks intelligent algorithm support, which is prone to unreasonable and inaccurate dispatch, resulting in waste of resources and rescue delays. At the same time, the traditional system is weak in collaborative decision-making, and there is a lack of effective communication and collaboration mechanisms between various emergency rescue entities, making it impossible to achieve dynamic resource optimization and coordinated actions. For example, in the face of complex situations such as multiple disasters or disaster chain reactions, it is difficult to quickly form a unified command and coordinated rescue operations, reducing the overall effectiveness of emergency response.

[0005] In addition, with the rapid development of information technology, the application potential of emerging technologies such as big data, cloud computing, and mobile Internet in the field of emergency command and dispatch has not been fully explored. Traditional systems have failed to fully integrate and utilize these technologies, and are unable to achieve in-depth analysis and mining of data, and it is difficult to extract valuable information from massive emergency data to provide strong support for decision-making. In terms of mobile Internet command, it lacks real-time and convenience, and cannot meet the dynamic information interaction needs of emergency rescue sites. Summary of the invention

[0006] The purpose of the present invention is to provide a distributed architecture and collaborative decision-making method for an intelligent emergency command and dispatch system to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a distributed architecture and collaborative decision-making method of a smart emergency command and dispatch system, comprising the following steps: Construct a distributed architecture based on a multi-agent system, which includes a basic resource layer, a support service layer and an application layer; the basic resource layer includes a basic environment, network and security resources and a cloud platform support environment, which are used to provide the underlying resources required for system operation; the support service layer provides various database supports and services, including an integrated business database, a basic database, an external exchange database, a theme database, a full-text database, a one-picture service and analysis tools; the application layer includes a combat command platform, an intelligent police reception and handling platform, a data visualization platform and a mobile combat APP; Utilize big data, cloud computing, and mobile Internet technologies to achieve unified access to multiple types of alarm methods, including fixed-line, mobile, WeChat public account, SMS, and IoT alarms, and automatically obtain the location, pictures, and video information of the alarm person, and automatically fill in the key information of the alarm situation; Through Internet positioning, place name address positioning, geographic element positioning, mobile application positioning technology means, combined with the mobile phone's own positioning function, operator base station positioning and installation address, using the similarity matching mechanism, the alarm positioning is achieved accurately, and the positioning information is automatically filled in the alarm receipt form; Based on time and geographic location analysis and deep learning intelligent algorithm means, an intelligent force dispatch model is established to achieve one-click precise dispatch of vehicles, equipment, plans, and formations according to different levels and types of alarm situations; At the fire rescue station, the alarm receiving terminal is linked with the garage door, emergency broadcast, warning lights, and alarm bells to automatically generate electronic dispatch orders, standardize alarm broadcasts and warning lights and alarm bells, and improve the efficiency of wartime dispatch; Using mobile Internet and big data technologies, a three-party video conference mechanism based on 5G high-speed video backhaul technology will be established among the alarm person, the command center, and the participating forces before the combatants arrive at the scene, so that they can share police information, upload and issue combat orders, and query auxiliary information anytime and anywhere; The coordinates, equipment information, and contact information of the city's key units, joint support units, micro fire stations, and government (enterprise and institution) professional teams are entered into the system database, and big data-assisted decision-making analysis is used to achieve "one map" to dispatch pre-positioned forces to participate in the disposal in advance; Comprehensively integrate the internal and external firefighting data resources of the fire department, accurately collect data on fire hydrants, key units, and rescue force elements, establish data analysis models and data visualization displays, and provide data support for command decision-making and force dispatch; The system adopts modular design, complies with relevant specifications and standards, and uses the advantages of plug-ins to expand functional modules, so as to realize rapid development of user unit functions and autonomous loading of personalized applications; Establish a collaborative decision-making mechanism among multiple agents, including problem modeling, communication protocol design, negotiation mechanism development, dynamic adjustment and decision execution, so that each agent can dynamically adjust resource allocation according to emergency situations and achieve rapid response and optimal decision-making.

[0008] Specifically, the basic environment in the basic resource layer includes a combat command center and a central computer room. The combat command center is equipped with advanced communication facilities and display equipment, which can display emergency site conditions and command and dispatch information in real time, and its layout design conforms to ergonomic principles, which is convenient for command personnel to operate and collaborate. The central computer room is equipped with high-performance servers and storage devices, and adopts redundant design and intelligent cooling system to ensure the stability of system operation and the security of data storage.

[0009] Specifically, the routers, switches and firewalls in the network and security resources have the following characteristics: the router supports multi-protocol high-speed forwarding, has intelligent routing selection and load balancing functions, and can dynamically adjust the data transmission path according to network traffic; the switch uses a full Gigabit Ethernet port, has VLAN division and port mirroring functions, which is convenient for network management and data monitoring; the firewall has intrusion detection and prevention, antivirus, content filtering and other advanced security functions, and uses state-based detection packet filtering technology to effectively prevent external illegal access, while supporting real-time updates of security policies to respond to ever-changing network security threats.

[0010] Specifically, the resource pooling function provided by the cloud platform support environment can realize the dynamic allocation and recovery of computing resources, storage resources and network resources, automatically adjust resource configuration according to the system operation load, and improve resource utilization; the application management function supports the unified deployment, upgrade and monitoring of various application programs to ensure the stability and compatibility of applications; the service management function provides service registration, discovery and orchestration functions to facilitate interaction and collaboration between different services; the security management function includes identity authentication, access authorization, data encryption and auditing, and adopts multi-factor authentication and encrypted transmission technology to ensure the security of system data and operations.

[0011] Specifically, the one-picture alarm receiving function in the one-picture service can display the alarm point location, alarm type and alarm time information in real time on the electronic map, and intuitively distinguish the urgency of different alarm situations through colors and icons; the one-picture dispatching function allows commanders to directly dispatch fire trucks and rescue personnel resources on the map, plan the optimal route in real time, and display the movement status of resources; the one-picture command function supports commanders to mark combat areas, set mission objectives and issue command instructions on the map, and can also view the location and action trajectory of each rescue force in real time; the one-picture analysis function can conduct a comprehensive analysis of historical alarm data, resource distribution and geographical environment information, generate hot spot area analysis and resource demand forecast visualization reports, and provide data support for decision-making; the one-picture decision-making function uses intelligent algorithms based on data analysis results to provide commanders with a variety of decision-making plans, and evaluates the feasibility and risks of each plan to assist commanders in making the best decision.

[0012] Specifically, the analysis tool includes a data mining module, a statistical analysis module and a simulation module. The data mining module uses a machine learning algorithm to mine and analyze massive emergency data to discover potential rules and correlation patterns, such as the relationship between the time and location of the fire and the surrounding environmental factors; the statistical analysis module provides a variety of statistical methods to perform statistical analysis on police data and resource usage data, and generate various reports and charts, such as police trend charts and resource allocation efficiency analysis tables; the simulation module can build a virtual model based on the actual emergency scenario, simulate the rescue process and effects under different decision-making plans, provide decision-making references for commanders, and evaluate possible problems in advance and optimize decisions by simulating rescue operations under different disaster scenarios.

[0013] Specifically, the process of establishing the intelligent force dispatch model is as follows: first, collect and organize a large amount of historical police data, including the type of police data, location of occurrence, time of occurrence, disaster severity information, and fire resource data, such as fire vehicle type, equipment configuration, rescue personnel skill level information, and geographic information data, such as road conditions, building distribution, and water source location information; then, use a deep learning algorithm to train these data to build a neural network-based force dispatch model. The model can automatically analyze and predict the required fire resource type, quantity, and optimal deployment path based on real-time police information; at the same time, the model has self-learning and adaptive capabilities, and can continuously adjust and optimize the dispatch strategy according to actual rescue results to improve the accuracy and efficiency of dispatch. For example, after multiple fire rescues, the vehicle dispatch plan is optimized according to the actual fire extinguishing effect.

[0014] Specifically, the dynamic adjustment mechanism runs in real time when an emergency occurs, specifically: through sensor networks, surveillance cameras, and satellite positioning technology, real-time information on the development trend of the disaster is obtained, such as the direction of fire spread, changes in the size of the fire, and resource usage, such as the fuel consumption of fire trucks, the remaining amount of fire extinguishing agent, the physical consumption of rescue personnel, and the progress of rescue, such as the number of people evacuated and the degree of fire control; this real-time information is input into the collaborative decision-making system, and the intelligent agents in the system evaluate the current decision and resource allocation according to preset rules and algorithms to determine whether adjustments are needed; if adjustments are needed, they communicate and collaborate with other intelligent agents through a negotiation mechanism to reformulate resource allocation plans and combat strategies, and update the task allocation and action instructions of each intelligent agent in real time to ensure that the entire emergency rescue process can be continuously optimized according to actual conditions, for example, when the wind direction at the fire scene changes suddenly, the position and attack direction of the fire-fighting vehicle are adjusted in time.

[0015] Specifically, the decision-making execution process includes the following steps: after the command center issues a dispatch order, the order is transmitted in real time to the mobile terminal devices of each fire truck and rescue personnel through the wireless communication network. The mobile terminal has voice prompts, text display and map navigation functions to ensure that the order is clearly conveyed; during the execution of the order, each execution unit uses the on-board sensors and personnel positioning equipment to provide real-time feedback on its own position, action status and task completion status, such as the driving speed, driving route, and arrival time of the fire truck, the entry time of the rescue personnel, and the progress information of the rescue operation; the command center tracks and monitors the dispatch order based on the feedback information. If deviations are found during the execution process or emergencies are encountered, such as road congestion that prevents the vehicle from arriving on time, the feedback adjustment mechanism is immediately activated to re-plan the route or adjust resource allocation to ensure that the order is executed accurately and correctly, and to ensure the smooth progress of emergency rescue work.

[0016] Specifically, the system's real-time monitoring and management functions for various emergency resources are achieved in the following ways: installing sensors and Internet of Things devices on fire trucks, fire equipment, and rescue material emergency resources to collect real-time status information of resources, such as the operating status of the vehicle, the integrity of the equipment, and the inventory quantity of materials; establishing a resource management database to store and update the collected information in real time, and at the same time interact with the command and dispatch system for data; through data analysis and early warning algorithms, real-time monitoring and early warning of abnormal resource conditions, such as vehicle failure warnings and material shortage warnings; when an early warning occurs, the system automatically starts the resource allocation process, and allocates corresponding resources from other regions or reserves according to preset rules and priorities, to ensure that emergency rescue work is not affected by resource shortages and to achieve efficient use and timely allocation of resources.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The distributed architecture design based on the multi-agent system realizes the decoupling of each level of the system and improves the flexibility and scalability of the system. The basic resource layer, support service layer and application layer each perform their duties and work together to quickly adapt to emergency events of different scales and types, effectively integrate various resources, and provide solid underlying support for emergency command and dispatch.

[0018] The deep integration of advanced technologies such as big data, cloud computing, and mobile Internet has significantly improved the system's intelligence level and emergency response capabilities. The unified access of multiple types of alarm means has greatly shortened the time to obtain alarm information and improved the richness and accuracy of alarm information. Accurate alarm location technology can quickly determine the location of the accident and buy precious time for the rapid arrival of rescue forces.

[0019] The establishment of an intelligent force dispatch model, based on the comprehensive analysis of multiple data using deep learning algorithms, has achieved accurate and rapid force dispatch. According to the real-time changes in the police situation and the actual situation of resources, the dispatch plan is dynamically optimized to improve resource utilization and avoid resource waste. For example, when responding to traffic accident rescue, the system intelligently selects the most suitable rescue vehicles and personnel based on the type of accident, severity, surrounding traffic conditions and distribution of rescue resources, and plans the optimal route to ensure efficient rescue work.

[0020] The wartime linkage control mechanism that can be activated upon hearing an alarm has realized the automated coordination between the alarm receiving terminal and the garage door, emergency broadcasting and other equipment, improving the wartime dispatch efficiency of the fire rescue station. The standardized alarm broadcasting and alarm light and bell settings have enhanced the visualization and standardization of command, helping to improve the response speed and accuracy of rescue personnel.

[0021] The collaborative consultation mechanism supported by 5G high-speed video backhaul technology enables real-time information sharing and efficient communication between the alarm person, the command center, and the participating forces. The fully integrated internal and external data resources of the fire department provide a rich data basis for decision-making, and assist the commanders in making scientific and reasonable decisions through data analysis models and visualization displays.

[0022] The modular design and plug-in function expansion method enable the system to quickly respond to the personalized needs of user units and facilitate function development and upgrades. Users can load the required application modules according to actual conditions, which improves the adaptability and customizability of the system.

[0023] The perfect collaborative decision-making mechanism ensures that all intelligent agents can collaborate efficiently in emergency situations, and achieves optimal resource allocation and rapid response through problem modeling, communication protocol design, negotiation mechanism development and dynamic adjustment. When faced with complex and changing emergency situations, the system can quickly coordinate the forces of all parties to form a unified command and action plan, improving the overall efficiency of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 , a specific implementation method of a distributed architecture and collaborative decision-making method of a smart emergency command and dispatch system provided by the present invention: 1. System architecture construction The site was selected to build a combat command center, equipped with a high-definition large-screen display, high-performance communication equipment, and an ergonomically designed command workbench to ensure that command personnel can obtain real-time and clear emergency site information and work together efficiently. At the same time, a central computer room was built, high-performance servers were selected, redundant power supplies, redundant network connections, and other designs were adopted, and an intelligent cooling system was installed to ensure stable system operation and safe data storage.

[0027] Deploy network and security equipment such as routers, switches and firewalls. The routers are enterprise-level devices that support multi-protocol high-speed forwarding and have intelligent routing selection and load balancing functions. They dynamically adjust the data transmission path in real time according to network traffic to ensure efficient data transmission. The switches use full Gigabit Ethernet ports and have VLAN division and port mirroring functions. The firewalls have advanced security functions such as intrusion detection and prevention, antivirus, content filtering, etc. They prevent external illegal access through stateful detection-based packet filtering technology, and regularly update security policies to respond to new network security threats.

[0028] Build a cloud platform support environment to achieve resource pooling, application management, service management and security management.

[0029] Build an integrated business database, integrate various emergency-related data, design a reasonable data structure and data storage method, and ensure efficient storage and rapid retrieval of data. At the same time, establish a basic database, external exchange database, subject database and full-text database.

[0030] Develop a one-map service to realize one-map alarm reception, one-map dispatch, one-map command, one-map analysis and one-map decision-making for fire emergency rescue based on the geographic information system platform. Provide analysis tools, including data mining module, statistical analysis module and simulation module.

[0031] Develop a practical command platform with functions such as real-time monitoring of emergency scene conditions, command and dispatch resources, information management, and decision-making assistance. Design an intelligent alarm receiving and handling platform to achieve unified access to multiple types of alarm methods, automatically obtain the location, pictures, videos and other information of the alarm person, and automatically fill in key alarm information.

[0032] Build a data visualization platform to display various emergency data in the form of intuitive charts, maps, dashboards, etc., so that commanders can quickly understand and analyze data. Develop a mobile combat APP to provide rescue personnel with functions such as on-site information acquisition, information reporting, and collaborative work, and support offline working mode.

[0033] 2. Realization of multi-technology integration functions Develop alarm access interfaces and cooperate with telecom operators and Internet platforms to achieve seamless connection of multiple alarm methods such as fixed-line phones, mobile phones, WeChat public accounts, text messages, and IoT alarms. When an alarm is received, the system automatically analyzes the alarm information, obtains the location information of the alarmist through positioning services, extracts key information using image recognition technology and video analysis technology, and automatically fills it into the alarm record, while pushing the alarm information to the command center in real time.

[0034] After receiving the alarm information, the system first uses the mobile phone's built-in positioning function and the operator's base station positioning information, combined with the place name and address positioning and geographic element positioning technology based on the place name and address database, to preliminarily determine the alarm location range. Then, the similarity matching mechanism is used to further refine the location information, automatically fill in the positioning results into the alarm receipt, and accurately mark the alarm location on the electronic map, while displaying the surrounding geographical environment information.

[0035] Collect historical alarm data, fire resource data and geographic information data, and clean, organize and annotate these data. Use deep learning algorithms to build an intelligent force dispatch model. The model analyzes the input real-time alarm information to predict the required fire resource type, quantity and optimal deployment path. During the model training process, continuously adjust the algorithm parameters to optimize the model performance, and use the validation data set for verification and evaluation. In the actual dispatch process, based on the model output results and combined with the real-time status of the current resources, one-click precise dispatch of various forms such as vehicle dispatch, equipment dispatch, plan dispatch, and formation dispatch is achieved, and the dispatch process is tracked in real time, and dynamic adjustments are made according to actual conditions.

[0036] An intelligent alarm receiving and handling system terminal is installed at the fire rescue station and integrated with the garage door control system, emergency broadcast system, and warning light and alarm control system. When an alarm is received, the alarm receiving and handling system automatically generates an electronic dispatch order and sends the command to the garage door control system to automatically open the garage door; at the same time, the emergency broadcast system automatically broadcasts the details of the alarm case, the dispatch voice and the countdown, and standardizes the alarm broadcast content and playback method; the warning light and alarm system activates the corresponding light and sound prompts according to the alarm level, realizes the three-color classification of the warning light and differentiated alarm settings, and ensures that rescue personnel can respond quickly and board the vehicle in an orderly manner.

[0037] Develop a mobile combat APP based on mobile Internet technology to provide real-time warning information push, combat command reception and auxiliary information query functions for combatants. Use big data technology to integrate and analyze information from the entire process of firefighting and rescue, and realize real-time information sharing and collaborative work. For example, on-site rescue personnel can upload the on-site situation through the APP, and the command center will adjust the command strategy in time based on this information, and issue the adjusted instructions to the rescue personnel through the APP in real time. At the same time, the APP supports voice communication and video call functions, which facilitates communication and collaboration between combatants and improves command efficiency.

[0038] A collaborative consultation system based on 5G high-speed video backhaul technology is established in the command center, and video terminal equipment of the alarm person and the participating forces is connected. When an emergency occurs, the command center can conduct a video call with the alarm person in real time to obtain more detailed on-site conditions; at the same time, a multi-party video consultation is conducted with the participating forces. Commanders can mark the combat area, mission objectives and other information on the map in real time, and issue command instructions through the video conferencing system. The system comprehensively integrates internal and external firefighting combat data resources of the fire department, establishes a data collection interface, obtains relevant data from the firefighting facility monitoring system, meteorological department, transportation department, etc., and accurately collects and updates data on elements such as fire hydrants, key units, and rescue forces in real time. Using data cleaning, conversion and fusion technology, various types of data are integrated into a unified data platform, and data analysis models and data visualization displays are established to provide comprehensive and accurate data support for command decision-making and force dispatch.

[0039] 3. Collaborative decision-making mechanism The emergency dispatch problem is modeled as a multi-agent collaboration problem, and the goals and constraints of each agent are clarified. The communication protocol between agents is designed, and the message queue is used to realize the asynchronous transmission of information to ensure the reliable transmission and efficient processing of information. The communication protocol specifies the format, content and communication method of the message to ensure that the agents can accurately understand and exchange information.

[0040] Develop negotiation mechanisms, adopt methods such as contract network protocols, and negotiate resource allocation and task execution plans between agents through bidding, tendering, and winning bids. For example, when responding to a large fire, the fire agent publishes bidding information for firefighting tasks, including the location of the fire, the size of the fire, and the required resources. The rescue team agent bids according to its own capabilities and resources. The fire agent selects the most suitable rescue team to win the bid and assign tasks based on the bidding situation. During the emergency response, according to the development trend of the disaster, resource usage, and rescue progress, the agent uses reinforcement learning algorithms to dynamically adjust its decisions and actions, optimize resource allocation plans, and ensure that the entire emergency rescue work is always moving towards the optimal goal.

[0041] The agent executes the decision according to the negotiation results, such as the fire agent directing the fire truck to the designated location to put out the fire, and the rescue agent organizing the rescue personnel to carry out the rescue work for the wounded. During the execution process, the location, status and action information of the resources are collected in real time through vehicle sensors, personnel positioning equipment, surveillance cameras, etc., and this information is fed back to the command center. The command center uses this feedback information to track and monitor the dispatch instructions. If deviations or emergencies are found during the execution process, the feedback adjustment mechanism is immediately activated. According to the preset rules and algorithms, the resource allocation plan is re-formulated, and the adjusted instructions are issued to the relevant agents in real time to ensure that the instructions are executed accurately and correctly, and to ensure the smooth progress of emergency rescue work. At the same time, the system monitors the status of various emergency resources in real time, and monitors and warns abnormal situations of resources in real time by setting thresholds and early warning mechanisms. When an early warning occurs, the system automatically starts the resource allocation process, and allocates corresponding resources from other regions or reserves according to the preset rules and priorities to ensure that emergency rescue work is not affected by resource shortages and to achieve efficient utilization and timely allocation of resources.

Claims

1. A distributed architecture and collaborative decision-making method for a smart emergency command and dispatch system, characterized in that: The following steps are involved: Construct a distributed architecture based on a multi-agent system, which includes a basic resource layer, a support service layer and an application layer; the basic resource layer includes a basic environment, network and security resources and a cloud platform support environment, which are used to provide the underlying resources required for system operation; the support service layer provides various database supports and services, including an integrated business database, a basic database, an external exchange database, a theme database, a full-text database, a one-picture service and analysis tools; the application layer includes a combat command platform, an intelligent police reception and handling platform, a data visualization platform and a mobile combat APP; Utilize big data, cloud computing, and mobile Internet technologies to achieve unified access to multiple types of alarm methods, including fixed-line, mobile, WeChat public account, SMS, and IoT alarms, and automatically obtain the location, pictures, and video information of the alarm person, and automatically fill in the key information of the alarm situation; Through Internet positioning, place name address positioning, geographic element positioning, mobile application positioning technology means, combined with the mobile phone's own positioning function, operator base station positioning and installation address, using the similarity matching mechanism, the alarm positioning is achieved accurately, and the positioning information is automatically filled in the alarm receipt form; Based on time and geographic location analysis and deep learning intelligent algorithm means, an intelligent force dispatch model is established to achieve one-click precise dispatch of vehicles, equipment, plans, and formations according to different levels and types of alarm situations; At the fire rescue station, the alarm receiving terminal is linked with the garage door, emergency broadcast, warning lights, and alarm bells to automatically generate electronic dispatch orders, standardize alarm broadcasts and warning lights and alarm bells, and improve the efficiency of wartime dispatch; Using mobile Internet and big data technologies, a three-party video conference mechanism based on 5G high-speed video backhaul technology will be established among the alarm person, the command center, and the participating forces before the combatants arrive at the scene, so that they can share police information, upload and issue combat orders, and query auxiliary information anytime and anywhere; The coordinates, equipment information, and contact information of the city's key units, joint support units, micro fire stations, and government (enterprise and institution) professional teams are entered into the system database, and based on big data-assisted decision-making analysis, "one map" is used to dispatch pre-positioned forces to participate in the disposal in advance; Comprehensively integrate the internal and external firefighting data resources of the fire department, accurately collect data on fire hydrants, key units, and rescue force elements, establish data analysis models and data visualization displays, and provide data support for command decision-making and force dispatch; The system adopts modular design, complies with relevant specifications and standards, and uses the advantages of plug-ins to expand functional modules, so as to realize rapid development of user unit functions and autonomous loading of personalized applications; Establish a collaborative decision-making mechanism among multiple agents, including problem modeling, communication protocol design, negotiation mechanism development, dynamic adjustment and decision execution, so that each agent can dynamically adjust resource allocation according to emergency situations and achieve rapid response and optimal decision-making.

2. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The basic environment in the basic resource layer includes a combat command center and a central computer room. The combat command center is equipped with advanced communication facilities and display equipment, which can display emergency site conditions and command and dispatch information in real time. Its layout design conforms to ergonomic principles, making it convenient for command personnel to operate and work together. The central computer room is equipped with high-performance servers and storage devices, and adopts redundant design and intelligent cooling system to ensure the stability of system operation and the security of data storage.

3. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The routers, switches and firewalls in the network and security resources have the following characteristics: the router supports multi-protocol high-speed forwarding, has intelligent routing selection and load balancing functions, and can dynamically adjust the data transmission path according to network traffic; the switch uses a full Gigabit Ethernet port, has VLAN division and port mirroring functions, which is convenient for network management and data monitoring; the firewall has intrusion detection and prevention, antivirus, content filtering and other advanced security functions, and uses state-based detection packet filtering technology to effectively prevent external illegal access. It also supports real-time updates of security policies to cope with ever-changing network security threats.

4. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The resource pooling function provided by the cloud platform support environment can realize the dynamic allocation and recovery of computing resources, storage resources and network resources, automatically adjust resource configuration according to the system operation load, and improve resource utilization; the application management function supports the unified deployment, upgrade and monitoring of various application programs to ensure the stability and compatibility of applications; the service management function provides service registration, discovery and orchestration functions to facilitate the interaction and collaborative work between different services; Security management functions include identity authentication, access authorization, data encryption and auditing, and use multi-factor authentication and encrypted transmission technology to ensure the security of system data and operations.

5. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The one-picture alarm receiving function in the one-picture service can display the alarm point location, alarm type and alarm time information in real time on the electronic map, and intuitively distinguish the urgency of different alarm situations through colors and icons; the one-picture dispatching function allows commanders to directly dispatch fire trucks and rescue personnel resources on the map, plan the optimal route in real time, and display the movement status of resources; the one-picture command function supports commanders to mark combat areas, set mission objectives and issue command instructions on the map, and can also view the location and action trajectory of each rescue force in real time; the one-picture analysis function can conduct a comprehensive analysis of historical alarm data, resource distribution and geographical environment information, generate hot spot area analysis and resource demand forecast visualization reports, and provide data support for decision-making; the one-picture decision-making function uses intelligent algorithms based on data analysis results to provide commanders with a variety of decision-making plans, evaluate the feasibility and risks of each plan, and assist commanders in making the best decision.

6. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The analysis tool includes a data mining module, a statistical analysis module and a simulation module. The data mining module uses a machine learning algorithm to mine and analyze massive emergency data to discover potential rules and association patterns, such as the relationship between the time and location of the fire and surrounding environmental factors; The statistical analysis module provides a variety of statistical methods to conduct statistical analysis on police data and resource usage data, and generate various reports and charts, such as police trend charts and resource allocation efficiency analysis tables; the simulation module can build virtual models based on actual emergency scenarios, simulate the rescue process and effects under different decision-making plans, provide decision-making references for commanders, and evaluate possible problems in advance and optimize decisions by simulating rescue operations under different disaster scenarios.

7. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The process of establishing the intelligent force dispatch model is as follows: first, collect and organize a large amount of historical police data, including the type of police data, location, time of occurrence, disaster severity information, and fire resource data, such as the type of fire vehicles, equipment configuration, rescue personnel skill level information, and geographic information data, such as road conditions, building distribution, and water source location information; then, use a deep learning algorithm to train these data to build a neural network-based force dispatch model. The model can automatically analyze and predict the required fire resource type, quantity, and optimal deployment path based on real-time police information; at the same time, the model has self-learning and adaptive capabilities, and can continuously adjust and optimize the dispatch strategy according to actual rescue results to improve the accuracy and efficiency of dispatch. For example, after multiple fire rescues, the vehicle dispatch plan is optimized according to the actual fire extinguishing effect.

8. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1 is characterized in that: The dynamic adjustment mechanism operates in real time when an emergency occurs, specifically: obtaining real-time information on the development trend of the disaster, such as the direction of fire spread, changes in the size of the fire, and resource usage, such as fuel consumption of fire trucks, the remaining amount of fire extinguishing agent, and the physical exertion of rescuers, as well as rescue progress, such as the number of people evacuated and the degree of fire control, through sensor networks, surveillance cameras, and satellite positioning technology; This real-time information is input into the collaborative decision-making system. The intelligent agents in the system evaluate the current decisions and resource allocation according to preset rules and algorithms to determine whether adjustments are needed. If adjustments are needed, they communicate and collaborate with other intelligent agents through a negotiation mechanism to reformulate resource allocation plans and combat strategies, and update the task allocation and action instructions of each intelligent agent in real time to ensure that the entire emergency rescue process can be continuously optimized according to actual conditions. For example, when the wind direction at the fire scene changes suddenly, the position and attack direction of the fire-fighting vehicle can be adjusted in time.

9. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1, characterized in that: The decision-making execution process includes the following steps: after the command center issues a dispatch order, the order is transmitted in real time to the mobile terminal devices of each fire truck and rescue personnel through the wireless communication network. The mobile terminal has voice prompts, text display and map navigation functions to ensure that the order is clearly conveyed; during the execution of the order, each execution unit uses the on-board sensors and personnel positioning equipment to provide real-time feedback on its own position, action status and task completion status, such as the driving speed, driving route, and arrival time of the fire truck, the entry time of the rescue personnel, and the progress information of the rescue operation; the command center tracks and monitors the dispatch order based on the feedback information. If deviations are found during the execution process or emergencies are encountered, such as road congestion that prevents the vehicle from arriving on time, the feedback adjustment mechanism is immediately activated to re-plan the route or adjust resource allocation to ensure that the order is executed accurately and correctly, and to ensure the smooth progress of emergency rescue work.

10. The distributed architecture and collaborative decision-making method of the intelligent emergency command and dispatch system according to claim 1, characterized in that: The system's real-time monitoring and management functions for various emergency resources are achieved in the following ways: installing sensors and IoT devices on firefighting vehicles, firefighting equipment, and emergency rescue materials to collect real-time status information of resources, such as the operating status of vehicles, the integrity of equipment, and the inventory quantity of materials; Establish a resource management database to store and update the collected information in real time, and interact with the command and dispatch system at the same time; through data analysis and early warning algorithms, conduct real-time monitoring and early warning of abnormal resource conditions, such as vehicle failure warning and material shortage warning; When an early warning occurs, the system automatically starts the resource allocation process and allocates corresponding resources from other regions or reserves based on preset rules and priorities, ensuring that emergency rescue work is not affected by resource shortages and achieving efficient use and timely allocation of resources.

Images