CN113537740A - Intelligent management system for emergency live-action - Google Patents

Intelligent management system for emergency live-action Download PDF

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CN113537740A
CN113537740A CN202110749529.3A CN202110749529A CN113537740A CN 113537740 A CN113537740 A CN 113537740A CN 202110749529 A CN202110749529 A CN 202110749529A CN 113537740 A CN113537740 A CN 113537740A
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张永超
李梦颖
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Suzhou Industrial Park Management Committee
Suzhou Industrial Park Surveying Mapping And Geoinformation Co ltd
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Suzhou Industrial Park Surveying Mapping And Geoinformation Co ltd
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Abstract

The invention discloses an intelligent management system for emergency live-action, which comprises an internet of things layer, a data access layer, a global security perception system layer, a map live-action emergency management layer, a platform layer, a full-flow supervision and command layer and a fusion communication layer, wherein the internet of things layer is connected with the data access layer; the data access layer comprises unmanned aerial vehicle equipment, an unmanned aerial vehicle airborne device, a remote control system and a fleet acquisition management system; the platform layer comprises a video cloud service, an AI middle platform and a big data platform; the whole-process supervision and command layer comprises a visual component and a movable client terminal; the invention aims to provide an intelligent management system for emergency live-action, which has strong real-time sensing and intelligent monitoring capabilities and can be used as a professional system support to further realize linkage command and rescue deployment.

Description

Intelligent management system for emergency live-action
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to an intelligent management system for emergency live-action.
Background
The existing management system usually only monitors public safety events by deploying a camera and a limited internet of things sensing device in a key monitoring area, the capabilities of real-time sensing and intelligent monitoring are not enough, meanwhile, the emergency capacity of the monitoring mode is unbalanced, important emergency information is reported, the important emergency information is reported to an emergency office through offline materials or an office system (OA), and no professional system supports emergency command scheduling management in an emergency state. Therefore, for an intelligent management system for emergency live-action, the currently adopted management system has the problems that the capabilities of real-time perception and intelligent monitoring are insufficient, and no professional system support exists.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide an intelligent management system for emergency live-action, which has strong real-time sensing and intelligent monitoring capabilities and can be used as a professional system support to further realize linkage command and rescue deployment.
In order to achieve the above purposes, the invention adopts the technical scheme that: an intelligent management system for emergency live-action comprises an internet of things layer, a data access layer, a global security perception system layer, a map live-action emergency management layer, a platform layer, a full-flow supervision and command layer and a fusion communication layer; the Internet of things layer is used for monitoring disaster information;
the data access layer comprises unmanned aerial vehicle equipment, an unmanned aerial vehicle airborne device used for acousto-optic warning and obtaining disaster information, a remote control system used for remote calling and a fleet management system used for obtaining flight data of the unmanned aerial vehicle equipment;
the global security perception system layer is used for receiving disaster information monitored by the Internet of things platform;
the map live-action emergency management layer is used for receiving disaster information monitored by the Internet of things platform, calling cameras around the disaster information for automatic identification, and transmitting a first automatic identification result to an AI middle station of the platform layer;
the platform layer comprises a video cloud service, an AI middle platform and a big data platform, wherein the video cloud service is used for receiving disaster information acquired by an airborne device of the unmanned aerial vehicle, storing and managing the information, and opening a capturing service and a retrieving service of the disaster information to the AI middle platform; the AI central station is used for receiving disaster information acquired by the unmanned aerial vehicle airborne device, carrying out reasoning calculation to obtain preliminary disaster identification data, sending alarm data to the remote control system, and combining the acquired first automatic identification result with the preliminary disaster identification data to calculate to obtain a final disaster identification result; the big data platform is used for receiving the final disaster identification data obtained by combining the AI middle station with calculation and receiving the flight data of the unmanned aerial vehicle equipment acquired by the fleet management system;
the whole-process supervision and command layer comprises a visual component and a movable client terminal, the visual component is used for receiving flight data of the unmanned aerial vehicle equipment acquired by the big data platform, and the movable client terminal is used for generating a work order and receiving disaster identification data acquired by the big data platform;
the fusion communication layer is connected with the map real scene emergency management layer through various audio/video terminals.
The intelligent management system for the emergency live action has the advantages that the Internet of things layer monitors disaster information, the disaster information can be smoking behavior identification, firework identification, intrusion detection, key post off duty, non-worn industrial and clothing caps, high-density people, crowd fighting, handheld knife and gun sticks, dangerous goods left over, dangerous vehicle identification and the like, and the disaster information is not limited to fixed camera identification or mobile identification. When disaster information occurs (for example, the gas transmission tank body leaks suddenly).
The Internet of things layer senses and sends disaster information to the global security sensing system layer and the map live-action emergency management layer, the global security sensing system layer and the map live-action emergency management layer both obtain information of the disaster information, the map live-action emergency management layer can call cameras around the disaster information for automatic identification while obtaining the information of the disaster information, and a first automatic identification result is transmitted to an AI middle station of the platform layer. The map live-action emergency management layer comprises functions of video front-end perception, video preview, video patrol, video map, high-low linkage, live-action plotting, video return and the like.
When unmanned aerial vehicle equipment began to open the flight, unmanned aerial vehicle machine carried device, remote control system and fleet of aircraft management system can realize unmanned aerial vehicle full automatic scheduling, autopilot, shoots video or picture, supports 4G/5G network, internet high-speed connection. The fleet management system opens an API interface and transmits the flight data (such as flight data chain, task and hangar state messages) of the unmanned aerial vehicle equipment to a big data platform. Video or picture that unmanned aerial vehicle machine carried device to shoot upload to AI middle stage, AI middle stage receives video or picture after, combines the video time point, obtains AI discernment result promptly preliminary disaster identification data (the video or the picture that gas transmission tank body proruption was revealed) through the message queue mechanism to combine the calculation with the first automatic identification result that obtains and preliminary disaster identification data and obtain final disaster identification result. Carry final calamity identification result to big data platform again, big data platform carries final calamity identification result to universe safety perception system, simultaneously with final calamity identification data and report to giving the fire control department, the AI middock can send alarm data to remote control system when obtaining final calamity identification result, remote control system regulation and control unmanned aerial vehicle airborne device carries out the acousto-optic warning, reveals near crowd evacuation in the gas transmission tank body proruption. And meanwhile, the ship can be dispatched by law enforcement personnel nearby to operate the ship to the position nearby the sudden leakage of the gas transmission tank body for field evacuation. The remote law enforcement personnel can observe the flight track, the position state and the like of the unmanned aerial vehicle equipment through the visualization component; meanwhile, the final disaster identification result can be checked through the movable client terminal, the work order can be downloaded and generated through the movable client terminal, and manual processing links are effectively reduced. The fusion communication layer is connected with the map real-scene emergency management layer through various audio/video terminals, and a basic platform serving as a command center can realize multi-channel remote fusion communication command through the audio/video terminals (mainly comprising a fixed telephone terminal, a video conference terminal, a video monitoring terminal and the like).
The intelligent management system for the emergency live-action has strong real-time sensing and intelligent monitoring capabilities, and can be used as a professional system support to further realize linkage command and rescue deployment. Enabling a system: based on a powerful platform layer, AI algorithm energization of a front-end common camera is supported, and AI energization and scheduling capability clouding are achieved for common unmanned aerial vehicle equipment, a full-process supervision and command layer and emergency process management. Emergency timeliness: through the linkage of the 5G + mobile hangar + AI intermediate station, the Internet of things layer + AI intermediate station, the transition of the conventional work order of public safety and social treatment to an emergency state, the judgment of energized field conditions of the AI intermediate station and manual auxiliary confirmation, all the measures push the emergency task to be changed from the original first-known and later-perceived emergency behavior to the current first-known and earlier-perceived prevention behavior.
The capability can be programmed: through the AI middle platform, AI discernment, picture and video processing, risk model, data analysis, visualization, position location, airborne device control, work order preliminary treatment, unmanned aerial vehicle management form atom and general service, support the emergent outdoor scene plotting interface of 3D that user experience is better, nimble adaptation business process reduces the development degree of difficulty of 3D interface application integration, realizes that new business is quick to go on-line.
The scene can be linked: in the pre-prevention stage of the on-duty state, the linkage of dangerous vehicle identification, crowd density identification, firework identification, Internet of things perception and other AI middleboxes can be realized in a business arrangement mode, the early warning attracts factory security and government law enforcement personnel to pay attention and timely performs treatment intervention on site, and the hidden trouble of the state which is not yet developed can be eliminated. In a range or an area where the manual ability is difficult to reach, AI middleboxes and Internet of things layer identification and emergency flow and personnel behavior linkage can be arranged, and the complexity of field disposal is reduced.
And the traceability is enhanced: the big data platform supports digital recording, data fusion and mining from occurrence to disposal of a full-flow event, different data topics can be formed by scene granularity, resource arrangement and task inheritance, data service is provided for an emergency command system, a full-flow supervision and global security perception system, and the traditional complex multi-source factor, multi-field professional knowledge and multi-event disposal links are not the difficult challenges of an event backtracking responsibility confirmation task.
Data accuracy: the risk data analysis of building site and danger enterprise realizes the modeling, under the energizing of digital base, can continuously develop, self evolution, help reminding law enforcement office to discover self not enough work, the link of dredging neglect, and promote the targetness of emergency task rehearsal, make the emergency plan to different risk sources of different enterprises have more pertinence, unmanned aerial vehicle flight task, rescue organization route design, law enforcement personnel site resource input, site disposal operation flow will have more foresight and pertinence, reduce the personal risk of site disposal, practice thrift man-hour and consumption resource, promote law enforcement office work efficiency.
And (3) operability: the management committee emergency supervisor leader, the law enforcement agency leader can use the mobile APP, look over real-time and historical videos, work order disposition, operation history, the statistics of emergency events, can be directed at the supervision task that the higher authority assigned at every turn and manage the generation analysis report of the local conditions, the key target of warning and supervising, scope and the risk source characteristic index that needs key inspection, the form that can serve, with the SMS, the mode of believe and printing the report in the overall process, subscribe the release on the big, medium and small screen of user.
The invention further improves that the airborne device of the unmanned aerial vehicle comprises a variable-focus tripod head far infrared camera, a flashing lamp strip, a directional sound amplifying device and a highlight searchlight, wherein the variable-focus tripod head far infrared camera, the flashing lamp strip, the directional sound amplifying device and the highlight searchlight are connected with an AI middle platform through a remote control system to realize remote calling. Once suspicious disaster information occurs, the AI console controls the unmanned aerial vehicle to hover in a programmable mode, points to the direction of a ship, and pulls a lens of the far infrared camera of the zoom holder to align the suspicious disaster information so as to perform more precise behavior identification and shooting; the flashing lamp band marks law enforcement roles of the unmanned aerial vehicle, so that the deterrence force is enhanced, the directional sound amplifying device and the searchlight accurately point to the ship and the peripheral position, a preset sound alarm is played for alarming, and the awareness and warning effects are enhanced.
The platform layer further comprises an API gateway, the API gateway comprises an application integration module, a message integration module and a data integration module, and the application integration module is used for receiving the primary disaster identification data obtained by the AI mesopodium reasoning calculation and transmitting the data to the message integration module; the information integration module is used for combining the acquired first automatic identification result with the primary disaster identification data to calculate final disaster identification data, arranging the final disaster identification data into an information queue through information middleware, transmitting the information queue to the data integration module, receiving alarm data sent by the AI central office and transmitting the alarm data to the remote control system; and the data integration module transmits the received final disaster identification data to the big data platform through the ETL tool. An application integration module: receiving disaster information, arranging the disaster information into a JSON format, and sending the disaster information to an application integration module of an API gateway by an AI middle platform in an API mode through an HTTP protocol or an HTTPS protocol; a message integration module: after the application integration module receives the preliminary disaster identification data, the acquired first automatic identification result and the preliminary disaster identification data are combined and calculated to obtain final disaster identification data, the final disaster identification data are arranged into a message queue through message middleware service and then sent to the data integration module, alarm data sent by the AI central station are received by the message integration module and then sent to the remote control system, and the remote control system calls the unmanned aerial vehicle equipment; a data integration module: and the data integration module extracts the received final disaster identification data to a big data platform through an ETL tool, and the big data platform can form a persistent base table which is incrementally stored along with time.
The platform layer further comprises a GIS middle station, and the GIS middle station is used for receiving the flight data acquired by the fleet management system and transmitting the flight data to the big data platform. Unmanned aerial vehicle equipment carries camera control each place calamity information in the air flight at night, and the GIS middleman provides the projected geographical position information in unmanned aerial vehicle equipment place in real time, helps law enforcement personnel to carry out quick location to the scene. The big data platform collects and stores flight tracks, position states and the like of the unmanned aerial vehicle equipment.
As a further improvement of the invention, the big data platform comprises a temporary library, an aggregation library, a central library and a special topic library. The large data platform can form a persistent base table which is stored in an increment mode along with time and is placed in the temporary database. The collection library collects final disaster identification data from the temporary library, the central library is a data warehouse and realizes the similar combination, historical slicing, data translation, scene association and fusion treatment of the final disaster identification data, and the special subject library is a data mart and realizes the business logic integration with other subject domain data, the wide table is drawn, the data dimension structure is defined, and the supported business logic of user interface visualization is realized. The big data platform will store the final disaster identification data.
The movable client terminal comprises a leader cockpit APP and a work order generation system which are arranged in the movable client terminal, the leader cockpit APP and the work order generation system are both connected with a big data platform, the leader cockpit APP is used for receiving disaster identification data obtained by AI middling station reasoning calculation, and the work order generation system is used for receiving flight data of the unmanned aerial vehicle equipment acquired by the remote control system. The leadership cockpit APP and the work order generation system are connected with the big data platform, and the leadership cockpit APP can check the final disaster identification data at any time through the final disaster identification data stored in the big data platform. Before disaster treatment begins, according to preset event levels and the water area position where final disaster identification data occurs, preprocessing can be performed in time, a detailed work order is generated rapidly and issued to business systems such as grid communication and the like, and field law enforcement personnel can perform preprocessing according to the work order information: for example, the final disaster identification data is subjected to longitude and latitude, ship range, personnel number, real-time geographic position and the like, and an attendance route, police resources and a disposal scheme are planned in advance. After disaster treatment is completed, law enforcement officers can download in the work order generation system through the movable client terminal to automatically generate work orders.
The invention further improves the system and the method, and further comprises an FTP server, wherein the FTP server is connected with the AI middle station and used for storing disaster identification data obtained by AI middle station reasoning calculation, and the work order generation system is connected with the FTP server. The AI middle station can transmit the final disaster identification data to the FTP server for centralized storage; the work order system can access the FTP server through an HTTP protocol, and further can better perform work order preprocessing according to the final disaster identification data.
The unmanned aerial vehicle device further comprises an unmanned aerial vehicle hangar which is used for parking, flying and charging the unmanned aerial vehicle device. Unmanned aerial vehicle equipment can park, charge and fly off at the hangar automation.
As a further improvement of the invention, the map live-action emergency management layer can be a 3D map live-action emergency management layer or an IOC emergency live-action management layer.
Drawings
FIG. 1 is a general technical architecture diagram of the first embodiment;
fig. 2 is a system deployment diagram of the first embodiment.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
Referring to fig. 1-2, an embodiment of an intelligent management system for emergency live-action includes an internet of things layer, a data access layer, a global security awareness system layer, a map live-action emergency management layer, a platform layer, a full-flow supervision and command layer, and a converged communication layer; the Internet of things layer is used for monitoring disaster information;
the data access layer comprises unmanned aerial vehicle equipment, an unmanned aerial vehicle airborne device used for acousto-optic warning and obtaining disaster information, a remote control system used for remote calling and a fleet management system used for obtaining flight data of the unmanned aerial vehicle equipment;
the global security perception system layer is used for receiving disaster information monitored by the Internet of things platform;
the map live-action emergency management layer is used for receiving disaster information monitored by the Internet of things platform, calling cameras around the disaster information for automatic identification, and transmitting a first automatic identification result to an AI middle station of the platform layer;
the platform layer comprises a video cloud service, an AI middle platform and a big data platform, wherein the video cloud service is used for receiving disaster information acquired by an airborne device of the unmanned aerial vehicle, storing and managing the information, and opening a capturing service and a retrieving service of the disaster information to the AI middle platform; the AI central station is used for receiving disaster information acquired by the unmanned aerial vehicle airborne device, carrying out reasoning calculation to obtain preliminary disaster identification data, sending alarm data to the remote control system, and combining the acquired first automatic identification result with the preliminary disaster identification data to calculate final disaster identification data; the big data platform is used for receiving the final disaster identification data obtained by combining the AI middle station with calculation and receiving the flight data of the unmanned aerial vehicle equipment acquired by the fleet management system;
the whole-process supervision and command layer comprises a visual component and a movable client terminal, the visual component is used for receiving flight data of the unmanned aerial vehicle equipment acquired by the big data platform, and the movable client terminal is used for generating a work order and receiving disaster identification data acquired by the big data platform;
the fusion communication layer is connected with the map real scene emergency management layer through various audio/video terminals. The internet of things layer monitors disaster information, the disaster information can be smoking behavior identification, firework identification, intrusion detection, key post off duty, non-wearing industrial service caps, high-density people, people fighting, handheld knife and rod, dangerous goods left over, dangerous vehicle identification and the like, and the disaster information is not limited to fixed camera identification or mobile identification. When disaster information occurs (for example, the gas transmission tank body leaks suddenly).
The Internet of things layer senses and sends disaster information to the global security sensing system layer and the map live-action emergency management layer, the global security sensing system layer and the map live-action emergency management layer both obtain information of the disaster information, the map live-action emergency management layer can call cameras around the disaster information for automatic identification while obtaining the information of the disaster information, and a first automatic identification result is transmitted to an AI middle station of the platform layer. The map live-action emergency management layer comprises functions of video front-end perception, video preview, video patrol, video map, high-low linkage, live-action plotting, video return and the like.
When unmanned aerial vehicle equipment began to open the flight, unmanned aerial vehicle machine carried device, remote control system and fleet of aircraft management system can realize unmanned aerial vehicle full automatic scheduling, autopilot, shoots video or picture, supports 4G/5G network, internet high-speed connection. The fleet management system opens an API interface and transmits the flight data (such as flight data chain, task and hangar state messages) of the unmanned aerial vehicle equipment to a big data platform. Video or picture that unmanned aerial vehicle machine carried device to shoot upload to AI middle stage, AI middle stage receives video or picture after, combines the video time point, obtains AI discernment result promptly preliminary disaster identification data (the video or the picture that gas transmission tank body proruption was revealed) through the message queue mechanism to combine the calculation with the first automatic identification result that obtains and preliminary disaster identification data and obtain final disaster identification result. The final disaster identification result is transmitted to a big data platform, the big data platform transmits the final disaster identification result to a global security sensing system, and the final disaster identification data is reported to a fire department; when obtaining final disaster recognition result, the AI middle station can send alarm data to remote control system, and remote control system regulates and control unmanned aerial vehicle airborne device and carries out acousto-optic warning, reveals near crowd evacuation in the gas transmission tank body proruption. And in the acousto-optic driving away alarm, nearby law enforcement officers can be appointed to operate the ship to the position nearby the sudden leakage of the gas transmission tank body for field evacuation. The remote law enforcement personnel can observe the flight track, the position state and the like of the unmanned aerial vehicle equipment through the visualization component; meanwhile, the final disaster identification result can be checked through the movable client terminal, the work order can be downloaded and generated through the movable client terminal, and manual processing links are effectively reduced. The fusion communication layer is connected with the map real-scene emergency management layer through various audio/video terminals, and a basic platform serving as a command center can realize multi-channel remote fusion communication command through the audio/video terminals (mainly comprising a fixed telephone terminal, a video conference terminal, a video monitoring terminal and the like).
The intelligent management system for the emergency live-action has strong real-time sensing and intelligent monitoring capabilities, and can be used as a professional system support to further realize linkage command and rescue deployment. Enabling a system: based on a powerful platform layer, AI algorithm energization of a front-end common camera is supported, and AI energization and scheduling capability clouding are achieved for common unmanned aerial vehicle equipment, a full-process supervision and command layer and emergency process management. Emergency timeliness: through the linkage of the 5G + mobile hangar + AI intermediate station, the Internet of things layer + AI intermediate station, the transition of the conventional work order of public safety and social treatment to an emergency state, the judgment of energized field conditions of the AI intermediate station and manual auxiliary confirmation, all the measures push the emergency task to be changed from the original first-known and later-perceived emergency behavior to the current first-known and earlier-perceived prevention behavior.
The capability can be programmed: through the AI middle platform, AI discernment, picture and video processing, risk model, data analysis, visualization, position location, airborne device control, work order preliminary treatment, unmanned aerial vehicle management form atom and general service, support the emergent outdoor scene plotting interface of 3D that user experience is better, nimble adaptation business process reduces the development degree of difficulty of 3D interface application integration, realizes that new business is quick to go on-line.
The scene can be linked: in the pre-prevention stage of the on-duty state, the linkage of dangerous vehicle identification, crowd density identification, firework identification, Internet of things perception and other AI middleboxes can be realized in a business arrangement mode, the early warning attracts factory security and government law enforcement personnel to pay attention and timely performs treatment intervention on site, and the hidden trouble of the state which is not yet developed can be eliminated. In a range or an area where the manual ability is difficult to reach, AI middleboxes and Internet of things layer identification and emergency flow and personnel behavior linkage can be arranged, and the complexity of field disposal is reduced.
And the traceability is enhanced: the big data platform supports digital recording, data fusion and mining from occurrence to disposal of a full-flow event, different data topics can be formed by scene granularity, resource arrangement and task inheritance, data service is provided for an emergency command system, a full-flow supervision and global security perception system, and the traditional complex multi-source factor, multi-field professional knowledge and multi-event disposal links are not the difficult challenges of an event backtracking responsibility confirmation task.
Data accuracy: the risk data analysis of building site and danger enterprise realizes the modeling, under the energizing of digital base, can continuously develop, self evolution, help reminding law enforcement office to discover self not enough work, the link of dredging neglect, and promote the targetness of emergency task rehearsal, make the emergency plan to different risk sources of different enterprises have more pertinence, unmanned aerial vehicle flight task, rescue organization route design, law enforcement personnel site resource input, site disposal operation flow will have more foresight and pertinence, reduce the personal risk of site disposal, practice thrift man-hour and consumption resource, promote law enforcement office work efficiency.
And (3) operability: the management committee emergency supervisor leader, the law enforcement agency leader can use the mobile APP, look over real-time and historical videos, work order disposition, operation history, the statistics of emergency events, can be directed at the supervision task that the higher authority assigned at every turn and manage the generation analysis report of the local conditions, the key target of warning and supervising, scope and the risk source characteristic index that needs key inspection, the form that can serve, with the SMS, the mode of believe and printing the report in the overall process, subscribe the release on the big, medium and small screen of user.
The embodiment is that a man-machine carries the device and includes the cloud platform far infrared camera of can zooming, scintillation lamp area, directional public address set and highlight searchlight, and cloud platform far infrared camera of can zooming, scintillation lamp area, directional public address set and highlight searchlight all realize long-range the calling through remote control system and AI zhongtai connection. Once suspicious disaster information occurs, the AI console controls the unmanned aerial vehicle to hover in a programmable mode, points to the direction of a ship, and pulls a lens of the far infrared camera of the zoom holder to align the suspicious disaster information so as to perform more precise behavior identification and shooting; the flashing lamp band marks law enforcement roles of the unmanned aerial vehicle, so that the deterrence force is enhanced, the directional sound amplifying device and the searchlight accurately point to the ship and the peripheral position, a preset sound alarm is played for alarming, and the awareness and warning effects are enhanced.
The platform layer of the embodiment also comprises an API gateway, wherein the API gateway comprises an application integration module, a message integration module and a data integration module, and the application integration module is used for receiving the primary disaster identification data obtained by the AI mesopic reasoning calculation and transmitting the data to the message integration module; the information integration module is used for combining the acquired first automatic identification result with the primary disaster identification data to calculate a final disaster identification result, arranging the final disaster identification data into an information queue through the information middleware and transmitting the information queue to the data integration module, receiving alarm data sent by the AI central office and transmitting the alarm data to the remote control system; and the data integration module transmits the received final disaster identification data to the big data platform through the ETL tool. An application integration module: receiving disaster information, arranging the disaster information into a JSON format, and sending the disaster information to an application integration module of an API gateway by an AI middle platform in an API mode through an HTTP protocol or an HTTPS protocol; a message integration module: after the application integration module receives the preliminary disaster identification data, the acquired first automatic identification result and the preliminary disaster identification data are combined and calculated to obtain final disaster identification data, the final disaster identification data are arranged into a message queue through message middleware service and then sent to the data integration module, alarm data sent by the AI central station are received by the message integration module and then sent to the remote control system, and the remote control system calls the unmanned aerial vehicle equipment; a data integration module: and the data integration module extracts the received final disaster identification data to a big data platform through an ETL tool, and the big data platform can form a persistent base table which is incrementally stored along with time.
The platform layer of the embodiment also comprises a GIS middle station which is used for receiving the flight data acquired by the fleet management system and transmitting the flight data to the big data platform. Unmanned aerial vehicle equipment carries camera control each place calamity information in the air flight at night, and the GIS middleman provides the projected geographical position information in unmanned aerial vehicle equipment place in real time, helps law enforcement personnel to carry out quick location to the scene. The big data platform collects and stores flight tracks, position states and the like of the unmanned aerial vehicle equipment.
An embodiment of a big data platform comprises a temporary library, an aggregation library, a central library and a special topic library. The large data platform can form a persistent base table which is stored in an increment mode along with time and is placed in the temporary database. The collection library collects final disaster identification data from the temporary library, the central library is a data warehouse and realizes the similar combination, historical slicing, data translation, scene association and fusion treatment of the final disaster identification data, and the special subject library is a data mart and realizes the business logic integration with other subject domain data, the wide table is drawn, the data dimension structure is defined, and the supported business logic of user interface visualization is realized. The big data platform will store the final disaster identification data.
The movable client terminal comprises a leader cockpit APP and a work order generation system which are arranged in the movable client terminal, the leader cockpit APP and the work order generation system are both connected with a big data platform, the leader cockpit APP is used for receiving disaster identification data obtained through AI central station reasoning calculation, and the work order generation system is used for receiving flight data of unmanned aerial vehicle equipment acquired by a remote control system. The leadership cockpit APP and the work order generation system are connected with the big data platform, and the leadership cockpit APP can check the final disaster identification data at any time through the final disaster identification data stored in the big data platform. Before disaster treatment begins, according to preset event levels and the water area position where final disaster identification data occurs, preprocessing can be performed in time, a detailed work order is generated rapidly and issued to business systems such as grid communication and the like, and field law enforcement personnel can perform preprocessing according to the work order information: for example, the final disaster identification data is subjected to longitude and latitude, ship range, personnel number, real-time geographic position and the like, and an attendance route, police resources and a disposal scheme are planned in advance. After disaster treatment is completed, law enforcement officers can download in the work order generation system through the movable client terminal to automatically generate work orders.
The first embodiment further comprises an FTP server, the FTP server is connected with the AI middle station and used for storing disaster identification data obtained through AI middle station reasoning calculation, and the work order generating system is connected with the FTP server. The AI middle station can transmit the final disaster identification data to the FTP server for centralized storage; the work order system can access the FTP server through an HTTP protocol, and further can better perform work order preprocessing according to the final disaster identification data.
The first embodiment further comprises an unmanned aerial vehicle hangar, and the unmanned aerial vehicle hangar is used for parking, flying and charging of unmanned aerial vehicle equipment. Unmanned aerial vehicle equipment can park, charge and fly off at the hangar automation.
The map live-action emergency management layer of the first embodiment may be a 3D map live-action emergency management layer or an IOC emergency live-action management layer.
Example two the exercise execution activity process is as follows:
disaster prior perception: the gas transmission tank body leaks suddenly and is sensed by the monitoring terminal of the Internet of things.
Triggering and alarming before disaster: and (4) suddenly leaking the gas transmission tank, triggering an alarm in the north direction after the gas transmission tank reaches a threshold value, and reporting to the global safety perception and 3D map live-action emergency management system according to rules.
Analyzing the situation of the disaster in advance: and linking the videos, verifying the alarm site by using an AI identification technology, extracting the evidence, synchronously uploading the evidence to a full-process supervision and command layer and a global security sensing system layer, and reporting the evidence to a fire department by the full-process supervision and command layer. Meanwhile, the 3D map live-action emergency management layer or the IOC emergency live-action management layer starts AI linkage action, cameras around the IoT terminal are called to rotate the holder to perform automatic identification, primary disaster identification data comprises a judgment result of an artificial site, the global security sensing system layer integrates the alarm of the Internet of things layer, AI video identification evidence, photos manually shot, videos and description contents and uploads the photos to the full-flow supervision and command layer, information is quickly reported by the full-flow supervision and command layer, a firework identification work order is generated, a first-level event level is matched, and a factory and nearby fire brigades start to implement fire extinguishing measures.
Remote or on-site identification in disaster: and the IOC watching personnel and the field disposal personnel judge the event disposal result and the peripheral risk source, and if the event is out of the control of the field personnel, the emergency state is ready to enter. At this stage, AI identification continuously sends out firework identification alarm, the IoT terminals and the cameras of the surrounding Internet of things are involved and are consistent with manual identification on the spot, which indicates that the scene rescue team cannot control the scene, meanwhile, AI equipment and the layer of the Internet of things continuously output firework alarms in a larger range, all signs show that the scene is out of control, and the scene must be immediately entered into an emergency state.
Emergency response in disaster events: and the whole flow supervision and command layer enters an emergency state, and the leader of the supervisor and related departments are combined to a hall and a site to conduct situation research and judgment, command and disposition. And the whole-process supervision and command layer or the 33D map live-action emergency management layer starts to check peripheral risk sources and rescue teams, dispose emergency plan contents and other operations, generate information quick report and inform the information quick report to the global security perception system layer, a leader site or a remote site listens to the required condition quick report and indicates a starting plan and a front finger for establishment, and the leader instruction is synchronized to the whole-process supervision and command layer.
Linkage command in disaster: and establishing a field command department, jointly executing a plan by crossing departments, and efficiently processing and eliminating disaster events by the cooperation of the front fingers and the rear fingers so as to rescue the lives and properties of the public on the field. The emergency command system issues a task two-dimensional code, the condition of a front-finger member is presented, task information is issued according to the requirements of a plan, the front-finger member responds to the site and synchronizes task confirmation, an attendant starts a video conference of the front finger and the rear finger, a mobile phone terminal of the task information is handed to a leader of the rear finger, the leader listens to reports of all member units, and a placement point, a key protection target and the real-time condition of the site can be checked in video monitoring.
Rescue deployment in disaster: and meanwhile, a full-flow supervision and command layer or a 3D map live-action emergency management layer and a mobile terminal can be ensured to always keep consistent presentation interface and content interaction through map auxiliary flow. The former finger member or the related personnel determines that the task is completed, and the processing result is synchronous with the global security perception system layer and the whole process supervision and command layer. And the leader of the supervisor confirms that the emergency rescue command task is completed and relieves the emergency state.
Conclusion of disaster post-incident feedback: and checking after events, generating a full-flow statistical report from the emergency state to the emergency task disposal end, sending the full-flow statistical report to an IOC visualization system, copying the report at a global security perception system layer and a full-flow supervision and command layer respectively, taking the report as a basis for work order and event ending, and taking the report as an important reference for responsibility tracing and emergency response maturity assessment.
The above embodiments are merely illustrative of the technical concept and features of the present invention, and the present invention is not limited thereto, and any equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.

Claims (9)

1. An intelligent management system for emergency live-action is characterized in that: the system comprises an internet of things layer, a data access layer, a global security perception system layer, a map real-scene emergency management layer, a platform layer, a full-flow supervision and command layer and a fusion communication layer; the Internet of things layer is used for monitoring disaster information;
the data access layer comprises unmanned aerial vehicle equipment, an unmanned aerial vehicle airborne device used for acousto-optic warning and obtaining disaster information, a remote control system used for remote calling and a fleet management system used for obtaining flight data of the unmanned aerial vehicle equipment;
the global security perception system layer is used for receiving disaster information monitored by the Internet of things platform;
the map live-action emergency management layer is used for receiving disaster information monitored by the Internet of things platform, calling cameras around the disaster information for automatic identification, and transmitting a first automatic identification result to an AI middle platform of the platform layer;
the platform layer comprises a video cloud service, an AI middle platform and a big data platform, wherein the video cloud service is used for receiving disaster information acquired by an airborne device of the unmanned aerial vehicle, storing the information and managing the information, and opening a capturing service and a retrieving service of the disaster information to the AI middle platform; the AI central station is used for receiving disaster information acquired by the unmanned aerial vehicle airborne device, carrying out reasoning calculation to obtain preliminary disaster identification data, sending alarm data to the remote control system, and combining the acquired first automatic identification result with the preliminary disaster identification data to calculate final disaster identification data; the big data platform is used for receiving final disaster identification data obtained by combining the AI middle station with calculation and receiving flight data of the unmanned aerial vehicle equipment acquired by the fleet management system;
the whole-process supervision and command layer comprises a visualization component and a movable client terminal, the visualization component is used for receiving flight data of the unmanned aerial vehicle equipment acquired by the big data platform, and the movable client terminal is used for generating a work order and receiving disaster identification data acquired by the big data platform;
the fusion communication layer is connected with the map live-action emergency management layer through various audio/video terminals.
2. An intelligent management system for emergency reality according to claim 1, wherein: the unmanned aerial vehicle machine carries the device and includes cloud platform far infrared camera, scintillation lamp area, directional public address set and the highlight searchlight of zooming, cloud platform far infrared camera, scintillation lamp area, directional public address set and the highlight searchlight of zooming all realize long-range the calling through remote control system and AI midsill connection.
3. An intelligent management system for emergency reality according to claim 1, wherein: the platform layer further comprises an API gateway, the API gateway comprises an application integration module, a message integration module and a data integration module, and the application integration module is used for receiving the primary disaster identification data obtained by AI middling reasoning calculation and transmitting the data to the message integration module; the information integration module is used for combining the acquired first automatic identification result with the primary disaster identification data to calculate a final disaster identification result, arranging the final disaster identification data into an information queue through the information middleware and transmitting the information queue to the data integration module, receiving alarm data sent by the AI central office and transmitting the alarm data to the remote control system; and the data integration module transmits the received final disaster identification data to a big data platform through an ETL tool.
4. An intelligent management system for emergency reality according to claim 1, wherein: the platform layer further comprises a GIS middle station, and the GIS middle station is used for receiving the flight data acquired by the fleet management system and transmitting the flight data to the big data platform.
5. An intelligent management system for emergency reality according to claim 1, wherein: the big data platform comprises a temporary library, an aggregation library, a central library and a special question library.
6. An intelligent management system for emergency reality according to claim 1, wherein: the movable client terminal comprises a leader cockpit APP and a work order generation system which are arranged in the movable client terminal, the leader cockpit APP and the work order generation system are both connected with a big data platform, the leader cockpit APP is used for receiving AI midstation reasoning data and calculating disaster identification data, and the work order generation system is used for receiving flight data of unmanned aerial vehicle equipment acquired by a remote control system.
7. An intelligent management system for emergency reality according to claim 1, wherein: the disaster identification system further comprises an FTP server, the FTP server is connected with the AI middle station and used for storing disaster identification data obtained by AI middle station reasoning calculation, and the work order generation system is connected with the FTP server.
8. An intelligent management system for emergency reality according to claim 1, wherein: still include the unmanned aerial vehicle hangar, the unmanned aerial vehicle hangar is used for parking, letting fly and charging of unmanned aerial vehicle equipment.
9. An intelligent management system for emergency reality according to claim 1, wherein: the map live-action emergency management layer can be a 3D map live-action emergency management layer or an IOC emergency live-action management layer.
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