CN111343598B - Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability - Google Patents
Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability Download PDFInfo
- Publication number
- CN111343598B CN111343598B CN202010078940.8A CN202010078940A CN111343598B CN 111343598 B CN111343598 B CN 111343598B CN 202010078940 A CN202010078940 A CN 202010078940A CN 111343598 B CN111343598 B CN 111343598B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- edge computing
- fire
- computing node
- wearable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C27/00—Fire-fighting land vehicles
-
- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C37/00—Control of fire-fighting equipment
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/48—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Traffic Control Systems (AREA)
- Fire-Extinguishing By Fire Departments, And Fire-Extinguishing Equipment And Control Thereof (AREA)
Abstract
The embodiment of the application provides an intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability. The method comprises the following steps: each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval; after the wearable edge computing node collects the field fire, the field fire range, the fire intensity and the characteristics of the combustion objects are identified and sent to the vehicle-mounted master control edge computing node; the vehicle-mounted master control edge computing nodes collect fire scene ranges, intensity and comburent characteristics returned by each wearable edge computing node to obtain holographic characteristic information of the whole fire scene, and a vehicle dispatching strategy and a fireman operation strategy are generated according to the holographic information of the fire scene, the running state of each vehicle-mounted edge computing node and the state of fire-fighting materials. The efficiency and the user experience are improved through the prediction algorithm.
Description
Technical Field
The application relates to the field of edge computing and fire control, in particular to an intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability.
Background
In the existing fire fighting process, the cooperation between fire fighting vehicles is still carried out through the dispatching of a command center or the telephone communication between the fire fighting vehicles, and the fire fighting vehicles are still in a manual control stage. For example, when a fire fighter extinguishes a fire in a fire scene, but the fire fighting vehicle stores insufficient disaster relief materials, a resource supply shortage strategy should be sent to the fire fighter in advance, so that the fire fighter is prevented from meaningless operation and even sacrifice the fire fighter. For the decision-making of fire fighting and emergency rescue, on one hand, fire fighters are required to accurately master the front fire situation and carry out fire extinguishing operation according to the resource condition of a fire engine, on the other hand, the fire fighters are required to carry out scientific overall decision-making on various fire engines with different functions and adapt to the change of the front fire situation in real time to continuously adjust, so that the fire fighting and emergency rescue can be realized quickly and efficiently.
Disclosure of Invention
In view of this, an object of the present application is to provide an intelligent fire fighting vehicle-mounted platform operation method and system with edge computing capability, so as to improve registration efficiency and solve the technical problem that in the current population process, special population verification is difficult.
Based on the above purpose, the present application provides an intelligent fire fighting vehicle-mounted platform operation method and system with edge computing capability, including:
arranging a vehicle-mounted edge computing node on each fire truck, and selecting one or more vehicle-mounted edge computing nodes from the vehicle-mounted edge computing nodes as vehicle-mounted master control edge computing nodes; a wearable edge computing node is arranged on each firefighter, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network;
the wearable edge computing node comprises a plurality of sensors and is used for collecting the scene fire; each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval;
after the wearable edge computing node collects the site fire, the site fire range, the fire intensity and the characteristics of the comburent are identified and sent to the vehicle-mounted master control edge computing node;
the vehicle-mounted master control edge computing nodes collect fire scene ranges, intensity and burning object characteristics returned by each wearable edge computing node to obtain holographic characteristic information of the whole fire scene, and generate a vehicle dispatching strategy and a fireman operation strategy according to the holographic information of the fire scene, the running state of each vehicle-mounted edge computing node and the fire-fighting material state.
In some embodiments, the method further comprises:
the vehicle-mounted master control edge computing node sends the firefighter operation strategy to a corresponding firefighter;
and the vehicle-mounted master control edge computing nodes send scheduling instructions to the vehicle-mounted edge computing nodes according to the vehicle scheduling strategy.
In some embodiments, selecting one or more of the in-vehicle edge computing nodes as an in-vehicle turnkey edge computing node comprises:
under the initial condition, selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node;
when the performance of the edge computing nodes exceeds a preset threshold value, increasing the number of vehicle-mounted general control edge computing nodes until the performance of all the vehicle-mounted general control edge computing nodes is lower than the preset threshold value;
and the vehicle-mounted master control edge computing nodes process the computing tasks in parallel through a dynamic strategy.
In some embodiments, the wearable edge computing node comprises a plurality of sensors, the wearable edge computing node collecting an on-site fire, comprising:
the sensor collects field fire range information through a visual collection device, and the wearable edge computing node computes a field fire range according to the fire range information;
the sensor collects field fire intensity information through temperature collection equipment, and the wearable edge computing node computes a field fire range according to the fire intensity information;
the sensor collects feature information of the combustion object through gas collection equipment, and the wearable edge calculation node calculates the feature of the combustion object according to the feature information of the combustion object.
In some embodiments, after the wearable edge computing node collects the field fire, the field fire range, the fire intensity, and the characteristics of the combustibles are identified, and the wearable edge computing node sends the field fire range, the fire intensity, and the characteristics of the combustibles to the vehicle-mounted general control edge computing node, where the method includes:
calculating the confidence level of the field fire by a formula L (Range) multiplied by Intensity, wherein L is the confidence level of the field fire, Range is the Range of the field fire, and Intensity is the Intensity of the fire;
and aiming at different combustion objects, comparing whether the site fire confidence level exceeds a corresponding level threshold value, and sending a fire upgrading alarm to the main control vehicle-mounted edge computing node under the condition that the site fire confidence level exceeds the level threshold value.
In some embodiments, generating a vehicle dispatch strategy and a firefighter operating strategy based on the holographic information of the fire scene and the operating state and the fire supply state of each on-board edge computing node comprises:
the vehicle-mounted edge computing nodes are combined into corresponding fire fighting vehicle groups according to the vehicle scheduling strategy to carry out continuous supply of fire fighting resources;
and the wearable edge computing nodes are combined into corresponding firefighter groups according to the firefighter operation strategies to assist in carrying out fire fighting task operation.
In some embodiments, the sending, by the vehicle-mounted total control edge computing node, a scheduling instruction to each vehicle-mounted edge computing node according to the vehicle scheduling policy includes:
when the vehicle-mounted edge computing node fails, sending a distress signal to a neighboring vehicle-mounted edge computing node;
and after receiving the signal, the neighbor vehicle-mounted edge computing node executes the rest tasks of the vehicle-mounted edge computing node and requests the vehicle-mounted master control node to carry out task scheduling.
Based on above-mentioned purpose, this application has still provided an on-vehicle platform operating system of wisdom fire control with marginal computing power, includes:
the building module is used for setting a vehicle-mounted edge computing node on each fire truck, and selecting one or more vehicle-mounted edge computing nodes from the vehicle-mounted edge computing nodes as vehicle-mounted master control edge computing nodes; a wearable edge computing node is arranged on each firefighter, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network;
the acquisition module is used for acquiring the field fire condition by the wearable edge computing node which comprises a plurality of sensors; each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval;
the recognition module is used for recognizing the field fire range, the fire intensity and the characteristics of the combustion objects after the wearable edge computing node collects the field fire, and sending the field fire range, the fire intensity and the characteristics of the combustion objects to the vehicle-mounted master control edge computing node;
and the scheduling module is used for summarizing the fire scene range, the intensity and the characteristics of the comburent returned by each wearable edge computing node by the vehicle-mounted master control edge computing node to obtain the holographic characteristic information of the whole fire scene, and generating a vehicle scheduling strategy and a fireman operation strategy according to the holographic information of the fire scene and the running state and the fire-fighting material state of each vehicle-mounted edge computing node.
In some embodiments, the system further comprises:
the fire fighter control module is used for sending the fire fighter operation strategy to a corresponding fire fighter by the vehicle-mounted general control edge computing node;
and the vehicle scheduling module is used for sending a scheduling instruction to each vehicle-mounted edge computing node by the vehicle-mounted total control edge computing node according to the vehicle scheduling strategy.
In some embodiments, the building module comprises:
the initial unit is used for selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node under the initial condition;
the expanding unit is used for increasing the number of the vehicle-mounted total control edge computing nodes when the performance of the edge computing nodes exceeds a preset threshold value until the performance of all the vehicle-mounted total control edge computing nodes is lower than the preset threshold value;
and the parallel unit is used for the vehicle-mounted total control edge computing node to process the computing task in parallel through a dynamic strategy.
In general, the idea of the application is that a vehicle-mounted edge computing node is arranged on each fire engine, and the edge computing nodes collect the conditions of fire fighting resources, fire rescue states, traffic running states and the like of the vehicles through sensors and analyze the conditions on local edge nodes; configuring a wearable edge computing node for each firefighter to acquire the scene of fire; data and instructions after calculation and analysis are exchanged between the wearable edge calculation node and the vehicle-mounted edge calculation node, so that intelligent fire truck scheduling and configuration of fire fighting resources are realized.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
FIG. 1 is a flow chart illustrating a method for operating an intelligent fire fighting vehicle platform with edge computing capability according to an embodiment of the invention.
FIG. 2 is a flow chart illustrating a method for operating an intelligent fire fighting vehicle platform with edge computing capability according to an embodiment of the invention.
Fig. 3 is a block diagram illustrating an intelligent fire fighting vehicle platform operating system with edge computing capability according to an embodiment of the present invention.
Fig. 4 is a block diagram illustrating an intelligent fire fighting vehicle platform operating system with edge computing capability according to an embodiment of the present invention.
Fig. 5 shows a composition diagram of a building block according to an embodiment of the present invention.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
FIG. 1 is a flow chart illustrating a method for operating an intelligent fire fighting vehicle platform with edge computing capability according to an embodiment of the invention. As shown in fig. 1, the method for operating an intelligent fire fighting vehicle-mounted platform with edge computing capability includes:
s11, setting a vehicle-mounted edge computing node on each fire truck, and selecting one or more vehicle-mounted edge computing nodes from the vehicle-mounted edge computing nodes as vehicle-mounted master control edge computing nodes; each firefighter is provided with a wearable edge computing node, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network.
Specifically, when a fire alarm occurs, a fire fighting fleet composed of fire fighting vehicles with different functions reaches a fire scene, each fire fighting vehicle in the fire fighting fleet is provided with a vehicle-mounted edge computing node, on one hand, fire fighting resources and action positions of the fire fighting vehicle are collected, and on the other hand, the rescue state of the fire fighting vehicle is subjected to prediction analysis. Fire resources may include such things as water production, fire equipment quantity, personnel skills, etc.; the action location may include geographic coordinates of the vehicle, driving speed, etc.
In one embodiment, selecting one or more of the in-vehicle edge computing nodes as an in-vehicle grandmaster edge computing node comprises:
under the initial condition, selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node;
when the performance of the edge computing nodes exceeds a preset threshold value, increasing the number of vehicle-mounted general control edge computing nodes until the performance of all the vehicle-mounted general control edge computing nodes is lower than the preset threshold value;
and the vehicle-mounted master control edge computing nodes process the computing tasks in parallel through a dynamic strategy.
Specifically, since the urgency of the fire extinguishing action time is particularly high, the demand for calculation efficiency is also particularly high, and therefore if the calculation pressure exists in the selected vehicle-mounted total control edge calculation node, the number of vehicle-mounted edge calculation nodes can be increased, and the parallel calculation of the multiple edge calculation nodes can be performed, thereby improving the calculation efficiency.
Step S12, the wearable edge computing node comprises a plurality of sensors, and the wearable edge computing node collects the scene fire; and each vehicle-mounted edge computing node sends the running state of the vehicle and the state of the fire-fighting materials to the vehicle-mounted main control edge computing node according to a preset time interval.
For example, the vehicle-mounted edge computing node may be provided with a sensor at a water outlet of a water gun of the vehicle, collect water pressure and flow rate of the water outlet, and return to the vehicle-mounted main control edge computing node according to a frequency of 10 seconds.
In one embodiment, the wearable edge computing node includes a plurality of sensors, the wearable edge computing node collecting a live fire, comprising:
the sensor collects field fire range information through a visual collection device, and the wearable edge computing node computes a field fire range according to the fire range information;
the sensor collects field fire intensity information through temperature collection equipment, and the wearable edge computing node computes a field fire range according to the fire intensity information;
the sensor collects feature information of the combustion object through gas collection equipment, and the wearable edge calculation node calculates the feature of the combustion object according to the feature information of the combustion object.
In particular, the sensor may be directly connected with the wearable edge computing node or communicatively connected with the wearable edge computing node. For example, the field fire range and intensity may be collected by a camera mounted on the head of a firefighter.
And S13, after the wearable edge computing node collects the field fire, recognizing the field fire range, the fire intensity and the characteristics of the comburent, and sending the scene fire range, the fire intensity and the characteristics of the comburent to the vehicle-mounted master control edge computing node.
In one embodiment, after the wearable edge computing node collects the field fire, the wearable edge computing node identifies the field fire range, the fire intensity and the characteristics of the comburent, and sends the identified field fire range, fire intensity and characteristics of the comburent to the vehicle-mounted general control edge computing node, and the method includes the following steps:
calculating the confidence level of the field fire by a formula L (Range) multiplied by Intensity, wherein L is the confidence level of the field fire, Range is the Range of the field fire, and Intensity is the Intensity of the fire;
and aiming at different combustion objects, comparing whether the site fire confidence level exceeds a corresponding level threshold value, and sending a fire upgrading alarm to the main control vehicle-mounted edge computing node under the condition that the site fire confidence level exceeds the level threshold value.
Specifically, by multiplying the fire range and the fire intensity, a fire confidence level for the fire scene may be calculated. However, for different combustibles, the corresponding fire confidence levels are different, and therefore different level thresholds need to be set. For example, the range is generally large during gas combustion, more fixed during solid combustion, and more complex mixtures, thus requiring specific confidence levels to be set for different combustibles.
And S14, summarizing the fire scene range, the intensity and the characteristics of the comburent returned by each wearable edge computing node by the vehicle-mounted total control edge computing node to obtain the holographic characteristic information of the whole fire scene, and generating a vehicle dispatching strategy and a fireman operation strategy according to the holographic information of the fire scene and the running state and the fire-fighting material state of each vehicle-mounted edge computing node.
Specifically, the fire handling process is a process of coordination and resource allocation, and therefore an optimal strategy needs to be obtained according to the condition of the fire on site and the condition of the fire fighting fleet.
In one embodiment, generating a vehicle dispatch strategy and a firefighter operation strategy based on the holographic information of the fire scene and the operating state and the fire material state of each on-board edge computing node comprises:
the vehicle-mounted edge computing nodes are combined into corresponding fire fighting vehicle groups according to the vehicle scheduling strategy to carry out continuous supply of fire fighting resources;
and the wearable edge computing nodes are combined into corresponding firefighter groups according to the firefighter operation strategies to assist in carrying out fire fighting task operation.
FIG. 2 is a flow chart illustrating a method for operating an intelligent fire fighting vehicle platform with edge computing capability according to an embodiment of the invention. As shown in fig. 2, the method for operating an intelligent fire fighting vehicle-mounted platform with edge computing capability further includes:
and S15, the vehicle-mounted main control edge computing node sends the firefighter operation strategy to a corresponding firefighter.
Specifically, each firefighter has different skills and is located at different positions in a fire scene, so that it is necessary to decompose the firefighter operating strategy on each firefighter for specific configuration according to the skill, state, and the like of each firefighter.
And S16, the vehicle-mounted main control edge computing node sends a scheduling instruction to each vehicle-mounted edge computing node according to the vehicle scheduling strategy.
In one embodiment, the sending, by the vehicle-mounted general control edge computing node, a scheduling instruction to each vehicle-mounted edge computing node according to the vehicle scheduling policy includes:
when the vehicle-mounted edge computing node fails, sending a distress signal to a neighboring vehicle-mounted edge computing node;
and after receiving the signal, the neighbor vehicle-mounted edge computing node executes the rest tasks of the vehicle-mounted edge computing node and requests the vehicle-mounted master control node to carry out task scheduling.
Specifically, if one fire truck in the fire truck fleet breaks down, another or more neighbor fire trucks need to be found in the fire truck fleet immediately to replace the function of the fire truck, so that the efficiency of fire handling is prevented from being affected.
Fig. 3 is a block diagram illustrating an intelligent fire fighting vehicle platform operating system with edge computing capability according to an embodiment of the present invention. As shown in fig. 3, the intelligent fire fighting vehicle-mounted platform operation system with edge computing capability can be divided into:
the building module 31 is configured to set a vehicle-mounted edge computing node on each fire truck, and select one or more vehicle-mounted edge computing nodes as vehicle-mounted general control edge computing nodes from the vehicle-mounted edge computing nodes; a wearable edge computing node is arranged on each firefighter, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network;
an acquisition module 32, configured to enable the wearable edge computing node to include a plurality of sensors, and enable the wearable edge computing node to acquire an on-site fire; each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval;
the identification module 33 is used for identifying the field fire range, the fire intensity and the characteristics of the combustion objects after the wearable edge computing node collects the field fire, and sending the field fire range, the fire intensity and the characteristics of the combustion objects to the vehicle-mounted master control edge computing node;
and the scheduling module 34 is used for summarizing the fire scene range, the intensity and the characteristics of the comburent returned by each wearable edge computing node by the vehicle-mounted total control edge computing node to obtain the holographic characteristic information of the whole fire scene, and generating a vehicle scheduling strategy and a fireman operation strategy according to the holographic information of the fire scene and the running state and the fire-fighting material state of each vehicle-mounted edge computing node.
Fig. 4 is a block diagram illustrating an intelligent fire fighting vehicle platform operating system with edge computing capability according to an embodiment of the present invention. As shown in fig. 4, the intelligent fire fighting vehicle-mounted platform operation system with edge computing capability integrally further includes:
the fireman control module 35 is used for sending the fireman operation strategy to the corresponding fireman by the vehicle-mounted main control edge computing node;
and the vehicle scheduling module 36 is configured to send a scheduling instruction to each vehicle-mounted edge computing node by the vehicle-mounted total control edge computing node according to the vehicle scheduling policy.
Fig. 5 shows a composition diagram of a building block according to an embodiment of the present invention. As shown in fig. 5, the building block 31 includes:
an initial unit 311, configured to select one of the vehicle-mounted edge computing nodes as a vehicle-mounted general control edge computing node in an initial condition;
the expanding unit 312 is configured to increase the number of vehicle-mounted total control edge computing nodes when the performance of the edge computing node exceeds a preset threshold value until the performance of all vehicle-mounted total control edge computing nodes is lower than the preset threshold value;
and the parallel unit 313 is used for the vehicle-mounted total control edge computing node to process the computing tasks in parallel through a dynamic strategy.
The functions of the modules in the systems in the embodiments of the present application may refer to the corresponding descriptions in the above methods, and are not described herein again.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (8)
1. An intelligent fire-fighting vehicle-mounted platform operation method with edge computing capability is characterized by comprising the following steps:
each fire truck is provided with a vehicle-mounted edge computing node, one or more vehicle-mounted edge computing nodes are selected from the vehicle-mounted edge computing nodes to serve as vehicle-mounted general control edge computing nodes, and the method comprises the following steps: under the initial condition, selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node; when the performance of the edge computing nodes exceeds a preset threshold value, increasing the number of vehicle-mounted general control edge computing nodes until the performance of all the vehicle-mounted general control edge computing nodes is lower than the preset threshold value; the vehicle-mounted general control edge computing nodes process computing tasks in parallel through a dynamic strategy; a wearable edge computing node is arranged on each firefighter, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network;
the wearable edge computing node comprises a plurality of sensors and is used for collecting the scene fire; each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval;
wearable edge calculation node gathers the back at scene fire, discerns scene fire scope, fire intensity and comburent characteristic send to on-vehicle edge calculation node of always controlling includes: calculating the confidence level of the field fire by a formula L (Range) multiplied by Intensity, wherein L is the confidence level of the field fire, Range is the Range of the field fire, and Intensity is the Intensity of the fire; aiming at different combustion objects, comparing whether the site fire confidence level exceeds a corresponding level threshold value, and sending a fire upgrading alarm to the main control vehicle-mounted edge computing node under the condition that the site fire confidence level exceeds the level threshold value;
the vehicle-mounted master control edge computing nodes collect fire scene ranges, intensity and burning object characteristics returned by each wearable edge computing node to obtain holographic characteristic information of the whole fire scene, and generate a vehicle dispatching strategy and a fireman operation strategy according to the holographic information of the fire scene, the running state of each vehicle-mounted edge computing node and the fire-fighting material state.
2. The method of claim 1, further comprising:
the vehicle-mounted general control edge computing node sends the firefighter operation strategy to a corresponding firefighter;
and the vehicle-mounted master control edge computing nodes send scheduling instructions to the vehicle-mounted edge computing nodes according to the vehicle scheduling strategy.
3. The method of claim 1, wherein the wearable edge computing node comprises a plurality of sensors, wherein the wearable edge computing node collects a live fire comprising:
the sensor collects field fire range information through a visual collection device, and the wearable edge computing node computes a field fire range according to the fire range information;
the sensor collects field fire intensity information through temperature collection equipment, and the wearable edge computing node computes a field fire range according to the fire intensity information;
the sensor collects feature information of the combustion object through gas collection equipment, and the wearable edge calculation node calculates the feature of the combustion object according to the feature information of the combustion object.
4. The method of claim 1, wherein generating a vehicle dispatch strategy and a firefighter operating strategy based on the holographic information of the fire scene and the operating status and the fire supply status of each on-board edge computing node comprises:
the vehicle-mounted edge computing nodes are combined into corresponding fire fighting vehicle groups according to the vehicle scheduling strategy to carry out continuous supply of fire fighting resources;
and the wearable edge computing nodes are combined into corresponding firefighter groups according to the firefighter operation strategies to assist in carrying out fire task operation.
5. The method according to claim 2, wherein the sending of the scheduling instruction to each vehicle-mounted edge computing node by the vehicle-mounted general control edge computing node according to the vehicle scheduling policy comprises:
when the vehicle-mounted edge computing node fails, sending a distress signal to a neighboring vehicle-mounted edge computing node;
and after receiving the signal, the neighbor vehicle-mounted edge computing node executes the rest tasks of the vehicle-mounted edge computing node and requests the vehicle-mounted master control node to carry out task scheduling.
6. An intelligent fire-fighting vehicle-mounted platform operation system with edge computing capability, comprising:
the building module is used for setting a vehicle-mounted edge computing node on each fire truck, and selecting one or more vehicle-mounted edge computing nodes from the vehicle-mounted edge computing nodes as vehicle-mounted general control edge computing nodes, and comprises the following steps: under the initial condition, selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node; when the performance of the edge computing nodes exceeds a preset threshold value, increasing the number of vehicle-mounted general control edge computing nodes until the performance of all the vehicle-mounted general control edge computing nodes is lower than the preset threshold value; the vehicle-mounted general control edge computing nodes process computing tasks in parallel through a dynamic strategy; a wearable edge computing node is arranged on each firefighter, and the vehicle-mounted edge computing node and the wearable edge computing node are connected in a wireless mode to form an edge computing network;
the acquisition module is used for acquiring the field fire condition by the wearable edge computing node which comprises a plurality of sensors; each vehicle-mounted edge computing node sends the running state of the vehicle and the state of fire-fighting materials to the vehicle-mounted master control edge computing node according to a preset time interval;
the identification module is used for wearable edge calculation node discerns after the scene fire is gathered scene fire scope, fire intensity and comburent characteristic, send to on-vehicle edge calculation node of always controlling includes: calculating a site fire confidence level by a formula L (Range) multiplied by Intensity, wherein L is the site fire confidence level, Range is the site fire Range, and Intensity is the fire Intensity; aiming at different combustion objects, comparing whether the site fire confidence level exceeds a corresponding level threshold value, and sending a fire upgrading alarm to the main control vehicle-mounted edge computing node under the condition that the site fire confidence level exceeds the level threshold value;
and the scheduling module is used for summarizing the fire scene range, the intensity and the characteristics of the combustion objects returned by each wearable edge computing node by the vehicle-mounted master control edge computing node to obtain the holographic characteristic information of the whole fire scene, and generating a vehicle scheduling strategy and a fireman operation strategy according to the holographic information of the fire scene and the running state and the fire-fighting material state of each vehicle-mounted edge computing node.
7. The system of claim 6, further comprising:
the fire fighter control module is used for sending the fire fighter operation strategy to a corresponding fire fighter by the vehicle-mounted general control edge computing node;
and the vehicle scheduling module is used for sending a scheduling instruction to each vehicle-mounted edge computing node by the vehicle-mounted total control edge computing node according to the vehicle scheduling strategy.
8. The system of claim 6, wherein the building block comprises:
the initial unit is used for selecting one vehicle-mounted edge computing node from the vehicle-mounted edge computing nodes as a vehicle-mounted master control edge computing node under the initial condition;
the expanding unit is used for increasing the number of the vehicle-mounted total control edge computing nodes when the performance of the edge computing nodes exceeds a preset threshold value until the performance of all the vehicle-mounted total control edge computing nodes is lower than the preset threshold value;
and the parallel unit is used for the vehicle-mounted total control edge computing node to process the computing task in parallel through a dynamic strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010078940.8A CN111343598B (en) | 2020-02-03 | 2020-02-03 | Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010078940.8A CN111343598B (en) | 2020-02-03 | 2020-02-03 | Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111343598A CN111343598A (en) | 2020-06-26 |
| CN111343598B true CN111343598B (en) | 2022-05-17 |
Family
ID=71186785
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010078940.8A Active CN111343598B (en) | 2020-02-03 | 2020-02-03 | Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111343598B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111786839B (en) | 2020-07-15 | 2021-09-07 | 南通大学 | Calculation unloading method and system for energy efficiency optimization in vehicle-mounted edge calculation network |
| CN115938065B (en) * | 2022-09-21 | 2023-09-15 | 慧之安信息技术股份有限公司 | Fire engine intelligent identification system based on edge calculation |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107510914A (en) * | 2017-08-21 | 2017-12-26 | 山东省科学院自动化研究所 | A kind of wisdom fire-fighting remote monitoring system and its method towards garden |
| CN108810096A (en) * | 2018-05-18 | 2018-11-13 | 上海波宝仟赫科技股份有限公司 | Fire-fighting and rescue resource management system |
| CN109857023A (en) * | 2019-01-30 | 2019-06-07 | 亿江(北京)科技发展有限公司 | A kind of vehicle-mounted multi-service gateway of internet of things of fire fighting truck and working method |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3069465B2 (en) * | 1993-04-30 | 2000-07-24 | 松下電工株式会社 | Fire alarm system |
| KR101514177B1 (en) * | 2005-01-12 | 2015-04-22 | 이클립스 에어로스페이스, 인크. | Fire suppression systems |
| CN103914942A (en) * | 2014-04-15 | 2014-07-09 | 北京百纳威尔科技有限公司 | Mobile terminal alarm method and device |
| CN105184668A (en) * | 2015-08-24 | 2015-12-23 | 国家电网公司 | Forest fire risk area dividing method for power transmission line based on cluster analysis |
| CN113450551A (en) * | 2017-03-09 | 2021-09-28 | 宁波鼎翔消防技术有限公司 | Automatic fire alarm equipment management system |
| CN109830092A (en) * | 2017-12-30 | 2019-05-31 | 湖南汇博电子科技股份有限公司 | Fire-fighting and rescue dispatching method, device and readable storage medium storing program for executing |
| CN109785570A (en) * | 2017-12-31 | 2019-05-21 | 湖南汇博电子科技股份有限公司 | A kind of fire-fighting and rescue method, apparatus, system and readable storage medium storing program for executing |
| CN108939386A (en) * | 2018-07-05 | 2018-12-07 | 天津市瑞傲特科技发展有限公司 | Comprehensive administration of the prevention and control platform |
| CN109800961A (en) * | 2018-12-27 | 2019-05-24 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of fire rescue decision-making technique, device, storage medium and terminal device |
-
2020
- 2020-02-03 CN CN202010078940.8A patent/CN111343598B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107510914A (en) * | 2017-08-21 | 2017-12-26 | 山东省科学院自动化研究所 | A kind of wisdom fire-fighting remote monitoring system and its method towards garden |
| CN108810096A (en) * | 2018-05-18 | 2018-11-13 | 上海波宝仟赫科技股份有限公司 | Fire-fighting and rescue resource management system |
| CN109857023A (en) * | 2019-01-30 | 2019-06-07 | 亿江(北京)科技发展有限公司 | A kind of vehicle-mounted multi-service gateway of internet of things of fire fighting truck and working method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111343598A (en) | 2020-06-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111343598B (en) | Intelligent fire-fighting vehicle-mounted platform operation method and system with edge computing capability | |
| CN107510914B (en) | Intelligent fire-fighting remote monitoring system and method for park | |
| CN110390789A (en) | Forest fire protection data analysis system based on big data | |
| CN101625789A (en) | Method for monitoring forest fire in real time based on intelligent identification of smoke and fire | |
| CN109376987A (en) | Pilotless automobile dispatching method, device, equipment and storage medium | |
| CN111179586A (en) | Traffic guidance method, equipment and storage medium based on big data analysis | |
| CN101607668A (en) | Embedded computer vision escalator pedestrian flow supervision and alarm device | |
| CN110309735A (en) | Exception detecting method, device, server and storage medium | |
| CN111063252B (en) | Scenic spot navigation method and system based on artificial intelligence | |
| CN109920099B (en) | Movable modular intelligent fire-fighting on-duty guarantee equipment and related products | |
| CN113506454A (en) | Expressway induction lamp system and method based on audio event detection | |
| CN111862656A (en) | Map updating method and device for intelligent carrier, storage medium and processor | |
| CN116863708A (en) | Smart city scheduling distribution system | |
| CN114299739A (en) | Target road section passing method, system, storage medium and electronic device | |
| CN116758490B (en) | Construction site safety helmet wearing detection and early warning method and system based on AI algorithm | |
| KR102475148B1 (en) | ESS Fire Prevention System and the method thereof | |
| CN117180687A (en) | Intelligent fire-fighting remote monitoring system and method | |
| CN111815893A (en) | Fire-fighting early warning method based on Internet of things and storage medium | |
| CN113559442B (en) | Electric automobile charging pile fire partition intelligent prevention and control method and system | |
| US20150134298A1 (en) | System and method for generating forest fire airtanker operations data | |
| CN112530165B (en) | Method and device for judging violation of regulations of apron support vehicle based on Internet of things | |
| CN113095959A (en) | Design method of scenic spot fire safety information system | |
| Chandarana et al. | Hybrid model for a priori performance prediction of multi-job type swarm search and service missions | |
| CN111338768A (en) | Public security resource scheduling system utilizing urban brain | |
| CN111160780A (en) | Dispatching robot and dispatching method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |