CN111882809A - Method and system for guaranteeing fire safety of residential area based on Internet of things - Google Patents
Method and system for guaranteeing fire safety of residential area based on Internet of things Download PDFInfo
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- 238000013135 deep learning Methods 0.000 abstract description 6
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- 238000011161 development Methods 0.000 abstract description 3
- 230000006870 function Effects 0.000 description 8
- 239000007921 spray Substances 0.000 description 6
- 208000037170 Delayed Emergence from Anesthesia Diseases 0.000 description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
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- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C31/00—Delivery of fire-extinguishing material
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B7/00—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00
- G08B7/06—Signalling systems according to more than one of groups G08B3/00 - G08B6/00; Personal calling systems according to more than one of groups G08B3/00 - G08B6/00 using electric transmission, e.g. involving audible and visible signalling through the use of sound and light sources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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Abstract
The invention discloses a method and a system for guaranteeing fire safety of a community based on the Internet of things, wherein the method comprises the following steps: according to the sensor comprehensive signal, analyzing and obtaining the fire condition degree and a corresponding fire expressing data result; generating a plan model based on the existing plan; selecting a proper plan according to the plan model, and starting automatic intervention; if the intervention is successful, ending the intervention, and if the intervention is failed for a plurality of times, starting the artificial intervention until the intervention is successful; forming a new plan according to the behavior implementation process of human intervention and the corresponding event form information, storing the new plan into a plan library, and correcting the plan model, wherein the system is based on the method. The invention can autonomously decide the type of the adopted plan, and can realize the continuous correction of the plan through deep learning; meanwhile, the system can control the occurrence and development degree of safety accidents in real time, and relieves the fire safety risk caused by personnel negligence or untimely intervention.
Description
Technical Field
The invention relates to the field of intelligent communities, in particular to a method and a system for guaranteeing fire safety of a community based on the Internet of things.
Background
It is well known that the losses to mankind caused by building fires are enormous. The fire disaster often occurs in the places with dense crowd and concentrated material, the expansion speed is high, the fire fighting channels in the areas are often blocked, the fire fighting truck is difficult to enter, the fire fighting work is difficult to expand, and the small fire disaster often causes a large disaster. The invention provides a fire safety Internet of things autonomous decision system which takes 'risk control, hidden danger elimination and scientific rescue' as a core.
The traditional fire safety management has the following problems: 1. precaution and post-remedial measures are not strict in preventive supervision, such as: when the fire extinguisher is overdue, the device is aged; the smoke alarm has unstable connection or is arranged; the water pressure of the fire hydrant is insufficient or the water is cut off; the fire fighting access serves as a blockage for other uses; the fire safety equipment is displaced and cannot be found when the fire safety equipment is displaced; and the like. 2. The fire hazard of high-rise buildings is caused by various electrical equipment, large electricity consumption, high load density, complex electrical system, multiple electrical lines, multiple electrical rooms and the like. When a fire disaster occurs, the fire spread is fast, the evacuation is difficult, and the fire fighting difficulty is high.
Disclosure of Invention
In view of the above, one of the objectives of the present invention is to provide a method for guaranteeing fire safety of a cell based on the internet of things, which can autonomously determine the type of a plan to be adopted, and can implement continuous correction of the plan through deep learning; the second purpose is to provide a system based on the method, which can control the occurrence and development degree of safety accidents in real time and relieve the fire safety risk caused by personnel negligence or untimely intervention.
The purpose of the invention is realized by the following technical scheme:
a method for guaranteeing fire safety of a community based on the Internet of things,
according to the sensor comprehensive signal, analyzing and obtaining the fire condition degree and a corresponding fire expressing data result;
generating a plan model based on the existing plan;
selecting a proper plan according to the plan model, and starting automatic intervention;
if the intervention is successful, ending the intervention, and if the intervention is failed for a plurality of times, starting the artificial intervention until the intervention is successful;
and forming a new plan according to the behavior implementation process of human intervention and the corresponding event form information, storing the new plan into a plan library, and correcting the plan model.
Further, the method for establishing the plan model specifically comprises the following steps:
and according to the existing plan, taking the abstract data set of the data result expressing the fire in the plan as an input layer, taking the abstract data set of the behavior as an output layer, adopting a residual error neural network as a weight layer between the input layer and the output layer, changing the input layer and the output layer, and correcting the middle weight layer to obtain the plan model.
Further, including sensor layer and hardware facilities, still include:
the communication module is used for transmitting information;
the processor layer is used for analyzing the comprehensive signals transmitted by the sensor layer to obtain the degree of the fire condition and a corresponding data result expressing the fire;
the decision layer is used for establishing and correcting a plan model, receiving the fire condition degree and the corresponding fire expressing data result transmitted by the processor layer and deciding the type of the plan;
the plan storage layer is used for storing plans and taking corresponding plans according to the decision of the decision layer;
and the control module controls the hardware facilities according to the corresponding plan to carry out fire fighting.
Further, when the corresponding plan cannot complete fire extinguishing, the decision layer sends out a decision of manual intervention through the communication module.
Further, the sensor layer includes smoke sensors, temperature sensors, and light sensors arranged in respective areas of the cell.
And further, the system also comprises a video monitoring layer for positioning fire accidents in the community.
And further, the system also comprises a fire fighting channel management layer which is used for overall management of all fire fighting channels in the community.
The invention has the beneficial effects that:
the invention can autonomously decide the type of the adopted plan, and can realize the continuous correction of the plan through deep learning; meanwhile, the system can control the occurrence and development degree of safety accidents in real time, and relieves the fire safety risk caused by personnel negligence or untimely intervention.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a manner of training a model and applying the model;
FIG. 2 is a schematic diagram of a deep learning network structure;
FIG. 3 is a ResNet structure in a network structure of a plan model;
FIG. 4 is a schematic diagram of a deep residual neural network;
FIG. 5 is a flow chart of example 2.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
Example 1
The embodiment provides a method for guaranteeing fire safety of a community based on the internet of things, which specifically comprises the following steps:
and analyzing and obtaining the fire condition degree and a corresponding fire expressing data result according to the sensor comprehensive signal. Wherein the sensor integrated signal comprises a smoke sensor signal, a light sensor signal and a temperature sensor signal. The manner in which the model is trained and applied is shown in FIG. 1.
And generating a plan model based on the existing plan.
The generation method of the plan model comprises the following steps: training Data { X _ Data, Y _ Data } is front-end thing networking sensor collection equipment such as smoke sensor, temperature sensor, light sense sensor, to the collection of reality fire control Data and the collection of fire control Data correspondence grade label, and the model is through the study to training Data, trains out the model that is applicable to this fire control environment, predicts and makes a decision to new fire control Data.
The important structure in the deep learning network is Residual Block, the structure is shown in FIG. 2, and the whole deep learning network is composed of 34 layers of sub-Residual blocks. As shown in fig. 3, the data of the abstract fire scene in the plan is used as the input of the whole network, the predicted fire class is used as the output, the weight layer between the input layer and the output layer adopts a deep residual error neural network, the process of minimizing the distance between the tag data and the true value is used for correcting the middle weight layer, a stable prediction model is obtained, and the system implements the corresponding plan according to the prediction result.
The depth residual error neural network is composed of matrix multiplication of an input layer and a middle layer, a residual error structure and pooling, the input passes through a full connection layer, a residual error network layer, an output full connection layer and a softmax layer to obtain an output layer, the back propagation of the output layer is based on a cross entropy loss function, and the parameter change of the whole model is adjusted by conforming to a function derivative chain rule.
The network formula is expressed as:
X*[resnet resnet]＝y (2)
y*Wfc→Softmax＝Y (3)
the ResNet neutron block structure in the network structure is as follows:
the overall ResNet is made up of 34 residual blocks.
As shown in fig. 4, the important hierarchy of each residual block includes:
ReLu layer: (x) max (0, x) | f' (x) ═ 0
SoftMax layer:
cross entropy loss function L (y, a) ═ Σiyilog(ai)
Chain rule of complex function derivatives: let f and g be derivable functions for x, the composite function y ═ f [ g (x) ].
Derivative of complex function y with respect to x
That is, in the formula (3), y1 ═ F (X, { Wi }) + WsX
Its inverse image propagation autograd:
establishing high-matching applicable model of specific scene and equipment
And inputting the fire condition degree and the corresponding fire expressing data result into a plan model, selecting a proper plan, and starting automatic intervention. Automatic intervention is based on the thing networking, through control mechanism control if intelligent fire hydrant, spray set etc. adopt spray area and the volume of spraying in the suitable plan put out a fire.
If the intervention is successful, ending, and if the intervention is failed for a plurality of times, starting the artificial intervention until the intervention is successful.
The signs of successful intervention are: the smoke sensor signal, the light sensor signal and the temperature sensor signal return to normal values.
And forming a new plan according to the behavior implementation process of human intervention and the corresponding event form information, storing the new plan into a plan library, and correcting the plan model.
The manual intervention is to inform fire brigade or property management personnel, remotely control or control facilities such as fire hydrant on the spot, extinguish the fire, and input the recorded signals of fire smoke, temperature and the like and the implementation process of the manual intervention into the existing plan model to correct the plan model.
The whole network architecture is designed to serve the decision FASD system, and when the decision system is stable. When the method is applied to an actual scene, the work flow is shown in fig. 5.
Example 2
The embodiment also provides an internet of things-based security system for guaranteeing fire protection of a residential area, as shown in fig. 5, the system comprises a sensor layer, a hardware facility, a communication module, a processor layer, a decision layer, a plan storage layer and a control module, wherein the sensor layer is used for transmitting information, the processor layer, the decision layer, the plan storage layer and the control module. The sensor layer is including installing smoke alarm, temperature sensor and light sensor etc. in district each position, and the hardware facilities is district fire control common use facility, including fire hydrant, automatic fire extinguishing device, spray set, dredge device etc..
The bottom hardware equipment facilities have a certain intelligent foundation, have the function of the Internet of things, can independently or semi-independently (externally connected with an Internet of things sensor) automatically upload abnormality, obstacle information and periodic signals, receive control information and execute simple control commands (such as opening and closing equipment or triggering sound or other alarm positioning information).
The wiring board of a certain office in district A in this embodiment has taken place the wiring board accident of catching fire because transshipping, at first through the video monitoring layer, fixes a position to the concrete position that takes place the conflagration, and the video monitoring layer is for installing the video monitor in district inside, and video monitor can fix a position the position of catching fire through the emergence position of gathering smog. Meanwhile, the three sensors can collect the smoke concentration, the fire temperature and the flame intensity of a fire point and transmit the smoke concentration, the fire temperature and the flame intensity to the processor layer through the communication module.
The communication module is used for transmitting information, and the action mode of the communication module is as follows: the primary server receives the three sets of data and transmits the three sets of data to the processor layer through one of the secondary servers.
The processor layer is used for analyzing the comprehensive signals transmitted by the sensor layer, obtaining the fire condition degree and a corresponding data result expressing the fire, and judging the fire condition at the position as follows: and 4, predicting dangerous cases according to the model, pointing to a plan table implementation scheme, and solving the problems:
TABLE 1 plan database plan data
With three data input plans models of group, decision-making layer decision adopts plan 4 to put out a fire, open audible-visual annunciator promptly, open spray set (increase second grade water pressure), increase 1 time and spray the area, it is sparse to open personnel, simultaneously through fire control passageway management layer, manage all fire control passageways of district overall, fire control passageway management layer adopts infrared binocular camera or other suitable camera sensor monitoring fire control passageway unobstructed circumstances, monitor that there is the unconventional condition such as legacy (parking, stack article, the nail, leave over time in fire control passageway etc.), start-up mediation plan (the broadcast moves the position, the direct managers of early warning information remain on-the-spot evidence information simultaneously), in order to avoid the condition that the fire engine can't drive into the district.
And then drive audible and visual annunciator, spray set, etc. through the control module (controller) and carry on work, when finishing carrying out, however the data display of the three sensors does not finish putting out a fire, the smog concentration and temperature of the area are higher than the normal value, therefore the sensor layer transmits these data to the decision-making layer through the communication module, the decision-making layer gives an alarm through the controller, adopt the manual intervention, the manual intervention can put out a fire for notifying the property personnel, can also put out a fire for notifying the fire fighter. The present embodiment is the former.
After the property personnel arrive, according to experience, measures such as controlling the area and the water pressure of the spraying device, starting the intelligent fire hydrant and the like are adopted to extinguish the lives in the area, when the sensor layer displays that the smoke concentration and the temperature are normal values, the decision layer can read the measures through the control layer, and revise the plan model to obtain a new plan which is stored in the plan library layer, and the plan library layer is big data. The data and operational procedures (this regulation) are stored as new blocks as new regulations for the current class of equipment. Namely a block chain type storage mode.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (7)
1. A method for guaranteeing fire safety of a community based on the Internet of things is characterized by comprising the following steps:
according to the sensor comprehensive signal, analyzing and obtaining the fire condition degree and a corresponding fire expressing data result;
generating a plan model based on the existing plan;
selecting a proper plan according to the plan model, and starting automatic intervention;
if the intervention is successful, ending the intervention, and if the intervention is failed for a plurality of times, starting the artificial intervention until the intervention is successful;
and forming a new plan according to the behavior implementation process of human intervention and the corresponding event form information, storing the new plan into a plan library, and correcting the plan model.
2. The method for guaranteeing fire safety of a community based on the internet of things as claimed in claim 1, wherein: the method for establishing the plan model specifically comprises the following steps:
and according to the existing plan, taking the abstract data set of the data result expressing the fire in the plan as an input layer, taking the abstract data set of the behavior as an output layer, adopting a residual error neural network as a weight layer between the input layer and the output layer, changing the input layer and the output layer, and correcting the middle weight layer to obtain the plan model.
3. The method for guaranteeing fire safety of a community based on the internet of things as claimed in any one of claims 1 to 2, wherein the method comprises the following steps: including sensor layer and hardware facilities, still include:
the communication module is used for transmitting information;
the processor layer is used for analyzing the comprehensive signals transmitted by the sensor layer to obtain the degree of the fire condition and a corresponding data result expressing the fire;
the decision layer is used for establishing and correcting a plan model, receiving the fire condition degree and the corresponding fire expressing data result transmitted by the processor layer and deciding the type of the plan;
the plan storage layer is used for storing plans and taking corresponding plans according to the decision of the decision layer;
and the control module controls the hardware facilities according to the corresponding plan to carry out fire fighting.
4. The internet of things-based cell fire safety system of claim 3, wherein:
and when the corresponding plan can not finish fire extinguishing, the decision layer sends out a manual intervention decision through the communication module.
5. The internet of things-based cell fire safety system of claim 3, wherein: the sensor layer includes smoke sensors, temperature sensors and light sensors arranged in various areas of the cell.
6. The internet of things-based cell fire safety system of claim 3, wherein: the system also comprises a video monitoring layer for positioning fire accidents in the community.
7. The internet of things-based cell fire safety system of claim 3, wherein: the system also comprises a fire fighting access management layer for overall management of all fire fighting accesses in the community.
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Cited By (1)
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