CN107564231A - Building fire early warning and fire disaster situation assessment system and method based on Internet of Things - Google Patents
Building fire early warning and fire disaster situation assessment system and method based on Internet of Things Download PDFInfo
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Abstract
The invention discloses a kind of building fire early warning based on Internet of Things and fire disaster situation assessment system and method, the present invention has the environmental information being closely connected, electric information with fire by building Internet of Things and gathering in building, feature extraction and data fusion are carried out to the collection information, fire alarm is carried out to building；The present invention gathers building information using Internet of things system is built, the comprehensive information from multiple sensors, data analysis is carried out using data fusion method, accurate, quick early warning is carried out to building fire situation, pass through the weight calculation of much information, effectively filtering interference signal, overcome the uncertainty and limitation of single sensor, improve building fire early-warning and predicting accuracy and effectively condition of a fire situation can be carried out and assess.
Description
Technical field
The present invention relates to a kind of building fire early warning based on Internet of Things and fire disaster situation assessment system and method.
Background technology
With the continuous development of all kinds of buildings, construction scope is increasing, level more and more higher, and the standard of building is also more next
It is higher.Fire detector is system " sense organ ", and it, which is acted on, is monitored in environment either with or without the generation of fire.Once have
The condition of a fire, just by the characteristic vector of fire, such as temperature, smog, gas and radiation light intensity are converted into electric signal, and act immediately to
Fire alarm control unit sends alarm signal.
At present, it is common to use fire detector include smoke detector, heat detector is special gas detector, red
Outer flame detector and ultraviolet flame detector etc..Above-mentioned fire detector is just for a variety of physics occurred in fire simultaneously
One kind in amount is detected, then is inevitably influenceed by some similar factors in environment, so as to cause false alarm.Solution
Certainly false alarm problem turns into the key point for improving detection accuracy.On the other hand, existing fire alarm mode lacks
Assessment to condition of a fire situation, it is difficult to judge that (the building personnel amount feelings of the condition of a fire such as occur for condition of a fire development trend, the extent of injury
Condition), it is unfavorable for accurately and rapidly rescuing.In fact, fire generating process is related to multiple physical messages such as sound, light, electricity, temperature
Change, while fire generation is closely related with domestic electrical parameter (electric current, voltage change) etc..
Therefore effective collection, comprehensive a variety of fire relevant informations, by the coordination between them and performance complement, pass through
The weight calculation of much information, effectively filters interference signal, overcomes the uncertainty and limitation of single sensor, is to improve to build
Build the key of the forecast of thing fire alarm and condition of a fire Situation Assessment.
The content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of building fire early warning and fire disaster situation based on Internet of Things
Assessment system and method, the present invention have the environment being closely connected to believe by building Internet of Things and gathering with fire in building
Breath, electric information, feature extraction and data fusion are carried out to the collection information, fire alarm is carried out to building.
To achieve these goals, the present invention adopts the following technical scheme that：
A kind of building fire early warning and fire disaster situation assessment system based on Internet of Things, including smart jack, environment ginseng
Number acquisition module, electric parameter acquisition module, Internet of things node and Cloud Server, wherein：
The smart jack has several, is arranged at monitoring area diverse location, and built-in temperature sensor detection is inserted
Seat temperature, and the socket temperature of detection is sent to the Internet of things node and stored；
The electric parameter acquisition module, be configured as gathering the electric current of electrical equipment of monitoring area, power, whether
Phase shortage and/or the information whether leaked electricity, and the information transfer of collection is stored to Internet of things node；
The ambient parameter acquisition module, it is configured as detecting the environment temperature of monitoring area, illumination, temperature, smog
And CO2Concentration parameter, and the ambient parameter of collection is sent to Internet of things node and stored；
The Internet of things node is arranged in monitoring area, is configured as receiving smart jack, ambient parameter collection mould
The collection information of block and electric parameter acquisition module simultaneously stores, and is transmitted to Cloud Server；
The Cloud Server, it is configured as establishing fire alarm model, the historical record stored by Internet of things node is believed
The message sample of building Internet of Things collection in breath structure building fire generating process, it is pre- to the fire of structure using the sample
Alert model is trained, and is carried out fire alarm to the data gathered in real time using the model after training, is carried out fire disaster situation assessment,
Send fire alarm signal.
Further, described Cloud Server provide electric fire disaster warning system output and long-distance user's intelligent terminal without
The interface of line connection.
Further, the Cloud Server utilizes BP neural network structure fire alarm model, the input layer unit of model
Number is six fire characteristic information as target, respectively smokescope, CO2Concentration, environment temperature, smart jack temperature, electricity
Stream and leakage current, output layer unit number are three, respectively without fire, have fire, the uncertain judgement as fire disaster situation, hidden
Layer unit number is 11.
Further, the system also includes personnel's detection module and video detection module, and the data of collection are transferred to cloud
Server, the Cloud Server utilize personnel's detection module, personnel positions are determined, with reference to the video of video detection module, in fire
During calamity early warning, the most short evacuation path of outlet is calculated according to the position of personnel, and is guided.Meanwhile by obtaining
Personal information, it can accurately implement rescue plan for fire fighter and help is provided.
Further, the system also includes the light guidance system for being arranged at monitoring area, including multiple guide lamp including sames,
Carry out lighting guide according to the most short evacuation path that Cloud Server is calculated.
Further, the system also includes the multiple audible-visual annunciators for being arranged at monitoring area.
Further, the cloud server ambient parameter and electrical equipment parameter information, as fire characteristic
Information, all information are merged, determine influence degree of each fire characteristic information to fire disaster situation, and according to influence journey
Degree assigns corresponding weight to each fire characteristic information.
Further, the smart jack, including the control of micro controller module, power module, temperature detecting module, break-make
Module and wireless communication module, wherein, the micro-control module and temperature detecting module, break-make control module and radio communication mold
Block connects, and the temperature detecting module is used for the temperature of test socket body, and temperature signal is sent to micro controller module.
Further, the Internet of things node, including micro controller module, wireless communication module, ethernet communication module,
Flash memory modules and power module, wherein, micro controller module and wireless communication module, ethernet communication module, Flash
Memory module is connected with power module, and Flash memory modules storage smart jack and ambient parameter acquisition module, electric parameter are adopted
Collect the signal that module uploads, form historical record.
Preferably, the Internet of things node judges temperature gap, finds temperature by the temperature of more each smart jack
Abnormal smart jack, the Internet of things node sends alarm signal to intelligent terminal, and disconnects the smart jack, inquires about simultaneously
The ID number of temperature anomaly smart jack on intelligent terminal, determine the position of the smart jack.This set can conveniently carry out different
Standing standby positioning, carries out replacing socket, prevents electric disaster hidden-trouble.
Preferably, the degree of heat of the smart jack or its temperature rising degree and institute's on-load be big and environment temperature because
Be known as pass, Internet of things node when judging smart jack temperature gap, according to difference variation degree judge smart jack whether band
There is load, difference variation is bigger, illustrates that smart jack carries load.
Method of work based on said system, comprises the following steps：
(1) information gathering and pretreatment, the collection environment relevant with fire and electric parameter information, Internet of things node will eventually
End data is transferred in Cloud Server, extraction wherein with fire associated temperature, smokescope and CO2Concentration, electric power and leakage
Current characteristic information simultaneously carries out Classifying Sum；
(2) using characteristic information as input layer information, the fire alarm model based on neutral net is established, passes through historical record
The message sample of building Internet of Things collection in information architecture building fire generating process, using the sample to fire alarm mould
Type is trained, and fire alarm is carried out to the data gathered in real time using the model completed after training；
(3) output result and complete fire disaster situation assessment, corresponding assessment result is sent by Internet of things system, carry out
Fire alarm signal.
In the step (1), Internet of things node receives and collects the temperature of each smart jack in affiliated node region
And environment temperature, carry out judging whether that fire occurs with reference to the temperature of smart jack, detailed process includes：
(1-1) stores the temperature and environment temperature of smart jack, obtains historical record temperature；
All smart jack temperature values, calculate all intelligence under same node in the region that (1-2) thing net node receives
Temperature difference between energy socket, is designated as the first temperature difference；
The temperature difference for the historical temperature average value that (1-3) Internet of things node intelligent socket temperature value stores up with node memory is big
It is small, it is poor to obtain second temperature；
The temperature difference that (1-4) Internet of things node intelligent socket temperature value detects current environment temperature with environment module is big
It is small, obtain the 3rd temperature difference；
(1-5) is according to relation between first temperature gap and the first preset difference value, the second temperature difference and the
The relation between relation and the 3rd temperature gap and the second preset difference value between two preset difference values, Internet of things node judge
Smart jack whether there is electric disaster hidden-trouble, and send alarm signal to intelligent terminal.
In the step (1-5), when first temperature gap is more than the first preset difference value, the second temperature difference
During more than the second preset difference value and when the 3rd temperature value is more than three preset difference values, Internet of things node judges that smart jack is deposited
In electric disaster hidden-trouble, smart jack is disconnected by on-off circuit automatically, and alarm signal is sent to intelligent terminal APP.
In the step (2), structure be based on BP neural network fire alarm model, for each neuron input to this
The effect of neuron is embodied in the size of connection weight, determines the weight between each sensor, selects smokescope, CO2Concentration,
6 environment temperature, socket temperature, electric current and leakage current fire characteristic information select no fire, have fire, be not true as target
It is set for the judgement for fire disaster situation, in BP neural network, input layer unit number is 6, and output layer unit number is 3, selects hidden layer
Unit number is 11.
Further, in the step (2), specifically include：
(2-1) sample data is normalized pretreatment, determines the topological structure of BP neural network；
(2-2) defines input layer to the connection weight between hidden layer, hidden layer to connection weight, the hidden layer section for exporting node layer
The threshold value of point, the threshold value for exporting node layer and hidden layer and the output function for exporting node layer；
The parameter of the definition of (2-3) to the BP neural network topological structure of structure carries out assignment, calculates output node layer
Output and desired output error, until error meets to impose a condition, target component and result are formed into sample set as BP
The learning sample of network is trained, and determines influence degree of each fire characteristic information to result；
(2-4) carries out weight distribution according to influence degree, obtains the weight distribution vector of target factor, obtains final pre-
Alert model.
Compared with prior art, beneficial effects of the present invention are：
(1) building illumination, temperature, smog, CO are gathered comprehensively by building Internet of things system2Concentration, room personnel etc.
Information and building electric equipment work electricity condition, data fusion judgement is carried out to information by Internet of things node, by god
BP algorithm through network is incorporated into the judgement of fire alarm, the data fusion of fire signal is realized with fuzzy logic, by more
The weight calculation of kind information, effectively filters interference signal；
(2) network is learnt and trained by BP algorithm, reach the Intellective Fire Alarm System of self learning type, effectively
The accuracy of warning information, and the fire condition and development trend that can be grasped in time are improved, reduces building as far as possible
Fire damage；
(3) relative to traditional single fire sensor warning system, the present invention is substantially a kind of Multi-information acquisition
Method, effectively overcome the precision of same sensor in itself and individual difference, installation site, environment temperature and electromagnetic interference be present
Wrong report, failing to report phenomenon Deng the caused fire of environment；
(4) present invention is all can be obtained using information by building Internet of things system, and fire alarm arbitration functions all may be used
To be realized by Internet of things system itself, without specially additionally increasing a set of fire sensor and warning system, it is effectively saved soft
Hardware cost.
Brief description of the drawings
The Figure of description for forming the part of the application is used for providing further understanding of the present application, and the application's shows
Meaning property embodiment and its illustrate be used for explain the application, do not form the improper restriction to the application.
Fig. 1 is the electric fire disaster warning system architecture diagram of Internet of Things；
Building fire early warning and the schematic diagram of fire disaster situation assessment system and method for the Fig. 2 based on Internet of Things；
The model structure of fire alarm processing steps of the Fig. 3 based on fuzzy neural network.
Embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is noted that described further below is all exemplary, it is intended to provides further instruction to the application.It is unless another
Indicate, all technologies used herein and scientific terminology are with usual with the application person of an ordinary skill in the technical field
The identical meanings of understanding.
It should be noted that term used herein above is merely to describe embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singulative
It is also intended to include plural form, additionally, it should be understood that, when in this manual using term "comprising" and/or " bag
Include " when, it indicates existing characteristics, step, operation, device, component and/or combinations thereof.
In the present invention, term as " on ", " under ", "left", "right", "front", "rear", " vertical ", " level ", " side ",
The orientation or position relationship of instructions such as " bottoms " are based on orientation shown in the drawings or position relationship, only to facilitate describing this hair
Bright each part or component structure relation and the relative determined, not refer in particular to either component or element in the present invention, it is impossible to understand
For limitation of the present invention.
In the present invention, term such as " affixed ", " connected ", " connection " should be interpreted broadly, and expression can be fixedly connected,
Can also be integrally connected or be detachably connected；Can be joined directly together, can also be indirectly connected by intermediary.For
The related scientific research of this area or technical staff, the concrete meaning of above-mentioned term in the present invention can be determined as the case may be,
It is not considered as limiting the invention.
As background is introduced, false alarm in the prior art be present, lack assessment to fire disaster situation, in order to solve such as
On technical problem, the present invention propose a kind of building fire early warning based on Internet of Things and fire disaster situation assessment system and side
Method, building illumination, temperature, smog, CO are gathered comprehensively by building Internet of things system2The information such as concentration, room personnel and
Building electric equipment work electricity condition, data fusion judgement is carried out to information by Internet of things node, by neutral net
BP algorithm is incorporated into the judgement of fire alarm, and the data fusion of fire signal is realized with fuzzy logic, passes through the power of multi information
Re-computation, interference signal is effectively filtered, network is learnt and trained by BP algorithm, reach the intelligent fire of self learning type
Early warning system, the accuracy of warning information is effectively improved, and the fire condition and development trend that can be grasped in time, as far as possible
Reduction building fire loss.
In order to solve the above technical problems, the technical solution used in the present invention is：A kind of building based on Internet of Things is provided
Thing Intellective Fire Alarm System, as shown in figure 1, it includes server, some Internet of things node, some intelligent terminals, Ruo Gan electricity
Gas equipment, for architectural electricity equipment information collection, transmission and processing.
The office building electric equipment intelligent fire alarm management software run in cloud platform is responsible for the fire of electrical equipment
Computing, displaying, statistics, analysis, alarm, the optimization of calamity information.App on smart mobile phone or other intelligent terminal can be inquired about
Relevant information.
Intelligent terminal of the present invention has corresponding relation with fire detector in building, and Internet of things node is responsible for intelligent terminal
Wireless self-networking, server communicated by Internet of things node with the fire detector of intelligent terminal side.
Described internet-of-things terminal includes environment terminal, can gather including building illumination, temperature, smog, CO2Concentration
Deng data message.
Described electric terminal includes smart jack, intelligent switch etc., can gather including building electric device temperature,
Electric current, power, whether the information such as phase shortage, electric leakage and closing related electric equipment can be opened.
Fig. 2 is building fire early warning and the original of fire disaster situation assessment system and method for the present invention based on Internet of Things
Reason figure, including：
(1) gather and pre-process, the intelligent terminal for being primarily based on building Internet of Things gathers the environment and electricity relevant with fire
The information such as gas；Fire information is transmitted and storage, Internet of things node by terminal data transmission to cloud platform, to cloud platform and store to
In data server；Fire information feature extraction, terminal transmission information first pass around filtering etc. pretreatment after, extraction wherein with
Fire relevant environment temperature, smokescope and CO2The characteristic informations such as concentration, socket temperature, electric power, leakage current are simultaneously divided
Class collects.
(2) fire based on BP neural network judges, establishes the fire alarm model based on BP neural network；By a variety of
The weight calculation of information, interference signal is effectively filtered, building fire is then built by the experience of experiment or standard
During building Internet of Things collection message sample, fire alarm model is trained using the sample；Finally complete instruction
Practice, Evaluated effect simultaneously carries out fire alarm using the model to the data gathered in real time.
(3) output result and complete fire disaster situation assessment, by Internet of things system send fire alarm signal, confirm fire
In the case that calamity is alarmed, Internet of things system will reduce fire damage to all electrical equipment power down.
A kind of the Internet of Things data fusion and fire determination methods based on BP neural network of the present invention, as shown in figure 3, institute
The method stated includes a BP neural network fire alarm model, can receive Internet of things system collection environment, electrically etc. letter
Cease and judge whether early warning.
Based on BP neural network fire alarm model, effect of the input to the neuron for each neuron is embodied in
In the size of connection weight, therefore the weight between each sensor is rationally determined, can effectively reduce uncertain factor, improve more letters
The reliability that breath fusion judges.
Multi information weight calculation based on neutral net, in calculating process, select smokescope (x1), CO2Concentration
(x2), environment temperature (x3), socket temperature (x4), electric current (x5), leakage current (x6) etc. 6 fire characteristic information as target, choosing
Select no fire, have fire, the uncertain judgement as fire disaster situation.In BP neural network, input layer unit number is 6, output
Layer unit number is 3, and it is 11 to select Hidden unit number.
Multi information weight calculation based on neutral net, calculation procedure are as follows：
The sample data of the first step, selection on target component X and result Y, selects smokescope (x1), CO2Concentration
(x2), environment temperature (x3), socket temperature (x4), electric power (x5), leakage current (x6) etc. 6 fire characteristic information as mesh
Mark, selection have fire, judged without fire, the uncertain result as fire disaster situation；
Second step, sample data is normalized pretreatment, the target component after being normalized is X=[x1,
x2,x3,x4,x5,x6,], Y=[y1, y2, y3], yjValue size represent X on the resulting influence journey of jth kind fire disaster situation
Degree.
3rd step, the topological structure for determining BP neural network, input layer unit number are 6, and output layer unit number is 3, choosing
Hidden unit number is selected as 11；
4th step, input layer is defined as W to the connection weight between hidden layerih, connection of the hidden layer to output node layer
Power is defined as Vhj, θhFor the threshold value of hidden node, βhTo export the output letter of the threshold value of node layer, hidden layer and output node layer
Number, it is respectively
When the 5th step, BP network trainings, first to Wih, Vhj, θh, βhLess value is assigned, then uses bh、yjEnter
Row calculates, so as to further calculate the output Y and desired output y of output node layerj' error, i.e.,Make ejMeet to require.
6th step, using target component X and result Y composition sample set be trained as the learning sample of BP networks, if Wih
And WkhRespectively target xiAnd xkLink weight coefficients between corresponding input block i and k and Hidden unit h, note
If | Wih|>|Wkh|, then show target xiX is compared to the influence degree of resultkBy force.
Wherein, the fire characteristic information xiTo fire yjInfluence degree be
Order
It can thus be concluded that
Further,
Fire characteristic information x can similarly be obtainedkTo fire disaster situation yjInfluence degree be
Thus, it is not difficult to find out, if
Therefore, if | Wi1| ＞ | Wk1|, | Wi2| ＞ | Wk2| ..., | Wir| ＞ | Wkr|
Then
Now fire characteristic information xiTo fire disaster situation yjInfluence than fire characteristic information xkTo fire disaster situation yjInfluence
Degree is strong.
7th step, it is assumed that the weighing factor of n target is assigned as λ1, λ2, λN,AndIt must can weigh
Re-computation formula
The weight of target factor is solved, so as to finally determine weight distribution vector λ=(λ of n factor1,
λ2, λn)
Weight distribution to fire information can be realized by above calculating process.
The present invention proposes a kind of fire disaster situation appraisal procedure, by analyzing fire hazard environment and electric data rate of change, in advance
Fire detecting calamity development trend, by Internet of Things human body detection terminal judge in room whether someone, assess fire hazard degree.
Preferably, by personnel module's testing staff's information, with reference to video monitoring, personnel positions is determined, work as fire alarm
After generation, Internet of things system calculates the most short evacuation path of outlet according to the position of personnel, walking along the street footpath light guiding of going forward side by side,
Meanwhile the personal information by obtaining, it can accurately implement rescue plan for fire fighter and help is provided.
Preferably, in addition to the fire alarm function based on Internet of Things, after fire alarm occurs, Internet of things system is opened
The alarm such as acousto-optic in building, while warning message is ejected by the cell phone application of Physical Network system, webpage etc., while also may be used
To be linked with fire department.
Preferably, after above-mentioned early warning system sends fire alarm, building Internet of things system will be automatically to institute in building
Some electrical equipment power down.
Meanwhile temperature detection also is carried out to smart jack, fire alarm is carried out from source.Internet of things node, which passes through, to be compared
Temperature between smart jack, judges temperature gap, finds the smart jack of temperature anomaly, and Internet of things node sends alarm signal
To intelligent terminal, and the smart jack is disconnected, while user can inquire about the ID number of temperature anomaly smart jack on intelligent terminal, really
The position of the fixed smart jack, to change socket, prevents electric disaster hidden-trouble；
The degree of heat (temperature rise degree) of same smart jack has with factors such as the big, indoor environment temperatures of institute's on-load
Close, Internet of things node can determine whether smart jack carries when judging smart jack temperature gap, according to difference variation degree
Load.Difference variation is bigger, illustrates that smart jack carries load.
The first step, pass through the temperature sensor module test socket temperature T built in smart jackC, examined by environment module
Survey current environment temperature TE, and temperature signal is uploaded to Internet of things node.Internet of things node analyzing and processing data is simultaneously carried out
Storage, obtains historical record temperature TCH、TEH；
Second step, the temperature difference between all smart jacks under same node is calculated, be designated as the first temperature difference.The Internet of Things
All smart jack temperature value T in the room that net node receivesC1、TC2、···TCi、TCn, it is Δ to obtain the first temperature difference
T1=TCi-TCn；
3rd step, Internet of things node intelligent socket temperature value TCWith the historical temperature average value T of node memory storageCH's
Temperature difference size, it is Δ T2=T to obtain second temperature differenceC-TCH；
4th step, Internet of things node intelligent socket temperature value TCCurrent environment temperature T is detected with environment moduleE's
Temperature difference size, it is Δ T3=T to obtain the 3rd temperature differenceC-TE；
5th step, the first preset difference value is set to be 15 DEG C, the second preset difference value is 15 DEG C, and the 3rd preset difference value is 20 DEG C.
When first temperature gap is more than the first preset difference value, the second temperature difference it is when being more than the second preset difference value and described
When 3rd temperature value is more than three preset difference values, Internet of things node judges that smart jack temperature is too high, electric disaster hidden-trouble be present,
Smart jack is disconnected by on-off circuit automatically, and alarm signal is sent to intelligent terminal APP.User can inquire about intelligent end simultaneously
The ID number of temperature anomaly smart jack on end, the position of the smart jack is determined, to change socket, prevents electric disaster hidden-trouble.
Relative to traditional electric fire disaster warning system, the present invention has more higher accuracy.
The preferred embodiment of the application is the foregoing is only, is not limited to the application, for the skill of this area
For art personnel, the application can have various modifications and variations.It is all within spirit herein and principle, made any repair
Change, equivalent substitution, improvement etc., should be included within the protection domain of the application.
Although above-mentioned the embodiment of the present invention is described with reference to accompanying drawing, model not is protected to the present invention
The limitation enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not
Need to pay various modifications or deformation that creative work can make still within protection scope of the present invention.
Claims (10)
1. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things, it is characterized in that：Including smart jack,
Ambient parameter acquisition module, electric parameter acquisition module, Internet of things node and Cloud Server, wherein：
The smart jack has several, is arranged at monitoring area diverse location, built-in temperature sensor test socket temperature
Degree, and the socket temperature of detection is sent to the Internet of things node and stored；
The electric parameter acquisition module, be configured as gathering the electric current of electrical equipment of monitoring area, power, whether phase shortage
And/or the information whether leaked electricity, and the information transfer of collection is stored to Internet of things node；
The ambient parameter acquisition module, it is configured as detecting environment temperature, illumination, temperature, smog and the CO of monitoring area2
Concentration parameter, and the ambient parameter of collection is sent to Internet of things node and stored；
The Internet of things node is arranged in monitoring area, be configured as receive smart jack, ambient parameter acquisition module and
The collection information of electric parameter acquisition module simultaneously stores, and is transmitted to Cloud Server；
The Cloud Server, it is configured as establishing fire alarm model, the history information structure stored by Internet of things node
The message sample for the building Internet of Things collection built in building fire generating process, the fire alarm mould using the sample to structure
Type is trained, and is carried out fire alarm to the data gathered in real time using the model after training, is carried out fire disaster situation assessment, send
Fire alarm signal.
2. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：Described Cloud Server provides the output of electric fire disaster warning system and connecing for long-distance user's intelligent terminal wireless connection
Mouthful.
3. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：For the Cloud Server using BP neural network structure fire alarm model, the input layer unit number of model is six fire
Characteristic information is as target, respectively smokescope, CO2Concentration, environment temperature, smart jack temperature, electric current and leakage current, it is defeated
Go out layer unit number for three, respectively without fire, have fire, the uncertain judgement as fire disaster situation, Hidden unit number is 11
It is individual.
4. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：The system also includes personnel's detection module and video detection module, and the data of collection are transferred to Cloud Server, the cloud
Server by utilizing personnel's detection module, determines personnel positions, with reference to the video of video detection module, in fire alarm, according to
The position of personnel calculates the most short evacuation path of outlet, and is guided.
5. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：The system also includes the light guidance system for being arranged at monitoring area, including multiple guide lamp including sames, according to Cloud Server
The most short evacuation path being calculated carries out lighting guide.
6. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：The smart jack, including micro controller module, power module, temperature detecting module, break-make control module and channel radio
Believe module, wherein, the micro-control module is connected with temperature detecting module, break-make control module and wireless communication module, described
Temperature detecting module is used for the temperature of test socket body, and temperature signal is sent to micro controller module.
7. a kind of building fire early warning and fire disaster situation assessment system based on Internet of Things as claimed in claim 1, it is special
Sign is：The Internet of things node, including micro controller module, wireless communication module, ethernet communication module, Flash storage mould
Block and power module, wherein, micro controller module and wireless communication module, ethernet communication module, Flash memory modules and electricity
Source module connects, and Flash memory modules storage smart jack and ambient parameter acquisition module, electric parameter acquisition module upload
Signal, form historical record.
8. based on the method for work of the system as described in claim any one of 1-7, it is characterized in that：Comprise the following steps：
(1) information gathering and pretreatment, the collection environment relevant with fire and electric parameter information, Internet of things node is by number of terminals
According to being transferred in Cloud Server, extraction wherein with fire associated temperature, smokescope and CO2Concentration, electric power and leakage current
Characteristic information simultaneously carries out Classifying Sum；
(2) using characteristic information as input layer information, the fire alarm model based on neutral net is established, passes through history information
The message sample of the building Internet of Things collection in building fire generating process is built, fire alarm model is entered using the sample
Row training, fire alarm is carried out to the data gathered in real time using the model completed after training；
(3) output result and fire disaster situation assessment is completed, sends corresponding assessment result by Internet of things system, carry out fire
Alarm signal.
9. method of work as claimed in claim 8, it is characterized in that：In the step (1), Internet of things node receives and collects institute
Belong to the temperature and environment temperature of each smart jack in node region, carry out judging whether to send out with reference to the temperature of smart jack
Light a fire calamity, detailed process includes：
(1-2) stores the temperature and environment temperature of smart jack, obtains historical record temperature；
All smart jack temperature values in the region that (1-2) thing net node receives, calculate all intelligence under same node and insert
Temperature difference between seat, is designated as the first temperature difference；
The temperature difference size of (1-3) Internet of things node intelligent socket temperature value and the historical temperature average value of node memory storage,
It is poor to obtain second temperature；
(1-4) Internet of things node intelligent socket temperature value detects the temperature difference size of current environment temperature with environment module,
Obtain the 3rd temperature difference；
(1-5) is pre- according to relation, the second temperature difference and second between first temperature gap and the first preset difference value
If the relation between relation and the 3rd temperature gap and the second preset difference value between difference, Internet of things node judges intelligence
Socket whether there is electric disaster hidden-trouble, and send alarm signal to intelligent terminal.
10. method of work as claimed in claim 8, it is characterized in that：In the step (2), specifically include：
(2-1) sample data is normalized pretreatment, determines the topological structure of BP neural network；
(2-2) defines input layer to the connection weight between hidden layer, hidden layer to the output connection weight of node layer, hidden node
Threshold value, the threshold value for exporting node layer and hidden layer and the output function for exporting node layer；
The parameter of the definition of (2-3) to the BP neural network topological structure of structure carries out assignment, calculates the defeated of output node layer
Go out the error with desired output, until error meets to impose a condition, target component and result are formed into sample set as BP networks
Learning sample be trained, determine influence degree of each fire characteristic information to result；
(2-4) carries out weight distribution according to influence degree, obtains the weight distribution vector of target factor, obtains final early warning mould
Type.
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