CN111028745A - Multifunctional intelligent fire-fighting emergency system - Google Patents
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Abstract
The invention discloses a multifunctional intelligent fire-fighting emergency system, which comprises intelligent fire-fighting emergency lamps arranged at each entrance and exit and key positions in a building, wherein the intelligent fire-fighting emergency lamps are used for detecting the vibration state of the building, the temperature value and the smoke concentration in a channel in real time and displaying the temperature value and the smoke concentration on site; the monitoring room receives the detection data uploaded by all the intelligent fire-fighting emergency lamps, and three-dimensional full-coverage information display of the building is achieved. The monitoring room displays various detection data, the intelligent fire-fighting emergency lamp with faults is maintained and replaced in time according to the state information of the equipment, the building health state is established based on the detection data of the intelligent fire-fighting emergency lamp, the early judgment of the building health state in a future period is realized by combining the neural network algorithm of the extreme learning machine, when a disaster occurs in the early judgment, the alarm is sent to the intelligent fire-fighting emergency lamps, meanwhile, the alarm is sent to related departments, and the rescue efficiency is improved when the disaster occurs.
Description
Technical Field
The invention relates to an environment detection, equipment maintenance and multi-target coordination system, in particular to a multifunctional intelligent safety fire-fighting emergency system.
Background
With the rapid development of urban construction, buildings gradually change from the past monomer structure to large-scale complex and large-scale buildings, more and more multifunctional complex large-scale buildings appear, the buildings generally have the characteristics of more layers, complex structures, numerous personnel and the like, and when a disaster happens, if the personnel cannot be reasonably and effectively evacuated to a safe area, the life and property loss is easily caused.
The fire-fighting emergency lamp is widely applied as equipment with an evacuation indication function and emergency lighting, and has very important practical value and significance. But the traditional emergency fire-fighting lamp with single function at present is difficult to meet the conditions of complicated building structure and diversified application requirements.
Disclosure of Invention
The invention aims to design a multifunctional intelligent fire-fighting emergency system integrating illumination, multi-sensor detection, data fusion analysis, positioning and multi-target path planning.
The technical scheme adopted by the invention for solving the technical problem is as follows:
the intelligent fire-fighting emergency lamp comprises intelligent fire-fighting emergency lamps which are arranged at each entrance and exit and key positions in a building, wherein the intelligent fire-fighting emergency lamps are connected with commercial power under a normal working state; detecting the vibration state of the building, the temperature value and the smoke concentration in the channel in real time, and displaying the temperature value and the smoke concentration on site in an intelligent fire-fighting emergency lamp; meanwhile, a human-type emergency sign, a safety passage arrow and floor information are displayed; the monitoring room receives the detection data uploaded by all the intelligent fire-fighting emergency lamps, and three-dimensional full-coverage information display of the building is achieved.
The intelligent fire-fighting emergency lamp is composed of a shell, and a microprocessor, an OLED module, an LED dot matrix module, a temperature sensing module, a smoke sensing module, a vibration sensing module, a voice module, a power management module and a communication module which are arranged in the shell.
The temperature sensing module, the smoke sensor module, the vibration sensing module, the voice module, the power management module, the communication module, the OLED module and the LED dot matrix module are all connected with the microprocessor.
The communication module adopts a radio frequency communication technology and can communicate with a semi-active RFID device carried by a person.
The LED dot matrix module displays a human-shaped emergency mark, escape direction indication and current floor position information at the center position in a normal working state; when an emergency occurs, the LED dot matrix module enters an alarm state, the escape direction is adjusted in real time according to the actual disaster situation, and meanwhile, the voice module is matched with the escape path algorithm to repeatedly broadcast and report, so that personnel are quickly guided to safely evacuate from the disaster site.
And the OLED module displays real-time state information of the real-time temperature, the smoke value and the vibration value of the environment.
The microprocessor receives the temperature, the smoke value and the vibration intensity which are acquired by the temperature sensing module, the smoke sensing module and the vibration sensing module in real time, the internal temperature, the voltage, the lighting lamp state and the standby battery electric quantity state of the intelligent fire-fighting emergency lamp, and uploads information to the monitoring room through the networking of the radio frequency communication module.
The monitoring room displays various detection data, the intelligent fire-fighting emergency lamp with faults is maintained and replaced in time according to the state information of the equipment, the building health state is established based on the detection data of the intelligent fire-fighting emergency lamp, the early judgment of the building health state in a future period is realized by combining the neural network algorithm of the extreme learning machine, when a disaster occurs in the early judgment, the alarm is sent to the intelligent fire-fighting emergency lamps, meanwhile, the alarm is sent to related departments, and the rescue efficiency is improved when the disaster occurs.
The escape path algorithm realizes multi-path planning by utilizing a multi-path dynamic planning algorithm and combining the position information of escapers and the detection states of various intelligent fire-fighting emergency lamps, and specifically comprises the following steps:
step1, carrying out topology construction on structures in a building, considering houses, doors, elevators, escalators and stairs as nodes, considering corridors and rooms as edges, and realizing the connection of the edges by using the nodes. Constructing a three-dimensional road network model in a building;
step2, increasing factors of path complexity, congestion degree and blocking events;
step3, designing an escape passing cost function G:
fcrowd(vi,vj)＝αD(vi,vj)
wherein, wcA weight coefficient being a degree of congestion; w is atWeight coefficients that are path complexity; w is aeFor the weight coefficient of blocking events, the three weights are between (0,1), and the sum is equal to 1. f. ofcrowd(vi,vj) Is a node viAnd vjCost function of inter-path congestion degree α is coefficient representing congestion degree D (v)i,vj) Characterizing a node viAnd vjThe inter-Euclidean distance; f. ofturn(Vm) Cost function of path complexity, VmIs a set of turns in the escape path; f. ofevent(Vn) As a function of the cost of the blocking time, VnTo affect the node set by blocking, the node can reach fevent(Vn) If the node is not reachable, it is 0.
Step4, determining the optimal path by adopting a Dijkstra algorithm, wherein the method specifically comprises the following steps:
step 1: determining a starting point S and a multi-target set TargetList, wherein the TargetList is { T1,T2,...,Tk}，TkSetting a kth target point in multiple targets, and initializing a path set PathList and a closed set CloseList as empty sets, wherein the PathList is used for storing escape paths from a starting point to the target point, and the CloseList is used for storing traversed target points;
step 2: calculating navigation passing cost G according to the actual environment;
step 3: starting from the starting point S, finding the shortest path D (T) from the starting point S to each target point in the TargetList by using Dijkstra algorithm1),D(T2),....,D(Tk) Destination point T of shortest pathLSatisfies D (T)L)＝min{D(T1),D(T2),....,D(Tk)}；
Step 4: if all the targets are unreachable, ending the algorithm; otherwise, the starting point S is led to the target point TLAdd navigation Path of to PathList, and target point TLMove from TargetList into CloseList;
step 5: judging whether the TargetList is empty or not, if so, indicating that all targets are traversed, and ending; if not, set TLAs a new starting point, i.e. S ═ TLAnd jumping to step2, and continuing to loop.
The neural network algorithm of the extreme learning machine is a neural network discrimination algorithm with a single hidden layer, can carry out prejudgment in the future according to detection data of a period of time in the past, and has the following main structure:
T＝Hβ
wherein T is the output of the neural network and is used for judging whether the interior of the building is a normal label, H is the output of a hidden layer of the neural network, β is a weight matrix of the neural network from the hidden layer to an output layer, and the weight matrix is respectively expressed as:
for the above formula, there are a total of N samples (X)i,Ti) Wherein T isi＝[ti1,ti2,...,tim]T∈Rm，Xi＝[xi1,xi2,...,xin]T∈RnThe expression that m outputs and n inputs exist at the ith moment; l represents the output number of the hidden layer of the neural network; g (-) is expressed as a hidden layer excitation function, Wi×XjRepresents WiAnd XjInner product of (b), wherein WiAnd biRespectively representing the weight of the ith input layer to the hidden layer and the bias of the ith input layer to the hidden layer.
Wherein WiAnd biRandom generation is carried out according to certain requirements, historical data is needed for training and learning from the hidden layer to the output layer weight β, the historical data is used for training, and the following formula can be obtained according to a definition formula:
wherein
β, the estimated value can be used to predict instead of the true value,
Moore-Penrose generalized inverse matrix, matrix H.
Wherein, the standby power supply and the commercial power are connected with the microprocessor through the power supply management module. Commercial power, stand-by power supply and power management module provide stable power for intelligent fire control emergency light, and wherein the commercial power is used for normal operating condition, and when emergency condition takes place under the circumstances that the commercial power is cut off, power management system switches to stand-by power supply, continues to maintain equipment normal work.
Furthermore, the semi-active RFID device is a positioning device, the device maintains a state to be activated by means of self electric quantity in the non-activated state of the intelligent fire-fighting emergency lamp, and after activation, the device is communicated with the intelligent fire-fighting emergency lamp to position the specific position of an escape person, so that the specific distribution of the escape person is provided for setting an escape path algorithm, and meanwhile, the device is also beneficial to quickly rescue trapped persons who cannot escape in time.
The invention has the beneficial effects that: the system mainly uses the intelligent fire-fighting emergency lamp as a hardware carrier, and can start the standby power supply to realize emergency lighting when a disaster happens. Under the normal condition, at first detect equipment running state such as internal voltage, light intensity, utilize vibration, temperature and smoke transducer to detect the actual conditions of building internal environment simultaneously, and upload two kinds of detection data to the control end in real time, the control end carries out equipment operation health status to intelligent fire control emergency light on the basis of running state and sensor return data and assesses, maintain the equipment that has the trouble, utilize the detection data of building internal environment simultaneously, combine prediction algorithm to carry out the prejudgement to building internal vibration intensity, temperature value and smog concentration, when reaching emergency, then trigger the early warning, strive for the time for fleing for. Everybody in the building can all carry one with oneself and carry a semi-active RFID device, and the device generally combines in same device with the entrance guard, and when the calamity took place, thereby each intelligent fire control emergency light all can confirm personnel's position according to semi-active RFID's location to according to disaster situation, the jam condition, combine to carry out the rational planning of route of fleing. Because everybody in the process of escaping can ignore the change of the sign because of confusion, needs to combine the pronunciation to remind repeatedly to ensure that more people more effectively flee to the safe position.
Drawings
FIG. 1 is a flow chart of the operation of the system of the present invention.
Fig. 2 is a flow chart of an escape path algorithm.
Detailed Description
The invention mainly realizes the work through the following technical scheme: the intelligent fire-fighting emergency lamp is mainly composed of an intelligent fire-fighting emergency lamp and an algorithm system, wherein the intelligent fire-fighting emergency lamp is composed of a shell, and a microprocessor, an OLED module, an LED dot matrix module, a temperature sensing module, a smoke sensing module, a vibration sensing module, a voice module, a power management module and a communication module which are arranged in the shell.
The temperature sensing module, the smoke sensor module, the vibration sensing module, the voice module, the power management module, the communication module, the OLED module and the LED dot matrix module are all connected with the microprocessor; the standby power supply and the commercial power are connected with the microprocessor through the power management module; the communication module adopts a radio frequency communication technology and can communicate with a semi-active RFID device carried by a person. The algorithm system mainly comprises an early warning system formed by an extreme learning machine neural network algorithm and a multi-target path planning algorithm formed by dynamic planning.
The microprocessor mainly has the functions of receiving building health conditions such as temperature, smoke value and vibration intensity acquired by the temperature sensing module, the smoke sensing module and the vibration sensing module in real time, and equipment states such as internal temperature, voltage, lighting lamp state and standby battery electric quantity of equipment, uploading information to a monitoring room through networking of the radio frequency communication module, and displaying the information through an OLED screen. The monitoring room displays various detection data, equipment with faults is maintained and replaced in time according to the state information of the equipment, building health states are built based on the detection data of the intelligent fire-fighting emergency lamps, the early judgment of the building health states in a future period is realized by combining an extreme learning machine neural network algorithm, when a disaster occurs in the early judgment, the alarm is sent to each intelligent fire-fighting emergency device, meanwhile, the alarm is sent to related departments, and the rescue efficiency is improved when the disaster occurs.
Semi-active RFID device is positioner, and under the not active state of intelligence fire control lamp, the device relies on self electric quantity to maintain and treats the active state, then through with the communication of intelligence fire control emergency light after the activation, fixes a position personnel of fleing's concrete position, for setting up the scheme of fleing and providing personnel of fleing concrete distribution, also help simultaneously in the quick rescue in time the stranded personnel who escape.
The LED dot matrix module displays a human-shaped emergency sign, escape direction indication, current floor position information and the like at the center of a normal working state, and the OLED screens which are inlaid together display real-time state information of environment such as real-time temperature, smoke value and vibration value. When an emergency happens, the LED dot matrix enters an alarm state, the escape direction is adjusted in real time according to the actual disaster situation, and meanwhile, the voice module can be matched with the escape path to conduct repeated broadcasting so as to rapidly guide personnel to safely evacuate a disaster site.
Commercial power, stand-by power supply and power management module provide stable power for intelligent fire control emergency light, and wherein the commercial power is used for when that operating condition normally, takes place under the circumstances that the commercial power was cut off when emergency situation, and power management system switches over to system stand-by power supply, continues to maintain equipment normal work.
The health state evaluation method mainly comprises the steps that a threshold value is set for each sensing detection value, when all detection values do not exceed the threshold value, the health state is normal, if one of the sensing detection values is not in the range constrained by the threshold value, equipment faults are displayed, the results are uploaded to a monitoring end, and timely maintenance and replacement are carried out.
The neural network of the extreme learning machine is a neural network discrimination method with a single hidden layer, and can carry out prejudgment in the future according to detection data of a period of time in the past. The main structure is as follows:
T＝Hβ
wherein T is the output of the neural network and is used for judging whether the interior of the building is a normal label, H is the output of a hidden layer of the neural network, β is a weight matrix of the neural network from the hidden layer to an output layer, and the weight matrix is respectively expressed as follows:
for the above formula, there are a total of N samples (X)i,Ti) Wherein T isi＝[ti1,ti2,...,tim]T∈Rm，Xi＝[xi1,xi2,...,xin]T∈RnThe expression that m outputs and n inputs exist at the ith moment; l represents the output number of the hidden layer of the neural network; g (-) is expressed as a hidden layer excitation function, optionally RBF, sigmod, or other, Wi·XjRepresents WiAnd XjInner product of (b), wherein WiAnd biRespectively representing the weight of the ith input layer to the hidden layer and the bias of the ith input layer to the hidden layer.
Wherein WiAnd biRandom generation is carried out according to certain requirements, historical data is needed for training and learning from the hidden layer to the output layer weight β, the historical data is used for training, and the following formula can be obtained by a defined formula:
wherein
β, the estimated value can be used to predict instead of the true value,
m being a matrix HThe oore-Penrose generalized inverse matrix,
the path planning algorithm realizes multiple path planning by utilizing a multi-path dynamic planning algorithm and combining the position information of escapers and the detection states of various intelligent fire-fighting emergency lamps, and dynamically and reasonably dredges the escapers by utilizing a direction indicating lamp board and voice broadcasting, so that the escape efficiency is improved, and secondary injuries such as extrusion treading and the like caused by congestion due to the fact that too many persons select the same escape path are avoided.
The specific algorithm for planning the internal path of the building is as follows:
1. firstly, the topology construction is carried out on the structure in the building, the house, the door, the elevator, the staircase, the stair and the like are considered as nodes, the corridor, the house and the like are considered as edges, and the connection of the edges is realized by the nodes. Constructing a three-dimensional road network model in a building;
2. factors such as increased path complexity, congestion, blocking events (i.e., emergency events, facility failures, etc.);
3. designing an escape passing cost function G:
fcrowd(vi,vj)＝αD(vi,vj)
wherein, wcA weight coefficient being a degree of congestion; w is atWeight coefficients that are path complexity; w is aeFor the weight coefficient of blocking events, the three weights are between (0,1), and the sum is equal to 1. f. ofcrowd(vi,vj) Is a node viAnd vjCost function of inter-path congestion degree α is coefficient representing congestion degree D (v)i,vj) Characterizing a node viAnd vjThe inter-Euclidean distance; f. ofturn(Vm) Cost function of path complexity, VmIs a set of turns in the escape path; f. ofevent(Vn) As a function of the cost of the blocking time, VnTo affect the node set by blocking, the node can reach fevent(Vn) If the node is not reachable, it is 0.
4. The optimal path is realized by adopting a Dijkstra algorithm, and the method has the advantages of simplicity, easiness in operation, capability of obtaining an optimal solution and the like. The specific steps are as follows:
step 1: determining a starting point S and a multi-target set TargetList, wherein the TargetList is { T1,T2,...,Tk}，TkSetting a kth target point in multiple targets, and initializing a path set PathList and a closed set CloseList as empty sets, wherein the PathList is used for storing escape paths from a starting point to the target point, and the CloseList is used for storing traversed target points;
step 2: calculating navigation passing cost G according to the actual environment;
step 3: starting from S, using Dijkstra algorithm, finding the shortest path D (T) from S to each target point in TargetList1),D(T2),....,D(Tk) And finding the target point T of the shortest pathLSatisfies D (T)L)＝min{D(T1),D(T2),....,D(Tk)}；
Step 4: if all the targets are unreachable, ending the algorithm; otherwise, the starting point S is led to the target point TLAdd the navigation path of (a) to the PathList algorithm, and add T to the PathList algorithmLMove from TargetList into CloseList;
step 5: judging whether the TargetList is empty or not, if so, indicating that all targets are traversed, and ending; if not, set TLAs a new starting point, i.e. S ═ TLJumping to step2, and continuing to circularly calculate; as shown in fig. 2.
Example, see fig. 1:
1. installing intelligent fire control emergency light in each access & exit and the key position of building inside, insert the commercial power under the normal operating condition, real-time detection building's vibration state, temperature value and smog concentration in the passageway to show on the spot at intelligent fire control emergency light, still should show simultaneously including type of people sign, escape way arrow point, floor information etc.. After receiving the detection data uploaded by all the intelligent fire-fighting emergency lamps, the monitoring room can realize three-dimensional full-coverage information display of the building.
2. Whether its operating condition is normal is judged to inside voltage, temperature and pilot lamp state of intelligence fire control emergency light according to, if abnormal, then need in time maintain and change to when the guarantee calamity, the system can play normal function.
3. Deployment server is used for receiving wisdom fire control emergency light detection information in the control room, including intelligence fire control emergency light label, temperature, smog, reputation and building vibration information.
4. The server utilizes an extreme learning machine neural network prediction algorithm and combines the detection values of a past period of time and the current moment to prejudge the state in a future period of time, so that the occurrence of disasters can be predicted in advance, and time is saved for prevention and escape. The disaster type is fire, toxic gas leakage or earthquake, alarm signals are sent to related departments according to different modes, and different escape schemes are formulated at the same time.
5. When the commercial power is interrupted in an unexpected emergency situation, each fire-fighting emergency lamp starts a standby power supply, except for basic detection communication, an emergency lighting function is started, a positioning function is started, the specific positions of escape personnel in a building are detected, and meanwhile, emergency indication directions are uniformly adjusted and dispatched by a dynamic path planning algorithm.
6. When an accident emergency occurs, the server starts a dynamic planning algorithm, comprehensive grasping of the disaster state is achieved by utilizing the disaster state, the current situation of building escape channels, the position distribution of escape personnel and the like, the optimal solution of escape paths is solved, the escape personnel are effectively dredged to be evacuated from multiple paths in a scattered manner nearby, injuries such as extrusion and treading injuries caused by overcrowding are avoided, and meanwhile, the reduction of escape efficiency caused by congestion is also avoided.
7. Aiming at the personnel who can not escape in time, the specific position of the personnel trapped can be determined through the positioning information, the monitoring room guides the personnel who cannot escape in time to avoid nearby by means of voice broadcasting, escape indication arrow lamps and the like, and meanwhile, based on the positioning information, relevant rescue departments can timely take targeted rescue.
In conclusion, the invention provides a multifunctional intelligent fire-fighting emergency system integrating illumination, multi-sensing detection, positioning, data fusion analysis and multi-target path planning on the basis of ensuring basic functions of emergency lighting, evacuation indication and the like of an emergency fire-fighting lamp. The multi-sensing detection means that the temperature and the smoke in the building and the vibration detection of the building can be detected, the electric quantity in the fire-fighting emergency lamp, the function of a sensor and the illumination brightness can be detected, the three-dimensional full-coverage information acquisition is realized, and effective help is provided for system maintenance and disaster relief; the data analysis and fusion is to combine various sensing information, realize the prejudgment of the current building state based on an intelligent algorithm and provide early warning information for the personnel in the building; the positioning means that the device can realize positioning on the flow of peripheral personnel and dynamically know the specific distribution of the current escape personnel; the multi-target path planning refers to the steps of dynamically designing a plurality of lifesaving channels by using an algorithm according to disaster conditions, personnel position information and building internal structures, and rapidly guiding personnel to safely evacuate from a disaster site, so that the loss of personnel and property in the disaster is reduced to the maximum extent.
Claims (3)
1. A multifunctional intelligent fire-fighting emergency system comprises intelligent fire-fighting emergency lamps arranged at each entrance and exit and key positions in a building, wherein the intelligent fire-fighting emergency lamps are connected to mains supply under the normal working state, detect the vibration state of the building, the temperature value and the smoke concentration in a channel in real time and display the temperature value and the smoke concentration on site on the intelligent fire-fighting emergency lamps; meanwhile, the display also comprises a human-type emergency sign, a safe passage arrow and floor information; the monitoring room receives all intelligent fire-fighting emergency lamps and uploads detection data, and the three-dimensional full-coverage information display of the building is realized, and the intelligent fire-fighting emergency lamp is characterized in that:
the intelligent fire-fighting emergency lamp consists of a shell, and a microprocessor, an OLED module, an LED dot matrix module, a temperature sensing module, a smoke sensing module, a vibration sensing module, a voice module, a power management module and a communication module which are arranged in the shell;
the temperature sensing module, the smoke sensor module, the vibration sensing module, the voice module, the power management module, the communication module, the OLED module and the LED dot matrix module are all connected with the microprocessor;
the communication module adopts a radio frequency communication technology and can communicate with a semi-active RFID device carried by a person;
the LED dot matrix module displays a human-shaped emergency mark, escape direction indication and current floor position information at the center position in a normal working state; when an emergency occurs, the LED dot matrix module enters an alarm state, the escape direction is adjusted in real time according to the actual disaster situation, and meanwhile, the voice module is matched with the escape path algorithm to repeatedly broadcast and report, so that personnel are quickly guided to safely evacuate from the disaster site;
the OLED module displays real-time state information of environment real-time temperature, smoke value and vibration value;
the microprocessor receives the temperature, the smoke value and the vibration intensity which are acquired by the temperature sensing module, the smoke sensing module and the vibration sensing module in real time, the internal temperature, the voltage, the lighting lamp state and the standby battery electric quantity state of the intelligent fire-fighting emergency lamp, and uploads information to a monitoring room through networking of the radio frequency communication module;
the monitoring room displays various detection data, the intelligent fire-fighting emergency lamp with faults is maintained and replaced in time according to the state information of the equipment, the building health state is established based on the detection data of the intelligent fire-fighting emergency lamp, the early judgment of the building health state in a future period is realized by combining the neural network algorithm of the extreme learning machine, when a disaster occurs in the early judgment, the alarm is sent to the intelligent fire-fighting emergency lamps, meanwhile, the alarm is sent to related departments, and the rescue efficiency is improved when the disaster occurs.
2. The multifunctional intelligent fire-fighting emergency system according to claim 1, characterized in that: the standby power supply and the commercial power are connected with the microprocessor through the power management module; commercial power, stand-by power supply and power management module provide stable power for intelligent fire control emergency light, and wherein the commercial power is used for normal operating condition, and when emergency condition takes place under the circumstances that the commercial power is cut off, power management system switches to stand-by power supply, continues to maintain equipment normal work.
3. The multifunctional intelligent fire-fighting emergency system according to claim 1, characterized in that: the semi-active RFID device is a positioning device, and the device is maintained in a state of waiting to be activated by means of self electric quantity under the state of not activating the intelligent fire-fighting emergency lamp, and after activation, the device is communicated with the intelligent fire-fighting emergency lamp to position the specific position of an escape person, so that the escape person is specifically distributed for setting an escape path algorithm, and meanwhile, the device is also helpful for rapidly rescuing trapped persons who cannot escape in time.
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| CN114842599A (en) * | 2022-04-28 | 2022-08-02 | 东南大学 | Distributed intelligent emergency fire-fighting and evacuation indicating system and method |
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