CN117114222A - Intelligent emergency evacuation line optimization method and system based on real-time data of Internet of things - Google Patents
Intelligent emergency evacuation line optimization method and system based on real-time data of Internet of things Download PDFInfo
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Abstract
The application solves the problems that the manual command evacuation rescue efficiency is low, a safe and reliable emergency evacuation line cannot be provided scientifically and accurately, and the evacuation line cannot be displayed visually, and provides an intelligent emergency evacuation line optimization method and system based on real-time data of an Internet of things.
Description
Technical Field
The application relates to the field of emergency evacuation of building sites, in particular to an intelligent emergency evacuation line optimization method and system based on real-time data of an Internet of things.
Background
Emergency evacuation is an important technology that is valued by various countries, has a history of years of development and ensures the life safety of personnel when related to building fires. In recent years, with the rapid development of technology, high and complex intelligent buildings are increasingly increased, fire-fighting emergency evacuation standards are continuously sound and perfect, tool varieties required by fire-fighting emergency are continuously increased, performances are continuously improved, the technical level is greatly improved, and the intelligent building fire-fighting emergency evacuation system is widely applied and developed. The fire emergency evacuation in European and American countries is early and fast in development, is in the leading position, and is fast in development although the China starts late.
At present, people's study on emergency evacuation is mainly focused on evacuation inside a building when a fire occurs and how to accurately and rapidly extinguish the fire. However, in large places such as buildings, scenic spots and parks, when a fire accident occurs, personnel or rescue workers are required to evacuate, rescue evacuation efficiency is low, the crowd evacuating mode is inflexible, and once accidents occur at the evacuation intersections, larger disturbance is easily caused, so that the disaster accident is aggravated, property of people is damaged, and life safety of the people is threatened.
The application discloses an emergency evacuation system and an emergency evacuation method, which have the defects of only improving the manual fire extinguishing efficiency and reducing the casualties possibly caused by fire extinguishment, have no good effect on emergency evacuation of crowds in large building sites, cannot scientifically and accurately evacuate the crowds, and cannot guide the crowds to safely evacuate and escape from fire sites.
Disclosure of Invention
The application solves the problems that the traditional personnel manually command the evacuation rescue efficiency is low, an intelligent means is lacking, a safe and reliable emergency evacuation line cannot be scientifically and accurately provided, the evacuation line is lacking in real-time performance, people cannot be guided to evacuate in real time, and meanwhile, the evacuation line cannot be visually displayed, and provides an intelligent emergency evacuation line optimization method and system based on real-time data of an Internet of things.
In order to achieve the aim of the application, the application adopts the following technical scheme:
an intelligent emergency evacuation line optimization method based on real-time data of an internet of things comprises the following steps:
s1, constructing an electronic map model according to a building scene, and marking the positions of sensing equipment and evacuation facilities;
s2, acquiring and planning an emergency evacuation line according to the occurrence condition of an emergency;
and S3, ranking the emergency evacuation lines and pushing the emergency evacuation lines. The intelligent emergency evacuation line optimization method further comprises the step S4 of sensing changes in the area in real time by sensing equipment in a building scene. Aiming at the problem that people need to be evacuated in emergency when a fire disaster happens in a large-scale place, an electronic map model of an actual building scene is constructed, wherein the electronic map model comprises 2D/2.5D/3D and the like, the position of current people and the position of nearby emergency exits are obtained through an emergency evacuation platform according to the marked sensing equipment position, the position of emergency exits and the position of evacuation facilities and the specific position of the occurrence of the fire disaster emergency, and an emergency evacuation line is planned,
according to comprehensive evaluation indexes of the emergency evacuation lines, ranking of the emergency evacuation lines is carried out, the emergency evacuation lines are pushed to evacuation crowds, a scientific and accurate evacuation line optimization scheme is provided, the problem that people blindly follow in selection of the evacuation lines when a fire disaster occurs is solved, the evacuation success rate of the people is greatly improved, the conditions of each evacuation line can be intelligently detected in real time, road conditions of the evacuation lines are updated and guided in real time, information transmission is facilitated, and the corresponding speed of the occurrence of the fire disaster event is improved.
Preferably, the step S1 is further expressed as:
s1.1, constructing an electronic map model according to an actual building scene;
s1.2, each sensing device is in communication connection through the Internet of things, and the positions of the sensing devices are marked;
s1.3, marking evacuation facilities and temporary refuge places. The step S1 further comprises the step S1.4 of configuring an alarm threshold of the sensing equipment. The electronic map model of the actual building scene is built and comprises 2D/2.5D/3D and the like, the sensing devices are all internet of things sensing devices, and real-time information and data transmission is carried out through a network. The internet of things sensing equipment comprises equipment used in all buildings such as a CO sensor, an intelligent camera and the like. The alarm threshold of the sensing equipment is regulated and controlled according to the instruction of the emergency evacuation platform, flexible threshold configuration is carried out to adapt to the alarm caused by the accidental event, and the alarm error rate of the internet of things sensing equipment is reduced.
Preferably, the step S2 is further expressed as:
s2.1, judging whether an emergency event occurs or not, and acquiring the current emergency event occurrence position and the emergency exit position;
s2.2, acquiring and updating reported data of the sensing equipment in real time. The step S2 also comprises the step S2.3 of generating and planning an emergency evacuation line according to an emergency evacuation line optimization algorithm. In the step S2.3, the evacuation route calculates in real time according to the real-time location of the evacuation user and the line-sensing equipment data. The intelligent and rapid emergency evacuation of people is realized, the response speed and the personnel rescue speed of a fire event are improved, and the concurrent accidents possibly caused by the fire event are avoided greatly.
In the step S2.3, the emergency evacuation line optimization algorithm sets an impact factor weight according to different equipment types. In addition, the emergency evacuation line optimization algorithm sets different influence factor weights for various influence factors and various types of devices according to different building sites and the influence of various facilities in the building sites on evacuation lines.
In the step S2.3, the setting of the influence factor weight is performed by combining an FAHP fuzzy analytic hierarchy process and a variation coefficient process to comprehensively judge the evacuation line. The FAHP fuzzy analytic hierarchy process combines the advantages of the fuzzy method and the analytic hierarchy process to form the fuzzy analytic hierarchy process, can well solve the problem that thinking consistency is difficult to guarantee when a certain level of evaluation indexes are more than four (for example), and improves the reliability of decision.
The coefficient of variation method is a method for calculating the degree of variation of each index of the system according to a statistical method, and the weight of each index is obtained by directly utilizing information contained in each index and calculating. The variation coefficient method weights each index according to the variation degree of the current value and the target value of each evaluation index, and if the numerical value difference of a certain index is large, each evaluated object can be clearly distinguished, and the index is indicated to have rich resolution information, so that the index is given a large weight; conversely, if the difference in the numerical value of each object to be evaluated is small in a certain index, the index is weak in the ability to distinguish each object to be evaluated, and thus the index should be given a small weight.
An intelligent emergency evacuation line system based on real-time data of an internet of things, comprising: the emergency evacuation platform is respectively connected with the evacuation line display module and the risk perception module, and the emergency evacuation platform comprehensively calculates comprehensive evaluation indexes of the evacuation line in real time according to equipment data and states fed back by the risk perception module.
The emergency evacuation platform is provided with a central server module, an audio output module, a video output module and an emergency alarm module, wherein the central server module receives data detected by equipment of each risk sensing module in a building site and state data of the equipment, and combines the data of historical annual equipment to carry out comprehensive analysis and judgment, calculates risk indexes of evacuation lines, and visually displays obtained results through the evacuation line display module, so that people evacuating each evacuation line, the safety indexes of the evacuation lines and possible problems are discovered in the first time, and the event prediction capacity and the coping speed are improved.
The risk perception module is arranged at each position of a building site and is composed of a plurality of sensors of different types, the sensors are connected with the emergency evacuation platform through wired or wireless communication, each type of sensor is reasonably arranged in a corresponding area of the building site, and the event risk degree of the area where the sensor is located is monitored.
The central server module is respectively connected with the audio output module, the video output module and the emergency alarm module, and the audio output module receives the audio file from the central server module and plays or closes the audio file information according to the received instruction of the central server module; the video output module receives the video file from the central server module and plays or closes the video file information according to the received command of the central server module.
The emergency alarming module comprises an alarming module and an emergency power-off module, when an emergency fault occurs on the emergency evacuation platform and the emergency event occurs in a building site, the alarming module automatically alarms to a nearby fire department or police office, and assistance seeking information is sent.
The beneficial effects of the application are as follows: the intelligent emergency evacuation line optimization method and system based on the real-time data of the Internet of things are characterized in that an electronic map model of a building site is built, sensing equipment and evacuation facilities in the site are marked, different equipment and different influence factor weights are set according to the regional risk degree detected by the sensing equipment, the scientificity and the accuracy of risk evaluation of the evacuation line are further improved by combining the FAHP fuzzy analytic hierarchy process with the coefficient of variation method, comprehensive evaluation indexes of the evacuation line are calculated by integrating the data of all the Internet of things equipment, the evacuation line is ranked, intelligent visual display of the evacuation line is realized, the emergency evacuation line is pushed to evacuation crowd, the evacuation success rate of personnel is greatly improved, the conditions of all the evacuation lines can be intelligently detected in real time, the road conditions of the evacuation line are updated and guided in real time, the information transmission is convenient, and the response speed of a fire event is improved.
Drawings
Figure 1 is a preferred flow chart of the intelligent emergency evacuation line of the present application;
figure 2 is a flow chart of the intelligent evacuation route planning of the present application;
FIG. 3 is a diagram of an evaluation index system of the FAHP fuzzy analytic hierarchy process of the present application;
fig. 4 is a visual representation of the evacuation route of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without making any inventive effort, are intended to be within the scope of the present application based on the embodiments herein.
As shown in fig. 1, an intelligent emergency evacuation line optimization method based on real-time data of the internet of things comprises the following steps: s1, constructing an electronic map model according to a building scene, and marking the positions of sensing equipment and evacuation facilities;
s2, acquiring and planning an emergency evacuation line according to the occurrence condition of an emergency;
and S3, ranking the emergency evacuation lines and pushing the emergency evacuation lines. The intelligent emergency evacuation line optimization method further comprises the step S4 of sensing the changes in the area in real time by sensing equipment in the building scene. Aiming at the problem that people need to be evacuated in a large-scale place when a fire accident happens, the scheme comprises the steps of constructing an electronic map model of an actual building scene, including 2D/2.5D/3D and the like, acquiring the current position of the people and the position of the nearby emergency exit through an emergency evacuation platform according to the marked sensing equipment position, the emergency exit position and the evacuation facility position, planning an emergency evacuation line, ranking the emergency evacuation line according to the comprehensive evaluation index of the emergency evacuation line, pushing the emergency evacuation line to the people to be evacuated, providing a scientific and accurate evacuation line optimization scheme, solving the problem that people blindly follow the selection of the evacuation line when the fire accident happens, greatly improving the evacuation success rate of the people, intelligently detecting the conditions of each evacuation line in real time, updating and guiding the road condition of the evacuation line in real time, facilitating information transmission and improving the response speed of the occurrence of the fire accident.
As shown in fig. 1, step S1 is further expressed as:
s1.1, constructing an electronic map model according to an actual building scene;
s1.2, each sensing device is in communication connection through the Internet of things, and the positions of the sensing devices are marked;
s1.3, marking evacuation facilities and temporary refuge places. Step S1 further includes step S1.4, configuring an alarm threshold of the sensing device. The electronic map model of the actual building scene is built, the electronic map model comprises 2D/2.5D/3D and the like, sensing equipment is the Internet of things sensing equipment, and real-time information and data transmission is carried out through a network. The internet of things sensing equipment comprises equipment used in all buildings such as a CO sensor, an intelligent camera and the like. The alarm threshold of the sensing equipment is regulated and controlled according to the instruction of the emergency evacuation platform, flexible threshold configuration is carried out to adapt to the alarm caused by the accidental event, and the alarm error rate of the internet of things sensing equipment is reduced.
As shown in fig. 1, step S2 is further expressed as:
s2.1, judging whether an emergency event occurs or not, and acquiring the current emergency event occurrence position and the emergency exit position;
s2.2, acquiring and updating reported data of the sensing equipment in real time. The step S2 also comprises the step S2.3 of generating and planning an emergency evacuation line according to an emergency evacuation line optimization algorithm. In step S2.3, the evacuation route calculates the evacuation route in real time according to the real-time position of the evacuation user and the line sensing equipment data. The intelligent and rapid emergency evacuation of people is realized, the response speed and the personnel rescue speed of a fire event are improved, and the concurrent accidents possibly caused by the fire event are avoided greatly.
In step S2.3, the emergency evacuation line optimization algorithm sets an impact factor weight according to different equipment types. In addition, the emergency evacuation line optimization algorithm sets different influence factor weights for various influence factors and various types of equipment according to different building sites and the possible influence of various facilities in the building sites on the evacuation line.
In step S2.3, the setting of the influence factor weight is performed by combining an FAHP fuzzy analytic hierarchy process and a variation coefficient method to comprehensively judge the evacuation line. The fuzzy analytic hierarchy process formed by combining the advantages of the fuzzy method and the analytic hierarchy process can well solve the problem that the thinking consistency is difficult to guarantee when a certain level of evaluation indexes are more than four (such as more than four), and improves the reliability of decision.
The coefficient of variation method is a method for calculating the degree of variation of each index of the system according to a statistical method, and the weight of each index is obtained by directly utilizing information contained in each index and calculating. The variation coefficient method weights each index according to the variation degree of the current value and the target value of each evaluation index, if the numerical value difference of a certain index is large, each evaluated object can be clearly distinguished, and the index is provided with large weight because the resolution information is rich; conversely, if the difference in the numerical value of each object to be evaluated is small in a certain index, the index is weak in the ability to distinguish each object to be evaluated, and thus the index should be given a small weight.
In step S2.3, the emergency evacuation line optimization algorithm is specifically as follows:
1. enumerating the influencing factors of each device and detection
Building material and safety device
Switching value monitoring device and state representation
A fire detector: if a flame is detected, the route is ignored.
Fire door system: if the fire door is open, the route is ignored if the fire door is closed.
A camera head: if there is a temporary obstacle, the route is ignored.
Smoke detector: whether the smoke concentration exceeds a threshold value or not, whether the instrument alarms or not, and if so, neglecting the route.
Non-switching value monitoring equipment and monitoring data
Binocular camera, infrared camera: saturation of people flow
Temperature sensor: real-time temperature data
Gas detector: carbon monoxide, hydrogen sulfide, ammonia, chlorine, oxygen, phosphine, sulfur dioxide, hydrogen chloride, chlorine dioxide and other toxic and harmful gases.
Light sensor: real-time illumination intensity. In the event of a fire, smoke may cause a decrease in visibility, making escape difficult. The light sensor can detect the intensity change of light, so that the position of a fire disaster ignition point can be monitored, and on the other hand, good light can help people to escape safely.
Residual pressure monitor: the real-time residual pressure value ensures that the residual pressure of the front chamber, the stairwell and the refuge room can be in an effective controlled state when a fire disaster occurs through monitoring the residual pressure, and ensures the safety of the evacuation channel.
2. Setting an influence factor weight
As shown in fig. 3, the combined weighting method of the FAHP fuzzy analytic hierarchy process and the coefficient of variation process is used to comprehensively consider the influence of the change rules of the historical data of different environments and seasons on the basis of subjective experience judgment of an expert, and finally a reasonable comprehensive weight value which can more reflect the situation of a real scene is obtained.
And carrying out expert subjective judgment assignment by using an FAHP fuzzy hierarchy analysis method, wherein the specific steps are as follows:
1. establishing an evaluation index system
The evacuation line is used as a target layer, and different types of equipment and various safety evacuation influencing factors are used as standard layers, such as distance, people flow saturation, temperature, illumination intensity, toxic gas concentration, residual pressure, building materials, safety equipment, ignition points/accident points, entrance guard/fireproof doors/barriers, smoke concentration and the like.
2. Expert scoring
By comparing the importance degrees of the factors, scoring according to a relief standard method, and defining according to a fuzzy complementary matrix, wherein the scoring needs to meet the requirement of the fuzzy complementary matrix, and the definition is as follows:
0≤A ij ≤1，A ii ＝0.5
A ij +A ji ＝1，(ij＝1，2，3，…，n)
calculating average division of all the expert pairs of factors, and constructing a judgment matrix T, wherein the distance is as follows: where A1 is the result of summing matrix A by rows.
| A | Flow of people | Temperature (temperature) | Intensity of illumination | Toxic gas | Residual pressure | Building material | Barrier object | Smoke concentration | A1 |
| Flow of people | 0.50 | 0.40 | 0.60 | 0.30 | 0.40 | 0.80 | 0.30 | 0.60 | 3.90 |
| Temperature (temperature) | 0.60 | 0.50 | 0.80 | 0.40 | 0.40 | 0.80 | 0.30 | 0.60 | 4.40 |
| Intensity of illumination | 0.40 | 0.20 | 0.50 | 0.60 | 0.60 | 0.40 | 0.20 | 0.60 | 3.50 |
| Toxic gas | 0.70 | 0.60 | 0.40 | 0.50 | 0.70 | 0.80 | 0.30 | 0.60 | 4.60 |
| Residual pressure | 0.60 | 0.60 | 0.40 | 0.30 | 0.50 | 0.30 | 0.20 | 0.40 | 3.30 |
| Building material | 0.20 | 0.20 | 0.60 | 0.20 | 0.70 | 0.50 | 0.20 | 0.70 | 3.30 |
| Barrier object | 0.70 | 0.70 | 0.80 | 0.70 | 0.80 | 0.80 | 0.50 | 0.80 | 5.80 |
| Smoke concentration | 0.40 | 0.40 | 0.40 | 0.40 | 0.60 | 0.30 | 0.20 | 0.50 | 3.20 |
According to determinant A1, constructing a fuzzy consistency matrix B, and adopting the following calculation formula:
wherein n is the dimension of the matrix
The weights of the factors are calculated, and the row summation and determinant sub-B1 is calculated according to the matrix B, wherein the weights are calculated as follows:
| B | flow of people | Temperature (temperature) | Intensity of illumination | Toxic gas | Residual pressure | Building material | Barrier object | Smoke concentration | 81 | W |
| Flow of people | 0.50 | 0.47 | 0.53 | 0.46 | 0.54 | 0.54 | 0.38 | 0.54 | 3.95 | 0.1232 |
| Temperature (temperature) | 0.53 | 0.50 | 0.56 | 0.49 | 0.57 | 0.57 | 0.41 | 0.58 | 4.20 | 0.1321 |
| Intensity of illumination | 0.48 | 0.44 | 0.50 | 0.43 | 0.51 | 0.51 | 0.36 | 0.52 | 3.75 | 0.1161 |
| Toxic gas | 0.54 | 0.51 | 0.57 | 0.50 | 0.58 | 0.58 | 0.43 | 0.59 | 4.30 | 0.1357 |
| Residual pressure | 0.46 | 0.43 | 0.49 | 0.42 | 0.50 | 0.50 | 0.34 | 0.51 | 3.65 | 0.1125 |
| Building material | 0.46 | 0.43 | 0.49 | 0.42 | 0.50 | 0.50 | 0.34 | 0.51 | 3.65 | 0.1125 |
| Barrier object | 0.62 | 0.59 | 0.54 | 0.58 | 0.66 | 0.66 | 0.50 | 0.66 | 4.00 | 0.1571 |
| Smoke concentration | 0.46 | 0.43 | 0.48 | 0.41 | 0.49 | 0.49 | 0.34 | 0.50 | 3.60 | 0.1107 |
Objective weight assignment using coefficient of variation
The coefficient of variation method determines the weight of an index by calculating information contained in data, and if a certain index value difference is small, the ability of the index evaluation object is weak, whereas if the index value difference is large, the ability of the index evaluation object is strong. According to the application, by taking statistics of historical data of various devices as sample data and considering the influence on the data under different environments, seasons and other factor environments, a more objective, reasonable and dynamic weight value is provided by a variation coefficient weighting mode, and the calculation steps and formulas are as follows:
1. mean value of each factor sample data is set；
2. Calculating standard deviation of each factor sample data:
3. calculating the coefficient of variation of each factor:
4. calculating the variation coefficient weight of each factor, and setting m factors, wherein the variation coefficient weight calculation formula of the ith factor is as follows:
and (5) comprehensive weight calculation. For the ith influencing factor, the weight obtained according to the FAHP fuzzy analytic hierarchy processWeight obtained by objective assignment method of variation coefficient>The comprehensive weight is calculated as follows:
firstly, calculating a factor evaluation composite coefficient according to the weight values of each factor subjective assignment method and objective assignment method, wherein the factor evaluation composite coefficient comprises the following steps:
because different influence factors exist on the evacuation line and differences exist among the influence factors, the influence factors in different evaluation models cannot balance the difference of authority degree and statistical rule of index data corresponding to the influence factors in the comprehensive evaluation process, and the evaluation composite coefficient theta is selected i To reduce and balance the differences in the weight assignment evaluation process of multiple factors in different evaluation models.
The composite coefficients are added as weight ratios to the comprehensive score calculation as follows:
3. comprehensive evaluation index of evacuation line is calculated
And calculating the ratio of the actual value of each influence factor to the threshold value as an influence coefficient, synthesizing the weight of each factor, calculating a weighted average value, and adding the weighted average value into the ratio calculation of the actual length of the route to obtain a comprehensive evaluation index.
Taking the flow rate of people, the temperature, the concentration of CO2, fireproof materials and safety equipment as influence factors as examples to calculate the influence coefficients:
people flow influencing index (RF)
C＝W*D*V
RF＝[N/C]]*Q r
C represents the maximum traffic capacity of the channel, W represents the effective width of the channel, D represents the pedestrian density of the channel, V represents the average traveling speed, and N represents the real-time pedestrian flow.
Temperature influence index (TF)
K(i)＝T i /N e
T i Representing the current real-time temperature value, N e Indicating a safe temperature threshold that a person can withstand.
CO impact index (COF)
K(i)＝CO i /N e
CO i Represents the current real-time CO concentration value, N e Indicating a safe CO concentration threshold that a person can withstand.
CO 2 Impact index
Represents the current real-time CO2 concentration value, N e Indicating human affordable safe CO 2 Concentration threshold.
Fireproof material impact index (FDF)
FDF＝FDL/L
FDL represents the length of the range covered by the fire-retardant material on the current route, and L represents the length of the current route.
Safety equipment impact index (SEF)
SEF＝N sE /N
N sE Indicating the number of security devices on the current route and N indicating the total number of security devices in the venue.
As shown in fig. 4, the weighted average is performed according to the influence coefficient of each factor and the corresponding weight, and the weighted average is added into the calculation of the actual path length to obtain the comprehensive evaluation index CEI of the evacuation line, and the calculation formula is as follows:
an intelligent emergency evacuation line system based on real-time data of an internet of things, comprising: the emergency evacuation platform is respectively connected with the evacuation line display module and the risk perception module, and comprehensively calculates comprehensive evaluation indexes of the evacuation line in real time according to equipment data and states fed back by the risk perception module.
The risk perception module is provided with a plurality of emergency evacuation platforms, the emergency evacuation platform is provided with a central server module, an audio output module, a video output module and an emergency alarm module, the central server module receives data detected by equipment of each risk perception module in a building site and state data of the equipment, and combines the data of historical annual equipment to carry out comprehensive analysis and judgment, calculates risk indexes of evacuation lines, and visually displays obtained results through the evacuation line display module, so that people evacuating people find each evacuation line at the first time, the safety indexes of the evacuation lines and possible problems, and the event prediction capacity and coping speed are improved.
The risk sensing module is arranged at each position of the building site and consists of a plurality of sensors of different types, the sensors of each type are connected with the emergency evacuation platform through wired or wireless communication, the sensors of each type are reasonably arranged in corresponding areas of the building site respectively, and the event risk degree of the area where the sensors are located is monitored.
The central server module is respectively connected with the audio output module, the video output module and the emergency alarm module, and the audio output module receives the audio file from the central server module and plays or closes the audio file information according to the received instruction of the central server module; the video output module receives the video file from the central server module and plays or closes the video file information according to the received instruction of the central server module.
The emergency alarm module comprises an alarm module and an emergency power-off module, and when an emergency evacuation platform fails in an emergency and an emergency event occurs in a building site, the alarm module automatically alarms to a nearby fire department or police office to send help seeking information.
In this embodiment, as shown in fig. 4, the emergency evacuation line is pushed to the crowd, and the advertisement screen can be arranged in the wall of the building site or placed in the center of each layer of the building site by arranging a plurality of advertisement screens, so that three pieces of preferable evacuation line and line equipment information under the current personnel position are displayed in real time for the emergency evacuation crowd. The emergency evacuation line is pushed to the crowd, short messages can be sent through the mobile terminal, the short messages comprise emergency event safety reminding and website linking of the emergency evacuation line, and the real-time optimal emergency evacuation line can be updated according to the real-time position of the mobile terminal.
Short messages are pushed to emergency evacuation people through an LBS electronic fence technology, and real-time navigation service is provided based on indoor positioning data such as mobile phone Bluetooth/RFID/UWB.
In this embodiment, as shown in fig. 2, the evacuation route intelligent planning flow is as follows:
firstly, selecting one line, judging the state of key factors (such as entrance guard, obstacle, flame, smoke feeling and the like) in the line, judging whether the state is closed or alarmed, if the state is closed or alarmed, indicating that the line is not open, and opening the entrance guard and treating the obstacle; if the state is not closed, the next step is carried out, whether the type of the equipment is detected along the line in an alarm mode is judged, if yes, the equipment state is judged, if the equipment state is abnormal or alarm, the route is marked as an unsafe route, and if the equipment state is normal, the early warning alarm and the equipment data detection along the line are collected; if the route is not a switching value device, the step of collecting the line early warning alarm and detecting the device data is also entered, then judging whether the device data exceeds a threshold value, if the device data exceeds the threshold value, marking the route as a non-safety route, if the device data does not exceed the threshold value, marking the route as a safety route, and if the device data does not exceed the threshold value, the two judging thresholds are all entered into the same step finally, calculating the comprehensive evaluation index of the route according to a route optimization algorithm, and next judging whether all routes are already calculated, if not, returning to an initial flow, and selecting one route; if the route is calculated, the next step is carried out to judge whether the route is marked as unsafe, if yes, the emergency evacuation crowd is searched and pushed with the nearest refuge layer and the evacuation route guidance is given, if not, evacuation route data are integrated, the evacuation route is ordered according to the comprehensive evaluation index, an analysis result is output, and finally the process is ended.
Claims (10)
1. The intelligent emergency evacuation line optimization method based on the real-time data of the Internet of things is characterized by comprising the following steps of:
s1, constructing an electronic map model according to a building scene, and marking the positions of sensing equipment and evacuation facilities;
s2, acquiring and planning an emergency evacuation line according to the occurrence condition of an emergency;
and S3, ranking the emergency evacuation lines and pushing the emergency evacuation lines.
2. The intelligent emergency evacuation line optimization method based on the real-time data of the internet of things according to claim 1, wherein the intelligent emergency evacuation line optimization method further comprises the step of S4, sensing changes occurring in a real-time sensing area of sensing equipment in a building scene.
3. The method for optimizing an intelligent emergency evacuation line based on real-time data of the internet of things according to claim 1, wherein the step S1 is further expressed as:
s1.1, constructing an electronic map model according to an actual building scene;
s1.2, each sensing device is in communication connection through the Internet of things, and the positions of the sensing devices are marked;
s1.3, marking evacuation facilities and temporary refuge places.
4. A method for optimizing an intelligent emergency evacuation line based on real-time data of an internet of things according to claim 1 or 3, wherein the step S1 further comprises the step S1.4 of configuring an alarm threshold of a sensing device.
5. The method for optimizing an intelligent emergency evacuation line based on real-time data of the internet of things according to claim 1, wherein the step S2 is further expressed as:
s2.1, judging whether an emergency event occurs or not, and acquiring the current emergency event occurrence position and the emergency exit position;
s2.2, acquiring and updating reported data of the sensing equipment in real time.
6. The method for optimizing an intelligent emergency evacuation line based on real-time data of the internet of things according to claim 5, wherein the step S2 further comprises the step S2.3 of generating and planning the emergency evacuation line according to an emergency evacuation line optimizing algorithm.
7. The method for optimizing intelligent emergency evacuation lines based on real-time data of internet of things according to claim 6, wherein in the step S2.3, the evacuation lines calculate the evacuation lines in real time according to real-time positions of evacuation users and along-line sensing equipment data.
8. The method according to claim 6, wherein in step S2.3, the emergency evacuation line optimization algorithm sets the impact factor weight according to different device types.
9. The method for optimizing intelligent emergency evacuation lines based on real-time data of the internet of things according to claim 8, wherein in the step S2.3, the setting of the influence factor weight is performed by comprehensively judging the evacuation lines by combining an FAHP fuzzy analytic hierarchy process and a coefficient of variation method.
10. An intelligent emergency evacuation line system based on real-time data of the internet of things, adapted for an intelligent emergency evacuation line optimization method according to any one of claims 1-9, comprising: the emergency evacuation platform is respectively connected with the evacuation line display module and the risk perception module, and comprehensively calculates comprehensive evaluation indexes of the evacuation line in real time according to equipment data and states fed back by the risk perception module.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117784692A (en) * | 2023-12-29 | 2024-03-29 | 营口天成消防设备有限公司 | Wisdom fire emergency lighting and evacuation indicating system |
| CN117789398A (en) * | 2023-12-29 | 2024-03-29 | 京彩未来智能科技股份有限公司 | Campus emergency guiding management system |
| CN117784692B (en) * | 2023-12-29 | 2024-06-04 | 营口天成消防设备有限公司 | Wisdom fire emergency lighting and evacuation indicating system |
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2023
- 2023-08-29 CN CN202311094113.8A patent/CN117114222A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117784692A (en) * | 2023-12-29 | 2024-03-29 | 营口天成消防设备有限公司 | Wisdom fire emergency lighting and evacuation indicating system |
| CN117789398A (en) * | 2023-12-29 | 2024-03-29 | 京彩未来智能科技股份有限公司 | Campus emergency guiding management system |
| CN117784692B (en) * | 2023-12-29 | 2024-06-04 | 营口天成消防设备有限公司 | Wisdom fire emergency lighting and evacuation indicating system |
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