Building fire emergency management system based on BIM

Abstract

The invention relates to the technical field of fire emergency management, in particular to a BIM-based building fire emergency management system, which comprises a fire intelligent monitoring module, a fire early warning module, a fire response module, a fire disposal module and a database management module, the intelligent fire monitoring module monitors fire information; the fire early warning module takes corresponding countermeasures according to different behavior decisions of trapped persons; the fire response module determines the decision behavior difference of different users and determines the positions of different decision makers; the fire disposal module confirms the optimal route and guides the users to complete their behavior decisions as soon as possible; the database management module obtains monitoring data for definition, storage and extraction; organizing and maintaining a database, and performing data connection when the operating system performs online and remote operation; and the data is fed back to mobile equipment of related operators and users in a central control room.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fire emergency management, in particular to a building fire emergency management system based on BIM. BACKGROUND

[0002] At present, fire is a major threat to the safe operation of buildings, and the complex indoor space and the rapid development of fire situation increase the difficulty of evacuation of personnel and rescue of firefighters. In order to reduce casualties and property losses, indoor fire emergency management in fire scenarios is particularly important. Due to the suddenness and complexity of building fires, future will rely on intelligent and integrated technology ecology, which can realize continuous dynamic observation of fire conditions. The present application is a fire emergency management system considering the behavior decision of building users.

[0003] Existing research focuses on developing advanced technologies and improving the effectiveness of fire emergency management, such as: multi-source data fusion under BIM platform; using wireless sensor network (WSNs) and Internet of Things (loT) technology for fire monitoring and data collection; using Global System for Mobile Communications (GSM) and Internet of Things (loT) to realize the connection of fire monitoring and other fire emergency management modules; using Dijkstra algorithm for path planning; using Pathfinder software to build building fire evacuation building model, etc.

[0004] Current fire management only focuses on fire monitoring, evaluation, early warning, evacuation decision-making, and preventive maintenance. The evacuation of personnel in the event of fire remains in static evacuation, which cannot adapt to various emergency risks, including dynamic risks caused by different behavior decisions and psychology, physical fitness of people at the fire scene; dynamic risks caused by stair congestion when personnel escape. This will affect the effectiveness of fire emergency management, raise the use threshold of fire emergency management, and the consciousness and behavior of people are also important factors, which should be considered when converting to data.

[0005] Therefore, in order to fill the research gap, we developed a fire emergency management system (FEMS) that takes into account user behavior decisions and fire emergency management technology in fire scenarios. SUMMARY

[0006] The purpose of the present application is to provide a building fire emergency management system based on BIM to solve the problems raised in the background art.

[0007] In order to solve the above technical problems, the present application provides the following technical solutions:

[0008] The building fire emergency management system based on BIM, the system comprises: a fire intelligent monitoring module, a fire warning module, a fire response module, a fire disposal module and a database management module.

[0009] The fire intelligent monitoring module monitors fire information through data sensors, cameras, voice broadcast equipment, building users' smartphones and equipment installed in the central control room arranged in the building;

[0010] Based on the information detected by multiple data sensors, including temperature data, smoke data, and carbon dioxide data, the online closed-circuit camera video data of the fire scene is received and uploaded to the BIM model in Revit2018;

[0011] The fire warning module determines whether the fire accident is controllable and takes corresponding countermeasures based on the different behavioral decisions of the trapped people;

[0012] The fire response module determines the differences in decision-making behaviors of different users and determines the locations of different decision makers;

[0013] The fire handling module determines the optimal route and guides users to complete their behavioral decisions as quickly as possible;

[0014] The database management module obtains the monitoring data of the intelligent monitoring module and defines, stores and extracts it; organizes and maintains the database, and connects the data when the operating system performs online and remote operations; and feeds the data back to the relevant operators in the central control room and the users' mobile devices.

[0015] These five modules complement each other and encompass the diverse behavioral decisions of trapped individuals. Throughout the system's operation, a comprehensive array of technologies, including a WiFi-based wireless sensor network, WiFi fingerprint recognition, a group SMS system, an e-building platform, and a SQL server, were implemented to ensure the technical feasibility of the FEMS. In the event of a fire, the system captures fire information, including the location of the fire source, sensor data, and combustion characteristics. The fire warning module determines whether the fire is controllable or uncontrollable. If the fire is determined to be uncontrollable, appropriate countermeasures are implemented based on the diverse behavioral decisions of the trapped individuals.

[0016] Integrating BIM with user behavior and decision-making primarily involves identifying user behavior decisions during building fires and determining their location. This location is then reflected in the Revit 3D model. Dijkstra's algorithm is used to plan optimal paths for different decisions, and intelligent navigation provides guidance for users. This emergency management system generates targeted countermeasures for different users, helping them escape or extinguish fires with less effort and time, fostering the integration of technology and users.

[0017] Preferably, the behavior of different groups of people in building fires is analyzed before the FEMS system performs tasks: the differences in decision-making of people in fire situations are studied and the user groups are divided into three categories based on past evacuation experiments, namely escapees, waiters and firefighters.

[0018] This is due to some external environment and individual differences form this decision difference, external environment including building layout, smoke diffusion, fire size, individual differences including fire response knowledge, psychological quality, physical quality (disability, age) and so on.

[0019] Preferably, the fire warning module comprises:

[0020] The flow chart is as shown in Figure 2

[0021] The system starts the fire warning management, evaluates the fire category, including controllable fire and uncontrollable fire, and combines the video monitoring near the fire point to check the equipment failure, and finally determines whether the comprehensive fire emergency management mechanism should be started;

[0022] When a fire occurs, the multiple data sensors placed in the building quickly locate the fire point and immediately collect data; the data is first stored in the SQL server and imported into Excel, and then the facility manager clicks the "Confirm" button in the Revit2018 "FireSense" plug-in to obtain the result; The plug-in analyzes whether the "temperature", "smoke" and "carbon dioxide" data exceed the limit value, and the manager makes a final judgment on the fire category according to the plug-in and the on-site video.

[0023] Preferably, the fire response module comprises: a WiFi fingerprint positioning stage and a decision maker behavior difference classification stage;

[0024] First, in order to accurately represent the location of the user, and facilitate the subsequent shortest path optimization of the evacuation route, rescue route and fire extinguishing route, and facilitate the data processing of the Dijkstra algorithm, the environmental information of the real building is presented in the form of a grid map; In the grid map, the environmental information is divided into individual grid units, which are divided into black obstacle grids and white feasible grids according to the actual environmental information of the building, for example: walls, columns and other obstacles that hinder passage are displayed as black grid grids, and areas that can be passed are displayed as white grids. The grid size is scaled according to the real building;

[0025] The WiFi fingerprint positioning stage comprises:

[0026] Construct an offline fingerprint library: place specific reference points in the grid converted from the real building, confirm the coordinates of each reference point, and then scale and convert to the real building environment; record the data from each​ signal strength data of the current environment and the coordinate information and signal strength information are stored in the SQL server to form a fingerprint library;

[0027] Online positioning phase: when a fire occurs, the building user opens the WiFi with the mobile phone, the mobile phone collects the signal strength data of the current environment of the current environment , and uses the NNSS algorithm to calculate the signal strength similarity and difference from the fingerprint library, finds the nearest reference point according to the principle of minimum length, and outputs the coordinates of the reference point as the definition result in the grid map to realize position determination;

[0028] It is particularly pointed out that the "fingerprint" in the WiFi fingerprint positioning is not the biological fingerprint of a person, but a wireless signal strength feature that is unique to different positions and similar to the fingerprint of a person. The AP is a wireless access point that continuously transmits wireless signals and needs to be accessed in advance in the building.

[0029] The decision maker behavior difference classification phase includes:

[0030] Information in the central control room: the monitoring computer in the central control room is installed with a short message mass sending system and is connected with the SQL server; the IMEI code (international mobile equipment identity code) of the user's mobile phone in the short message mass sending system has been pre-input and is bound with the fingerprint data of each mobile phone; the IMEI code is the unique "identity card number" of the mobile phone, and each mobile phone has a unique IMEI code, which can be used to identify the user's mobile phone equipment and distinguish different mobile phones in the communication network;

[0031] After the fire response module is started, the decision information is sent to the user through the short message mass sending system, and the user replies to the decision result through the short message; the system then decodes the short message, restores the respective IMEI code, and then matches the WiFi fingerprint to obtain the position of the decision maker; further, the spatial position of the decision maker with different behaviors is marked in the grid map of the respective floor by using the "Human Location" plug-in, and is restored to the real building view according to the proportional relationship, and the position is displayed on the BIM model;

[0032] Among them, the gray point position represents the position of the escapee, the red point position represents the position of the person to be rescued, and the black point position represents the position of the fire extinguisher.

[0033] Preferably, the fire disposal module includes a path optimization phase and a dynamic route guidance phase.

[0034] The path optimization phase includes a path selection layer, a path allocation layer, and a path adjustment layer.

[0035] The flow chart is as followsFigure 3 As shown:

[0036] The path selection layer optimizes the path according to the position coordinates of the crowd in the fire response module:

[0037] Wherein, the end point is the stairwell of each floor, and the route from each stairwell to the exit of a floor is fixed, so that only the path from the start point to the stairwell needs to be optimized.

[0038] The improved Dijkstra algorithm is used to search the shortest path: the points on the extracted grid map are referred to as a vertex set, the vertex set is divided into a shortest path set and an unmarked path set; each time, the node closest to the starting point is selected from the unmarked points and marked, and is included in the optimal path set; the distance from the newly added point A to the adjacent point B is calculated, wherein the node B is not contained in the marked node; if (the distance of node A + the distance from node A to node B) < the distance of node B, the distance of node B is updated to the optimal path set S; the operation is repeated; finally, the shortest distance from the starting point to each point in the path is found, so that the shortest path is found; wherein, the optimal path set S represents the vertex set of the shortest path, and the sum of the distances between the vertex sets of the shortest path is the length of the shortest path;

[0039] It is worth mentioning that, due to the diversified needs of fire emergency management, and the fact that the obstacles and dangerous areas at the fire scene can change at any time, it is necessary to dynamically optimize multiple paths, and when multiple shortest paths are reserved, the Dijkstra algorithm is improved to meet the requirements, so that multiple shortest paths can be saved.

[0040] According to the formula: , multiple shortest paths are obtained;

[0041] Wherein, represents the i-th path, represents the length of the i-th path, S represents the vertex set of the found shortest path, and U represents the unmarked shortest path vertex set.

[0042] The path allocation layer allocates paths according to the allocation model according to different decisions of different crowds:

[0043] In order to associate the optimal path with the decision of the person, it is thought that paths are allocated according to different decisions of different crowds.

[0044] The basic principles of the allocation model of the application are as follows:

[0045] The route of each person includes parallel path of floor and path of stairs. Due to the large horizontal space of the building, the multiple evacuation passages of stairs and the multiple exits, the route selection is various, and the stairs are the only passage for the evacuation personnel and the rescue personnel, and the selection of different stairs represents different paths. The fire personnel do not suggest to occupy the passage which is necessary for evacuation, i.e. the relatively narrow stairs, and the limited space should be used to give priority to the passage of the escape and the rescue personnel. Therefore, the fire personnel do not perform the cross-floor behavior, and the distribution model only considers the path selection of the escape and the rescue personnel. The distribution model is as follows:

[0046] According to the distribution model formula: ; , the total path sum of the escape group and the rescue group is calculated respectively, and then the optimal path with the shortest distance is distributed according to the target;

[0047] Wherein, S represents the total sum of the complete route of each kind of personnel in the building, represents the total sum of the jth stair path selected by the ith person; wherein the path is composed of the length of the stair corridor, the horizontal distance from the starting point to the stair and the distance from the stair to the exit; represents whether the jth stair is selected as the path by the ith person, and represents the safety capacity of the jth corridor;

[0048] Finally, considering the carrying capacity of the stairs, avoiding the occurrence of stampede and maintaining the safety, the number of evacuation personnel on the stairs is adjusted in real time, and the basis is the traffic state coefficient of the stairs. The model is as follows:

[0049] According to the traffic state coefficient: , the number of evacuation personnel on the stairs is adjusted in real time;

[0050] Wherein, represents the traffic state coefficient of the stair at t time, represents the traffic capacity of the stair when it is unblocked, represents the number of passengers at t time;

[0051] The path adjustment layer collects the passenger number data on the stairs through dynamic monitoring during the system operation, and transmits the data to the central control room; when the number of passengers of a certain stair exceeds the traffic capacity at a certain time, the traffic state coefficient of other stairs is immediately checked to determine the number of passengers that can be carried when the two stairs are balanced at the same time, and the number of passengers of the over-capacity stair is adjusted according to the number of passengers, so as to realize the reasonable distribution of the evacuation stairs;

[0052] For the crowd of escaping and waiting for rescue, only the optimization of the path from the starting point to the stairwell is considered. For the crowd of taking fire-fighting actions, the optimization of the two paths from the crowd area to the fire-fighting facility area and then from the fire-fighting facility area to the fire starting point position is considered. The Dijkstra algorithm is used to calculate the shortest path from the crowd area to the stairwell area. Secondly, the total path length between different groups for all available stairwells is calculated, and the stairwell with the shortest total path length is selected as the final evacuation stairwell of the specific group. Finally, according to the number of people using the stairwell obtained by the fire intelligent monitoring module, the respective traffic state coefficient is calculated, and the number of people using the evacuation stairwell is dynamically adjusted when the stairwell exceeds the maximum load.

[0053] The dynamic route guidance stage comprises:

[0054] The position information of each grid point in the optimized route is recorded in the grid map and imported into the SQL server, and the original grid points are converted to the floor to form a 3D map by programming; the different optimized routes of the decision makers are displayed as red channels in the 3D model; our purpose is to enable building users and rescue personnel to see the optimized route through the mobile app, so as to achieve the effect of evacuation guidance;

[0055] The e-building is adopted as the platform connecting the PC and the mobile app, and the facility managers upload 3D models for the three groups on the PC end of the central control room, which are downloaded through the e-building on the mobile phone, and the optimized route is displayed in the form of a red channel in the BIM model; at the same time, the facility managers observe the action route of the building users and rescue personnel through the video, and when the route deviates or the evacuation stairwell needs to be modified, the facility managers can use the voice broadcasting equipment to warn.

[0056] The e-building is excellent in real-time updating of the BIM model and is a platform focusing on model management and sharing.

[0057] Compared with the prior art, the present application has the beneficial effects that:

[0058] (1) The evacuation distance and time are shortened. The fire monitoring data is used to determine whether the fire is controllable, the fire warning efficiency is improved, and false alarms caused by equipment failure are prevented. In the simulation experiment, the moving distance of the test group equipped with the FEMS is shortened by 30% compared with the control group, and the total time consumption is reduced by 48%. And the performance of different behavior decision makers in the test group is better than that in the control group, which verifies the effectiveness of the system.

[0059] (2) The multi-data sensor network and video monitoring are combined, and real-time detection of fire dynamics is realized through the SQL server.

[0060] (3) Precise positioning is achieved. By using WiFi fingerprint identification and IMEI matching technology, the system can accurately locate the position of building users and provide personalized guidance according to their behavior decisions (such as evacuation, waiting for rescue or fire extinguishing).

[0061] (4) Deep integration with user behavior. FEMS takes into account the behavior differences that may exist in actual fires, quickly collects user decision information through the SMS mass sending system and realizes efficient classification, and matches the best emergency management method for each behavior decision.

[0062] (5) Dynamic path adjustment mechanism. FEMS dynamically adjusts path allocation according to real-time fire data and stair congestion, avoiding the limitations of traditional static path planning.

[0063] (6) Visual navigation and voice prompts make users more operational when using FEMS. The mobile terminal APP provides three-dimensional visual path navigation through the BIM model, and the central control room issues real-time announcements through voice broadcasting equipment. This reduces the user's operation threshold and is more effective in emergency situations.

[0064] (7) Multi-technology and application integration. This system combines BIM, WSNs, WiFi fingerprint positioning, Dijkstra algorithm and user behavior to realize the whole chain technology from fire monitoring, fire classification judgment, user positioning, user decision collection, path planning, path dynamic adjustment and user path guidance.

[0065] (8) Building users can obtain the most practical fire response measures, and it also helps rescue personnel to participate in rescue operations more safely and effectively. This invention remedies the defects of traditional fire emergency management that ignores user behavior decisions. The four functional modules of intelligent detection, early warning, response and processing realize data sharing and collaboration through the SQL server, thereby improving the pertinence and effectiveness of fire emergency management.

[0066] (9) The fire emergency management system provides a new solution for building fire emergency management and has important theoretical and practical value. It can also be associated with other intelligent systems in other fields to realize technology sharing and promote multi-technology integration. BRIEF DESCRIPTION OF DRAWINGS

[0067] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application and are used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0068] Figure 1 is a structure diagram of the building fire emergency management system based on BIM of the present application;

[0069] Figure 2 is a flow chart of the fire warning module of the present application;

[0070] Figure 3 is a flow chart of the path optimization stage in the fire response module of the present application;

[0071] Figure 4 is a diagram of information transmission between the e-building platform and a PC. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0073] Referring to Figures 1-4 , the present application provides technical solutions:

[0074] Example 1: In order to verify the function of the FEMS (fire emergency management system), a fire scene simulation experiment was performed, and the results showed that the use of the system significantly reduced the moving distance and action time of the trapped personnel.

[0075] Considering safety, site restrictions, and experimental personnel restrictions, we used a fire scene simulation instead of creating a real fire scene. Therefore, the intelligent monitoring module and the fire warning module were not used in this experiment, and the fire sensor data was given by data simulation. The fire response module and the fire disposal module closely related to user action and user decision-making after the occurrence of a fire were mainly applied.

[0076] 1) Overview of the experimental site:

[0077] 2) Preparation work for the experiment:

[0078] (1) Test personnel composition:

[0079] (2) Draw the test site grid map:

[0080] In order to facilitate data processing, complex indoor furniture, office equipment, and other elements were removed, and only walls were used as obstacles to hinder route passage. Small rooms were combined and divided into 8 large rooms. The first floor with a length of 37.66 m and a width of 31.65 m was drawn as a plane network diagram according to a scale of 1:2, and the trapped personnel position was displayed as a point in the grid map.

[0081] (3) Collect the fingerprint samples of the participants' cellphones. First, select the point where the trapped person is in the room, then bind the IMEI cellphone code with the fingerprint data of each cellphone and input them into the SQL server.

[0082] (4) Check the relevant equipment:

[0083] Check if the emergency lights, safety exit signs, voice broadcast speakers, cameras, fire sensing devices, fire alarms, APs, fire hydrants, and fire extinguisher boxes can work normally.

[0084] The normal operation of these devices is the key to the success of the test, playing a dual role of lighting and guiding, and constitutes the basic guarantee system for fire emergency escape.

[0085] 3) Experimental process:

[0086] (1) Test process of experimental group A:

[0087] The experimental group will choose to escape, wait for rescue, and extinguish the fire with the assistance of FEMS.

[0088] When a fire occurs, the fire location is displayed in the BIM model through sensors. First, the area around the fire point is set as an obstacle area. Second, send decision-making consultations to the 20 trapped people through the SMS mass messaging system, and obtain the IMEI code of the user's cellphone and the location information of different decision-makers in the room by decoding the reply information. Third, according to the location of different decision-makers, combined with the obstacle area, the shortest path under different decisions is obtained through Dijkstra algorithm, and the path is synchronized to the BIM model and displayed with a red channel. Finally, the trapped personnel and rescue personnel download the corresponding BIM model on the e-building platform of the mobile terminal, and proceed according to the route in the model. The volunteers' movement path, path length, and route time are recorded in real time during the whole process.

[0089] (2) Test process of control group B:

[0090] The control group will not be assisted by FEMS, and will escape, wait for rescue, and extinguish the fire according to their personal experience. In order to show the comparison effect of the experiment, the positions of the decision-makers are arranged the same as in the test group. The volunteers' movement path, path length, and route time are recorded in real time during the whole process.

[0091] (3) Experimental route following: The real-time positions of the volunteers are located through WiFi fingerprint positioning, imported into the SQL server, and connected with Revit 2018 through a plug-in. Revit 2018 can intuitively display the routes of the participants.

[0092] (4) The time spent by the experimenters on the path: by letting each volunteer carry a stopwatch, the starting time was unified, and the ending time of the escapees and the rescued was the time when each person reached the exit of a floor, and the ending time of the firefighters was the time when they reached the fire point with fire-fighting tools.

[0093] 4) Experimental data:

[0094] (1) The distribution of the experimenters

[0095] The escapees were distributed in rooms 2, 4, 5, 7, and 8, the rescued were distributed in rooms 3 and 6, and the firefighters were distributed in room 1.

[0096] (2) Comparison of the characteristics of the moving routes:

[0097] (3) Comparison of the distances of the moving routes:

[0098] (4) Comparison of the action times:

[0099] The total time of the test group was shortened by 48% compared with the control group, and the action time difference in room 6 was the largest, which was 139s. The difference in room 8 was the second largest.

[0100] 5) Experimental analysis:

[0101] For the rooms with large differences in data between the test group and the control group, we further analyzed to find out the conditions under which the FEMS is more effective, and found the reasons for the differences.

[0102] Room 6:

[0103] Data characteristics: the distance difference of the moving routes between the two groups was large, and the action time difference between the two groups was the largest;

[0104] Reason analysis: first, we know that the population in this room decided to wait for rescue. Since the room is far from the only exit, the rescue personnel of the control group had to spend a lot of time finding the escape route. However, the rescue personnel of the test group can directly act according to the optimized route map on the application, greatly saving time and distance.

[0105] Room 7:

[0106] Data characteristics: the distance difference of the moving routes between the two groups was large;

[0107] Reason analysis: First, we know that the crowd in the room decides to escape. By observing the movement trajectory, they first walk towards exit 1 and then turn back to leave from exit 3. The room is closest to exit 1, and since the control group has no knowledge of the fire location, they choose exit 1 as the preferred escape exit. However, exit 1 is within the range of the fire point and cannot be left from exit 1. The evacuation personnel must choose the nearest exit according to the nearest principle. In the process of repeated searching and changing routes, the moving distance gradually increases, and the time is wasted. However, FEMS optimizes the route in advance to ensure the shortest route and avoid obstacle areas, and can directly see the fire point, thereby significantly improving the efficiency of fire emergency handling.

[0108] Room 8:

[0109] Data characteristics: the maximum distance between the two groups of moving routes, and the large difference in action time between the two groups;

[0110] Reason analysis: First, we know that the crowd in the room decides to put out the fire. The extinguishing action is divided into two steps: first, move from the firefighter's position to the fire-fighting facility, and then move to the fire source. Since there is only one fire hydrant on the first floor of the building, the moving distance and action time of the two groups in the first stage are almost the same. The firefighter of the control group spends a lot of time looking for the location of the fire source. On the contrary, the firefighter equipped with FEMS can quickly find the fire source and put out the fire according to the shortest route in the BIM model.

[0111] In summary, the following two processes have a certain degree of invalid or repeated routes:

[0112] (1) The escapees of the control group choose the nearest exit to evacuate at the first time, but some exits are cut off by the fire and cannot be passed, so they have to find other exits.

[0113] (2) Firefighters blindly search for fire extinguishers and fire points.

[0114] 6) Experimental conclusion:

[0115] (1) The use of FEMS can rationally respond to the fire in terms of moving distance and time consumption. The presence of technology helps to reduce panic. In contrast, participants without FEMS assistance (control group) may walk into blocked routes, causing U-turns and confusion.

[0116] (2) Through WiFi fingerprint comparison, the spatial location of different behavior groups in the building can be quickly located.

[0117] (3) Based on Dijkstra's algorithm, the optimal path is provided for different behavior decision makers. The optimal evacuation route is set for the evacuees, the optimal rescue route is set for the rescued, and the optimal extinguishing route is set for the firefighters.

[0118] (4) The route is visualized and guided by the e-building platform, which greatly enhances the operability and the connection between people and the emergency management system.

[0119] (5) The system can provide targeted countermeasures according to the behavior of the crowd after the fire occurs, thereby ensuring safety and reducing casualties. BIM technology is applied to different decision analysis. The route optimization of firefighters is the path from the location of the person to the fire-fighting facility and then to the fire source location, which does not involve stair roads. The route optimization of escapees and rescuers is the path from the location of the person to the exit location on the first floor, which involves stair roads.

[0120] Compared with the previous fire emergency management concept, the biggest improvement of the present application is to integrate user behavior decision-making into technology application, so that the technology application always meets the needs of user decision-making, including:

[0121] The behavior decision-making information of building users can be accurately collected and their positions can be quickly locked. The decision-making information is sent to the user through the SMS mass sending system, and the user replies to the decision-making result through the SMS. The system then decodes the SMS and restores the respective IMEI code to match the WiFi fingerprint to obtain the location of the decision-maker.

[0122] The optimal path is calculated according to the location of the user and the exit using Dijkstra algorithm.

[0123] The Dijkstra algorithm is improved to retain multiple optimal paths with the same distance, meeting the diversified needs.

[0124] It has a dynamic adjustment mechanism. The staircase traffic state coefficient is considered in the optimization path process. The traffic state coefficient of "1" represents that the number of people on the staircase does not exceed the limit, and the traffic state coefficient < "1" represents that the number of people on the staircase exceeds the limit, and the people on each evacuation staircase will be distributed according to the safety state.

[0125] The action path of different decision-makers is realized by the mobile terminal app. The spatial position of different behavior decision-makers is recorded in the grid map of each floor using the "HumanLocation" plug-in, and is restored to the real building model view according to the proportional relationship. The e-building is used to connect the PC and the mobile app, so that the user can intuitively see the fire point and the optimized path in the 3D BIM model on the mobile app, reducing the delay and errors of human intervention.

[0126] Finally, it should be noted that the above only describes the preferred embodiments of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art will appreciate that the technical solutions described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalent ones. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. BIM-based building fire emergency management system, characterized by: The system includes: a fire intelligent monitoring module, a fire early warning module, a fire response module, a fire disposal module and a database management module; The fire intelligent monitoring module monitors fire information through data sensors, cameras, voice broadcast equipment, building users' smartphones and equipment installed in the central control room arranged in the building; The fire warning module determines whether the fire accident is controllable and takes corresponding countermeasures based on the different behavioral decisions of the trapped people; The fire response module determines the differences in decision-making behaviors of different users and determines the locations of different decision makers; The fire handling module determines the optimal route and guides users to complete their behavioral decisions as quickly as possible; The database management module obtains the monitoring data of the intelligent monitoring module and defines, stores and extracts it; organizes and maintains the database, and connects the data when the operating system performs online and remote operations; and feeds the data back to the relevant operators in the central control room and the users' mobile devices.

2. The BIM-based building fire emergency management system according to claim 1, characterized in that: The fire warning module includes: The system initiates fire early warning management, assesses fire types, including controllable and uncontrollable fires, and conducts equipment troubleshooting in conjunction with video surveillance near the fire point, ultimately determining whether a comprehensive fire emergency management mechanism should be activated; When a fire occurs, multiple data sensors placed inside the building quickly locate the fire point and immediately collect data. The data is first stored in a SQL server and imported into Excel. Facility managers then click the "Confirm" button in the Revit 2018 "Fire Sense" plug-in to obtain the results. The plug-in analyzes whether the three data values ​​of "temperature," "smoke," and "carbon dioxide" exceed the limit values. The manager then makes a final judgment on the fire type based on the plug-in and on-site video.

3. The BIM-based building fire emergency management system according to claim 1, characterized in that: The fire response module includes: a WiFi fingerprint positioning stage and a decision maker behavior difference classification stage; First, the environmental information of the real building is presented in the form of a grid map. In the grid map, the environmental information is broken down into individual grid cells. Based on the actual environmental information of the building, it is divided into black obstacle grids and white feasible grids. The grid size is scaled according to the actual building. The WiFi fingerprint positioning stage includes: Construct offline fingerprint library: Arrange specific reference points in the grid converted from real buildings, confirm the coordinates of each reference point, and then convert it into the real building environment in equal proportion; record the fingerprints from each reference point. Signal strength data at The coordinate information and signal strength information are stored in the SQL server to form a fingerprint library; Online positioning stage: When a fire occurs, building users use their mobile phones to turn on WiFi, and the mobile phones collect various Signal strength data , and use the NNSS algorithm to calculate the signal strength similarity and difference from the fingerprint library, find the nearest reference point based on the minimum length principle, and calculate the coordinates of the reference point. The output of the definition result is recorded in the grid map to achieve position determination; The decision maker behavior difference classification stage includes: Information in the central control room: A group text messaging system is installed on the monitoring computer in the central control room and connected to the SQL server. The IMEI code of each user's mobile phone is pre-entered into the group text messaging system and bound to the fingerprint data of each mobile phone. The IMEI code is the unique "identity card number" of the mobile phone. Each mobile phone has a unique IMEI code, which can be used to identify the user's mobile device and distinguish different mobile phones on the communication network. After the fire response module is activated, the decision information is sent to the user via a group SMS system, and the user responds with the decision result via SMS. The system then decodes the SMS, recovers the individual IMEI codes, and matches the WiFi fingerprint to obtain the decision maker's location. Furthermore, using the "Human Location" plug-in, the spatial locations of different decision makers are marked on the grid map of their respective floors. This is then restored to the real building view based on the scale relationship and displayed on the BIM model. Among them, the gray dots represent the position of the escapee, the red dots represent the position of the person to be rescued, and the black dots represent the position of the fire extinguisher.

4. The BIM-based building fire emergency management system according to claim 1, characterized in that: The fire handling module includes: a path optimization stage and a dynamic route guidance stage; The path optimization stage includes: a path selection layer, a path allocation layer and a path adjustment layer; The path selection layer performs path optimization based on the position coordinates of the crowd in the fire response module: The improved Dijkstra algorithm is used to search for the shortest path: the points on the extracted grid map are called vertex sets, and the vertex sets are divided into the shortest path set and the unmarked path set; each time, the node mark closest to the starting point is selected from the unmarked points and included in the optimal path set; the distance from the newly added point A to the adjacent point B is calculated, which does not include the marked nodes. If (the distance from node A + the distance from node A to node B) is less than the distance from node B, the distance of node B is updated to the optimal path set S; the operation is repeated; finally, the shortest distance from the starting point to each point in the path is found, thereby finding the shortest path; the optimal path set S represents the vertex set of the shortest path, and the sum of the distances between the vertex sets of the shortest path is the shortest path length; According to the formula: , get multiple shortest paths; in, represents the i-th path, represents the length of the i-th path, S represents the set of vertices for which the shortest path has been found, and U represents the set of unmarked shortest path vertices; The path allocation layer allocates paths to different groups of people based on the allocation model: According to the distribution model formula: ; ; Calculate the sum of the paths for the escape group and the group to be rescued respectively, and then assign the shortest optimized path according to the target; Among them, S represents the total number of complete routes of each group of people in the building, Then represents the sum of the j-th stair paths chosen by the i-th person; where the path is composed of the length of the stair corridor, the horizontal distance from the starting point to the stairs, and the distance from the stairs to the exit; Indicates whether the i-th person chooses the j-th staircase as the path, and Then it represents the safety capacity of the jth corridor; Further according to the traffic state coefficient: ; Adjust the number of people evacuating on the stairs in real time; in, represents the traffic state coefficient of the stairs at time t, Indicates the traffic capacity when the stairs are unobstructed, represents the number of passengers at time t; During system operation, the path adjustment layer collects data on the number of passengers on the stairs through dynamic monitoring and transmits it to the central control room. When the number of people on a certain staircase exceeds the traffic capacity at a specific time, the traffic status coefficients of other stairs are immediately checked to determine the number of passengers that can be carried by the two stairs at the same time while maintaining balance. The number of passengers on the staircase exceeding the capacity is then adjusted based on the number of people, thus achieving a reasonable allocation of evacuation stairs. The dynamic route guidance stage includes: The location information of each grid point in the optimized route is recorded in a grid map and imported into the SQL server. A program is written to convert the original grid points to floors to form a 3D map. The decision maker's different optimized routes are displayed as red channels in the 3D model. Using e-building as a platform connecting PCs and mobile apps, facility managers upload 3D models for three groups on the PC in the central control room. These models are then downloaded via e-building on mobile phones, and the optimized routes are displayed in the BIM model as red channels. At the same time, facility managers observe the routes of building users and rescuers through video. When route deviations occur or evacuation stairs need to be modified, facility managers can use voice broadcasting equipment to issue warnings.

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