Evacuation indication route design method, system and equipment considering fire position

Abstract

The invention relates to the technical field of intelligent fire fighting, and discloses an evacuation indication route design method, system and equipment considering a fire position, and the method comprises the steps: building a first ant colony algorithm model, determining the direction of a daily evacuation indication lamp under the condition of no fire, and determining a daily evacuation room set of each evacuation indication lamp pointing to each emergency exit, the first generation module generates a first evacuation indication route; judging whether the available time of each emergency exit is greater than or equal to the evacuation time from the room to the corresponding evacuation indicating lamp to point to each emergency exit when no fire occurs; if yes, continuing to execute the first evacuation indication route; and if not, dynamically planning a second evacuation indication route by adopting a second ant colony algorithm model. Through scientific model establishment and a dynamic adjustment mechanism, the situation that people select unavailable or crowded emergency exits in the evacuation process can be effectively avoided, the panic feeling of evacuated people caused by confusion and congestion in the evacuation process is reduced, and therefore the overall evacuation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart firefighting technology, and in particular to a method, system and equipment for designing an evacuation route taking into account the location of a fire. Background Art

[0002] In modern life, where electrical equipment and electronic products are ubiquitous, fires are caused by a variety of causes. Furthermore, with the increasing scarcity of land resources and the rapid development of the construction industry, the centralization of building functions and the complexity of their structures have become increasingly evident, making fire escape more difficult. To protect public safety, research is needed on how to provide scientific, reasonable, accurate, easily identifiable, and safe and reliable fire escape routes. Existing studies have mostly relied on simplified models (e.g., uniform channel resistance and static obstacles) and employed fixed preset paths or simple distance-priority strategies. These studies fail to fully consider the diversity of evacuation routes resulting from the centralization of functions and complex structures (e.g., multi-story atriums and staggered-level layouts) in modern buildings. Furthermore, when fires cause changes in the availability of emergency exits, people familiar with their usual routes may become disoriented and panicked due to the sudden change in their routes. Existing evacuation signage systems often fail to consider the connection between daily evacuation routes and dynamic fire routes, failing to meet the complex fire evacuation needs of modern buildings, which require coordinated optimization of multiple factors, including dynamic environmental adaptation and psychological counseling. Summary of the Invention

[0003] In view of this, the present invention provides a method, system and equipment for designing evacuation routes taking into account the location of the fire, so as to solve the problem of how to meet the fire evacuation needs of the coordinated optimization of multiple factors such as dynamic environmental adaptation and psychological counseling of personnel in complex scenes of modern buildings.

[0004] In a first aspect, the present invention provides a method for designing an evacuation route taking into account a fire location, the method comprising:

[0005] Establishing a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of a fire, and the set of daily evacuation rooms where each evacuation indicator light points to each safety exit, for use in generating a first evacuation route. The heuristic function of the first ant colony algorithm model is determined based on the distance from each building location to the safety exit, the passage difficulty coefficient, and the congestion level of the safety exit;

[0006] Based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spread speed, a safety exit availability time model is established to determine the availability time of each safety exit;

[0007] Determine whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit when the evacuation indicator light points to the room when there is no fire;

[0008] If yes, keep the corresponding room in the daily evacuation room set and continue to execute the first evacuation instruction route;

[0009] If not, the second ant colony algorithm model is used to dynamically plan the second evacuation route. The heuristic function of the second ant colony algorithm model is determined by establishing a safety index model based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation passage, the speed of personnel movement and the equivalent path length.

[0010] The embodiment of the present invention provides a method for designing an evacuation route that takes the location of a fire into consideration. By establishing a first ant colony algorithm model, the direction of daily evacuation indicator lights and the set of daily evacuation rooms in the absence of a fire are determined, and a first evacuation route is generated. This provides a basic solution for the evacuation of personnel in a building under normal conditions, ensuring the orderliness and efficiency of daily evacuation. At the same time, the subsequent dynamic planning for fire situations enables this method to adapt to different scenarios. The dynamic planning method of the second ant colony algorithm model can adjust the evacuation path in a timely manner according to the real-time situation at the fire scene, avoid unavailable or unsafe safety exits, and select a better evacuation plan, thereby improving the safety and efficiency of personnel evacuation. Through scientific model establishment and dynamic adjustment mechanisms, it is possible to effectively avoid personnel from choosing unavailable or crowded safety exits during the evacuation process, reduce the panic caused to personnel by the chaos and congestion during the evacuation process, and thus improve the overall evacuation efficiency.

[0011] In an optional implementation, the heuristic function of the first ant colony algorithm model is:

[0012] η ij (t)=1 / t sm +μ s

[0013] t sm =∑ξ ij ×L ij / v r

[0014] Among them, t sm The time required for evacuation from room m to the safety exit s without considering external dangers; L ij is the equivalent length between the current node i and the next node j; v r μ is the speed of movement of people in an obstacle-free situation; s is the attractiveness of the safety exit s.

[0015] This embodiment of the present invention calculates the time required to evacuate room m to emergency exit s, ignoring external hazards (e.g., fire or other emergencies). This provides a scientific basis for determining the direction of daily evacuation indicator lights and generating daily evacuation room sets. This helps establish a stable and efficient evacuation indication system under normal circumstances, familiarizing personnel with the locations of evacuation routes and emergency exits, enabling faster response in emergencies and improving evacuation success rates.

[0016] In an optional embodiment, the passage difficulty coefficient is calculated according to the following formula:

[0017] ξ ij =v r / v t

[0018] Among them, ξ ij v is the difficulty coefficient of traveling from node i to node j; t The speed of people passing under specific road conditions, taking into account the effective width, slope and curvature of the passage without considering smoke;

[0019] The congestion level of the emergency exit is calculated according to the following formula:

[0020] P s =(∑P fs -v s ×t sm ) / S

[0021] v s =a×v p

[0022] Among them, P s is the congestion level at the emergency exit s; ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing through the safety exit; a is the number of people who can pass through the safety exit at one time; v p is the flow rate of people at the safety exit; S is the area of the safety exit platform; P s When P >δ, it is considered that the exit is congested; on the contrary, when P s When ≤δ, the exit is considered unobstructed, and δ is the preset threshold.

[0023] The passage difficulty coefficient of the embodiment of the present invention can be flexibly calculated based on the effective width, slope, and curvature of the specific passage, and has strong adaptability. Whether it is a large commercial building, a high-rise residential building, or an industrial plant, the formula can accurately assess the passage difficulty of the passage, providing a reliable basis for evacuation route planning; the formula for calculating the congestion level of the emergency exit comprehensively considers multiple factors such as the number of people in all rooms that choose the emergency exit, the speed of passing the emergency exit, the number of people who can pass through at a time, the speed of the flow of people, and the area of the emergency exit platform. The emergency exit congestion level is quantified through mathematical calculations, making the judgment of the emergency exit congestion situation more objective and accurate.

[0024] In an optional implementation, the safe exit available time model is:

[0025] T=L / v y

[0026] Where T is the available time for the safety exit; L is the equivalent path length for smoke to spread to the safety exit; v y is the average spreading speed of smoke;

[0027] L=∑L ij ×(1+d×d)

[0028] Among them, ∑L ij is the sum of the equivalent length between the original node i and node j and the equivalent length between all updated nodes i and node j, and d is the number of other safety exits passed by the path from each safety exit to the fire location.

[0029] The emergency exit availability time model in this embodiment of the present invention can update emergency exit availability time in real time based on the actual situation at the fire scene, adapting promptly to the development of the fire and providing more accurate and effective evacuation guidance. Emergency exit availability may change at different stages of a fire, and this model can promptly reflect these changes, improving the flexibility and adaptability of evacuation plans.

[0030] In an optional embodiment, the safety indicator model is:

[0031] P′ s =(∑P fs -v s ×T) / S

[0032] Among them, ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing the safety exit; T is the available time of the safety exit; S is the area of the safety exit platform.

[0033] The safety index calculated by the safety index model provided by the embodiment of the present invention can provide more accurate heuristic information for the second ant colony algorithm model, helping the algorithm to more rationally plan evacuation routes. When planning a route, the algorithm will tend to select safe exits with higher safety indicators, that is, exits with better overall safety after comprehensively considering factors such as the number of people, passage speed, available time, and platform area. This can prevent excessive concentration of people at certain unsafe exits, achieve reasonable diversion of people, improve evacuation efficiency, and reduce safety risks during the evacuation process.

[0034] In an optional implementation, the heuristic function of the second ant colony algorithm model is:

[0035] η′ ij (t) = 1 / (X ij (t)+C ij )+μ′ s

[0036] X ij (t) = ξ ij ×L ij / P′ s

[0037] C ij =△t / (L ij ×v y )

[0038] Among them, η′ ij (t) is the heuristic function value from node i to node j at time t after the fire occurs; X ij (t) is the equivalent length of the evacuation channel ij at time t; C ij is the smoke concentration between nodes i and j; △t is the time since the fire occurred; μ ′ s is the attraction of the safety exit s after the fire occurs, μ′ s =1-P′ s +T / t sm .

[0039] The heuristic function design provided by this embodiment of the present invention enables the second ant colony algorithm model to be flexibly adjusted to different fire scenarios and real-time data, demonstrating its high versatility and adaptability. Regardless of the type of building, scale, or development of fire, the algorithm can effectively plan evacuation routes based on the information provided by the heuristic function, enhancing its reliability and practicality in practical applications.

[0040] In a second aspect, the present invention provides a system for designing an evacuation route taking into account a fire location, the system comprising:

[0041] A first evacuation route acquisition module is configured to establish a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of a fire, and a set of daily evacuation rooms where each evacuation indicator light points to each safety exit, wherein the heuristic function of the first ant colony algorithm model is determined based on the distance from each location in the building to the safety exit, the passage difficulty coefficient, and the congestion level of the safety exit;

[0042] The safety exit available time calculation module is used to establish a safety exit available time model based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spread speed, so as to determine the available time of each safety exit;

[0043] The evacuation indicator light pointing consistency screening module is used to determine whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit pointed by the evacuation indicator light when there is no fire;

[0044] A first evacuation route execution module, configured to, if yes, keep the corresponding room in the daily evacuation room set and continue to execute the first evacuation route;

[0045] The second evacuation route execution module is used to dynamically plan the second evacuation route using the second ant colony algorithm model if no. The heuristic function of the second ant colony algorithm model is determined by establishing a safety index model based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation channel, the speed of personnel movement and the equivalent path length.

[0046] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the method for designing an evacuation route taking into account the fire location of the first aspect or any corresponding embodiment thereof.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for designing an evacuation route taking into account the fire location according to the first aspect or any corresponding embodiment thereof.

[0048] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to execute the method for designing an evacuation route taking into account the fire location according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 is a flow chart of a method for designing an evacuation route taking into account a fire location according to an embodiment of the present invention;

[0051] Figure 2 is a structural block diagram of a system for designing an evacuation route considering fire locations according to an embodiment of the present invention;

[0052] Figure 3 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0054] This embodiment provides a method for designing an evacuation route taking into account the location of a fire. Figure 1 1 is a flow chart of a method for designing an evacuation route considering fire locations according to an embodiment of the present invention. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer device such as a set of computer executable instructions, and although a logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in an order different from that shown here. Figure 1 As shown, the process includes the following steps:

[0055] Step S101, establish a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of fire, and the set of daily evacuation rooms where each evacuation indicator light points to each safety exit, to generate a first evacuation indication route. The heuristic function of the first ant colony algorithm model is determined based on the distance from each location of the building to the safety exit, the difficulty coefficient of the passage, and the congestion level of the safety exit.

[0056] Specifically, the first ant colony algorithm model in the embodiment of the present invention can scientifically and rationally plan daily evacuation routes based on factors such as the distance from each location of the building to the emergency exit, the difficulty coefficient of the passage, and the congestion level of the emergency exit. The distance factor allows the algorithm to give priority to paths with shorter distances, thereby improving evacuation efficiency; the difficulty coefficient of the passage comprehensively considers the actual passage conditions of the passage (such as effective width, slope, curvature, etc.) to avoid people choosing passages that are difficult to pass; the consideration of the congestion level of the emergency exit helps to distribute people more evenly to various emergency exits, preventing the occurrence of excessive congestion at individual exits. By combining these factors, the generated daily evacuation instruction planning route is more in line with the actual situation and can effectively guide people to evacuate quickly and orderly.

[0057] By establishing the first ant colony algorithm model, the direction of the daily evacuation indicator lights and the daily evacuation room set in the absence of fire are determined, and the set Q (s) , s = {s1, s2, ...}. This helps building occupants familiarize themselves with evacuation routes and emergency exit locations in advance. When a real fire occurs, familiarity with the evacuation routes allows them to react more quickly and evacuate according to instructions. This reduces panic-induced errors and increases the success rate of emergency evacuations.

[0058] Specifically, the heuristic function of the first ant colony algorithm model is:

[0059] η ij (t)=1 / t sm +μ s (1)

[0060] t sm =∑ξ ij ×L ij / v r (2)

[0061] Among them, η ij (t) represents the heuristic function value from node i to node j at time t; t sm The time required for evacuation from room m to the safety exit s without considering external dangers; L ij is the equivalent length between the current node i and the next node j; v r μ is the speed of movement of people in an obstacle-free situation; s is the attractiveness of the safety exit s, μ s =1-P s , P s is the congestion level at the emergency exit s, ξ ij is the difficulty coefficient of traveling from node i to node j.

[0062] It should be noted that the above-mentioned nodes refer to nodes of the building plan, including room locations, exit locations, bathroom locations, corners, etc., which are only examples and are not limited to this.

[0063] The embodiments of the present invention incorporate the actual building structure, taking into account the difficulty of passageways and the level of congestion at emergency exits, making evacuation plans more realistic. Different passageways and emergency exits have various differences in actual use. For example, narrow passageways can slow down traffic, while crowded emergency exits can cause confusion and stampedes. By incorporating these factors into the algorithm, the generated evacuation route planning can better address these potential issues, enhancing the reliability and stability of the evacuation plan and ensuring that personnel can reach a safe area safely and smoothly during routine evacuations.

[0064] Specifically, the channel difficulty coefficient is calculated according to the following formula:

[0065] ξ ij =v r / v t (3)

[0066] Among them, ξ ij v is the difficulty coefficient of traveling from node i to node j; t The speed of people passing under specific road conditions, taking into account the effective width, slope and curvature of the passage without considering smoke;

[0067] The congestion level of the emergency exit is calculated according to the following formula:

[0068] P s =(∑P fs -v s ×t sm ) / S (4)

[0069] v s =a×v p (5)

[0070] Among them, P s is the congestion level at the emergency exit s; ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing through the safety exit; a is the number of people who can pass through the safety exit at one time; v p is the flow rate of people at the safety exit; S is the area of the safety exit platform; P s When P >δ, it is considered that the exit is congested; on the contrary, when P s When ≤δ, the exit is considered unobstructed, and δ is the preset threshold.

[0071] In one embodiment, for example, when P s When P > 0.5, it is considered that the exit is congested; on the contrary, when Ps When ≤0.5, the outlet is considered unobstructed. s =1-P s , when P s >1, take P s =1.

[0072] The first ant colony algorithm mathematical model established by the present invention has the following formulas for selecting the next node j:

[0073]

[0074]

[0075] in, is the probability that the person in room m moves from node i to node j at time t;

[0076] represents another node in the channel with the largest amount of information associated with node i; τ ij (t) is the pheromone between node i and node j at time t; α is the pheromone heuristic factor; β is the expected heuristic factor; allowed is the node that has not been visited, allowed∈s; R is the total number of people in the building; R ij is the number of people passing between node i and node j; q0=max(dis(i),q min ), dis(i) represents the dispersion of node i, and the number of evacuees of node i can be calculated using Σ j R ij The ratio of q to the total number R indicates that min is the minimum threshold, q0∈(0,1), is a given constant; q is a random variable in the range of (0,1) that obeys a uniform distribution.

[0077] Step S102: Based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spreading speed, a safety exit availability time model is established to determine the availability time of each safety exit.

[0078] The safe exit availability time model provided by the embodiments of the present invention comprehensively considers multiple key factors and can more accurately calculate the time it takes for smoke to spread to each safe exit, namely the safe exit availability time. Specifically, the equivalent path length takes into account the complexity of the actual path, rather than simply the straight-line distance, making the calculation more realistic. The average smoke spread velocity is a key factor affecting the safe exit availability time. By accurately measuring or estimating this velocity, the time it takes for smoke to reach the safe exit can be more accurately predicted. The number of other safe exits passed by each safe exit from the fire location takes into account the complex paths that may exist in a fire scenario. Since smoke may be affected by different paths and exits during its spread, the more other safe exits it passes through, the more complex the smoke propagation path may be, and the greater the impact on the safe exit availability time. By comprehensively considering these factors, the model can provide an accurate time reference for personnel evacuation.

[0079] Specifically, the safety exit available time model is:

[0080] T=L / v y (9)

[0081] Where T is the available time for the safety exit; L is the equivalent path length for smoke to spread to the safety exit; v y is the average spreading speed of smoke;

[0082] L=∑L ij ×(1+d×d) (10)

[0083] Among them, ∑L ij is the sum of the equivalent length between the original node i and node j and the equivalent length between all updated nodes i and node j, and d is the number of other safety exits passed by the path from each safety exit to the fire location.

[0084] By integrating these factors, the available time for emergency exits can be calculated more scientifically and accurately, providing a critical time reference for evacuation in the event of a fire. Based on the calculated available time for each emergency exit, people can quickly determine which exits offer more time for evacuation, thereby choosing the optimal evacuation route. This helps avoid choosing exits that may be quickly blocked by smoke due to lack of understanding, thereby improving evacuation efficiency and safety.

[0085] Step S103 , determining whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit pointed to by the evacuation indicator light when there is no fire.

[0086] Specifically, the available time T of each safety exit is greater than or equal to the evacuation time t from the room to the corresponding evacuation indicator light pointing to each safety exit when there is no fire. sm , the set Q(s) It is decomposed into two parts. One part is that the direction of the evacuation indicator light in the room is consistent with the direction of the daily evacuation indicator light, and the corresponding evacuation exit can be determined. The other part is that the direction of the evacuation indicator light in the room needs to be combined with the remaining available evacuation exits in the set s to obtain the exit set that the evacuees are allowed to choose next at time t. This judgment method can help people understand the relationship between daily evacuation routes and the available time of safe exits, help enhance their fire safety awareness, make them pay more attention to the location of evacuation passages and safe exits in their daily work and life, and improve their ability to deal with emergencies such as fires.

[0087] Step S1041: If yes, the corresponding room is retained in the daily evacuation room set, and the first evacuation instruction route is continued to be executed.

[0088] Specifically, T ≥ t sm , the direction of the evacuation indicator lights within the radiation range of the safety exit remains unchanged, which can ensure that when a fire occurs, people have enough time to evacuate from the room to the safety exit according to the daily evacuation route, reducing the risk of people encountering smoke or other dangers during the evacuation process. Since people are familiar with the daily evacuation routes, it can reduce panic and confusion among people in the event of a fire, make the evacuation process more orderly, and thus improve the overall evacuation efficiency and shorten the evacuation time.

[0089] Step S1042: If not, the second ant colony algorithm model is used to dynamically plan the second evacuation route. The heuristic function of the second ant colony algorithm model is based on the safety index model established based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation passage, the speed of personnel movement and the equivalent path length.

[0090] Specifically, the situation at the fire scene is dynamic. For example, the smoke density changes over time, and the congestion level of the emergency exit also changes due to the flow of people. <t sm When the evacuation is underway, the second ant colony algorithm model uses heuristic functions for dynamic planning, which can generate the optimal evacuation path based on real-time data in a short time, provide timely and effective guidance for personnel evacuation, and improve emergency response capabilities.

[0091] The heuristic function of the second ant colony algorithm model of the present invention is:

[0092] η′ ij (t) = 1 / (X ij (t)+C ij )+μ′ s (11)

[0093] X ij (t) = ξ ij ×L ij j / P′s (12)

[0094] C ij =△t / (L ij ×v y ) (13)

[0095] Among them, η′ ij (t) is the heuristic function value from node i to node j at time t after the fire occurs; X ij (t) is the equivalent length of the evacuation channel ij at time t; C ij is the smoke concentration between nodes i and j; △t is the time since the fire occurred; μ′ s is the attraction of the safety exit s after the fire occurs, μ′ s =1-P′ s +T / t sm .

[0096] In this embodiment of the present invention, the equivalent length of evacuation route ij at time t takes into account the actual length of the route as well as factors that may affect travel speed, such as its width and the presence of obstacles. This allows the algorithm to more accurately assess the "actual difficulty" of different routes when planning a route, avoiding the selection of seemingly short but actually difficult routes, thereby improving the rationality and practicality of route planning. After a fire occurs, the longer it takes for smoke concentration in a route to reach the same level as the fire location, the less suitable the route is for evacuation. The time elapsed since the fire outbreak, combined with various factors, can reflect the dynamic changes in the fire scene. As smoke concentration changes over time, the safety of the route also changes. The heuristic function can dynamically adjust the route assessment based on this real-time information, ensuring that the route planning is always adapted to the actual situation at the fire scene. The attractiveness of the emergency exit s after a fire occurs integrates multiple factors in the safety index model, such as the number of people choosing the emergency exit, travel speed, available time, and platform area. This allows the algorithm to not only consider the conditions of the passage itself when selecting a path, but also to holistically evaluate the comprehensive safety and attractiveness of each safety exit, guiding people to choose the safety exit that is most conducive to evacuation.

[0097] By comprehensively considering the above multiple factors, the heuristic function can provide accurate information for the ant colony algorithm, guiding it to choose the optimal evacuation path that takes into account both the actual conditions of the channel and the comprehensive conditions of the safe exit. This can ensure that people avoid dangerous areas as much as possible during the evacuation process and choose a safer and more efficient path to reach the safe exit.

[0098] Furthermore, the present invention establishes a safety index model for the emergency exit based on the congestion level at the emergency exit s at time t and the available time of the emergency exit:

[0099] P′s =(∑P fs -v s ×T) / S (14)

[0100] Among them, ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing the safety exit; T is the available time of the safety exit; S is the area of the safety exit platform.

[0101] The safety index model of the embodiment of the present invention quantifies the various factors affecting emergency exits and represents their safety through a comprehensive index. This allows for convenient comparison and assessment of the safety of different emergency exits in the event of a fire. For example, by calculating the safety index values for different emergency exits, it is possible to intuitively identify which emergency exits have relatively few evacuees, faster transit speeds, longer available time, and larger platform areas. This provides a scientific basis for selecting the optimal evacuation route, guiding personnel to prioritize safer emergency exits for evacuation, and improving overall evacuation efficiency and safety.

[0102] The second ant colony algorithm mathematical model established by the present invention has the following rules and probabilities for selecting the next node j:

[0103]

[0104] in, is the probability that the person in room m moves from node i to node j at time t;

[0105] represents another node in the channel with the largest amount of information associated with node i; τ ij (t) is the pheromone between node i and node j at time t; α is the pheromone heuristic factor; β is the expected heuristic factor; γ is the path attractiveness factor; allowed is the node that has not been visited, allowed∈s; R is the total number of people in the building; R ij is the number of people passing between node i and node j;

[0106] q0=max(dis(i),q min ), dis(i) represents the dispersion of node i, and the number of evacuees of node i can be calculated using Σ j R ij The ratio of q to the total number R indicates that min is the minimum threshold, q0∈(0,1), is a given constant; q is a random variable in the range of (0,1) that obeys a uniform distribution.

[0107] The heuristic function of the second ant colony algorithm model determined based on the above multiple factors can obtain this information in real time and dynamically adjust the evacuation path according to the changes, so that the path planning always adapts to the actual situation on site and ensures the effectiveness and safety of the evacuation plan.

[0108] This embodiment also provides a system for designing evacuation routes that takes fire locations into consideration. This system is used to implement the aforementioned embodiments and preferred implementations, and details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the systems described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0109] This embodiment provides an evacuation route design system that takes into account the fire location. Figure 2 As shown, including:

[0110] A first evacuation route acquisition module 201 is configured to establish a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of a fire, and a set of daily evacuation rooms where each evacuation indicator light points to each safety exit. The heuristic function of the first ant colony algorithm model is determined based on the distance from each building location to the safety exit, the passage difficulty coefficient, and the congestion level of the safety exit.

[0111] The safety exit available time calculation module 202 is used to establish a safety exit available time model based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spread speed, so as to determine the available time of each safety exit;

[0112] The evacuation indicator light pointing consistency screening module 203 is used to determine whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit pointed by the evacuation indicator light when there is no fire;

[0113] The first evacuation route execution module 2041 is configured to, if yes, keep the corresponding room in the daily evacuation room set and continue to execute the first evacuation route;

[0114] The second evacuation route execution module 2042, if not, adopts the second ant colony algorithm model to dynamically plan the second evacuation route, the heuristic function of the second ant colony algorithm model adopts a safety index model based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation channel, the speed of personnel movement and the equivalent path length.

[0115] In some optional implementations, the heuristic function of the first ant colony algorithm model in the first evacuation instruction route acquisition module 201 is:

[0116] η ij (t)=1 / t sm +μ s

[0117] t sm =∑ξ ij ×L ij / v r

[0118] Among them, η ij (t) represents the heuristic function value from node i to node j at time t; t sm The time required for evacuation from room m to the safety exit s without considering external dangers; L ij is the equivalent length between the current node i and the next node j; v r μ is the speed of movement of people in an obstacle-free situation; s is the attractiveness of the safety exit s, μ s =1-P s , P s is the congestion level at the emergency exit s, ξ ij is the difficulty coefficient of traveling from node i to node j.

[0119] In some optional implementations, the passage difficulty coefficient is calculated according to the following formula:

[0120] ξ ij =v r / v t

[0121] Among them, ξ ij v is the difficulty coefficient of traveling from node i to node j; t The speed of people passing under specific road conditions, taking into account the effective width, slope and curvature of the passage without considering smoke;

[0122] The congestion level of the emergency exit is calculated according to the following formula:

[0123] P s =(∑P fs -v s ×t sm ) / S

[0124] v s =a×v p

[0125] Among them, P s is the congestion level at the emergency exit s; ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing through the safety exit; a is the number of people who can pass through the safety exit at one time; vp is the flow rate of people at the safety exit; S is the area of the safety exit platform; P s When P >δ, it is considered that the exit is congested; on the contrary, when P s When ≤δ, the exit is considered unobstructed, and δ is the preset threshold.

[0126] In some optional implementations, the safe exit available time model in the safe exit available time calculation module 202 is:

[0127] T=L / v t

[0128] Where T is the available time for the safety exit; L is the equivalent path length for smoke to spread to the safety exit; v y is the average spreading speed of smoke;

[0129] L=∑L ij ×(1+d×d)

[0130] Among them, ∑L ij is the sum of the equivalent length between the original node i and node j and the equivalent length between all updated nodes i and node j, and d is the number of other safety exits passed by the path from each safety exit to the fire location.

[0131] In some optional implementations, the safety index model in the second evacuation route execution module 2042 is:

[0132] P′ s =(∑P fs -v s ×T) / S

[0133] Among them, ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing the safety exit; T is the available time of the safety exit; S is the area of the safety exit platform.

[0134] In some optional implementations, the heuristic function of the second ant colony algorithm model of the second evacuation instruction route execution module 2042 is:

[0135] η′ ij (t) = 1 / (X ij (t)+C ih )+μ′ s

[0136] X ij (t) = ξ ij ×L ij / P′ s

[0137] C ij=△t / (L ij ×v y )

[0138] Among them, η′ ij (t) is the heuristic function value from node i to node j at time t after the fire occurs; X ij (t) is the equivalent length of the evacuation channel ij at time t; C ij is the smoke concentration between nodes i and j; △t is the time since the fire occurred; μ′ s is the attraction of the safety exit s after the fire occurs, μ′ s =1-P′ s +T / t sm .

[0139] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0140] The evacuation route design system considering the fire location in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0141] The embodiment of the present invention also provides a computer device having the above Figure 2 The evacuation route design system shown takes into account the fire location.

[0142] See also Figure 3 , Figure 3 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 3 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0143] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0144] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0145] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0146] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0147] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0148] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0149] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0150] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for designing an evacuation route considering the location of a fire, characterized in that: include: Establishing a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of a fire, and the set of daily evacuation rooms where each evacuation indicator light points to each safety exit, for use in generating a first evacuation route. The heuristic function of the first ant colony algorithm model is determined based on the distance from each building location to the safety exit, the passage difficulty coefficient, and the congestion level of the safety exit; Based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spread speed, a safety exit availability time model is established to determine the availability time of each safety exit; Determine whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit when the evacuation indicator light points to the room when there is no fire; If yes, keep the corresponding room in the daily evacuation room set and continue to execute the first evacuation instruction route; If not, the second ant colony algorithm model is used to dynamically plan the second evacuation route. The heuristic function of the second ant colony algorithm model is determined by establishing a safety index model based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation passage, the speed of personnel movement and the equivalent path length.

2. The method according to claim 1, characterized in that The heuristic function of the first ant colony algorithm model is: or ij (t)=1 / t sm +m s t sm =∑ξ ij ×L ij / v r Among them, η ij (t) represents the heuristic function value from node i to node j at time t; t sm The time required for evacuation from room m to the safety exit s without considering external dangers; L ij is the equivalent length between the current node i and the next node j; v r μ is the speed of movement of people in an obstacle-free situation; s is the attractiveness of the safety exit s, μ s =1-P s , P s is the congestion level at the emergency exit s, ξ ij is the difficulty coefficient of traveling from node i to node j.

3. The method according to claim 2, characterized in that The passage difficulty coefficient is calculated according to the following formula: ξ ij =v r / v t Among them, ξ ij v is the difficulty coefficient of traveling from node i to node j; t The speed of people passing under specific road conditions, taking into account the effective width, slope and curvature of the passage without considering smoke; The congestion level of the emergency exit is calculated according to the following formula: P s =(∑P fs -v s ×t sm ) / S in s =a×v p Among them, P s is the congestion level at the emergency exit s; ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing through the safety exit; a is the number of people who can pass through the safety exit at one time; v p is the flow rate of people at the safety exit; S is the area of the safety exit platform; P s When P >δ, it is considered that the exit is congested; on the contrary, when P s When ≤δ, the exit is considered unobstructed, and δ is the preset threshold.

4. The method according to claim 1 or 3, characterized in that The safety exit available time model is: T=L / v y Where T is the available time for the safety exit; L is the equivalent path length for smoke to spread to the safety exit; v y is the average spreading speed of smoke; L=∑L ij ×(1+d×d) Among them, ∑L ij is the sum of the equivalent length between the original node i and node j and the equivalent length between all updated nodes i and node j, and d is the number of other safety exits passed by the path from each safety exit to the fire location.

5. The method according to claim 4, characterized in that The safety index model is: P′ s =(∑P fs -v s ×T) / S Among them, ∑P fs is the number of people in all rooms who choose the emergency exit s; s is the speed of passing the safety exit; T is the available time of the safety exit; S is the area of the safety exit platform.

6. The method according to claim 5, characterized in that The heuristic function of the second ant colony algorithm model is: or' ij (t)=1 / (X ij (t)+C ij )+μ′ s X ij (t)=ξ ij ×L ij / P′ s C ij =△t / (L ij ×v y ) Among them, η′ ij (t) is the heuristic function value from node i to node j at time t after the fire occurs; X ij (t) is the equivalent length of the evacuation channel ij at time t; C ij is the smoke concentration between nodes i and j; △t is the time since the fire occurred; μ′ s is the attraction of the safety exit s after the fire occurs, μ′ s =1-P′ s +T / t sm .

7. A system for designing evacuation routes taking into account fire locations, characterized in that: The system comprises: A first evacuation route acquisition module is configured to establish a first ant colony algorithm model to determine the direction of daily evacuation indicator lights in the absence of a fire, and a set of daily evacuation rooms where each evacuation indicator light points to each safety exit, wherein the heuristic function of the first ant colony algorithm model is determined based on the distance from each location in the building to the safety exit, the passage difficulty coefficient, and the congestion level of the safety exit; The safety exit available time calculation module is used to establish a safety exit available time model based on the equivalent path length of smoke spreading to the safety exit, the number of other safety exits passed by each safety exit from the fire location, and the average smoke spread speed, so as to determine the available time of each safety exit; The evacuation indicator light pointing consistency screening module is used to determine whether the available time of each safety exit is greater than or equal to the evacuation time from the room to the corresponding safety exit pointed by the evacuation indicator light when there is no fire; A first evacuation route execution module, configured to, if yes, keep the corresponding room in the daily evacuation room set and continue to execute the first evacuation route; The second evacuation route execution module is used to dynamically plan the second evacuation route using the second ant colony algorithm model if no. The heuristic function of the second ant colony algorithm model is determined by establishing a safety index model based on the congestion level and available time of the safety exit, combined with the real-time smoke concentration of the evacuation channel, the speed of personnel movement and the equivalent path length.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the evacuation route design method considering the fire location according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for designing an evacuation route considering a fire location according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for designing an evacuation route considering a fire location according to any one of claims 1 to 6.

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