Urban fire early warning reaction system based on Internet of Things and edge computing

Abstract

The invention discloses an urban fire early warning reaction system based on the Internet of Things and edge computing, and relates to the technical field of urban fire rescue. The first edge computing node is responsible for extracting fire data and judging whether fire rescue power is sufficient or not, the fire range, position and level can be rapidly recognized, and therefore an accurate basis is provided for configuration of the rescue power; the edge computing node II can plan a fire rescue path and screen an optimal path according to the traffic data to ensure that the fire rescue force can reach the fire scene at the fastest speed; most importantly, the path with the highest smoothness coefficient can be identified by analyzing the road image and the predicted passing time, and weighted evaluation is carried out by combining the predicted passing time of each path section, so that the fire rescue path with the highest passing efficiency is selected, the real-time traffic condition of the road is considered, and the traffic efficiency of the fire rescue path is improved. The arrival time of the fire rescue force is optimized, and the rescue vehicle can arrive at the fire scene at the fastest speed.

Description

Technical Field

[0001] This invention belongs to the field of urban fire rescue, specifically an urban fire early warning and response system based on the Internet of Things and edge computing. Background Technology

[0002] In the field of urban fire early warning and rescue, while existing technologies have achieved a certain level of fire monitoring and alarm capabilities, there are still many shortcomings in emergency response after a fire breaks out. In particular, even when fire and rescue forces are fully prepared, it is difficult to quickly reach the fire site to carry out fire and rescue operations.

[0003] In current fire and rescue operations, fire and rescue forces typically reach fire sites based on static map data, failing to consider real-time road traffic conditions such as congestion and illegally parked vehicles. This can lead to traffic jams and other problems for fire and rescue vehicles en route to the fire scene, preventing them from reaching the scene in a timely manner and thus impacting rescue efficiency.

[0004] This invention provides an urban fire early warning and response system based on the Internet of Things and edge computing to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes an urban fire early warning and response system based on the Internet of Things and edge computing. This system can identify the path with the highest traffic flow coefficient by analyzing road images and predicting travel time. It also performs a weighted evaluation based on the predicted travel time of each path segment to select the fire rescue path with the highest traffic efficiency. This system not only considers the real-time traffic conditions of the road, but also optimizes the arrival time of fire rescue forces, ensuring that rescue vehicles can reach the fire scene as quickly as possible.

[0006] To achieve the above objectives, the first aspect of the present invention provides an urban fire early warning and response system based on the Internet of Things and edge computing, including several levels of edge computing units, wherein the edge computing unit includes edge computing node one and edge computing node two;

[0007] Edge computing node one: used to extract fire data within its coverage area; whereby the fire data includes fire extent, fire location, and fire severity; and,

[0008] Used to determine whether sufficient fire and rescue forces are configured based on fire data; if yes, analyze the fire data and organize fire and rescue forces; if no, report to the next higher-level edge computing unit for assistance based on the fire data.

[0009] Edge computing node two: used to plan several basic paths between the location of fire and rescue forces and the fire point; to filter these basic paths based on traffic data to obtain fire and rescue routes; and,

[0010] This is used to determine whether the time of obtaining the fire rescue route is earlier than that of the fire evacuation route; if yes, plan the fire evacuation route based on the fire rescue route; if no, adjust the fire evacuation route based on the fire rescue route.

[0011] Preferably, several basic routes are planned between the location of fire and rescue forces and the fire point, including:

[0012] The location of the fire and rescue forces is designated as Location 1, and the location of the fire point is designated as Location 2.

[0013] The target area is determined based on location one and location two; wherein, the target area is a continuous area that includes both location one and location two.

[0014] Plan the path between location one and location two in the target area to obtain several basic paths.

[0015] Preferably, the target area is determined based on location one and location two, including:

[0016] Use the line connecting position one and position two as the baseline, and the straight line passing through position one and perpendicular to the baseline as the target line.

[0017] Divide the planar regions containing position one and position two using the target line as the dividing line, and take the region containing position two as the target region.

[0018] Preferably, several basic routes are selected based on traffic data, including:

[0019] Sort the basic paths in ascending order of length to obtain a path sequence; then use the basic paths in the path sequence as the target paths in turn.

[0020] The traffic data of the target route is matched, and the traffic efficiency of the target route is evaluated based on the traffic data. The target route with the highest traffic efficiency is selected as the fire rescue route. The traffic data includes road images and predicted travel time.

[0021] Preferably, the assessment of the traffic efficiency of a target route based on traffic data includes:

[0022] Extract road images and predict travel time from traffic data; identify illegally parked vehicles in road images and set traffic flow coefficients;

[0023] The travel time is predicted by matching the arrival time of fire and rescue forces to each segment of the target route; the travel time of the target route is calculated based on the predicted travel time of each segment.

[0024] The traffic efficiency of the target path is obtained by weighted summation of the traffic flow coefficient and the travel time of the target path; where the travel time of the target path is the sum of the travel times of each path segment.

[0025] Preferably, fire evacuation routes are planned based on fire rescue routes, including:

[0026] Based on the information about the area where the fire is located, several evacuation routes for personnel and several evacuation routes for vehicles are planned.

[0027] Determine whether the vehicle evacuation route will affect the fire rescue route; if yes, remove the vehicle evacuation route; otherwise, retain the vehicle evacuation route.

[0028] Several personnel evacuation routes and several retained vehicle evacuation routes will be integrated into a fire evacuation route.

[0029] Preferably, adjusting fire evacuation routes based on fire rescue routes includes:

[0030] When obtaining fire rescue routes, determine in turn whether fire evacuation routes will affect fire rescue routes;

[0031] Yes, set the route change time according to the fire rescue route, and adjust the fire evacuation route according to the route change time;

[0032] No, then there is no need to adjust the fire evacuation routes.

[0033] Preferably, several edge computing units simultaneously plan fire rescue routes and merge these routes, including:

[0034] The edge computing unit with the largest number of organized fire and rescue forces is identified as the main edge computing unit, and the fire and rescue path planned by the main edge computing unit is taken as the main fire and rescue path.

[0035] The terminal paths of other fire rescue routes are adjusted based on the main fire rescue route, so that the terminal paths of several fire rescue routes overlap.

[0036] Preferably, the terminal routes of other fire rescue routes are adjusted based on the main fire rescue route, including:

[0037] Determine the Nth intersection point from the end of the main fire rescue route as the path feature point, and take the path between the path feature point and the fire point as the terminal path; where N≥1;

[0038] Based on the end path, other fire rescue paths are adjusted to obtain several adjusted fire rescue paths.

[0039] Preferably, other fire rescue routes are adjusted based on the end path, including:

[0040] Other fire and rescue routes will be used as the basic rescue routes in sequence;

[0041] Identify the location of the fire and rescue forces corresponding to the basic rescue route, take the intersection of the next path along the basic rescue route as the initial location, and take the path feature point as the end location.

[0042] Based on traffic data, the route with the highest traffic efficiency between the initial and final locations is planned as route one; route one and the final route are then combined to obtain the adjusted fire rescue route.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. This invention achieves efficient collaboration between fire early warning and fire rescue by constructing a multi-level edge computing unit. First, edge computing node one is responsible for extracting fire data and determining whether fire rescue forces are sufficient, and can quickly identify the fire's range, location, and level, thus providing accurate basis for the allocation of rescue forces. Second, edge computing node two can plan fire rescue routes and select the optimal route based on traffic data, ensuring that fire rescue forces can arrive at the fire scene as quickly as possible. Most importantly, by analyzing road images and predicting travel time, it can identify the route with the highest traffic flow coefficient, and perform a weighted evaluation based on the predicted travel time of each route segment, thereby selecting the fire rescue route with the highest traffic efficiency. This not only considers the real-time traffic conditions of the road, but also optimizes the arrival time of fire rescue forces, ensuring that rescue vehicles can arrive at the fire scene as quickly as possible.

[0045] 2. This invention optimizes the mechanism for collaborative planning of fire rescue routes by multiple edge computing units. By determining the primary edge computing unit and using its planned route as a basis, the paths planned by other edge computing units are adjusted and merged at the end, ensuring that the ends of multiple fire rescue routes overlap, enabling all rescue forces to enter the fire scene from the same direction. This method not only improves the flexibility and adaptability of fire rescue route planning but also avoids potential conflicts between different rescue routes, ensuring the orderliness and efficiency of rescue operations. Simultaneously, dynamic route adjustment based on traffic data can respond to complex traffic conditions in real time, further guaranteeing the traffic efficiency of fire rescue routes. In multi-edge computing unit collaborative rescue scenarios, this invention significantly improves the scientific nature and practicality of fire rescue route planning, providing a more reliable route planning scheme for urban fire rescue and effectively improving the overall efficiency and success rate of fire rescue. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the system principle of the urban fire early warning and response system in Embodiment 1 of the present invention;

[0048] Figure 2 This is a schematic diagram illustrating the basic path filtering principle in Embodiment 1 of the present invention;

[0049] Figure 3 This is a schematic diagram illustrating the principle of merging and adjusting several fire rescue routes in Embodiment 2 of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 The first aspect of the present invention provides an urban fire early warning and response system based on the Internet of Things and edge computing, including several levels of edge computing units, wherein the edge computing unit includes edge computing node one and edge computing node two;

[0052] Edge computing node one: used to extract fire data within its coverage area; whereby the fire data includes fire extent, fire location, and fire severity; and,

[0053] Used to determine whether sufficient fire and rescue forces are configured based on fire data; if yes, analyze the fire data and organize fire and rescue forces; if no, report to the next higher-level edge computing unit for assistance based on the fire data.

[0054] Edge computing node two: used to plan several basic paths between the location of fire and rescue forces and the fire point; to filter these basic paths based on traffic data to obtain fire and rescue routes; and,

[0055] This is used to determine whether the time of obtaining the fire rescue route is earlier than that of the fire evacuation route; if yes, plan the fire evacuation route based on the fire rescue route; if no, adjust the fire evacuation route based on the fire rescue route.

[0056] As areas with a high density of population, buildings, and facilities, urban areas have significant unique characteristics in the occurrence and spread of fires, which are mainly reflected in the complex environment and dense population.

[0057] From an environmental complexity perspective, urban areas are densely populated and diverse in their buildings, including high-rises, older residential buildings, commercial complexes, and underground spaces (subways, shopping malls), making them highly susceptible to fires spreading rapidly through heat radiation and embers, potentially leading to widespread fires. Furthermore, urban areas have a high concentration of combustible materials; flammable interior decoration materials and goods in shopping malls can provide ample fuel for fires, accelerating their spread. Clearly, due to the complexity of the urban environment, fires in urban areas tend to spread much faster and cause greater property damage than those in rural or industrial areas.

[0058] From the perspective of population density, the population density in the core urban area is significantly higher than in other areas. In the event of a fire, a large number of people need to be evacuated, which can easily lead to secondary disasters such as congestion and stampedes. Moreover, the population composition in urban areas is complex, including a large number of elderly, infirm, disabled, pregnant women, migrant workers, and tourists. These groups are unfamiliar with evacuation routes and have varying self-rescue capabilities, which further increases the difficulty of evacuation.

[0059] Furthermore, urban traffic and infrastructure limitations create numerous obstacles for rescue efforts. For instance, fire lanes are often blocked by private cars and street vendors, hindering fire trucks from reaching the fire quickly. If a fire occurs during peak traffic hours, even if vehicles actively give way, it can still impede the efficiency of fire truck passage.

[0060] When building an urban fire early warning and response system based on the Internet of Things and edge computing, it is necessary to use edge computing technology to process the collected data at the edge to improve data processing efficiency. It is also necessary to use the Internet of Things to report and summarize the edge processing results so that decisions can be made quickly and accurately.

[0061] Edge computing technology is a distributed computing architecture whose core idea is to move data processing, storage, and application services from traditional centralized cloud data centers to edge nodes closer to the data source or user, thereby reducing latency, saving bandwidth, and enhancing real-time performance.

[0062] The edge computing unit consists of edge computing node one and edge computing node two. Edge computing node one analyzes fire data and determines whether sufficient fire and rescue forces are available. Edge computing node two organizes fire and rescue forces, plans fire and rescue routes, and interacts with other edge computing nodes that need to plan fire and rescue routes.

[0063] Edge computing node 1: Used to extract fire data in its coverage area; the fire data includes the fire range, fire location and fire level; and used to determine whether sufficient fire rescue forces are configured based on the fire data; if yes, it analyzes the fire data and organizes fire rescue forces; if no, it reports to the next higher-level edge computing unit for assistance based on the fire data.

[0064] In this embodiment, edge computing technology is used for data acquisition and processing. Data acquisition is primarily achieved through multi-level edge computing units, such as level 1, level 2, and level 3 edge computing units. A higher level edge computing unit indicates a larger coverage area. Furthermore, a higher-level edge computing unit's coverage area contains several lower-level edge computing units; for example, the area corresponding to a level 3 edge computing unit is composed of the areas corresponding to multiple level 2 edge computing units.

[0065] As an example, and not a limitation, taking a prefecture-level city as an example, three-level edge computing units are set up in several districts under the jurisdiction of the prefecture-level city; two-level edge computing units are set up in several streets under the jurisdiction of several districts; and first-level edge computing units are set up in several residential communities under the jurisdiction of several streets. The three-level edge computing units set up in the districts are used to receive data uploaded by the corresponding two-level edge computing units in the several streets; the two-level edge computing units in the streets are used to receive data uploaded by the corresponding first-level edge computing units in the several residential communities; and the first-level edge computing units are used to receive data from various types of fire sensors or fire-fighting facilities installed in the residential communities.

[0066] A primary edge computing unit typically does not have subordinate edge computing units. It directly collects fire-related data through various fire sensors or fire-fighting facilities installed within the residential community. The primary edge computing unit uses built-in algorithms to derive fire data from the collected fire-related data, specifically the fire's location, orientation, and severity. If no fire occurs in the covered area, no fire data is transmitted. This fire data primarily serves as a reference for fire and rescue departments in organizing their rescue efforts.

[0067] Firefighting sensors include fire sensors, smoke sensors, and temperature sensors, used to detect whether a fire has occurred and the corresponding fire situation. Firefighting facilities include emergency evacuation facilities, fire communication facilities, and fire water supply facilities, used to provide data support for fire rescue and personnel evacuation.

[0068] After analyzing the fire data, the primary edge computing unit determines whether it has fire and rescue capabilities. If so, it organizes these capabilities for firefighting. Simultaneously, it assesses whether the available fire and rescue resources meet the firefighting requirements. If not, it reports to and requests assistance from the secondary edge computing unit. Furthermore, the primary edge computing unit can collect and analyze fire data not only from residential areas but also from commercial centers, industrial parks, and other areas.

[0069] The secondary edge computing unit receives fire data collected by its subordinate edge computing units, determines whether it has fire and rescue capabilities, and if so, organizes these capabilities for firefighting. Simultaneously, it needs to determine whether the fire and rescue capabilities meet the firefighting requirements; if not, it must report to and request assistance from the next higher (tertiary) edge computing unit. Similarly, tertiary edge nodes and even higher-level edge computing units should follow this process.

[0070] If fire and rescue forces exist within the coverage area of ​​each level of edge computing unit, they should be associated with the edge computing unit after the unit is set up. For example, if a residential community is equipped with a mini fire station, after the (Level 1) edge computing unit corresponding to that community is set up, the relevant parameters of the fire station, including fire and rescue personnel, fire and rescue equipment, and the fire levels it can handle, should be associated with the edge computing unit. If the mini fire station in the residential community cannot handle the fire, it should be dispatched to handle the necessary situation first, while simultaneously reporting to the next higher-level edge computing unit, namely the Level 2 edge computing unit corresponding to the street. Upon receiving the fire data, the Level 2 edge computing unit will organize the street's fire and rescue forces based on the fire data.

[0071] When organizing fire and rescue forces in secondary edge computing units, the fire and rescue forces configured in primary edge computing units can generally be disregarded and treated as redundant to ensure sufficient fire and rescue resources reaching the fire site, thereby improving fire control efficiency. However, if the fire and rescue forces configured in the secondary edge computing units are insufficient to extinguish the fire, the fire and rescue forces configured in the next lower level edge computing units can be included in the organization.

[0072] As an example, and not a limitation, if a fire breaks out in a residential community, its corresponding primary edge computing unit analyzes the fire data based on detection data. If it determines that the micro fire station associated with the primary edge computing unit is unable to complete the firefighting task, it reports the fire to the secondary edge computing unit of the corresponding street for assistance. If the fire and rescue forces associated with the secondary edge computing unit can extinguish the fire in the residential community, then the fire and rescue forces of the street can be organized. If the fire and rescue forces of the street are also insufficient to extinguish the fire, the secondary edge computing unit can mobilize the fire and rescue forces of several primary edge computing units under its jurisdiction, including the fire and rescue forces of the residential community. If the fire and rescue forces of the secondary edge computing unit and its subordinate primary edge computing units are still insufficient, it reports the fire to the tertiary edge computing unit for assistance. When organizing fire and rescue forces, the tertiary edge computing unit may consider the fire and rescue forces of the secondary edge computing unit and its subordinate primary edge computing units.

[0073] In layman's terms, if the edge computing unit has sufficient fire and rescue capabilities, it can directly organize these capabilities based on the received fire data. If the fire and rescue capabilities are insufficient, it can request assistance from the next higher-level edge computing unit or integrate the fire and rescue capabilities of its subordinate lower-level edge computing units. The higher-level edge computing unit can organize its own associated fire and rescue capabilities, as well as those of its subordinate lower-level edge computing units and their even lower-level subordinate edge computing units. During the organization of fire and rescue capabilities, the relative location of the fire and rescue capabilities to the fire location must be comprehensively considered. Furthermore, if an edge computing unit is associated with fire and rescue capabilities, that edge computing unit should be located at the location of the fire and rescue capabilities to ensure a rapid response by firefighters.

[0074] It is worth noting that fire and rescue forces include fire and rescue personnel, fire trucks, and fire equipment. The organization of fire and rescue forces can refer to the above process, but the reporting of fire data must strictly comply with relevant fire management regulations. During the process of reporting upwards and requesting assistance, fire data and the fire and rescue forces of the current edge computing unit and its subordinate edge computing units should be reported together to ensure that accurate and sufficient fire and rescue forces can be organized.

[0075] In this embodiment, data uploading is primarily based on Internet of Things (IoT) technology. Furthermore, each edge computing unit is not only responsible for collecting data but also for processing the collected data using built-in algorithms. During data upload, only the data processing results can be uploaded, or both the collected data and the corresponding processing results can be uploaded together.

[0076] The urban fire early warning and response system based on the Internet of Things and edge computing provided in this embodiment uses edge computing technology to build the basic framework of the system. Each edge computing unit is responsible for a different area, and the edge computing units interact with each other through Internet of Things technology to ensure that fire rescue forces at different levels can assist each other and improve the early warning and response speed of urban fire rescue.

[0077] Edge computing node 2: Used to plan several basic paths between the location of fire rescue forces and the fire point; to filter several basic paths based on traffic data to obtain fire rescue paths; and to determine whether the acquisition time of the fire rescue path is earlier than the fire evacuation path; if yes, to plan the fire evacuation path based on the fire rescue path; if no, to adjust the fire evacuation path based on the fire rescue path.

[0078] In this embodiment, after any edge computing unit completes fire data collection, the fire and rescue department needs to organize fire and rescue forces based on the fire data and dispatch the organized forces to the fire site in a timely manner. The response speed of the fire and rescue department and the efficiency of the fire and rescue personnel are not a concern; however, the arrival of the fire and rescue forces at the fire site is affected by various factors. For example, fire and rescue personnel may be blocked by illegally parked private cars on the road upon arrival, preventing them from passing through for a short period. This can render many fire and rescue resources unusable, affecting the efficiency of fire and rescue operations.

[0079] After assessing the fire situation, while organizing fire and rescue forces and allocating fire and rescue resources, it is necessary to quickly determine the fire and rescue route. This route must ensure that fire and rescue forces can reach the fire site as quickly as possible to complete the fire rescue.

[0080] In a preferred embodiment, several basic paths are planned between the location of fire and rescue forces and the fire point, including:

[0081] The location of the fire and rescue forces is designated as Location 1, and the location of the fire point is designated as Location 2.

[0082] The target area is determined based on location one and location two; wherein, the target area is a continuous area that includes both location one and location two.

[0083] Plan the path between location one and location two in the target area to obtain several basic paths.

[0084] Before fire and rescue forces can set off for a rescue operation, it is necessary to plan the departure routes of the fire trucks in advance to guide their direction. If the planning is done directly using navigation software, the navigation routes provided cannot take into account the size of the fire trucks and related fire-fighting facilities, which may result in situations where the fire and rescue forces cannot pass through the given navigation routes. Therefore, in this embodiment, several basic paths are first provided based on set conditions, and then these basic paths are selected and the path with the highest traffic efficiency is used as the fire and rescue route.

[0085] Meanwhile, in order to avoid increasing the difficulty of data processing due to too many basic paths, this embodiment first delineates the target area based on the location of the fire rescue force (location 1) and the location of the fire (location 2), and then plans several basic paths between location 1 and location 2 within the target area.

[0086] It should be noted that if there are no basic paths in the target area or the number of planned basic paths is very small, then the restriction of the target area can be waived, and all paths between location one and location two can be directly planned as basic paths. Also, edge computing node two may be working because its parent edge computing unit determines that the fire and rescue forces are sufficient and issues an instruction, or it may be due to a fire and rescue assistance instruction issued by other edge computing units.

[0087] In a preferred embodiment, determining the target area based on location one and location two includes:

[0088] Use the line connecting position one and position two as the baseline, and the straight line passing through position one and perpendicular to the baseline as the target line.

[0089] Divide the planar regions containing position one and position two using the target line as the dividing line, and take the region containing position two as the target region.

[0090] Please see Figure 2 Point A represents the location of the fire and rescue forces, i.e., location one; point B represents the location of the fire, i.e., location two. The dashed line between points A and B is the target line, and another dashed line D is perpendicular to the target line. Therefore... Figure 2 The area to the right of the dashed line D, including positions one and two, is the target area. All paths within the target area can be used as base paths. It can be seen that paths C1, C2, and C4 are base paths, but some path segments in C4 are not within the target area; therefore, C4 is not considered a base path.

[0091] After determining several basic routes, it is naturally necessary to select the route with the highest traffic efficiency as the final fire rescue route. Fire rescue route planning differs from general route planning, strictly requiring maximum traffic efficiency while ensuring passability. In general route planning, navigation software analyzes existing road sections to provide passable routes, and drivers generally do not encounter problems due to temporary road congestion or other unconventional reasons. However, in fire rescue, if a planned fire rescue route becomes impassable due to special circumstances, such as insufficient road width or illegally parked vehicles, fire rescue forces will be unable to reach the fire site in a timely manner, delaying rescue efforts and leading to greater property damage. Therefore, after planning several basic routes, it is necessary to select the passable and most efficient route as the fire rescue route.

[0092] In a preferred embodiment, several basic routes are filtered based on traffic data, including:

[0093] Sort the basic paths in ascending order of length to obtain a path sequence; then use the basic paths in the path sequence as the target paths in turn.

[0094] The traffic data of the target route is matched, and the traffic efficiency of the target route is evaluated based on the traffic data. The target route with the highest traffic efficiency is selected as the fire rescue route. The traffic data includes road images and predicted travel time.

[0095] When planning fire and rescue routes, edge computing units need to interact with traffic management departments. With authorization or verification, they extract traffic data from the traffic management department's database and use this data to select the most efficient route for fire and rescue.

[0096] After authorization or verification, the edge computing unit can extract traffic data from the traffic management department's database for all paths leading to the fire site. This traffic data includes road images and road travel times.

[0097] After acquiring traffic data for all routes, the road images within the traffic data are used to identify whether each road segment is clear. Based on the predicted travel time, the efficiency of fire trucks reaching the corresponding road segments is calculated. If a route is clear and has the highest overall efficiency across all road segments, then that route can be used as a fire rescue route.

[0098] During the selection process, the planned basic paths are first sorted according to their path length to obtain a path sequence. The paths at the beginning of this sequence have a shorter total length than the paths at the end. Then, each basic path in the path sequence is used as a target path, and the traffic efficiency of the target path is evaluated.

[0099] During the organization of fire and rescue forces, the required fire and rescue forces can be determined based on fire data. Fire and rescue routes can be planned and selected simultaneously during the organization process. When the fire and rescue forces depart, they can directly follow the given fire and rescue routes to reach the fire point with maximum efficiency. This parallel processing does not affect the efficiency of fire and rescue operations.

[0100] In a preferred embodiment, evaluating the traffic efficiency of a target route based on traffic data includes:

[0101] Extract road images and predict travel time from traffic data; identify illegally parked vehicles in road images and set traffic flow coefficients;

[0102] The travel time is predicted by matching the arrival time of fire and rescue forces to each segment of the target route; the travel time of the target route is calculated based on the predicted travel time of each segment.

[0103] The traffic efficiency of the target path is obtained by weighted summation of the traffic flow coefficient and the travel time of the target path; where the travel time of the target path is the sum of the travel times of each path segment.

[0104] When evaluating a target route, it is necessary to obtain the traffic data corresponding to the target route from the transportation department after authorization. This includes road images and predicted travel times for each route segment within the target route. The traffic data is processed internally by the traffic management department and then authorized for use.

[0105] During the assessment, it is necessary to first ensure that fire and rescue forces can pass through, i.e., obtain the accessibility coefficient; at the same time, it is also necessary to assess the time required for fire and rescue forces to pass through the basic path, i.e., obtain the passage time.

[0106] As an example, and not a limitation, the maximum accessibility coefficient is achieved when fire and rescue forces can smoothly traverse all segments of the target route. This can be expressed by the formula. Calculate the smoothness coefficient , ;in, For path segment The smoothness coefficient, For path segment The number of illegally parked vehicles For the preset path segment The maximum number of illegally parked vehicles allowed. When At that time, the traffic flow coefficient decreased linearly with the increase in the number of illegally parked vehicles. At that time, the smoothness coefficient was fixed at 0.1; It uses geometric mean processing to avoid the dominance of extreme values ​​in a single path segment.

[0107] After obtaining the predicted travel times for each path segment, these are summed to obtain the travel time for the target path. This travel time is then normalized to obtain the processed travel time. During the normalization process, the shortest and longest travel times for the target path can be statistically analyzed and used to complete the normalization. Alternatively, a baseline travel time can be set, which is greater than the maximum travel time required for the target path. Dividing the target path's travel time by this baseline time yields the normalized result. Of course, the predicted travel times for each path segment within the target path can also be normalized before summing.

[0108] After obtaining the traffic flow coefficient and travel time of the target path, a pre-set weighting coefficient is extracted. The weighting coefficient for the traffic flow coefficient is... The weighting factor for travel time is Under normal circumstances ,like , The traffic efficiency can then be obtained by using the formula and applying a weighted sum. ;in, This represents the travel time for the target path.

[0109] After calculating the traffic efficiency of all basic paths in the path sequence, the basic path with the highest traffic efficiency is selected as the fire rescue path of the fire rescue force associated with the corresponding edge computing unit, and the fire rescue force is guided to the fire point in real time based on the fire rescue path.

[0110] After a fire occurs, both fire evacuation routes and fire rescue routes are planned simultaneously. Several fire evacuation routes and fire rescue routes can be planned. The fire rescue routes are planned by the edge computing unit that dispatches fire rescue forces, while the fire evacuation routes are planned by the edge computing unit covering the area where the fire is located; if necessary, they can also be planned by a higher-level edge computing unit.

[0111] In a preferred embodiment, planning fire evacuation routes based on fire rescue routes includes:

[0112] Based on the information about the area where the fire is located, several evacuation routes for personnel and several evacuation routes for vehicles are planned.

[0113] Determine whether the vehicle evacuation route will affect the fire rescue route; if yes, remove the vehicle evacuation route; otherwise, retain the vehicle evacuation route.

[0114] Several personnel evacuation routes and several retained vehicle evacuation routes will be integrated into a fire evacuation route.

[0115] When planning fire evacuation routes, separate routes for personnel and vehicles should be planned if possible. After planning fire evacuation routes based on fire escape routes, emergency evacuation facilities, such as evacuation signs and emergency lighting, should be controlled by edge computing units. Fire data should be incorporated into the planning of fire evacuation routes to minimize dangerous intersections between fire evacuation routes and fire points (dangerous intersections refer to intersections that pose a danger to personnel or vehicles).

[0116] Multiple evacuation routes can be planned based on safety exits and the location of people, allowing them to evacuate using the most efficient routes. Similarly, multiple vehicle evacuation routes can be planned based on the number of exits. It should be noted that vehicle evacuation routes generally refer to motor vehicles. If non-motorized vehicles and motorized vehicles are parked near each other, non-motorized vehicles should also evacuate in an orderly manner according to the vehicle evacuation routes.

[0117] If the fire rescue route is planned before the fire evacuation route, people can spontaneously organize and evacuate in an orderly manner along both sides of the road, generally without affecting fire rescue vehicles. However, if the vehicle evacuation route overlaps with the fire rescue route, it is highly likely to hinder the rescue operation. Therefore, if the planned vehicle rescue route affects the fire rescue route, i.e., the two routes intersect, the vehicle evacuation route should be removed. If there is only one vehicle evacuation route, it does not need to be removed, but the arrival time of the fire rescue vehicles should be indicated, and personnel should be automatically organized to control the vehicles if necessary.

[0118] In a preferred embodiment, adjusting the fire evacuation route based on the fire rescue route includes:

[0119] When obtaining fire rescue routes, determine in turn whether fire evacuation routes will affect fire rescue routes;

[0120] Yes, set the route change time according to the fire rescue route, and adjust the fire evacuation route according to the route change time;

[0121] No, then there is no need to adjust the fire evacuation routes.

[0122] If the planning of fire rescue routes is completed no earlier than that of fire evacuation routes, then orderly evacuation will be carried out according to the fire evacuation routes first. That is, personnel evacuation will be organized based on personnel evacuation routes, and vehicle evacuation will be organized based on vehicle evacuation routes. Once the fire rescue routes are obtained, it will be determined whether the fire evacuation routes (mainly vehicle evacuation routes, and personnel evacuation routes can also be analyzed if necessary) will affect the fire rescue routes. If they will affect the fire rescue routes, a route change time will be set, and the fire evacuation routes will be adjusted according to the route change time. If they will not affect the fire rescue routes, personnel or vehicle evacuation will still be carried out according to the fire evacuation routes.

[0123] As an example, and not a limitation, after planning fire rescue routes, if it is found that personnel evacuation route L1 and vehicle evacuation route L2 will affect the fire rescue route, and fire rescue forces will enter the affected area after 10 minutes, this 10-minute period is the route change time. Therefore, after 8 minutes, a voice reminder can be issued in personnel evacuation route L1 to evacuate along non-motorized vehicle routes; after 7 minutes, a reminder can be issued in vehicle evacuation route L2 to keep traffic stationary in the area affecting the fire rescue route. If there are enough vehicle evacuation routes, vehicle evacuation route L2 can be directly closed. When making adjustments, sufficient advance time should be allowed to avoid issuing reminders or closing routes only after fire rescue forces have already arrived in the affected area.

[0124] Example 2:

[0125] In planning fire rescue routes, efficiency is the primary consideration, ensuring that fire rescue forces can reach the fire site efficiently from the designated routes. If the fire rescue forces of an edge computing unit are sufficient, it only needs to plan fire rescue routes for its associated fire rescue forces, resulting in a single route. However, if the fire rescue forces of an edge computing unit are insufficient, and it is necessary to call upon fire rescue forces associated with other edge computing units, then each edge computing unit requiring the dispatch of fire rescue forces must plan a fire rescue route. All fire rescue routes have different starting points but the same destination.

[0126] In a preferred embodiment, several edge computing units simultaneously plan fire rescue routes and merge these routes, including:

[0127] The edge computing unit with the largest number of organized fire and rescue forces is identified as the main edge computing unit, and the fire and rescue path planned by the main edge computing unit is taken as the main fire and rescue path.

[0128] The terminal paths of other fire rescue routes are adjusted based on the main fire rescue route, so that the terminal paths of several fire rescue routes overlap.

[0129] If the fire and rescue forces associated with a single edge computing unit are insufficient to meet firefighting requirements, then the fire and rescue forces configured by other edge computing units of the same or different levels need to cooperate. In this case, the cooperating edge computing units will all plan fire and rescue routes, and these fire and rescue routes need to be processed to avoid conflicts between the planned fire and rescue routes and fire evacuation routes, or even conflicts between different fire and rescue routes.

[0130] Before path fusion, it's crucial to determine which fire and rescue path will be the primary one. Generally, the fire and rescue path planned by the edge computing unit that organizes the largest number of fire and rescue forces is selected. However, other rules can also be used to determine the primary fire and rescue path, such as the fastest arrival time for fire and rescue forces. Other fire and rescue paths are adjusted according to this primary path, primarily at the path's endpoint.

[0131] In a preferred embodiment, adjusting the end paths of other fire rescue routes based on the main fire rescue route includes:

[0132] Determine the Nth intersection point from the end of the main fire rescue route as the path feature point, and take the path between the path feature point and the fire point as the terminal path; where N≥1;

[0133] Based on the end path, other fire rescue paths are adjusted to obtain several adjusted fire rescue paths.

[0134] Select the terminal path on the main fire rescue route and use it as the only rescue route to the fire point. Then, adjust other fire rescue routes based on this terminal path, that is, adjust the terminals of other fire rescue routes so that all fire rescue routes have the same terminal path.

[0135] It is worth noting that if other fire rescue routes cannot be adjusted according to the end route, such as when the fire rescue force corresponding to a certain fire rescue route cannot reach the end route, a second end route can be determined, and the fire rescue routes can be merged and adjusted according to these two end routes.

[0136] In a preferred embodiment, adjusting other fire rescue routes based on the end path includes:

[0137] Other fire and rescue routes will be used as the basic rescue routes in sequence;

[0138] Identify the location of the fire and rescue forces corresponding to the basic rescue route, take the intersection of the next path along the basic rescue route as the initial location, and take the path feature point as the end location.

[0139] Based on traffic data, the route with the highest traffic efficiency between the initial and final locations is planned as route one; route one and the final route are then combined to obtain the adjusted fire rescue route.

[0140] When fusing based on the end path, the characteristic path points in the end path can be used as the endpoint positions. The positions of the fire and rescue forces within their fire and rescue routes can be identified, and the intersection point of the next path corresponding to that position can be extracted as the initial position. According to the aforementioned fire and rescue route planning scheme, the routes to the initial and endpoint positions are replanned based on traffic data, and then concatenated with the end path to obtain the fused and adjusted fire and rescue route.

[0141] Please see Figure 3 Points A1, A2, and A3 represent the locations of fire and rescue forces, and point B represents the fire location. Point A1 has the largest number of fire and rescue forces, and its corresponding fire and rescue route is the main fire and rescue route. Setting N to 1, point E is the characteristic point of the route, and the path segment between E and B is the terminal path.

[0142] Depend on Figure 3 It can be seen that the fire rescue path corresponding to point A2 coincides with the end path of the main fire rescue path, so this fire rescue path does not need to be merged or adjusted. However, the fire rescue path of point A3 does not coincide with the end path of the main fire rescue path, so the fire rescue path of point A3 needs to be adjusted. During the adjustment, the fire rescue force of point A3 is identified as being at point W, and the next path intersection is identified as point Q. Based on traffic data planning, the optimal path between point Q and point E is Q→F→E. The path Q→F→E is then spliced ​​with the end path to realize the adjustment of the fire rescue path of point A3. The fire rescue force of point A3 reaches the fire point B via Q→F→E.

[0143] Therefore, multiple edge computing units may need to plan fire rescue routes. Once all edge computing units have completed their fire rescue route planning, a primary fire rescue route is selected from these routes. This primary route is then aligned with the primary fire rescue route near the fire site, allowing all fire rescue forces to enter the fire site from the same direction. Other routes can then be used for organizing the evacuation of personnel and vehicles without affecting fire rescue capabilities. Furthermore, using the endpoint routes to integrate and adjust other fire rescue routes will not affect fire evacuation routes, achieving multiple benefits.

[0144] In this embodiment, after a fire breaks out, it is also necessary to evacuate personnel from the fire area to avoid difficulties in rapid evacuation due to unfamiliarity with the environment. During evacuation, personnel should be evacuated according to the planned evacuation routes. Emergency lights, voice prompts, and assistance from professional personnel should be used during the evacuation process to ensure rapid evacuation without affecting vehicle evacuation or the work of fire and rescue personnel.

[0145] Meanwhile, community or property management staff also need to clear the interior of the fire-affected area to ensure fire trucks can quickly reach the fire site. External public roads are typically included in fire rescue routes, but the specific details of the fire area are unclear. Therefore, necessary preparations for guiding and extinguishing fires should be made before firefighters arrive, allowing for rapid deployment of firefighting efforts upon arrival.

[0146] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An urban fire early warning and response system based on the Internet of Things and edge computing, characterized in that, It includes several levels of edge computing units, which include edge computing node one and edge computing node two. Edge computing node one: used to extract fire data within its coverage area; whereby the fire data includes fire extent, fire location, and fire severity; and, The system is used to determine whether sufficient fire and rescue forces are configured based on the fire data; if yes, it analyzes the fire data and organizes fire and rescue forces; if no, it reports to the next higher-level edge computing unit for assistance based on the fire data. Edge computing node two: used to plan several basic paths between the location of fire rescue forces and the fire point; to filter the basic paths based on traffic data to obtain fire rescue routes; and, The method is used to determine whether the acquisition time of the fire rescue route is earlier than that of the fire evacuation route; if yes, the fire evacuation route is planned based on the fire rescue route; if no, the fire evacuation route is adjusted based on the fire rescue route; wherein, the fire evacuation route is used for the evacuation of personnel and vehicles in the fire area.

2. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 1, characterized in that, Plan several basic routes between the location of fire and rescue forces and the fire site, including: The location of the fire and rescue forces is designated as location one, and the location of the fire point is designated as location two. The target region is determined based on position one and position two; wherein the target region is a continuous region that includes both position one and position two. Plan the path between location one and location two in the target area to obtain several basic paths.

3. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 2, characterized in that, Determining the target area based on location one and location two includes: The line connecting position one and position two is used as the base line, and the straight line passing through position one and perpendicular to the base line is used as the target line. The planar regions where position one and position two are located are divided using the target line as the dividing line, and the region containing position two after the division is taken as the target region.

4. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 1, characterized in that, Based on traffic data, several basic routes are filtered, including: The basic paths are sorted in ascending order of length to obtain a path sequence; each basic path in the path sequence is then used as the target path. The traffic data of the target route is matched, and the traffic efficiency of the target route is evaluated based on the traffic data; the target route with the highest traffic efficiency is selected as the fire rescue route; wherein, the traffic data includes road images and predicted travel time.

5. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 4, characterized in that, Evaluating the traffic efficiency of the target route based on the traffic data includes: Extract road images and predict travel times from the traffic data; identify illegally parked vehicles in the road images and set a traffic flow coefficient; The travel time is predicted by matching the arrival time of fire and rescue forces to each segment of the target route; the travel time of the target route is calculated based on the predicted travel time of each segment. The traffic efficiency of the target path is obtained by weighted summation of the traffic flow coefficient and the travel time of the target path; wherein the travel time of the target path is the sum of the travel times of each path segment.

6. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 1, characterized in that, Based on the fire rescue route, the fire evacuation route is planned, including: Based on the information about the area where the fire is located, several evacuation routes for personnel and several evacuation routes for vehicles are planned. Determine sequentially whether the vehicle evacuation route will affect the fire rescue route; if yes, remove the vehicle evacuation route; otherwise, retain the vehicle evacuation route. The aforementioned personnel evacuation routes and the retained aforementioned vehicle evacuation routes are integrated into the fire evacuation route.

7. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 1, characterized in that, Adjusting the fire evacuation route based on the fire rescue route includes: When obtaining the fire rescue route, it is determined in turn whether the fire evacuation route will affect the fire rescue route; Yes, the fire rescue route is set with a route change time, and the fire evacuation route is adjusted according to the route change time; No, then there is no need to adjust the fire evacuation routes.

8. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 1, characterized in that, Several edge computing units simultaneously plan fire rescue routes, and merge these fire rescue routes, including: The edge computing unit with the largest organized fire and rescue force is identified as the main edge computing unit, and the fire and rescue path planned by the main edge computing unit is taken as the main fire and rescue path. Based on the main fire rescue route, the end routes of other fire rescue routes are adjusted so that the end routes of several fire rescue routes overlap.

9. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 8, characterized in that, Adjusting the end routes of other fire rescue routes based on the main fire rescue route includes: The Nth intersection point from the end of the main fire rescue route is determined as the path feature point, and the path between the path feature point and the fire point is taken as the terminal path; where N≥1; Based on the terminal path, other fire rescue paths are adjusted to obtain several adjusted fire rescue paths.

10. The urban fire early warning and response system based on the Internet of Things and edge computing according to claim 9, characterized in that, Adjusting other fire and rescue routes based on the terminal path includes: The other fire rescue routes will be used as the basic rescue routes in sequence; Identify the location of the fire and rescue force corresponding to the basic rescue path, take the intersection of the next path along the basic rescue path as the initial location, and take the path feature point as the end location. Based on traffic data, the route with the highest traffic efficiency between the initial and final locations is planned as route one; route one and the final route are then combined to obtain the adjusted fire rescue route.

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