Fire scene auxiliary decision support system based on mobile ad hoc network

Abstract

The invention discloses a fire scene auxiliary decision support system based on a mobile ad hoc network, and particularly relates to the technical field of fire decision, which comprises the steps of constructing a fire-based mobile ad hoc network according to a fire occurrence area, and determining fixed nodes, movable nodes and alternative nodes, the risk and the influence of the alternative nodes are determined through map analysis, uncertainty entropy analysis is carried out on the fire information data, node importance data of the alternative nodes are collected, and the delay time of fire spreading is determined through self-correlation analysis of the temperature and the time of the lost nodes. The method comprises the following steps: acquiring importance data of a node, comparing the importance data of the node with the time from an alternative node to a lost node, acquiring node timeliness data of the alternative node, comprehensively evaluating the importance data of the node and the node timeliness data, and quantifying the priority of each alternative node. And the method can adapt to fire scenes with different scales and complexity.

Description

Technical Field

[0001] The present invention relates to the technical field of fire decision-making, and more specifically, to a fire scene auxiliary decision support system based on a mobile self-organizing network. Background Art

[0002] With the acceleration of urbanization and the increase in the frequency of fires, traditional fire emergency response systems often face problems such as delayed information transmission, unstable network structure, and untimely response when fires spread rapidly and the environment is complex. Especially in complex environments such as large buildings, forests or high-risk areas, the collection and transmission of fire information is crucial for emergency decision-making. However, traditional emergency response systems rely on fixed sensors and monitoring equipment. When a fire occurs, fixed nodes may fail due to the impact of the fire, resulting in information loss or delays, which in turn affects decision-making efficiency. Therefore, it is not easy to ensure information flow and effective monitoring when nodes are lost or fail.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a fire scene auxiliary decision support system based on a mobile self-organizing network to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A fire scene auxiliary decision support system based on a mobile self-organizing network, comprising a mobile self-organizing network module, a node importance data module, a node timeliness data module and an evaluation module, and signal connections between the modules;

[0007] The mobile self-organizing network module is used to construct a fire-based mobile self-organizing network according to the fire occurrence area and determine the fixed nodes, movable nodes and alternative nodes;

[0008] The node importance data module is used to determine the danger and influence of the replacement node through graph analysis, and to perform uncertainty entropy analysis on the fire information data to collect the node importance data of the replacement node;

[0009] The node timeliness data module is used to determine the delay time of fire spread by analyzing the autocorrelation between temperature and time at the lost node, and compare it with the time from the replacement node to the lost node to collect the node timeliness data of the replacement node;

[0010] The evaluation module is used to comprehensively evaluate the node importance data and node timeliness data to quantify the priority of each alternative node.

[0011] In a preferred embodiment, the node importance data includes:

[0012] The node importance data is represented by the graph key node coefficient and uncertainty entropy coefficient;

[0013] The acquisition logic of the key node coefficients of the graph is: taking fixed nodes and movable nodes as nodes of the graph, connecting two nodes through edges, and representing the relationship between the two nodes through data transmission links and communication connections between nodes;

[0014] The influence of alternative nodes is evaluated by the degree centrality of alternative nodes in the graph, and the hazard weight of alternative nodes is determined based on the flammability of the alternative nodes' geographical locations. The influence of alternative nodes is marked as: , mark the dangerous weight of the alternative node as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the replacement node;

[0015] The calculation formula for calculating the importance coefficient of the replacement node is: ;in, is the importance coefficient of the nth alternative node;

[0016] Determine the geographical location of the lost node and the geographical location of the replacement node, obtain the shortest distance from the replacement node to the nearest fire site, and obtain the shortest distance from the lost node to the fire site, and calculate the key node coefficient of the graph. The calculation formula is: ;in, is the key node coefficient of the graph, is the shortest distance from the alternative node to the nearest fire location, is the shortest distance from the lost node to the fire location.

[0017] In a preferred embodiment, the uncertainty entropy coefficient includes:

[0018] The acquisition logic of the uncertainty entropy coefficient is: collect fire information data of the replacement node, the fire information data includes temperature, smoke concentration, gas concentration, and humidity, and use the Gaussian kernel function to estimate the probability density of the collected fire information data. The calculation formula is: ;in, is the probability density of the i-th fire information data, i is the number of the fire information data, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the replacement node collecting the i-th fire information data, h is the bandwidth parameter, and K is the Gaussian kernel function;

[0019] Calculate the uncertainty entropy coefficient, the calculation formula is: ;in, is the uncertainty entropy coefficient of the nth alternative node.

[0020] In a preferred embodiment, the node importance data includes:

[0021] The node timeliness data is represented by the autocorrelation timeliness coefficient;

[0022] The acquisition logic of the autocorrelation timeliness coefficient is: according to the temperature time series at the lost node, the autocorrelation function of temperature and time is calculated, and the correlation between temperature and time is evaluated through different time lags;

[0023] The correlation between temperature and time at different time lags was calculated using the formula: ;in, is the correlation between temperature and time at the kth time lag, t is 1, 2, 3, ..., T, T is a positive integer, and t is the number of each temperature unit time collected, is the temperature at the alternative node at the tth unit time, The average temperature at the alternative node, k = 0, 1, 2, 3, ..., K, k is the number of unit time of lag;

[0024] The correlation between temperature and time under the optimal time lag is obtained, and the calculation formula is: ;in, is the correlation between temperature and time at the optimal time lag;

[0025] Set a correlation threshold. If the correlation between temperature and time under the optimal time lag is greater than the correlation threshold, the time required for the replacement node to go to the lost node is obtained, and the time required for the replacement node to go to the lost node is marked as: , calculate the autocorrelation timeliness coefficient, the calculation formula is: ;in, is the autocorrelation timeliness coefficient of the nth alternative node;

[0026] If the correlation between temperature and time at the optimal time lag is less than the correlation threshold, the autocorrelation time-effect coefficient is recorded as: 0.

[0027] In a preferred embodiment, the node importance data and the node timeliness data are comprehensively evaluated, including:

[0028] The graph key node coefficient, uncertainty entropy coefficient and autocorrelation timeliness coefficient are used to construct an alternative node evaluation model to generate an alternative node evaluation coefficient. The calculation formula for the alternative node evaluation coefficient is: ;in, is the alternative node evaluation coefficient of the nth alternative node, , , are the proportional coefficients of the graph key node coefficient, uncertainty entropy coefficient, and autocorrelation timeliness coefficient, respectively. , , Both are greater than 0.

[0029] In a preferred embodiment, quantifying the priority of each replacement node includes:

[0030] Set an alternative node evaluation coefficient threshold, compare the alternative node evaluation coefficient of each alternative node with the alternative node evaluation coefficient threshold, if the alternative node evaluation coefficient is greater than the alternative node evaluation coefficient threshold, then the alternative node is used as a replacement for the lost node and goes to the lost node, if the alternative node evaluation coefficient is less than or equal to the alternative node evaluation coefficient threshold, then the alternative node is not used as a replacement for the lost node and goes to the lost node, collect alternative nodes greater than the alternative node evaluation coefficient threshold, generate a priority selection order for the alternative nodes based on the size order of the alternative node evaluation coefficients greater than the alternative node evaluation coefficient threshold, and select the alternative node with the largest alternative node evaluation coefficient to go to the lost node.

[0031] Technical effects and advantages of the present invention:

[0032] The present invention establishes a self-organizing network consisting of fixed nodes, movable nodes and alternative nodes, and uses graph analysis to evaluate the danger and influence of nodes. Combined with uncertainty entropy analysis, the information diversity and uncertainty of each node are obtained to further evaluate the importance of the node; at the same time, by performing autocorrelation analysis on the fire data at the lost node, the delay time of the fire spread is determined and compared with the response time of the alternative node. Finally, the importance and timeliness data of the node are integrated to quantify the priority of the alternative node, so that when a fire occurs, the most appropriate node can be quickly selected for intervention and monitoring, thereby optimizing the fire response strategy and ensuring the stability and real-time performance of information transmission and on-site monitoring. The present invention helps to quickly select the most appropriate node for intervention and monitoring, and can adapt to fire scenes of different scales and complexities. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0034] Figure 1 The present invention is a structural schematic diagram of a fire scene auxiliary decision support system based on a mobile self-organizing network. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] Figure 1 A structural diagram of a fire scene auxiliary decision support system based on a mobile self-organizing network is given, which specifically includes a mobile self-organizing network module, a node importance data module, a node timeliness data module and an evaluation module, and signal connections between the modules;

[0038] The mobile self-organizing network module is used to construct a fire-based mobile self-organizing network according to the fire occurrence area and determine the fixed nodes, movable nodes and alternative nodes;

[0039] The node importance data module is used to determine the danger and influence of the replacement node through graph analysis, and to perform uncertainty entropy analysis on the fire information data to collect the node importance data of the replacement node;

[0040] The node timeliness data module is used to determine the delay time of fire spread by analyzing the autocorrelation between temperature and time at the lost node, and compare it with the time from the replacement node to the lost node to collect the node timeliness data of the replacement node;

[0041] The evaluation module is used to comprehensively evaluate the node importance data and node timeliness data to quantify the priority of each alternative node.

[0042] A mobile self-organizing network is built at the location where the fire occurs, and the labels of each node are defined. The node labels include fixed nodes and movable nodes. The mobile self-organizing network combines fixed nodes and movable nodes (such as drones, firefighting robots, etc.) to work together to provide efficient fire monitoring, data transmission and emergency response capabilities, including:

[0043] Fixed nodes: These nodes are usually deployed in various areas of the fire scene and are responsible for monitoring environmental changes, such as temperature, smoke concentration, gas composition, fire source location, etc. These sensor nodes can be temperature sensors, smoke sensors, CO2 sensors, temperature and humidity sensors, etc. They share data with other nodes through wireless communication;

[0044] Mobile nodes (drones, firefighting robots, etc.): Mobile nodes can be moved dynamically at the fire scene, with higher flexibility and maneuverability. Drones can obtain real-time images or video data of the fire area by flying in the air, and firefighting robots can enter the fire scene to perform firefighting or search tasks. They act as communication nodes in the network, helping to ensure network coverage and stability through dynamic path planning and data transmission;

[0045] Sensor data collection: Each node (whether fixed or mobile) is equipped with different sensors to collect fire-related data. The environmental information collected by these sensors (such as temperature, smoke, humidity, etc.) can reflect the status and development of the fire;

[0046] Node communication: Fixed nodes and mobile nodes are connected to each other through wireless communication technologies (such as Wi-Fi, Zigbee, LoRa, LTE, etc.) to form a self-organizing network. At the fire scene, both fixed nodes and mobile nodes can transmit data to each other and share information, thereby achieving real-time monitoring and dynamic assessment of the fire.

[0047] Based on the position of fixed nodes, the position of mobile nodes is adjusted to ensure effective information monitoring of the fire area and its surrounding areas. According to the speed and direction of fire spread, mobile nodes can obtain sensor data in the fire area in real time, and adjust their paths through adaptive routing protocols or task planning algorithms to ensure coverage of hot spots where fires occur and adjacent areas that may be affected by the spread of fire, thereby forming full coverage of fire information in the fire area and its surrounding areas.

[0048] Since fixed nodes have a high risk of loss in a fire environment, such as building collapse, equipment damage, fire spread, and drastic environmental changes, resulting in the loss of fire information data in the lost area, when a fixed node is lost, a loss signal is generated, and the loss signal is broadcast to the mobile ad hoc network. After receiving the loss signal, the nearby mobile nodes update their task queues to determine whether they need to go to the location of the lost node for monitoring or replacement. Among them, the basic factors for the mobile nodes to consider going to the location of the lost node include:

[0049] Battery power: If the battery power of the mobile node is low, it may not be able to complete the task, so it is necessary to select a node with sufficient power;

[0050] Distance: The distance between the node and the lost location. Farther nodes may require more time or energy. Choosing closer nodes can improve response efficiency.

[0051] Current task load: Some nodes may already be performing other tasks. If there are too many tasks, they may not be able to respond to the lost signal in time;

[0052] Route selection: It is necessary to assess the surrounding fire spread, ensure the path is safe, and avoid passing through areas with strong fire.

[0053] After determining the movable nodes that can replace the lost nodes, the movable nodes that can replace the lost nodes are marked as replacement nodes, and the node importance data and node timeliness data of each replacement node are collected. The node importance data is represented by the graph key node coefficient and the uncertainty entropy coefficient, and the node timeliness data is represented by the autocorrelation timeliness coefficient.

[0054] The key node coefficient of the graph helps to evaluate the priority order of alternative nodes to the lost nodes for the following reasons:

[0055] It can help identify the most influential nodes in the network topology. By evaluating the importance of the lost nodes, it can quickly find the most suitable replacement nodes and dispatch them first, which can speed up emergency response and reduce network communication interruptions caused by node loss.

[0056] If a key node is lost, it may affect a large number of communication links. The key node coefficient of the graph can be used to predict the impact of the failure on the network, and the spread of network failures can be reduced by timely adjusting routing strategies and giving priority to alternative nodes.

[0057] In disaster scenarios such as fire, the availability and importance of nodes change over time. Through the key node coefficients in the graph, the priority of nodes can be adjusted in real time according to the trend of fire spread.

[0058] The acquisition logic of the key node coefficients of the graph is: taking fixed nodes and movable nodes as nodes of the graph, connecting two nodes through edges, and representing the relationship between the two nodes through data transmission links and communication connections between nodes;

[0059] The influence of alternative nodes is evaluated by the degree centrality of alternative nodes in the graph, and the hazard weight of alternative nodes is determined based on the flammability of the alternative nodes' geographical locations. The influence of alternative nodes is marked as: , mark the dangerous weight of the alternative node as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the replacement node;

[0060] It should be noted that alternative nodes with high degree centrality usually have greater influence, which means that they can quickly transmit information and provide key data or signals when a fire occurs. The flammability of the geographical location of the alternative node is evaluated and determined by analyzing the environmental characteristics of the area.

[0061] The calculation formula for calculating the importance coefficient of the replacement node is: ;in, is the importance coefficient of the nth alternative node;

[0062] It should be noted that the greater the importance coefficient of the replacement node, the higher the importance of the replacement node position, and the lower the priority of going to the lost node for the replacement task.

[0063] Determine the geographical location of the lost node and the geographical location of the replacement node, obtain the shortest distance from the replacement node to the nearest fire site, and obtain the shortest distance from the lost node to the fire site, and calculate the key node coefficient of the graph. The calculation formula is: ;in, is the key node coefficient of the graph, is the shortest distance from the alternative node to the nearest fire location, is the shortest distance from the lost node to the fire location.

[0064] It can be seen from the formula that the larger the coefficient of the key node of the graph is, the more important the replacement node is relative to the lost node. If the replacement node is used as a replacement for the lost node, it is easy to cause the loss of fire information at the original replacement node.

[0065] The uncertainty entropy coefficient helps to evaluate the priority order of alternative nodes to the lost nodes. The uncertainty entropy can effectively quantify the information uncertainty of nodes in a fire environment. When the uncertainty entropy value of a node is high, it means that the information source of the node is not reliable enough or there is a greater risk of change. By using the entropy value as part of the node priority, it can help evaluate which nodes need to be restored or replaced first to ensure the accuracy and stability of the information flow. For example, if a node is at the edge of the fire spread, its entropy value may be high, indicating that the information it collects is not accurate enough or is more likely to change. At this time, the node should give priority to sending alternative nodes to supplement the information, thereby ensuring the real-time nature of the fire dynamics.

[0066] The acquisition logic of the uncertainty entropy coefficient is: collect fire information data of the replacement node, the fire information data includes temperature, smoke concentration, gas concentration, and humidity, and use the Gaussian kernel function to estimate the probability density of the collected fire information data. The calculation formula is: ;in, is the probability density of the i-th fire information data, i is the number of the fire information data, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the replacement node collecting the i-th fire information data, h is the bandwidth parameter, and K is the Gaussian kernel function;

[0067] Calculate the uncertainty entropy coefficient, the calculation formula is: ;in, is the uncertainty entropy coefficient of the nth alternative node.

[0068] It can be seen from the formula that the larger the uncertainty entropy coefficient is, the more difficult it is to predict the fire information data, and new fire situations may be more likely to occur. Therefore, nodes need to monitor the area closely, and the priority of replacing nodes to go to the lost node location is lower.

[0069] The autocorrelation timeliness coefficient helps to evaluate the priority order of alternative nodes to the lost node for the following reasons:

[0070] In a fire scene, the spread of fire is not completely random, but is affected by many factors (such as wind speed, temperature, humidity, terrain, combustible materials, etc.), and usually has a certain time pattern. The autocorrelation time coefficient can capture these regularities, allowing us to infer the delay time for the fire to spread to a certain node;

[0071] If the delay time is greater than or close to the consumption time of the mobile node, it means that the fire spread may not reach the node quickly before the mobile node reaches the lost node. Therefore, the larger the delay time of the node, the slower the fire spread rate may be. The change of the fire can be evaluated within a certain period of time, giving the mobile node more time to prepare and deploy;

[0072] If the delay time is large and the mobile node can reach the lost node within a reasonable time, the spread of the fire is more stable (not changing drastically), and the system can perform more effective monitoring, information collection, and decision-making intervention by reaching the node in advance. This will help to formulate reasonable resource scheduling and fire fighting plans.

[0073] The acquisition logic of the autocorrelation timeliness coefficient is: according to the temperature time series at the lost node, the autocorrelation function of temperature and time is calculated, and the correlation between temperature and time is evaluated through different time lags;

[0074] The correlation between temperature and time at different time lags was calculated using the formula: ;in, is the correlation between temperature and time at the kth time lag, t is 1, 2, 3, ..., T, T is a positive integer, and t is the number of each temperature unit time collected, is the temperature at the alternative node at the tth unit time, The average temperature at the alternative node, k = 0, 1, 2, 3, ..., K, k is the number of unit time of lag;

[0075] It should be noted that the unit time is a smaller specific time period set by staff in the professional field.

[0076] The correlation between temperature and time under the optimal time lag is obtained, and the calculation formula is: ;in, is the correlation between temperature and time at the optimal time lag;

[0077] Set a correlation threshold. If the correlation between temperature and time under the optimal time lag is greater than the correlation threshold, the time required for the replacement node to go to the lost node is obtained, and the time required for the replacement node to go to the lost node is marked as: , calculate the autocorrelation timeliness coefficient, the calculation formula is: ;in, is the autocorrelation timeliness coefficient of the nth alternative node;

[0078] If the correlation between temperature and time at the optimal time lag is less than the correlation threshold, the autocorrelation time-effect coefficient is recorded as: 0.

[0079] It should be noted that if the correlation between temperature and time under the optimal time lag is greater than the correlation threshold, it means that the spread of fire in the area is correlated with time, which means that the spread of fire in the area has a certain regularity and predictability. This regularity is relatively important for optimizing resource scheduling and emergency response. If the correlation between temperature and time under the optimal time lag is less than the correlation threshold, it means that the spread of fire in the area is not correlated with time and is not easy to predict, and the priority of the replacement node to the lost node is low.

[0080] It can be seen from the formula that the larger the autocorrelation timeliness coefficient is, the stronger the correlation between the spread of fire in the lost node area and time is, and the replacement node can go to the lost node in time to collect fire information data, which means that the replacement node has a higher priority to go to the lost node.

[0081] The graph key node coefficient, uncertainty entropy coefficient and autocorrelation timeliness coefficient are used to construct an alternative node evaluation model to generate an alternative node evaluation coefficient. The calculation formula for the alternative node evaluation coefficient is: ;in, is the alternative node evaluation coefficient of the nth alternative node, , , are the proportional coefficients of the graph key node coefficient, uncertainty entropy coefficient, and autocorrelation timeliness coefficient, respectively. , , Both are greater than 0.

[0082] Set an alternative node evaluation coefficient threshold, compare the alternative node evaluation coefficient of each alternative node with the alternative node evaluation coefficient threshold, if the alternative node evaluation coefficient is greater than the alternative node evaluation coefficient threshold, then the alternative node is used as a replacement for the lost node and goes to the lost node, if the alternative node evaluation coefficient is less than or equal to the alternative node evaluation coefficient threshold, then the alternative node is not used as a replacement for the lost node and goes to the lost node, collect alternative nodes greater than the alternative node evaluation coefficient threshold, generate a priority selection order for the alternative nodes based on the size order of the alternative node evaluation coefficients greater than the alternative node evaluation coefficient threshold, and select the alternative node with the largest alternative node evaluation coefficient to go to the lost node.

[0083] The present invention establishes a self-organizing network consisting of fixed nodes, movable nodes and alternative nodes, and uses graph analysis to evaluate the danger and influence of nodes. Combined with uncertainty entropy analysis, the information diversity and uncertainty of each node are obtained to further evaluate the importance of the node; at the same time, by performing autocorrelation analysis on the fire data at the lost node, the delay time of the fire spread is determined and compared with the response time of the alternative node. Finally, the importance and timeliness data of the node are integrated to quantify the priority of the alternative node, so that when a fire occurs, the most appropriate node can be quickly selected for intervention and monitoring, thereby optimizing the fire response strategy and ensuring the stability and real-time performance of information transmission and on-site monitoring. The present invention helps to quickly select the most appropriate node for intervention and monitoring, and can adapt to fire scenes of different scales and complexities.

[0084] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0085] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0086] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0087] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0091] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fire scene auxiliary decision support system based on mobile self-organizing network, characterized in that: It includes a mobile self-organizing network module, a node importance data module, a node timeliness data module and an evaluation module, and signal connections between the modules; The mobile self-organizing network module is used to construct a fire-based mobile self-organizing network according to the fire occurrence area and determine the fixed nodes, movable nodes and alternative nodes; The node importance data module is used to determine the danger and influence of the replacement node through graph analysis, and to perform uncertainty entropy analysis on the fire information data to collect the node importance data of the replacement node; The node timeliness data module is used to determine the delay time of fire spread by analyzing the autocorrelation between temperature and time at the lost node, and compare it with the time from the replacement node to the lost node to collect the node timeliness data of the replacement node; The evaluation module is used to comprehensively evaluate the node importance data and node timeliness data to quantify the priority of each alternative node.

2. According to claim 1, a fire scene auxiliary decision support system based on a mobile self-organizing network is characterized in that: Node importance data, including: The node importance data is represented by the graph key node coefficient and uncertainty entropy coefficient; The acquisition logic of the key node coefficients of the graph is: taking fixed nodes and movable nodes as nodes of the graph, connecting two nodes through edges, and representing the relationship between the two nodes through data transmission links and communication connections between nodes; The influence of alternative nodes is evaluated by the degree centrality of alternative nodes in the graph, and the hazard weight of alternative nodes is determined based on the flammability of the alternative nodes' geographical locations. The influence of alternative nodes is marked as: , mark the dangerous weight of the alternative node as: , where n=1, 2, 3, ..., N, N is a positive integer, and n is the number of the replacement node; The calculation formula for calculating the importance coefficient of the replacement node is: ;in, is the importance coefficient of the nth alternative node; Determine the geographical location of the lost node and the geographical location of the replacement node, obtain the shortest distance from the replacement node to the nearest fire site, and obtain the shortest distance from the lost node to the fire site, and calculate the key node coefficient of the graph. The calculation formula is: ;in, is the key node coefficient of the graph, is the shortest distance from the alternative node to the nearest fire location, is the shortest distance from the lost node to the fire location.

3. A fire scene auxiliary decision support system based on mobile self-organizing network according to claim 2, characterized in that: Uncertainty entropy coefficients, including: The acquisition logic of the uncertainty entropy coefficient is: collect fire information data of the replacement node, the fire information data includes temperature, smoke concentration, gas concentration, and humidity, and use the Gaussian kernel function to estimate the probability density of the collected fire information data. The calculation formula is: ;in, is the probability density of the i-th fire information data, i is the number of the fire information data, m=1, 2, 3, ..., M, M is a positive integer, m is the number of the replacement node collecting the i-th fire information data, h is the bandwidth parameter, and K is the Gaussian kernel function; Calculate the uncertainty entropy coefficient, the calculation formula is: ;in, is the uncertainty entropy coefficient of the nth alternative node.

4. A fire scene auxiliary decision support system based on a mobile self-organizing network according to claim 3, characterized in that: Node importance data, including: The node timeliness data is represented by the autocorrelation timeliness coefficient; The acquisition logic of the autocorrelation timeliness coefficient is: according to the temperature time series at the lost node, the autocorrelation function of temperature and time is calculated, and the correlation between temperature and time is evaluated through different time lags; The correlation between temperature and time at different time lags was calculated using the formula: ;in, is the correlation between temperature and time at the kth time lag, t is 1, 2, 3, ..., T, T is a positive integer, and t is the number of each temperature unit time collected, is the temperature at the alternative node at the tth unit time, The average temperature at the alternative node, k = 0, 1, 2, 3, ..., K, k is the number of unit time of lag; The correlation between temperature and time under the optimal time lag is obtained, and the calculation formula is: ;in, is the correlation between temperature and time at the optimal time lag; Set a correlation threshold. If the correlation between temperature and time under the optimal time lag is greater than the correlation threshold, the time required for the replacement node to go to the lost node is obtained, and the time required for the replacement node to go to the lost node is marked as: , calculate the autocorrelation timeliness coefficient, the calculation formula is: ;in, is the autocorrelation timeliness coefficient of the nth alternative node; If the correlation between temperature and time at the optimal time lag is less than the correlation threshold, the autocorrelation time-effect coefficient is recorded as:

0.

5. The fire scene auxiliary decision support system based on mobile self-organizing network according to claim 4 is characterized in that: Comprehensively evaluate node importance data and node timeliness data, including: The graph key node coefficient, uncertainty entropy coefficient and autocorrelation timeliness coefficient are used to construct an alternative node evaluation model to generate an alternative node evaluation coefficient. The calculation formula for the alternative node evaluation coefficient is: ;in, is the alternative node evaluation coefficient of the nth alternative node, , , are the proportional coefficients of the graph key node coefficient, uncertainty entropy coefficient, and autocorrelation timeliness coefficient, respectively. , , Both are greater than 0.

6. A fire scene auxiliary decision support system based on mobile self-organizing network according to claim 5, characterized in that: Quantify the priority of each alternative node, including: Set an alternative node evaluation coefficient threshold, compare the alternative node evaluation coefficient of each alternative node with the alternative node evaluation coefficient threshold, if the alternative node evaluation coefficient is greater than the alternative node evaluation coefficient threshold, then the alternative node is used as a replacement for the lost node and goes to the lost node, if the alternative node evaluation coefficient is less than or equal to the alternative node evaluation coefficient threshold, then the alternative node is not used as a replacement for the lost node and goes to the lost node, collect alternative nodes greater than the alternative node evaluation coefficient threshold, generate a priority selection order for the alternative nodes based on the size order of the alternative node evaluation coefficients greater than the alternative node evaluation coefficient threshold, and select the alternative node with the largest alternative node evaluation coefficient to go to the lost node.

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