A fire rescue path planning method based on artificial intelligence

Abstract

The present invention relates to the technical field of firefighting and rescue path planning, and discloses an artificial intelligence-based firefighting and rescue path planning method, comprising: step 1, establishing a dynamic environment model by modeling the firefighter's state, wherein the firefighter's state is represented by multiple sets of dimensional variables to form a state vector, which is updated by changes in time to dynamically reflect the firefighter's current state; and step 2, constructing an optimization objective function. By adopting blockchain technology to achieve decentralized data sharing in firefighting and rescue path planning, the bottleneck of limited information transmission in traditional centralized systems is avoided, ensuring that firefighters, command centers, and participants can seamlessly share real-time path planning, fire dynamics, and building structure data. The decentralized data transmission mechanism improves the efficiency of collaborative operations among all parties, ensures the efficiency and immediacy of data transmission, and achieves rapid response and decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of fire rescue path planning, and in particular to a fire rescue path planning method based on artificial intelligence. Background Art

[0002] The rapid development of modern cities has led to an increasing frequency and complexity of fire disasters. Traditional fire rescue route planning methods rely on centralized information management and route optimization systems, which suffer from issues such as limited data sharing, delayed information transmission, and single points of failure. These issues lead to firefighters facing insufficient and untimely information during the rescue process, which in turn affects rescue efficiency and safety.

[0003] When responding to fire disasters, information exchange between firefighters and command centers is crucial. Traditional centralized systems are prone to data lags and coordination difficulties, hindering the timely sharing of critical fire dynamics and on-site information. Furthermore, current fire safety information systems present significant trust risks. Data is controlled by a small number of central nodes, making it vulnerable to tampering or leakage, and the reliability of the information cannot be guaranteed.

[0004] To improve fire safety information management and ensure firefighters can complete rescue missions efficiently and safely, a new technical solution is urgently needed to address data transmission bottlenecks, trust issues, and system failure risks in existing systems. In this context, blockchain technology, with its decentralized and tamper-proof nature, offers a new solution for fire rescue route planning. Blockchain technology enables decentralized data sharing, ensuring efficient and immediate information transmission, enhancing data trust, and ensuring data security and high system availability through distributed ledger technology and redundant backup mechanisms.

[0005] Therefore, the present invention provides an artificial intelligence-based fire rescue path planning method for fire rescue tasks by combining an artificial intelligence-based path planning method with blockchain technology to solve the above-mentioned problems. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a fire rescue path planning method based on artificial intelligence to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a fire rescue path planning method based on artificial intelligence, comprising:

[0008] Step 1: Establish a dynamic environment model by modeling the firefighter's state. The firefighter's state is represented by multiple sets of variables to form a state vector. The state vector is updated with time to dynamically reflect the firefighter's current state.

[0009] Step 2: Construct an optimization objective function. The optimization objective function evaluates the quality of the firefighter's path by comprehensively considering the time consumption, path length, control energy, and potential danger factors of the firefighter's path. The objective function is designed based on the dynamic distribution of the firefighter's current position, target position, and environmental factors.

[0010] Step 3: Apply optimal control theory to solve the optimal path. By setting control variables, the firefighter's movement pattern is adjusted to minimize the objective function. Then, based on the optimal control equation, a control strategy is derived to enable the firefighter to move along the predetermined path and respond to dynamic environmental changes in real time.

[0011] Step 4: Based on the obtained path planning results, deep reinforcement learning is used to dynamically optimize the path planning. At each moment, according to the environmental state and path planning feedback, the intelligent agent adjusts the path planning strategy so that the firefighter can adapt to environmental changes and choose the optimal path;

[0012] Step 5: Combined with the path planning results, blockchain technology is used to achieve data sharing and security. Firefighters, the command center, and other participants share real-time location information, fire dynamics, and building structure data through the blockchain system. The data is verified and stored through encryption and consensus mechanisms.

[0013] Step 6: Based on the distributed ledger technology of blockchain, if some nodes fail or are attacked, the remaining nodes can continue to provide complete path planning and environmental data, ensuring that the firefighters' path planning system continues to operate in a disaster environment.

[0014] Preferably, the state vector in step 1 In the figure, x1(t) represents the position of the firefighter in two-dimensional and three-dimensional space, x2(t) represents the speed of the firefighter, and x n (t) represents the dynamic factors of posture and acceleration, and the state vector is updated in real time according to time changes to dynamically reflect the status of the firefighter.

[0015] Preferably, the optimization objective function J in step 2 is designed as:

[0016]

[0017] in, represents the speed of the firefighter, u(t) represents the control input, ||u(t)|| 2 To control energy consumption, f h (x(t)) represents the impact of the dangerous area on the path, f f(x(t)) is the influence of fire spread, and w1, w2, w3, w4 are the corresponding weight coefficients, is the rate of change of the firefighter's position, J represents the overall cost of path planning, T is the total time of planning, and dt represents the time step.

[0018] Preferably, in step 3, the optimal control theory is applied through the optimal control equation:

[0019]

[0020] in, represents the rate of change with respect to time t, H is the Hamiltonian, is the derivative of the state variable, is the partial derivative of the Hamiltonian with respect to the rate of change of state x, Hamiltonian versus state variables The partial derivative of

[0021] To solve the control strategy of the optimal path, the Hamiltonian H(x(t),u(t),λ(t)) is:

[0022]

[0023] Among them, H(x(t),u(t),λ(t)) is the Hamiltonian, which contains the state variable x(t), the control variable u(t) and the Lagrange multiplier λ(t), L(x(t),u(t)) is the path optimization cost function, is the rate of change of the state variable.

[0024] Preferably, in step 4, deep reinforcement learning is used to optimize path planning, and the reward function R(S(t), A(t)) is designed as:

[0025] R(S(t),A(t))=-γ·di(S(t),go)-λ·ha(S(t))+μ·time e (A(t)),

[0026] Among them, R(S(t), A(t)) is the reward value under the current state S(t) and the action A(t), γ is the discount factor,

[0027] di(S(t),go) is the distance from the current state S(t) to the target position go, λ is the weight coefficient,

[0028] ha(S(t)) is the risk level of the current state S(t), μ is the weight coefficient,

[0029] time e (A(t)) is the time efficiency of the current action A(t).

[0030] Preferably, the deep reinforcement learning in step 4 adopts Q-learning to update the path planning strategy, and the update formula is:

[0031]

[0032] Among them, Q(S t ,A t ) is the current state S t Next, take action A t The Q value, α is the learning rate,

[0033] R t+1 is the immediate reward from the current moment t to the next moment t+1,

[0034] γ is the discount factor, In the next state S t+1 Next take action a ′ The maximum Q value,

[0035] Q(S t+1 ,a ′ ) is in the next state S t+1 Next take action a ′ Q value.

[0036] Preferably, in step 5, blockchain technology is used to achieve data sharing and security assurance. Firefighters, command centers, and participants share real-time data through blockchain, including firefighters' location information, fire dynamics, and building structure, and data is verified and stored through encryption and consensus mechanisms.

[0037] Preferably, the data is verified and stored through the smart contract of the blockchain to ensure the authenticity and non-tamperability of the shared data. The smart contract automatically executes the data update, verification and recording process according to pre-set rules.

[0038] Preferably, the blockchain technology in step 5 ensures decentralized sharing of fire safety information, and through the mechanism of distributed ledger, achieves instant synchronization of data among firefighters, command centers and participants, ensuring that all parties do not need to rely on centralized servers.

[0039] Preferably, the distributed ledger technology of the blockchain ensures data redundancy and high availability. If a node fails or is attacked, the remaining nodes can continue to provide path planning and environmental data, ensuring that the firefighters' path planning system can operate continuously and stably in a disaster environment.

[0040] The present invention provides a fire rescue path planning method based on artificial intelligence. It has the following beneficial effects:

[0041] 1. This invention uses blockchain technology to achieve decentralized data sharing in fire rescue route planning, avoiding the bottleneck of limited information transmission in traditional centralized systems, ensuring that firefighters, command centers and participants can seamlessly share real-time route planning, fire dynamics and building structure data. The decentralized data transmission mechanism improves the efficiency of collaborative operations among all parties, ensures the efficiency and immediacy of data transmission, and achieves rapid response and decision support.

[0042] 2. The present invention solves the trust risk of fire safety information through the immutability and consensus mechanism of blockchain. Shared fire data are encrypted and stored and verified through the consensus mechanism to ensure the authenticity and consistency of the data. Data change records are public and traceable, which greatly reduces the risk of information tampering and ensures that decisions are made based on trusted data, thereby enhancing the reliability of fire rescue decisions.

[0043] 3. The present invention ensures the autonomy and redundancy of data through the distributed ledger technology and redundant backup mechanism of blockchain, avoiding the risk of system crash caused by single point failure. Each node can store and update complete fire rescue path planning and environmental data. Even if some nodes fail or are attacked, the remaining nodes can continue to provide complete data, ensuring the high availability and data security of the system, and achieving the effect of continuous and stable operation in disaster scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0045] To help those skilled in the art understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0046] The present invention is described in detail below with reference to the accompanying drawings:

[0047] Example:

[0048] Please see the attached Figure 1 , an embodiment of the present invention provides a fire rescue path planning method based on artificial intelligence, comprising:

[0049] Step 1: Establish a dynamic environment model by modeling the firefighter's state. The firefighter's state is represented by multiple sets of dimensional variables to form a state vector. The state vector is updated with time to dynamically reflect the firefighter's current state.

[0050] Step 2: Construct an optimization objective function. This function evaluates the quality of a firefighter's path by comprehensively considering factors such as time consumption, path length, control energy, and potential danger. The objective function is designed based on the dynamic distribution of the firefighter's current position, target position, and environmental factors.

[0051] Step 3: Apply optimal control theory to solve the optimal path. By setting control variables, the firefighter's movement pattern is adjusted to minimize the objective function. Then, based on the optimal control equation, a control strategy is derived to enable the firefighter to move along the predetermined path and respond to dynamic environmental changes in real time.

[0052] Step 4: Based on the obtained path planning results, deep reinforcement learning is used to dynamically optimize the path planning. At each moment, according to the environmental state and path planning feedback, the intelligent agent adjusts the path planning strategy so that the firefighter can adapt to environmental changes and choose the optimal path;

[0053] Step 5: Combined with the path planning results, blockchain technology is used to achieve data sharing and security. Firefighters, the command center, and other participants share real-time location information, fire dynamics, and building structure data through the blockchain system. The data is verified and stored through encryption and consensus mechanisms.

[0054] Step 6: Based on the distributed ledger technology of blockchain, if some nodes fail or are attacked, the remaining nodes can continue to provide complete path planning and environmental data, ensuring that the firefighters' path planning system continues to operate in a disaster environment.

[0055] The benefit of step 1 is that it can achieve real-time updates and adaptive adjustments to path planning, ensuring that firefighters can respond to changes in the disaster environment in a timely manner.

[0056] The benefit of step 2 is that it effectively avoids dangerous areas and optimizes energy consumption. Through comprehensive optimization goals, it ensures that firefighters choose paths that are both short and safe, effectively improving rescue efficiency and safety.

[0057] The benefit of step 3 is that it can provide firefighters with the optimal exercise plan under different environmental conditions, ensuring that firefighters follow the most effective path in a dynamic environment, while also having the ability to adjust in real time to adapt to sudden environmental changes.

[0058] The benefit of step 4 is that deep reinforcement learning can gradually improve the efficiency and reliability of path planning strategies through continuous interaction with the environment, enabling firefighters to make accurate and efficient path choices when faced with complex and unpredictable disaster environments.

[0059] The benefit of step 5 is that the decentralized nature of blockchain can resolve the bottleneck of centralized data management, enabling instant and highly reliable information sharing. Encrypted storage and consensus mechanisms can effectively prevent data tampering and leakage, enhancing system security.

[0060] The benefit of step 6 is that it ensures high data availability and system stability through distributed storage and multi-redundancy mechanisms. Even in a disaster environment, the system can still maintain stable operation and ensure the smooth progress of the rescue mission.

[0061] The state vector in step 1 In the figure, x1(t) represents the position of the firefighter in two-dimensional and three-dimensional space, x2(t) represents the speed of the firefighter, and x n (t) represents the dynamic factors of posture and acceleration, and the state vector is updated in real time according to time changes to dynamically reflect the status of the firefighter.

[0062] The state modeling method can provide real-time information of spatial location and capture the motion state of firefighters, ensuring that path planning takes into account the actual motion situation.

[0063] The state vector is updated in real time over time, enabling rapid response to changes in a firefighter's status in complex environments. This means that whether during a rescue operation or as the fire or building structure changes, the firefighter's status information is updated promptly, providing the latest environmental data for subsequent route planning, enabling it to adapt to the ever-changing on-site environment.

[0064] By dynamically updating the state vector, path planning can more accurately predict firefighters' next actions and optimize path selection. Especially in emergencies such as fire spread and building collapse, timely state updates can improve the system's response speed, ensuring firefighters choose the safest and most efficient path, reducing risks during the rescue process.

[0065] The optimization objective function J in step 2 is designed as:

[0066]

[0067] in, represents the speed of the firefighter, u(t) represents the control input, ||u(t)|| 2 To control energy consumption, f h (x(t)) represents the impact of the dangerous area on the path, f f (x(t)) is the influence of fire spread, and w1, w2, w3, w4 are the corresponding weight coefficients, is the rate of change of the firefighter's position, J represents the overall cost of path planning, T is the total time of planning, and dt represents the time step.

[0068] The optimization objective function J in step 2 comprehensively considers multiple important factors in the firefighter's path. This multi-objective optimization approach ensures that path planning focuses on the shortest path while also taking into account safety and energy efficiency. This comprehensive consideration improves the scientific nature of path planning and ensures that firefighters complete their mission in the shortest possible time and in the safest manner.

[0069] The objective function incorporates factors related to route safety. This design minimizes firefighters' entry into hazardous areas and proximity to fire sources during route optimization, ensuring their safety. By adjusting the weighting coefficients, the system flexibly balances route safety and efficiency, ensuring both firefighter productivity and life safety.

[0070] The optimization objective function includes an energy consumption term. By optimizing the control input u(t), the energy consumption of firefighters during the mission can be effectively reduced. This means that path planning focuses on speed and safety, while also reducing unnecessary energy waste, improving mission sustainability, and reducing fatigue.

[0071] By adding the time factor, the objective function can optimize the time consumption of the path, ensuring that firefighters can complete the rescue mission efficiently, shorten the response time, extinguish the fire source and rescue trapped people in time when performing the mission.

[0072] In step 3, the optimal control theory is applied through the optimal control equation:

[0073]

[0074] in, represents the rate of change with respect to time t, H is the Hamiltonian, is the derivative of the state variable, is the partial derivative of the Hamiltonian with respect to the rate of change of state x, Hamiltonian versus state variables The partial derivative of

[0075] To solve the control strategy of the optimal path, the Hamiltonian H(x(t),u(t),λ(t)) is:

[0076]

[0077] Among them, H(x(t),u(t),λ(t)) is the Hamiltonian, which contains the state variable x(t), the control variable u(t) and the Lagrange multiplier λ(t), L(x(t),u(t)) is the path optimization cost function, is the rate of change of the state variable.

[0078] Optimal control theory provides a rigorous mathematical framework for path planning problems. By applying optimal control equations, it can accurately solve how to optimize the path under given constraints and determine the optimal choice of control variables.

[0079] The Hamiltonian, H, is the core of the optimal control problem. It integrates the system's state variables, control inputs, and cost functions, and is the key to measuring system optimality during path optimization. By setting the Hamiltonian, firefighters are guided along the optimal path from a control theory perspective, ensuring an efficient and safe path and avoiding the risks associated with blindly selecting a path.

[0080] In the optimal control equation, the state variable x(t) and the control variable u(t) are interconnected through the partial derivatives of the Hamiltonian. A real-time feedback mechanism dynamically adjusts the path plan based on the current state at each moment. If the environment changes, optimal control theory can update the control strategy in real time, ensuring that firefighters always follow the optimal path.

[0081] In step 4, deep reinforcement learning is used to optimize path planning, and the reward function R(S(t), A(t)) is designed as:

[0082] R(S(t),A(t))=-γ·di(S(t),go)-λ·ha(S(t))+μ·time e (A(t)),

[0083] Among them, R(S(t), A(t)) is the reward value under the current state S(t) and the action A(t), γ is the discount factor,

[0084] di(S(t),go) is the distance from the current state S(t) to the target position go, λ is the weight coefficient,

[0085] ha(S(t)) is the risk level of the current state S(t), μ is the weight coefficient,

[0086] time e (A(t)) is the time efficiency of the current action A(t).

[0087] Deep reinforcement learning optimizes path planning in step 4, leveraging a reward function to comprehensively consider factors such as path safety, time efficiency, and riskiness, providing an adaptive, flexible, and continuously optimized path selection method. By dynamically adjusting weight coefficients, the system optimizes path selection based on specific mission requirements. Furthermore, by introducing discount factors, the intelligent agent can balance long-term and short-term goals, ensuring comprehensive and efficient path planning. Deep reinforcement learning improves path planning accuracy, enhances the system's adaptability to environmental changes, and strengthens its ability to cope with complex situations, thereby improving the efficiency and safety of firefighting and rescue missions.

[0088] The deep reinforcement learning in step 4 uses Q-learning to update the path planning strategy. The update formula is:

[0089]

[0090] Among them, Q(S t ,A t ) is the current state S t Next, take action A t The Q value, α is the learning rate,

[0091] R t+1 is the immediate reward from the current moment t to the next moment t+1,

[0092] γ is the discount factor, In the next state S t+1 Next take action a ′ The maximum Q value,

[0093] Q(S t+1 ,a ′ ) is in the next state S t+1 Next take action a ′ Q value.

[0094] By updating the path planning strategy through Q-learning, the system continuously optimizes path selection based on immediate feedback from each decision. This continuous learning mechanism enables the system to flexibly adapt to complex and dynamic environmental conditions, and balances short-term and long-term objectives through a discount factor to ensure efficient and safe path planning. The introduction of the Q-learning algorithm enables path planning to rapidly respond to the current environment and optimize based on potential future changes, providing an adaptive and reliable path planning solution that significantly improves adaptability and decision-making quality during firefighting and rescue missions.

[0095] In step 5, blockchain technology is used to achieve data sharing and security. Firefighters, command centers, and participants share real-time data through blockchain, including firefighters' location information, fire dynamics, and building structure, and verify and store data through encryption and consensus mechanisms.

[0096] Data is verified and stored through blockchain smart contracts to ensure the authenticity and immutability of shared data. Smart contracts automatically execute data updates, verification, and recording processes according to pre-set rules.

[0097] The blockchain technology in step 5 ensures the decentralized sharing of fire safety information. Through the distributed ledger mechanism, data can be synchronized instantly between firefighters, command centers, and participants, ensuring that all parties do not need to rely on centralized servers.

[0098] Blockchain's distributed ledger technology ensures data redundancy and high availability. If a node fails or is attacked, the remaining nodes can continue to provide path planning and environmental data, ensuring that the firefighters' path planning system can operate continuously and stably in a disaster environment.

[0099] The blockchain technology in Step 5 effectively addresses data transmission bottlenecks, information tampering, and single points of failure in traditional firefighting and rescue route planning through decentralized data sharing, cryptographic verification, smart contracts, and distributed ledger mechanisms. Through decentralized data storage and synchronization, the system can share and verify data in real time, ensuring the accuracy and security of firefighter route planning. Furthermore, blockchain's redundant backups and high-availability design ensure the system's continued stable operation in disaster environments, providing reliable and intelligent technical support for firefighting and rescue missions.

[0100] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A fire rescue path planning method based on artificial intelligence, characterized in that: include: Step 1: Establish a dynamic environment model by modeling the firefighter's state. The firefighter's state is represented by multiple sets of variables to form a state vector. The state vector is updated with time to dynamically reflect the firefighter's current state. The state vector in step 1 In the figure, x1(t) represents the position of the firefighter in two-dimensional and three-dimensional space, x2(t) represents the speed of the firefighter, and x n (t) represents the dynamic factors of posture and acceleration, and the state vector is updated in real time according to time changes to dynamically reflect the state of the firefighter; Step 2: Construct an optimization objective function. The optimization objective function evaluates the quality of the firefighter's path by comprehensively considering the time consumption, path length, control energy, and potential danger factors of the firefighter's path. The objective function is designed based on the dynamic distribution of the firefighter's current position, target position, and environmental factors. Step 3: Apply optimal control theory to solve the optimal path. By setting control variables, the firefighter's movement pattern is adjusted to minimize the objective function. Then, based on the optimal control equation, a control strategy is derived to enable the firefighter to move along the predetermined path and respond to dynamic environmental changes in real time. Step 4: Based on the obtained path planning results, deep reinforcement learning is used to dynamically optimize the path planning. At each moment, according to the environmental state and path planning feedback, the intelligent agent adjusts the path planning strategy so that the firefighter can adapt to environmental changes and choose the optimal path; In step 4, deep reinforcement learning is used to optimize path planning, and the reward function R(S(t), A(t)) is designed as: R(S(t),A(t))=-γ·di(S(t),go)-λ·ha(S(t))+μ·time e (A(t)), Among them, R(S(t), A(t)) is the reward value under the current state S(t) and the action A(t), γ is the discount factor, di(S(t), go) is the distance from the current state S(t) to the target position go, λ is the weight coefficient, ha(S(t)) is the danger level on the current state S(t), μ is the weight coefficient, and time e (A(t)) is the time efficiency of the current action A(t); The deep reinforcement learning in step 4 adopts Q-learning to update the path planning strategy, and the update formula is: Among them, Q(S t ,A t ) is the current state S t Next, take action A t Q value, α is the learning rate, R t+1 is the immediate reward from the current moment t to the next moment t+1, γ is the discount factor, In the next state S t+1 The maximum Q value of taking action a′, Q(S t+1 ,a′) is in the next state S t+1 The Q value of taking action a′; Step 5: Combined with the path planning results, blockchain technology is used to achieve data sharing and security. Firefighters, the command center, and other participants share real-time location information, fire dynamics, and building structure data through the blockchain system. The data is verified and stored through encryption and consensus mechanisms. Step 6: Based on the distributed ledger technology of blockchain, if some nodes fail or are attacked, the remaining nodes can continue to provide complete path planning and environmental data, ensuring that the firefighters' path planning system continues to operate in a disaster environment.

2. The fire rescue path planning method based on artificial intelligence according to claim 1, characterized in that: The optimization objective function J in step 2 is designed as: in, represents the speed of the firefighter, u(t) represents the control input, ||u(t)|| 2 To control energy consumption, f h (x(t)) represents the impact of the dangerous area on the path, f f (x(t)) is the influence of fire spread, and w1, w2, w3, w4 are the corresponding weight coefficients, is the rate of change of the firefighter's position, J represents the overall cost of path planning, T is the total time of planning, and dt represents the time step.

3. The fire rescue path planning method based on artificial intelligence according to claim 1, characterized in that: In step 3, optimal control theory is applied through the optimal control equation: in, represents the rate of change with respect to time t, H is the Hamiltonian, is the derivative of the state variable, is the partial derivative of the Hamiltonian with respect to the rate of change of state x, Hamiltonian versus state variables The partial derivative of To solve the control strategy of the optimal path, the Hamiltonian H(x(t),u(t),λ(t)) is: Among them, H(x(t),u(t),λ(t)) is the Hamiltonian, which contains the state variable x(t), the control variable u(t) and the Lagrange multiplier λ(t), L(x(t),u(t)) is the path optimization cost function, is the rate of change of the state variable.

4. The fire rescue path planning method based on artificial intelligence according to claim 1, characterized in that: In step 5, blockchain technology is used to achieve data sharing and security. Firefighters, the command center, and participants share real-time data through blockchain, including firefighter location information, fire dynamics, and building structure. The data is verified and stored through encryption and consensus mechanisms. The data is verified and stored through smart contracts on the blockchain to ensure the authenticity and immutability of shared data. Smart contracts automatically execute the data update, verification and recording process according to pre-set rules; The blockchain technology in step 5 above ensures decentralized sharing of fire safety information. Through the distributed ledger mechanism, data is synchronized instantly between firefighters, command centers, and participants, ensuring that all parties do not need to rely on centralized servers. The blockchain's distributed ledger technology ensures data redundancy and high availability. If a node fails or is attacked, the remaining nodes can continue to provide path planning and environmental data, ensuring that the firefighters' path planning system operates continuously and stably in disaster environments.

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