An emergency decision-making intelligent agent system and method based on the OODA cycle and a storage medium

Abstract

The application provides an emergency decision-making intelligent agent system based on an OODA cycle, which comprises an observation module, a direction module, a decision module and an action module, and each module cooperatively works for emergency decision-making based on the OODA cycle, and through multi-technology fusion (the OODA cycle, quantum optimization, block chain, reinforcement learning and the like), realizes core advantages of efficient real-time response, intelligent collaborative decision-making, safe transparent execution and complex problem optimization, effectively solves technical bottlenecks of a traditional emergency system in dynamicity, cooperativity, safety and the like, and is suitable for large-scale and high-complexity emergency management scenes.

Description

Technical Field

[0001] This invention relates to the field of emergency management technology, and in particular to an emergency decision-making intelligent agent system based on the OODA loop. Background Technology

[0002] In the field of emergency management, efficient and accurate decision-making is crucial for responding to emergencies and protecting lives and property. With social development and technological advancements, emergency scenarios are becoming increasingly complex, placing higher demands on decision-making systems. However, traditional emergency decision-making systems have revealed significant limitations when facing numerous challenges.

[0003] Emergency decision-making relies on massive amounts of real-time data, including not only various environmental conditions such as temperature, humidity, and smoke concentration, but also data on environmental trends. However, traditional systems struggle to efficiently process such large volumes of dynamic data. Delays in data acquisition, transmission, and processing often cause decisions to lag behind the event's development, missing the optimal response window. For example, in fire accidents, the inability to obtain crucial data such as the fire's spread rate and changes in toxic gas concentrations in a timely manner can lead to flawed rescue decisions and negatively impact rescue effectiveness.

[0004] Traditional emergency decision-making systems suffer from insufficient decision-making coordination. In complex emergency scenarios involving multiple tasks and resources, it is necessary to coordinate multiple agents or resources to jointly complete rescue missions. However, traditional decision-making methods lack effective coordination mechanisms, making it difficult to rationally allocate tasks and schedule resources, resulting in low decision-making efficiency. For example, in post-earthquake rescue operations, multiple departments and related resources, such as fire fighting, medical services, and transportation, need to work together, but traditional systems cannot accurately coordinate the forces of all parties, which may lead to chaotic and disorderly rescue work.

[0005] Data security is also a major problem with traditional emergency decision-making systems. These systems often lack data transparency and robust security mechanisms, making them vulnerable to malicious attacks or data tampering. Once data is compromised, the accuracy and effectiveness of decisions will be significantly reduced. For example, if hackers maliciously tamper with casualty data at a disaster site, it could lead to misallocation of rescue resources and delays in the rescue process.

[0006] Traditional methods are ill-suited to complex and ever-changing emergency environments. Emergency scenarios are characterized by rapid environmental changes and inherent uncertainties, while traditional decision-making systems, largely based on pre-defined rules and models, struggle to adapt quickly to these changes. In flood disasters, real-time fluctuations in water levels and flow rates prevent traditional systems from updating their decision-making models in a timely manner, leading to a disconnect between decisions and reality.

[0007] Task allocation and optimization for large-scale emergency missions face numerous challenges. Traditional optimization methods are computationally intensive and inefficient, making it difficult to complete complex task allocation and resource scheduling in a short period. In large-scale public health events, tasks such as allocating medical supplies and deploying medical personnel are complex, and traditional methods cannot quickly provide optimal solutions, thus affecting the efficiency of epidemic prevention and control.

[0008] In recent years, the development of technologies such as multi-agent systems, reinforcement learning, quantum optimization algorithms, game theory, edge computing, and blockchain has provided new ideas for improving emergency decision-making systems. Agents in multi-agent systems possess independence and autonomy, enabling them to complete tasks through cooperation and competition. Reinforcement learning allows agents to optimize their behavioral strategies by interacting with the environment and receiving rewards or penalties. Quantum optimization algorithms utilize quantum properties to handle complex optimization problems. Game theory analyzes the interaction of decision-makers' strategies, optimizing resource sharing and coordination among multiple agents. Edge computing reduces data transmission latency. Blockchain technology ensures data security and transparency. However, research on combining these technologies with the OODA loop (Observe, Orient, Decide, Act) for emergency decision-making agent systems is still in its developmental stage, with significant room for innovation and improvement. A new emergency decision-making system is urgently needed to address existing problems and meet the needs of complex emergency scenarios. Summary of the Invention

[0009] This invention proposes an emergency decision-making intelligent agent system, method, and device based on the OODA loop, solving the problems of low data standardization and governance efficiency and the inability to comprehensively cover the entire process from data governance to intelligent analysis in existing technologies. The technical solution of this invention is implemented as follows:

[0010] An emergency decision-making intelligent agent system based on the OODA loop, comprising:

[0011] The observation module is used to collect multimodal emergency data in real time, and to preprocess and fuse the data through edge computing nodes to generate dynamic situation reports;

[0012] The targeting module integrates real-time and historical data based on reinforcement learning and game theory models to generate emergency decision-making objectives and optimize multi-agent collaborative strategies.

[0013] The decision-making module uses a quantum optimization algorithm combined with the emergency decision-making objectives to generate the optimal solution for task allocation and resource scheduling;

[0014] The action module executes task allocation through blockchain smart contracts, monitors task status in real time, and provides feedback on execution results.

[0015] The modules work collaboratively to make emergency decisions based on the OODA loop, achieving closed-loop control of observation, orientation, decision-making, and action.

[0016] As a preferred technical solution, the observation module includes:

[0017] Multimodal sensor networks are used to collect temperature, humidity, gas composition, and video stream data;

[0018] Edge computing nodes are used for data denoising, normalization, and preliminary fusion.

[0019] A situation prediction model based on deep learning is used to generate dynamic emergency situation reports.

[0020] As a preferred technical solution, the multi-sensor data fusion algorithm of the observation module includes Kalman filtering algorithm and Bayesian fusion algorithm, and the deep learning algorithm includes convolutional neural network (CNN) and recurrent neural network (RNN), which are used to fuse data from different sources and of different types.

[0021] As a preferred technical solution, the targeting module generates emergency response targets based on reinforcement learning. The target generation formula is as follows:

[0022] G(t) = goal(D(t), H(t))

[0023] G(t): represents the emergency decision objective generated at time t, specifically the objective for the current emergency situation;

[0024] D(t): represents the real-time data collected at time t;

[0025] H(t): Represents historical data, which is the recorded data of past events at time t;

[0026] goal(.): Represents a function that generates emergency decision-making objectives based on current real-time and historical data;

[0027] Meanwhile, multi-agent resource allocation and task priority scheduling are performed based on a utility function derived from game theory. The specific function is as follows:

[0028]

[0029] Among them, U agent (t) = Payoff(G(t), A(t)) is the utility function.

[0030] As a preferred technical solution, the quantum optimization algorithm of the decision-making module utilizes the properties of quantum superposition and entanglement to perform combined optimization on large-scale task allocation problems, generating optimal solutions:

[0031] Decide(G(t))=QuantumOptimize(G(t))

[0032] Where Decide(G(t)) represents the specific emergency decision scheme generated based on the objective G(t);

[0033] G(t): represents the emergency decision objective generated at time t;

[0034] Quantum Optimize(x): Represents a function that generates the optimal decision scheme using a quantum optimization algorithm.

[0035] As a preferred technical solution, the action module includes:

[0036] Blockchain-based smart contracts automatically assign tasks to up to multiple agents and record execution status.

[0037] The dynamic feedback mechanism updates the agent's execution result through a state transition function, where:

[0038] The task execution formula is:

[0039] Execute(G(t))=SmartContract(G(t))

[0040] Execute(G(t)) represents the operation of executing a task based on the generated emergency decision objective G(t);

[0041] G(t) represents the emergency decision objective generated at time t;

[0042] Smart Contract(x) represents a function that executes a task through a smart contract;

[0043] The dynamic feedback mechanism is as follows:

[0044] S i (t+1)=f(S i (t), Act i (t))

[0045] Among them, S i (t+1) represents the state of the i-th agent at time t+1, reflecting the state update after task execution;

[0046] S i (t) represents the state of the i-th agent at time t;

[0047] Act i (t) represents the action performed by the i-th agent at time t;

[0048] f(.) represents the state transition function, which calculates the state at the next moment based on the current state and the action to be performed.

[0049] As a preferred technical solution, the formula for data fusion is:

[0050]

[0051] This indicates the fused emergency situation report generated at time t, which includes the results of multimodal fusion.

[0052] m represents the number of different sensors or data sources;

[0053] ω i This represents the weight of the i-th data source;

[0054] D i (t) represents the data collected by the i-th data source at time t.

[0055] An emergency decision-making method, employing an emergency decision-making intelligent agent system based on an OODA loop, includes the following steps:

[0056] Step S1: Collect data in real time through a multimodal sensor network and generate a dynamic situation report using edge computing;

[0057] Step S2: Combine reinforcement learning and game theory to generate emergency objectives and multi-agent collaborative strategies;

[0058] Step S3: Use a quantum optimization algorithm to generate a task allocation and resource scheduling scheme;

[0059] Step S4: Execute the task and report the status through the blockchain smart contract to complete the OODA loop.

[0060] As a preferred technical solution, quantum optimization algorithms prioritize addressing the following problems:

[0061] NP-hard problems of path planning, resource allocation, and task scheduling;

[0062] Optimization solutions based on quantum annealing or variable quantum algorithms.

[0063] A non-transitory storage medium for storing a program that causes an emergency decision-making agent system based on an OODA loop to perform the aforementioned emergency decision-making method.

[0064] Compared with existing technologies, this solution has the following advantages:

[0065] (1) Improve the real-time nature of emergency decision-making; real-time data acquisition through a multimodal sensor network, combined with local preprocessing by edge computing nodes, significantly reduces data transmission latency and ensures that the decision response speed meets the timeliness requirements of emergency scenarios. Based on a deep learning-based dynamic situation analysis model, accurate emergency situation reports are quickly generated, providing real-time support for subsequent decision-making.

[0066] (2) Enhance multi-agent collaboration and self-adaptation capabilities; By dynamically adjusting strategies through reinforcement learning and combining game theory models to coordinate resource allocation and task priorities among multiple agents, the problems of low agent collaboration efficiency and resource conflicts in traditional methods are solved. Based on the integration of real-time and historical data, emergency targets are adaptively generated to adapt to complex and ever-changing emergency environments.

[0067] (3) Ensuring data security and execution transparency: The task allocation and execution process is recorded through blockchain technology, ensuring that the data is tamper-proof and the execution process is transparent and traceable, preventing malicious attacks or data leaks. Smart contracts are executed automatically, and the task allocation, status update, and feedback mechanisms do not require manual intervention, reducing the risk of human operation and improving execution reliability.

[0068] (4) Efficiently solve complex optimization problems; for NP-hard problems such as large-scale emergency task scheduling and path planning, the parallelism and superposition characteristics of quantum computing can provide the optimal solution that is difficult to achieve by traditional algorithms in a short time, significantly improving resource utilization and task execution efficiency.

[0069] (5) Multimodal data fusion and accurate decision-making; weighted fusion and situation modeling: multimodal data is integrated through multi-source data fusion formula to generate high-precision emergency situation reports and avoid the bias of a single data source. Attached Figure Description

[0070] 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.

[0071] Figure 1 This is a structural block diagram of an emergency decision-making intelligent agent system based on the OODA loop of the present invention. Detailed Implementation

[0072] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0073] Reference Figure 1 This application provides an emergency decision-making intelligent agent system based on the OODA loop. By combining various advanced technologies (such as multi-agent collaboration, reinforcement learning, quantum optimization, game theory optimization, edge computing, and blockchain), it offers an efficient, intelligent, and secure emergency decision-making solution. The system uses the OODA loop for emergency decision-making and is divided into four modules: observation, orientation, decision, and action. Each module involves multiple sub-modules and specific implementation processes. The implementation flow of each module will be described in detail below to ensure that the work of each module is interconnected, forming an efficient decision-making closed loop.

[0074] 1. Observation Module

[0075] 1.1 Data Acquisition and Fusion

[0076] Data acquisition is the starting point for emergency decision-making. Its purpose is to obtain real-time information about the on-site environment and the emergency event, providing foundational data support for subsequent decision-making. The system collects data through multiple devices (such as sensors, drones, and cameras). The data collected includes not only current on-site status information but also trend data on environmental changes.

[0077] Data acquisition process:

[0078] Multimodal data collection: Different types of sensors (such as temperature, humidity, and smoke detectors) and devices such as drones work simultaneously to collect data on temperature, humidity, gas composition, and video streams in real time. These sensors and devices are distributed in various key locations throughout the emergency area to ensure broad data coverage and high real-time performance.

[0079] Data is transmitted to edge computing nodes: Data collected by sensors is transmitted to nearby edge computing nodes via wireless networks. The edge nodes perform preliminary processing of the data locally, reducing data transmission latency and alleviating the burden on the central processing system.

[0080] Data preprocessing: Edge nodes perform data preprocessing, mainly including data cleaning, noise removal, and data normalization. The preprocessed data is then transmitted to the central decision-making system via a secure communication protocol to ensure its accuracy and integrity.

[0081] Data collection formula:

[0082]

[0083] D(t): represents the real-time data collected at time t, which includes the summary of data from all sensors.

[0084] N: Indicates the number of sensors.

[0085] Sensor i (t): Represents the data collected by the i-th sensor at time t. Different sensors may collect different types of data, such as temperature, humidity, video streams, etc.

[0086] 1.2 Data Fusion and Situation Generation

[0087] Through the data fusion module, the system comprehensively analyzes multimodal data collected from various sensors to form a unified emergency response model. Data fusion not only integrates real-time field data but also combines historical data to ensure a comprehensive understanding of event developments in a dynamically changing environment.

[0088] Data fusion process:

[0089] Data denoising and normalization: Data collected by different types of sensors may contain noise, so denoising algorithms (such as Kalman filtering, mean filtering, etc.) are needed to remove outliers from the data. Subsequently, all data is standardized or normalized to ensure comparability across different data sources.

[0090] Multimodal data fusion: The system employs fusion methods (such as weighted averaging, Kalman filtering, etc.) to fuse real-time data from different sensors. Each type of data (temperature, humidity, smoke concentration, video data, etc.) is assigned different weight values ​​based on its importance and reliability.

[0091] Situation Report Generation: The fused data is analyzed using deep learning models (such as LSTM and GRU), and the system predicts the current emergency situation and generates corresponding reports. These reports include detailed information about the current emergency event, potential risks, resource requirements, etc., providing support for subsequent orientation and decision-making modules.

[0092] Data fusion formula:

[0093]

[0094] This indicates the fused emergency situation report generated at time t, which includes the results of multimodal fusion.

[0095] m: represents the number of different sensors or data sources.

[0096] ω i: Represents the weight of the i-th data source. Different weights are assigned based on the reliability and importance of the sensor.

[0097] D i (t): represents the data collected by the i-th data source at time t.

[0098] S current (t)=LSTM(S prev (t-1), D(t) (LSTM-based situation prediction)

[0099] S current (t): Represents the current emergency situation predicted at time t.

[0100] S prev (t-1): Represents the emergency situation at the previous time t-1.

[0101] D(t): represents the real-time data collected at time t.

[0102] LSTM(.): This function is used to predict the current situation by processing data through a Long Short-Term Memory (LSTM) network.

[0103] 2. Orientation Module

[0104] 2.1 Data Integration and Target Generation

[0105] In the targeting module, the system combines real-time and historical data to formulate emergency decision-making goals and strategies. At this stage, the system intelligently evaluates emergency decision-making goals under different scenarios using artificial intelligence algorithms, especially reinforcement learning and game theory models.

[0106] Data integration process:

[0107] Reinforcement learning optimization strategy: The system will analyze the relationship between historical and real-time data using reinforcement learning algorithms to dynamically adjust decision-making objectives. For example, if the current environment changes, the system can adjust its strategy in real time to adapt to new emergency needs.

[0108] Goal Generation: Based on multi-dimensional data input, the system generates emergency response goals according to the urgency of the task, resource availability, time constraints, etc. These goals will be the key driving force for the system's next decision.

[0109] Target generation formula:

[0110] G(t) = goal(D(t), H(t))

[0111] G(t): represents the emergency decision target generated at time t, specifically the target for the current emergency situation.

[0112] D(t): represents the real-time data collected at time t.

[0113] H(t): Represents historical data, which is the recorded data of past events at time t. Historical data helps to compare and analyze the similarity between current events and past events.

[0114] goal(.): This function generates emergency decision-making objectives based on current real-time and historical data.

[0115] 2.2 Multi-agent cooperation and game theory optimization

[0116] The system optimizes the allocation of emergency resources and the execution sequence of tasks through a game theory model and a multi-agent collaborative mechanism. The game theory model ensures efficient coordination of the system in complex environments with multiple tasks and constraints by modeling the interactions between agents.

[0117] Game theory optimization process:

[0118] Resource and task allocation: The system analyzes the resource needs and task urgency of each agent using a game theory model to formulate reasonable resource and task allocation strategies. The system will dynamically adjust based on the current state of each agent (such as available resources and execution capabilities).

[0119] Cooperation and Competition: In a multi-task environment, competitive relationships may exist between agents. Game theory models optimize the cooperative relationships among all agents by calculating the utility functions of each agent, thereby maximizing the overall system benefit.

[0120] Game theory formula:

[0121]

[0122] Among them, U agent (t) = Payoff(G(t), A(t)) is the utility function.

[0123] 3. Decision Module

[0124] 3.1 Strategy Generation and Quantum Optimization

[0125] Based on the objectives generated by the orientation module, the decision-making module uses quantum optimization algorithms and traditional optimization methods to generate specific solutions for emergency decisions. Quantum optimization can effectively handle large-scale, complex combinatorial optimization problems, improving the system's computational efficiency and decision quality.

[0126] Strategy generation process:

[0127] Quantum optimization: Based on the superposition and entanglement properties of quantum computing, quantum optimization algorithms can handle optimization problems that are difficult for traditional computers to solve. In tasks such as task scheduling, resource allocation, and path planning, quantum optimization can provide optimal solutions in a short time.

[0128] Combining Game Theory and Reinforcement Learning: In multi-agent systems, the decision-making process needs to consider cooperation and competition among agents. Combining game theory and reinforcement learning algorithms to generate policies ensures the optimality of decision-making schemes.

[0129] Quantum optimization formula:

[0130] Decide(G(t))=QuantumOptimize(G(t))

[0131] Where Decide(G(t)) represents the specific emergency decision scheme generated based on the objective G(t);

[0132] G(t): represents the emergency decision objective generated at time t;

[0133] Quantum Optimize(x): Represents a function that generates the optimal decision scheme using a quantum optimization algorithm.

[0134] 4. Action Module

[0135] 4.1 Task Allocation and Execution

[0136] Task allocation and execution is the final step in emergency decision-making. During this process, the system uses smart contracts and blockchain technology to ensure the secure, efficient, and transparent execution of tasks.

[0137] Task allocation process:

[0138] Smart contract mechanism: Task allocation is automatically executed through smart contracts, ensuring that all tasks are assigned to appropriate smart agents according to priority. Each smart agent, upon receiving a task, automatically updates its task status and reports its progress.

[0139] Dynamic adjustment: Task allocation is dynamically adjusted based on real-time data feedback. For example, if an agent is unable to complete a task due to a malfunction, the system will reassign the task to another agent in real time.

[0140] Task execution feedback process:

[0141] Execution process monitoring and status updates: When an intelligent agent executes a task, the progress, results, and status of the task execution are recorded using blockchain technology to ensure the transparency and immutability of the task execution process. Execution results include information such as success status, time consumed, and resource consumption.

[0142] Execution result feedback: The execution results will be transmitted back to the central decision-making system through a feedback mechanism and used for the next round of decision-making and adjustments.

[0143] Task execution formula:

[0144] Execute(G(t)) = SmartContract(G(t)) (Smart contract execution task)

[0145] Execute(G(t)): Represents the operation of executing a task based on the generated emergency decision objective G(t).

[0146] G(t): represents the emergency decision target generated at time t.

[0147] Smart Contract(.): Represents a function that executes a task through a smart contract. Smart contracts can automatically execute tasks without human intervention and ensure that the execution of the task is not tampered with.

[0148] S i (t+1)=f(S i (t), Act i (t))(State Transition and Feedback)

[0149] S i (t+1): Represents the state of the i-th agent at time t+1, reflecting the state update after task execution.

[0150] S i (1): Represents the state of the i-th agent at time t.

[0151] Act i (t): Represents the action performed by the i-th agent at time t.

[0152] f(.): Represents the state transition function, which calculates the state at the next moment based on the current state and the action performed. Compared with the prior art, this application has the following advantages:

[0153] 1. Improve the real-time nature of emergency decision-making

[0154] By collecting data in real time through multimodal sensor networks (such as temperature, humidity, smoke sensors, drones, etc.) and combining it with edge computing nodes for local preprocessing (denoising, normalization), data transmission latency is significantly reduced, ensuring that the decision response speed meets the timeliness requirements of emergency scenarios.

[0155] Dynamic situational analysis models based on deep learning (such as LSTM) can quickly generate accurate emergency situation reports, providing real-time support for subsequent decision-making.

[0156] 2. Enhance multi-agent collaboration and adaptive capabilities

[0157] By dynamically adjusting strategies through reinforcement learning and combining game theory models to coordinate resource allocation and task priorities among multiple agents, the problems of low agent collaboration efficiency and resource conflicts in traditional methods can be solved.

[0158] By integrating real-time and historical data, emergency objectives (such as resource scheduling priorities and task allocation strategies) are adaptively generated to adapt to complex and ever-changing emergency environments.

[0159] 3. Ensure data security and operational transparency

[0160] The task allocation and execution process is recorded using blockchain technology to ensure that the data is immutable, the execution process is transparent and traceable, and to prevent malicious attacks or data leaks.

[0161] Task allocation, status updates, and feedback mechanisms require no manual intervention, reducing the risk of human error and improving execution reliability.

[0162] 4. Efficiently solve complex optimization problems

[0163] For NP-hard problems such as large-scale emergency task scheduling and path planning, the parallelism and superposition characteristics of quantum computing (such as quantum annealing and variable quantum algorithms) are utilized to provide optimal solutions that are difficult to achieve with traditional algorithms in a short time, significantly improving resource utilization and task execution efficiency.

[0164] 5. Multimodal data fusion and precise decision-making

[0165] By integrating multi-source data fusion formulas (such as LSTM models) and multi-modal data (sensor data, video streams, etc.), high-precision emergency situation reports can be generated, avoiding the bias of a single data source.

[0166] 6. System scalability and scenario adaptability

[0167] The modular architecture based on the OODA loop (Observe, Orient, Decision, Action) supports flexible expansion of new functions (such as adding new sensor types and optimizing algorithms) to adapt to different emergency scenarios (such as fire, earthquake, and public health events).

[0168] Cross-platform compatibility: Compatible with various hardware devices (drones, edge nodes) and software protocols, facilitating integration with existing emergency systems.

[0169] 7. Reduce the risk of human decision-making

[0170] Automated closed-loop control: The entire process of "data acquisition → analysis → decision-making → execution → feedback" is automated through the OODA loop, reducing misjudgments or delays caused by human intervention and improving the objectivity and accuracy of emergency response.

[0171] This system integrates multiple technologies (OODA loop, quantum optimization, blockchain, reinforcement learning, etc.) to achieve core advantages such as efficient real-time response, intelligent collaborative decision-making, secure and transparent execution, and optimization of complex problems. It effectively solves the technical bottlenecks of traditional emergency systems in terms of dynamism, collaboration, and security, and is suitable for large-scale and highly complex emergency management scenarios.

[0172] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An emergency decision-making intelligent agent system based on the OODA loop, characterized in that, include: The observation module is used to collect multimodal emergency data in real time, and to preprocess and fuse the data through edge computing nodes to generate dynamic situation reports; The targeting module integrates real-time and historical data based on reinforcement learning and game theory models to generate emergency decision-making objectives and optimize multi-agent collaborative strategies. The decision-making module uses a quantum optimization algorithm combined with the emergency decision-making objectives to generate the optimal solution for task allocation and resource scheduling; The action module executes task allocation through blockchain smart contracts, monitors task status in real time, and provides feedback on execution results. The modules work collaboratively to make emergency decisions based on the OODA loop, achieving closed-loop control of observation, orientation, decision-making, and action. The targeting module generates emergency response targets based on reinforcement learning, and the target generation formula is as follows: ； G(t): represents the emergency decision objective generated at time t, specifically the objective for the current emergency situation; D(t): represents the real-time data collected at time t; H(t): Represents historical data, which is the recorded data of past events at time t; goal(.): Represents a function that generates emergency decision-making objectives based on current real-time and historical data; Meanwhile, multi-agent resource allocation and task priority scheduling are performed based on a utility function derived from game theory. The specific function is as follows: ； in, It is a utility function; The quantum optimization algorithm of the decision-making module utilizes the properties of quantum superposition and entanglement to perform combined optimization on large-scale task allocation problems, generating the optimal solution: ； Where Decide(G(t)) represents the specific emergency decision scheme generated based on the objective G(t); G(t): represents the emergency decision objective generated at time t; QuantumOptimize(x): Represents a function that generates the optimal decision scheme using a quantum optimization algorithm; The action module includes: Blockchain-based smart contracts automatically assign tasks to up to multiple agents and record execution status. The dynamic feedback mechanism updates the agent's execution result through a state transition function, where: The task execution formula is: ； Execute(G(t)) represents the operation of executing a task based on the generated emergency decision objective G(t); G(t) represents the emergency decision objective generated at time t; SmartContract(x) represents a function that executes a task through a smart contract; The dynamic feedback mechanism is as follows: ； in, This represents the state of the i-th agent at time t+1, reflecting the state update after task execution; This represents the state of the i-th agent at time t; This represents the action performed by the i-th agent at time t; This represents the state transition function, which calculates the state at the next moment based on the current state and the action to be performed.

2. The emergency decision-making intelligent agent system based on the OODA loop as described in claim 1, characterized in that, The observation module includes: Multimodal sensor networks are used to collect temperature, humidity, gas composition, and video stream data; Edge computing nodes are used for data denoising, normalization, and preliminary fusion. A situation prediction model based on deep learning is used to generate dynamic emergency situation reports.

3. The emergency decision-making intelligent agent system based on the OODA loop as described in claim 2, characterized in that, The observation module employs multi-sensor data fusion algorithms including Kalman filtering and Bayesian fusion algorithms, and deep learning algorithms including convolutional neural networks (CNN) and recurrent neural networks (RNN), to fuse data from different sources and of different types.

4. An emergency decision-making method, characterized in that, An emergency decision-making intelligent agent system based on an OODA loop, as described in any one of claims 1 to 3, comprises the following steps: Step S1: Collect data in real time through a multimodal sensor network and generate a dynamic situation report using edge computing; Step S2: Combine reinforcement learning and game theory to generate emergency objectives and multi-agent collaborative strategies; Step S3: Use a quantum optimization algorithm to generate a task allocation and resource scheduling scheme; Step S4: Execute the task and report the status through the blockchain smart contract to complete the OODA loop.

5. An emergency decision-making method as described in claim 4, characterized in that, The quantum optimization algorithm prioritizes the following problems: NP-hard problems of path planning, resource allocation, and task scheduling; Optimization solutions based on quantum annealing or variable quantum algorithms.

6. A non-transitory storage medium, characterized in that, It is used to store a program that causes an emergency decision-making intelligent agent system based on an OODA loop as described in any one of claims 1 to 3 to perform the following action: execute an emergency decision-making method as described in any one of claims 4 to 5.

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