Emergency decision-making agent system and method based on OODA circulation and storage medium

Abstract

The invention provides an emergency decision-making agent system based on OODA circulation, and the system comprises an observation module, an orientation module, a decision-making module, and an action module, all the modules carry out the cooperative work of emergency decision-making based on the OODA circulation, and through the multi-technology fusion (OODA circulation, quantum optimization, block chain, reinforcement learning, etc. The core advantages of efficient real-time response, intelligent collaborative decision making, safe and transparent execution, complex problem optimization and the like are achieved, the technical bottlenecks of a traditional emergency system in the aspects of dynamics, collaboration, safety and the like are effectively solved, and the method is suitable for large-scale and high-complexity emergency management scenes.

Description

Technical Field

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

[0002] In the field of emergency management, efficient and accurate decision-making is crucial for responding to emergencies and protecting life and property. With the development of society and advancements in technology, emergency scenarios are becoming increasingly complex, placing increasing demands on decision-making systems. However, traditional emergency decision-making systems have exposed significant limitations in the face of numerous challenges.

[0003] Emergency decision-making relies on massive amounts of real-time data. This data includes not only various on-site environmental status information, such as temperature, humidity, and smoke concentration, but also data on environmental trends. However, traditional systems struggle to efficiently process massive amounts of dynamic data. Delays in data collection, transmission, and processing often cause decisions to lag behind the development of events, missing the optimal time to respond. For example, in a fire incident, the inability to obtain timely data such as the speed of fire spread and changes in toxic gas concentrations can lead to deviations in rescue decisions, compromising rescue effectiveness.

[0004] Traditional emergency decision-making systems lack collaborative decision-making capabilities. Complex emergency scenarios involving multiple tasks and resources require coordination of multiple agents or resources to complete rescue missions. However, traditional decision-making methods lack effective collaborative mechanisms, making it difficult to rationally allocate tasks and schedule resources, resulting in inefficient decision execution. For example, in post-earthquake rescue operations, multiple departments and related resources, such as firefighting, medical care, and transportation, must collaborate. However, traditional systems are unable to accurately coordinate these various forces, potentially leading to chaotic and disorganized rescue efforts.

[0005] Data security is also a major issue with traditional emergency decision-making systems. These systems often lack data transparency and robust security mechanisms, making them vulnerable to malicious attacks and data tampering. Once data corruption occurs, the accuracy and effectiveness of decisions are significantly compromised. For example, hackers maliciously tampering with casualty data at a disaster site could lead to misallocation of rescue resources and delay the rescue process.

[0006] Traditional methods are poorly adaptable to complex and ever-changing emergency environments. Emergency scenarios are characterized by rapid change and uncertainty, and traditional decision-making systems, often based on pre-set rules and models, struggle to adapt quickly to these changes and make adjustments. During flood disasters, flood levels and flow rates fluctuate in real time, making it difficult for traditional systems to update their decision-making models in a timely manner. This results in decisions being out of sync with actual conditions.

[0007] Task allocation and optimization for large-scale emergency response missions are fraught with difficulties. Traditional optimization methods are computationally intensive and inefficient, making it difficult to quickly complete complex task allocation and resource scheduling. In large-scale public health incidents, the deployment of medical supplies and the arrangement of medical personnel are complex tasks, and traditional methods are unable to quickly generate optimal solutions, hindering the effectiveness of anti-epidemic efforts.

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

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

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

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

[0012] The directional module integrates real-time and historical data based on reinforcement learning and game theory models, generates emergency decision-making goals, and optimizes multi-agent coordination strategies;

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

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

[0015] Among them, the modules work together to make emergency decisions based on the OODA cycle, realizing closed-loop control of observation, orientation, decision-making and action.

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

[0017] Multimodal sensor networks for collecting temperature, humidity, gas composition, and video streaming data;

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

[0019] A situation prediction model based on a deep learning model generates dynamic emergency situation reports.

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

[0021] As a preferred technical solution, the directional 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-making goal generated at time t, specifically the specific goal 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 the function that generates the emergency decision goal, based on the current real-time data and historical data;

[0027] At the same time, multi-agent resource allocation and task priority scheduling are performed based on the utility function of 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 module uses the characteristics of quantum superposition and entanglement to perform combinatorial optimization on large-scale task allocation problems and generate the optimal solution:

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

[0032] Among them, Decide(G(t)) represents the specific emergency decision-making plan generated based on the target G(t);

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

[0034] Quantum Optimize(x): represents a function that generates an optimal decision solution through a quantum optimization algorithm.

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

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

[0037] Dynamic feedback mechanism, which updates the agent execution results through the state transfer function, where:

[0038] The task execution formula is:

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

[0040] Execute(G(t)) represents the operation of executing the task according to the generated emergency decision goal G(t);

[0041] G(t) represents the emergency decision-making target generated at time t;

[0042] Smart Contract (x) represents the function that executes tasks through smart contracts;

[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 the task is executed;

[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 transfer function, which calculates the state at the next moment based on the current state and the execution action.

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

[0050]

[0051] represents the fused emergency situation report generated at time t, which contains the results of multimodal fusion;

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

[0053] ω i 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 using an emergency decision-making 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 goals and multi-agent collaboration strategies;

[0058] Step S3: Using quantum optimization algorithm to generate task allocation and resource scheduling solutions;

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

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

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

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

[0063] A non-transitory storage medium is used to store a program, which is used to enable an emergency decision-making intelligent agent system based on an OODA loop to perform the following actions: executing the emergency decision-making method.

[0064] Compared with the existing technology, this solution has the following beneficial effects:

[0065] (1) Improve the real-time nature of emergency decision-making; by collecting data in real time through a multimodal sensor network and combining it with edge computing nodes for local pre-processing, data transmission latency is significantly reduced, ensuring that decision-making response speeds meet the timeliness requirements of emergency scenarios. A dynamic situation analysis model based on deep learning can quickly generate accurate emergency situation reports, providing real-time support for subsequent decision-making.

[0066] (2) Enhance the collaborative and adaptive capabilities of multiple agents. By dynamically adjusting strategies through reinforcement learning and combining game theory models to coordinate resource allocation and task priorities among multiple agents, this approach addresses the low efficiency of agent collaboration and resource conflicts encountered in traditional approaches. Based on the integration of real-time and historical data, emergency response targets are adaptively generated to adapt to complex and changing emergency environments.

[0067] (3) Ensure data security and execution transparency; task allocation and execution processes are recorded through blockchain technology to ensure that data cannot be tampered with and the execution process is transparent and traceable, preventing malicious attacks or data leaks. Smart contracts are automatically executed, and task allocation, status updates, and feedback mechanisms do not require human 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 are used to provide optimal solutions that are difficult to achieve with traditional algorithms in a short period of time, significantly improving resource utilization and task execution efficiency.

[0069] (5) Multimodal data fusion and accurate decision-making; weighted fusion and situation modeling: Integrate multimodal data through multi-source data fusion formulas to generate high-precision emergency situation reports and avoid the deviation of a single data source. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[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 DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall 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, which provides an efficient, intelligent and safe emergency decision-making solution by combining a variety of advanced technologies (such as multi-agent collaboration, reinforcement learning, quantum optimization, game theory optimization, edge computing, blockchain, etc.). The system makes emergency decisions through the OODA loop and is divided into four modules: observation, orientation, decision-making and action. Each module involves multiple sub-modules and specific implementation processes. The implementation process of each module will be elaborated in detail below to ensure that the work of each module is linked together to form an efficient decision-making closed loop.

[0074] 1. Observation module

[0075] 1.1 Data Collection and Fusion

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

[0077] Data collection process:

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

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

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

[0081] Data collection formula:

[0082]

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

[0084] N: represents 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 stream, etc.

[0086] 1.2 Data Fusion and Situation Generation

[0087] The system uses a data fusion module to comprehensively analyze multimodal data collected from various sensors to form a unified emergency situation model. Data fusion not only integrates real-time field data but also combines historical data to ensure a comprehensive understanding of the development of events 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 and mean filtering) are needed to remove outliers. Subsequently, all data is standardized or normalized to ensure comparability across different data sources.

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

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

[0092] Data fusion formula:

[0093]

[0094] It represents the fused emergency situation report generated at time t, which contains the result 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 according to the credibility 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)) (Situation prediction based on LSTM)

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

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

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

[0102] LSTM(.): Represents a function processed by a long short-term memory (LSTM) network to predict the current situation.

[0103] 2. Orientation module

[0104] 2.1 Data integration and target generation

[0105] In the directional module, the system combines integrated real-time and historical data to formulate emergency decision-making objectives and strategies. At this stage, the system uses artificial intelligence algorithms, particularly reinforcement learning and game theory models, to intelligently assess emergency decision-making objectives in different scenarios.

[0106] Data integration process:

[0107] Reinforcement Learning Optimization Strategy: The system uses reinforcement learning algorithms to analyze the relationship between historical and real-time data and 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 based on 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-making goal generated at time t, specifically the specific goal 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 record of past events at time t. Historical data helps to compare and analyze the similarities between current events and past events.

[0114] goal(.): represents the function that generates the emergency decision goal based on the current real-time data and historical data.

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

[0116] The system optimizes the allocation of emergency resources and the order of task execution through a game theory model and multi-agent coordination mechanism. The game theory model models the interactions between agents, ensuring efficient coordination in a complex environment with multiple tasks and constraints.

[0117] Game theory optimization process:

[0118] Resource and Task Allocation: The system uses game theory models to analyze each agent's resource needs and task urgency, formulating appropriate resource and task allocation strategies. The system dynamically adjusts these strategies based on each agent's current state (e.g., available resources, execution capabilities).

[0119] Collaboration and Competition: In a multi-task environment, agents may compete with each other. Game theory models optimize the collaborative relationships among all agents by calculating their utility functions to maximize the overall benefits of the system.

[0120] Game theory formula:

[0121]

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

[0123] 3. Decision-making module

[0124] 3.1 Strategy Generation and Quantum Optimization

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

[0126] Strategy generation process:

[0127] Quantum optimization: Leveraging the superposition and entanglement properties of quantum computing, quantum optimization algorithms can tackle optimization problems that are difficult for traditional computers. For tasks like scheduling, resource allocation, and path planning, quantum optimization can provide optimal solutions in a fraction of the time.

[0128] Combining game theory with reinforcement learning: In a multi-agent system, the decision-making process needs to consider the collaboration and competition between agents. Game theory and reinforcement learning algorithms are combined to generate strategies and ensure the optimality of the decision-making plan.

[0129] Quantum optimization formula:

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

[0131] Among them, Decide(G(t)) represents the specific emergency decision-making plan generated based on the target G(t);

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

[0133] Quantum Optimize(x): represents a function that generates an optimal decision solution through a quantum optimization algorithm.

[0134] 4. Action Module

[0135] 4.1 Task Allocation and Execution

[0136] Task allocation and execution are the final stages of 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 the appropriate agents according to priority. After receiving a task, each agent automatically updates the task status and provides feedback on the execution progress.

[0139] Dynamic Adjustment: Task assignments are dynamically adjusted based on real-time data feedback. For example, if an agent fails 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 monitoring and status updates: When an agent executes a task, the progress, results, and status of the task are recorded through blockchain technology, ensuring transparency and immutability of the task execution process. Execution results include information such as success, time consumption, and resource consumption.

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

[0143] Task execution formula:

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

[0145] Execute(G(t)): represents the operation of executing the task according to the generated emergency decision-making target G(t).

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

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

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

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

[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 executed action. Compared with the existing technology, this application has the following beneficial effects:

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

[0154] Real-time data collection through multimodal sensor networks (such as temperature, humidity, smoke sensors, drones, etc.) and local preprocessing (denoising, normalization) in combination with edge computing nodes can significantly reduce data transmission delays and ensure that decision response speeds meet the timeliness requirements of emergency scenarios.

[0155] Dynamic situation analysis models based on deep learning (such as LSTM) can quickly generate accurate emergency situation reports and provide 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 are solved.

[0158] Based on the integration of real-time data and historical data, emergency goals (such as resource scheduling priorities and task allocation strategies) are adaptively generated to adapt to complex and changing emergency environments.

[0159] 3. Ensure data security and execution transparency

[0160] The task allocation and execution process is recorded through blockchain technology to ensure that the data cannot be tampered with, the execution process is transparent and traceable, and to prevent malicious attacks or data leaks.

[0161] Task allocation, status update and feedback mechanisms do not require human intervention, reducing the risk of human operation 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 state characteristics of quantum computing (such as quantum annealing and variational quantum algorithms) are utilized to provide optimal solutions that are difficult to achieve with traditional algorithms in a short period of time, significantly improving resource utilization and task execution efficiency.

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

[0165] Through multi-source data fusion formulas (such as) and LSTM models, multimodal data (sensors, video streams, etc.) are integrated to generate high-precision emergency situation reports and avoid the deviation of a single data source.

[0166] 6. System scalability and scenario adaptability

[0167] The modular architecture based on the OODA loop (observe, orient, decide, act) supports flexible expansion of new functions (such as adding new sensor types and optimizing algorithms) and adapts to different emergency scenarios (such as fire, earthquake, and public health events).

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

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

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

[0171] Through the integration of multiple technologies (OODA loop, quantum optimization, blockchain, reinforcement learning, etc.), this system has achieved core advantages such as efficient real-time response, intelligent collaborative decision-making, safe and transparent execution, and optimization of complex problems. It has effectively solved the technical bottlenecks of traditional emergency systems in terms of dynamics, collaboration, and security, and is suitable for large-scale, 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 in the scope of protection of the present invention.

Claims

1. An emergency decision-making intelligent agent system based on OODA loop, characterized by: include: The observation module is used to collect multimodal emergency data in real time, pre-process and fuse the data through edge computing nodes, and generate dynamic situation reports; The directional module integrates real-time and historical data based on reinforcement learning and game theory models, generates emergency decision-making goals, and optimizes multi-agent coordination strategies; The decision-making module uses quantum optimization algorithms combined with the emergency decision-making goals to generate the optimal solution for task allocation and resource scheduling; The action module executes task assignments through blockchain smart contracts, monitors task status in real time, and provides feedback on execution results; Among them, the modules work together to make emergency decisions based on the OODA cycle, realizing closed-loop control of observation, orientation, decision-making and action.

2. The OODA loop-based emergency decision-making agent system according to claim 1, characterized in that: The observation module includes: Multimodal sensor networks for collecting temperature, humidity, gas composition, and video streaming data; Edge computing nodes, used for data denoising, normalization, and preliminary fusion; A situation prediction model based on a deep learning model generates dynamic emergency situation reports.

3. The OODA loop-based emergency decision-making agent system according to claim 2, characterized in that: The multi-sensor data fusion algorithm of the observation module includes the Kalman filter algorithm and the Bayesian fusion algorithm, and the deep learning algorithm includes the convolutional neural network CNN and the recurrent neural network RNN, which are used to fuse data from different sources and of different types.

4. The OODA loop-based emergency decision-making agent system according to claim 1, characterized in that: The directional module generates emergency response targets based on reinforcement learning. The target generation formula is as follows: G(t)=goal(D(t),H(t)) G(t): represents the emergency decision-making goal generated at time t, specifically the specific goal 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 the function that generates the emergency decision goal, based on the current real-time data and historical data; At the same time, multi-agent resource allocation and task priority scheduling are performed based on the utility function of game theory. The specific function is as follows: Among them, U agent (t)=Payoff(G(t), A(t)) is the utility function.

5. The OODA loop-based emergency decision-making agent system according to claim 1, characterized in that: The quantum optimization algorithm of the decision module uses quantum superposition and entanglement properties to perform combinatorial optimization on large-scale task allocation problems and generate the optimal solution: Decide(G(t))=QuantumOptimize(G(t)) Among them, Decide(G(t)) represents the specific emergency decision-making plan generated based on the target G(t); G(t): represents the emergency decision-making target generated at time t; Quantum Optimize(x): represents a function that generates an optimal decision solution through a quantum optimization algorithm.

6. The OODA loop-based emergency decision-making agent system according to claim 1, characterized in that: The action module includes: Blockchain-based smart contracts that automatically assign tasks to multiple agents and record execution status; Dynamic feedback mechanism, which updates the agent execution results through the state transfer function, where: The task execution formula is: Execute(G(t))=SmartContract(G(t)) Execute(G(t)) represents the operation of executing the task according to the generated emergency decision goal G(t); G(t) represents the emergency decision-making target generated at time t; Smart Contract (x) represents the function that executes the task through the smart contract; The dynamic feedback mechanism is as follows: S i (t+1)=f(S i (t),Act i (t)) Among them, S i (t+1) represents the state of the i-th agent at time t+1, reflecting the state update after the task is executed; S i (t) represents the state of the i-th agent at time t; Act i (t) represents the action performed by the i-th agent at time t; f(.) represents the state transfer function, which calculates the state at the next moment based on the current state and the execution action.

7. The OODA loop-based emergency decision-making agent system according to claim 1, characterized in that: The formula for data fusion is: S i (t) represents the fused emergency situation report generated at time t, which contains the results of multimodal fusion; m represents the number of different sensors or data sources; ω i represents the weight of the i-th data source; D i (t) represents the data collected by the i-th data source at time t.

8. An emergency decision-making method, characterized in that: An emergency decision-making agent system based on an OODA loop as claimed in any one of claims 1 to 7 is used, comprising 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 goals and multi-agent collaboration strategies; Step S3: Using quantum optimization algorithm to generate task allocation and resource scheduling solutions; Step S4: Execute the task and feedback the status through the blockchain smart contract to complete the OODA closed loop.

9. An emergency decision-making method according to claim 8, characterized in that: The quantum optimization algorithm prioritizes the following issues: NP-hard problems of path planning, resource allocation, and task scheduling; Optimization solutions based on quantum annealing or variational quantum algorithms.

10. A non-transitory storage medium, characterized in that: It is used to store a program, which is used to enable an emergency decision-making intelligent agent system based on an OODA loop as described in any one of claims 1 to 7 to perform the following actions: executing an emergency decision-making method as described in any one of claims 8 to 9 above.

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