Emergency drilling system based on big data

Abstract

The invention relates to the technical field of emergency management and big data processing, and discloses an emergency drilling system based on big data, and the system comprises a data collection and recognition module which is used for collecting on-site images and video data in real time through a camera, unmanned aerial vehicle equipment and a mobile terminal, and recognizing the key information of an accident site through an image recognition algorithm; the big data analysis and prediction module is used for processing and analyzing the real-time data acquired from the data acquisition and identification module and predicting the development of the emergency event; and the hierarchical scene analysis and simulation module is used for decomposing the emergency event into a plurality of hierarchies for independent analysis and simulation, and the hierarchies are associated through a preset rule. By means of weapon deduction, multi-level analysis, real-time data processing and intelligent scheduling modules, the problems of experience dependence, decision lag, unreasonable resource scheduling and the like in traditional emergency management are solved, and the ability of coping with complex emergencies is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency management and big data processing, and in particular to an emergency drill system based on big data. Background Art

[0002] With the acceleration of globalization and urbanization, the frequency of various emergencies has increased significantly, including natural disasters, industrial accidents and public safety incidents. These incidents not only threaten people's lives and safety, but may also cause serious economic losses and social unrest. Therefore, it is particularly important to improve the efficiency and scientificity of emergency management.

[0003] However, existing emergency management technologies usually rely on traditional methods and have many shortcomings. First, many emergency drill systems rely on static data and past experience, and lack effective integration and analysis of real-time data. This results in decision makers being unable to quickly obtain accurate information when emergencies occur, affecting the speed and effectiveness of emergency response.

[0004] Secondly, existing technologies are also insufficient in dynamically adapting to changing on-site conditions. Traditional drills often lack flexibility and cannot effectively simulate complex situations that actually occur, resulting in poor drill results. In addition, resource scheduling usually relies on manual judgment and lacks scientific data support, which may lead to waste of resources or improper configuration, further affecting the rescue effect.

[0005] In order to solve the above problems, the present invention proposes an emergency drill system based on big data. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides an emergency drill system based on big data, which solves the problems of insufficient real-time data integration, lack of dynamic adaptability and unscientific resource scheduling in traditional emergency management.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an emergency drill system based on big data, comprising:

[0008] Data collection and recognition module, which is used to collect on-site images and video data in real time using cameras, drone equipment and mobile terminals, and use image recognition algorithms to identify key information at the accident scene;

[0009] Big data analysis and prediction module, used to process and analyze real-time data obtained from the data collection and recognition module, and predict the development of emergency events;

[0010] The hierarchical scenario analysis and simulation module is used to decompose emergency events into multiple levels for independent analysis and simulation, and each level is associated through preset rules;

[0011] Multi-level decision-making and simulation module, used to make decisions and simulate tasks at different levels;

[0012] Dynamic scenario generation and simulation module, which is used to generate multiple possible scenarios based on the set initial conditions and the development of events, and conduct war games in real time;

[0013] Intelligent scheduling and resource optimization module, which is used to optimize the scheduling and allocation of emergency resources based on the results of the big data analysis and prediction module;

[0014] The event chain trigger and adaptive feedback module is used to automatically trigger secondary events according to the development of emergency events, and to provide dynamic feedback and adjustments during the exercise;

[0015] The virtual rehearsal interface module is used to present the results of the layered scenario analysis and simulation module on a virtual reality or augmented reality device.

[0016] Preferably, the data collection and identification module includes:

[0017] An image recognition unit for identifying key objects on site;

[0018] Video analysis unit, used to analyze dynamic changes at the accident scene;

[0019] A multimodal sensor fusion unit that integrates data from multiple sensors to generate a complete scene description;

[0020] The identity and authority identification unit is used to verify the identity of the personnel participating in the exercise and assign corresponding tasks according to their authority.

[0021] Preferably, the big data analysis and prediction module includes:

[0022] A historical data analysis unit, used to analyze data of historical emergency events;

[0023] The real-time data prediction unit is used to predict the development trend of events based on real-time data.

[0024] Preferably, the intelligent scheduling and resource optimization module includes:

[0025] Resource availability monitoring unit, used to monitor the status of emergency resources in real time;

[0026] An adaptive scheduling unit, used to adjust resource scheduling strategies according to resource status changes;

[0027] The prediction and analysis unit is used to predict changes in resource demand based on historical data and real-time data and optimize resource allocation strategies.

[0028] Preferably, the event chain triggering and adaptive feedback module includes:

[0029] An event chain management unit, used to predefine and manage possible secondary event chains;

[0030] The feedback adjustment unit is used to dynamically adjust the emergency drill strategy according to the development of the event.

[0031] Preferably, the multi-level decision-making and simulation module includes:

[0032] Strategic decision-making unit, used to formulate overall resource deployment and rescue plans;

[0033] Tactical planning unit, used to develop specific action plans within the strategic framework and simulate possible sudden changes;

[0034] The operation execution unit is used to perform specific operation tasks at the micro level and adjust the operation steps through real-time feedback.

[0035] Preferably, the dynamic scenario generation and deduction module includes:

[0036] A scenario generation unit, used to automatically generate development scenarios according to the set initial parameters of the event, including normal scenarios and extreme scenarios;

[0037] The multi-path exploration unit is used to simultaneously deduce multiple possible development paths through a branching algorithm and evaluate the effectiveness of emergency plans under each path.

[0038] Preferably, the hierarchical scenario analysis and simulation module includes:

[0039] Hierarchical association unit, which is used to define the interaction between the levels and dynamically update them during the simulation;

[0040] The result evaluation unit is used to evaluate and provide feedback on analysis results at different levels.

[0041] Preferably, the virtual drill interface module includes:

[0042] Data visualization unit, used to convert analysis and simulation results into visual graphics for easy understanding by exercise personnel;

[0043] The interactive feedback unit is used to collect feedback from drill personnel in real time and adjust the drill strategy based on the feedback.

[0044] Working principle: When the system receives an alarm of an emergency, it will quickly start and collect real-time information on the scene through the data acquisition and recognition module. Drones and cameras deployed at the accident site begin to capture images and video data, while mobile terminal devices collect status information of on-site personnel. These data are transmitted to the central database in real time for storage.

[0045] The system then processes the collected data using image recognition algorithms and video analysis technology to automatically identify the source of the leak, the affected area, and the status of surrounding personnel.

[0046] The processed data is fed into the big data analysis and prediction module. Here, the system compares real-time data with historical emergency events, identifies the impact and consequences of similar events, and predicts the spread and environmental impact of the current event. After the analysis is completed, the system processes the emergency events in layers. The layered scenario analysis and simulation module breaks down the event into multiple layers for independent analysis, such as the strategic layer focusing on the overall deployment of resources, the tactical layer formulating specific rescue plans, and the operational layer performing specific rescue tasks.

[0047] Based on these analysis results, the system enters the multi-level decision-making and simulation stage. The strategic decision-making unit formulates the overall resource allocation plan, and the tactical planning unit is responsible for refining the specific action steps to ensure that each participating unit has a clear division of labor. At the same time, the operational execution unit performs tasks at the micro level and adjusts the operational steps based on real-time feedback to adapt to changes on the scene.

[0048] To cope with uncertainty, the dynamic scenario generation and deduction module generates multiple possible development scenarios based on the set initial conditions. These scenarios take into account factors such as weather changes and personnel behavior, and deduce the effects of emergency plans for different development paths through the multi-path exploration unit to evaluate the effectiveness of the response measures.

[0049] During the emergency response process, the intelligent dispatch and resource optimization module monitors the status of emergency resources on site in real time. The system automatically adjusts the resource dispatch strategy based on real-time data to ensure the optimal allocation of resources. For example, if the fire truck arrives late, the system can quickly mobilize other available resources for support.

[0050] When on-site conditions change, such as gas diffusion, the event chain trigger and adaptive feedback module will automatically trigger secondary events according to the preset conditions and quickly adjust the emergency strategy. This flexibility ensures a quick response in emergencies and reduces potential risks.

[0051] Finally, the system presents the analysis and simulation results in a visual form through the virtual exercise interface, which is convenient for the exercise personnel to understand and make decisions. After the exercise, the system will collect feedback from the participants, generate a summary report, record important data and lessons learned, and put forward improvement suggestions, providing valuable reference for future emergency management.

[0052] The present invention provides an emergency drill system based on big data, which has the following beneficial effects:

[0053] 1. Through war game simulation and multi-level analysis, the system of the present invention can simulate various emergency scenarios and provide scientific data support for decision makers, thereby improving the flexibility and effectiveness of emergency response, ensuring that correct decisions can be made quickly in complex situations, and solving the risks of decision-making relying on experience and misjudgment in traditional emergency management.

[0054] 2. The present invention generates multiple possible scenarios through real-time data, can adapt to on-site changes in a timely manner, and quickly adjust response plans. This dynamic adjustment capability reduces decision-making errors caused by information delays and improves the response speed to emergencies.

[0055] 3. The present invention uses an intelligent scheduling module to monitor the status of emergency resources in real time and automatically adjust resource allocation to ensure that resources are effectively deployed to where they are most needed, thereby maximizing the rescue effect. This solves the problem that traditional resource scheduling relies heavily on human judgment and may lead to waste or shortage of resources. Intelligent scheduling improves resource utilization efficiency.

[0056] 4. Through dynamic scenario generation and deduction, the system of the present invention can generate multiple possible scenarios under different initial conditions and conduct war game deductions in real time, so that the emergency team can foresee potential problems in advance and formulate corresponding strategies, which solves the problem that the existing technology usually lacks such flexibility and foresight and is difficult to cope with complex and changeable on-site conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a framework diagram of the system of the present invention;

[0058] Figure 2 It is a framework diagram of the data acquisition and identification module of the present invention;

[0059] Figure 3 This is a framework diagram of the big data analysis and prediction module of the present invention;

[0060] Figure 4 It is a framework diagram of the intelligent scheduling and resource optimization module of the present invention;

[0061] Figure 5 It is a framework diagram of the event chain triggering and adaptive feedback module of the present invention;

[0062] Figure 6 It is a framework diagram of the multi-level decision-making and simulation module of the present invention;

[0063] Figure 7 It is a framework diagram of the dynamic scenario generation and deduction module of the present invention;

[0064] Figure 8 It is a framework diagram of the hierarchical scenario analysis and simulation module of the present invention;

[0065] Fig. 9It is a framework diagram of the virtual rehearsal interface module of the present invention. DETAILED DESCRIPTION

[0066] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Please see attached Figure 1 , an embodiment of the present invention provides an emergency drill system based on big data, including:

[0068] Data collection and recognition module, which is used to collect on-site images and video data in real time using cameras, drone equipment and mobile terminals, and use image recognition algorithms to identify key information at the accident scene;

[0069] Big data analysis and prediction module, used to process and analyze real-time data obtained from the data collection and recognition module, and predict the development of emergency events;

[0070] The hierarchical scenario analysis and simulation module is used to decompose emergency events into multiple levels for independent analysis and simulation, and each level is associated through preset rules;

[0071] Multi-level decision-making and simulation module, used to make decisions and simulate tasks at different levels;

[0072] Dynamic scenario generation and simulation module, which is used to generate multiple possible scenarios based on the set initial conditions and the development of events, and conduct war games in real time;

[0073] Intelligent scheduling and resource optimization module, which is used to optimize the scheduling and allocation of emergency resources based on the results of the big data analysis and prediction module;

[0074] The event chain trigger and adaptive feedback module is used to automatically trigger secondary events according to the development of emergency events, and to provide dynamic feedback and adjustments during the exercise;

[0075] The virtual rehearsal interface module is used to present the results of the layered scenario analysis and simulation module on a virtual reality or augmented reality device.

[0076] Please see attached Figure 2 , the data acquisition and recognition module includes:

[0077] An image recognition unit for identifying key objects on site;

[0078] Video analysis unit, used to analyze dynamic changes at the accident scene;

[0079] A multimodal sensor fusion unit that integrates data from multiple sensors to generate a complete scene description;

[0080] The identity and authority identification unit is used to verify the identity of the personnel participating in the exercise and assign corresponding tasks according to their authority.

[0081] Specifically, the data collection and recognition module collects images and video data from the accident scene in real time through cameras, drones and mobile terminals deployed at the accident scene. These devices can cover a wide area and transmit the collected data to the system's central server. After receiving this data, the system will use image recognition algorithms to process the images and videos and automatically identify key information at the accident scene, such as the source of the leak, the location of the victim, and the surrounding environment.

[0082] Please see attached Figure 3 , the big data analysis and prediction modules include:

[0083] A historical data analysis unit, used to analyze data of historical emergency events;

[0084] The real-time data prediction unit is used to predict the development trend of events based on real-time data.

[0085] Specifically, the historical data analysis unit is responsible for retrieving and analyzing past emergency event data from the system database, using data mining and pattern recognition algorithms to identify historical events similar to the current event and their processing results. The historical events are classified through clustering algorithms, and the common characteristics and key influencing factors of the event development are discovered using association rule mining technology. At the same time, the time series analysis method is used to understand the time evolution of these events. The analysis results provide empirical support for the handling of current events, helping to predict possible event trends and the best response measures.

[0086] The real-time data prediction unit receives the latest on-site data from the data acquisition and recognition module, and analyzes the development trend of current events by combining machine learning models and prediction algorithms such as recursive neural networks and long short-term memory networks. Through these algorithms, the system can dynamically evaluate the potential development path of events and predict possible changes in the future, such as the spread of disasters and resource requirements. The Bayesian network is used to build a causal relationship model of events and further speculate on the possible evolution of events in the future.

[0087] Please see attached Figure 4 , the intelligent scheduling and resource optimization modules include:

[0088] Resource availability monitoring unit, used to monitor the status of emergency resources in real time;

[0089] An adaptive scheduling unit, used to adjust resource scheduling strategies according to resource status changes;

[0090] The prediction and analysis unit is used to predict changes in resource demand based on historical data and real-time data and optimize resource allocation strategies.

[0091] Specifically, the resource availability monitoring unit is responsible for real-time monitoring of the status of emergency resources. Emergency resources may include personnel, equipment, vehicles, medical supplies, etc. The unit obtains the current location, status (such as availability, working status) and other relevant information of these resources in real time through IoT devices, sensors and data acquisition systems, and transmits them to the central control system.

[0092] State monitoring model: Assume that each resource R i Status S i (t) at time t can be expressed by the following formula:

[0093] S i (t) = {L i (t),A i (t),C i (t)}

[0094] L i (t) represents resource R i position at time t.

[0095] A i (t) represents resource R i The availability status of the resource, such as whether it is in use or idle.

[0096] C i (t) represents resource R i Conditional status, such as power, fuel, functional status, etc.

[0097] By continuously updating S i (t), the system can grasp the status of each resource in real time, ensuring that the decision-makers have a comprehensive understanding of the resource status.

[0098] The adaptive scheduling unit dynamically adjusts the resource scheduling strategy based on the real-time data provided by the resource availability monitoring unit to respond to changes in demand caused by emergencies. The unit uses a dynamic optimization algorithm to balance resource supply and demand, ensuring that resources can be quickly and reasonably allocated to where they are most needed.

[0099] Scheduling optimization model:

[0100] The resource scheduling problem is usually modeled as an optimization problem, the goal is to minimize the resource scheduling time or cost. Assume that the objective function is Z:

[0101]

[0102] T ij Indicates resource R i From current location L i (t) Transportation time or cost to demand point j.

[0103] x ij is a decision variable, indicating whether to allocate resource R i Assigned to demand point j.

[0104] Constraints:

[0105]

[0106] x ij ∈{0,1}

[0107] These conditions ensure that each demand point j can get effective allocation of resources.

[0108] Dynamic adjustment formula:

[0109] As time t passes, resource status Si(t) and demand change, and the scheduling strategy needs to be adjusted dynamically:

[0110] x ij (t+1)=x ij (t)+α·ΔS j (t)

[0111] ΔS j (t) represents the state change of demand point j at time t.

[0112] α is the adjustment coefficient, which is used to control the adjustment speed.

[0113] Through this formula, the system can respond to changes in resource demand in real time and dynamically optimize resource scheduling strategies.

[0114] The forecasting and analysis unit uses historical data and real-time data to predict changes in resource demand in the future. The unit uses machine learning models (such as time series analysis, regression analysis) and statistical methods to infer the changing trend of resource demand under different scenarios, thereby optimizing resource allocation strategies in advance.

[0115] Demand forecasting model:

[0116] Resource demand forecast can be expressed by the following formula:

[0117] D j (t+1)=g(H j ,S j (t),t)

[0118] Dj (t+1) is the predicted value of resource demand for demand point j at the next time step t+1.

[0119] H j It is historical demand data.

[0120] S j (t) is the resource status at the current moment.

[0121] Optimization configuration formula:

[0122] To optimize resource allocation, the system can minimize the prediction error for the next time step:

[0123]

[0124] The goal of this optimization problem is to ensure that resource allocation matches the forecasted demand as closely as possible, reducing the risk of resource surplus or shortage.

[0125] Please refer to the attached Figure 5 , the event chain triggering and adaptive feedback module includes:

[0126] An event chain management unit, used to predefine and manage possible secondary event chains;

[0127] Feedback adjustment unit, used to dynamically adjust emergency drill strategies according to the development of events.

[0128] Specifically, the event chain model formula is:

[0129] Assume that the main event is E 0 , secondary event chain E 1 ,E 2 ,…,E n Defined by the causal matrix C, representing event E i Raise event E j Possibilities:

[0130]

[0131] c ij Indicates event E i After it occurs, event E is triggered j The probability or condition (e.g., chemical release E 1 May cause fire E 2 ).

[0132] In event chain management, the system predefines these secondary event chains and sets trigger conditions according to actual conditions. ij When a certain threshold is reached, event E j Will be triggered.

[0133] Dynamic Updates:

[0134] As the event progresses, the condition matrix C may change, reflecting the change in the probability of triggering the secondary event. The formula for dynamic update can be expressed as:

[0135] c ij (t+1)=f(c ij (t),S j (t))

[0136] S j (t) is related to event E j Related state variables (such as field temperature, gas concentration, etc.).

[0137] f is a function that represents the update mechanism of the condition matrix, which may be based on the trend of the current state change.

[0138] Through this dynamic update, the system can adjust the triggering conditions of the secondary event chain in real time, making event chain management more flexible.

[0139] 2. Feedback adjustment unit - dynamically adjust emergency drill strategies

[0140] The feedback adjustment unit dynamically adjusts the preset emergency drill strategy according to the real-time development of the incident to ensure the effectiveness of the emergency response. This unit achieves this goal by continuously monitoring the progress of the incident and evaluating the effectiveness of the drill.

[0141] Feedback adjustment formula:

[0142] Assume that the policy of the system at time t is π(t), and the goal of policy adjustment is to minimize the difference with the ideal policy π * The adjustment formula can be expressed as:

[0143] π(t+1)=π(t)+α·Δπ(t)

[0144] Among them, Δπ(t) represents the policy adjustment amount, which is calculated as follows:

[0145]

[0146] π * is the ideal strategy (the contingency plan based on the best current information).

[0147] α is the learning rate, which controls the speed of adjustment.

[0148] β and γ are adjustment coefficients that weigh the impact of current strategy deviation and strategy change trend, respectively.

[0149] Strategy Optimization:

[0150] The feedback adjustment unit not only adjusts the current strategy, but also continuously optimizes future strategies through historical data and real-time feedback, ensuring that the best response measures can be taken at each key node in the event chain.

[0151] Please refer to the attached Figure 6 , the multi-level decision-making and simulation modules include:

[0152] Strategic decision-making unit, used to formulate overall resource deployment and rescue plans;

[0153] Tactical planning unit, used to develop specific action plans within the strategic framework and simulate possible sudden changes;

[0154] The operation execution unit is used to perform specific operation tasks at the micro level and adjust the operation steps through real-time feedback.

[0155] Specifically, the multi-level decision-making and simulation module consists of a strategic decision-making unit, a tactical planning unit, and an operational execution unit, which are responsible for the overall deployment of resources, the formulation of specific action plans, and the execution of tasks at the micro level, respectively.

[0156] 1. Strategic Decision-making Unit - Resource Optimization and Deployment

[0157] At the strategic level, the optimal deployment of resources uses a linear programming algorithm to determine the optimal resource allocation plan.

[0158] Linear Programming Model:

[0159]

[0160] Z is the objective function to be maximized, representing the overall benefit of resource allocation (e.g., minimizing response time or maximizing rescue coverage).

[0161] x i It is a decision variable, which indicates the allocation amount of various resources (such as rescue personnel and equipment).

[0162] c i It is the efficiency coefficient of each type of resources (such as the unit efficiency of each type of resources).

[0163] a ij Indicates the consumption of resources under various constraints (such as time and budget).

[0164] b j is the upper limit value of each constraint.

[0165] By solving this linear programming problem, the system can derive the optimal resource allocation plan to ensure the best deployment of resources at the strategic level.

[0166] 2. Tactical Planning Unit - Dynamic Scenario Simulation and Action Plan

[0167] The tactical level involves the formulation of specific action plans, using the Markov decision process to deal with complex dynamic situations and simulate the possible outcomes of different decisions.

[0168] MDP Model:

[0169] State space (S): represents the different stages of the development of an event, such as the spread of a disaster or the progress of evacuation.

[0170] Action space (A): represents the emergency measures that can be taken at different stages, such as sending additional personnel and activating emergency facilities.

[0171] Transition probability (P(s'|s,a)): The probability of transitioning from state s to a new state s' by taking action a.

[0172] Reward function (R(s,a)): The reward value obtained by taking action a in each state s, which is used to measure the effectiveness of the action.

[0173] Optimization goal:

[0174]

[0175] π * is the optimal strategy, maximizing the cumulative reward from the current state to the future state.

[0176] γ is a discount factor, usually between 0 and 1, which is used to balance the weights of current and future rewards.

[0177] Through the MDP model, the system can simulate various emergencies and help formulate the optimal action plan under uncertain conditions.

[0178] 3. Operation execution unit - real-time feedback and task adjustment

[0179] At the operational level, the system uses reinforcement learning algorithms to provide real-time feedback and task adjustments to ensure flexibility and adaptability during task execution.

[0180] Q-learning algorithm:

[0181]

[0182] Q(s,a) represents the value of performing action a in state s.

[0183] α is the learning rate, which controls the impact of new information on existing Q values.

[0184] γ is a discount factor that balances the value of current and future rewards.

[0185] R(s,a) is the immediate reward for taking action a in state s.

[0186] The operation execution unit uses the Q-learning algorithm to continuously update the value assessment of each operation step and optimize the task execution process based on real-time feedback to cope with dynamic changes in on-site conditions.

[0187] Please refer to the attached Figure 7 , the dynamic scenario generation and deduction module includes:

[0188] A scenario generation unit, used to automatically generate development scenarios according to the set initial parameters of the event, including normal scenarios and extreme scenarios;

[0189] The multi-path exploration unit is used to simultaneously deduce multiple possible development paths through a branching algorithm and evaluate the effectiveness of emergency plans under each path.

[0190] Specifically, the dynamic scenario generation and deduction module consists of a scenario generation unit and a multi-path exploration unit. It is responsible for automatically generating multiple possible scenarios based on initial conditions in emergency event handling, and deducing the development path of each scenario through a branching algorithm to evaluate the effectiveness of the emergency plan.

[0191] 1. Scenario generation unit - generates multiple development scenarios

[0192] The scenario generation unit generates a variety of possible development scenarios based on the set initial event parameters (such as event type, weather conditions, time schedule, resource availability, etc.) using techniques such as random simulation and scenario planning.

[0193] Scenario generation model: Assuming the initial parameter is θ, the scenario generation formula is:

[0194] Scenario i =f(θ,ω i )

[0195] θ represents the initial condition parameters (such as resource status, external environmental factors, etc.).

[0196] ω i It is a random variable that represents the uncertainty factors in the scenario (such as the speed of accident spread, the occurrence of secondary disasters, etc.).

[0197] f(θ,ω i ) is a scenario generation function, which is used to calculate the event development path under different scenarios.

[0198] Through this formula, the system can generate multiple possible development paths, including normal scenarios and extreme scenarios, providing a basis for subsequent scenario deductions.

[0199] 2. Multi-path exploration unit - deduce and evaluate multiple development paths

[0200] The multi-path exploration unit uses branching algorithms and multi-path Monte Carlo simulations to deduce and evaluate multiple possible development paths under each scenario.

[0201] Path deduction formula:

[0202] P(s t ,a t )→P(s t+1 , a t+1 )

[0203] in:

[0204] P(s t ,a t ) means that at time t, the event is in state s t Take action when t The subsequent transition probability.

[0205] a t It is the state and action that the event may transfer to at the next time step t+1.

[0206] By repeating this deduction process, the system is able to generate a multi-path decision tree, in which each path represents the evolution of events that may occur under different conditions.

[0207] Path effect evaluation: To evaluate the effect of the emergency plan under each path, the system introduces the following objective function:

[0208]

[0209] E(Path) represents the effect evaluation value of the path (such as minimizing losses, maximizing rescue success rate).

[0210] R(s t ,a t ) is in state s t Take action t The immediate reward or penalty value (such as reducing the impact of disasters, casualties, etc.).

[0211] T represents the total time span of the simulation.

[0212] Through this evaluation function, the system can quantitatively compare emergency plans under different paths and select the plan with the best effect for implementation or further optimization.

[0213] The dynamic scenario generation and deduction module automatically generates multiple development scenarios based on different initial conditions through random simulation and scenario planning of the scenario generation unit; then, through the multi-path exploration unit, it uses branching algorithms and multi-path Monte Carlo simulation to deduce multiple possible development paths under each scenario, and quantitatively evaluates the effect of the emergency plan for each path. Ultimately, the system can provide decision makers with a variety of emergency response strategies to ensure that they can choose the best response plan when facing complex and changing emergencies.

[0214] Please refer to the attached Figure 8 , the hierarchical scenario analysis and simulation module includes:

[0215] Hierarchical association unit, which is used to define the interaction between the levels and dynamically update them during the simulation;

[0216] The result evaluation unit is used to evaluate and provide feedback on analysis results at different levels.

[0217] Specifically, the interactive relationship definition formula is:

[0218] Assume that the different levels in the system are L 1 ,L 2 ,…,L n , the interaction between each level can be expressed as a matrix R, where R ij Representation level L i For level L j Impact weight:

[0219]

[0220] R ij Representation level L i The decision or behavior of level L j The degree of influence of the strategic layer on the tactical layer (for example, the strategic layer has a higher influence on the tactical layer, then r 12 will be larger).

[0221] Dynamic update formula:

[0222] As the simulation progresses, the state S at a certain level i (t) will change dynamically at time t with the feedback from other levels. Assume that level L i The state update formula is:

[0223]

[0224] S i (t+1) is the level L i The status at time t (such as current resource configuration, plan execution progress, etc.).

[0225] ΔSj (t) is the level L j The amount of change in state at time t.

[0226] This means that at each time step, the level L i The states of each level will be affected by changes in other levels, and the weights are determined by the interaction matrix R.

[0227] Multi-dimensional evaluation formula:

[0228] Assuming that the execution effect of each level can be evaluated by multiple indicators, such as time T, cost C, effect E, etc., then level L i The comprehensive evaluation formula can be expressed as:

[0229]

[0230] Eval i It is level L i The comprehensive evaluation score.

[0231] T i ,C i ,E i Level L i The actual values ​​of execution time, cost and effect.

[0232] T opt ,C opt ,E opt It is the ideal value or benchmark value of the corresponding indicator.

[0233] w T ,w C ,w E It is the weight coefficient of the corresponding indicator, reflecting the importance of each indicator in the evaluation.

[0234] This formula is used to comprehensively consider indicators of multiple dimensions such as time, cost, and effect to generate a hierarchical overall evaluation score.

[0235] Feedback adjustment formula:

[0236] After receiving the evaluation results, the system will evaluate the current level of i Make adjustments. The adjustment formula can be expressed as:

[0237]

[0238] ΔS i (t+1) is the level L i The amount by which the state is adjusted at the next time step.

[0239] Eval targetis the target evaluation score (i.e., the evaluation score under ideal conditions).

[0240] α is the adjustment coefficient, which controls the speed and amplitude of the adjustment.

[0241] Indicates the rate of change of the current state and is used to predict the direction and magnitude of future adjustments.

[0242] Through this formula, the system will dynamically adjust the execution strategy and status of the hierarchy according to the difference between the actual and target evaluation scores to gradually optimize the entire emergency response process.

[0243] Please refer to the attached Fig. 9 , the virtual drill interface module includes:

[0244] Data visualization unit, used to convert analysis and simulation results into visual graphics for easy understanding by exercise personnel;

[0245] The interactive feedback unit is used to collect feedback from drill personnel in real time and adjust the drill strategy based on the feedback.

[0246] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The emergency drill system based on big data is characterized by: include: Data collection and recognition module, which is used to collect on-site images and video data in real time using cameras, drone equipment and mobile terminals, and use image recognition algorithms to identify key information at the accident scene; Big data analysis and prediction module, used to process and analyze real-time data obtained from the data collection and recognition module, and predict the development of emergency events; The hierarchical scenario analysis and simulation module is used to decompose emergency events into multiple levels for independent analysis and simulation, and each level is associated through preset rules; Multi-level decision-making and simulation module, used to make decisions and simulate tasks at different levels; Dynamic scenario generation and simulation module, which is used to generate multiple possible scenarios based on the set initial conditions and the development of events, and conduct war games in real time; Intelligent scheduling and resource optimization module, which is used to optimize the scheduling and allocation of emergency resources based on the results of the big data analysis and prediction module; The event chain trigger and adaptive feedback module is used to automatically trigger secondary events according to the development of emergency events, and to provide dynamic feedback and adjustments during the exercise; The virtual rehearsal interface module is used to present the results of the layered scenario analysis and simulation module on a virtual reality or augmented reality device.

2. The emergency drill system based on big data according to claim 1 is characterized in that: The data acquisition and identification module includes: An image recognition unit for identifying key objects on site; Video analysis unit, used to analyze dynamic changes at the accident scene; A multimodal sensor fusion unit that integrates data from multiple sensors to generate a complete scene description; The identity and authority identification unit is used to verify the identity of the personnel participating in the exercise and assign corresponding tasks according to their authority.

3. The emergency drill system based on big data according to claim 1 is characterized in that: The big data analysis and prediction module includes: A historical data analysis unit, used to analyze data of historical emergency events; The real-time data prediction unit is used to predict the development trend of events based on real-time data.

4. The emergency drill system based on big data according to claim 1 is characterized in that: The intelligent scheduling and resource optimization module includes: Resource availability monitoring unit, used to monitor the status of emergency resources in real time; An adaptive scheduling unit, used to adjust resource scheduling strategies according to resource status changes; The prediction and analysis unit is used to predict changes in resource demand based on historical data and real-time data and optimize resource allocation strategies.

5. The emergency drill system based on big data according to claim 1 is characterized in that: The event chain triggering and adaptive feedback module includes: An event chain management unit, used to predefine and manage possible secondary event chains; Feedback adjustment unit, used to dynamically adjust emergency drill strategies according to the development of events.

6. The emergency drill system based on big data according to claim 1 is characterized in that: The multi-level decision-making and simulation module includes: Strategic decision-making unit, used to formulate overall resource deployment and rescue plans; Tactical planning unit, used to develop specific action plans within the strategic framework and simulate possible sudden changes; The operation execution unit is used to perform specific operation tasks at the micro level and adjust the operation steps through real-time feedback.

7. The big data-based emergency drill system according to claim 1, characterized in that: The dynamic scenario generation and deduction module includes: A scenario generation unit, used to automatically generate development scenarios according to the set initial parameters of the event, including normal scenarios and extreme scenarios; The multi-path exploration unit is used to simultaneously deduce multiple possible development paths through a branching algorithm and evaluate the effectiveness of emergency plans under each path.

8. The big data-based emergency drill system according to claim 1, characterized in that: The hierarchical scenario analysis and simulation module includes: Hierarchical association unit, which is used to define the interaction between the levels and dynamically update them during the simulation; The result evaluation unit is used to evaluate and provide feedback on analysis results at different levels.

9. The emergency drill system based on big data according to claim 1, characterized in that: The virtual drill interface module includes: Data visualization unit, used to convert analysis and simulation results into visual graphics for easy understanding by exercise personnel; The interactive feedback unit is used to collect feedback from drill personnel in real time and adjust the drill strategy based on the feedback.

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