Disaster risk early warning method and system based on big data model

Abstract

The invention relates to the technical field of disaster early warning and risk management, and discloses a disaster risk early warning method based on a big data model, and the method comprises the following steps: S1, data collection: collecting environment data and social data through a sensor and Internet of Things equipment; s2, risk calculation: calculating an environment risk index according to the environment data, and calculating a social activity risk index according to the social data; the invention also discloses a disaster risk early warning system based on the big data model, and the system comprises a data collection module which is used for collecting the environment data and the social data through the sensor and the Internet of Things equipment. By constructing a society-environment interaction risk model, introducing a risk chain conduction mechanism and intervention measures, dynamically evaluating a disaster risk propagation process, simulating multiple disaster scenes, calculating emergency response time and optimizing emergency resource deployment, disaster extension is effectively predicted, loss is reduced, and the accuracy and efficiency of emergency response are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster warning and risk management, and in particular to a disaster risk warning method and system based on a big data model. Background Art

[0002] In recent years, natural disasters have occurred frequently around the world, including earthquakes, floods, typhoons, landslides and other forms of disasters, which pose a major threat to the safety of human society. With the acceleration of urbanization, the concentration of population and social activities has further aggravated the risks brought by disasters. Especially in densely populated metropolitan areas, once natural disasters occur, they are likely to cause secondary disasters and widespread socio-economic impacts. Therefore, how to improve the risk prediction capabilities of disasters and the response speed of disaster management has become an important issue of concern to countries around the world.

[0003] Traditional disaster warning systems often rely on a single data source or physical model, lacking dynamic simulation of multi-dimensional, multi-node disaster propagation processes, resulting in limited prediction accuracy. In addition, existing technologies are insufficient in dealing with the complexity of disasters, and it is difficult to comprehensively consider the impact of social activities on the spread of disasters, especially in areas with dense human activities. These systems lack sufficient foresight and cannot intervene in the expansion process of disasters in a timely manner. In this regard, the present invention proposes a disaster risk warning method and system based on a big data model. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a disaster risk warning method and system based on a big data model, which solves the problem of lack of multi-dimensional data fusion, dynamic simulation and timely intervention mechanism in the existing disaster warning system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a disaster risk early warning method based on a big data model, comprising the following steps:

[0006] S1. Data collection: Collect environmental and social data through sensors and IoT devices;

[0007] S2. Risk calculation: calculating an environmental risk index based on the environmental data, and calculating a social activity risk index based on the social data;

[0008] S3. Construction of social-environmental interaction model: Combine the environmental risk index and social activity risk index to construct a social-environmental interaction risk model and calculate the social-environmental interaction risk index of each region;

[0009] S4, Risk intervention and adjustment: Introduce intervention mechanisms at key nodes in the risk chain and adjust the risk values ​​of subsequent nodes to reduce the impact of disasters;

[0010] S5. Scenario simulation and emergency response: simulate various disaster scenarios, evaluate disaster damage under different scenarios, obtain risk assessment and scenario simulation results, and calculate emergency response time;

[0011] S6. Disaster warning release: Generate and release disaster warning information based on risk assessment and scenario simulation results.

[0012] Preferably, the environmental data in step S1 includes meteorological data, geological data and hydrological data, and the social data in step S1 includes population density, traffic flow and energy consumption data.

[0013] Preferably, the risk calculation in step S2 specifically includes the following steps:

[0014] S2.1. Calculation of environmental risk index: convert environmental data into a standardized format and calculate the environmental risk index according to a preset formula;

[0015] S2.2. Calculation of social activity risk index: Standardize the social activity data and calculate the social activity risk index according to the following formula:

[0016] S i =w " % i +w F ·' i +w $ ·( i ,in,% i ,' i 、( i They represent population density, traffic flow, and energy consumption level respectively;

[0017] S2.3. Calculation of social-environment interaction risk index: Combine the environmental risk index and the social activity risk index to construct a social-environment interaction risk model and calculate the social-environment interaction risk index for each region.

[0018] Preferably, the formula preset in step S2.1 is as follows:

[0019] E ％ =w & ·T ％ +w ' ·H ％ +w ( ·D i +w ) ·W ％ , where T ％ , H ％ , D i , W ％ Respectively represent temperature, humidity, rainfall and soil moisture content, and the weight coefficient w& 、w ' 、w ( 、w ) Adjusted by historical data;

[0020] The calculation formula for the social-environmental interaction risk index of each region in step S2.3 is as follows:

[0021] &'( $ =)·' $ ++& $ , where α and β are adjustment weights, representing the relative weights of environmental and social activities in the risk.

[0022] Preferably, the construction of the social-environment interaction model in step S3 specifically includes the following steps:

[0023] S3.1. Risk chain node setting: Set key risk chain nodes based on historical data and expert knowledge, and set an initial risk value R for each node. ％ ;

[0024] S3.2, Risk transmission model construction: According to the mutual relationship between the chain nodes, construct the risk transmission formula, set the transmission coefficient γ and the external input variable δ, and simulate the transmission of risks between different nodes;

[0025] S3.3. Chain risk accumulation calculation: Calculate the risk value at all chain nodes in turn, and calculate the final total risk value R of the chain. *+*,- .

[0026] Preferably, the risk transmission formula in step S3.2 is as follows:

[0027] R i. / =4(3 i )=γ·R ％ +δ, where R ％. / is the risk value of the next node, R ％ is the risk value of the current node, γ is the risk transmission coefficient, and δ is the external input variable;

[0028] The final total risk value R of the chain in step S3.3 *+*,- The calculation formula is as follows:

[0029] Among them, R *+*,- is the total risk value of the entire risk chain, R ％ is the risk value of the i-th risk node.

[0030] Preferably, the risk intervention and adjustment in step S4 specifically includes the following steps:

[0031] S4.1. Identification of intervention nodes: Identify the nodes most likely to cause disaster spread based on the risk chain model and select intervention points as key nodes for disaster control;

[0032] S4.2. Intervention mechanism design: Design an intervention plan through physical or management means, set the intervention effectiveness coefficient θ, and calculate the risk value of the node after intervention through the following formula:

[0033] R k"1 =R k ·$1-θ%, where R k is the node risk value before intervention, θ is the intervention effectiveness coefficient, which indicates the degree of blocking of risk transmission by intervention measures;

[0034] S4.3. Evaluation of intervention effectiveness: After simulating an intervention, recalculate the risk values ​​of subsequent nodes in the chain and evaluate their effectiveness by dynamically adjusting intervention measures.

[0035] Preferably, the scenario simulation and emergency response in step S5 specifically includes the following steps:

[0036] S5.1. Scenario creation: Create multiple scenarios according to different disaster types and set key influencing factors in the simulation environment;

[0037] S5.2. Calculation of scene damage index: Calculate the damage index according to the set scene damage formula:

[0038] Among them, D2 is the damage value, ρ2 is the damage weight;

[0039] S5.3. Emergency response time calculation: Simulate the deployment of emergency resources and calculate the emergency response time T4:

[0040] Among them, L is the dispatch distance of emergency resources, v is the transportation speed, and T7 is the time required for deployment.

[0041] Preferably, the disaster warning release in step S6 specifically includes the following steps:

[0042] S6.1. Risk level classification: Based on the risk calculation results, the risks are classified into different levels, and different emergency response plans are formulated for each risk level;

[0043] S6.2. Warning information generation: Generate specific warning information, including the affected area, possible disaster type and impact range, and release warning information to relevant departments and the public to guide disaster response;

[0044] S6.3. Dynamic feedback and adjustment: Monitor the development of disasters in real time, adjust the warning plan according to the actual situation, and update the warning information in a timely manner if the impact of the disaster expands or shrinks.

[0045] Disaster risk early warning system based on big data model, including:

[0046] Data collection module, which is used to collect environmental and social data through sensors and IoT devices;

[0047] Data processing module, used to standardize, clean and pre-process the collected environmental and social data;

[0048] An environmental risk calculation module is used to calculate the environmental risk index based on the collected environmental data;

[0049] A social activity risk calculation module is used to calculate the social activity risk index based on the collected social data;

[0050] The social-environmental interaction model building module is used to combine the environmental risk index and the social activity risk index to build and calculate the social-environmental interaction risk index of each region;

[0051] The risk chain model construction module is used to construct the disaster transmission chain, set key risk nodes, and calculate according to the risk transmission formula to evaluate the risk value of each node;

[0052] The intervention mechanism module is used to identify key nodes in the risk chain and set and implement intervention measures;

[0053] Scenario simulation module, which is used to simulate various disaster scenarios, evaluate the damage index of different disaster types, and create emergency response plans based on the scenarios;

[0054] The emergency response module is used to calculate the dispatch time, distance and response speed of emergency resources and generate a specific emergency response plan;

[0055] The warning release module is used to generate and release disaster warning information based on risk calculation and scenario simulation results, and notify relevant departments and the public;

[0056] The model optimization and feedback module is used to dynamically adjust the disaster warning model based on historical data and real-time monitoring results.

[0057] The present invention provides a disaster risk early warning method and system based on a big data model, which has the following beneficial effects:

[0058] 1. By constructing a social-environmental interaction risk model, the present invention can quantify the interaction between environmental risks and social activities and accurately assess the impact of social activities on disasters. The model can dynamically calculate the social-environmental interaction risk index of each region and help predict secondary disasters that may occur in areas with dense human activities.

[0059] 2. The present invention introduces a risk chain model, constructs a risk transmission mechanism, simulates the risk propagation process between different nodes, and evaluates the chain effect of multiple disaster events by setting the transmission coefficient, accurately predicts the expansion risk of disasters at different stages, and provides a scientific basis for timely intervention.

[0060] 3. The present invention introduces an intervention mechanism module to identify key nodes in the disaster transmission chain and designs intervention measures to block or reduce the transmission of risks at appropriate nodes. This mechanism helps optimize response measures and reduce losses caused by the spread of disasters.

[0061] 4. The present invention simulates various disaster scenarios, evaluates the damage index under various disaster conditions, and calculates the emergency response time, thereby optimizing the deployment of emergency resources, helping to shorten the emergency response time and ensuring the pertinence and effectiveness of the emergency plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flow chart of the method of the present invention;

[0063] Figure 2 It is a framework diagram of the system of the present invention. DETAILED DESCRIPTION

[0064] 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.

[0065] Please see attached Figure 1 The embodiment of the present invention provides a disaster risk early warning method based on a big data model, comprising the following steps:

[0066] S1. Data collection: Collect environmental and social data through sensors and IoT devices;

[0067] S2. Risk calculation: Calculate the environmental risk index based on environmental data and the social activity risk index based on social data;

[0068] S3. Construction of social-environmental interaction model: Combine the environmental risk index and social activity risk index to construct a social-environmental interaction risk model and calculate the social-environmental interaction risk index of each region;

[0069] S4, Risk intervention and adjustment: Introduce intervention mechanisms at key nodes in the risk chain and adjust the risk values ​​of subsequent nodes to reduce the impact of disasters;

[0070] S5. Scenario simulation and emergency response: simulate various disaster scenarios, evaluate disaster damage under different scenarios, obtain risk assessment and scenario simulation results, and calculate emergency response time;

[0071] S6. Disaster warning release: Generate and release disaster warning information based on risk assessment and scenario simulation results.

[0072] The environmental data in step S1 include meteorological data, geological data and hydrological data, and the social data in step S1 include population density, traffic flow and energy consumption data.

[0073] The risk calculation in step S2 specifically includes the following steps:

[0074] S2.1. Calculation of environmental risk index: convert environmental data into a standardized format and calculate the environmental risk index according to a preset formula;

[0075] S2.2. Calculation of social activity risk index: Standardize the social activity data and calculate the social activity risk index according to the following formula:

[0076] S i =w " % i +w F ·' i +w $ ·( i ,in,% i ,' i 、( i They represent population density, traffic flow, and energy consumption level respectively;

[0077] S2.3. Calculation of social-environment interaction risk index: Combine the environmental risk index and the social activity risk index to construct a social-environment interaction risk model and calculate the social-environment interaction risk index for each region.

[0078] The preset formula in step S2.1 is as follows:

[0079] E ％ =w & ·T ％ +w ' ·H ％ +w (·D i +w ) ·W ％ , where T ％ , H ％ , D i , W ％ Respectively represent temperature, humidity, rainfall and soil moisture content, and the weight coefficient w & 、w ' 、w ( 、w ) Adjusted by historical data;

[0080] The calculation formula for the social-environmental interaction risk index of each region in step S2.3 is as follows:

[0081] &'( $ =)·' $ ++& $ , where α and β are adjustment weights, representing the relative weights of environmental and social activities in the risk.

[0082] S3 step The construction of social-environment interaction model specifically includes the following steps:

[0083] S3.1. Risk chain node setting: Set key risk chain nodes based on historical data and expert knowledge, and set an initial risk value R for each node. ％ ;

[0084] S3.2, Risk transmission model construction: According to the mutual relationship between the chain nodes, construct the risk transmission formula, set the transmission coefficient γ and the external input variable δ, and simulate the transmission of risks between different nodes;

[0085] S3.3. Chain risk accumulation calculation: Calculate the risk value at all chain nodes in turn, and calculate the final total risk value R of the chain. *+*,- .

[0086] The risk transmission formula in step S3.2 is as follows:

[0087] R ％. / =f(R ％ )=γ·R ％ +δ, where R ％. / is the risk value of the next node, R ％ is the risk value of the current node, γ is the risk transmission coefficient, and δ is the external input variable, which is used to consider the impact of the external environment or intervention measures on risk transmission;

[0088] The final total risk value R of the chain in step S3.3 *+*,- The calculation formula is as follows:

[0089] Among them, R *+*,- is the total risk value of the entire risk chain, R ％ is the risk value of the i-th risk node.

[0090] S4 Step Risk Intervention and Adjustment specifically includes the following steps:

[0091] S4.1. Identification of intervention nodes: Identify the nodes most likely to cause disaster spread based on the risk chain model and select intervention points as key nodes for disaster control;

[0092] S4.2. Intervention mechanism design: Design an intervention plan through physical or management means, set the intervention effectiveness coefficient θ, and calculate the risk value of the node after intervention through the following formula:

[0093] R k"1 =R k ·$1-θ%, where R k is the node risk value before intervention, θ is the intervention effectiveness coefficient, which indicates the degree of blocking of risk transmission by intervention measures;

[0094] S4.3. Evaluation of intervention effectiveness: After simulating an intervention, recalculate the risk values ​​of subsequent nodes in the chain and evaluate their effectiveness by dynamically adjusting intervention measures.

[0095] S5 step scenario simulation and emergency response specifically includes the following steps:

[0096] S5.1. Scenario creation: Create multiple scenarios according to different disaster types and set key influencing factors in the simulation environment;

[0097] S5.2. Calculation of scene damage index: Calculate the damage index according to the set scene damage formula:

[0098] Among them, D2 is the damage value, ρ2 is the damage weight;

[0099] S5.3. Emergency response time calculation: Simulate the deployment of emergency resources and calculate the emergency response time T4:

[0100] Among them, L is the dispatch distance of emergency resources, v is the transportation speed, and T7 is the time required for deployment.

[0101] S6 step disaster warning release specifically includes the following steps:

[0102] S6.1. Risk level classification: Based on the risk calculation results, the risks are classified into different levels, and different emergency response plans are formulated for each risk level;

[0103] S6.2. Warning information generation: Generate specific warning information, including the affected area, possible disaster type and impact range, and release warning information to relevant departments and the public to guide disaster response;

[0104] S6.3. Dynamic feedback and adjustment: Monitor the development of disasters in real time, adjust the warning plan according to the actual situation, and update the warning information in a timely manner if the impact of the disaster expands or shrinks.

[0105] Specifically, step S6.1, based on the previously calculated social-environmental interaction risk index &'( $ And the total risk value R of the risk chain *+*,- , classifying disaster risks into multiple levels, such as low risk, medium risk and high risk.

[0106] Implementation steps:

[0107] Obtain the comprehensive risk index for each region from the risk calculation module.

[0108] Set risk thresholds and classify risk levels according to different disaster types, such as floods, fires, earthquakes, etc. Example:

[0109] Low risk: Risk index &'( $ Below the set threshold.

[0110] Medium risk: The risk index is within the set middle range.

[0111] High Risk: The risk index exceeds the highest risk threshold.

[0112] Different emergency response plans are formulated for each risk level, including evacuation routes, emergency resource allocation, etc.

[0113] In step S6.2, specific warning information is generated based on the results of the risk level classification, including the affected area, disaster type, risk level, impact range and recommended emergency measures.

[0114] Implementation steps:

[0115] Based on the risk assessment results of each area, the impact range of the disaster is determined, and early warning information is generated according to the type of disaster, such as landslide, typhoon, earthquake, etc.

[0116] The information includes:

[0117] Impacted Area: Identify the geographic area that may be affected by a disaster (for example, a specific neighborhood in a city or village).

[0118] Disaster Type: Indicate the possible type of disaster, such as flood, fire, earthquake, etc.

[0119] Risk Level: The risk level assigned to each area based on the risk index.

[0120] Impact scope: Estimate the expansion scope of the disaster and the related areas that may be affected.

[0121] Emergency Measures: Provides suggested evacuation routes, emergency assembly points, and shelter information.

[0122] Warning information is released to relevant departments and the public through various channels, such as: SMS notifications, broadcast alerts, social media, government websites, television, etc.

[0123] S6.3 Dynamic feedback and adjustment:

[0124] By continuously monitoring the development of disasters in real time, if the impact of the disaster changes, the system can adjust and update the early warning plan based on real-time data.

[0125] Implementation steps:

[0126] Acquire data from sensors, IoT devices and other sources in real time, and continuously track the actual situation of disaster expansion, such as the intensity of earthquake aftershocks and changes in flood water levels.

[0127] If the monitoring data indicates that the impact of the disaster is expanding or shrinking, the early warning information will be dynamically adjusted through the feedback mechanism. For example:

[0128] Disaster expansion: If the affected area is found to be expanding, the system will generate new warning information, expand the scope of the affected area, and update the emergency response plan.

[0129] Disaster Mitigation: If the impact of a disaster is reduced, the system will reduce the risk level and notify relevant departments and the public.

[0130] Adjustments to warning information will be updated in real time through the automated system and quickly disseminated through corresponding warning release channels to ensure the timeliness of the information.

[0131] Please see attached Figure 2 , a disaster risk early warning system based on big data models, including:

[0132] Data collection module, which is used to collect environmental and social data through sensors and IoT devices;

[0133] Data processing module, used to standardize, clean and pre-process the collected environmental and social data;

[0134] An environmental risk calculation module is used to calculate the environmental risk index based on the collected environmental data;

[0135] A social activity risk calculation module is used to calculate the social activity risk index based on the collected social data;

[0136] The social-environmental interaction model building module is used to combine the environmental risk index and the social activity risk index to build and calculate the social-environmental interaction risk index of each region;

[0137] The risk chain model construction module is used to construct the disaster transmission chain, set key risk nodes, and calculate according to the risk transmission formula to evaluate the risk value of each node;

[0138] The intervention mechanism module is used to identify key nodes in the risk chain and set and implement intervention measures;

[0139] Scenario simulation module, which is used to simulate various disaster scenarios, evaluate the damage index of different disaster types, and create emergency response plans based on the scenarios;

[0140] The emergency response module is used to calculate the dispatch time, distance and response speed of emergency resources and generate a specific emergency response plan;

[0141] The warning release module is used to generate and release disaster warning information based on risk calculation and scenario simulation results, and notify relevant departments and the public;

[0142] The model optimization and feedback module is used to dynamically adjust the disaster warning model based on historical data and real-time monitoring results.

[0143] 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. A disaster risk early warning method based on a big data model, characterized in that: The following steps are involved: S1. Data collection: Collect environmental and social data through sensors and IoT devices; S2. Risk calculation: calculating an environmental risk index based on the environmental data, and calculating a social activity risk index based on the social data; S3. Construction of social-environmental interaction model: Combine the environmental risk index and social activity risk index to construct a social-environmental interaction risk model and calculate the social-environmental interaction risk index of each region; S4, Risk intervention and adjustment: Introduce intervention mechanisms at key nodes in the risk chain and adjust the risk values ​​of subsequent nodes to reduce the impact of disasters; S5. Scenario simulation and emergency response: simulate various disaster scenarios, evaluate disaster damage under different scenarios, obtain risk assessment and scenario simulation results, and calculate emergency response time; S6. Disaster warning release: Generate and release disaster warning information based on risk assessment and scenario simulation results.

2. The disaster risk early warning method based on big data model according to claim 1 is characterized in that: The environmental data in step S1 include meteorological data, geological data and hydrological data, and the social data in step S1 include population density, traffic flow and energy consumption data.

3. The disaster risk early warning method based on big data model according to claim 1 is characterized in that: The risk calculation in step S2 specifically includes the following steps: S2.

1. Calculation of environmental risk index: convert environmental data into a standardized format and calculate the environmental risk index according to a preset formula; S2.

2. Calculation of social activity risk index: Standardize the social activity data and calculate the social activity risk index according to the following formula: S i =w " % i +w F ·' i +w $ ·( i ,in,% i ,' i 、( i Respectively represent population density, traffic flow, energy consumption level, weight coefficient w " 、w F and w $ Adjusted by historical data; S2.

3. Calculation of social-environment interaction risk index: Combine the environmental risk index and the social activity risk index to construct a social-environment interaction risk model and calculate the social-environment interaction risk index for each region.

4. The disaster risk early warning method based on big data model according to claim 3 is characterized in that: The preset formula in step S2.1 is as follows: E ％ =w & ·T ％ +w ' ·H ％ +w ( ·D i +w ) ·W ％ , where T ％ , H ％ , D i , W ％ Respectively represent temperature, humidity, rainfall and soil moisture content, and the weight coefficient w & 、w ' 、w ( 、w ) Adjusted by historical data; The calculation formula for the social-environmental interaction risk index of each region in step S2.3 is as follows: S( / i =0·( i +1 S i , where α and β are adjustment weights, representing the relative weights of environmental and social activities in the risk.

5. The disaster risk early warning method based on big data model according to claim 1 is characterized in that: The construction of the social-environment interaction model in step S3 specifically includes the following steps: S3.

1. Risk chain node setting: Set key risk chain nodes based on historical data and expert knowledge, and set an initial risk value R for each node. ％ ; S3.2, Risk transmission model construction: According to the mutual relationship between the chain nodes, construct the risk transmission formula, set the transmission coefficient γ and the external input variable δ, and simulate the transmission of risks between different nodes; S3.

3. Chain risk accumulation calculation: Calculate the risk value at all chain nodes in turn, and calculate the final total risk value R of the chain. *+*,- .

6. The disaster risk early warning method based on big data model according to claim 5 is characterized in that: The risk transmission formula in step S3.2 is as follows: R i. / =4(3 i )=γ·R ％ +δ, where R ％. / is the risk value of the next node, R ％ is the risk value of the current node, γ is the risk transmission coefficient, and δ is the external input variable; The final total risk value R of the chain in step S3.3 *+*,- The calculation formula is as follows: Among them, R *+*,- is the total risk value of the entire risk chain, R ％ is the risk value of the i-th risk node.

7. The disaster risk early warning method based on big data model according to claim 1 is characterized in that: The risk intervention and adjustment in step S4 specifically includes the following steps: S4.

1. Identification of intervention nodes: Identify the nodes most likely to cause disaster spread based on the risk chain model and select intervention points as key nodes for disaster control; S4.

2. Intervention mechanism design: Design an intervention plan through physical or management means, set the intervention effectiveness coefficient θ, and calculate the risk value of the node after intervention through the following formula: R k"1 =R k ·$1-θ%, where R k is the node risk value before intervention, θ is the intervention effectiveness coefficient, which indicates the degree of blocking of risk transmission by intervention measures; S4.

3. Evaluation of intervention effectiveness: After simulating an intervention, recalculate the risk values ​​of subsequent nodes in the chain and evaluate their effectiveness by dynamically adjusting intervention measures.

8. The disaster risk early warning method based on big data model according to claim 1 is characterized in that: The scenario simulation and emergency response in step S5 specifically include the following steps: S5.

1. Scenario creation: Create multiple scenarios according to different disaster types and set key influencing factors in the simulation environment; S5.

2. Calculation of scene damage index: Calculate the damage index according to the set scene damage formula: Among them, D2 is the damage value, ρ2 is the damage weight; S5.

3. Emergency response time calculation: Simulate the deployment of emergency resources and calculate the emergency response time T4: Among them, L is the dispatch distance of emergency resources, v is the transportation speed, and T7 is the time required for deployment.

9. The disaster risk early warning method based on big data model according to claim 8 is characterized in that: The disaster warning release in step S6 specifically includes the following steps: S6.

1. Risk level classification: Based on the risk calculation results, the risks are classified into different levels, and different emergency response plans are formulated for each risk level; S6.

2. Warning information generation: Generate specific warning information, including the affected area, possible disaster type and impact range, and release warning information to relevant departments and the public to guide disaster response; S6.

3. Dynamic feedback and adjustment: Monitor the development of disasters in real time, adjust the warning plan according to the actual situation, and update the warning information in a timely manner if the impact of the disaster expands or shrinks.

10. The disaster risk early warning system based on big data model is characterized by: include: Data collection module, which is used to collect environmental and social data through sensors and IoT devices; Data processing module, used to standardize, clean and pre-process the collected environmental and social data; An environmental risk calculation module is used to calculate the environmental risk index based on the collected environmental data; A social activity risk calculation module is used to calculate the social activity risk index based on the collected social data; The social-environmental interaction model building module is used to combine the environmental risk index and the social activity risk index to build and calculate the social-environmental interaction risk index of each region; The risk chain model construction module is used to construct the disaster transmission chain, set key risk nodes, and calculate according to the risk transmission formula to evaluate the risk value of each node; The intervention mechanism module is used to identify key nodes in the risk chain and set and implement intervention measures; Scenario simulation module, which is used to simulate various disaster scenarios, evaluate the damage index of different disaster types, and create emergency response plans based on the scenarios; The emergency response module is used to calculate the dispatch time, distance and response speed of emergency resources and generate a specific emergency response plan; The warning release module is used to generate and release disaster warning information based on risk calculation and scenario simulation results, and notify relevant departments and the public; The model optimization and feedback module is used to dynamically adjust the disaster warning model based on historical data and real-time monitoring results.

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