Snow disaster grade monitoring and early warning management system

Abstract

The invention discloses a snow disaster grade monitoring and early warning management system, which comprises the following steps: S1, constructing a multi-factor correlation analysis model, and collecting and cleaning related data to form a data set; s2, establishing a high-frequency self-adjusting environment interaction model based on the data set, and dynamically adjusting the sampling frequency; s3, utilizing self-adaptive climate variation analysis to adjust model parameters in real time; s4, the adjusted parameters are input into a fluctuation threshold value self-calibration predictor, and a detection threshold value is automatically adjusted; s5, constructing a microclimate customization model, and generating a regional snow disaster risk level; s6, generating a multi-scene snow disaster scene by using a climate condition evolution simulator, and simulating and predicting risk trends under different environment changes; s7, applying an intelligent resource preset algorithm to dynamically adjust the distribution and supply period; and S8, adjusting the resource consumption and the supply period in real time by using a self-adaptive resource supply interval algorithm. The invention provides an efficient snow disaster monitoring and early warning scheme which is suitable for emergency management under complex climate conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring and emergency management, and in particular to a snow disaster level monitoring and early warning management system. Background Art

[0002] As climate change intensifies, the frequency of extreme weather events has increased significantly. Snowstorms, as a typical extreme weather phenomenon, have brought severe challenges to transportation, energy supply, ecological environment and other fields. Traditional snowstorm warning systems mostly use static monitoring and single variable prediction methods, which are difficult to fully respond to complex and changeable climate conditions. In current technologies, snowstorm warning monitoring systems face the following significant deficiencies:

[0003] 1. Lack of multi-factor correlation analysis and multi-source data integration: Traditional snow disaster monitoring systems usually rely only on meteorological data and fail to effectively combine multi-source data such as ecology, land use, and traffic flow. The singleness of this data leads to insufficient accuracy of the system in dealing with complex environmental changes. For example, land use and traffic flow data are crucial to judging the actual impact of snow disasters. Traditional systems that do not take these factors into account have difficulty providing regional and personalized risk forecasts, limiting the adaptability of the early warning system.

[0004] 2. Lack of dynamic sampling and adaptive adjustment mechanism: Snow disaster monitoring systems in existing technologies mostly collect data based on fixed sampling frequencies, and cannot respond dynamically and sensitively to different meteorological conditions. This fixed sampling method will result in the failure to capture key data in a timely manner during periods of drastic climate fluctuations, thereby delaying the timeliness of snow disaster monitoring and early warning. In addition, traditional systems usually lack environmental interaction models and adaptive sampling mechanisms, and cannot adjust the sampling frequency in real time to more accurately capture snow disaster precursor information.

[0005] 3. The prediction model is not adaptive and forward-looking: The prediction models of traditional snowstorm warning systems mostly rely on static or semi-static meteorological models, which make it difficult to adaptively adjust parameters according to extreme weather changes. For example, under existing abnormal weather conditions, meteorological model parameters may require frequent manual corrections to maintain prediction accuracy, increasing the burden of manual operations. This static model lacks a flexible adjustment mechanism to adapt to extreme weather, and cannot quickly respond and calibrate prediction values ​​in extreme climate events, thus affecting the real-time and accuracy of the system.

[0006] 4. Risk assessment lacks regionalization and personalization: Traditional snow disaster warning systems often only provide unified risk level warnings, and fail to conduct differentiated risk assessments based on the actual conditions of different regions. Due to the large differences in climate and terrain conditions in different regions, unified risk assessments often cannot meet the specific needs of different regions. This lack of regional customized risk assessments results in poor warning effects, which can easily lead to false alarms or missed alarms, affecting the application value of the system.

[0007] 5. Insufficient simulation of climate condition evolution and limited multi-scenario analysis capabilities: Current snow disaster warning systems generally lack multi-scenario analysis capabilities in climate simulation and only provide a single weather forecast model. When faced with drastic climate changes or extreme weather events, a single simulation method cannot predict multiple possible development trends, resulting in insufficient prediction accuracy and stability of the system under complex circumstances. In particular, in the case of sudden changes in climate conditions, the lack of multi-scenario simulation makes it difficult for the system to timely and accurately judge the potential risks of snow disasters to different regions, and is unable to provide effective emergency support to decision makers.

[0008] Therefore, how to provide a snow disaster level monitoring and early warning management system is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a snow disaster level monitoring and early warning management system. The present invention utilizes technologies such as multi-factor correlation analysis, adaptive climate variation analysis, and multi-scenario simulation, and describes in detail the complete process from multi-source data collection, dynamic monitoring, trend prediction to resource management, and constructs a real-time and sensitive snow disaster monitoring and early warning system with the advantages of fast response speed, high prediction accuracy, and reasonable resource allocation.

[0010] A snow disaster level monitoring and early warning management system according to an embodiment of the present invention includes the following steps:

[0011] S1. Build a multi-factor correlation analysis model, collect and clean meteorological data, ecological parameters, land use information and traffic flow data to form an initial data set;

[0012] S2. Based on the generated initial data set, a high-frequency self-regulating environmental interaction model is established to sensitively monitor the precursors of snow disasters by dynamically adjusting the sampling frequency;

[0013] S3. Use adaptive climate variability analysis to adjust environmental interaction model parameters in real time, so that the system has the ability to respond quickly to extreme weather events;

[0014] S4, inputting the adjusted parameters into the fluctuation threshold self-calibration predictor, automatically adjusting the detection threshold at each time step, making a forward-looking prediction of the snow disaster trend, and generating a prediction result;

[0015] S5. Based on the prediction results, a microclimate customized model is constructed for different regional characteristics to generate a preliminary regional snow disaster risk level and provide characteristic input parameters for the early warning plan of each region;

[0016] S6. Based on the generated regional risk level input parameters, a self-driving simulator for climate condition evolution is used to generate multi-scenario snowstorm scenarios, simulate the dynamic evolution of regional snowstorm risks under extreme climate conditions, and predict regional risk trends under different environmental changes through a progressive future state generation mechanism, providing fine-grained multi-scenario risk analysis;

[0017] S7. Apply intelligent predictive resource pre-positioning algorithms to allocate resources to high-risk areas in advance based on multi-scenario analysis results, monitor in real time, and dynamically adjust resource allocation and replenishment cycles;

[0018] S8. Based on resource pre-positioning and replenishment plans, an adaptive resource replenishment interval algorithm is used to adjust the resource consumption rate and risk level in real time, automatically shortening the resource replenishment cycle when secondary disasters occur.

[0019] Optionally, S2 includes the following steps:

[0020] S21, collect and receive the generated initial data set, divide the data into four environmental variables: meteorology, ecology, land use and traffic flow, and divide them into micro, meso and macro layers according to the characteristics of the variables, set the initial values ​​of data change rate ΔV1, ΔV2 and ΔV3, and the initial values ​​of sampling frequency f1, f2 and f3 for each layer, satisfying the relationship f1>f2>f3;

[0021] S22, based on the generated data change rate ΔV of each layer n and the initial value of the sampling frequency f n , where n∈{1,2,3} represents the level, and the sampling frequency is dynamically adjusted through the sampling frequency adaptive adjustment formula:

[0022]

[0023] Among them, f n represents the adjusted sampling frequency of the nth layer, and k is the short-term change rate ΔV n The relevant adjustment coefficient, δ is the multi-level data smoothing adjustment coefficient, w i Indicates the importance weight of each level of data, T i is the trend factor of the i-th layer of data, m is the total number of data layers, η is the sensitivity adjustment coefficient, Indicates the instantaneous change of the data change rate at the nth layer;

[0024] S23, the generated adjusted sampling frequency f nPerform smoothing to optimize the fluctuations caused by frequent switching and set the adaptive smoothing coefficient α n And apply the adaptive dynamic smoothing formula:

[0025]

[0026] Among them, f smooth,n represents the sampling frequency after smoothing of the nth layer, f n is the current sampling frequency after adjustment, f prev,n is the sampling frequency of the previous time step, α n is the adaptive smoothing coefficient, β is the dynamic smoothing adjustment factor, and λ is the rate of change ΔV n The intensity of the response;

[0027] S24, through the multi-level feedback mechanism, the sampling frequency f after smoothing each layer smooth,n Associated with real-time environmental data changes, the sampling frequency is adjusted according to feedback optimization, and layered sensitive monitoring of snow disaster precursors is dynamically carried out to optimize the system's response accuracy and stability under multi-layer environmental conditions.

[0028] Optionally, S4 includes the following steps:

[0029] S41, smooth the sampling frequency f of each layer generated smooth,n and the adjusted parameter input fluctuation threshold self-calibration predictor, which detects the data at each time step t, records the data value V(t) of the current time step, and combines it with environmental parameters, including temperature T(t), snowfall S(t), and wind speed W(t);

[0030] S42, setting basic detection threshold T base , and real-time calibration is performed based on a dynamic multi-factor adjustment strategy. The adjusted detection threshold is:

[0031]

[0032] Among them, T adjusted (t) is the detection threshold after dynamic calibration at time step t, T base is the basic detection threshold, f smooth,n is the smoothing sampling frequency of the nth layer, which indicates the sensitivity weights of different environmental parameters. α, β, γ, and δ are the adjustment coefficients of environmental parameters, which are used to adjust the influence of each factor on the threshold.

[0033] S43, the generated dynamic calibration detection threshold T adjusted (t) Adaptive update of the adjustment coefficient is introduced to optimize the self-calibration capability of the detection threshold under different environmental conditions:

[0034]

[0035] Among them, α t+1 , β t+1 , γ t+1 , δ t+1 are the update coefficients for time step t+1, T adjusted (t) is the calibration threshold generated for the current time step, T base is the basic detection threshold, T std is the standard deviation of the detection threshold, μ, ν, ξ, ζ are adaptive update adjustment factors;

[0036] S44, when the detection data V(t) exceeds the calibrated threshold T at time step t adjusted At (t), the snow disaster trend signal is recorded and alarm data is generated. If the threshold condition is met within n consecutive time steps, the forward-looking warning mechanism is triggered, and the current change data is transmitted to the microclimate customized model to evaluate the regional snow disaster risk level;

[0037] S45. Apply the updated detection threshold and adjustment coefficient to the analysis of the next time step, and use the real-time feedback mechanism to optimize the response accuracy of the detection system, and optimize the sensitivity and accuracy of the system under complex environmental conditions.

[0038] Optionally, S5 includes the following steps:

[0039] S51, receiving the generated calibration threshold T adjusted (t), adjustment coefficients α(t), β(t), γ(t), δ(t) and forward-looking warning data, the environmental parameters of different regions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), are input into the regional feature module in the microclimate customization model to generate the dynamic weight of each environmental parameter;

[0040] S52. Based on the generated dynamic weights, combined with the historical meteorological data and current meteorological conditions of each region, an adaptive microclimate model is constructed, and the regional characteristic factor R is generated by dynamically adjusting the weights. i :

[0041]

[0042] Among them, R i is the characteristic factor of region i, X j (t) represents the jth environmental parameter at time step t, and the dynamic weight α j , β j Adaptively adjust γ, δ, ψ and ξ are feedback adjustment parameters;

[0043] S53, the generated characteristic factor R iInput into the regional risk level calculation model, according to the characteristic factor R i and calibration threshold T adjusted (t), calculate the regional risk level L i , and introduce adaptive feedback mechanisms to optimize responses to extreme climate conditions:

[0044]

[0045] Among them, L i is the risk level of area i, T adjusted (t) is the generated calibration threshold, λ is the smoothing factor, ρ and σ are adaptive feedback adjustment parameters used to adjust the impact of humidity on the risk level, and the standard deviation of humidity H std Used for smooth feedback regulation;

[0046] S54, the generated regional risk level L i The data is transmitted to the regional early warning module, which combines the forward-looking warning data to generate personalized snow disaster warning plans and regional emergency response plans to optimize the dynamic management of regional risk characteristics.

[0047] Optionally, the S6 comprises the following steps:

[0048] S61. Receive the generated regional risk level L i and regional characteristic factor R i , which is input into the environment initialization module of the self-driving simulator, and combined with the current climate conditions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), to generate the climate benchmark parameters C for each region i , used to initialize the simulator state;

[0049] S62, based on the generated climate reference parameter C i and regional characteristic factor R i , generating the climate state E at time step t+1 i (t+1):

[0050] E i (t+1)=R i ·C i ·(1+η·sin(σ·T(t)+θ·S(t)));

[0051] Among them, E i (t+1) represents the climate state of region i at time step t+1, C i is the regional climate benchmark parameter, η, σ, and θ are adjustment factors, which adjust the impact of temperature and snowfall on the climate state respectively;

[0052] S63. Using the generated climate state E i(t+1) and the risk level changes of each area, and the risk level L of the future t+n time step is generated through the adaptive scenario generation mechanism. i (t+n) to make predictions:

[0053]

[0054] Among them, L i (t+n) is the risk level of region i at time step t+n, E i (t+k) is the climate state at the future time step t+k, and λ is the time decay factor, which is used to control the decreasing effect of the risk level;

[0055] S64, the predicted regional risk level L i (t+n) is passed to the early warning module to generate a personalized snow disaster warning plan, and provide a regional emergency response plan based on the risk level analysis of multiple future time steps, and a detailed early warning of the evolution of different climate conditions.

[0056] A snow disaster level monitoring and early warning management system according to an embodiment of the present invention includes:

[0057] Data collection and preprocessing module: used to collect multi-source environmental data, including meteorological data, ecological data, land use information and traffic flow information. After data cleaning, denoising and normalization, the initial data set is formed and transmitted to the environmental interaction module as system input data;

[0058] High-frequency self-adjusting environmental interaction module: Based on a multi-factor correlation model and an adaptive sampling mechanism, the module uses the initial data set to sensitively monitor the precursors of snowstorms. The module dynamically adjusts the sampling frequency and automatically adjusts the sampling density for different environmental variables, so that environmental changes are reflected in the system monitoring results.

[0059] Threshold self-calibration prediction module: Based on the dynamically collected environmental data and the fluctuating threshold self-calibration mechanism, the prediction threshold is adjusted in real time through a multi-factor adjustment strategy, so that the system can adapt to changing climate conditions and maintain a high prediction accuracy;

[0060] Microclimate customized model module: Build adaptive microclimate models according to the characteristics of different regions, generate regional characteristic factors and risk levels based on the historical meteorological data and current climate conditions of each region, and provide this information to the system's early warning module;

[0061] Climate Condition Evolution Self-Driving Simulator Module: Generates multiple snowstorm scenarios in real time based on environmental conditions through an adaptive scenario generation mechanism to predict regional risk changes under extreme climate conditions;

[0062] Adaptive resource replenishment module: Based on the risk level and climate scenario generated by the system, it dynamically calculates the resource consumption rate and replenishment interval, adjusts the resource allocation plan through an adaptive algorithm, and automatically shortens the resource replenishment cycle when emergencies or secondary disasters occur, so that resources can be replenished and allocated in a timely manner;

[0063] Early warning and response module: Integrate the risk level and climate status generated by the above modules, combine the risk level analysis of multiple time steps in the future to generate a personalized snow disaster early warning plan, and provide a regional emergency response plan to accurately warn and respond to different climate conditions.

[0064] The beneficial effects of the present invention are:

[0065] (1) The present invention integrates multi-source data such as meteorology, ecology, land use and traffic flow by constructing a multi-factor correlation analysis model and a high-frequency self-regulating environmental interaction model, significantly improving the sensitivity and accuracy of snowstorm precursor monitoring and effectively overcoming the limitations of the single data source in traditional methods.

[0066] (2) By introducing adaptive climate variation analysis and fluctuation threshold self-calibration predictor, the present invention can adjust model parameters and detection thresholds in real time to adapt to the rapid changes in extreme weather conditions, thereby improving the system's response speed and prediction accuracy under variable climates.

[0067] (3) The present invention uses an intelligent predictive resource pre-positioning algorithm and an adaptive resource replenishment interval algorithm to dynamically adjust resource allocation and replenishment cycles according to real-time risk levels, and maintain a continuous supply of resources in the event of sudden and secondary disasters. This method optimizes the rationality of resource management and improves the efficiency of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0069] Figure 1 This is a flow chart of a snow disaster level monitoring and early warning management system proposed by the present invention. DETAILED DESCRIPTION

[0072] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0073] refer to Figure 1 , a snow disaster level monitoring and early warning management system, comprising the following steps:

[0074] S1. Build a multi-factor correlation analysis model, collect and clean meteorological data, ecological parameters, land use information and traffic flow data to form an initial data set;

[0075] S2. Based on the generated initial data set, a high-frequency self-regulating environmental interaction model is established to sensitively monitor the precursors of snow disasters by dynamically adjusting the sampling frequency;

[0076] S3. Use adaptive climate variability analysis to adjust environmental interaction model parameters in real time, so that the system has the ability to respond quickly to extreme weather events;

[0077] S4, inputting the adjusted parameters into the fluctuation threshold self-calibration predictor, automatically adjusting the detection threshold at each time step, making a forward-looking prediction of the snow disaster trend, and generating a prediction result;

[0078] S5. Based on the prediction results, a microclimate customized model is constructed for different regional characteristics to generate a preliminary regional snow disaster risk level and provide characteristic input parameters for the early warning plan of each region;

[0079] S6. Based on the generated regional risk level input parameters, a self-driving simulator for climate condition evolution is used to generate multi-scenario snowstorm scenarios, simulate the dynamic evolution of regional snowstorm risks under extreme climate conditions, and predict regional risk trends under different environmental changes through a progressive future state generation mechanism, providing fine-grained multi-scenario risk analysis;

[0080] S7. Apply intelligent predictive resource pre-positioning algorithms to allocate resources to high-risk areas in advance based on multi-scenario analysis results, monitor in real time, and dynamically adjust resource allocation and replenishment cycles;

[0081] S8. Based on resource pre-positioning and replenishment plans, an adaptive resource replenishment interval algorithm is used to adjust the resource consumption rate and risk level in real time, automatically shortening the resource replenishment cycle when secondary disasters occur.

[0082] In this implementation, S2 includes the following steps:

[0083] S21, collect and receive the generated initial data set, divide the data into four environmental variables: meteorology, ecology, land use and traffic flow, and divide them into micro, meso and macro layers according to the characteristics of the variables, set the initial values ​​of data change rate ΔV1, ΔV2 and ΔV3, and the initial values ​​of sampling frequency f1, f2 and f3 for each layer, satisfying the relationship f1>f2>f3;

[0084] S22, based on the generated data change rate ΔV of each layer n and the initial value of the sampling frequency f n, where n∈{1,2,3} represents the level, and the sampling frequency is dynamically adjusted through the sampling frequency adaptive adjustment formula:

[0085]

[0086] Among them, f n represents the adjusted sampling frequency of the nth layer, and k is the short-term change rate ΔV n The relevant adjustment coefficient, δ is the multi-level data smoothing adjustment coefficient, w i Indicates the importance weight of each level of data, T i is the trend factor of the i-th layer of data, m is the total number of data layers, η is the sensitivity adjustment coefficient, Indicates the instantaneous change of the data change rate at the nth layer;

[0087] S23, the generated adjusted sampling frequency f n Perform smoothing to optimize the fluctuations caused by frequent switching and set the adaptive smoothing coefficient α n And apply the adaptive dynamic smoothing formula:

[0088]

[0089] Among them, f smooth,n represents the sampling frequency after smoothing of the nth layer, f n is the current sampling frequency after adjustment, f prev,n is the sampling frequency of the previous time step, α n is the adaptive smoothing coefficient, β is the dynamic smoothing adjustment factor, and λ is the rate of change ΔV n The intensity of the response;

[0090] S24, through the multi-level feedback mechanism, the sampling frequency f after smoothing each layer smooth,n Associated with real-time environmental data changes, the sampling frequency is adjusted according to feedback optimization, and layered sensitive monitoring of snow disaster precursors is dynamically carried out to optimize the system's response accuracy and stability under multi-layer environmental conditions.

[0091] In this implementation manner, S4 includes the following steps:

[0092] S41, smooth the sampling frequency f of each layer generated smooth,n and the adjusted parameter input fluctuation threshold self-calibration predictor, which detects the data at each time step t, records the data value V(t) of the current time step, and combines it with environmental parameters, including temperature T(t), snowfall S(t), and wind speed W(t);

[0093] S42, setting basic detection threshold T base , and real-time calibration is performed based on a dynamic multi-factor adjustment strategy. The adjusted detection threshold is:

[0094]

[0095] Among them, T adjusted (t) is the detection threshold after dynamic calibration at time step t, T base is the basic detection threshold, f smooth,n is the smoothing sampling frequency of the nth layer, which indicates the sensitivity weights of different environmental parameters. α, β, γ, and δ are the adjustment coefficients of environmental parameters, which are used to adjust the influence of each factor on the threshold.

[0096] S43, the generated dynamic calibration detection threshold T adjusted (t) Adaptive update of the adjustment coefficient is introduced to optimize the self-calibration capability of the detection threshold under different environmental conditions:

[0097]

[0098] Among them, α t+1 , β t+1 , γ t+1 ,δ t+1 are the update coefficients for time step t+1, T adjusted (t) is the calibration threshold generated for the current time step, T base is the basic detection threshold, T std is the standard deviation of the detection threshold, μ, ν, ξ, ζ are adaptive update adjustment factors;

[0099] S44, when the detection data V(t) exceeds the calibrated threshold T at time step t adjusted At (t), the snow disaster trend signal is recorded and alarm data is generated. If the threshold condition is met within n consecutive time steps, the forward-looking warning mechanism is triggered, and the current change data is transmitted to the microclimate customized model to evaluate the regional snow disaster risk level;

[0100] S45. Apply the updated detection threshold and adjustment coefficient to the analysis of the next time step, and use the real-time feedback mechanism to optimize the response accuracy of the detection system, and optimize the sensitivity and accuracy of the system under complex environmental conditions.

[0101] In this implementation manner, S5 includes the following steps:

[0102] S51, receiving the generated calibration threshold T adjusted (t), adjustment coefficients α(t), β(t), γ(t), δ(t) and forward-looking warning data, the environmental parameters of different regions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), are input into the regional feature module in the microclimate customization model to generate the dynamic weight of each environmental parameter;

[0103] S52. Based on the generated dynamic weights, combined with the historical meteorological data and current meteorological conditions of each region, an adaptive microclimate model is constructed, and the regional characteristic factor R is generated by dynamically adjusting the weights. i :

[0104]

[0105] Among them, R i is the characteristic factor of region i, X j (t) represents the jth environmental parameter at time step t, and the dynamic weight α j , β j Adaptively adjust γ, δ, ψ and ξ are feedback adjustment parameters;

[0106] S53, the generated characteristic factor R i Input into the regional risk level calculation model, according to the characteristic factor R i and calibration threshold T adjusted (t), calculate the regional risk level L i , and introduce adaptive feedback mechanisms to optimize responses to extreme climate conditions:

[0107]

[0108] Among them, L i is the risk level of area i, T adjusted (t) is the generated calibration threshold, λ is the smoothing factor, ρ and σ are adaptive feedback adjustment parameters used to adjust the impact of humidity on the risk level, and the standard deviation of humidity H std Used for smooth feedback regulation;

[0109] S54, the generated regional risk level L i The data is transmitted to the regional early warning module, which combines the forward-looking warning data to generate personalized snow disaster warning plans and regional emergency response plans to optimize the dynamic management of regional risk characteristics.

[0110] In this implementation manner, S6 includes the following steps:

[0111] S61. Receive the generated regional risk level L i and regional characteristic factor R i , which is input into the environment initialization module of the self-driving simulator, and combined with the current climate conditions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), to generate the climate benchmark parameters C for each region i , used to initialize the simulator state;

[0112] S62, based on the generated climate reference parameter C iand regional characteristic factor R i , generating the climate state E at time step t+1 i (t+1):

[0113] E i (t+1)=R i ·C i ·(1+η·sin(σ·T(t)+θ·S(t)));

[0114] Among them, E i (t+1) represents the climate state of region i at time step t+1, C i is the regional climate benchmark parameter, η, σ, and θ are adjustment factors, which adjust the impact of temperature and snowfall on the climate state respectively;

[0115] S63. Using the generated climate state E i (t+1) and the risk level changes of each area, and the risk level L of the future t+n time step is generated through the adaptive scenario generation mechanism. i (t+n) to make predictions:

[0116]

[0117] Among them, L i (t+n) is the risk level of region i at time step t+n, E i (t+k) is the climate state at the future time step t+k, and λ is the time decay factor, which is used to control the decreasing effect of the risk level;

[0118] S64, the predicted regional risk level L i (t+n) is passed to the early warning module to generate a personalized snow disaster warning plan, and provide a regional emergency response plan based on the risk level analysis of multiple future time steps, and a detailed early warning of the evolution of different climate conditions.

[0119] A snow disaster level monitoring and early warning management system according to an embodiment of the present invention includes:

[0120] Data collection and preprocessing module: used to collect multi-source environmental data, including meteorological data, ecological data, land use information and traffic flow information. After data cleaning, denoising and normalization, the initial data set is formed and transmitted to the environmental interaction module as system input data;

[0121] High-frequency self-adjusting environmental interaction module: Based on a multi-factor correlation model and an adaptive sampling mechanism, the module uses the initial data set to sensitively monitor the precursors of snowstorms. The module dynamically adjusts the sampling frequency and automatically adjusts the sampling density for different environmental variables, so that environmental changes are reflected in the system monitoring results.

[0122] Threshold self-calibration prediction module: Based on the dynamically collected environmental data and the fluctuating threshold self-calibration mechanism, the prediction threshold is adjusted in real time through a multi-factor adjustment strategy, so that the system can adapt to changing climate conditions and maintain a high prediction accuracy;

[0123] Microclimate customized model module: Build adaptive microclimate models according to the characteristics of different regions, generate regional characteristic factors and risk levels based on the historical meteorological data and current climate conditions of each region, and provide this information to the system's early warning module;

[0124] Climate Condition Evolution Self-Driving Simulator Module: Generates multiple snowstorm scenarios in real time based on environmental conditions through an adaptive scenario generation mechanism to predict regional risk changes under extreme climate conditions;

[0125] Adaptive resource replenishment module: Based on the risk level and climate scenario generated by the system, it dynamically calculates the resource consumption rate and replenishment interval, adjusts the resource allocation plan through an adaptive algorithm, and automatically shortens the resource replenishment cycle when emergencies or secondary disasters occur, so that resources can be replenished and allocated in a timely manner;

[0126] Early warning and response module: Integrate the risk level and climate status generated by the above modules, combine the risk level analysis of multiple time steps in the future to generate a personalized snow disaster early warning plan, and provide a regional emergency response plan to accurately warn and respond to different climate conditions.

[0127] Embodiment 1:

[0128] In order to verify the feasibility of the present invention in implementation, the present invention is applied to extreme snowfall events in winter in a northern city to help the city monitor and respond to snow disaster risks in real time. During the continuous snowfall in the city, due to the lag of the traditional early warning system, traffic jams and frequent accidents, and the untimely dispatch of emergency resources, the lives of citizens and the operation of the city were affected. For this reason, the city management department introduced a snow disaster level monitoring and early warning management system of the present invention to improve the accuracy of early warning and the efficiency of emergency response.

[0129] In this embodiment, the system first builds a multi-factor correlation analysis model, collects and cleans meteorological, ecological, land use and traffic flow data, and generates an initial data set. Data collection covers all areas of the city, and focuses on setting monitoring points in traffic-intensive areas and suburbs. Subsequently, the system establishes a high-frequency self-regulating environmental interaction model to monitor the precursors of snowstorms in real time by dynamically adjusting the sampling frequency. For example, when the intensity of snowfall increases, the system automatically increases the sampling frequency from every hour to every half hour, sensitively capturing snowstorm risk signals.

[0130] The system then uses adaptive climate variation analysis to adjust model parameters in real time so that it can quickly adapt to extreme weather changes. Taking the morning of the second day of snowfall as an example, when the system detects a sudden drop in temperature and an increase in wind speed, the adaptive module automatically adjusts the relevant parameters to ensure the accuracy of the prediction. Through the fluctuation threshold self-calibration predictor, the system automatically adjusts the detection threshold at each time step to generate a snowstorm trend forecast result, indicating that snowfall will continue in the next 48 hours and the snowfall will reach a historical high.

[0131] Based on trend prediction, the system constructs customized microclimate models in different regions to generate specific snow disaster risk levels for the city center and suburbs. For example, the traffic-intensive area in the city center is assessed as a yellow warning, and snow removal vehicles and anti-skid materials need to be prepared; due to the complex terrain in the suburbs, the risk level is raised to a red warning, and emergency points need to be set up in advance. Subsequently, the system uses the climate condition evolution self-driving simulator to generate multi-scenario snow disaster scenarios, simulate regional risk changes under continuous snowfall and low temperatures, and finds that some roads in the suburbs may have snow accumulation of more than 50 cm. Based on this, the system recommends setting up emergency supply stations at transportation hubs.

[0132] Table 1 Comparison of snow disaster monitoring and early warning data between the city center and the suburbs

[0133]

[0134]

[0135] After identifying high-risk areas, the system applies an intelligent predictive resource pre-positioning algorithm to pre-configure snow removal equipment, anti-skid materials, and emergency supplies in the city center and suburbs based on simulation results. The system monitors the resource consumption rate in real time and adjusts the resource replenishment interval in the city center from 4 hours to 2 hours in the evening when the snow intensity increases to keep the roads open. Emergency supplies such as sandbags and lighting equipment are given priority to high-risk areas in the suburbs to cope with possible secondary impacts of snowstorms.

[0136] To verify the effectiveness of the system, we conducted a number of data comparisons under extreme weather conditions. In the past, it took more than 2 hours to remove snow from the main roads in the city center during heavy snow. With the support of this invention, the clearing time was shortened to less than 1 hour, and the traffic efficiency of the main roads was improved by 50%. Through the regional early warning and risk assessment generated by the microclimate customized model, emergency resources in the city center and suburbs are reasonably allocated, avoiding emergencies caused by uneven resource allocation in the past. At the same time, by dynamically adjusting the sampling frequency and automatic threshold self-calibration prediction, the system's snow disaster warning accuracy has been increased to 85%, which can predict the risk of snow accumulation during peak traffic flow periods and arrange resources reasonably.

[0137] In the case of secondary disasters such as snow on roads and local landslides, the system optimizes the replenishment cycle through an adaptive resource replenishment interval algorithm, automatically shortens the replenishment time when resource demand peaks, and ensures that suburban rescue points and transportation hubs have sufficient supplies. After a 48-hour monitoring cycle, the system predicts the risk index within 48 hours through multi-scenario analysis. The final data shows that suburban traffic flow remains above 70% of the normal level, effectively avoiding serious traffic interruptions caused by snow accumulation, and significantly improving the overall emergency response efficiency of the city.

[0138] By applying the snow disaster level monitoring and early warning system of the present invention, the city has successfully solved the problems of delayed snow disaster early warning, unreasonable resource allocation, and untimely emergency response in the past, and achieved efficient monitoring and early warning management of snow disaster levels under extreme climatic conditions.

[0139] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A snow disaster level monitoring and early warning management system, characterized in that: The steps include: S1. Build a multi-factor correlation analysis model, collect and clean meteorological data, ecological parameters, land use information and traffic flow data to form an initial data set; S2. Based on the generated initial data set, a high-frequency self-regulating environmental interaction model is established to sensitively monitor the precursors of snow disasters by dynamically adjusting the sampling frequency; S3. Use adaptive climate variability analysis to adjust environmental interaction model parameters in real time, so that the system has the ability to respond quickly to extreme weather events; S4, inputting the adjusted parameters into the fluctuation threshold self-calibration predictor, automatically adjusting the detection threshold at each time step, making a forward-looking prediction of the snow disaster trend, and generating a prediction result; S5. Based on the prediction results, a microclimate customized model is constructed for different regional characteristics to generate a preliminary regional snow disaster risk level and provide characteristic input parameters for the early warning plan of each region; S6. Based on the generated regional risk level input parameters, a self-driving simulator for climate condition evolution is used to generate multi-scenario snowstorm scenarios, simulate the dynamic evolution of regional snowstorm risks under extreme climate conditions, and predict regional risk trends under different environmental changes through a progressive future state generation mechanism, providing fine-grained multi-scenario risk analysis; S7. Apply intelligent predictive resource pre-positioning algorithms to allocate resources to high-risk areas in advance based on multi-scenario analysis results, monitor in real time, and dynamically adjust resource allocation and replenishment cycles; S8. Based on resource pre-positioning and replenishment plans, an adaptive resource replenishment interval algorithm is used to adjust the resource consumption rate and risk level in real time, automatically shortening the resource replenishment cycle when secondary disasters occur.

2. A snow disaster level monitoring and early warning management system according to claim 1, characterized in that: The S2 specifically includes: S21, collect and receive the generated initial data set, divide the data into four environmental variables: meteorology, ecology, land use and traffic flow, and divide them into micro, meso and macro layers according to the characteristics of the variables, set the initial values ​​of data change rate ΔV1, ΔV2 and ΔV3, and the initial values ​​of sampling frequency f1, f2 and f3 for each layer, satisfying the relationship f1>f2>f3; S22, based on the generated data change rate ΔV of each layer n and the initial value of the sampling frequency f n , where n∈{1,2,3} represents the level, and the sampling frequency is dynamically adjusted through the sampling frequency adaptive adjustment formula: Among them, f n represents the adjusted sampling frequency of the nth layer, and k is the short-term change rate ΔV n The relevant adjustment coefficient, δ is the multi-level data smoothing adjustment coefficient, w i Indicates the importance weight of each level of data, T i is the trend factor of the i-th layer of data, m is the total number of data layers, η is the sensitivity adjustment coefficient, Indicates the instantaneous change of the data change rate at the nth layer; S23, the generated adjusted sampling frequency f n Perform smoothing to optimize the fluctuations caused by frequent switching and set the adaptive smoothing coefficient α n And apply the adaptive dynamic smoothing formula: Among them, f smooth,n represents the sampling frequency after smoothing of the nth layer, f n is the current sampling frequency after adjustment, f prev,n is the sampling frequency of the previous time step, α n is the adaptive smoothing coefficient, β is the dynamic smoothing adjustment factor, and λ is the rate of change ΔV n The intensity of the response; S24, through the multi-level feedback mechanism, the sampling frequency f after smoothing each layer smooth,n Associated with real-time environmental data changes, the sampling frequency is adjusted according to feedback optimization, and layered sensitive monitoring of snow disaster precursors is dynamically carried out to optimize the system's response accuracy and stability under multi-layer environmental conditions.

3. A snow disaster level monitoring and early warning management system according to claim 1, characterized in that: The S4 specifically includes: S41, smooth the sampling frequency f of each layer generated smooth,n and the adjusted parameter input fluctuation threshold self-calibration predictor, which detects the data at each time step t, records the data value V(t) of the current time step, and combines it with environmental parameters, including temperature T(t), snowfall S(t), and wind speed W(t); S42, setting basic detection threshold T base , and real-time calibration is performed based on a dynamic multi-factor adjustment strategy. The adjusted detection threshold is: (α·|V(t)-V(t-1)|+β·|T(t)-T(t-1)|+γ·|S(t)-S(t-1)|+δ·|W(t)-W(t-1)|); Among them, T adjusted (t) is the detection threshold after dynamic calibration at time step t, T base is the basic detection threshold, f smooth,n is the smoothing sampling frequency of the nth layer, which indicates the sensitivity weights of different environmental parameters. α, β, γ, and δ are the adjustment coefficients of environmental parameters, which are used to adjust the influence of each factor on the threshold. S43, the generated dynamic calibration detection threshold T adjusted (t) Adaptive update of the adjustment coefficient is introduced to optimize the self-calibration capability of the detection threshold under different environmental conditions: Among them, α t+1 , β t+1 , γ t+1 , δ t+1 are the update coefficients for time step t+1, T adjusted (t) is the calibration threshold generated for the current time step, T base is the basic detection threshold, T std is the standard deviation of the detection threshold, μ, ν, ξ, ζ are adaptive update adjustment factors; S44, when the detection data V(t) exceeds the calibrated threshold T at time step t adjusted At (t), the snow disaster trend signal is recorded and alarm data is generated. If the threshold condition is met within n consecutive time steps, the forward-looking warning mechanism is triggered, and the current change data is transmitted to the microclimate customized model to evaluate the regional snow disaster risk level; S45. Apply the updated detection threshold and adjustment coefficient to the analysis of the next time step, and use the real-time feedback mechanism to optimize the response accuracy of the detection system, and optimize the sensitivity and accuracy of the system under complex environmental conditions.

4. A snow disaster level monitoring and early warning management system according to claim 1, characterized in that: The S5 specifically includes: S51, receiving the generated calibration threshold T adjusted (t), adjustment coefficients α(t), β(t), γ(t), δ(t) and forward-looking warning data, the environmental parameters of different regions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), are input into the regional feature module in the microclimate customization model to generate the dynamic weight of each environmental parameter; S52. Based on the generated dynamic weights, combined with the historical meteorological data and current meteorological conditions of each region, an adaptive microclimate model is constructed, and the regional characteristic factor R is generated by dynamically adjusting the weights. i : Among them, R i is the characteristic factor of region i, X j (t) represents the jth environmental parameter at time step t, and the dynamic weight α j , β j Adaptively adjust γ, δ, ψ and ξ are feedback adjustment parameters; S53, the generated characteristic factor R i Input into the regional risk level calculation model, according to the characteristic factor R i and calibration threshold T adjusted (t), calculate the regional risk level L i , and introduce adaptive feedback mechanisms to optimize responses to extreme climate conditions: Among them, L i is the risk level of area i, T adjusted (t) is the generated calibration threshold, λ is the smoothing factor, ρ and σ are adaptive feedback adjustment parameters used to adjust the impact of humidity on the risk level, and the standard deviation of humidity H std Used for smooth feedback regulation; S54, the generated regional risk level L i The data is transmitted to the regional early warning module, which combines the forward-looking warning data to generate personalized snow disaster warning plans and regional emergency response plans to optimize the dynamic management of regional risk characteristics.

5. The snow disaster level monitoring and early warning management system according to claim 1 is characterized in that: The S6 specifically includes: S61. Receive the generated regional risk level L i and regional characteristic factor R i , which is input into the environment initialization module of the self-driving simulator, and combined with the current climate conditions, including temperature T(t), snowfall S(t), wind speed W(t) and humidity H(t), to generate the climate benchmark parameters C for each region i , used to initialize the simulator state; S62, based on the generated climate reference parameter C i and regional characteristic factor R i , generating the climate state E at time step t+1 i (t+1): E i (t+1)=R i ·C i ·(1+η·sin(σ·T(t)+θ·S(t))); Among them, E i (t+1) represents the climate state of region i at time step t+1, C i is the regional climate benchmark parameter, η, σ, and θ are adjustment factors, which adjust the impact of temperature and snowfall on the climate state respectively; S63. Using the generated climate state E i (t+1) and the risk level changes of each area, and the risk level L of the future t+n time step is generated through the adaptive scenario generation mechanism. i (t+n) to make predictions: Among them, L i (t+n) is the risk level of region i at time step t+n, E i (t+k) is the climate state at the future time step t+k, and λ is the time decay factor, which is used to control the decreasing effect of the risk level; S64, the predicted regional risk level L i (t+n) is passed to the early warning module to generate a personalized snow disaster warning plan, and provide a regional emergency response plan based on the risk level analysis of multiple future time steps, and a detailed early warning of the evolution of different climate conditions.

6. A snow disaster level monitoring and early warning management system, characterized in that: include: Data collection and preprocessing module: used to collect multi-source environmental data, including meteorological data, ecological data, land use information and traffic flow information. After data cleaning, denoising and normalization, the initial data set is formed and transmitted to the environmental interaction module as system input data; High-frequency self-adjusting environmental interaction module: Based on a multi-factor correlation model and an adaptive sampling mechanism, the module uses the initial data set to sensitively monitor the precursors of snowstorms. The module dynamically adjusts the sampling frequency and automatically adjusts the sampling density for different environmental variables, so that environmental changes are reflected in the system monitoring results. Threshold self-calibration prediction module: Based on the dynamically collected environmental data and the fluctuating threshold self-calibration mechanism, the prediction threshold is adjusted in real time through a multi-factor adjustment strategy, so that the system can adapt to changing climate conditions and maintain a high prediction accuracy; Microclimate customized model module: Build adaptive microclimate models according to the characteristics of different regions, generate regional characteristic factors and risk levels based on the historical meteorological data and current climate conditions of each region, and provide this information to the system's early warning module; Climate Condition Evolution Self-Driving Simulator Module: Generates multiple snowstorm scenarios in real time based on environmental conditions through an adaptive scenario generation mechanism to predict regional risk changes under extreme climate conditions; Adaptive resource replenishment module: Based on the risk level and climate scenario generated by the system, it dynamically calculates the resource consumption rate and replenishment interval, adjusts the resource allocation plan through an adaptive algorithm, and automatically shortens the resource replenishment cycle when emergencies or secondary disasters occur, so that resources can be replenished and allocated in a timely manner; Early warning and response module: Integrate the risk level and climate status generated by the above modules, combine the risk level analysis of multiple time steps in the future to generate a personalized snow disaster early warning plan, and provide a regional emergency response plan to accurately warn and respond to different climate conditions.

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