An agricultural insurance disaster loss assessment and early warning method based on thunderstorm gale grading identification technology

Abstract

The application discloses a kind of agricultural insurance disaster loss determination and early warning method based on thunderstorm gale grading identification technology, it is related to agricultural meteorology and insurance risk control technical field.The application collects weather, crop, disaster and policy data to construct thunderstorm gale database, and completes gale grading identification and global determination by fusing ground wind speed and radar data;Wind disaster vulnerability model is constructed by combining crop attributes, meteorological claim index is established, and the loss and compensation standard of disaster are quantified;Relying on real-time monitoring, carry out graded risk early warning and disaster prevention information push, reconstruct wind field spatial distribution after disaster, automatically complete loss determination and claim settlement, and the model is iteratively optimized through real loss data.The application overcomes the defects of traditional loss determination, such as subjectivity, lag, limited site recognition, poor index matching, realizes wind disaster early warning, precise loss determination and efficient claim settlement, reduces claim settlement disputes and basis risk, and is suitable for agricultural insurance control scene of crop thunderstorm gale disaster.

Description

Technical Field

[0001] This invention relates to the field of agricultural meteorological disaster assessment and insurance technology, specifically to an agricultural insurance disaster loss assessment and early warning method based on thunderstorm and strong wind classification identification technology. Background Technology

[0002] Agricultural insurance is a core tool for modern agricultural risk management. Traditional agricultural insurance loss assessment mainly relies on "post-disaster reporting, on-site inspection, and manual loss assessment," which suffers from problems such as low timeliness, strong subjectivity, high cost, and susceptibility to claims disputes. Although satellite remote sensing and drone loss assessment have improved the situation, they still have shortcomings such as data lag, high cost, and difficulty in achieving full automation coverage.

[0003] In the meteorological field, Doppler weather radar and automatic weather stations can already monitor strong winds in real time, but these technologies are only used for weather warnings and have not been deeply integrated with agricultural insurance. While existing weather index insurance can reduce operating costs, it generally suffers from basis risk, has a low degree of matching between weather indices and actual crop losses, and lacks refined vulnerability models for different crops and growth stages, making it impossible to achieve accurate loss assessment and pre-disaster prevention.

[0004] In summary, there is an urgent need for an agricultural insurance disaster assessment and early warning method based on thunderstorm and strong wind classification and identification technology, which can realize the identification of strong wind levels across the entire region, crop vulnerability modeling, automatic triggering of claims by meteorological index, pre-disaster early warning and disaster prevention, and rapid post-disaster loss assessment, forming a closed loop of the entire process. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides an agricultural insurance disaster loss assessment and early warning method based on thunderstorm and strong wind classification and identification technology. This method enables comprehensive identification of strong wind levels across the entire region, crop vulnerability modeling, automatic claims processing triggered by meteorological indices, pre-disaster early warning and disaster prevention, and rapid post-disaster loss assessment, forming a closed-loop process.

[0006] The technical solution of this invention is: A method for agricultural insurance disaster loss assessment and early warning based on thunderstorm and strong wind classification and identification technology includes the following steps: S1: Collect meteorological, crop, disaster information, and policy data to construct a thunderstorm and strong wind database; S2: Classify and identify thunderstorm and strong winds based on ground-measured wind speed and radar data to form wind level determination rules; S3: Construct a wind disaster vulnerability model based on crop attributes and establish a meteorological claims index; S4: Provide risk warnings and disaster prevention information based on real-time wind identification results; S5: After a disaster, automatically assess and process claims based on real-time wind data and the vulnerability model, and feed back actual losses to iteratively optimize the model. Through a closed-loop approach, this method achieves data interoperability between meteorological monitoring, crop damage, insurance claims, and model iteration, solving the problem of disconnect between meteorology and agricultural insurance business, and forming an intelligent risk control system.

[0007] Preferably, in step S2, the strong winds are classified according to the maximum ground wind speed: Level 8 is defined as 17.2 m / s ≤ <20.8m / s, Level 9 is 20.8m / s≤ <24.5m / s, level 10 and above ≥24.5m / s, This refers to the maximum ground wind speed. The national standard wind speed classification is adopted to standardize the determination of gale levels and improve the objectivity and universality of damage assessment criteria.

[0008] Preferably, in step S2, precipitation, thunderstorms, and strong winds are determined using the maximum radial velocity at the lower levels by radar: ≥ level 8 corresponds to... ≥20m / s, ≥9 level corresponds ≥30m / s, ≥10 level corresponding ≥35m / s, This represents the maximum radial velocity at the lower levels of the radar. By calibrating the radar radial velocity threshold, accurate identification of strong winds in areas without ground monitoring stations can be achieved, expanding the monitoring coverage.

[0009] Preferably, step S2 employs a fusion judgment: radar judgment is prioritized for areas with precipitation, ground measurements are used for areas without precipitation, and radar inversion interpolation is used for areas without monitoring stations. This multi-scenario fusion judgment rule improves the accuracy and applicability of wind level identification under different weather conditions.

[0010] Preferably, the wind disaster vulnerability model in step S3 adopts the formula: Where L is the crop loss rate, G is the wind level, C is the crop type coefficient, and P is the growth period coefficient. A quantitative vulnerability model is constructed for each crop and growth period to reduce basis risk and improve the accuracy of loss assessment.

[0011] Preferably, the weather claim index in step S3 is calculated using the following formula: Where I is the claims index, Here, K represents the gale level coefficient, and K represents the insurance policy coefficient. A standardized meteorological claims index will be established to achieve objectivity and automation in claims triggering, thereby shortening the claims processing cycle.

[0012] Preferably, the compensation amount is calculated using the following formula: Where S is the compensation amount, A is the land insurance amount, and L is the loss rate. By clarifying the compensation amount calculation formula, standardization of loss assessment and claims settlement is achieved, reducing claims disputes.

[0013] Preferably, in step S4, the strong wind warning is overlaid with crop spatial data to classify risk levels and target warnings and disaster prevention information accordingly. Through risk zoning and targeted delivery, accurate pre-disaster warnings and proactive disaster prevention are achieved, reducing disaster losses.

[0014] Furthermore, in step S5, the spatial distribution of strong winds is reconstructed through spatial interpolation, and the loss rate is automatically calculated by coupling the vulnerability model. High-resolution spatial interpolation enables accurate calculation of the affected area and loss rate, improving the precision of damage assessment.

[0015] Furthermore, in step S5, the following steps are adopted: Calibration model, To estimate the loss rate, This represents the true loss rate. Through closed-loop iterative calibration of the model, the accuracy of identification and loss assessment is continuously optimized, improving the long-term stability of the system.

[0016] The beneficial effects of this invention are: 1. Full-area coverage identification: The fusion of ground station and radar inversion judgment solves the technical problem of inaccurate identification of wind level in areas without meteorological stations, and realizes the spatial expansion from point observation to area coverage.

[0017] 2. Objective and efficient loss assessment: Based on meteorological monitoring data and quantitative vulnerability models, loss assessment eliminates the subjectivity of manual inspection and shortens the traditional loss assessment cycle of several days to minutes, greatly improving efficiency.

[0018] 3. Reduce basis risk: Develop refined vulnerability models for different crops and growth stages to ensure that the meteorological claims index is highly matched with the actual crop losses, thereby reducing claims disputes.

[0019] 4. Proactive disaster prevention and mitigation: Achieve tiered early warning and targeted prevention and control before disasters, shifting from "passive compensation after disasters" to "proactive disaster prevention before disasters", thereby reducing disaster losses and insurance compensation costs.

[0020] 5. Closed-loop end-to-end: Connecting data channels for meteorological monitoring, crop disasters, insurance claims, and model iteration to form a self-optimizing intelligent risk control system. Attached Figure Description

[0021] Figure 1 This is a flowchart of the agricultural insurance disaster assessment and early warning method based on the graded identification of thunderstorms and strong winds, according to the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0023] Example 1 This embodiment uses the main winter wheat producing area in North China as the research area, and selects the wheat heading stage (a high-wind-sensitive growth period) to carry out thunderstorm and strong wind insurance loss assessment and early warning, and details the specific implementation process of the present invention. Those skilled in the art can refer to this embodiment to extend the method to crops such as corn, rice, and fruit trees, as well as other growth stages.

[0024] The entire process of damage assessment and early warning for thunderstorms and strong winds during the heading stage of winter wheat. Step 1: Multi-source data acquisition and construction of thunderstorm and strong wind database Data collected from the study area over the past 5 years: Meteorological data: maximum wind speed from ground-based automatic weather stations, physical quantities of the upper and lower atmosphere, and Doppler weather radar data; Agricultural data: Winter wheat planting distribution, land use type, and observation data on heading / jointing / maturity stages; Insurance data: Farmers' policy information, insured amount, deductible, and reimbursement ratio clauses; Disaster data: historical wheat lodging area, measured yield reduction rate, and compensation results.

[0025] The samples of strong winds with wind speeds ≥17.2m / s (level 8 and above) were cleaned, deduplicated, and spatiotemporally matched; the authenticity of the samples was verified by comparing them with the actual situation using radar PUP products, and a historical case database of thunderstorms and strong winds was constructed.

[0026] The system deploys a real-time batch processing program that automatically captures meteorological and radar data when strong winds occur and automatically supplements disaster information from the disaster reporting system after the weather event ends, forming a dynamically updated real-time case database.

[0027] Step 2: Thunderstorm and Strong Wind Classification Identification and Fusion Judgment Gale winds are classified into three levels based on maximum ground wind speed: Level 8: 17.2 m / s ≤ <20.8m / s Level 9: 20.8 m / s ≤ <24.5m / s Level 10 and above: ≥24.5m / s For thunderstorms with precipitation and strong winds, calibrate the maximum radial velocity threshold of the lower radar layers: Level 8 or above: ≥20m / s Level 9 or above: ≥30m / s Level 10 or above: ≥35m / s The following fusion determination rules are adopted: Precipitation, thunderstorms, and strong winds: Radar radial velocity should be used to determine the severity level. Thunderstorms with strong winds and no precipitation: based on wind speed measured by automatic ground stations; Areas without meteorological station coverage: determined by interpolation of wind field obtained from radar inversion.

[0028] Step 3: Construct a wind disaster vulnerability model and a weather claims index Vulnerability Model Construction The wheat-growing area was rasterized using GIS, and historical disaster data was linked to wind intensity. A vulnerability model for wheat during the heading stage to wind disasters was then fitted: L = 0.12·G + 0.15·C + 0.20·P + 0.05 Where: L is the loss rate, G is the wind level, wheat type coefficient C=1.2, and heading period sensitivity coefficient P=1.3.

[0029] Construction of Weather Claims Index

[0030] in: Level coefficient: Level 8 = 1.0, Level 9 = 1.5, Level 10 and above = 2.0; Insurance clause coefficient: K=(1−D)×R=(1−0.1)×0.8=0.72 (10% deductible, 80% reimbursement ratio).

[0031] Compensation amount formula S=A×L×I Agreed-upon threshold =1.5, and claims will be automatically triggered when I≥1.5.

[0032] Step 4: Proximity Early Warning and Targeted Disaster Prevention and Mitigation The system accesses radar base data in real time, identifies radar radial velocity ≥35m / s, determines it as a level 10 or higher thunderstorm gale, and extrapolates the impact path and time.

[0033] The warning area is overlaid with the GIS data of the wheat-growing area, and risk zones are divided according to the following criteria: Red risk zones: winds of level 10 or above, wheat heading stage, and high-risk plots of land; Orange risk zone: Level 9 gale, wheat heading stage; Yellow risk zone: Level 8 gale, other wheat growth stages.

[0034] Push notifications to farmers in red-risk areas via app and SMS: Warning: Level 10 thunderstorms and strong winds are expected in the next hour. Wheat is in the heading stage and is prone to lodging. Please immediately reinforce field protection and take emergency measures to reduce losses. Simultaneously, a disaster prevention and mitigation activation notice was sent to the insurance company, and resources for investigation and assessment were deployed in advance.

[0035] Step 5: Rapid post-disaster damage assessment, automated claims processing, and model iteration 1. Real-time wind speed reconstruction Quality control and outlier removal were performed on ground-based measured and radar-retrieved data. Kriging interpolation was used to generate a 100m resolution real-time wind speed spatial distribution map to accurately delineate the disaster area and level.

[0036] 2. Loss Rate and Compensation Calculation The insured amount for a certain plot of land is A = 1500 yuan / mu, and the model calculates a loss rate of L = 0.45. Claims reimbursement index I = 2.0 × 1.2 × 1.3 × 0.72 = 2.2464 > =1.5, triggering compensation; The compensation amount S = 1500 × 0.45 × 2.2464 ≈ 1515 yuan / mu.

[0037] 3. Automatically generate damage assessment reports Within 10 minutes, the system generates a standardized loss assessment report that includes the land boundary, wind force level, loss rate, and compensation amount, and automatically pushes it to the core insurance business system to complete rapid direct compensation.

[0038] 4. Model Iterative Optimization The actual loss rate Lᵣ is fed back to the database to calculate the error. ; When Δ > 0.1, the vulnerability model coefficients are refitted, the radar threshold parameters are updated, and the system closed-loop iterative optimization is achieved.

[0039] The technical effect of this embodiment is as follows: 1. The accuracy of wind level identification has been significantly improved; 2. Damage assessment time has been reduced from the traditional 7 days to ≤30 minutes; 3. Claims disputes have also decreased accordingly; 4. The average increase in the amount of economic losses recovered through pre-disaster prevention, mitigation, and loss reduction; 5. Basis risk has been significantly reduced, and the correlation between the index and actual losses has improved.

[0040] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for agricultural insurance disaster assessment and early warning based on thunderstorm and strong wind classification and identification technology, characterized in that, Includes the following steps: S1: Collect meteorological, crop, disaster, and insurance policy data to build a thunderstorm and strong wind database; S2: Based on ground-measured wind speed and radar data, thunderstorm gale classification is identified, and gale level determination rules are formed. S3: Construct a wind disaster vulnerability model based on crop attributes and establish a meteorological claims index; S4: Based on real-time strong wind identification results, risk warnings and disaster prevention information are pushed out; S5: After a disaster, the system automatically assesses and processes damages and claims based on real-time wind data and vulnerability models, and feeds back actual losses to iteratively optimize the model.

2. The method according to claim 1, characterized in that, In step S2, strong winds are classified according to the maximum ground wind speed: Level 8 is defined as 17.2 m / s ≤ <20.8m / s, Level 9 is 20.8m / s≤ <24.5m / s, level 10 and above ≥24.5m / s, This represents the maximum wind speed at ground level.

3. The method according to claim 1, characterized in that, Step S2 involves precipitation, thunderstorms, and strong winds, determined by the maximum radial velocity at lower levels using radar: ≥ level 8 corresponds to... ≥20m / s, ≥9 level corresponds ≥30m / s, ≥10 level corresponding ≥35m / s, This represents the maximum radial velocity at the lower radar level.

4. The method according to claim 1, characterized in that, The wind disaster vulnerability model in step S3 uses the following formula: Where L is the crop loss rate, G is the wind level, C is the crop type coefficient, and P is the growth period coefficient.

5. The method according to claim 1, characterized in that, The weather claims index in step S3 uses the following formula: Where I is the claims index, K represents the gale level coefficient, and K represents the insurance policy coefficient.

6. The method according to claim 5, characterized in that, The compensation amount is calculated using the following formula: Where S is the compensation amount, A is the land insurance amount, and L is the loss rate.

7. The method according to claim 1, characterized in that, Step S2 employs a fusion-based determination: radar determination is prioritized for areas with precipitation, ground measurements are used for areas without precipitation, and radar inversion interpolation is used for areas without monitoring stations.

8. The method according to claim 1, characterized in that, In step S4, the gale warning is overlaid with crop spatial data to classify risk levels and push warning and disaster prevention information in a targeted manner.

9. The method according to claim 1, characterized in that, In step S5, the spatial distribution of strong winds is reconstructed through spatial interpolation, and the loss rate is automatically calculated by coupling the vulnerability model.

10. The method according to claim 1, characterized in that, Step S5 uses Calibration model, To estimate the loss rate, This represents the true loss rate.

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