Agricultural insurance claim risk assessment method and system

Abstract

The invention discloses an agricultural insurance claim risk assessment method and system, and the method comprises the steps: obtaining agricultural insurance contract data, extracting key information, such as an insured crop type, a growth cycle and an insurance amount, carrying out the matching of a risk assessment result with the contract information through a natural language processing technology, and automatically calculating the claim risk of each contract; according to the level of the claim settlement risk, performing level-to-level management on the contract, and taking corresponding risk control measures for the high-risk contract, such as adjustment of underwriting conditions, improvement of insurance premium and starting of a manual checking process for the contract with the claim settlement risk exceeding a preset threshold value; obtaining claim settlement case data, adopting a decision tree algorithm, analyzing a relationship between case characteristics and claim settlement results, summarizing a claim settlement law, optimizing a risk assessment model and a claim settlement process, and improving risk prevention and control and claim settlement efficiency.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and system for assessing agricultural insurance claims risk. Background Art

[0002] In the risk assessment of agricultural insurance claims, it is necessary to comprehensively consider a variety of factors, including plant disease factors, weather disaster factors and man-made disaster factors. These factors interact with each other and constitute a complex risk assessment system. In actual business scenarios, it is difficult to unify the weight distribution and quantitative standards of various factors, resulting in large deviations in the calculation results of risk probability and loss degree. In addition, there are many types of crops, and the growth cycle, stress resistance, and occurrence patterns of pests and diseases of different crops vary significantly, which further increases the difficulty of risk assessment. At the same time, agricultural production is significantly affected by regionality. The climate conditions, soil types, and planting habits in different regions are different, making it difficult for risk assessment models to be applied to all regions. Finally, agricultural insurance claims also involve a large amount of insurance contract data. How to effectively combine risk assessment results with contract data and automatically calculate future claims risks is also a technical problem that needs to be solved urgently. These complex factors are intertwined, which brings huge challenges to the risk assessment of agricultural insurance claims. There is an urgent need for a technical solution that can comprehensively consider various factors, adapt to the characteristics of different crops and regions, and automatically calculate claims risks. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method and system for assessing agricultural insurance claims risk.

[0004] A method for assessing agricultural insurance claims risk, comprising:

[0005] Step S101, obtaining a preliminary factor weight allocation scheme based on historical data including plant disease factors, weather disaster factors and man-made disaster factors;

[0006] Step S102: According to the regional differences in agricultural production, the climate conditions, soil types and planting habits of different regions are obtained, the weight distribution scheme of the adjustment factors is optimized, and the quantitative standards applicable to the region are determined;

[0007] Step S103, obtaining data on the stress resistance and occurrence patterns of pests and diseases of crops, and predicting the probability of risk occurrence and the extent of loss within a certain period of time in the future;

[0008] Step S104: Acquire agricultural insurance contract data, match the risk assessment results with the agricultural insurance contract data, and automatically calculate the claim risk of each contract;

[0009] Step S105: Manage contracts in different levels according to the risk of claims settlement, and take corresponding risk control measures for high-risk contracts;

[0010] Step S106: Obtain claim case data, analyze the relationship between case characteristics and claim results, and optimize the risk assessment model and claim process.

[0011] Preferably, in step S101, historical data including plant disease factors, weather disaster factors and man-made disaster factors are obtained, and data mining technology is used to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, so as to obtain a preliminary factor weight allocation plan.

[0012] Preferably, in step S102, based on the regional differences in agricultural production, data on climate conditions, soil types and planting habits in different regions are obtained, and the regions are divided into several similar areas through a clustering algorithm. For each region, the weight distribution scheme of the adjustment factors is optimized to determine the quantitative standards applicable to the region.

[0013] Preferably, in step S103, data on the stress resistance and occurrence patterns of pests and diseases of crops are obtained, and a support vector machine algorithm is used to train a risk assessment model to predict the probability of risk occurrence and the degree of loss within a certain period of time in the future. If the prediction result exceeds a preset threshold, an early warning mechanism is triggered.

[0014] The present invention also provides an agricultural insurance claim risk assessment system, comprising:

[0015] The first processing device is used to obtain a preliminary factor weight allocation scheme based on historical data including plant disease factors, weather disaster factors and man-made disaster factors;

[0016] The second processing device is used to obtain data on climate conditions, soil types and planting habits in different regions according to the regional differences in agricultural production, optimize the weight distribution plan of adjustment factors, and determine the quantitative standards applicable to the region;

[0017] The third processing device is used to obtain data on the stress resistance and occurrence patterns of pests and diseases of crops, and to predict the probability of risk occurrence and the extent of loss within a certain period of time in the future;

[0018] The fourth processing device is used to obtain agricultural insurance contract data, match the risk assessment results with the agricultural insurance contract data, and automatically calculate the claim risk of each contract;

[0019] The fifth processing device is used to manage contracts in different levels according to the level of claim risk and take corresponding risk control measures for high-risk contracts;

[0020] The sixth processing device is used to obtain claim case data, analyze the relationship between case characteristics and claim results, and optimize the risk assessment model and claim process.

[0021] Preferably, the first processing device obtains historical data including plant disease factors, weather disaster factors and man-made disaster factors, and uses data mining technology to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, and obtains a preliminary factor weight allocation plan.

[0022] Preferably, the second processing device obtains data on climate conditions, soil types and planting habits in different regions based on regional differences in agricultural production, divides the region into several similar areas through a clustering algorithm, optimizes and adjusts the weight distribution scheme of factors for each region, and determines the quantitative standards applicable to the region.

[0023] Preferably, the third processing device obtains data on the stress resistance and occurrence patterns of crops and pests and diseases, adopts a support vector machine algorithm, trains a risk assessment model, and predicts the probability of risk occurrence and the degree of loss within a certain period of time in the future. If the prediction result exceeds a preset threshold, an early warning mechanism is triggered.

[0024] The present invention obtains agricultural insurance contract data, extracts key information, such as the type of insured crops, growth cycle, insurance amount, etc., matches the risk assessment results with the contract information through natural language processing technology, and automatically calculates the claim risk of each contract; the contracts are managed in a graded manner according to the level of claim risk, and corresponding risk control measures are taken for high-risk contracts, such as adjusting the underwriting conditions, increasing the premium, etc. For contracts whose claim risk exceeds a preset threshold, a manual verification process is initiated; the claim case data is obtained, and a decision tree algorithm is used to analyze the relationship between case characteristics and claim results, summarize the claim rules, optimize the risk assessment model and claim process, and improve risk prevention and control and claim efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flow chart of the agricultural insurance claim risk assessment method. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0027] Embodiment 1:

[0028] like Figure 1 As shown, an agricultural insurance claim risk assessment system in this embodiment may specifically include:

[0029] Step S101, obtain historical data including plant disease factors, weather disaster factors and man-made disaster factors, use data mining technology to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, and obtain a preliminary factor weight allocation plan.

[0030] According to the crop types and growth cycles, historical data including plant disease factors, weather disaster factors and man-made disaster factors are obtained. Data preprocessing technology is used to clean, integrate, transform and reduce the acquired historical data to obtain a high-quality data set. Based on the preprocessed data set, the association rule mining algorithm is used to analyze the association rules between each disaster factor and the probability of risk occurrence and the degree of loss, and the frequent item set and association rule set are obtained. According to the association rule set obtained by mining, the fuzzy comprehensive evaluation method is used to calculate the membership function of each disaster factor and obtain the preliminary factor weight. According to the preliminary factor weight, the hierarchical analysis method is used, combined with expert experience, to correct and optimize the factor weight to obtain the final factor weight allocation scheme. The optimized factor weight allocation scheme is applied to the crop risk assessment model to predict and evaluate the risk level of different crop types in different growth cycles. According to the risk assessment results, targeted crop risk prevention and control measures and emergency plans are formulated, and the factor weight allocation scheme is dynamically adjusted to achieve precise prevention and control and dynamic management of crop risks.

[0031] Furthermore, crop risk assessment and prevention and control is a complex issue involving the combined effects of multiple factors. In order to conduct risk management more effectively, these factors need to be analyzed, quantified and evaluated. First, historical data needs to be obtained. For example, taking wheat as an example, pest and disease data, meteorological disaster data (such as drought, flood, hail, frost) and man-made disaster data (such as improper application of pesticides and insufficient irrigation) of wheat in different growth cycles (such as seedling stage, jointing stage, heading stage, and filling stage) in the past ten years can be collected. Data sources can include statistical data from agricultural meteorological stations, plant protection stations, agricultural and rural departments, and farmers' field records. Next, data preprocessing is performed. For example, the collected data may have missing values, inconsistent formats, outliers, and other problems. Data cleaning techniques can be used, such as filling missing values ​​with mean or median, unifying the date format to "year-month-day", deleting or correcting obviously erroneous data, etc. Then, data from different sources need to be integrated, such as integrating pest and disease data, meteorological data, and man-made disaster data into one data set. In order to facilitate subsequent analysis, data transformation may be required, such as converting text-type pest names into digital codes, or converting continuous temperature data into discrete "high temperature", "suitable temperature", "low temperature", etc. Finally, in order to improve computational efficiency, data reduction can be performed, such as retaining only key indicators or reducing the dimensionality of the data. After data preprocessing, association rule mining algorithms, such as the Apriori algorithm, can be used. Suppose the frequent item set is {high temperature, red spider}, the association rule is {high temperature} → {red spider}, and the confidence is 80%, which means that in high temperature weather, the probability of wheat being infected with red spider is 80%. Another example is {low temperature, frost, wheat yield decline}, with a confidence of 90% and a support of 20%, which means that in 20% of the years, low temperature, frost and wheat yield decline occurred at the same time, and in the years with low temperature and frost, wheat yield declined in 90% of the years. These rules can help understand the correlation between different disaster factors. Based on association rules, the weight of each disaster factor can be calculated using fuzzy comprehensive evaluation methods. For example, the degree of harm of "high temperature" to wheat can be defined as "high", "medium" and "low", and the membership function of "high temperature" at different levels can be determined based on historical data and expert experience. For example, when the temperature is higher than 35°C, the membership function value is 1 (completely "high"); when the temperature is between 30°C and 35°C, the membership function value is between 0 and 1; when the temperature is lower than 30°C, the membership function value is 0 (completely not "high"). By similar methods, the membership functions of other disaster factors can be calculated and preliminary factor weights can be obtained. Then, the preliminary factor weights can be modified using the analytic hierarchy process (AHP).For example, a hierarchical model can be constructed, with "crop risk" as the target layer, "pests and diseases", "meteorological disasters", and "man-made disasters" as the criterion layer, and specific disaster factors as the indicator layer. Through expert scoring, the relative importance of different factors is compared, and the final factor weight distribution scheme is calculated. For example, experts may believe that during the growth of wheat, the impact weight of pests and diseases is 4, meteorological disasters is 5, and man-made disasters is 1. Finally, the optimized weights are applied to the crop risk assessment model. For example, the risk level of wheat in different growth cycles can be calculated based on the degree of wheat's susceptibility to disasters in different growth cycles and the weights of different disasters, such as "high risk", "medium risk", and "low risk". For example, wheat is more sensitive to drought at the heading stage, and the weight of drought is also relatively large, so the risk level of wheat facing drought at the heading stage may be "high risk". According to the risk assessment results, targeted prevention and control measures can be formulated. For example, for the "high risk" level, irrigation preparations can be made in advance, or drought-resistant varieties can be selected; for the "medium risk" level, field monitoring can be strengthened and preventive measures can be taken in advance; for the "low risk" level, investment can be appropriately reduced to reduce costs. In order to cope with emergencies, emergency plans need to be formulated, such as activating emergency response mechanisms to reduce losses when encountering extreme weather. In order to improve the accuracy and adaptability of the model, the model needs to be evaluated and adjusted regularly, such as dynamically adjusting the factor weight allocation scheme based on new data and experience, so as to achieve precise prevention and control and dynamic management of crop risks.

[0032] Step S102, based on the regional differences in agricultural production, obtain the climate conditions, soil types and planting habits data of different regions, divide the regions into several similar areas through clustering algorithms, optimize and adjust the weight distribution scheme of factors for each region, and determine the quantitative standards applicable to the region.

[0033] Agricultural production data from different regions, including climate conditions, soil types, and planting habits, were obtained to construct an agricultural production feature data set. The agricultural production feature data were preprocessed to remove missing values ​​and outliers, and the data were standardized to eliminate the dimensional differences between different features. The K-means clustering algorithm was used to cluster the preprocessed agricultural production feature data, and different regions were divided into several similar agricultural production areas according to the clustering results. For each agricultural production area, the influence of each agricultural production factor on the yield in the area was analyzed, and the weight coefficient of each factor was determined by correlation analysis and regression analysis. According to the determined weight coefficient, an agricultural production evaluation model suitable for the agricultural production area was constructed, with climate conditions, soil types, and planting habits as the input variables of the model, and yield as the output variable. The constructed evaluation model was trained and verified using existing agricultural production data, and the accuracy and generalization ability of the model were evaluated by cross-validation and other methods, and the model was optimized and adjusted according to the evaluation results. The optimized agricultural production assessment model is applied to actual production. Based on data such as climate conditions, soil type and planting habits, the agricultural output of the region is predicted, and corresponding agricultural production management measures are formulated to guide agricultural production practices.

[0034] Furthermore, agricultural production data from different regions can be obtained. For example, historical data from wheat-growing areas in Hebei Province can be collected, including climate data (temperature, rainfall, sunshine duration, etc.), soil type data (soil texture, pH value, organic matter content, etc.), and planting habit data (sowing time, fertilizer application, irrigation method, etc.). Constructing an agricultural production feature data set is to integrate these different types of data into one data set for subsequent analysis. In the data preprocessing stage, assuming that the rainfall data in July 2022 in the wheat-growing areas of Hebei Province is missing, the average value filling method can be used to fill the missing values ​​with the average rainfall in July 2021 and 2023. For outliers, such as the amount of fertilizer applied in a certain year far exceeds the normal range, it may be a data recording error, and the outlier can be eliminated. Standardization is to eliminate the dimensional differences between different features. For example, the unit of temperature is Celsius and the unit of rainfall is millimeters. Through standardization, these data can be converted into dimensionless values ​​to avoid the impact of dimensional differences on the analysis results. The K-means clustering algorithm is used to perform cluster analysis on the preprocessed data. Assume that the wheat-growing area of ​​Hebei Province is divided into three agricultural production areas: the north, the middle and the south. The basis for division is the similarity of climate conditions, soil types and planting habits. For example, the northern region has a cold climate, alkaline soil, and a late sowing habit; the central region has a mild climate, fertile soil, and a relatively balanced planting habit; the southern region has a warm climate, acidic soil, and a early sowing habit. For the northern wheat-growing area, the impact of climate conditions, soil types and planting habits on wheat yield is analyzed. Through correlation analysis, it is found that temperature is negatively correlated with wheat yield, that is, the higher the temperature, the lower the wheat yield; rainfall is positively correlated with wheat yield, that is, the more rainfall (within a certain range), the higher the wheat yield. Through regression analysis, the weight coefficients of each factor can be determined, for example, the weight coefficient of temperature is -3, the weight coefficient of rainfall is 5, the weight coefficient of soil pH is 2, and so on. These weight coefficients reflect the degree of influence of each factor on wheat yield. According to the determined weight coefficients, an agricultural production evaluation model suitable for the northern wheat-growing area is constructed. The input variables of the model include temperature, rainfall, soil pH, etc., and the output variable is wheat yield. For example, assuming that the temperature in a certain year is 25 degrees Celsius, the rainfall is 500 mm, and the soil pH is 5, these data are input into the model to predict the wheat yield of that year. Use existing agricultural production data to train and verify the model. For example, the model can be trained with data from 2010 to 2020, and the accuracy of the model can be verified with data from 2021 and 2022. Through cross-validation, the generalization ability of the model can be evaluated, that is, the predictive ability of the model on a new data set. If the accuracy and generalization ability of the model are not ideal, the parameters of the model can be adjusted or new input variables can be added to optimize the model. Apply the optimized agricultural production evaluation model to actual production.For example, based on the weather forecast for the next month, the yield of wheat growing areas in the north is predicted, and corresponding agricultural production management measures are formulated based on the forecast results. If the predicted yield is low, measures such as increasing irrigation and applying foliar fertilizer can be taken to increase the yield. Doing so can help farmers better cope with climate change and market fluctuations, and improve the efficiency and stability of agricultural production.

[0035] Step S103, obtain the data on the stress resistance and occurrence patterns of crops and pests and diseases, use the support vector machine algorithm to train the risk assessment model, and predict the probability of risk occurrence and the degree of loss in a certain period of time in the future. If the prediction result exceeds the preset threshold, the early warning mechanism is triggered.

[0036] Obtain historical data on crop stress resistance and the occurrence of pests and diseases, and preprocess the data, including data cleaning, feature extraction, and data standardization, to obtain a data set suitable for training the model. Use the support vector machine algorithm to train the preprocessed data set, optimize the model hyperparameters through grid search and other methods, and obtain the risk assessment model with the best performance. Use the trained risk assessment model, combined with real-time data such as the current growth status of crops and environmental factors, to predict the probability of crop pests and diseases and the degree of loss that may be caused in the future. Set a risk warning threshold. When the predicted probability of risk occurrence or degree of loss exceeds the threshold, the warning mechanism is triggered and a warning message is sent to farmers. According to the warning information, farmers can take corresponding prevention and control measures in time, such as spraying pesticides, adjusting the planting environment, etc., to reduce the risk and loss of pests and diseases. Continuously collect various data during the growth process of crops, regularly retrain and optimize the risk assessment model, and improve the prediction accuracy and stability of the model. Establish a visual platform for crop disease and pest risk assessment and early warning to facilitate farmers to view the health status and potential risks of crops in real time, and arrange farming activities scientifically and rationally based on the decision support information provided by the system.

[0037] Furthermore, in modern agricultural production, the establishment and optimization of pest and disease risk assessment models are the key to improving crop yield and quality. First, data preprocessing is performed by collecting historical data, including crop stress resistance data and records of pest and disease occurrence. For example, for wheat rust data, first clean the outliers in the data, such as records with abnormally large lesion areas detected, which may be input errors and need to be eliminated. Next, extract key features such as temperature, humidity, and crop growth stage, which are important factors affecting the development of diseases. Data standardization is an indispensable step, which can eliminate the dimensional effects between different data indicators and make model training more accurate. The support vector machine algorithm is used for model training, and the hyperparameters of the model, such as kernel function type and penalty coefficient, are optimized through grid search technology to ensure that the model can achieve good performance in many cases. For example, when predicting rice bacterial blight, it may be found that the radial basis function (RBF) kernel is more suitable for dealing with nonlinear problems than the linear kernel function, thereby improving the prediction accuracy of the model. The optimized model is used in combination with real-time data to predict the risk of future pests and diseases. For example, if the model predicts that the probability of potato late blight in a certain area in the next week exceeds 40%, and the loss may exceed 20% of the yield, the system will automatically trigger the early warning mechanism. After receiving the early warning information, farmers can choose to spray copper preparations or use biological control methods during the predicted high-risk period to reduce the occurrence of diseases. In addition, continuous data collection and regular model updates are key to maintaining model accuracy. Through continuous training and optimization, the model can adapt to environmental changes and new types of pests and diseases. For example, by analyzing data from several consecutive seasons, some newly emerging pests and diseases or changes in resistance may be found, and timely adjustment of the model can more effectively respond to these changes. Finally, a visualization platform is established to enable farmers to monitor the health status and potential risks of crops in real time. The platform can display detailed pest and disease types, probability of occurrence, and recommended prevention and control measures to help farmers manage farms scientifically and optimize the arrangement of agricultural activities. For example, the platform can display the current field humidity and temperature data, predict future pest and disease development trends, and provide operational suggestions, such as adjusting the irrigation system to control field humidity, thereby reducing the risk of disease occurrence.

[0038] Step S104, obtain agricultural insurance contract data, extract key information, such as insured crop types, growth cycles, insurance amounts, etc., match risk assessment results with contract information through natural language processing technology, and automatically calculate the claims risk of each contract.

[0039] Obtain electronic text data of agricultural insurance contracts, and use natural language processing technology to identify and extract key information in the contracts, including insured crop types, growth cycles, insurance amounts, etc. Based on the extracted crop type information, obtain the historical yield of the crop, meteorological disasters and other risk data, and establish a crop risk assessment model through machine learning algorithms. Apply the risk assessment model to the current agricultural insurance contracts to calculate the risk score of each contract. According to the insurance amount and risk score of the contract, determine whether the contract is a high-risk contract through the set claim risk threshold. For high-risk contracts, the system automatically sends warning information to the underwriter and recommends appropriate adjustments to the insurance terms or rates. For contracts with lower claim risks, the system automatically conducts underwriting review and policy generation to improve underwriting efficiency. Regularly track changes in the risk status of insured contracts, dynamically adjust risk assessment results according to different stages of the crop growth cycle, and achieve continuous monitoring and early warning of contract claim risks.

[0040] Furthermore, in modern agricultural insurance business, it is an important step to identify and extract key information from insurance contracts through natural language processing technology. For example, from a wheat planting insurance contract, the system can automatically identify that the insured crop is wheat, the insurance amount is 100,000 yuan, and the expected growth period is 120 days. This information will serve as the basic data for subsequent risk assessment. Next, the system will obtain the historical yield data of wheat and the records of meteorological disasters in the past few years from the database based on the extracted crop type information. For example, the system may find that in the past five years, there have been three wheat yield reductions in the region due to drought, and each reduction ratio is about 20%. These historical risk data will be used to build a machine learning model to assess future risks. Using machine learning algorithms such as support vector machines or decision trees, a crop risk assessment model can be established. The model will input data such as crop types, historical yields, meteorological disasters, etc., and output a risk score. For example, for a wheat insurance contract, the model may calculate a high risk rating of 75, which means that the risk of the next season is high. Based on the insurance amount and risk score of the contract, the system can automatically determine whether the contract is a high-risk contract. If the risk score exceeds the set claim risk threshold, such as 7, the system will automatically send an early warning message to the underwriter and recommend adjustments to the insurance terms or rates. Such automated processing not only improves efficiency, but also helps insurance companies better manage risks. For those contracts with lower risk scores, such as contracts with a risk score of 3, the system can automatically complete underwriting review and policy generation. This automated process greatly improves underwriting efficiency and reduces manual errors and delays. In addition, the system regularly tracks changes in the risk status of insured contracts. As the crop growth cycle progresses, new risk factors may emerge, such as sudden floods or pests and diseases. The system can dynamically adjust the risk assessment results to ensure that the claim risk of the contract is continuously monitored and warned, thereby protecting the interests of farmers and insurance companies. Through this series of technical applications and data processing, agricultural insurance companies can assess and manage risks more scientifically, while providing farmers with more accurate and efficient insurance services.

[0041] The risk assessment model is applied to current agricultural insurance contracts to calculate the risk score for each contract.

[0042] Obtain agricultural insurance contract data, extract contract feature information, and construct a contract feature vector. According to the pre-established risk assessment model, input the contract feature vector into the model to calculate the risk score of each contract. Determine whether the risk score of the contract exceeds the preset risk threshold. If it exceeds the threshold, mark the contract as a high-risk contract. For contracts with risk scores below the threshold, sort them according to the score size to obtain the risk ranking of the contract. According to the risk score and ranking of the contract, divide the contract into different risk levels to form a contract classification result. For contracts of different risk levels, formulate differentiated management strategies and measures to achieve refined management. Continuously track changes in the risk status of the contract, regularly update the risk score and classification of the contract, dynamically adjust the management strategy, and ensure the stable operation of the agricultural insurance business.

[0043] Furthermore, obtaining agricultural insurance contract data is the starting point of the entire risk assessment process. Assume that an agricultural insurance company has a large number of electronic agricultural insurance contracts, which contain key information such as the type of insured crop, growth cycle, and insurance amount. Through natural language processing (NLP) technology, these key information can be automatically extracted from these texts. For example, a contract mentions "the insured crop is wheat, the growth cycle is 90 days, and the insurance amount is 100,000 yuan", and NLP technology can identify and extract these fields. After extracting the contract feature information, it is necessary to construct a contract feature vector. The feature vector is to convert text information into a numerical form for easy processing by the machine learning model. For example, the crop type can be encoded as a numerical value (such as wheat is 1, corn is 2), and the growth cycle and insurance amount directly use their numerical values. In this way, the feature vector of a contract may be \[1,90,100000\]. The pre-established risk assessment model is trained based on historical data and machine learning algorithms. Assuming that the decision tree algorithm is used, the model learns which feature combinations are likely to lead to high risks by analyzing the features and claims records in the historical contract data. Input the feature vector of the current contract \[1,90,100000\] into the model, and the model will output a risk score, such as 75. Determining whether the risk score of the contract exceeds the preset risk threshold is a key step. Assuming the threshold is set to 7, a score of 75 means that the contract is marked as a high-risk contract. High-risk contracts require special attention and may require further review by underwriters, or even adjustment of insurance terms or rates. For contracts with risk scores below the threshold, sort them according to the size of the score to obtain the risk ranking of the contract. For example, if there are three contracts with scores of 65, 55, and 45, respectively, their rankings are 1, 2, and 3. This ranking helps to prioritize contracts with higher risks. According to the risk score and ranking of the contract, the contract is divided into different risk levels. Assuming that the risk level is divided into three levels: high, medium, and low, the score above 7 is high risk, 5 to 7 is medium risk, and below 5 is low risk. In this way, the above three contracts belong to medium risk, medium risk, and low risk respectively. Differentiated management strategies are formulated for contracts of different risk levels. High-risk contracts may require a more stringent underwriting process and higher premiums, medium-risk contracts may require regular audits, and low-risk contracts can simplify the audit process and quickly underwrite. For example, for high-risk contracts, the system can automatically remind underwriters to conduct a detailed review and even recommend adding specific clauses; for low-risk contracts, the system can directly generate insurance policies to improve efficiency. Continuously tracking changes in the risk status of contracts is an important part of ensuring the accuracy of risk assessment. The risks at different stages of the crop growth cycle change dynamically. For example, wheat is more susceptible to meteorological disasters during the heading period. The system can dynamically adjust the risk assessment results based on real-time meteorological data and crop growth stages.Suppose a contract is initially assessed as low risk, but as the weather forecast shows heavy rainfall in the future, the system reassesses it and adjusts it to medium risk, and issues an early warning. Regularly update the risk score and classification of the contract, dynamically adjust the management strategy, and ensure the stable operation of the agricultural insurance business. For example, reassess the insured contracts once a quarter, and adjust the risk level and management measures based on the latest risk data. In this way, even if the initial assessment is inaccurate, it can be corrected in time through dynamic adjustment to avoid potential losses. Through the above process, agricultural insurance companies can not only improve underwriting efficiency, but also effectively control claims risks and ensure the stable development of the business. This refined management and dynamic monitoring mechanism enables the company to deal with complex and changing agricultural risks more calmly.

[0044] Step S105, manage contracts in different levels according to the level of claims risk, take corresponding risk control measures for high-risk contracts, such as adjusting underwriting conditions, increasing premiums, etc., and start the manual verification process for contracts whose claims risk exceeds the preset threshold.

[0045] Obtain the historical claims data and relevant risk factor information of the insurance contract, and establish a claims risk assessment model through data analysis and machine learning algorithms. Score the claims risk of each insurance contract according to the claims risk assessment model, and divide the risk level into three levels: high, medium and low, to form the contract risk classification result. Set corresponding risk control thresholds for contracts of different risk levels. If the claims risk score of the contract exceeds the threshold, the corresponding risk control measures will be triggered. For contracts with high risk levels, the system automatically adjusts the underwriting conditions, such as increasing the deductible, reducing the insured amount or shortening the insurance period, and increases the premium accordingly to control the risk exposure. When the claims risk score of the contract exceeds the preset manual verification threshold, the system automatically marks the contract as a key focus and starts the manual verification process. In the manual verification process, professional underwriters conduct in-depth investigations and assessments on the background and risk factors of the insured of high-risk contracts, and may require the insured to provide additional information or conduct on-site inspections if necessary. Based on the results of manual verification, underwriters can further adjust underwriting conditions and rates or decide whether to underwrite the contract to control the company's overall claims risk within an acceptable range.

[0046] Furthermore, obtaining the historical data of insurance contract claims and relevant risk factor information is the basis for establishing a claim risk assessment model. For example, an agricultural insurance company collected the claim data of all corn planting insurance contracts in the past five years, including the number of claims, the amount of claims, and the reasons for claims (such as drought, floods, pests and diseases, etc.). At the same time, relevant risk factor information was also collected, such as the climate conditions, soil types, and historical disaster records in the planting area. A claim risk assessment model was established through data analysis and machine learning algorithms. Assuming that a logistic regression algorithm is used, the number of claims, the amount of claims, the climate conditions, the soil type, etc. are used as feature variables, and whether a claim occurs is used as the target variable. By training the model, a model that can predict the probability of a new contract claim is obtained. The claim risk of each insurance contract is scored according to the claim risk assessment model. For example, for a corn planting insurance contract, the model predicts that its claim probability is 35. According to the preset risk level classification standard, the probability between 3-5 is classified as a medium risk level. The risk level is divided into three levels: high, medium, and low, forming a contract risk classification result. High-risk contracts may have the characteristics of high claim probability and high claim amount, medium-risk contracts are between the two, and low-risk contracts have the characteristics of low claim probability and low claim amount. Set corresponding risk control thresholds for contracts of different risk levels. For example, the threshold for high-risk contracts is set to 7, the threshold for medium-risk contracts is set to 4, and the threshold for low-risk contracts is set to 2. If the claim risk score of the contract exceeds the threshold, the corresponding risk control measures are triggered. For contracts with high risk levels, the system automatically adjusts the underwriting conditions. For example, for a high-risk corn planting insurance contract, the system increases its deductible from 5,000 yuan to 10,000 yuan, reduces the insured amount from 200,000 yuan to 150,000 yuan, shortens the insurance period from one year to half a year, and increases the premium from 5,000 yuan to 7,000 yuan to control risk exposure. When the claim risk score of a contract exceeds the preset manual verification threshold, the system automatically marks the contract as a key focus object. For example, if the claim risk score of a contract is 75, which exceeds the preset threshold of 7, the system automatically marks it and starts the manual verification process. In the manual verification process, professional underwriters conduct in-depth investigations and assessments of the insured's background, risk factors, etc. of high-risk contracts. For example, the underwriters found that the insured of the contract had multiple claims records in the past three years, and that droughts had frequently occurred in the planting area recently. The underwriters decided to require the insured to provide detailed planting management records and conduct on-site inspections. Based on the results of the manual verification, the underwriters can further adjust the underwriting conditions and rates or decide whether to underwrite the contract. For example, the underwriters found that there were major loopholes in the insured's planting management and decided to increase the premium to 8,000 yuan and shorten the insurance period to three months. If the insured disagrees, they decide not to underwrite the contract. Through the above measures, insurance companies can effectively control the overall claims risk within an acceptable range.Doing so can not only reduce the company's compensation pressure, but also improve the ability to accurately price insurance contracts and enhance customer satisfaction. For example, through risk grading and accurate pricing, low-risk customers can enjoy more favorable premiums, while high-risk customers can reduce the risk of claims by adjusting underwriting conditions, and both parties benefit. In addition, regularly tracking the changes in the risk status of insured contracts and dynamically adjusting the risk assessment results according to the different stages of the crop growth cycle are also key links. For example, the risk of corn growth is low in the early stage, it is susceptible to pests and diseases in the middle stage, and meteorological disasters need to be paid attention to in the later stage. The system dynamically adjusts the risk score and level of the contract according to these staged risk changes to ensure the timeliness and effectiveness of risk control measures. Through this series of processes, insurance companies can not only achieve continuous monitoring and early warning of contract claims risks, but also improve risk management levels and the scientific nature of business decisions through data-driven methods. For example, by accumulating a large amount of claims data and risk factor information, insurance companies can continuously optimize risk assessment models and improve prediction accuracy, thereby gaining an advantage in the fierce market competition.

[0047] Step S106, obtain claim case data, use decision tree algorithm to analyze the relationship between case characteristics and claim results, summarize claim rules, optimize risk assessment model and claim process, and improve risk prevention and control and claim efficiency.

[0048] Obtain a large amount of historical claims case data, pre-process the case data, and extract the key feature attributes of the case. According to the case feature attributes and claim results, use the decision tree algorithm to establish a claims prediction model, and determine the correlation and weight of each feature attribute with the claim result through model training. Use the trained claims prediction model to conduct risk assessment on new claims cases, predict the probability distribution of claims results, and identify high-risk cases. For high-risk cases predicted by the model, trigger additional risk prevention and control measures, such as conducting more detailed investigations and verifications, or raising the claim threshold, so as to reduce the risk of fraud. Continuously collect new claims data, retrain and optimize the model regularly, and improve the model's prediction accuracy and risk identification ability. According to the claims rules and risk characteristics summarized by the model analysis, optimize and improve the existing claims process, reduce unnecessary process links, and improve claims efficiency. Embed the optimized risk assessment model and claims process into the core business system to realize the automation and intelligence of risk prevention and control and claims processing, and further improve claims efficiency and customer experience.

[0049] Furthermore, a large amount of historical claims case data is obtained. For example, an insurance company can collect all closed claims case data in the past five years, including auto insurance, health insurance, life insurance and other types of insurance. These data include claim application time, policy information, insured information, cause of accident, claim amount, payment result, etc. Preprocess the case data and extract the key feature attributes of the case. For example, in auto insurance claims cases, key feature attributes may include vehicle type, driver age, driving experience, accident location, weather conditions, accident responsibility determination, etc. Clean, convert and standardize these data, such as converting text-type causes of accident into numerical type codes, dividing driver age into segments, etc., to facilitate subsequent model training. Based on the case feature attributes and claim results, a claim prediction model is established using the decision tree algorithm. The decision tree algorithm is a commonly used machine learning algorithm that simulates the decision-making process by building a tree structure. For example, based on historical data, it can be found that the two feature attributes of "driver age less than 25 years old" and "night driving" are strongly correlated with high claim risks. Then, in the decision tree model, these two feature attributes will become important decision nodes. The correlation and weight of each feature attribute and the claim result are determined through model training. For example, by training the decision tree model, the weight of the two feature attributes "driver age" and "night driving" on the claim result can be determined, for example, the weight of "driver age" is 0.6, and the weight of "night driving" is 0.4. Using the trained claim prediction model, new claim cases are risk assessed, the probability distribution of claim results is predicted, and high-risk cases are identified. For example, in a new car insurance claim case, the driver is 22 years old and the accident occurred at night. These features are input into the trained claim prediction model, and the model will output the probability that the case belongs to a high-risk claim, such as 80%. If this probability exceeds the preset threshold (such as 50%), the case will be identified as a high-risk case. For high-risk cases predicted by the model, additional risk prevention and control measures are triggered, such as conducting more detailed investigations and verifications, or raising the claim threshold, etc., to reduce the risk of fraud. For example, for claims cases identified as high-risk, the insurance company can arrange professional investigators to conduct on-site inspections and verify the accident situation to exclude the possibility of fraudulent claims. Alternatively, the insured can be required to provide more supporting materials, such as a hospital diagnosis certificate, a police traffic accident liability determination, etc. Continuously collect new claims data, regularly retrain and optimize the model, and improve the model's prediction accuracy and risk identification capabilities. For example, collect new claims data once a quarter and use this data to retrain the claims prediction model. This allows the model to continuously learn new claims rules and risk characteristics and improve the accuracy of predictions. Based on the claims rules and risk characteristics summarized by the model analysis, optimize and improve the existing claims process, reduce unnecessary process links, and improve claims efficiency.For example, through model analysis, it is found that certain types of claims cases (such as small property losses) have lower risks, and the claims process can be simplified, such as canceling the on-site inspection link and handling the claims procedures directly online. The optimized risk assessment model and claims process are embedded in the core business system to realize the automation and intelligence of risk prevention and control and claims processing, and further improve the claims efficiency and customer experience. For example, the claims prediction model is integrated into the insurance company's claims system. When a new claim case is submitted, the system can automatically conduct a risk assessment and automatically trigger the corresponding risk prevention and control measures or claims process based on the assessment results. This can greatly improve the efficiency of claims processing, shorten the claims cycle, and improve customer satisfaction.

[0050] Step S107, establish a visualization platform for agricultural insurance claims risk assessment, and present the risk assessment results and claims data of each region in the form of charts, so that business personnel can monitor risk changes in real time, adjust insurance products and service strategies in a timely manner, and continuously improve the level of refined management of agricultural insurance.

[0051] Obtain the historical claims data of agricultural insurance in each region, pre-process the claims data, remove outliers and missing values, and obtain the cleaned claims data set. Based on the cleaned claims data set, the random forest algorithm is used to establish an agricultural insurance claims risk prediction model, and the key factors affecting the claims risk are determined through feature importance analysis. Obtain external data such as real-time meteorological data and crop growth data in each region, and combine them with the claims risk prediction model to determine the current agricultural insurance claims risk level in each region. On the visualization platform, the distribution of claims risk levels in each region is displayed in the form of a heat map, and the trend of historical claims data in each region is displayed in the form of a line chart. According to the claims risk level of each region and the trend of historical claims data, the association rule mining algorithm is used to discover the common characteristics of high-risk regions, providing decision support for adjusting insurance products and service strategies. Set a risk warning threshold on the visualization platform. When the claims risk level in a certain area exceeds the warning threshold, the system automatically sends a warning prompt to the business personnel to remind them to pay attention to the agricultural insurance business in that area. Regularly evaluate the predictive effect of the agricultural insurance claims risk assessment model, optimize and improve the model based on the evaluation results, continuously improve the accuracy and reliability of risk assessment, and provide strong support for the refined management of agricultural insurance business.

[0052] Furthermore, historical claims data of agricultural insurance in various regions are obtained, such as collecting claims data of major agricultural provinces such as Sichuan, Henan, and Heilongjiang in the past five years. Preprocess the claims data, such as eliminating abnormal data with negative insured amounts due to recording errors, and records with missing key information such as insurance date and claim amount. These operations can obtain more reliable claims data sets and provide a high-quality data basis for subsequent modeling. The random forest algorithm is used to establish an agricultural insurance claims risk prediction model. The random forest algorithm can handle a large number of feature variables and effectively avoid overfitting problems, which is suitable for agricultural insurance claims data analysis. Through model training, the influence of each feature attribute on the claims result can be obtained, such as the influence of rainfall, temperature, soil moisture, etc. on crop yield, which in turn affects the claims risk. Feature importance analysis can help identify key risk factors, such as finding that continued low temperature is the main reason for the increase in the claims rate of citrus fruits. Obtain external data, such as obtaining real-time meteorological data such as temperature, rainfall, and light in each region through the Meteorological Bureau, and obtaining crop growth data such as leaf area index and vegetation coverage through remote sensing technology. Combining these external data with the claims risk prediction model can more accurately determine the current agricultural insurance claims risk level in each region. For example, the model predicts that there will be continuous heavy rainfall in a certain area of ​​Sichuan in the next week. Combined with the growth stage of rice in the area, it can be judged that the claims risk level of rice in the area is high. On the visualization platform, the distribution of claims risk levels in each region is displayed in the form of a heat map. For example, the darker the color, the higher the risk level. At the same time, the trend of historical claims data in each region is displayed in the form of a line chart. For example, the trend of the claims amount of wheat insurance in Henan Province in the past five years is displayed. It can be found that the claims amount peaks in some years, which may be related to the natural disasters of that year. These visualization charts can intuitively show the risk distribution and change trend, which is convenient for business personnel to quickly understand the situation. The association rule mining algorithm is used to find the common characteristics of high-risk areas. For example, through analysis, it is found that areas with high historical claims risk levels usually have common characteristics such as unstable climate conditions, weak agricultural infrastructure, and low risk awareness among farmers. This information can provide decision support for adjusting insurance products and service strategies, such as promoting crop varieties with stronger disaster resistance in high-risk areas, or increasing premiums to cover higher risks. Set risk warning thresholds on the visualization platform. For example, when the claim risk level in a certain area exceeds 80 points (out of 100 points), the system automatically sends a warning to the business personnel in charge of the area, reminding them to pay attention to the agricultural insurance business in the area and take corresponding risk prevention and control measures, such as increasing on-site survey personnel or strengthening the review of insurance information. Regularly evaluate the prediction effect of the agricultural insurance claim risk assessment model. For example, compare the claim risk level predicted by the model with the actual claim situation, and calculate the accuracy and recall rate of the model.Based on the evaluation results, the model is optimized and improved, such as adjusting model parameters or adding new feature variables. By continuously optimizing the model, the accuracy and reliability of risk assessment can be continuously improved, providing strong support for the refined management of agricultural insurance business. For example, after the model is optimized, the claim risk of different types of crops can be more accurately predicted, thereby formulating a more accurate premium pricing strategy.

[0053] Embodiment 2:

[0054] The embodiment of the present invention also provides an agricultural insurance claim risk assessment system, including:

[0055] The first processing device is used to obtain a preliminary factor weight allocation scheme based on historical data including plant disease factors, weather disaster factors and man-made disaster factors;

[0056] The second processing device is used to obtain data on climate conditions, soil types and planting habits in different regions according to the regional differences in agricultural production, optimize the weight distribution plan of adjustment factors, and determine the quantitative standards applicable to the region;

[0057] The third processing device is used to obtain data on the stress resistance and occurrence patterns of pests and diseases of crops, and to predict the probability of risk occurrence and the extent of loss within a certain period of time in the future;

[0058] The fourth processing device is used to obtain agricultural insurance contract data, match the risk assessment results with the agricultural insurance contract data, and automatically calculate the claim risk of each contract;

[0059] The fifth processing device is used to manage contracts in different levels according to the level of claim risk and take corresponding risk control measures for high-risk contracts;

[0060] The sixth processing device is used to obtain claim case data, analyze the relationship between case characteristics and claim results, and optimize the risk assessment model and claim process.

[0061] As an implementation method of an embodiment of the present invention, the first processing device obtains historical data including plant disease factors, weather disaster factors and man-made disaster factors, and uses data mining technology to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, and obtains a preliminary factor weight allocation plan.

[0062] As an implementation mode of an embodiment of the present invention, the second processing device obtains the climate conditions, soil types and planting habit data of different regions according to the regional differences in agricultural production, divides the region into several similar areas through a clustering algorithm, optimizes the weight distribution scheme of the adjustment factors for each region, and determines the quantitative standards applicable to the region.

[0063] As an implementation method of an embodiment of the present invention, the third processing device obtains data on the stress resistance and occurrence patterns of pests and diseases of crops, adopts a support vector machine algorithm, trains a risk assessment model, and predicts the probability of risk occurrence and the degree of loss within a certain period of time in the future. If the prediction result exceeds a preset threshold, an early warning mechanism is triggered.

[0064] The above is only a preferred embodiment of the present invention. It should be pointed out that ordinary technicians in this technical field can make several improvements and supplements without departing from the principle of the present invention. These improvements and supplements should also be regarded as the scope of protection of the present invention.

Claims

1. A method for assessing agricultural insurance claims risk, characterized in that: include: Step S101, obtaining a preliminary factor weight allocation scheme based on historical data including plant disease factors, weather disaster factors and man-made disaster factors; Step S102: According to the regional differences in agricultural production, the climate conditions, soil types and planting habits of different regions are obtained, the weight distribution scheme of the adjustment factors is optimized, and the quantitative standards applicable to the region are determined; Step S103, obtaining data on the stress resistance and occurrence patterns of pests and diseases of crops, and predicting the probability of risk occurrence and the extent of loss within a certain period of time in the future; Step S104: Acquire agricultural insurance contract data, match the risk assessment results with the agricultural insurance contract data, and automatically calculate the claim risk of each contract; Step S105: Manage contracts in different levels according to the risk of claims settlement, and take corresponding risk control measures for high-risk contracts; Step S106: Obtain claim case data, analyze the relationship between case characteristics and claim results, and optimize the risk assessment model and claim process.

2. The agricultural insurance claim risk assessment method according to claim 1, characterized in that: In step S101, historical data including plant disease factors, weather disaster factors and man-made disaster factors are obtained, and data mining technology is used to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, and a preliminary factor weight allocation plan is obtained.

3. The agricultural insurance claim risk assessment method according to claim 2, characterized in that: In step S102, based on the regional differences in agricultural production, the climate conditions, soil types and planting habits data of different regions are obtained, and the regions are divided into several similar areas through a clustering algorithm. For each region, the weight distribution scheme of the adjustment factors is optimized and the quantitative standards applicable to the region are determined.

4. The agricultural insurance claim risk assessment method according to claim 3, characterized in that: In step S103, the data on the stress resistance and occurrence patterns of pests and diseases of crops are obtained, and the support vector machine algorithm is used to train the risk assessment model to predict the probability of risk occurrence and the degree of loss in a certain period of time in the future. If the prediction result exceeds the preset threshold, the early warning mechanism is triggered.

5. An agricultural insurance claim risk assessment system, characterized in that: include: The first processing device is used to obtain a preliminary factor weight allocation scheme based on historical data including plant disease factors, weather disaster factors and man-made disaster factors; The second processing device is used to obtain data on climate conditions, soil types and planting habits in different regions according to the regional differences in agricultural production, optimize the weight distribution plan of adjustment factors, and determine the quantitative standards applicable to the region; The third processing device is used to obtain data on the stress resistance and occurrence patterns of pests and diseases of crops, and to predict the probability of risk occurrence and the extent of loss within a certain period of time in the future; The fourth processing device is used to obtain agricultural insurance contract data, match the risk assessment results with the agricultural insurance contract data, and automatically calculate the claim risk of each contract; The fifth processing device is used to manage contracts in different levels according to the level of claim risk and take corresponding risk control measures for high-risk contracts; The sixth processing device is used to obtain claim case data, analyze the relationship between case characteristics and claim results, and optimize the risk assessment model and claim process.

6. The agricultural insurance claim risk assessment system according to claim 5, characterized in that: The first processing device obtains historical data including plant disease factors, weather disaster factors and man-made disaster factors, and uses data mining technology to analyze the association rules between each factor and the probability of risk occurrence and the degree of loss for different crop types and growth cycles, and obtains a preliminary factor weight allocation plan.

7. The agricultural insurance claim risk assessment system according to claim 5, characterized in that: The second processing device obtains data on climate conditions, soil types and planting habits in different regions based on the regional differences in agricultural production, divides the region into several similar areas through a clustering algorithm, optimizes and adjusts the weight distribution scheme of factors for each region, and determines the quantitative standards applicable to the region.

8. The agricultural insurance claim risk assessment system according to claim 7, characterized in that: The third processing device obtains data on the stress resistance and occurrence patterns of crops and pests and diseases, uses a support vector machine algorithm to train a risk assessment model, and predicts the probability of risk occurrence and the degree of loss within a certain period of time in the future. If the prediction result exceeds a preset threshold, an early warning mechanism is triggered.

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