Flood data processing method and device, equipment and medium

Abstract

The present application belongs to the technical field of data processing, and is suitable for the financial field and the medical field, and discloses a flood data processing method, device, equipment and medium, the method comprises: spatially fusing double-source flood risk data, constructing a flood risk database based on the spatially fused double-source flood risk data; calling a standardized spatial data service interface, obtaining a risk hit result and a data source type corresponding to a geographical position of an insurance business target; executing a risk control strategy corresponding to the data source type on the insurance business target, and obtaining newly added claim settlement data generated after the risk control strategy is executed; backflowing the newly added claim settlement data to historical flood claim settlement data, updating the state of business experience annotation data, and driving the dynamic update of the flood risk database based on the backflowed historical flood claim settlement data and the state updated business experience annotation data. The accuracy of risk assessment is effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology and is applicable to the financial and medical fields. In particular, it relates to a method, apparatus, equipment and medium for processing flood data. Background Technology

[0002] In the financial and insurance sectors, flood risk assessment technology has been widely applied. For example, several insurance companies have published flood risk maps, using geographic information systems to mark information such as culvert flooding and historical incidents with different colored blocks for underwriting and risk control personnel to refer to.

[0003] However, current flood risk assessment schemes generally rely on historical claims records or physical model extrapolations, resulting in a single data source. This leads to significant blind spots in risk identification for newly developed areas or plots with low insurance penetration and insufficient claims samples. The actual flood risks in these areas are often rooted in the field experience of frontline surveyors and senior underwriters, and existing systems lack mechanisms for structured collection and integration of this tacit knowledge. Furthermore, the risk identification results of existing schemes are mostly limited to visualization, failing to be embedded as data services into core business processes such as pricing, underwriting, and early warning. Risk identification and business decision-making are disconnected at the system level. Additionally, existing schemes do not consider the payout amount when generating risk areas, assigning equal weight to minor flooding reports and large payout cases, making it difficult for risk grading to support accurate underwriting pricing.

[0004] Therefore, improving the accuracy of flood risk assessment is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This invention provides a flood data processing method, apparatus, equipment, and medium to address the technical problem of improving the accuracy of flood risk assessment.

[0006] In a first aspect, the present invention provides a flood data processing method, comprising: Historical flood claims data and business experience annotation data of insurance business are obtained, and the historical flood claims data and business experience annotation data are preprocessed respectively to generate dual-source flood risk data of insurance business. The dual-source flood risk data are spatially fused, and a flood risk database is constructed based on the spatially fused dual-source flood risk data. Based on the aforementioned flood risk database, a standardized spatial data service interface is constructed for online spatial queries by external business systems; Call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business object, and new claims data generated after the execution of the risk control strategy is obtained; The newly added claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data.

[0007] Secondly, the present invention provides a flood data processing apparatus, comprising: The first acquisition module is used to acquire historical flood claims data and business experience annotation data of insurance business, and preprocess the historical flood claims data and business experience annotation data respectively to generate dual-source flood risk data of insurance business. The fusion module is used to spatially fuse the dual-source flood risk data and construct a flood risk database based on the spatially fused dual-source flood risk data. The module is used to build a standardized spatial data service interface for online spatial queries by external business systems based on the flood risk database. The calling module is used to call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; The second acquisition module is used to execute a risk control strategy corresponding to the data source type on the insurance business target in at least one business processing stage of the insurance business, and to acquire the new claims data generated after the execution of the risk control strategy. The update module is used to feed the newly added claims data back to the historical flood claims data, update the status of the business experience annotation data, and drive the dynamic update of the flood risk database based on the fed-back historical flood claims data and the updated business experience annotation data.

[0008] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described flood data processing method.

[0009] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described flood data processing method.

[0010] The aforementioned flood data processing method, apparatus, equipment, and medium, in their implemented scheme, can obtain historical flood claims data and business experience annotation data of insurance business through a client. The historical flood claims data and business experience annotation data are preprocessed to generate dual-source flood risk data for insurance business. The dual-source flood risk data is spatially fused, and a flood risk database is constructed based on the spatially fused dual-source flood risk data. Based on the flood risk database, a standardized spatial data service interface is constructed for online spatial querying by external business systems. The standardized spatial data service interface is called to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target. In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business target, and new claims data generated after the execution of the risk control strategy is obtained. The new claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data. In this invention, a dual-source flood risk database is constructed by integrating historical claims data and business experience-annotated data, thus broadening the data sources and coverage for risk assessment. Simultaneously, by feeding new claims data back to historical data and triggering dynamic updates to the risk database, this, together with changes in the status of business experience-annotated data, forms a continuous learning loop from risk assessment and strategy execution to data feedback. This allows the flood risk database to be continuously updated as business operations progress, effectively improving the accuracy of risk assessment. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an application environment for a flood data processing method according to an embodiment of the present invention.

[0013] Figure 2 This is a flowchart illustrating a flood data processing method according to an embodiment of the present invention.

[0014] Figure 3 yes Figure 2 A schematic diagram of a specific implementation method for step S10.

[0015] Figure 4 yes Figure 2 A flowchart illustrating a specific implementation of step S40.

[0016] Figure 5 This is a schematic diagram of a flood data processing device according to an embodiment of the present invention.

[0017] Figure 6 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] Figure 7 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The flood data processing method provided in this embodiment of the invention can be applied to, for example... Figure 1 In the application environment, Figure 1This is a schematic diagram of an application environment for a flood data processing method according to an embodiment of the present invention; wherein, the client communicates with the server via a network. The server can obtain historical flood claims data and business experience annotation data of insurance business through the client, preprocess the historical flood claims data and business experience annotation data respectively to generate dual-source flood risk data of insurance business; spatially fuse the dual-source flood risk data, and construct a flood risk database based on the spatially fused dual-source flood risk data; based on the flood risk database, construct a standardized spatial data service interface for online spatial query of external business systems; call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; in at least one business processing link of the insurance business, execute a risk control strategy corresponding to the data source type for the insurance business target, and obtain the new claims data generated after executing the risk control strategy; the new claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data. In this invention, a dual-source flood risk database is constructed by integrating historical claims data and business experience-annotated data, thus broadening the data sources and coverage for risk assessment. Simultaneously, by feeding new claims data back to historical data and triggering dynamic updates to the risk database, this, together with changes in the status of business experience-annotated data, forms a continuous learning loop from risk assessment and strategy execution to data feedback. This allows the flood risk database to be continuously updated as business operations progress, effectively improving the accuracy of risk assessment. The invention will now be described in detail through specific embodiments.

[0021] Please see Figure 2 As shown, Figure 2 This is a flowchart illustrating a flood data processing method provided in an embodiment of the present invention. The flood data processing method specifically includes the following steps: S10: Obtain historical flood claims data and business experience annotation data for insurance business, and preprocess the historical flood claims data and business experience annotation data respectively to generate dual-source flood risk data for insurance business. Specifically, such as... Figure 3 The above, Figure 3 yes Figure 2 A schematic flowchart of a specific implementation of step S10 includes the following steps S11-S14: S11: Geocoding the accident addresses in the historical flood claims data generates spatial point data containing compensation amounts, and clustering the spatial point data based on a preset compensation amount threshold. Specifically, in this embodiment of the invention, the accident addresses recorded in text form in historical flood claims are converted into spatial point data with latitude and longitude coordinates through a geocoding service, enabling each claim record to be spatially located and calculated on an electronic map. Simultaneously, the compensation amount is added as attribute information to the spatial point data, and a compensation amount threshold is introduced as a filtering condition during the clustering process. When performing spatial clustering, not only is the spatial proximity of multiple claim points considered, but the cumulative compensation amount of each cluster group is also summarized. Cluster groups with total compensation amounts below a preset threshold, such as cumulative compensation of less than 50,000 yuan, are eliminated, retaining major risk clusters with actuarial significance. Taking a financial insurance claims scenario as an example, when an insurance company analyzed the past five years of car insurance flood claims data for a certain city, it generated a spatial point map by geocoding the accident addresses. It discovered over twenty claim points scattered near an underpass in the urban area. After spatial clustering of these points, the system found that fifteen of them were clustered within a 500-meter radius of the underpass, but the total compensation amount was only 30,000 yuan, mostly small payments due to minor flooding. Another cluster, located around an old residential area, had eight claim points with a total compensation amount as high as 800,000 yuan. Through a compensation amount threshold filtering, the first group of minor damage points was automatically removed, while the second group of large loss points was retained and entered into the subsequent processing flow.

[0022] S12: Generate a buffer for the clustered spatial point data, and spatially overlay the buffer with the pre-acquired building outline data of the insured business target to generate flood risk data corresponding to the historical flood claims data. Specifically, this includes the following steps S121-S123: S121: Generate closed polygonal surface features for each group of clustered spatial point data according to their outer boundaries, and generate a buffer zone for the polygonal surface features according to a preset radius. Specifically, in this embodiment of the invention, the clustering operation results in a group of points that are spatially adjacent to each other. These discrete point groups need to be transformed into continuous planar regions to more reasonably represent the actual impact range of flood risk. First, for each group of clustered spatial point data, a spatial geometric algorithm is used to calculate its outer boundary, i.e., a convex hull or concave hull polygon, generating a closed polygonal surface feature that can encompass all the claim points in that group. This polygonal surface feature represents the core clustering area of ​​flood risk. Then, considering that flood risk has spatial spread, buildings or vehicles around the core clustering area also face high risks. Therefore, the polygonal surface feature is extended outward according to a preset radius, such as 200 meters or 300 meters, to generate a buffer zone, expanding the risk range from the core clustering area to a planar region covering the surrounding continuous space. Taking a financial insurance claims scenario as an example, when processing flood claims data in a certain city, an insurance company's system processed a cluster containing 8 claims points with a cumulative payout of 800,000 yuan. These 8 claims points were scattered around 3 residential buildings in an old community. The system used spatial geometric algorithms to calculate the outer boundary of these 8 points, generating a closed polygonal surface feature covering these 3 residential buildings. Subsequently, considering that the terrain inside the community is generally low-lying and that floodwater often spreads to adjacent buildings, the system generated a buffer zone for this polygonal surface feature with a radius of 300 meters.

[0023] S122: Spatially overlay the buffer zone with the pre-acquired building outline data of the insured business objects to filter out the building outline surfaces that intersect with the buffer zone. Specifically, in this embodiment of the invention, the buffer area is a continuous geographical surface, but not all buildings within its coverage area are insured objects of the insurance company. Therefore, spatial overlay analysis is needed to accurately match the buffer zone with the building outline data of the insured objects of the insurance company. The building outline data is the precise outer outline surface element of each insured building, including the shape, area, and spatial location information of the building. Spatially intersecting the buffer zone with the building outline data, i.e., determining whether each building outline surface overlaps or contains the buffer zone in space, thereby filtering out all insured buildings located within the buffer zone. Taking a financial insurance claims scenario as an example, the buffer zone generated by the system covers an old residential area and a surrounding area of ​​approximately 500 meters. The insured objects of the insurance company in this area include the building outline surfaces of 6 residential buildings in the residential area and the building outline surface of an adjacent commercial office building. After spatially overlaying the buffer layer with the building outline data of these 7 buildings, the system found that 4 residential buildings were completely within the buffer, 1 residential building partially intersected with the buffer, and the commercial office building was completely outside the buffer. The system selected these 5 residential buildings that intersected with the buffer as risk-related buildings.

[0024] S123: Extract the selected building outlines as flood risk data corresponding to the historical flood claims data. Specifically, in this embodiment of the invention, the risk-related building outlines determined after spatial overlay filtering are extracted and encapsulated to form standardized flood risk data output. The extracted building outlines serve as the basic unit of risk data. Each data record not only contains the spatial geometric information of the building but also carries the risk source information corresponding to the building, such as attribute fields such as claims data-driven, cumulative compensation amount of associated cluster groups, and historical compensation frequency. This attribute information provides a data foundation for downstream business systems to determine risk levels and perform differentiated processing. Taking a financial insurance claims scenario as an example, the system extracts the building outlines of 5 residential buildings that intersect in the buffer zone and generates a risk data record for each building. Among them, the 4 residential buildings that fall completely within the buffer zone are marked as high-risk buildings, and the partially intersecting residential building is marked as a medium-risk building.

[0025] S13: Based on the building outlines, annotate the risk map of the insurance business to obtain the business experience annotation data generated by the annotation. Specifically, in this embodiment of the invention, to solve the problem of blind spots in historical claims data coverage, the annotation function can be opened by senior underwriters or surveyors with authorized permissions. These personnel can directly select existing building outlines on the electronic map or manually draw polygonal areas to annotate buildings that are known to have flooding risks but are not yet reflected in historical claims data. When annotating, mandatory information such as risk level and information source description must be filled in. The risk level is, for example, severe flooding or moderate flooding. The information source description is, for example, that the community floods every time there is a rainstorm or that the garage entrance is designed too low, causing backflow. On-site photos can be uploaded as supporting attachments. Taking a financial insurance claims scenario as an example, a senior surveyor found during a routine inspection that a high-end residential community built less than two years ago, although it has not yet generated any flooding claims, has an underground garage entrance elevation that is significantly lower than the surrounding municipal roads, and the drainage pipe network diameter in the area is too small. The surveyor located the building's outline in the system, marked it as having a moderate risk of flooding, filled in a description of the possibility of municipal road flooding into the garage during heavy rain, and uploaded a photo of the garage entrance taken on-site. After submission, the marking entered a pending review process, taking effect only after approval by the risk control management team.

[0026] S14: The flood risk data corresponding to the historical flood claims data and the business experience annotation data are used as dual-source flood risk data for insurance business. Specifically, in this embodiment of the invention, the flood risk data output from the objective data-driven and subjective experience-driven production lines are merged to form a unified dual-source flood risk data set. In the merged dual-source flood risk data, each record carries a risk source type attribute, i.e., claims data-driven or business experience annotation, for downstream business systems to perform differentiated processing.

[0027] S20: Spatially fuse the dual-source flood risk data, and construct a flood risk database based on the spatially fused dual-source flood risk data. Specifically, this includes the following steps S21-S24: S21: When both the historical flood claims data and the business experience annotation data exist simultaneously in the same spatial location, the flood risk data corresponding to the historical flood claims data is retained, and the business experience annotation data is stored as a supplementary attribute. Specifically, in this embodiment of the invention, the dual-source flood risk data originates from two independent production lines. The historical flood claims data is generated through objective claims analysis and spatial calculation, while the business experience annotation data originates from the on-site experience judgment of frontline personnel. Both may generate risk data for the same building outline. When such spatial overlap occurs, a priority rule for data fusion needs to be established. The historical flood claims data is the priority to be retained. Simultaneously, the manual judgment information carried by the business experience annotation data, such as on-site observation descriptions, on-site photos, and risk cause analysis recorded by frontline surveyors, is an important supplement to the claims data and should not be simply discarded but should be stored as a supplementary attribute. Taking a financial insurance claims scenario as an example, a residential building in an old urban community in a certain city has been marked as a flood-risk building in historical flood claims data because it has accumulated five flood claims in the past three years, with a total compensation amount of 500,000 yuan. Meanwhile, a senior surveyor also annotated the building during an inspection, recording detailed descriptions of the aging drainage pump in the underground garage and the fact that water depths could reach half a meter during heavy rains. During data fusion, the system detected that the building's outline existed in both data sources. Following priority rules, the historical flood claims data was retained as the master data record, with the risk level and compensation intensity supported by objective claims data. The surveyor's experience annotation data was then linked to the same record as supplementary attributes, including the annotator, annotation time, risk description, and on-site photos.

[0028] S22: Construct the flood risk database based on the spatially fused dual-source flood risk data, recording the risk source type attribute of each data entry in the flood risk database. Specifically, in this embodiment of the invention, the dual-source flood risk data after spatial fusion and deduplication are aggregated and databased to form a unified flood risk database. During the database construction process, the risk source type attribute is explicitly marked for each risk data record, such as historical claims data-driven or business experience-based annotation, so that downstream business systems can clearly distinguish the source of the risk record when calling risk data, and thus adopt differentiated business processing strategies. For risk records from historical claims data, the risk assessment is supported by actual claims cases and can be directly used for quantitative decisions such as rate adjustments and changes in underwriting conditions; for risk records from business experience-based annotations, the risk assessment is supported by the experience judgment of front-line personnel and can be used for qualitative decisions such as risk warnings and key monitoring. Taking the financial insurance claims scenario as an example, after completing the dual-source data fusion processing, an insurance company constructed a flood risk database covering the entire city, containing data records of more than 1,200 risky buildings. Each record in the database is assigned a clear risk source type label, of which more than 900 labels are driven by historical claims data and more than 200 labels are marked by business experience.

[0029] S30: Based on the aforementioned flood risk database, a standardized spatial data service interface is constructed for online spatial queries by external business systems. Specifically, in this embodiment of the invention, by constructing a standardized spatial data service interface, core geographic information processing capabilities such as spatial query and spatial retrieval are encapsulated into a unified network service. External business systems do not need to independently deploy a geographic information system engine; they can obtain spatial risk assessment capabilities simply through network requests. The standardized interface defines a unified input parameter format and output data structure, shielding the technical details of the underlying spatial database, enabling seamless integration between business systems with different technology stacks. Taking a financial insurance claims scenario as an example, an insurance company's flood risk database stores spatial data of tens of thousands of risky buildings nationwide. The company simultaneously operates four independent business systems: an insurance quotation system, an underwriting review system, an early warning notification system, and a claims scheduling system, each developed and maintained by different technical teams. If a traditional offline data distribution model is adopted, each system needs to import risk data separately and implement its own spatial query algorithm, which not only results in high development costs but also easily leads to inconsistencies in data versions between systems. Through the constructed standardized spatial data service interface, these four business systems can call the spatial query service simply through a unified network interface. For example, after the policyholder enters the insured address, the quotation system converts the address into latitude and longitude coordinates, calls the spatial query interface, and can obtain the determination result of whether the address falls under the category of flood-risk buildings and the corresponding risk details.

[0030] S40: Invoke the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target. Specifically, such as... Figure 4 The above, Figure 4 yes Figure 2 A schematic flowchart of a specific implementation of step S40 includes the following steps S41-S44: S41: Obtain the geographic coordinates of the insured object and call the standardized spatial data service interface based on the geographic coordinates. Specifically, in this embodiment of the invention, the insured object is usually recorded in the business system in the form of a text address, such as the insured house address in the insurance policy or the vehicle parking address in the policy. The premise of spatial query is to convert the text address into latitude and longitude coordinates that can be recognized by the geographic information system. The system first performs geocoding on the object address, parses it into a standard latitude and longitude coordinate pair, and then uses the coordinates as input parameters to call the standardized spatial data service interface through a network request. The standardized interface defines a unified request format and response format. External business systems do not need to understand the storage structure and query algorithm of the underlying spatial database. They only need to pass in the coordinate parameters according to the interface specification to obtain the risk assessment result. Taking the financial insurance claims scenario as an example, a customer submits a home insurance application to an insurance company. The insured address is Building 3, a community, a street, a district, a city. After receiving the address, the quotation system calls the geocoding service to parse it into latitude and longitude coordinates. Subsequently, the quotation system uses the coordinates as input parameters to initiate a query request to the standardized spatial data service interface. The request format is a standard network service call, which includes coordinate values ​​and interface authentication information.

[0031] S42: Perform a point-to-area relationship query between the geographical coordinates and the risk areas in the flood risk database. Specifically, in this embodiment of the invention, the point element is the geographical coordinate of the insured property, and the area element is the risk area surface corresponding to each risky building stored in the flood risk database. The system quickly retrieves the risk area surface adjacent to the coordinate space through spatial indexing, and then performs geometric calculations of the point within the polygon one by one to determine the inclusion relationship between the coordinate and each risk area surface. This calculation method has higher accuracy than traditional grid matching or administrative division matching, and can be accurate to the level of a single building. Taking the financial insurance claims scenario as an example, after the quotation system sends the latitude and longitude coordinates of a customer's insured house to the standardized spatial data service interface, the query engine inside the interface starts the point-to-area relationship query process. The query engine first uses spatial indexing to quickly locate dozens of candidate risk area surfaces adjacent to the coordinate space among the tens of thousands of risk area surfaces stored in the flood risk database, greatly narrowing the search range for accurate calculation.

[0032] S43: When the geographical coordinates belong to a risk area, obtain the risk hit result and the corresponding data source type for the risk area. Specifically, in this embodiment of the invention, when the coordinates of the target fall within a certain risk area surface determined by the point-to-surface relationship query, the system extracts the complete risk attribute information corresponding to the risk area surface from the flood risk database and encapsulates it into a structured risk hit result according to the interface specification, returning it to the caller. The risk hit result contains various risk attributes of the risk area, such as quantitative indicators like historical compensation intensity, compensation frequency, and risk level. Simultaneously, the result explicitly carries a data source type field, indicating whether the source of the risk record is historical claims data driven or business experience annotation. Taking a financial insurance claims scenario as an example, after the system determines through the point-to-surface relationship query that the coordinates of the customer's insured house fall within a risk area surface, it queries the database for detailed information about the risk area surface. The query result shows that the building corresponding to the risk area surface has experienced 5 flood claims in the past 3 years, with a cumulative compensation amount of 500,000 yuan, and the risk source type is historical claims data driven. The system encapsulates this information into a structured risk hit result, including a hit status of "hit," a cumulative payout of 500,000 yuan, 5 historical payouts, a high-risk level, and a data source type of historical claims data driven, and returns this information to the quotation system. Upon receiving the result, the quotation system automatically displays the property's historical flood risk information on the quotation page, alerting the quotation personnel that the building has a history of severe flooding and suggesting an appropriate increase in the premium rate.

[0033] S44: When the geographical coordinates do not belong to a risk area, obtain the risk assessment result of "missing risk area". Specifically, in this embodiment of the invention, the returned results of the risk query should fully cover both hit and miss situations to ensure that the downstream business system can obtain a clear risk assessment conclusion in any scenario. Taking a financial insurance claims scenario as an example, a customer insures a residence located in a newly developed area. No flood claims have occurred in this area in the past 5 years, and front-line surveyors have not marked this area with business experience. The quotation system sends the latitude and longitude coordinates of the house to the standardized spatial data service interface. After the system performs a point-to-surface relationship query, it confirms that the coordinates do not fall within any risk area surface in the flood risk database. The system then constructs a "missing risk assessment result", including basic information such as the hit status as "missing", the query timestamp, and the query coordinates, and returns it to the quotation system. After receiving the "missing result", the quotation system confirms that there is no flood risk record in the location of the property and generates a quotation plan according to the standard premium rate.

[0034] S50: In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business object, and new claims data generated after the execution of the risk control strategy is obtained. Specifically, this includes the following steps S51-S53: S51: When the data source type is historical flood claims data, the premium rate is adjusted for the insured business object based on the historical payout intensity in the risk hit result. Specifically, in this embodiment of the invention, the historical flood claims data comes from actual payout cases that have occurred. The payout amount and frequency recorded directly reflect the severity of economic losses due to flooding at the location of the object. When the data source type of the risk hit result is marked as historical flood claims data driven, it means that the risk assessment of the object is supported by objective data, and a quantitative premium rate adjustment can be made accordingly. The system automatically calculates the risk premium increase ratio or the recommended adjustment of underwriting conditions based on the historical payout intensity information carried in the risk hit result, i.e., the cumulative payout amount and the number of payouts, combined with the preset premium rate adjustment rules, thereby directly converting the risk assessment result into an insurance pricing decision. Taking a financial insurance claims scenario as an example, a customer insures his / her family car for vehicle damage insurance. The address filled in when applying for insurance is an old residential area in a certain city where the vehicle is parked at night. After the quotation system calls the standardized spatial data service interface, the returned risk hit result shows that the address is a building at risk of flooding. The data source type is historical flood claims data driven. In the past 3 years, there have been 8 flood claims in this risk area, with a total payout of 800,000 yuan. Based on this historical payout intensity, the system automatically triggers the rate adjustment logic, increasing the flood risk premium by 15 percentage points. The reason for the premium adjustment is clearly displayed on the quotation page: the building at this address has a serious history of flooding, with a cumulative payout of 800,000 yuan in the past 3 years, and it is recommended to increase the flood risk premium.

[0035] S52: When the data source type is business experience annotation, a risk warning is issued to the insured business object based on the experience risk level in the risk hit result. Specifically, in this embodiment of the invention, the business experience annotation data comes from the on-site observation and experience judgment of front-line surveyors and senior underwriters. It reflects potential flood hazards that have not yet appeared in the claims data but have been identified by the human eye. Taking a financial insurance claims scenario as an example, a company insured a warehouse under its name with property insurance. The warehouse is located in a new industrial park on the edge of the city. After the quotation system queries the flood risk database, the returned risk hit result shows that the warehouse address hits the flood risk, and the data source type is business experience annotation. This annotation was created by a senior surveyor 6 months ago, with a risk level of moderate water accumulation. The information source description is: the warehouse floor elevation is about 30 centimeters lower than the adjacent municipal road. During heavy rain, rainwater on the road surface is prone to backflow into the warehouse. On-site photos show obvious water stains at the warehouse entrance.

[0036] S53: Obtain new claims data generated after adjusting the premium rate or issuing risk warnings for the insured business object. Specifically, in this embodiment of the invention, the execution of risk control strategies is not the end point of risk management, but a key node in a continuously iterative closed loop. Whether the premium adjustment is based on historical claims data or the risk warning is based on business experience, the insurance business will continue to operate thereafter. When the insured business object experiences a flood accident and generates a claim during the subsequent insurance period, this new claim case becomes new claims data. Taking a financial insurance claims scenario as an example, an insurance company added a 15 percentage point flood risk premium to the vehicles of customers in an old residential area and added a special deductible for floods to a warehouse in an industrial park. During the following year of the insurance period, the city experienced a severe rainstorm, and many cars in the old residential area were flooded, including the aforementioned family car with the additional premium. The car incurred repair costs of 30,000 yuan due to water entering the engine. Meanwhile, warehouses in the industrial park were also flooded by the torrential rain. However, due to a deductible clause included in the insurance policy, the customer bore the 5,000 yuan deductible loss themselves. The system automatically retrieved these two new claim records by monitoring the reporting and settlement data in the claims system. These records included payouts of 30,000 yuan and 5,000 yuan, along with the corresponding accident addresses and times. These two new claim records were automatically fed back into the historical flood claims database, triggering re-clustering and buffer analysis of the spatial point data for the area, and updating the payout intensity and risk level information in the flood risk database.

[0037] S60: The newly added claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data. Specifically, this includes the following steps S61-S62: S61: The newly added claim data is written into the historical flood claim data, triggering a re-clustering of the historical flood claim data based on a preset compensation amount threshold, resulting in updated flood risk data corresponding to the historical flood claim data. Specifically, in this embodiment of the invention, the system automatically writes the newly added claim data into the historical flood claim dataset, continuously expanding the historical data sample. Then, the clustering analysis process is re-triggered, and all spatial point data, including both new and old data, are filtered and aggregated again based on the preset compensation amount threshold. Taking a financial insurance claim scenario as an example, after the summer flood season, an insurance company's claim system generated a large number of new flood claim cases. Among them, a newly built residential community in a city, which was not previously marked as a risk area, experienced severe flooding during this flood season due to municipal drainage network renovation work, resulting in 12 parked vehicles being flooded and a total compensation amount of 450,000 yuan. After writing these 12 new claim data entries into the historical flood claim database, the system automatically triggers a re-clustering analysis. The 12 claim settlement points in this community are highly clustered in space, and the cumulative compensation amount far exceeds the preset threshold of 50,000 yuan, so they have been successfully identified as a new cluster group.

[0038] S62: Receive the status change operation for the business experience annotation data, and drive the flood risk database to perform a version update based on the flood risk data corresponding to the updated claims data and the business experience annotation data after the status change. Specifically, in this embodiment of the invention, the update of dual-source flood risk data has two different driving modes. Historical flood claims data adopts an automatic trigger update mode, that is, after new claims data is written, new risk data is automatically re-clustered to generate new risk data; business experience annotation data adopts a manual status change mode, where authorized users perform status change operations on the marked risk buildings according to the results of on-site review or business needs, such as approving pending annotations to make them effective, marking effective annotations as invalid due to improvements in the on-site environment, or modifying risk levels and description information.

[0039] As can be seen, the above solution, by integrating historical claims data and business experience-annotated data to construct a dual-source flood risk database, broadens the data sources and coverage for risk assessment. Simultaneously, by feeding new claims data back to historical data and triggering dynamic updates to the risk database, this, together with changes in the status of business experience-annotated data, forms a continuous learning loop from risk assessment and strategy execution to data feedback. This ensures the flood risk database is constantly updated as business operations progress, effectively improving the accuracy of risk assessment.

[0040] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0041] In one embodiment, a flood data processing apparatus is provided, which corresponds one-to-one with the flood data processing methods described in the above embodiments. For example... Figure 5 As shown, Figure 5 This is a schematic diagram of a flood data processing device according to an embodiment of the present invention. The flood data processing device includes a first acquisition module 51, a fusion module 52, a construction module 53, a calling module 54, a second acquisition module 55, and an update module 56. Detailed descriptions of each functional module are as follows: The first acquisition module 51 is used to acquire historical flood claims data and business experience annotation data of insurance business, and preprocess the historical flood claims data and business experience annotation data respectively to generate dual-source flood risk data of insurance business. The fusion module 52 is used to spatially fuse the dual-source flood risk data and construct a flood risk database based on the spatially fused dual-source flood risk data. Module 53 is used to build a standardized spatial data service interface for online spatial querying by external business systems based on the flood risk database. Module 54 is used to call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; The second acquisition module 55 is used to execute a risk control strategy corresponding to the data source type on the insurance business target in at least one business processing stage of the insurance business, and to acquire the new claims data generated after the execution of the risk control strategy. The update module 56 is used to feed the newly added claims data back to the historical flood claims data, update the status of the business experience annotation data, and drive the dynamic update of the flood risk database based on the fed-back historical flood claims data and the updated business experience annotation data.

[0042] In one embodiment, the first acquisition module 51 is specifically used for: The accident addresses in the historical flood claims data are geocoded to generate spatial point data containing the compensation amount, and the spatial point data is clustered based on a preset compensation amount threshold. A buffer is generated from the clustered spatial point data. The buffer is then spatially overlaid with the pre-acquired building outline data of the insurance business target to generate flood risk data corresponding to the historical flood claims data. Based on the building outline, the risk map of the insurance business is marked, and the business experience annotation data generated by the annotation is obtained. The flood risk data corresponding to the historical flood claims data and the business experience-annotated data are used as dual-source flood risk data for insurance business.

[0043] In one embodiment, the first acquisition module 51 is further configured to: After clustering, each group of spatial point data is used to generate closed polygonal surface features according to its outer boundary, and a buffer is generated for the polygonal surface features according to a preset radius. The buffer is spatially superimposed with the pre-acquired building outline data of the insurance business target, and the building outline surfaces that intersect with the buffer are filtered out. The selected building outlines are extracted and used as the flood risk data corresponding to the historical flood claims data.

[0044] In one embodiment, the fusion module 52 is specifically used for: When both the flood risk data corresponding to the historical flood claims data and the business experience annotation data exist in the same spatial location, the flood risk data corresponding to the historical flood claims data is retained, and the business experience annotation data is stored as a supplementary attribute. The flood risk database is constructed based on the spatially fused dual-source flood risk data, and the risk source type attribute of each data point in the flood risk database is recorded.

[0045] In one embodiment, module 54 is invoked, specifically for: Obtain the geographic coordinates of the insurance business target, and call the standardized spatial data service interface based on the geographic coordinates; Perform a point-to-area relationship query between the geographical coordinates and the risk areas in the flood risk database; When the geographical coordinates belong to a risk area, obtain the risk hit result and the corresponding data source type for the risk area; When the geographical coordinates do not belong to the risk area, obtain the risk assessment result that the risk area was not hit.

[0046] In one embodiment, the second acquisition module 55 is specifically used for: When the data source type is historical flood claims data, the premium rate of the insurance business is adjusted based on the historical payout intensity in the risk hit results; When the data source type is business experience annotation, risk warnings are issued to the insurance business target based on the experience risk level in the risk hit results; Obtain new claims data generated after the implementation of premium rate adjustments or risk warnings for insurance business targets.

[0047] In one embodiment, the update module 56 is specifically used for: The newly added claims data is written into the historical flood claims data, triggering the re-clustering of the historical flood claims data based on a preset compensation amount threshold, to obtain the flood risk data corresponding to the updated historical flood claims data; The system receives status change operations on the business experience annotation data and, based on the flood risk data corresponding to the updated claims data and the business experience annotation data after the status change, drives the flood risk database to perform a version update.

[0048] This invention provides a flood disaster data processing device that constructs a dual-source flood disaster risk database by integrating historical claims data and business experience-annotated data, thus broadening the data sources and coverage for risk assessment. Simultaneously, by feeding new claims data back to historical data and triggering dynamic updates to the risk database, this, together with changes in the status of business experience-annotated data, forms a continuous learning loop from risk assessment and strategy execution to data feedback. This enables the flood disaster risk database to be continuously updated as business operations progress, effectively improving the accuracy of risk assessment.

[0049] Specific limitations regarding the flood data processing device can be found in the limitations of the flood data processing method described above, and will not be repeated here. Each module in the aforementioned flood data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0050] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of a computer device according to an embodiment of the present invention. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a flood data processing method on the server side.

[0051] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 7 As shown, Figure 7This is another schematic diagram of a computer device according to an embodiment of the present invention. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a flood data processing method on the client side.

[0052] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Historical flood claims data and business experience annotation data of insurance business are obtained, and the historical flood claims data and business experience annotation data are preprocessed respectively to generate dual-source flood risk data of insurance business. The dual-source flood risk data are spatially fused, and a flood risk database is constructed based on the spatially fused dual-source flood risk data. Based on the aforementioned flood risk database, a standardized spatial data service interface is constructed for online spatial queries by external business systems; Call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business object, and new claims data generated after the execution of the risk control strategy is obtained; The newly added claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data.

[0053] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Historical flood claims data and business experience annotation data of insurance business are obtained, and the historical flood claims data and business experience annotation data are preprocessed respectively to generate dual-source flood risk data of insurance business. The dual-source flood risk data are spatially fused, and a flood risk database is constructed based on the spatially fused dual-source flood risk data. Based on the aforementioned flood risk database, a standardized spatial data service interface is constructed for online spatial queries by external business systems; Call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business object, and new claims data generated after the execution of the risk control strategy is obtained; The newly added claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data.

[0054] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0055] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0057] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0058] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for processing flood disaster data, characterized in that, include: Historical flood claims data and business experience annotation data of insurance business are obtained, and the historical flood claims data and business experience annotation data are preprocessed respectively to generate dual-source flood risk data of insurance business. The dual-source flood risk data are spatially fused, and a flood risk database is constructed based on the spatially fused dual-source flood risk data. Based on the aforementioned flood risk database, a standardized spatial data service interface is constructed for online spatial queries by external business systems; Call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; In at least one business processing stage of the insurance business, a risk control strategy corresponding to the data source type is executed on the insurance business object, and new claims data generated after the execution of the risk control strategy is obtained; The newly added claims data is fed back to the historical flood claims data, the status of the business experience annotation data is updated, and the dynamic update of the flood risk database is driven based on the fed-back historical flood claims data and the status-updated business experience annotation data.

2. The flood data processing method according to claim 1, characterized in that, The process involves acquiring historical flood claims data and business experience annotation data for insurance business, preprocessing the historical flood claims data and business experience annotation data respectively, and generating dual-source flood risk data for insurance business, including: The accident addresses in the historical flood claims data are geocoded to generate spatial point data containing the compensation amount, and the spatial point data is clustered based on a preset compensation amount threshold. A buffer is generated from the clustered spatial point data. The buffer is then spatially overlaid with the pre-acquired building outline data of the insurance business target to generate flood risk data corresponding to the historical flood claims data. Based on the building outline, the risk map of the insurance business is marked, and the business experience annotation data generated by the annotation is obtained. The flood risk data corresponding to the historical flood claims data and the business experience-annotated data are used as dual-source flood risk data for insurance business.

3. The flood data processing method according to claim 2, characterized in that, The process of generating a buffer from the clustered spatial point data, and then spatially overlaying the buffer with the pre-acquired building outline data of the insured business target to generate flood risk data corresponding to the historical flood claims data, includes: After clustering, each group of spatial point data is used to generate closed polygonal surface features according to its outer boundary, and a buffer is generated for the polygonal surface features according to a preset radius. The buffer is spatially superimposed with the pre-acquired building outline data of the insurance business target, and the building outline surfaces that intersect with the buffer are filtered out. The selected building outlines are extracted and used as the flood risk data corresponding to the historical flood claims data.

4. The flood data processing method according to claim 2, characterized in that, The step of spatially fusing the dual-source flood risk data and constructing a flood risk database based on the spatially fused dual-source flood risk data includes: When both the flood risk data corresponding to the historical flood claims data and the business experience annotation data exist in the same spatial location, the flood risk data corresponding to the historical flood claims data is retained, and the business experience annotation data is stored as a supplementary attribute. The flood risk database is constructed based on the spatially fused dual-source flood risk data, and the risk source type attribute of each data point in the flood risk database is recorded.

5. The flood data processing method according to claim 1, characterized in that, By calling the standardized spatial data service interface, the risk hit result and data source type corresponding to the geographical location of the insurance business target are obtained, including: Obtain the geographic coordinates of the insurance business target, and call the standardized spatial data service interface based on the geographic coordinates; Perform a point-to-area relationship query between the geographical coordinates and the risk areas in the flood risk database; When the geographical coordinates belong to a risk area, obtain the risk hit result and the corresponding data source type for the risk area; When the geographical coordinates do not belong to the risk area, obtain the risk assessment result that the risk area was not hit.

6. The flood data processing method according to claim 1, characterized in that, In at least one business processing stage of the insurance business, the execution of a risk control strategy corresponding to the data source type on the insurance business object, and the acquisition of new claims data generated after the execution of the risk control strategy, includes: When the data source type is historical flood claims data, the premium rate of the insurance business is adjusted based on the historical payout intensity in the risk hit results; When the data source type is business experience annotation, risk warnings are issued to the insurance business target based on the experience risk level in the risk hit results; Obtain new claims data generated after the implementation of premium rate adjustments or risk warnings for insurance business targets.

7. The flood data processing method according to claim 2, characterized in that, The process of feeding the newly added claims data back to the historical flood claims data, updating the status of the business experience annotation data, and driving the dynamic update of the flood risk database based on the fed-back historical flood claims data and the updated business experience annotation data includes: The newly added claims data is written into the historical flood claims data, triggering a re-clustering of the historical flood claims data based on a preset compensation amount threshold, to obtain the flood risk data corresponding to the updated historical flood claims data; The system receives a status change operation on the business experience annotation data and, based on the flood risk data corresponding to the updated claims data and the business experience annotation data after the status change, drives the flood risk database to perform a version update.

8. A flood data processing device, characterized in that, include: The first acquisition module is used to acquire historical flood claims data and business experience annotation data of insurance business, and preprocess the historical flood claims data and business experience annotation data respectively to generate dual-source flood risk data of insurance business. The fusion module is used to spatially fuse the dual-source flood risk data and construct a flood risk database based on the spatially fused dual-source flood risk data. The module is used to build a standardized spatial data service interface for online spatial queries by external business systems based on the flood risk database. The calling module is used to call the standardized spatial data service interface to obtain the risk hit result and data source type corresponding to the geographical location of the insurance business target; The second acquisition module is used to execute a risk control strategy corresponding to the data source type on the insurance business target in at least one business processing stage of the insurance business, and to acquire the new claims data generated after the execution of the risk control strategy. The update module is used to feed the newly added claims data back to the historical flood claims data, update the status of the business experience annotation data, and drive the dynamic update of the flood risk database based on the fed-back historical flood claims data and the updated business experience annotation data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the flood data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the flood data processing method as described in any one of claims 1 to 7.

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