Flood insurance policy early warning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a flood insurance policy early warning method and device, electronic equipment and a storage medium, belongs to the technical field of disaster early warning, and is suitable for the financial field. The method comprises the steps of performing model updating on a static flood disaster prediction model based on current hydrological data to obtain a dynamic flood disaster prediction model; performing flood disaster prediction on the target area based on the dynamic flood disaster prediction model to obtain a flood inundation area and an area inundation risk label; on the basis of the target area, site water flow monitoring is carried out, site water flow early warning data are obtained, and the site water flow early warning data comprise early warning types; based on the early warning type and the regional inundation risk label, carrying out region screening on the flood inundation region to obtain a station inundation region; performing insurance policy screening on the flood disaster insurance policy data based on the station inundation area to obtain station risk insurance policy data; and performing insurance policy risk early warning based on the site risk insurance policy data. According to the embodiment of the invention, the accuracy of flood insurance policy early warning can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disaster early warning, and is suitable for the financial field, and particularly relates to a flood insurance early warning method and device, an electronic device and a storage medium. BACKGROUND

[0002] The flood insurance early warning is used for early warning of a user who has purchased a flood insurance in a scenario where there is a flood risk. For example, in the financial field, by early warning of the flood insurance in a target region, the user who has purchased the flood insurance can know in advance the arrival of the flood, thereby ensuring the safety of the property of the user.

[0003] At present, the method of flood insurance early warning is usually to train a flood prediction model according to historical data, and to early warn according to the flood prediction result of the flood prediction model. However, in the flood insurance early warning method, the flood prediction model lacks the response capability to real-time change data, resulting in insufficient accuracy and timeliness of flood prediction. Therefore, how to improve the accuracy of flood insurance early warning has become a technical problem to be solved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a flood insurance early warning method and device, an electronic device and a storage medium, which aims to improve the accuracy of flood insurance early warning.

[0005] To achieve the above-mentioned purpose, the first aspect of the embodiments of the present application provides a flood insurance early warning method, which comprises: obtaining current hydrological data of a target region and obtaining flood insurance data; based on the current hydrological data, updating a preset static flood prediction model to obtain a dynamic flood prediction model; based on the dynamic flood prediction model, performing flood prediction on the target region to obtain a flood inundation map, wherein the flood inundation map comprises a flood inundation region and a region inundation risk label; based on the target region, performing site water flow monitoring to obtain site water flow early warning data, wherein the site water flow early warning data comprises an early warning type; based on the early warning type and the region inundation risk label, performing region screening on the flood inundation region to obtain a site inundation region; based on the site inundation region, performing insurance screening on the flood insurance data to obtain site risk insurance data; based on the site risk insurance data, performing insurance risk early warning.

[0006] In some embodiments, the flood prediction based on the dynamic flood prediction model comprises: based on the preset flood risk data, performing hydrological data simulation to obtain regional hydrological simulation data; based on the regional hydrological simulation data and the dynamic flood prediction model, performing a flooded area prediction on the target region to obtain a flood inundated area; based on the flood risk data, performing a risk marking on the flood inundated area to obtain a regional inundation risk label; based on the flood inundated area and the regional inundation risk label, generating the flood inundation map.

[0007] In some embodiments, based on the warning type and the regional inundation risk label, performing a regional screening on the flood inundated area to obtain a site inundated area, comprising: based on the warning type, performing a label screening on the regional inundation risk label to obtain a target risk label; based on the target risk label, performing a regional search on the flood inundated area to obtain the site inundated area.

[0008] In some embodiments, based on the site inundated area, performing a policy screening on the flood insurance data to obtain site risk insurance data, comprising: performing a data analysis on the flood insurance data to obtain an insurance policy area; performing a spatial overlap on the insurance policy area and the site inundated area to obtain a target insurance area; based on the target insurance area, performing a policy query on the flood insurance data to obtain the site risk insurance data.

[0009] In some embodiments, based on the target region, performing a site water flow monitoring to obtain site water flow warning data, comprising: based on the target region, determining a hydrological site; performing a water flow monitoring on the hydrological site to obtain water flow monitoring data; based on the water flow monitoring data and a preset flow warning threshold, performing a data comparison to obtain data comparison information, wherein the data comparison information includes an over-threshold monitoring information; based on the over-threshold monitoring information, performing a data reading on the water flow monitoring data to obtain the site water flow warning data.

[0010] In some embodiments, based on the site risk insurance data, performing an insurance risk warning, comprising: performing a data analysis on the site risk insurance data to obtain insurance user information; Based on the policy user information, pre-warning information transmission is performed to obtain the flood pre-warning information.

[0011] In some embodiments, before the model updating based on the current hydrological data is performed on the preset static flood prediction model to obtain a dynamic flood prediction model, the method further includes: obtaining historical hydrological data of the target area; performing model training based on the historical hydrological data on a preset initial flood prediction model to obtain the static flood prediction model.

[0012] To achieve the above object, a second aspect of the embodiments of the present application provides a flood insurance pre-warning device, which includes: a data acquisition module configured to acquire current hydrological data of a target area and acquire flood insurance data; a model updating module configured to perform model updating based on the current hydrological data on a preset static flood prediction model to obtain a dynamic flood prediction model; a flood prediction module configured to perform flood prediction on the target area based on the dynamic flood prediction model to obtain a flood inundation map, wherein the flood inundation map includes a flood inundation area and a regional inundation risk label; a water flow monitoring module configured to perform site water flow monitoring based on the target area to obtain site water flow pre-warning data, wherein the site water flow pre-warning data includes a pre-warning type; a regional screening module configured to perform regional screening on the flood inundation area based on the pre-warning type and the regional inundation risk label to obtain a site inundation area; a policy screening module configured to perform policy screening on the flood insurance data based on the site inundation area to obtain site risk policy data; a policy pre-warning module configured to perform policy risk pre-warning based on the site risk policy data.

[0013] To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0014] To achieve the above object, a fourth aspect of the embodiments of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0015] The flood insurance early warning method and device, the electronic equipment and the storage medium provided by the application obtain current hydrological data of a target region, and based on the current hydrological data, a dynamic flood prediction model is obtained by performing real-time updating on a preset static flood prediction model, so that the trained dynamic flood prediction model can more accurately estimate the flood risk of the target region under the current hydrological data. Further, based on the dynamic flood prediction model, the flood of the target region is predicted to obtain a flood inundation map including a flood inundation region and a region inundation risk label, which provides more detailed information for risk assessment of the flood insurance and lays a data foundation for flood insurance early warning. Meanwhile, by monitoring the water flow of the station in the target region, the water flow early warning data of the station including the early warning type can be obtained, and then the flood inundation region is screened according to the water flow early warning data of the station and the region inundation risk label, so that the region division of the flood inundation region and the more accurate risk labeling of the divided flood inundation region can be realized, thereby obtaining the station inundation region. Further, based on the station inundation region, the flood insurance data obtained is screened, and the insurance located in the station inundation region can be accurately positioned, that is, the station risk insurance data, so that the flood insurance early warning is more accurate. Finally, based on the station risk insurance data, the insurance risk early warning is performed, so that the purchase user of the station risk insurance data can obtain the risk notification of the flood risk, thereby protecting the property safety of the insurance purchase user. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of the flood insurance early warning method provided by the embodiment of the application; Figure 2 is a flowchart of the flood insurance early warning method provided by another embodiment of the application; Figure 3 is a flowchart of step S103 in Figure 1 Figure 4 is a flowchart of step S104 in Figure 1 Figure 5 is a flowchart of step S105 in Figure 1 Figure 6 is a flowchart of step S106 in Figure 1 Figure 7 is a flowchart of step S107 in Figure 1 Figure 8 is a structural schematic diagram of the flood insurance early warning device provided by the embodiment of the application; Figure 9 is a hardware structural schematic diagram of the electronic equipment provided by the embodiment of the application.​​​​​ Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0020] First, let's analyze some of the terms used in this application: Flood Policy Early Warning System: The Flood Policy Early Warning System is an intelligent risk management system based on real-time hydrological monitoring and dynamic flood models, and using policy big data as its carrier. It continuously collects data such as rainfall, water level, and flow rate, updates flood inundation maps in real time, and automatically compares them with policy coordinates. Once the monitored value exceeds the threshold, it triggers a tiered early warning and pushes personalized information to policyholders within seconds, including risk avoidance guidance, green channels for claims, and emergency credit limits. At the same time, it drives the insurance company's back-end to adjust reinsurance limits, dispatch investigation resources, and pre-allocate claims funds, achieving a seamless transition from "flood is coming" to "insurance is ready".

[0021] Data assimilation technology is a technique that combines real-time observational data with model prediction data. Mathematical algorithms are used to fuse the two to improve the accuracy and reliability of the model. Simply put, it uses actually observed data to correct and optimize the model's predictions, making the model closer to reality. For example, in weather forecasting, by integrating real-time monitoring data such as temperature and humidity from weather stations with prediction data from atmospheric models, more accurate weather forecasts can be obtained.

[0022] Artificial intelligence: Artificial intelligence (AI) is a technology field that is formed by the cross-fusion of computer science, psychology, philosophy, and cognitive science. Artificial intelligence aims to develop systems and machines that can perform tasks that usually require human intelligence. These tasks include, but are not limited to, language understanding, learning, reasoning, perception, pattern recognition, problem solving, and planning. Artificial intelligence systems use algorithms and statistical models to simulate human cognitive functions, enabling computers to recognize language, images, understand natural language, and even make complex decisions and predictions.

[0023] The flood insurance early warning is used to early warn users who have purchased flood insurance in a flood risk scenario. For example, in the financial field, by early warning flood insurance in the target area, users who have purchased flood insurance can be early warned of the arrival of floods, thereby protecting the safety of the user's property.

[0024] Currently, the method of flood insurance early warning is usually to train a flood prediction model according to historical data, and to early warn according to the flood prediction result of the flood prediction model. However, in this flood insurance early warning method, the above flood prediction model lacks the response ability to real-time change data, resulting in insufficient accuracy and timeliness of flood prediction. Therefore, how to improve the accuracy of flood insurance early warning has become a technical problem to be solved.

[0025] Therefore, the embodiments of the present application provide a flood insurance early warning method and device, an electronic device and a storage medium, which aims to improve the accuracy of flood insurance early warning.

[0026] The flood insurance early warning method and device, the electronic device and the storage medium provided by the embodiments of the present application are specifically explained by the following embodiments. First, the flood insurance early warning method in the embodiments of the present application is described.

[0027] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0028] The basic technology of artificial intelligence generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The software technology of artificial intelligence mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0029] The flood insurance policy early warning method provided by the embodiments of the present application relates to the technical field of disaster early warning and is suitable for the financial field. The flood insurance policy early warning method provided by the embodiments of the present application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like; the server end can be configured as a stand-alone physical server, can be configured as a server cluster or a distributed system formed by multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms; and the software can be an application that implements the flood insurance policy early warning method, but is not limited to the above forms.

[0030] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0031] It should be noted that in each specific embodiment of the present application, when relevant processing needs to be performed on data related to the identity or characteristics of the user, such as user information, user behavior data, user history data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiments of the present application need to obtain sensitive personal information of the user, the separate permission or separate consent of the user will be obtained through a pop-up window or a jump to a confirmation page, and after obtaining the separate permission or separate consent of the user, the necessary user-related data for enabling the embodiments of the present application to normally operate will be obtained.

[0032] Figure 1 is an optional flowchart of the flood insurance policy early warning method provided by the embodiments of the present application, and the method can be used in a flood insurance policy early warning system, Figure 1The method in the method can include but is not limited to including steps S101 to S107.

[0033] Step S101, obtaining current hydrological data of the target area, obtaining flood insurance data; Step S102, based on the current hydrological data, updating the preset static flood prediction model to obtain a dynamic flood prediction model; Step S103, based on the dynamic flood prediction model, predicting the flood of the target area to obtain a flood inundation map, wherein the flood inundation map includes a flood inundation area and a regional inundation risk label; Step S104, based on the target area, monitoring the site water flow to obtain site water flow warning data, wherein the site water flow warning data includes a warning type; Step S105, based on the warning type and the regional inundation risk label, screening the flood inundation area to obtain a site inundation area; Step S106, based on the site inundation area, screening the flood insurance data to obtain site risk insurance data; Step S107, based on the site risk insurance data, performing insurance risk warning.

[0034] The steps S101 to S107 shown in the embodiments of the present application, by obtaining the current hydrological data of the target area, and based on the current hydrological data, updating the preset static flood prediction model in real time to obtain a dynamic flood prediction model, so that the trained dynamic flood prediction model can more accurately estimate the flood risk of the target area under the current hydrological data, further, based on the dynamic flood prediction model, predicting the flood of the target area to obtain a flood inundation map including a flood inundation area and a regional inundation risk label, providing more detailed information for risk assessment of flood insurance, and laying a data foundation for flood insurance warning, at the same time, by monitoring the site water flow of the target area, site water flow warning data including warning type can be obtained, and according to the site water flow warning data and the regional inundation risk label, the flood inundation area can be screened to realize regional division of the flood inundation area and more accurate risk labeling of the divided flood inundation area, so as to obtain the site inundation area, further, based on the site inundation area, screening the obtained flood insurance data to accurately locate the insurance in which the insured area is located in the site inundation area, i.e. site risk insurance data, so that the flood insurance warning is more accurate, finally, based on the site risk insurance data, performing insurance risk warning, so that the purchase user of the site risk insurance data can obtain the risk notification of the flood risk, thereby protecting the property safety of the insurance purchase user.

[0035] In step S101 of some embodiments, the target area refers to a specific geographical area that needs to be assessed and warned of flood risk. The current hydrological data refers to the hydrological condition data in the target area at the current time node, and the current hydrological data includes water level, flow, rainfall, topography, etc. It needs to be known that in the field of finance and insurance, the current hydrological data can be used to assess the risk of insurance products related to floods, such as pricing and risk assessment of flood insurance. The flood insurance policy data refers to the insurance policy information related to flood risk.

[0036] In the embodiments of the present application, the flood insurance policy warning system can be connected to the hydrological database of the hydrological department, meteorological bureau and other institutions in the target area, so as to obtain real-time hydrological data collected by various monitoring equipment arranged by the hydrological department, meteorological bureau and other institutions in the target area, to obtain the current hydrological data. Further, the flood insurance policy warning system can also be connected to the policy database of the financial institution, and specific screening conditions can be set to accurately extract the policy information related to flood disasters, i.e. flood insurance policy data, from the huge policy database.

[0037] In step S102 of some embodiments, the static flood prediction model refers to a flood prediction model established based on historical hydrological data and fixed parameters, and the static flood prediction model is not updated with time. The dynamic flood prediction model can be a model obtained by adjusting the parameters of the static flood prediction model in real time according to the current hydrological data.

[0038] In the embodiments of the present application, before updating the static flood prediction model, it is also necessary to ensure that the static flood prediction model has been trained.

[0039] In detail, please refer to Figure 2 In some embodiments, the flood insurance policy warning method can also include but is not limited to steps S201 to S202: Step S201, obtaining historical hydrological data of the target area; Step S202, based on the historical hydrological data, model training is performed on the preset initial flood prediction model to obtain a static flood prediction model.

[0040] In step S201 of some embodiments, the historical hydrological data refers to data collected about hydrological phenomena and flood phenomena in the past period of time, such as rainfall, river water level, flow, flood grade, etc.

[0041] In the embodiment of the present application, the flood insurance early warning system can query the hydrological data in the past time period by calling the hydrological database to obtain the historical data, and can also obtain the historical data by calling the database storing the key index data of rainfall, river level, flow, etc. constructed by itself. In addition, the historical data and the detailed records of historical flood events can also be obtained by network query method. Further, the historical data and the detailed records of historical flood events are spliced to obtain the historical hydrological data.

[0042] In step S202 of some embodiments, the initial flood prediction model refers to a flood disaster prediction model established based on theory and hypothesis without actual data training or calibration.

[0043] The embodiment of the present application can input the above historical hydrological data into the initial flood prediction model, and adjust the parameters of the initial flood prediction model through an iterative process until the initial flood prediction model can predict the historical flood event with acceptable accuracy, complete the model training of the initial flood prediction model, and obtain the static flood prediction model.

[0044] The steps S201 to S202 shown in the embodiment of the present application provide a data basis for model training by obtaining the historical hydrological data of the target area. Further, based on the historical hydrological data, the initial flood prediction model is trained to obtain the static flood prediction model, which improves the flood risk assessment accuracy of the model.

[0045] The embodiment of the present application can understand and analyze the static flood prediction model in depth, determine the model structure, parameter setting and historical hydrological data of the static flood prediction model, then compare and analyze the current hydrological data and the historical hydrological data in the static flood prediction model, find out the change trend and difference points between the hydrological data, based on the difference points between the current hydrological data and the historical hydrological data, use data assimilation technology to integrate the current hydrological data into the static flood prediction model, dynamically adjust the parameters of the static flood prediction model, and obtain the dynamic flood prediction model. The data assimilation technology includes data assimilation algorithms such as Kalman filter and particle filter. It needs to be known that while dynamically adjusting the parameters of the static flood prediction model, the physical process of the static flood prediction model also needs to be recalibrated, for example, considering the influence of soil moisture change on rainfall infiltration, the effect of river channel shape change on water flow resistance, etc., to improve the simulation accuracy of the dynamic flood prediction model on the flood scene.

[0046] In step S103 of some embodiments, the flood inundation map refers to a map of the area that can be inundated under a specific flood scenario. The flood inundated area refers to the area that can be inundated under a flood scenario. The area inundation risk label refers to a label for classifying the risk level of the flood inundated area, wherein the risk level includes five-year, ten-year, twenty-year, etc.

[0047] The present embodiment application can perform hydrological simulation on the preset flood risk data to obtain area hydrological simulation data, then input the area hydrological simulation data into the above dynamic flood prediction model to predict the inundated area of the target area, then use the flood risk data to mark the risk of the inundated area to generate the area inundation risk label, and finally combine the flood inundated area and the area inundation risk label to obtain the flood inundation map, so as to realize effective evaluation and visualization of the flood risk.

[0048] In detail, please refer to Figure 3 In some embodiments, step S103 can include but is not limited to steps S301 to S304: Step S301, based on the preset flood risk data, perform hydrological data simulation to obtain area hydrological simulation data; Step S302, based on the area hydrological simulation data and the dynamic flood prediction model, predict the inundated area of the target area to obtain the flood inundated area; Step S303, based on the flood risk data, mark the risk of the flood inundated area to obtain the area inundation risk label; Step S304, based on the flood inundated area and the area inundation risk label, generate the flood inundation map.

[0049] In step S301 of some embodiments, the flood risk data refers to various data in the past flood scenarios, such as water flow, rainfall, etc. The area hydrological simulation data refers to the hydrological condition data obtained by simulating the flood risk data in the target area. The area hydrological simulation data usually includes the simulated water level value, flow value, inundation duration, runoff depth, etc. of each sub-area in the target area.

[0050] The embodiment of the present application can obtain flood risk data by integrating and preprocessing historical flood event record information covering the target area. The historical flood event information generally includes historical flood water level, flow, inundation range, duration, flood return period, and inundation topography data, etc. Then, a suitable simulation model, such as a distributed hydrological model or a hydrology-hydraulics coupling model, is selected. The above flood risk data is used as model parameters and substituted into the simulation model for numerical calculation to simulate the hydrological dynamic processes such as surface runoff formation, river confluence process, water level change trend, and water flow propagation speed in the target area under different flood risk data. Finally, the regional hydrological element data corresponding to the simulation data of the target area is output by the simulation model, that is, the regional hydrological simulation data.

[0051] In step S302 of some embodiments, the regional hydrological simulation data can be converted into an input format recognizable by the dynamic flood prediction model through spatial and parameter adaptation processing, and associated with the pre-stored target area geographic spatial data in the dynamic flood prediction model. Then, the dynamic flood prediction model can use the associated regional hydrological simulation data as the basis to quantitatively calculate the flood state of each sub-region of the target area through the built-in hydrology calculation module. The probability of each sub-region being flooded, the specific spatial range of flooding, and the flooding depth can be obtained. Finally, the dynamic flood prediction model can output all land parcels in the target area that meet the flooding conditions according to the probability of each sub-region being flooded, the specific spatial range of flooding, and the flooding depth, forming a flood inundation area containing specific inundation boundaries and flooding depth classification.

[0052] In step S303 of some embodiments, the flood return period in the flood risk data can be extracted to obtain the risk label corresponding to each regional hydrological simulation data, such as a five-year risk label, a ten-year risk label, a twenty-year risk label, etc. Further, according to the risk label corresponding to each regional hydrological simulation data, the flood inundation area corresponding to the regional hydrological simulation data is risk labeled to obtain the regional inundation risk label.

[0053] In step S304 of some embodiments, the predicted flood inundation area and the regional inundation risk label can be visualized to generate an intuitive map, that is, a flood inundation map, for displaying the flood risk of each sub-region in the target area.

[0054] The steps S301 to S304 shown in the embodiments of the present application are based on the preset flood risk data to perform hydrological data simulation to obtain regional hydrological simulation data, which can make the regional hydrological simulation data more fit the actual hydrological characteristics and risk background of the target region, avoid abstract simulation that deviates from reality, and secondly, based on the regional hydrological simulation data and the dynamic flood prediction model, the target region is predicted for a flooded area to obtain a flood inundated area, and based on the flood risk data, the flood inundated area is marked for risk to obtain a regional inundation risk label, which can improve the regional pertinence of the regional inundation risk label, thereby improving the division precision of the flood inundated area, and finally, based on the flood inundated area and the regional inundation risk label, a flood inundation map is generated, which can convert the flood inundated area and the regional inundation risk label into intuitive visual results, facilitate relevant personnel to quickly grasp the core information, and reduce the decision-making cost.

[0055] In step S104 of some embodiments, the station water flow early warning data refers to flood warning information generated based on real-time data of a hydrological monitoring station. The hydrological monitoring station can be a station near a river for monitoring river water flow data, and the warning type refers to the flood risk level that the station water flow early warning data can reach. The warning type includes a five-year warning type, a ten-year warning type, a twenty-year warning type, etc.

[0056] The present embodiment can read the data monitored by the hydrological stations in the target region to obtain water flow monitoring data, and then extract the water flow monitoring data that exceeds the pre-set flow warning threshold to obtain station water flow early warning data.

[0057] In detail, please refer to Figure 4 In some embodiments, step S104 can include but is not limited to steps S401 to S404: Step S401, based on the target region, determining a hydrological station; Step S402, monitoring the water flow of the hydrological station to obtain water flow monitoring data; Step S403, based on the water flow monitoring data and the pre-set flow warning threshold, performing data comparison to obtain data comparison information, wherein the data comparison information includes over-threshold monitoring information; Step S404, based on the over-threshold monitoring information, performing data reading on the water flow monitoring data to obtain station water flow early warning data.

[0058] In step S401 of some embodiments, the hydrological station refers to a place for monitoring and recording hydrological data.

[0059] The embodiment of the present application can determine each hydrological station by searching for a station for monitoring hydrological data in the target area.

[0060] In step S402 of some embodiments, the water flow monitoring data refers to data about water flow monitored by the hydrological station.

[0061] The embodiment of the present application can achieve real-time monitoring of the water flow of the river near the hydrological station by installing flow meters, water level meters and other monitoring equipment at the hydrological station, thereby obtaining the water flow monitoring data.

[0062] In step S403 of some embodiments, the flow warning threshold refers to a set warning standard value. It is necessary to know that the flood warning will be triggered when the water flow monitoring data exceeds the warning standard value. The data comparison information refers to information obtained by comparing the water flow monitoring data with the flow warning threshold. The over-threshold monitoring information refers to information recorded when the water flow monitoring data exceeds the flow warning threshold.

[0063] The embodiment of the present application can set the flow warning threshold according to the historical flood event record information, and when the water flow monitoring data monitored by the hydrological station exceeds the flow warning threshold, the flood insurance warning system will automatically record this over-threshold event, thereby generating the data comparison information including the over-threshold monitoring information.

[0064] In step S404 of some embodiments, when the data comparison information contains the over-threshold monitoring information, the water flow monitoring data corresponding to the over-threshold monitoring information, i.e., the station water flow warning data, can be extracted from the water flow monitoring database of the hydrological station.

[0065] The steps S401 to S404 shown in the embodiment of the present application are based on the target area, determine the hydrological station, and monitor the water flow of the hydrological station to obtain the water flow monitoring data, which can dynamically capture the instantaneous change and period trend of the water flow, and at the same time avoids the hysteresis and data error of manual monitoring, provides data support for subsequent flood insurance warning, and further, based on the water flow monitoring data and the preset flow warning threshold, performs data comparison to obtain data comparison information, and when the data comparison information includes the over-threshold monitoring information, performs data reading on the water flow monitoring data to obtain the station water flow warning data, thereby enhancing the timeliness and accuracy of the flood insurance warning system in warning the flood risk.

[0066] In step S105 of some embodiments, the station inundation area refers to an area in the management area of the hydrological station corresponding to the station water flow warning data that may be flooded by the flood.

[0067] The embodiment application can screen the regional flood risk labels corresponding to the warning type from the regional flood risk labels to determine the target risk labels, and then accurately search in the flood inundated area of the corresponding hydrological station using the target risk labels, so as to identify and define the specific station inundated area.

[0068] In detail, please refer to Figure 5 In some embodiments, step S105 can include but is not limited to steps S501 to S502: Step S501, label screening of the regional flood risk labels based on the warning type to obtain target risk labels; Step S502, regional search of the flood inundated area based on the target risk labels to obtain the station inundated area.

[0069] In step S501 of some embodiments, the target risk label refers to the risk level label matched with the warning type screened from the regional flood risk labels, for example, when the warning type is a five-year warning type, the target risk label should be a five-year risk label.

[0070] The embodiment application can determine the risk label needed to be screened by reading the detailed information of the warning type, and further, traverse the regional flood risk labels to screen the regional flood risk labels corresponding to the warning type, i.e., the target risk labels.

[0071] In step S502 of some embodiments, after obtaining the target risk label, the catchment area of the hydrological station corresponding to the station water flow warning data is queried to determine the catchment area range of the hydrological station in the target area, and further, the smaller sub-area in the catchment area range whose regional flood risk label is the target risk label is regionally labeled, and the station inundated area can be obtained.

[0072] The steps S501 to S502 shown in the embodiment application screen the regional flood risk labels based on the warning type to obtain the target risk labels, so that the flood risk assessment is more detailed and specific, thereby improving the accuracy of the flood insurance policy warning, and further, the regional search of the flood inundated area based on the target risk labels obtains the station inundated area, so that the warning range of the flood insurance policy can be limited to the catchment area of the hydrological station corresponding to the station water flow warning data, thereby further improving the accuracy of the flood insurance policy warning.

[0073] In step S106 of some embodiments, the station risk policy data refers to the flood insurance policy data located in the station inundated area, which can help the insurance company to evaluate and manage the flood insurance risk in a specific area and optimize the risk diversification strategy in the financial field.

[0074] The embodiment application can identify target insurance areas by analyzing flood insurance data to determine insurance coverage areas, then performing spatial overlap analysis on the insurance coverage areas and site flooded areas, and finally querying relevant site risk insurance data from the flood insurance data based on the target insurance areas.

[0075] In detail, please refer to Figure 6 In some embodiments, step S106 can include but is not limited to steps S601-S603: Step S601, data analysis of flood insurance data to obtain insurance coverage areas; Step S602, spatial overlap of insurance coverage areas and site flooded areas to obtain target insurance areas; Step S603, based on the target insurance areas, insurance query of the flood insurance data to obtain site risk insurance data.

[0076] In step S601 of some embodiments, the insurance coverage area refers to a geographical area specified in the flood insurance data that is protected by insurance.

[0077] The embodiment application extracts geographical location information contained in each insurance policy from the flood insurance data through structured query language or data analysis technology, and further determines the insurance coverage range of each insurance policy, i.e., the insurance coverage area, according to the geographical location information. It is necessary to know that the geographical location information usually exists in the form of latitude and longitude coordinates.

[0078] In step S602 of some embodiments, the target insurance area refers to an area that belongs to both the insurance coverage area and the site flooded area.

[0079] The embodiment application analyzes the target insurance area by superimposing the insurance coverage area and the site flooded area, or calculates the intersection area of the insurance coverage area and the site flooded area to identify the geographical location that is located in both the insurance coverage area and the site flooded area, i.e., the target insurance area.

[0080] In step S603 of some embodiments, by database query, the geographical information of the target insurance area is taken as a screening condition to retrieve insurance records that meet the conditions from the flood insurance data, i.e., to obtain site risk insurance data. For example, an insurance company may design a query program that can extract all insurance policies located in these areas from the insurance database according to the geographical boundaries of the target insurance area, thereby obtaining site risk insurance data.

[0081] The steps S601 to S603 shown in the embodiments of the present application can accurately identify the insurance policy insured area by analyzing the flood insurance policy data, so that the geographical distribution of the flood insurance policy data in the target area is more intuitive. Secondly, the spatial overlap of the insurance policy insured area and the site flooded area can effectively identify the target insured area that is truly under the threat of flood, provide more accurate risk positioning for the flood insurance policy warning, and finally, based on the target insured area, the flood insurance policy data is queried to obtain the site risk policy data, so that the flood insurance policy warning system can communicate more accurately with the customers at risk, thereby improving the prevention ability of flood disasters.

[0082] In step S107 of some embodiments, the site risk policy data is analyzed to extract the policy user information, and then the delivery process of the warning information is performed according to the policy user information, so that the flood warning information for the policy user can be finally obtained to timely notify the policy user and take corresponding measures.

[0083] In detail, please refer to Figure 7 In some embodiments, step S107 can include but is not limited to steps S701 to S702: Step S701, data analysis is performed on the site risk policy data to obtain policy user information; Step S702, based on the policy user information, the transmission of the warning information is performed to obtain the flood warning information.

[0084] In step S701 of some embodiments, the policy user information includes the identity, contact information, and insurance amount of the policyholder, and it is necessary to know that the flood insurance policy warning system can use the policy user information to push personalized flood warning and renewal suggestions to the affected policy users.

[0085] The embodiments of the present application can use the policy number in the site risk policy data as an anchor point, extract and clean the customer name, ID number, contact information, communication address, policy subject coordinates, insurance amount, deductible, and historical claims records in the insurance company core database one by one through regular expression and field mapping rules to obtain structured data. Then, the structured data is cross-verified with the dynamic tags of the customer relationship management system in the insurance company, and the hash and fuzzy matching algorithm is used to remove the repeated or invalid data, so that a unique mapping table of the policy and the user can be output, and then according to the mapping table, the corresponding policy user information can be located from the site risk policy data.

[0086] In step S702 of some embodiments, the pre-warning information transmission refers to personalized pre-warning content generated based on the personalized information of the policy user, wherein the personalized pre-warning content can include risk positioning information, time limit and influence information, targeted response guide, and policy benefit prompt, etc. Specifically, the risk positioning information includes the specific area of the user's insurance and the current flood risk type faced by the area, the time limit and influence information includes the effective time of the flood warning, the expected duration, and the specific impact of the risk on the target, the targeted response guide includes the adaptive action suggestion given by the flood warning system in combination with the insurance coverage range, and the policy benefit prompt includes the effectiveness of the user's policy in the current warning scenario and the core process prompt for starting the claim after the disaster.

[0087] After obtaining the policy user information, the embodiments of the present application first generate personalized risk positioning information, time limit and influence information, targeted response guide, and policy benefit prompt, etc. according to the policy user information, then dynamically assemble the above-mentioned content to form a pre-warning transmission message, and then transmit the pre-warning transmission message to the policy user through a specific transmission channel, thereby forming the flood pre-warning information, wherein the specific transmission channel can be a mailbox, a message queue, a short message, an APP, etc.

[0088] The steps S701 to S702 shown in the embodiments of the present application can realize the accurate selection of the target user in the site flooded area by analyzing the site risk policy data to obtain the policy user information, avoid the resource waste and interference caused by generalized pre-warning information push to irrelevant users, further, based on the policy user information, the flood pre-warning information is obtained by transmitting the pre-warning information, which can realize the customization of the highly adaptive flood pre-warning content based on the policy user information, and the information transmission through the common channel of the policy user, thereby improving the efficiency and accuracy of the flood policy pre-warning.

[0089] The application obtains current hydrological data of a target area, and performs model real-time updating on a preset static flood disaster prediction model based on the current hydrological data to obtain a dynamic flood disaster prediction model, so that the trained dynamic flood disaster prediction model can more accurately predict the flood risk of the target area under the current hydrological data. Further, the target area is predicted for flood disaster based on the dynamic flood disaster prediction model to obtain a flood inundation map including a flood inundation area and a regional inundation risk label, which provides more detailed information for risk assessment of a flood insurance policy and lays a data foundation for flood insurance policy early warning. Meanwhile, by monitoring water flow at a station in the target area, water flow early warning data including an early warning type at the station can be obtained, and the flood inundation area is screened based on the water flow early warning data at the station and the regional inundation risk label, so that regional division of the flood inundation area and more accurate risk labeling of the divided flood inundation area can be realized to obtain a station inundation area. Further, the obtained flood insurance policy data is screened based on the station inundation area to accurately locate a policy whose insurable area is located in the station inundation area, i.e., station risk policy data, so that the flood insurance policy early warning is more accurate. Finally, based on the station risk policy data, policy risk early warning is performed, so that a user who purchases the station risk policy data can obtain a risk notification of the flood risk, thereby protecting the property safety of the policy purchase user.

[0090] Please refer to Figure 8 The application also provides a flood insurance policy early warning device, which can implement the flood insurance policy early warning method. The data acquisition module 801 is configured to acquire current hydrological data of a target area and acquire flood insurance policy data. The model updating module 802 is configured to perform model updating on a preset static flood disaster prediction model based on the current hydrological data to obtain a dynamic flood disaster prediction model. The flood disaster prediction module 803 is configured to predict a target area for flood disaster based on a dynamic flood disaster prediction model to obtain a flood inundation map, wherein the flood inundation map includes a flood inundation area and a regional inundation risk label. The water flow monitoring module 804 is configured to monitor water flow at a station in a target area based on the target area to obtain water flow early warning data at the station, wherein the water flow early warning data at the station includes an early warning type. The regional screening module 805 is configured to screen a flood inundation area based on an early warning type and a regional inundation risk label to obtain a station inundation area. The policy screening module 806 is configured to screen flood insurance policy data based on a station inundation area to obtain station risk policy data. The policy early warning module 807 is configured to perform policy risk early warning based on station risk policy data.

[0091] The specific implementation of the flood insurance early warning device is basically the same as the specific implementation of the flood insurance early warning method, and will not be repeated here.

[0092] The embodiment of the present application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor implements the flood insurance early warning method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0093] Please refer to Figure 9 , Figure 9 The hardware structure of the electronic device of another embodiment is illustrated, which includes: The processor 901 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application. The memory 902 can be implemented in the form of a ROM (ReadOnly Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 902 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 902 and are called and executed by the processor 901 to implement the flood insurance early warning method of the embodiments of the present application. The input / output interface 903 is used to realize information input and output. The communication interface 904 is used to realize the communication interaction between the device and other devices, and can realize communication through a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.). The bus 905 transmits information between various components (for example, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904) of the device. The processor 901, the memory 902, the input / output interface 903, and the communication interface 904 realize the communication connection between each other in the device through the bus 905.

[0094] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the flood insurance early warning method.

[0095] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include memory that is remotely located with respect to the processor, which can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0096] The flood insurance early warning method and device, the electronic equipment and the storage medium provided by the embodiments of the present application obtain current hydrological data of a target area, obtain flood insurance data, update a preset static flood prediction model based on the current hydrological data to obtain a dynamic flood prediction model, predict a flood of the target area based on the dynamic flood prediction model to obtain a flood inundation map, wherein the flood inundation map includes a flood inundation area and a regional inundation risk label, perform site water flow monitoring based on the target area to obtain site water flow early warning data, wherein the site water flow early warning data includes an early warning type, perform regional screening on the flood inundation area based on the early warning type and the regional inundation risk label to obtain a site inundation area, perform insurance screening on the flood insurance data based on the site inundation area to obtain site risk insurance data, and perform insurance risk early warning based on the site risk insurance data.

[0097] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0098] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps or different steps.

[0099] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0100] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0101] The terms "first", "second", "third", "fourth" and the like in the description of this application and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on completion of steps or events, based on other processes or items encountered, or the like. Moreover, the terms "comprise", "have" and any variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises, has or includes a list of steps or elements is not necessarily limited to those steps or elements but can include other not expressly listed steps or elements.

[0102] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including single or multiple combinations. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0103] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0104] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0105] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0106] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store programs.

[0107] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A flood disaster insurance policy early warning method, characterized in that, The method includes: Obtain current hydrological data for the target area and obtain flood insurance policy data; Based on the current hydrological data, the preset static flood prediction model is updated to obtain a dynamic flood prediction model. Based on the dynamic flood prediction model, flood prediction is performed on the target area to obtain a flood inundation map, wherein the flood inundation map includes flood inundation areas and regional inundation risk labels; Based on the target area, station water flow is monitored to obtain station water flow early warning data, wherein the station water flow early warning data includes early warning type; Based on the warning type and the regional flooding risk label, the flood-inundated areas are filtered to obtain the station flooding areas; Based on the flooded area of ​​the site, the flood insurance policy data is filtered to obtain site risk insurance policy data; Based on the risk policy data of the aforementioned sites, policy risk warnings are issued.

2. The method according to claim 1, characterized in that, The process of predicting floods in the target area based on the dynamic flood prediction model to obtain a flood inundation map includes: Based on preset flood risk data, hydrological data simulation is performed to obtain regional hydrological simulation data; Based on the regional hydrological simulation data and the dynamic flood prediction model, the inundation area of ​​the target area is predicted to obtain the flood inundation area. Based on the flood risk data, the flood-inundated areas are marked with risk labels to obtain the flood risk labels for the areas; The flood inundation map is generated based on the flood-inundated area and the area inundation risk label.

3. The method according to claim 1, characterized in that, Based on the warning type and the regional flooding risk label, the flood-inundated areas are filtered to obtain the station-inundated areas, including: Based on the aforementioned warning type, the flooding risk labels for the area are filtered to obtain target risk labels; Based on the target risk label, the flood-inundated area is searched to obtain the flood-inundated area of ​​the station.

4. The method according to claim 1, characterized in that, The process of filtering flood insurance policies based on the flooded area of ​​the site to obtain site risk insurance policy data includes: The flood insurance policy data is analyzed to obtain the insured area of ​​the policy. The insured area of ​​the policy and the flooded area of ​​the site are spatially overlapped to obtain the target insured area; Based on the target insured area, the flood-related policy data is queried to obtain the site-specific risk policy data.

5. The method according to claim 1, characterized in that, The process of monitoring station water flow based on the target area to obtain station water flow early warning data includes: Based on the target area, hydrological stations were identified; Water flow monitoring was performed at the hydrological stations to obtain water flow monitoring data; Based on the water flow monitoring data and the preset flow warning threshold, data comparison is performed to obtain data comparison information, wherein the data comparison information includes threshold monitoring information; Based on the above threshold monitoring information, the water flow monitoring data is read to obtain the station's water flow early warning data.

6. The method according to any one of claims 1-5, characterized in that, The process of issuing policy risk warnings based on the risk policy data from the aforementioned sites includes: Data parsing is performed on the risk policy data of the aforementioned sites to obtain policyholder information; Based on the policyholder information, early warning information is transmitted to obtain flood early warning information.

7. The method according to any one of claims 1-5, characterized in that, Before updating the preset static flood prediction model based on the current hydrological data to obtain the dynamic flood prediction model, the process further includes: Obtain historical hydrological data for the target area; Based on the historical hydrological data, the preset initial flood prediction model is trained to obtain the static flood prediction model.

8. A flood disaster insurance policy early warning device, characterized in that, The device includes: The data acquisition module is used to acquire current hydrological data for the target area and flood insurance policy data. The model update module is used to update the preset static flood prediction model based on the current hydrological data to obtain a dynamic flood prediction model. The flood forecasting module is used to forecast floods in the target area based on the dynamic flood forecasting model and obtain a flood inundation map, wherein the flood inundation map includes flood inundation areas and regional inundation risk labels. A water flow monitoring module is used to monitor the water flow at a station based on the target area and obtain station water flow early warning data, wherein the station water flow early warning data includes an early warning type; The regional filtering module is used to filter the flood-inundated areas based on the warning type and the regional flood risk label to obtain the flood-inundated areas of the stations; The policy filtering module is used to filter the flood-related policy data based on the flooded area of ​​the site to obtain site risk policy data; The policy early warning module is used to provide early warnings of policy risks based on the risk policy data of the site.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the flood insurance policy early warning method according to 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 the processor, it implements the flood insurance policy early warning method according to any one of claims 1 to 7.

Images