Waterlogging early warning method and device, electronic equipment and storage medium
Through screening and spatial clustering of waterlogging claims data, combined with future rainfall forecast information, the accuracy and cost reduction of waterlogging warnings are achieved, and the problems of high cost and poor accuracy of waterlogging warnings in the existing technology are solved.
Patent Information
- Application Number
- CN202411191706.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-28
- Publication Date
- 2024-12-13
AI Technical Summary
The existing flooding warning methods are costly or difficult to obtain data, which makes it difficult to guarantee the accuracy of early warnings and cannot be promoted on a large scale.
By obtaining original claims data, screening and spatial clustering to identify flooding risk areas, and conducting flooding warnings based on future rainfall forecast information to reduce hardware and data acquisition costs.
It has achieved the improvement of the accuracy of flooding warning while reducing costs, reminding users to respond in a timely manner, reducing the loss of life and property, and reducing the claims cost of insurance companies, achieving a win-win situation.
Smart Images
Figure CN119130138A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the fields of artificial intelligence and financial technology, and in particular to a method, device, electronic device and storage medium for early warning of urban flooding. Background Art
[0002] At present, waterlogging warning is mainly achieved through two methods. The first method is to monitor the waterlogging situation in the city in real time through sensors deployed in various places, but the hardware cost of this method is relatively high and it cannot be promoted on a large scale. The second method is to combine a large amount of historical waterlogging data and meteorological rainfall forecasts to build a waterlogging analysis model for each city, but this method requires a large amount of accurate waterlogging data. However, in actual application scenarios, it is difficult to obtain data. When data is limited, the accuracy of waterlogging warnings is difficult to guarantee.
[0003] Therefore, how to improve the accuracy of waterlogging warning while reducing costs has become a technical problem that needs to be solved urgently. Summary of the invention
[0004] The main purpose of the embodiments of the present application is to propose a waterlogging warning method, device, electronic device and storage medium, aiming to reduce costs while improving the accuracy of waterlogging warnings.
[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application provides a waterlogging early warning method, the method comprising:
[0006] Obtaining original claims data;
[0007] Screening and processing the original claims data to obtain waterlogging claims data;
[0008] Perform spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas;
[0009] Performing waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data;
[0010] Extracting future rainfall data for the waterlogging risk area from pre-acquired future rainfall forecast information;
[0011] A waterlogging warning is performed based on the future rainfall data and the waterlogging warning data.
[0012] In some embodiments, the original claim data includes risk factor data, and the screening and processing of the original claim data to obtain waterlogging claim data includes:
[0013] Classifying the original claim data based on the risk factor data to obtain claim classification data;
[0014] The waterlogging claim data of the waterlogging claim type is screened out from the claim classification data.
[0015] In some embodiments, the waterlogging claim data includes waterlogging claim events; and performing spatial clustering based on the waterlogging claim data to obtain waterlogging risk areas includes:
[0016] Obtaining the claim settlement location information of the waterlogging claim settlement event;
[0017] Convert the claim location information into longitude and latitude to obtain claim geographic information;
[0018] Clustering the claim geographical information based on the preset clustering area to obtain clustering area data; wherein the clustering area data includes clustering points, each of which corresponds to one of the waterlogging claim events;
[0019] The areas to which a preset number of the cluster points belong are screened out from the cluster area data to obtain the waterlogging risk areas.
[0020] In some embodiments, performing waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data includes:
[0021] For each of the waterlogging claim settlement events in the waterlogging risk area, obtaining the occurrence date of the waterlogging claim settlement event;
[0022] Filter out rainfall data on the accident date from the pre-acquired historical rainfall record data;
[0023] The waterlogging warning data is determined from the historical rainfall record data and the rainfall data of the plurality of danger days.
[0024] In some embodiments, determining the waterlogging warning data from the historical rainfall record data and the rainfall data of the plurality of danger days includes:
[0025] Filtering out historical daily rainfall peak data from the historical rainfall record data based on a preset statistical year threshold;
[0026] Sorting the historical daily rainfall peak data and the rainfall data of multiple accident days to obtain a rainfall data sequence;
[0027] The data with the smallest value is extracted from the rainfall data sequence to obtain the waterlogging warning data.
[0028] In some embodiments, extracting the future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information includes:
[0029] Filtering the future rainfall forecast information based on a preset time period to obtain target rainfall forecast information;
[0030] The target rainfall forecast information is regionally screened based on the waterlogging risk area to obtain the future rainfall data; wherein the future rainfall data is used to characterize the rainfall in the waterlogging risk area within the preset time period.
[0031] In some embodiments, performing waterlogging warning based on the future rainfall data and the waterlogging warning data includes:
[0032] Comparing the future rainfall data with the waterlogging warning data;
[0033] When the future rainfall data exceeds the waterlogging warning data, generating waterlogging warning information;
[0034] Based on the waterlogging warning information, a waterlogging warning is issued to users located in the waterlogging risk area.
[0035] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a waterlogging early warning device, the device comprising:
[0036] Claims data acquisition module, used to obtain original claims data;
[0037] A claims data screening module, used to screen the original claims data to obtain waterlogging claims data;
[0038] A spatial clustering module, used for performing spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas;
[0039] A waterlogging warning calculation module is used to perform waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data;
[0040] A future rainfall data acquisition module is used to extract future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information;
[0041] The early warning module is used to provide waterlogging early warning based on the future rainfall data and the waterlogging early warning data.
[0042] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.
[0043] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.
[0044] The waterlogging early warning method, device, electronic device and storage medium proposed in this application obtains the original claims data, screens out the claims data related to waterlogging, and then uses the waterlogging claims data for spatial clustering to identify waterlogging risk areas, and calculates waterlogging early warning data based on these waterlogging risk areas, and finally combines future rainfall forecast information to perform accurate waterlogging early warnings. The embodiments of this application do not require the deployment of a large number of sensors for real-time waterlogging monitoring, nor do they require the collection of a large amount of data to build a waterlogging analysis model, and can achieve accurate waterlogging early warnings, which reduces both hardware costs and data collection costs. In addition, waterlogging early warnings can remind users to take timely countermeasures to minimize loss of life and property, while also reducing the insurance company's waterlogging case claims costs, achieving a win-win situation. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] Figure 1 is a flow chart of a waterlogging early warning method provided by an embodiment of the present application;
[0046] Figure 2 yes Figure 1 Flow chart of step S102 in FIG.
[0047] Figure 3 yes Figure 1 Flow chart of step S103 in FIG.
[0048] Figure 4 yes Figure 1 Flow chart of step S104 in FIG.
[0049] Figure 5 yes Figure 4 Flow chart of step S403 in FIG.
[0050] Figure 6 yes Figure 1 Flow chart of step S105 in FIG.
[0051] Figure 7 yes Figure 1 Flow chart of step S106 in FIG.
[0052] Figure 8 is a schematic diagram of the structure of a waterlogging warning device provided in an embodiment of the present application;
[0053] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION
[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0055] It should be noted that, although the functional modules are divided in the device schematic diagram and the 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 above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.
[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0057] First, some nouns involved in this application are analyzed:
[0058] Artificial Intelligence (AI): AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information process of human consciousness and thinking, use digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, and perceive the environment, acquire knowledge, and use knowledge to obtain the best results. Theory, method, technology and application system.
[0059] Spatial clustering is an important research direction of cluster analysis. It refers to automatically classifying objects in a spatial data set according to their spatial and non-spatial features, thereby dividing the spatial objects in the data set into a series of data clusters with different properties according to a certain specified similarity or dissimilarity detection standard. In this process, data objects of the same type have a high degree of similarity, while data objects of different types have great differences. As an unsupervised learning method, spatial clustering does not require any prior knowledge and can automatically discover data classification knowledge of interest from a spatial database based on the characteristics of the data itself. Spatial clustering methods have been widely used in urban planning, environmental monitoring, earthquake prediction and other fields, and play a great role.
[0060] Waterlogging refers to the accumulation of water on the ground due to heavy rain or flood discharge from reservoirs or rivers, which results in flooding of roads, houses, farmland, and traffic blockage. Waterlogging may threaten the safety of life and property of users, and also bring huge claims pressure to insurance companies, especially in property insurance and auto insurance, where insurance companies need to bear more compensation liability.
[0061] At present, waterlogging early warning is mainly achieved through two methods:
[0062] The first method is to monitor the urban flooding situation in real time through sensors deployed in various places. However, the hardware cost of this method is relatively high and it cannot be widely promoted in all cities across the country.
[0063] The second method is to combine a large amount of historical waterlogging data and meteorological rainfall forecasts to build a waterlogging analysis model for each city. However, this method requires a large amount of accurate waterlogging data, urban geographic information, and urban building information. However, in actual application scenarios, it is difficult to obtain data. When data is limited, the accuracy of waterlogging warnings is difficult to guarantee.
[0064] Based on this, the embodiments of the present application provide a waterlogging warning method, device, electronic device and storage medium, aiming to reduce costs while improving the accuracy of waterlogging warnings.
[0065] The waterlogging warning method, device, electronic device and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the waterlogging warning method in the embodiments of the present application is described.
[0066] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
[0067] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.
[0068] The waterlogging warning method provided in the embodiment of the present application relates to the fields of artificial intelligence and financial technology. The waterlogging warning method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the waterlogging warning method, etc., but is not limited to the above forms.
[0069] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor 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, etc. 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, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.
[0070] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.
[0071] Figure 1 is an optional flow chart of the waterlogging early warning method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.
[0072] Step S101, obtaining original claim data;
[0073] Step S102, screening and processing the original claim settlement data to obtain waterlogging claim settlement data;
[0074] Step S103, performing spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas;
[0075] Step S104, performing waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data;
[0076] Step S105, extracting future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information;
[0077] Step S106: Perform waterlogging warning based on future rainfall data and waterlogging warning data.
[0078] Steps S101 to S106 shown in the embodiment of the present application obtain the original claims data, filter out the claims data related to waterlogging, and then use the waterlogging claims data for spatial clustering to identify waterlogging risk areas, and calculate waterlogging warning data based on these waterlogging risk areas, and finally combine future rainfall forecast information to perform accurate waterlogging warnings. The embodiment of the present application does not require the deployment of a large number of sensors for real-time waterlogging monitoring, nor does it require the collection of a large amount of data to build a waterlogging analysis model for each city, and can achieve accurate waterlogging warnings, which reduces both hardware costs and data collection costs.
[0079] In addition, for users, waterlogging warnings can remind them to take timely response measures to minimize property losses; for insurance companies, waterlogging warnings combined with historical claims data can help insurance companies assess risks more accurately and take corresponding prompt measures in time to remind users in risk areas that may be affected, thereby minimizing the claims costs of waterlogging cases, improving customer satisfaction and establishing a sustainable operating model to achieve a win-win situation.
[0080] In step S101 of some embodiments, there are multiple original claim data, and accurate and effective analysis of waterlogging risk can only be achieved after a certain amount of claim data has been collected.
[0081] Among them, the original claims data includes but is not limited to risk factor data, risk date, risk amount, relevant document information, etc.
[0082] The risk factor data records the cause of the claim case, whether it is due to a traffic accident, a waterlogging disaster, a fire disaster, or a medical incident. By screening the risk factor data, you can quickly and accurately screen out claim cases related to waterlogging disasters and improve processing efficiency.
[0083] Relevant document information may include certification documents, repair documents, inspection documents, etc. related to the claim, which are used to ensure that the claim application can be processed accurately.
[0084] The amount of insurance loss is used to guide insurance companies and relevant departments to formulate urban flood prevention and claims compensation strategies so as to better respond to urban flood disasters.
[0085] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S202:
[0086] Step S201, classifying the original claim data based on the risk factor data to obtain claim classification data;
[0087] Step S202, filtering out waterlogging claim data of waterlogging claim type from the claim classification data.
[0088] Steps S201 to S202 shown in the embodiment of the present application can accurately divide the data into different claim types by classifying the original claim data based on the risk factor data, and specifically screen out the waterlogging claim data, thereby clearly identifying claim cases related to waterlogging disasters, and providing more accurate data support for subsequent waterlogging risk analysis and early warning.
[0089] In step S201 of some embodiments, the categories of the original claim data may include traffic claim type, waterlogging claim type, fire claim type, property damage claim type, commercial claim type, medical claim type, etc., and multiple original claim data are classified according to categories to obtain claim classification data, which includes claim data of more than 50 categories, and the number of claim data of each category is multiple.
[0090] Then, the claim data of waterlogging claim type is screened out from the claim classification data to obtain the waterlogging claim data.
[0091] In step S202 of some embodiments, the waterlogging claim data includes multiple waterlogging claim events, each of which corresponds to an actual claim case, including but not limited to claim location information, accident date, accident cause, claim amount and other information. The specific information needs to be selected according to the actual application scenario, but is not limited to this.
[0092] See also Figure 3 In some embodiments, step S103 may include but is not limited to steps S301 to S304:
[0093] Step S301, obtaining the claim settlement location information of the waterlogging claim settlement event;
[0094] Step S302, converting the claim location information into longitude and latitude to obtain claim geographic information;
[0095] Step S303, clustering the claim geographic information based on the preset clustering area to obtain clustering area data; wherein the clustering area data includes clustering points, and each clustering point corresponds to an urban flooding claim event;
[0096] Step S304, filtering out the areas to which a preset number of cluster points belong from the cluster area data to obtain waterlogging risk areas.
[0097] Steps S301 to S304 shown in the embodiment of the present application, by converting the longitude and latitude of the claim location information of the waterlogging claim event, obtain accurate claim geographic information, and cluster these claim geographic information based on preset clustering areas, which can accurately identify waterlogging risk areas and quickly locate geographical areas where waterlogging disasters frequently occur, provide data support for subsequent waterlogging warning, disaster prevention and mitigation work, help relevant departments optimize resource allocation, improve disaster response efficiency, and reduce the impact of disasters on society and the economy.
[0098] In some embodiments, the claim location information is a detailed address, down to the house number, building name or other detailed description to identify a specific location.
[0099] In step S302 of some embodiments, the conversion of location information into longitude and latitude may be achieved by using geocoding services provided by various map service providers.
[0100] For example, the claim location information of location A is converted into longitude and latitude information (39.9042°N, 116.4074°E).
[0101] In step S303 of some embodiments, spatial clustering processing can be performed using a K-means clustering algorithm, or using a DBSCAN algorithm, an OPTI CS algorithm, or other methods. The specific selection needs to be made in combination with the actual application scenario, but is not limited thereto.
[0102] It should be noted that the number of clustered area data is at least one, and each clustered area data includes at least one cluster point, and each cluster point corresponds to the claim geographic information of a waterlogging claim event, that is, the latitude and longitude information;
[0103] The preset clustering area is used to define the parameters of the neighborhood distance range between cluster points. By setting the preset clustering area, you can control which cluster points are considered adjacent and which are not adjacent during the clustering process, thereby completing clustering and obtaining the final clustering result.
[0104] Specifically, the preset clustering area may be a circular area formed by a clustering radius, may be a rectangular area, or may be a polygonal area, but is not limited thereto.
[0105] For example, the preset clustering area can be an area with a clustering radius of 500 meters, which can cover a residential area, industrial park, commercial park, etc.; or an area divided by the shape of the plot, such as a planned street, planned park, etc., but not limited to this.
[0106] In step S304 of some embodiments, areas where the number of cluster points is greater than or equal to a preset number are screened out from all cluster area data to obtain waterlogging risk areas.
[0107] Specifically, the number of flood risk areas is at least one.
[0108] In some embodiments, the preset number can be set to 5 clustering points, 8 clustering points, 10 clustering points, etc., and the specific setting needs to be combined with the actual application scenario, and is not limited to this.
[0109] See also Figure 4 In some embodiments, step S104 may include but is not limited to steps S401 to S403:
[0110] Step S401, for each waterlogging claim event in the waterlogging risk area, obtaining the occurrence date of the waterlogging claim event;
[0111] Step S402, filtering out rainfall data on the accident date from the pre-acquired historical rainfall record data;
[0112] Step S403, determining waterlogging warning data from historical rainfall record data and rainfall data on multiple dangerous days.
[0113] Steps S401 to S403 shown in the embodiment of the present application, by obtaining the date of occurrence of each waterlogging claim event in the waterlogging risk area and screening out the corresponding rainfall data on the day of occurrence from the historical rainfall record data, can closely combine the actual waterlogging disaster with its corresponding rainfall data, thereby more accurately identifying and analyzing the rainfall pattern that causes waterlogging, helping to predict and assess the risk of waterlogging under similar rainfall conditions in the future, providing a scientific basis for disaster warning, disaster prevention and mitigation, and emergency response, and effectively reducing the losses caused by waterlogging disasters.
[0114] In some embodiments, for each waterlogging claim event in the waterlogging risk area, the pre-acquired historical rainfall record data is screened with the date of accident and the claim location information (or the claim geographic information) of the waterlogging claim event to obtain the rainfall data on the day of accident corresponding to each waterlogging claim event, wherein the rainfall data on the day of accident is the rainfall data of the waterlogging claim event on the day of accident.
[0115] It should be noted that the historical rainfall record data can be obtained from meteorological stations, meteorological departments or relevant scientific research institutions, and the specific acquisition method is not limited in the embodiments of the present application.
[0116] See also Figure 5 In some embodiments, step S403 may also include but is not limited to steps S501 to S503:
[0117] Step S501, filtering out historical daily rainfall peak data from historical rainfall record data based on a preset statistical year threshold;
[0118] Step S502, sorting the historical daily rainfall peak data and the rainfall data of multiple accident days to obtain a rainfall data sequence;
[0119] Step S503, extracting the data with the smallest value from the rainfall data sequence to obtain waterlogging warning data.
[0120] Steps S501 to S503 shown in the embodiment of the present application screen out historical daily rainfall peak data from historical rainfall record data based on a preset statistical year threshold, and sort these peak data and the rainfall data on the dangerous day to obtain a complete rainfall data sequence, and then extract the data with the smallest value from the rainfall data sequence as urban waterlogging warning data, and obtain a relatively conservative but more reliable warning threshold, i.e., urban waterlogging warning data.
[0121] The embodiment of the present application combines historical rainfall peaks with rainfall data of specific waterlogging claims events to ensure that waterlogging risk prediction takes into account both extreme rainfall conditions and actual rainfall levels at which disasters occur. The calculated warning threshold is closer to actual conditions, which helps to improve the accuracy and effectiveness of warnings and provide a scientific basis for relevant departments to formulate disaster prevention and mitigation measures.
[0122] It should be noted that the preset statistical year threshold can be set to 5 years, 10 years, etc. The specific year value needs to be selected based on the actual application scenario, but is not limited to this.
[0123] In step S501 of some embodiments, in addition to the preset statistical year threshold, it is also necessary to combine with the geographical information of the waterlogging risk area to accurately obtain the historical daily rainfall peak data, that is: based on the preset statistical year threshold, the historical daily rainfall peak data corresponding to each waterlogging risk area is filtered out from the historical rainfall record data.
[0124] In step S502 of some embodiments, for each waterlogging risk area, the historical daily rainfall peak data and the rainfall data on multiple risk days are sorted and processed to obtain a rainfall data sequence; wherein the rainfall data sequence corresponds to the waterlogging risk area, and therefore, waterlogging warning data can be obtained from the rainfall data sequence.
[0125] It should be noted that the sorting can be done by sorting from small to large values, or by sorting from large to small values, and is not limited thereto, as long as the sorting can be achieved. The sorting process helps to find a specific value or element more quickly, making it more efficient to filter the data with the smallest value in subsequent processing.
[0126] It should be noted that extracting the data with the smallest value from the rainfall data series as waterlogging warning data can help identify potential waterlogging risks as early as possible, leaving time for taking appropriate protective measures and ensuring the safety of life and property.
[0127] In some embodiments, for areas without waterlogging risk, the historical daily rainfall peak data of the area can be used as waterlogging warning data, thereby extending the waterlogging warning method provided in the embodiments of the present application to areas where no waterlogging claims have occurred or where the number of cluster points is less than a preset number, thereby achieving large-scale promotion and enhancing the applicability and applicability of the waterlogging warning method.
[0128] In addition, in step S403 of some embodiments, waterlogging warning data may be determined based only on rainfall data of multiple dangerous days, and the specific implementation process may include but is not limited to the following steps:
[0129] The rainfall data of multiple accident days are sorted and processed to obtain a rainfall data sequence;
[0130] Extract the data with the smallest value from the rainfall data sequence to obtain the waterlogging warning data.
[0131] See also Figure 6 In some embodiments, step S105 includes but is not limited to steps S601 to S602:
[0132] Step S601, filtering future rainfall forecast information based on a preset period to obtain target rainfall forecast information;
[0133] Step S602, performing regional screening on the target rainfall forecast information based on the waterlogging risk area to obtain future rainfall data; wherein the future rainfall data is used to characterize the rainfall in the waterlogging risk area within a preset time period.
[0134] Steps S601 to S602 shown in the embodiment of the present application screen future rainfall forecast information by preset time periods, and then perform regional screening for waterlogging risk areas to obtain accurate future rainfall data, and can obtain rainfall forecasts for waterlogging risk areas within a specific time period, thereby improving the relevance and pertinence of data and making waterlogging warnings more accurate and timely. It is convenient to take corresponding disaster prevention and mitigation measures in advance, thereby effectively reducing the loss of life and property caused by waterlogging disasters.
[0135] It should be noted that the preset time period may be the next two days, the next three days, or the next day from the current time, and the specific setting needs to be made according to the actual application scenario, but is not limited thereto.
[0136] In some embodiments, future rainfall prediction information can be a high-resolution precipitation forecast, which is obtained by analyzing factors such as the atmospheric environment, cloud maps, and air pressure systems. Among them, the high-resolution precipitation forecast can detect precipitation anomalies in advance, including heavy rain, drought, etc., providing an important basis for disaster warning and prevention, and helping to reduce disaster losses.
[0137] In some embodiments, the preset time period is the next three days from the current moment, and the rainfall data within the next three days is filtered from the future rainfall forecast information to obtain the target rainfall forecast data, and then the geographic information of each waterlogging risk area is filtered from the target rainfall forecast data to obtain the future rainfall data corresponding to each waterlogging risk area within the next three days.
[0138] See also Figure 7 In some embodiments, step S106 may include but is not limited to steps S701 to S703:
[0139] Step S701, comparing the future rainfall data with the waterlogging warning data;
[0140] Step S702, when the future rainfall data exceeds the waterlogging warning data, waterlogging warning information is generated;
[0141] Step S703: issuing a waterlogging warning to users in the waterlogging risk area based on the waterlogging warning information.
[0142] Steps S701 to S703 shown in the embodiment of the present application, by numerically comparing future rainfall data with waterlogging warning data, when it is found that future rainfall may exceed a preset waterlogging risk threshold, waterlogging warning information is immediately generated and sent to users in waterlogging risk areas. This can identify potential waterlogging risks in real time and accurately, and quickly convey warning information to users in affected areas, so that users can prepare in advance and take necessary protective measures to minimize the losses and impacts caused by waterlogging disasters and ensure the safety of life and property.
[0143] In addition, for insurance companies, issuing flood warnings when it is found that future rainfall may exceed the preset flood risk threshold can help insurance companies assess risks more accurately and take corresponding prompt measures in time to remind users in risk areas that may be affected, thereby minimizing the claims costs of flood cases. It can also improve customer satisfaction and establish a sustainable operating model to achieve a win-win situation.
[0144] In some embodiments, it can be determined whether the user is in a flood risk area based on the user's registration information, the positioning information of the smart device and other positioning parameters. When future rainfall data exceeds the flood warning data, a flood warning is issued to users in the flood risk area based on the flood warning information.
[0145] In step S703 of some embodiments, waterlogging warning information may be sent to users in waterlogging risk areas through applications, mobile operators, network warnings, etc., so as to provide waterlogging warnings.
[0146] In some embodiments, the waterlogging warning information includes:
[0147] Waterlogging warning data, used to characterize the rainfall threshold that may cause waterlogging disasters in the area;
[0148] Future rainfall data, used to characterize the predicted rainfall in the area within a preset period in the future;
[0149] Current surface water level data, which is used to characterize the monitoring data of surface water level so that users can understand the rising of water level;
[0150] Waterlogging disaster warning level, which is used to generate different warning levels, such as yellow warning, orange warning and red warning, based on waterlogging warning data, future rainfall data and current surface water level data, so that users and relevant departments can take corresponding prevention and rescue measures;
[0151] Recommended measures: Provide corresponding recommended measures for different warning levels, including but not limited to evacuation guidelines, flood prevention measures, etc., to help users take precautions and self-rescue measures.
[0152] The waterlogging warning method provided in the embodiment of the present application can realize high-precision waterlogging warning across the country based on a large amount of claims data and high-resolution future rainfall forecast information. There is no need to build a waterlogging analysis model for each city, and there is no need to deploy a large number of sensors for real-time waterlogging monitoring. It can be promoted on a large scale.
[0153] In addition, analysis and clustering based on spatial information can quickly and accurately locate the scope of waterlogging warnings, and can provide accurate waterlogging warnings to users in waterlogging risk areas, thereby improving the accuracy of waterlogging warnings.
[0154] In addition, for users, waterlogging warnings can remind them to take timely response measures to minimize property losses; for insurance companies, waterlogging warnings combined with historical claims data can help insurance companies assess risks more accurately and take corresponding prompt measures in time to remind users in risk areas that may be affected, thereby minimizing the claims costs of waterlogging cases, improving customer satisfaction and establishing a sustainable operating model to achieve a win-win situation.
[0155] See also Figure 8 The embodiment of the present application also provides a waterlogging early warning device, which can implement the waterlogging early warning method described above, and the device includes:
[0156] Claim data acquisition module 801, used to acquire original claim data;
[0157] The claim data screening module 802 is used to screen the original claim data to obtain waterlogging claim data;
[0158] The spatial clustering module 803 is used to perform spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas;
[0159] The waterlogging warning calculation module 804 is used to perform waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data;
[0160] A future rainfall data acquisition module 805 is used to extract future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information;
[0161] The warning module 806 is used to issue waterlogging warning based on future rainfall data and waterlogging warning data.
[0162] In some embodiments of the claim data screening module 802, the claim data screening module 802 further includes:
[0163] A claim data classification unit, used to classify the original claim data based on the risk factor data to obtain claim classification data;
[0164] The data screening unit is used to screen out waterlogging claim data of waterlogging claim type from the claim classification data.
[0165] In the spatial clustering module 803 of some embodiments, the spatial clustering module 803 further includes:
[0166] A location information acquisition unit, used to acquire the claim location information of the waterlogging claim event;
[0167] A latitude and longitude conversion unit is used to convert the longitude and latitude of the claim location information to obtain the claim geographic information;
[0168] A cluster processing unit, used for clustering the claim geographic information based on a preset cluster area to obtain cluster area data; wherein the cluster area data includes cluster points, and each cluster point corresponds to an urban flood claim event;
[0169] The risk area screening unit is used to screen out the areas to which a preset number of cluster points belong from the cluster area data to obtain the waterlogging risk areas.
[0170] In some embodiments of the waterlogging early warning calculation module 804, the waterlogging early warning calculation module 804 further includes:
[0171] The accident date acquisition unit is used to acquire the accident date of the waterlogging claim event for each waterlogging claim event in the waterlogging risk area;
[0172] The rainfall data acquisition unit on the day of the accident is used to filter out the rainfall data on the day of the accident from the historical rainfall record data acquired in advance;
[0173] The warning data determination unit is used to determine waterlogging warning data from historical rainfall record data and rainfall data on multiple dangerous days.
[0174] In some embodiments of the early warning data determination unit, the early warning data determination unit further includes:
[0175] A peak data acquisition subunit is used to filter out historical daily rainfall peak data from historical rainfall record data based on a preset statistical year threshold;
[0176] A data sorting subunit is used to sort the historical daily rainfall peak data and the rainfall data of multiple accident days to obtain a rainfall data sequence;
[0177] The data extraction subunit is used to extract the data with the smallest value from the rainfall data sequence to obtain waterlogging warning data.
[0178] In some embodiments of the future rainfall data acquisition module 805, the future rainfall data acquisition module 805 further includes:
[0179] A forecast information screening unit, used to screen future rainfall forecast information based on a preset time period to obtain target rainfall forecast information;
[0180] The rainfall area screening unit is used to perform regional screening of target rainfall forecast information based on the waterlogging risk area to obtain future rainfall data; wherein the future rainfall data is used to characterize the rainfall in the waterlogging risk area within a preset time period.
[0181] In some embodiments of the early warning module 806, the early warning module 806 further includes:
[0182] A numerical comparison unit is used to compare the future rainfall data with the waterlogging warning data;
[0183] A warning information generating unit, used for generating waterlogging warning information when future rainfall data exceeds waterlogging warning data;
[0184] The waterlogging risk warning unit is used to issue waterlogging warnings to users in waterlogging risk areas based on waterlogging warning information.
[0185] The specific implementation of the waterlogging warning device is basically the same as the specific implementation of the waterlogging warning method described above, and will not be repeated here.
[0186] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned waterlogging early warning method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.
[0187] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:
[0188] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.
[0189] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the waterlogging warning method of the embodiment of this application;
[0190] Input / output interface 903, used to implement information input and output;
[0191] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);
[0192] A bus 905 that transmits information between the various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);
[0193] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .
[0194] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned waterlogging warning method is implemented.
[0195] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0196] The waterlogging early warning method, device, electronic device and storage medium provided in the embodiments of the present application obtain original claims data, filter out claims data related to waterlogging, and then use the waterlogging claims data for spatial clustering to identify waterlogging risk areas, and calculate waterlogging early warning data based on these waterlogging risk areas, and finally combine future rainfall forecast information to perform accurate waterlogging early warning. The embodiments of the present application do not need to deploy a large number of sensors for real-time waterlogging monitoring, nor do they need to collect a large amount of data to build a waterlogging analysis model, and can achieve accurate waterlogging early warning, which reduces both hardware costs and data collection costs.
[0197] In addition, for users, waterlogging warnings can remind them to take timely response measures to minimize property losses; for insurance companies, waterlogging warnings combined with historical claims data can help insurance companies assess risks more accurately and take corresponding prompt measures in time to remind users in risk areas that may be affected, thereby minimizing the claims costs of waterlogging cases, improving customer satisfaction and establishing a sustainable operating model to achieve a win-win situation.
[0198] The embodiments described in the embodiments of the present application are intended 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 in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
[0199] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.
[0200] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0201] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.
[0202] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0203] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: 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 indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. 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, c can be single or multiple.
[0204] In the several embodiments provided in the present 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 schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0205] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0206] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0207] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.
[0208] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.
Claims
1. A waterlogging early warning method, characterized in that: The method comprises: Obtaining original claims data; Screening and processing the original claims data to obtain waterlogging claims data; Perform spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas; Performing waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data; Extracting future rainfall data for the waterlogging risk area from pre-acquired future rainfall forecast information; A waterlogging warning is performed based on the future rainfall data and the waterlogging warning data.
2. The method according to claim 1, characterized in that: The original claim data includes risk factor data, and the original claim data is screened to obtain waterlogging claim data, including: Classifying the original claim data based on the risk factor data to obtain claim classification data; The waterlogging claim data of the waterlogging claim type is screened out from the claim classification data.
3. The method according to claim 1, characterized in that: The waterlogging claims data includes waterlogging claims events; The spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas includes: Obtaining the claim settlement location information of the waterlogging claim settlement event; Convert the claim location information into longitude and latitude to obtain claim geographic information; Clustering the claim geographical information based on the preset clustering area to obtain clustering area data; wherein the clustering area data includes clustering points, each of which corresponds to one of the waterlogging claim events; The areas to which a preset number of the cluster points belong are screened out from the cluster area data to obtain the waterlogging risk areas.
4. The method according to claim 3, characterized in that The waterlogging early warning calculation is performed on the waterlogging risk area to obtain waterlogging early warning data, including: For each of the waterlogging claim settlement events in the waterlogging risk area, obtaining the occurrence date of the waterlogging claim settlement event; Filter out rainfall data on the accident date from the pre-acquired historical rainfall record data; The waterlogging warning data is determined from the historical rainfall record data and the rainfall data of the plurality of danger days.
5. The method according to claim 4, characterized in that The determining of the waterlogging warning data from the historical rainfall record data and the rainfall data of the plurality of danger days comprises: Filtering out historical daily rainfall peak data from the historical rainfall record data based on a preset statistical year threshold; Sorting the historical daily rainfall peak data and the rainfall data of multiple accident days to obtain a rainfall data sequence; The data with the smallest value is extracted from the rainfall data sequence to obtain the waterlogging warning data.
6. The method according to claim 1, characterized in that The extracting future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information includes: Filtering the future rainfall forecast information based on a preset time period to obtain target rainfall forecast information; The target rainfall forecast information is regionally screened based on the waterlogging risk area to obtain the future rainfall data; wherein the future rainfall data is used to characterize the rainfall in the waterlogging risk area within the preset time period.
7. The method according to claim 1, characterized in that The performing waterlogging warning based on the future rainfall data and the waterlogging warning data comprises: Comparing the future rainfall data with the waterlogging warning data; When the future rainfall data exceeds the waterlogging warning data, generating waterlogging warning information; Based on the waterlogging warning information, a waterlogging warning is issued to users located in the waterlogging risk area.
8. A waterlogging early warning device, characterized in that: The device comprises: Claims data acquisition module, used to obtain original claims data; A claims data screening module, used to screen the original claims data to obtain waterlogging claims data; A spatial clustering module, used for performing spatial clustering based on the waterlogging claims data to obtain waterlogging risk areas; A waterlogging warning calculation module is used to perform waterlogging warning calculation on the waterlogging risk area to obtain waterlogging warning data; A future rainfall data acquisition module is used to extract future rainfall data of the waterlogging risk area from the pre-acquired future rainfall forecast information; The early warning module is used for providing waterlogging early warning based on the future rainfall data and the waterlogging early warning data.
9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the waterlogging early warning method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the waterlogging early warning method according to any one of claims 1 to 7 is implemented.