Waterlogging risk early warning threshold calculation method and system based on multi-source data analysis

Abstract

The invention discloses a waterlogging risk early warning threshold calculation method and system based on multi-source data analysis, and relates to the technical field of big data analysis and risk early warning, and the method comprises the steps: obtaining the multi-source data of a to-be-analyzed target disaster-bearing body; generating a target disaster-bearing body elevation model containing the building height information according to the building height information and the surrounding area elevation model data so as to analyze a surrounding runoff path of a target disaster-bearing body; correcting a surrounding runoff path according to the surrounding area satellite image to determine a corresponding disaster-bearing body catchment area and a disaster-bearing volume water area; calculating a rainfall ponding relation of the target disaster-bearing body; and acquiring a ponding height threshold value for the disaster-bearing volume water area, and calculating a rainfall early warning threshold value of the waterlogging risk corresponding to the target disaster-bearing body according to the ponding height threshold value and the rainfall ponding relationship. Therefore, personalized rainfall early warning thresholds for different disaster-bearing bodies are obtained through fine-grained analysis, and more accurate decision support is provided.

Description

Technical Field

[0001] The present application relates to the technical field of big data analysis and risk warning, and in particular to a method and system for calculating urban flood risk warning thresholds based on multi-source data analysis. Background Art

[0002] With the intensification of global climate change and the acceleration of urbanization, the frequency and intensity of extreme weather events (such as rainstorms) have increased year by year, leading to increasingly serious urban waterlogging problems around the world. Especially in high-density urban areas, waterlogging, traffic disruptions, building damage, and loss of life and property caused by rainstorms have become a major threat to urban safety and development.

[0003] At present, the early warning system is usually based on meteorological data, with cities or urban areas as the minimum warning unit, ignoring the impact of factors such as terrain, building distribution, and drainage systems on urban waterlogging. This coarse-grained risk assessment method limits the practicality of urban waterlogging early warning. However, urban waterlogging is a localized disaster type, especially in highly urbanized areas, where urban buildings and terrain features have a significant impact on the convergence and drainage of rainwater. For example, areas with high-rise buildings often change the natural flow path of water, causing the flow to be obstructed during the convergence process, greatly increasing the risk of waterlogging.

[0004] Therefore, traditional early warning systems are usually unable to accurately identify a single disaster-prone object (such as a specific building or facility). The warning range is usually based on cities or urban areas, and a unified precipitation warning threshold is used for urban flooding risks without considering the local risk situation of the disaster-prone object. It is difficult to support personalized early warning services for different disaster-prone objects in the city.

[0005] In response to the above problems, the industry has not yet proposed a better technical solution. Summary of the invention

[0006] The present application provides a method, system, storage medium, computer program product and electronic device for calculating urban waterlogging risk warning thresholds based on multi-source data analysis, which are used to at least solve the limitation of traditional urban waterlogging warning systems in current related technologies in using a unified warning threshold for large-scale urban geographic information and the problem that they cannot provide effective warnings for different types of disaster-prone bodies.

[0007] In the first aspect, an embodiment of the present application provides a method for calculating a waterlogging risk warning threshold based on multi-source data analysis, including: obtaining multi-source data of a target disaster-prone body to be analyzed, the multi-source data including building height information, drainage facility information, surrounding area elevation model data, and surrounding area satellite images for a surrounding area of ​​the disaster-prone body; the surrounding area is an area corresponding to a preset distance resolution around the target disaster-prone body; generating a target disaster-prone body elevation model containing building height information based on the building height information and the surrounding area elevation model data, and parsing the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model; and calculating a surrounding runoff path based on the surrounding area satellite images. The surrounding runoff path is corrected according to the road structured data of each road in the surrounding area shown, so as to determine the disaster-bearing body catchment area and the disaster-bearing body waterlogging area corresponding to the target disaster-bearing body; the road structured data includes the slope, material and width of the road; the rainfall-water accumulation relationship of the target disaster-bearing body is calculated according to the drainage facility information, the disaster-bearing body catchment area and the disaster-bearing body waterlogging area; the rainfall-water accumulation relationship represents the relationship between the rainfall and the water depth of the disaster-bearing body waterlogging area; the waterlogging height threshold for the disaster-bearing body waterlogging area is obtained, and the rainfall warning threshold for the waterlogging risk corresponding to the target disaster-bearing body is calculated according to the waterlogging height threshold and the rainfall-water accumulation relationship.

[0008] In a second aspect, an embodiment of the present application provides a waterlogging risk warning threshold calculation system based on multi-source data analysis, including: a data acquisition unit, used to acquire multi-source data of a target disaster-prone body to be analyzed, the multi-source data including building height information, drainage facility information, surrounding area elevation model data and surrounding area satellite images for the surrounding area of ​​the disaster-prone body; the surrounding area is an area corresponding to a preset distance resolution around the target disaster-prone body; an elevation data analysis unit, used to generate a target disaster-prone body elevation model containing building height information based on the building height information and the surrounding area elevation model data, and to parse the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model; a satellite data correction unit, used to correct the surrounding area satellite images based on the surrounding area satellite images; The road structured data of each road in the surrounding area indicated by the star image corrects the surrounding runoff path to determine the disaster-bearing body catchment area and the disaster-bearing volume water area corresponding to the target disaster-bearing body; the road structured data includes the slope, material and width of the road; a rainfall and water accumulation fitting unit is used to calculate the rainfall and water accumulation relationship of the target disaster-bearing body according to the drainage facility information, the disaster-bearing body catchment area and the disaster-bearing volume water area; the rainfall and water accumulation relationship represents the relationship between rainfall and the water accumulation depth in the disaster-bearing volume water area; an early warning threshold calculation unit is used to obtain the water accumulation height threshold for the disaster-bearing volume water area, and calculate the rainfall early warning threshold of the target disaster-bearing body corresponding to the waterlogging risk according to the water accumulation height threshold and the rainfall and water accumulation relationship.

[0009] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the method for calculating the waterlogging risk warning threshold based on multi-source data analysis of any embodiment of the present application.

[0010] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, the steps of the method for calculating the urban flood risk warning threshold based on multi-source data analysis of any embodiment of the present application are implemented.

[0011] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a method for calculating a waterlogging risk warning threshold based on multi-source data analysis of any embodiment of the present application.

[0012] The method for calculating the waterlogging risk warning threshold based on multi-source data analysis provided by this application can produce at least the following technical effects:

[0013] (1) By introducing multi-source data such as building height information, drainage facility information, elevation model data, and satellite images, the multi-dimensional characteristics of the area where the disaster-prone body is located can be comprehensively considered, such as terrain undulation, building distribution, and the effectiveness of the drainage system. The local runoff path and catchment area of ​​the disaster-prone body can be analyzed, and the differentiated physical characteristics of different disaster-prone bodies can be effectively considered. It can also achieve personalized and accurate early warning of local waterlogging risks for different types of disaster-prone bodies in the city.

[0014] (2) In urban areas with high-density buildings, waterlogging caused by heavy rains is highly localized. Through this solution, localized analysis of the target disaster-prone body can be carried out. According to the combined impact of buildings, roads and drainage facilities, the catchment area and water accumulation area of ​​the target disaster-prone body can be accurately determined, which helps to achieve personalized and dynamic runoff path adjustment and provide personalized and targeted early warning services for local buildings, streets and other facilities.

[0015] (3) The building density, drainage system effectiveness and road structure of different regions will significantly affect the local waterlogging situation. Therefore, it is difficult to adapt the differentiated needs of all regions to a unified precipitation warning threshold. In view of this, in this technical solution, by combining the waterlogging height threshold with the rainfall-waterlogging relationship, a personalized warning threshold is dynamically calculated according to the specific water collection and waterlogging characteristics of the disaster-prone body, which can more accurately predict the critical rainfall value at which waterlogging may occur in a specific area. Through a differentiated threshold setting mechanism, especially in high-density urban areas, personalized risk management strategies can be provided for different disaster-prone bodies.

[0016] Through this technical solution, based on the specific information of different disaster-prone bodies, the water collection path and water accumulation risk in local areas can be accurately identified. Through fine-grained analysis, personalized rainfall warning thresholds can be obtained to achieve personalized warnings, provide more accurate decision-making support, optimize the allocation of rescue resources, reduce the threat of urban waterlogging disasters to life and property, and improve the level of urban safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart showing an example of a method for calculating a waterlogging risk warning threshold based on multi-source data analysis according to an embodiment of the present application is shown;

[0019] Figure 2 Shown according to Figure 1An exemplary operation flow chart of step S150 in FIG.

[0020] Figure 3 A flowchart showing an example of a method for early warning of waterlogging risk according to an embodiment of the present application is shown;

[0021] Figure 4 Shown according to Figure 1 An exemplary operation flow chart of step S120 in FIG.

[0022] Figure 5 Shown according to Figure 1 An exemplary operation flow chart of step S130 in FIG.

[0023] Figure 6 A structural block diagram of an example of a waterlogging risk warning threshold calculation system based on multi-source data analysis according to an embodiment of the present application is shown;

[0024] Figure 7 It is a schematic structural diagram of an embodiment of an electronic device of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0027] Figure 1 A flowchart of an example of a method for calculating a waterlogging risk warning threshold based on multi-source data analysis according to an embodiment of the present application is shown.

[0028] Regarding the execution subject of the method of the embodiment of the present application, it can be any controller or processor with computing or processing capabilities, and can adapt to the diversity of different urban areas by dynamically analyzing the specific conditions of the water collection and water accumulation areas of the disaster-prone body. Whether it is a commercial area with dense high-rise buildings, an old city with poor drainage facilities, or a mountainous city with significant road slopes, through comprehensive analysis of multi-source data, it can effectively generate personalized warning thresholds in different scenarios, which are suitable for various complex urban environments.

[0029] In some examples, it may be integrated and configured in an electronic device or terminal by means of software, hardware, or a combination of software and hardware, and the type of the terminal or electronic device may be diverse, such as a mobile phone, a tablet computer, or a desktop computer, etc.

[0030] like Figure 1 As shown, in step S110, multi-source data of the target disaster-prone body to be analyzed is obtained.

[0031] Here, the multi-source data includes building height information, drainage facility information, elevation model data of the surrounding area and satellite images of the surrounding area for the disaster-prone body. The surrounding area is an area around the target disaster-prone body corresponding to a preset distance resolution.

[0032] In some embodiments, the terminal receives input data through various communication interfaces to obtain multi-source data of the target disaster-prone body. Specifically, it is obtained through LiDAR (Light Detection and Ranging, high-resolution laser radar) data or urban building planning data. LiDAR can accurately obtain three-dimensional information of buildings, including height, shape, floor distribution, etc., which is helpful for subsequent analysis. In addition, it is obtained through GIS (geographic information system) data of the urban drainage system or planning drawings of the underground drainage network, which can include parameters such as the layout, diameter, and flow of drainage pipes to evaluate drainage capacity. In addition, by obtaining DEM (Digital Elevation Model) data, DEM data can reflect the elevation fluctuations of the surface, and a more refined three-dimensional model can be generated in combination with building height information. In addition, it is obtained through commercial remote sensing satellites or public satellite image libraries (such as Landsat, Sentinel), which have high spatial resolution and temporal resolution, which helps to identify surface features such as roads, green spaces, and water bodies.

[0033] Specifically, these data come from different collection methods, such as satellite remote sensing, lidar data, and urban infrastructure databases. Building height information can accurately model the obstruction and impact of urban buildings on runoff, drainage facility information can effectively evaluate regional drainage capacity, elevation model data can determine the height changes of terrain, and satellite images can provide a more detailed reference for the overall situation of the entire surface. Therefore, integrating these multi-source data enables the system to fully understand the geographical and architectural environmental characteristics of the target disaster-prone body and its surroundings. High-resolution building height information and drainage facility data can accurately capture subtle differences in the region, providing reliable data support for refined waterlogging warnings.

[0034] In step S120, a target disaster-prone body elevation model including building height information is generated according to the building height information and the surrounding area elevation model data, and the surrounding runoff path of the target disaster-prone body is parsed from the target disaster-prone body elevation model.

[0035] In some embodiments, a high-precision three-dimensional terrain model including the building height is generated by combining the building height information and the elevation model data of the surrounding area, including the undulating changes in the terrain and the height differences of the buildings, which can reflect the impact of the building on the water flow obstruction. In addition, through hydrological analysis tools (such as the water flow direction calculation function in the GIS system), the natural flow direction of the water flow is calculated according to the elevation model to determine the path of rainwater runoff. The basis for calculating the direction of the water flow is the elevation data of each pixel point, and the water flow will flow along the lowest elevation point. Therefore, through the analysis of the elevation model, the system can accurately predict the runoff path after rainfall, especially in a complex urban environment, the impact of the building height on the runoff can be accurately modeled. In addition, the addition of buildings ensures that the impact of the urban building layout on the water flow path is fully considered, ensuring the accuracy of the water flow path prediction.

[0036] In step S130, the surrounding runoff path is corrected according to the road structure data of each road in the surrounding area indicated by the satellite image of the surrounding area to determine the disaster-bearing body water catchment area and disaster-bearing body water volume area corresponding to the target disaster-bearing body.

[0037] Here, the road structured data includes the slope, material and width of the road. The slope represents the flow rate and direction that affect the water flow. In areas with larger slopes, the water flows faster and the risk of waterlogging is lower. In areas with smaller slopes or negative slopes, the water flow will be retained and the risk of waterlogging will increase. The permeability of road materials, such as asphalt and concrete, has an important impact on the convergence of water flow. Roads with poor permeability will increase the risk of waterlogging. For example, wide roads can accommodate more water flows, while narrow roads are prone to local waterlogging.

[0038] In some embodiments, various image recognition technologies (such as convolutional neural networks) are used to extract road information from satellite images, and the road slope, material, width and other attributes are annotated in combination with the road database. Furthermore, the previously analyzed runoff path is corrected using the road structured data, and the runoff path can be corrected according to information such as the road slope to ensure more accurate path analysis, thereby better simulating the impact of roads in the city on rainwater runoff.

[0039] In step S140, the rainfall-water accumulation relationship of the target disaster-affecting body is calculated based on the drainage facility information, the water catchment area of ​​the disaster-affecting body and the water accumulation area of ​​the disaster-affecting body. The rainfall-water accumulation relationship represents the relationship between the rainfall and the water accumulation depth of the water accumulation area of ​​the disaster-affecting body.

[0040] Here, the drainage facility information is combined with the catchment area of ​​the disaster-prone body to simulate the water volume in the catchment area and the processing capacity of the drainage facilities under different rainfall intensities, and analyze the drainage situation of the drainage facilities under rainfall. Furthermore, by simulating the changes in the depth of water accumulation in the catchment area under different rainfall intensities, a mathematical relationship model between rainfall and water accumulation depth is obtained, which shows how the depth of water accumulation in the water accumulation area of ​​the target disaster-prone body changes with rainfall under given rainfall conditions. Therefore, by integrating drainage facility information, catchment area and water accumulation area, the system can more accurately evaluate the relationship between rainfall and water accumulation, especially when the drainage capacity is limited or the catchment area is complex, and can identify potential water accumulation risks in advance.

[0041] In step S150, a water accumulation height threshold for the flooding area of ​​the disaster-bearing body is obtained, and a rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body is calculated based on the water accumulation height threshold and the relationship between rainfall and water accumulation.

[0042] In some embodiments, the water accumulation height threshold can be obtained through empirical data or data statistics of historical waterlogging events. For example, there are differences between the water accumulation height thresholds corresponding to different building types. In addition, it can also be determined by information input by the user into the terminal. Then, by substituting the water accumulation height threshold into the rainfall-water accumulation relationship, the corresponding rainfall warning threshold is determined. For example, if the water accumulation height threshold of a certain area is 30 cm, the rainfall required to reach 30 cm of water accumulation is calculated according to the water accumulation relationship model and set as the waterlogging warning threshold of the disaster-sustaining body.

[0043] Through the embodiments of the present application, combined with multi-source data of disaster-prone bodies (such as elevation models, satellite images, drainage facility information, etc.), the early warning system can show strong adaptability and robustness under different disaster-prone bodies. Comprehensive utilization of multi-source data and highly refined waterlogging risk analysis are carried out. By extracting information such as building heights, elevation models, road structures and drainage facilities from multi-source data, a refined three-dimensional elevation model is generated, the runoff path during heavy rain is analyzed, and combined with information such as drainage system capacity, a personalized rainfall warning threshold for the disaster-prone body is obtained. Therefore, by introducing a water accumulation height threshold, a personalized and accurate waterlogging risk warning can be provided for each target disaster-prone body, which not only solves the problems of coarse-grained and unified thresholds of traditional early warning systems, but can also be refined to a single disaster-prone body to achieve accurate early warning of local risks, greatly enhancing the ability to predict urban waterlogging risks.

[0044] In some examples of the embodiments of the present application, after calculating the rainfall warning threshold, the waterlogging disaster record of the target carrier can also be obtained, and the historical waterlogging rainfall corresponding to the waterlogging risk is queried from the waterlogging disaster record. Then, the rainfall warning threshold is calibrated according to the deviation between the historical waterlogging rainfall and the rainfall warning threshold.

[0045]

[0046] Where M historical represents the rainfall when the flood risk is triggered in the flood disaster record, T current represents the rainfall warning threshold calculated based on the relationship between rainfall and water accumulation, and ΔM represents T current With M historical The difference between calibrated Represents the calibrated rainfall warning threshold.

[0047] Here, by introducing data such as historical waterlogging rainfall and waterlogging depth, the system can dynamically calibrate the rainfall warning threshold. Since the waterlogging risks in different regions under the same rainfall conditions vary greatly, the calibration method based on historical data can significantly improve the pertinence and accuracy of the warning threshold. In addition, the system warning threshold is no longer the result of static calculation, but an adaptive dynamic adjustment based on multi-source data (historical data and real-time data), which greatly reduces the risk of false alarms and missed alarms. In particular, when rainfall conditions similar to those in history appear in the region, the system can more accurately predict the waterlogging risk in the region.

[0048] On the other hand, rainfall patterns at different times and locations may have different impacts on waterlogging risk. For example, the waterlogging risk of short-term heavy rainfall is very different from that of continuous light rain. Through historical data feedback, the system can identify and adapt to these differences, making the warning model more flexible. As a result, the system can learn rainfall patterns that have triggered waterlogging in the past, and adaptively adjust the rainfall warning threshold, accurately distinguishing the impact of different types of rainfall events on waterlogging, thereby improving the accuracy of the warning and enabling the system to quickly identify and issue corresponding warnings.

[0049] In some examples of the embodiments of the present application, in order to more accurately calculate the relationship between rainfall and water accumulation in the target disaster-prone body, the influence of building height and terrain undulation on the runoff path is added, and the drainage capacity of the rainwater pipe is combined, so that the system can adapt to complex urban terrain and building layout. Through accurate elevation model data (such as elevation difference, height influencing factor, etc.), the flow and water accumulation trend of rainfall in a complex urban environment can be well captured, thereby improving the accuracy of early warning, especially for waterlogging prediction in densely built areas. Therefore, through water accumulation prediction, the influence of comprehensive factors such as rainfall intensity, duration, catchment area, and drainage facility efficiency on the depth of water accumulation is directly reflected. By refining the parameters of buildings, drainage facilities and other disaster-prone bodies, the depth of water accumulation and waterlogging risk of each disaster-prone body can be personalized, which greatly improves the accuracy of the prediction.

[0050] Specifically, the rainfall-water relationship of the target disaster-bearing body is expressed by the following formula:

[0051] q=((h×S) / t+Q 泵 *η1+Q 管 *η2) / F, Formula (2)

[0052] Q 泵 =(n×P×1000×1000) / (ρ×g×H+0.5×ρ×v 3 ), Formula (3)

[0053]

[0054] In the formula, q represents rainfall intensity, h represents water depth, S represents water area, t represents rainfall duration, Q 泵 is the drainage volume of the drainage pump, η1 is the drainage efficiency of the pump, Q 管 is the drainage volume of the rainwater pipe, η2 is the drainage efficiency of the rainwater pipe, and F is the area of ​​the catchment area; n is the efficiency of the water pump, P is the rated power of the water pump, ρ is the weight of rainwater per unit volume, g is the acceleration of gravity, H is the head of the water pump, and v is the water flow velocity in the pipeline; A is the cross-sectional area of ​​the drainage pipe, λ is the roughness coefficient of the drainage pipe, and R is the hydraulic radius when fully discharged; I is the hydraulic gradient value, which indicates the height of the water level drop per unit distance.

[0055] Here, the roughness coefficient of the drainage channel can be determined according to the material of the drainage channel. The roughness coefficients of common materials can be obtained by looking up the table. For example, the roughness of the channel wall materials of concrete, steel or soil is different. The hydraulic radius is expressed as the ratio of the wetted area of ​​the channel to the perimeter. The hydraulic gradient is the height of the water level drop per unit distance.

[0056] In some examples of the embodiments of the present application, through detailed calculation of the water pump drainage and rainwater pipe drainage, combined with the efficiency parameters η1 and η2 of the drainage facilities, the calculation results of the drainage capacity can be dynamically adjusted according to the actual performance of the drainage facilities, so that factors such as the working efficiency of the drainage facilities and equipment aging will directly affect the calculation of the warning threshold, ensuring that the warning system can reflect the actual changes in drainage capacity in real time, thereby improving the timeliness and reliability of urban waterlogging warnings.

[0057] By calculating the rainfall-water accumulation relationship of each disaster-prone body, and combining the catchment area F and the water accumulation area S of different disaster-prone bodies, the waterlogging warning threshold of each disaster-prone body can be dynamically calculated based on its unique environmental conditions, taking into account the terrain, building height, drainage facilities and other factors of each disaster-prone body, significantly improving the accuracy and applicability of the warning model, and making the risk management of different disaster-prone bodies in the same area more detailed. In addition, by dynamically calculating the rainfall-water accumulation relationship and drainage capacity, the waterlogging warning threshold can be continuously adjusted according to the real-time rainfall situation, the working status of the drainage facilities, etc. In particular, under the changes of rainfall duration t and catchment area F, the warning system can respond quickly and adjust the threshold calculation through a real-time feedback mechanism. In this way, when the intensity of the rainstorm increases or the efficiency of the drainage facilities decreases, the system can dynamically adjust the waterlogging warning level to ensure the timeliness of the warning measures.

[0058] In addition, by comprehensively considering the efficiency, head, pipe roughness, pipe slope and other parameters of the water pump, the scheme can more accurately simulate complex drainage conditions. Especially in cities, where drainage facilities are complex and aging conditions vary, combining these parameters can better reflect the drainage capacity of drainage facilities in different environments.

[0059] Through the embodiments of the present application, based on the calculation of the water accumulation depth h, the water accumulation area S and the rainfall intensity q, combined with the actual efficiency of the drainage system, the water accumulation height under different rainfall scenarios can be accurately predicted, providing a more scientific basis for urban flood risk warning, and helping to more effectively avoid the problems of warning failure or excessive warning due to rainfall prediction errors.

[0060] Figure 2 Shown according to Figure 1 An example operation flow chart of step S150 in FIG.

[0061] like Figure 2 As shown, in step S210, user input information is obtained, and height threshold input information corresponding to each level of waterlogging risk level is extracted from the user input information by keyword matching.

[0062] In some embodiments, based on the field survey results and the definition of risk level in the table below, the water accumulation height thresholds corresponding to different risk levels are determined. For example, the risk level can be defined according to property loss and casualties. For example, the risk level corresponding to "causing property loss and risk of casualties" is level I, and the risk level corresponding to "causing property loss and risk of injury" is level II. The risk levels that can be triggered by different water accumulation height thresholds are also diverse. Furthermore, researchers conduct field surveys on the target disaster-prone body and then input the height threshold input information corresponding to each risk level.

[0063] In step S220, each height threshold input information is substituted into the rainfall-water accumulation relationship to calculate the rainfall warning thresholds corresponding to each level of waterlogging risk level of the target disaster-bearing body.

[0064] Through the embodiments of the present application, the system can obtain user input information and dynamically match different waterlogging thresholds and risk levels when calculating the warning threshold, and ensure that differentiated warning thresholds can be provided for different disaster-prone bodies based on personalized warning methods. For example, some low-lying areas may be more sensitive to smaller rainfall, while some areas with good drainage facilities may only have waterlogging risks when there is higher rainfall. Therefore, personalized warnings can reduce the situation of excessive or insufficient warnings, making the prediction of waterlogging risks more accurate.

[0065] Therefore, combined with the waterlogging height threshold, the system can provide more detailed waterlogging risk classification management, not only based on rainfall, but also combined with the waterlogging depth, and can provide detailed response plans for different levels of waterlogging risks. For example, for different risk levels, city managers can take graded response measures, such as early warning information release, early drainage preparation measures, etc., which can improve the efficiency of urban flood control and drainage.

[0066] Figure 3 A flowchart of an example of a method for early warning of urban flood risk according to an embodiment of the present application is shown.

[0067] In step S310, a meteorological observation data set is obtained.

[0068] Here, each meteorological observation data in the meteorological observation data set package uniquely corresponds to a meteorological observation site, and the distance between each meteorological observation site and the target disaster-prone body is less than a preset distance threshold.

[0069] In some embodiments, the data set can be obtained by connecting to a meteorological service platform, a meteorological database, or a real-time observation station. Each meteorological observation data usually includes data such as rainfall, temperature, humidity, wind speed, etc., but the focus is on rainfall data. In addition, in order to ensure the timeliness and spatial accuracy of the data, the system will filter according to the geographical location of the target disaster-prone body, and only retain the data of meteorological observation stations that are close to the target disaster-prone body (less than a preset distance threshold).

[0070] In order to ensure the accuracy and timeliness of early warning data, meteorological observation stations that are close to the target disaster-prone body are selected. For example, in dense urban areas, the threshold can be set to a few kilometers, while in wider suburbs, the threshold can be larger. The rainfall data of each observation station records the time, precipitation, etc. In addition, in order to cope with the rapidly changing rainfall conditions under extreme weather, meteorological observation data should be updated as frequently as possible, and ideally, rainfall data should be updated at the minute level.

[0071] In step S320, the meteorological observation data set is interpolated by using the inverse distance weighted method to obtain target rainfall data corresponding to the location of the target disaster-prone body.

[0072] It should be noted that the Inverse Distance Weighting (IDW) method is an interpolation method. The closer the observation data is to the target point, the greater the impact on the target point, and the contribution of the farther station is relatively small. Specifically, the degree to which the rainfall at a certain point in space is affected by the rainfall at its surrounding stations is inversely proportional to the distance.

[0073] In step S330, an early warning is issued for the waterlogging risk of the target disaster-bearing body according to the target rainfall data and the rainfall early warning threshold.

[0074] In some embodiments, the system can classify waterlogging risk into different levels according to the extent to which rainfall exceeds a threshold. For example, if the rainfall is close to the threshold, a yellow warning is issued; if the rainfall exceeds the threshold by a large amount, an orange or red warning is issued. At the same time, the system can also dynamically adjust the waterlogging risk level according to the actual location of the target disaster-bearing body and the water accumulation in the catchment area. In addition, due to the dynamic changes in meteorological conditions, rainfall data should be updated in real time. The system sends warning information to urban management departments, emergency response departments or related users according to the waterlogging risk level to prevent waterlogging disasters.

[0075] It should be noted that, considering that the target disaster-prone bodies are usually located in urban environments, the layout of meteorological observation sites may not be uniform and the distances between sites may vary, so directly applying the conventional inverse distance weighting method may not be able to capture the subtle differences in the local environment well.

[0076] In view of this, with respect to the implementation details of the above step S320, in some implementations, the target rainfall data corresponding to the position of the target disaster-prone body is calculated in the following manner.

[0077] For densely populated urban areas, the layout of meteorological observation stations is restricted by the geographical environment and human construction, resulting in weak local representativeness of meteorological data. Therefore, in the embodiment of the present application, it is necessary to optimize the strategy of weight distribution in IDW, not only considering the distance between the observation station and the disaster-prone body, but also introducing other geographical factors (for example, elevation difference, building occlusion, etc.) to improve the accuracy of the interpolation results. Factors such as building height and terrain undulation around the target disaster-prone body have a direct impact on the distribution of rainfall. The optimized interpolation calculation should take these environmental factors into account to achieve a precise prediction of local rainfall.

[0078] In some embodiments, by collecting data (rainfall, rainfall intensity, etc.) of meteorological stations that are close to the target disaster-prone body, the distance between these stations and the disaster-prone body should be less than a preset distance threshold (such as 10 kilometers). The elevation information of the disaster-prone body location and the elevation information of the meteorological observation station are obtained from high-precision DEM and building height data. In addition, the degree of building obstruction between the disaster-prone body and each meteorological station is calculated based on satellite images or elevation data, and the impact of buildings on rainfall can be evaluated by the line of sight blocking angle.

[0079] Specifically, in the optimized algorithm, not only the horizontal distance between the observation station and the target disaster-prone body is considered, but also the influence of elevation difference and building occlusion is introduced. The optimized interpolation calculation formula is as follows:

[0080]

[0081] Where q(p) is the interpolation result corresponding to the position p of the target disaster-prone body, that is, the target rainfall data; q i is the rainfall data of the ith meteorological observation station, w i (p) is the weight of the ith station to the location of the disaster-prone body, and N is the total number of meteorological observation stations whose corresponding distance is less than the preset distance threshold; d i is the distance between the ith meteorological observation station and the target disaster-prone body, h i is the elevation difference between the i-th meteorological observation station and the target disaster-prone body, α is the elevation difference adjustment coefficient, and θ i is the building shielding angle between the i-th meteorological observation station and the target disaster-sustaining body, and β is the building shielding influence coefficient.

[0082] Through the embodiment of the present application, combined with the elevation difference and the building shielding angle, not only the horizontal distance is considered, but also the impact of terrain and urban buildings on rainfall distribution is accurately reflected. Therefore, the rainfall data calculated for the target disaster-prone body is more in line with the actual situation, especially in urbanized areas, and the accuracy of rainfall data can be significantly improved.

[0083] In waterlogging risk warning, the rainfall changes in the region are usually highly heterogeneous, especially in urban areas with large terrain undulations or dense buildings. By introducing the consideration of elevation difference and building occlusion through the optimized inverse distance weighting method, more accurate rainfall prediction can be made for the actual geographical environment of different disaster-prone bodies. In this way, in the scenario of multiple disaster-prone bodies, the system can generate more adaptive warning thresholds according to the terrain characteristics and environment of disaster-prone bodies in different locations, and effectively deal with the differences in local rainfall.

[0084] It should be noted that the effect of the conventional inverse distance weighting method is often limited by the density of meteorological stations. If the meteorological stations are too sparse, the interpolation calculation accuracy will decrease. By introducing factors such as elevation difference and building occlusion, even if the distribution of meteorological stations is not dense enough, the optimized interpolation method can better make up for this deficiency. In addition, the addition of building height and terrain reduces the dependence on the density of meteorological stations to a certain extent, and can capture the impact of these complex environments on rainfall distribution in a more detailed manner, so that even in areas with fewer meteorological observation stations, local rainfall can still be calculated more accurately.

[0085] Figure 4 Shown according to Figure 1 An example operation flow chart of step S120 in FIG.

[0086] like Figure 4 As shown, in step S410, according to the building height information, the elevation value of the area where each building is located in the surrounding area elevation model is updated pixel by pixel through the overlay matrix operation to obtain the target disaster-prone body elevation model.

[0087] H total (x,y)=H DEM (x,y)+H building (x,y), formula (7)

[0088] In the formula, H total (x, y) is the target disaster-prone body elevation model, which represents the result of superimposing the basic terrain height and building height corresponding to the pixel unit position (x, y); H DEM (x, y) is the basic elevation model, which represents the elevation value distribution of the basic digital elevation model data. It does not include any building information and only reflects the terrain height of the natural terrain at the pixel unit position (x, y); H building (x, y) is the building height information at the corresponding pixel unit position (x, y).

[0089] By refining the elevation model of the area surrounding the target disaster-prone body and superimposing the building height information, the optimized elevation model can more realistically reflect the complex environment of the urban area, including the combined effects of natural terrain and artificial buildings. The original elevation model can only reflect the natural terrain, but after adding building information, it can more accurately capture various height changes in the urban environment, which is crucial for the simulation of water flow. As a result, the runoff path after rainfall is more in line with the actual situation, especially in urban areas with dense buildings. The height of buildings has a significant effect on blocking and guiding water flow. The optimized elevation model greatly improves the simulation accuracy.

[0090] In step S420, a hydrological analysis tool is called to perform flow direction analysis on the elevation model of the target hazard-prone body to determine the corresponding surrounding runoff path.

[0091] f(x,y)=argmin(H total (x±1,y±1)), formula (8)

[0092] Where f(x,y) represents the flow direction of the surrounding runoff path at (x,y), H total (x±1, y±1) represents the distribution of elevation values ​​in the eight adjacent pixel units around (x, y).

[0093] By finding the direction of the pixel with the lowest surrounding elevation, the flow direction of the water can be accurately obtained, the flow direction of each pixel can be dynamically calculated, and the runoff path can be automatically adjusted according to the elevation change. As a result, the flow path is no longer determined solely based on the terrain, but takes into account the blocking effect of buildings and elevation details, ensuring that the flow of water in the city is more in line with actual environmental conditions, especially in areas with tall buildings, where it can be foreseen that the runoff will be more diverted or blocked, and the optimized flow direction analysis results are more accurate.

[0094] By using hydrological analysis tools (such as ArcGIS), the natural flow direction and drainage path of water at each location can be automatically simulated, allowing the system to quickly respond to different terrain features and building layouts, making it highly adaptable in different urban environments. Since the optimized water flow direction analysis can take into account the terrain and building height in real time, the generated drainage area is also more accurate, which helps to delineate potential waterlogging risk areas. Therefore, under the joint action of building height information and elevation data, the system can dynamically correct the runoff path and simulate in real time how the water flows around buildings or flows to other low-lying areas, greatly improving the response speed of the system, and being able to quickly adjust the runoff simulation results. The dynamic correction of the runoff path enhances the robustness of the system, ensuring that it can provide accurate water flow predictions and waterlogging warnings in various complex environments.

[0095] Figure 5 Shown according to Figure 1 An example operation flow chart of step S130 in FIG.

[0096] In step S510, correction coefficients for water flow guidance at different road locations are calculated.

[0097]

[0098] In the formula, R road (x, y) is the correction coefficient of the road to the water flow, which represents the correction amount of the water flow path at the pixel unit position (x, y). road represents the slope of the road, ηroad Represents the material coefficient of the road, W road Indicates the width of the road.

[0099] Here, by introducing the influence of three key factors on water flow guidance, namely the road slope, material coefficient and road width, the guiding role of roads in the actual environment can be more accurately reflected. Therefore, by combining the physical characteristics of the road, the system can more accurately adjust the flow path of water on different roads. Based on the correction of actual road characteristics, it can effectively avoid the wrong prediction of runoff path. The slope and material changes of the road have a significant effect on guiding water flow, which can better simulate the water flow path in the real environment.

[0100] In step S520, the surrounding runoff path is adjusted according to the calculated correction coefficient.

[0101] f road (x,y)=f(x,y)×R road (x,y), formula (10)

[0102] In the formula, f road (x,y) represents the corrected runoff direction at (x,y);

[0103] Here, combining the slope, material and width of the road to guide the water flow will help better identify which areas of rainwater will converge to the target disaster-prone body. In addition, due to the correction of the guiding effect of the road on the water flow, the delineation of the catchment area will be more accurate, ensuring that all rainwater collection areas are reasonably considered and avoiding missing potential risk points.

[0104] In step S530, the paths that converge to the low-lying area where the target disaster-affected body is located are screened from the surrounding runoff paths to identify the corresponding disaster-affected body water catchment area and disaster-affected volume water storage area.

[0105] Here, the formation of waterlogged areas is often closely related to the drainage capacity and water flow guidance capacity of the road. By considering factors such as the slope, material and width of the road, the system can dynamically adjust the identification of waterlogged areas, especially for roads with weak drainage capacity or obvious guidance. The system can identify potential waterlogging risk areas on the road. By dynamically adjusting the delineation of waterlogged areas, the system can identify high-risk waterlogging areas caused by poor road drainage, low slope or small width, provide accurate waterlogging warnings, and help city managers rationally plan the layout of drainage facilities and improve drainage capacity.

[0106] Through the embodiments of the present application, in complex urban environments with large terrain diversity and complex road systems, multiple factors affecting the road can be integrated into the calculation of the runoff path, ensuring that the system can still provide high-precision predictions under various urban road conditions. As a result, the system has stronger adaptability. Whether it is a flat avenue, a mountain road with a large slope, or a street with a variety of materials, the system can automatically adjust the water flow path and the identification of the waterlogged area according to the actual road conditions, ensuring the generalization ability and robustness of the model.

[0107] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of actions combined, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application. In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0108] Figure 6 A structural block diagram of an example of a waterlogging risk warning threshold calculation system based on multi-source data analysis according to an embodiment of the present application is shown.

[0109] like Figure 6 As shown, the waterlogging risk warning threshold calculation system 600 based on multi-source data analysis includes a data acquisition unit 610, an elevation data analysis unit 620, a satellite data correction unit 630, a rainfall and water accumulation fitting unit 640 and a warning threshold calculation unit 650.

[0110] The data acquisition unit 610 is used to acquire multi-source data of the target disaster-prone body to be analyzed, wherein the multi-source data includes building height information, drainage facility information, surrounding area elevation model data and surrounding area satellite images for the surrounding area of ​​the disaster-prone body; the surrounding area is an area corresponding to a preset distance resolution around the target disaster-prone body.

[0111] The elevation data analysis unit 620 is used to generate a target disaster-prone body elevation model containing building height information according to the building height information and the surrounding area elevation model data, and to analyze the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model.

[0112] The satellite data correction unit 630 is used to correct the surrounding runoff path according to the road structure data of each road in the surrounding area indicated by the satellite image of the surrounding area, so as to determine the disaster-bearing body watershed area and disaster-bearing body watershed area corresponding to the target disaster-bearing body; the road structure data includes the slope, material and width of the road.

[0113] The rainfall-water accumulation fitting unit 640 is used to calculate the rainfall-water accumulation relationship of the target disaster-affecting body according to the drainage facility information, the water catchment area of ​​the disaster-affecting body and the water accumulation area of ​​the disaster-affecting body; the rainfall-water accumulation relationship represents the relationship between the rainfall and the water accumulation depth of the water accumulation area of ​​the disaster-affecting body.

[0114] The warning threshold calculation unit 650 is used to obtain the water accumulation height threshold for the flooding area of ​​the disaster-bearing body, and calculate the rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body according to the water accumulation height threshold and the rainfall-water accumulation relationship.

[0115] In some embodiments, an embodiment of the present application provides a non-volatile computer-readable storage medium, which stores one or more programs including execution instructions, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to execute the steps of any of the above-mentioned methods for calculating urban waterlogging risk warning thresholds based on multi-source data analysis in the present application.

[0116] In some embodiments, the embodiments of the present application also provide a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes any step of the above-mentioned method for calculating the urban flood risk warning threshold based on multi-source data analysis.

[0117] In some embodiments, the embodiments of the present application also provide an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of a method for calculating a waterlogging risk warning threshold based on multi-source data analysis.

[0118] Figure 7 is a schematic diagram of the hardware structure of an electronic device for executing a method for calculating a waterlogging risk warning threshold based on multi-source data analysis provided by another embodiment of the present application, such as Figure 7 As shown, the device includes:

[0119] One or more processors 710 and memory 720, Figure 7A processor 710 is taken as an example.

[0120] The device for executing the method for calculating the waterlogging risk warning threshold based on multi-source data analysis may further include: an input device 730 and an output device 740 .

[0121] The processor 710, the memory 720, the input device 730 and the output device 740 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0122] The memory 720, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the method for calculating the threshold value of waterlogging risk warning based on multi-source data analysis in the embodiment of the present application. The processor 710 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 720, that is, the method for calculating the threshold value of waterlogging risk warning based on multi-source data analysis in the above method embodiment is realized.

[0123] The memory 720 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 720 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 720 may optionally include a memory remotely arranged relative to the processor 710, and these remote memories may be connected to the electronic device 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.

[0124] The input device 730 may receive input digital or character information and generate signals related to user settings and function control of the electronic device. The output device 740 may include a display device such as a display screen.

[0125] The one or more modules are stored in the memory 720, and when executed by the one or more processors 710, the method for calculating the waterlogging risk warning threshold based on multi-source data analysis in any of the above method embodiments is executed.

[0126] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0127] The electronic devices of the embodiments of the present application exist in various forms, including but not limited to:

[0128] (1) Mobile communication equipment: This type of equipment is characterized by having mobile communication functions and its main purpose is to provide voice and data communications. This type of terminal includes: smart phones, multimedia phones, functional phones, and low-end phones.

[0129] (2) Ultra-mobile personal computer devices: These devices belong to the category of personal computers, have computing and processing functions, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, etc.

[0130] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players, handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0131] (4) Other onboard electronic devices with data interaction functions, such as on-board devices installed in vehicles.

[0132] The device embodiments described above are merely illustrative, wherein the units described 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 modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for calculating waterlogging risk warning threshold based on multi-source data analysis, comprising: Acquire multi-source data of the target disaster-prone body to be analyzed, wherein the multi-source data includes building height information, drainage facility information, elevation model data of the surrounding area, and satellite images of the surrounding area for the disaster-prone body; the surrounding area is an area corresponding to a preset distance resolution around the target disaster-prone body; Generate a target disaster-prone body elevation model containing the building height information according to the building height information and the surrounding area elevation model data, and parse the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model; Correcting the surrounding runoff path according to the road structure data of each road in the surrounding area indicated by the satellite image of the surrounding area to determine the disaster-bearing body water catchment area and the disaster-bearing body water storage area corresponding to the target disaster-bearing body; the road structure data includes the slope, material and width of the road; Calculate the rainfall-water accumulation relationship of the target disaster-affected body according to the drainage facility information, the water catchment area of ​​the disaster-affected body and the water accumulation area of ​​the disaster-affected body; the rainfall-water accumulation relationship represents the relationship between the rainfall and the water accumulation depth of the water accumulation area of ​​the disaster-affected body; A water accumulation height threshold for the flooding area of ​​the disaster-bearing body is obtained, and a rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body is calculated according to the water accumulation height threshold and the rainfall-water accumulation relationship.

2. The method according to claim 1, wherein: The step of obtaining a water accumulation height threshold for the flood area of ​​the disaster-bearing body, and calculating a rainfall warning threshold for the waterlogging risk of the target disaster-bearing body according to the water accumulation height threshold and the rainfall-water accumulation relationship, includes: Obtain user input information, and extract height threshold input information corresponding to each level of waterlogging risk level from the user input information by keyword matching; Substitute the height threshold input information into the rainfall-water accumulation relationship to calculate the rainfall warning thresholds of the target disaster-bearing body corresponding to each level of waterlogging risk level.

3. The method according to claim 1, wherein: The rainfall-water accumulation relationship of the target disaster-bearing body is expressed by the following formula: q=((h×S) / t+Q 泵 *η1+Q 管 *η2) / F, In the formula, q represents rainfall intensity, h represents water depth, S represents water area, t represents rainfall duration, Q 泵 is the drainage volume of the drainage pump, η1 is the drainage efficiency of the pump, Q 管 is the drainage volume of the rainwater pipe, η2 is the drainage efficiency of the rainwater pipe, and F is the area of ​​the catchment area; n is the efficiency of the water pump, P is the rated power of the water pump, ρ is the weight of rainwater per unit volume, g is the acceleration of gravity, H is the head of the water pump, and v is the water flow velocity in the pipeline; A is the cross-sectional area of ​​the drainage pipe, λ is the roughness coefficient of the drainage pipe, and R is the hydraulic radius when fully discharged; I is the hydraulic gradient value, which indicates the height of the water level drop per unit distance.

4. According to the method of claim 1, after obtaining the water accumulation height threshold for the flooding area of ​​the disaster-bearing body, and calculating the rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body according to the water accumulation height threshold and the rainfall-water accumulation relationship, the method further comprises: Get meteorological observation datasets; Each meteorological observation data in the meteorological observation data set package is uniquely corresponding to a meteorological observation site, and the distance between each meteorological observation site and the target disaster-prone body is less than a preset distance threshold; Performing interpolation calculation on the meteorological observation data set by using an inverse distance weighted method to obtain target rainfall data corresponding to the location of the target disaster-prone body; According to the target rainfall data and the rainfall warning threshold, an early warning is issued for the waterlogging risk of the target disaster-bearing body.

5. The method according to claim 4, wherein: The interpolation calculation of the meteorological observation data set by using the inverse distance weighted method to obtain target rainfall data corresponding to the position of the target disaster-prone body includes: Where q(p) is the interpolation result corresponding to the position p of the target disaster-prone body, that is, the target rainfall data; q i is the rainfall data of the ith meteorological observation station, w i (p) is the weight of the ith station to the location of the disaster-prone body, and N is the total number of meteorological observation stations whose corresponding distance is less than the preset distance threshold; d i is the distance between the ith meteorological observation station and the target disaster-prone body, h i is the elevation difference between the i-th meteorological observation station and the target disaster-prone body, α is the elevation difference adjustment coefficient, and θ i is the building shielding angle between the i-th meteorological observation station and the target disaster-sustaining body, and β is the building shielding influence coefficient.

6. The method according to claim 1, wherein: The step of generating a target disaster-prone body elevation model including the building height information according to the building height information and the surrounding area elevation model data, and parsing the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model comprises: According to the building height information, the elevation value of each building in the surrounding area elevation model is updated pixel by pixel through superposition matrix operation to obtain the target disaster-prone body elevation model: H total (x,y)=H DEM (x,y)+H building (x,y), In the formula, H total (x, y) is the target disaster-prone body elevation model, which represents the result of superimposing the basic terrain height and building height corresponding to the pixel unit position (x, y); H DEM (x, y) is the basic elevation model, which represents the elevation value distribution of the basic digital elevation model data. It does not include any building information and only reflects the terrain height of the natural terrain at the pixel unit position (x, y); H building (x, y) is the building height information at the corresponding pixel unit position (x, y); Call the hydrological analysis tool to perform flow direction analysis on the elevation model of the target disaster-prone body to determine the corresponding surrounding runoff path: f(x,y)=argmin(H total (x±1,y±1)), Where f(x,y) represents the flow direction of the surrounding runoff path at (x,y), H total (x±1, y±1) represents the distribution of elevation values ​​in the eight adjacent pixel units around (x, y).

7. The method according to claim 6, wherein: The method of correcting the surrounding runoff path according to the road structure data of each road in the surrounding area indicated by the satellite image of the surrounding area to determine the disaster-bearing body water catchment area and the disaster-bearing body water volume area corresponding to the target disaster-bearing body includes: Calculate the correction factor for water flow guidance at different road locations: In the formula, R road (x, y) is the correction coefficient of the road to the water flow, which represents the correction amount of the water flow path at the pixel unit position (x, y); G road represents the slope of the road, η road Represents the material coefficient of the road, W road Indicates the width of the road; Adjust the surrounding runoff path according to the calculated correction factor: f road (x,y)=f(x,y)×R road (x,y), In the formula, f road (x,y) represents the corrected runoff direction at (x,y); The paths that converge to the low-lying area where the target disaster-prone body is located are screened from the surrounding runoff paths to identify the corresponding disaster-prone body water catchment area and disaster-prone body water storage area.

8. According to the method of any one of claims 1 to 7, after obtaining the water accumulation height threshold for the flooding area of ​​the disaster-bearing body, and calculating the rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body according to the water accumulation height threshold and the rainfall-water accumulation relationship, the method further comprises: Obtaining the waterlogging disaster record of the target carrier, and querying the historical waterlogging rainfall corresponding to the waterlogging risk triggering from the waterlogging disaster record; According to the deviation between the historical waterlogging rainfall and the rainfall warning threshold, the rainfall warning threshold is calibrated: Where M historical represents the rainfall when the flood risk is triggered in the flood disaster record, T current represents the rainfall warning threshold calculated based on the relationship between rainfall and water accumulation, and ΔM represents T current With M historical The difference between calibrated Represents the calibrated rainfall warning threshold.

9. A waterlogging risk warning threshold calculation system based on multi-source data analysis, comprising: A data acquisition unit is used to acquire multi-source data of a target disaster-prone body to be analyzed, wherein the multi-source data includes building height information, drainage facility information, elevation model data of the surrounding area, and satellite images of the surrounding area for the disaster-prone body; the surrounding area is an area corresponding to a preset distance resolution around the target disaster-prone body; An elevation data analysis unit, configured to generate an elevation model of a target disaster-prone body including the building height information according to the building height information and the surrounding area elevation model data, and to analyze the surrounding runoff path of the target disaster-prone body from the target disaster-prone body elevation model; A satellite data correction unit is used to correct the surrounding runoff path according to the road structure data of each road in the surrounding area indicated by the satellite image of the surrounding area, so as to determine the disaster-bearing body water catchment area and the disaster-bearing body water volume area corresponding to the target disaster-bearing body; the road structure data includes the slope, material and width of the road; A rainfall-water accumulation fitting unit is used to calculate the rainfall-water accumulation relationship of the target disaster-bearing body according to the drainage facility information, the water catchment area of ​​the disaster-bearing body and the water accumulation area of ​​the disaster-bearing body; the rainfall-water accumulation relationship represents the relationship between the rainfall and the water accumulation depth of the water accumulation area of ​​the disaster-bearing body; The warning threshold calculation unit is used to obtain the water accumulation height threshold for the flooding area of ​​the disaster-bearing body, and calculate the rainfall warning threshold corresponding to the waterlogging risk of the target disaster-bearing body according to the water accumulation height threshold and the rainfall-water accumulation relationship.

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