Flood disaster multi-source information monitoring method, system and electronic equipment

Abstract

The application provides a flood disaster multi-source information monitoring method and system and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: determining a rainfall area and accumulated rainfall according to ground observation rainfall data; identifying a town in the rainfall area, determining a confluence area in the town, and determining a real-time area and an initial water depth of the confluence area according to satellite remote sensing data and a digital elevation model; dividing the rainfall area according to a terrain to obtain a plurality of monitoring areas and a terrain confluence factor of the monitoring areas according to the digital elevation model; when a preset condition is reached, updating real-time data, inputting the real-time data and the terrain confluence factor into a water depth prediction model to obtain a predicted water depth; and monitoring and warning the waterlogging condition of the monitoring area according to the predicted water depth and a preset depth. The predicted water depth can be continuously obtained without relying on satellite remote sensing data, so that the monitoring can be continuously and timely performed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a flood disaster multi-source information monitoring method and system and electronic equipment. BACKGROUND

[0002] It is of great significance to obtain high-precision spatiotemporal distribution information of precipitation for improving the ability of flood disaster prevention, promoting efficient allocation and utilization of water and soil resources, etc. However, due to the combined influence of weather situation, geographical location, topography and human activities, the precipitation aggregation presents complex variability.

[0003] At present, the way to monitor precipitation is increasingly multi-source, mainly including meteorological satellite and weather radar remote sensing inversion, and atmospheric reanalysis. Remote sensing inversion and atmospheric reanalysis can obtain the precipitation on the ground, but due to the running characteristics of the satellite, the precipitation of a certain place can only be obtained during the satellite transit, and the information obtained by monitoring is not timely and continuous, which is not conducive to the monitoring of flood disasters. SUMMARY

[0004] The problem to be solved by the present application is that the existing monitoring method lacks timeliness and continuity, which is not conducive to the monitoring of flood disasters.

[0005] To solve the above problems, in a first aspect, the present application provides a flood disaster multi-source information monitoring method, comprising:

[0006] obtaining satellite remote sensing data, ground observation precipitation data and a digital elevation model;

[0007] determining a precipitation area and a cumulative precipitation amount according to the ground observation precipitation data;

[0008] identifying a town in the precipitation area according to the satellite remote sensing data and the digital elevation model, determining a confluence area in the town and real-time area and initial water depth of the confluence area;

[0009] dividing the precipitation area according to topography according to the digital elevation model to obtain a plurality of monitoring areas and a topographic confluence factor of each monitoring area;

[0010] when a preset condition is reached, updating real-time data, inputting the real-time data and the topographic confluence factor in each monitoring area into a water depth prediction model to obtain a predicted water depth of the confluence area in the corresponding monitoring area, wherein the real-time data includes the cumulative precipitation amount, real-time waterway depth, real-time area and initial water depth;

[0011] monitoring and warning the waterlogging situation of the monitoring area according to the predicted water depth and a preset depth.

[0012] Optionally, the preset condition is that a statistical time length of the accumulated rainfall is equal to a preset time length.

[0013] The updating of the real-time data when the preset condition is reached comprises:

[0014] When the statistical time length of the accumulated rainfall is equal to the preset time length, the predicted water depth of the previous prediction stage is taken as a new initial water depth.

[0015] The real-time area of the confluence area is updated according to the predicted water depth of the previous prediction stage and the digital elevation model.

[0016] The sum of the rainfall between a prediction time point of the previous prediction stage and a prediction time point of the current prediction stage is taken as the accumulated rainfall, wherein the prediction time point of the current prediction stage is the time point when the statistical time length of the accumulated rainfall is equal to the preset time length.

[0017] Optionally, the preset condition is that the accumulated rainfall is equal to a preset accumulated amount.

[0018] The updating of the real-time data when the preset condition is reached comprises:

[0019] When the accumulated rainfall is equal to the preset accumulated amount, the statistical time length of the accumulated rainfall is extracted.

[0020] The statistical time length is taken as the preset time length of the previous prediction stage and is input into the water depth prediction model to re-determine the predicted water depth of the previous prediction stage.

[0021] The predicted water depth of the previous prediction stage is taken as a new initial water depth.

[0022] The real-time area of the confluence area is updated according to the predicted water depth of the previous prediction stage and the digital elevation model.

[0023] The sum of the rainfall between a prediction time point of the previous prediction stage and a prediction time point of the current prediction stage is taken as the accumulated rainfall, wherein the prediction time point of the current prediction stage is the time point when the accumulated rainfall is equal to the preset accumulated amount.

[0024] Optionally, the preset condition is that satellite remote sensing data is re-acquired.

[0025] The updating of the real-time data when the preset condition is reached comprises:

[0026] When the satellite remote sensing data is re-acquired, the real-time area of the confluence area is updated according to the re-acquired satellite remote sensing data.

[0027] According to satellite remote sensing data, a digital elevation model, and real-time area of a confluence area, an initial waterlogging depth of the confluence area is updated, and the updated initial waterlogging depth is taken as a predicted waterlogging depth of a previous prediction stage;

[0028] A sum of precipitation between a prediction time point of the previous prediction stage and a prediction time point of a current prediction stage is taken as accumulated precipitation, where the prediction time point of the current prediction stage is a time point when satellite remote sensing data is reacquired.

[0029] Optionally, when the preset condition is reached, the updating of the real-time data further includes:

[0030] Real-time waterway depth of a drainage channel at the current time point is acquired.

[0031] Optionally, the water depth prediction model is:

[0032]

[0033] wherein, H(t) represents the predicted waterlogging depth in a preset time period t; C represents a comprehensive runoff coefficient; P represents the accumulated precipitation in the preset time period t; Qmax represents a drainage flow limit of the drainage pipeline; S(t) represents the real-time area of the confluence area; and t represents the preset time period. R represents an impervious ground runoff coefficient; P represents an impervious area proportion; R represents a pervious ground runoff coefficient; P represents a pervious area proportion; H0 represents the initial waterlogging depth of the confluence area; and h represents the real-time waterway depth in the drainage pipeline, Hmax represents a height of the drainage pipeline, and β represents a topographic confluence factor.

[0034] Optionally, the monitoring and early warning of the waterlogging situation of the monitoring area according to the predicted waterlogging depth and the preset depth includes:

[0035] When the predicted waterlogging depth is greater than or equal to the preset depth, comprehensive early warning information is generated, and the comprehensive early warning information is pushed to each user terminal in the monitoring area.

[0036] Optionally, the monitoring and early warning of the waterlogging situation of the monitoring area according to the predicted waterlogging depth and the preset depth further includes:

[0037] When the predicted waterlogging depth is less than the preset depth, a submergence boundary of the confluence area is determined according to the real-time area of the confluence area and the digital elevation model;

[0038] According to the position of the protected object and the submergence boundary marked in the digital elevation model, the transverse distance and the longitudinal distance between the protected object and the submergence boundary are determined;

[0039] According to the current predicted water depth and the longitudinal distance, the target water depth is determined;

[0040] According to the current real-time area of the confluence area and the cross-sectional area of the confluence area corresponding to the position of the protected object, the average area is obtained;

[0041] According to the current cumulative precipitation, the average area and the target water depth, the prediction time is determined by inputting into the water depth prediction model;

[0042] According to the satellite remote sensing data, it is judged whether it rains in the prediction time;

[0043] When it rains in the prediction time, and the prediction time is less than the time threshold or the transverse distance is less than the preset distance, point-to-point warning information is generated, and the point-to-point warning information is pushed to the protected object.

[0044] In a second aspect, the present application also provides a flood disaster multi-source information monitoring system, comprising:

[0045] A data acquisition module is configured to acquire satellite remote sensing data, ground observation precipitation data and a digital elevation model;

[0046] A data analysis module is configured to determine a precipitation area and a cumulative precipitation according to the ground observation precipitation data; also configured to identify a town in the precipitation area, determine a confluence area in the town and the real-time area and the initial water depth of the confluence area according to the satellite remote sensing data and the digital elevation model; also configured to divide the precipitation area according to the terrain to obtain a plurality of monitoring areas and a terrain confluence factor of each monitoring area according to the digital elevation model;

[0047] A water depth prediction module is configured to update real-time data when a preset condition is reached, input the real-time data and the terrain confluence factor in each monitoring area into a water depth prediction model to obtain a predicted water depth of the confluence area in the corresponding monitoring area, wherein the real-time data includes the cumulative precipitation, the real-time waterway depth of the drainage channel, the real-time area of the confluence area and the initial water depth of the confluence area; a water level monitoring sensor is arranged in the drainage channel in each monitoring area to collect the real-time waterway depth of the drainage channel;

[0048] A monitoring and warning module is configured to monitor and warn the waterlogging situation of the monitoring area according to the predicted water depth and a preset depth.

[0049] In a third aspect, the present application provides an electronic device comprising a memory and a processor;

[0050] The memory is configured to store the computer program.

[0051] The processor is configured to implement the flood disaster multi-source information monitoring method according to the first aspect when executing the computer program.

[0052] The application provides a flood disaster multi-source information monitoring method, system and electronic equipment. Compared with the prior art, the following beneficial effects are achieved:

[0053] According to the ground observation precipitation data, the precipitation area and the cumulative precipitation amount are determined; according to the satellite remote sensing data and the digital elevation model, the town in the precipitation area is identified, the confluence area in the town and the real-time area and the initial water depth of the confluence area are determined; according to the digital elevation model, the precipitation area is divided according to the terrain, and a plurality of monitoring areas and the terrain confluence factor of each monitoring area are obtained; when the preset condition is reached, the real-time data is updated, and the obtained real-time data and the terrain confluence factor in each monitoring area are input into the water depth prediction model to obtain the predicted water depth of the confluence area in the corresponding monitoring area, for example, the last predicted water depth is combined with the digital elevation model to deduce the real-time area of the confluence area as the input value of the current prediction stage, at this time, the real-time area is obtained without relying on satellite remote sensing data, the predicted water depth can be continuously obtained, the monitoring of the precipitation aggregation can be continuously and timely performed, and the error between the predicted water depth and the actual value can be controlled within an acceptable range, the waterlogging condition of the monitoring area is monitored and warned according to the predicted water depth and the preset depth, and the monitoring of the flood disaster is facilitated. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0055] Figure 1 A flowchart of a flood disaster multi-source information monitoring method provided by an embodiment of the application is shown in the figure.

[0056] Figure 2 A structural schematic diagram of a flood disaster multi-source information monitoring system provided by an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0057] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0058] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the drawings of the specification and specific embodiments.

[0059] As shown in the figure, the flood disaster multi-source information monitoring method provided by the embodiments of the present application comprises: Figure 1

[0060] S1: acquiring satellite remote sensing data, ground observation precipitation data and digital elevation model;

[0061] S2: determining the precipitation area and the cumulative precipitation according to the ground observation precipitation data;

[0062] S3: identifying the town in the precipitation area according to the satellite remote sensing data and the digital elevation model, determining the confluence area in the town and the real-time area and the initial water depth of the confluence area;

[0063] S4: dividing the precipitation area according to the terrain according to the digital elevation model, obtaining a plurality of monitoring areas and the terrain confluence factor of each monitoring area;

[0064] S5: when the preset condition is reached, updating the real-time data, inputting the real-time data and the terrain confluence factor in each monitoring area into the water depth prediction model to obtain the predicted water depth of the confluence area in the corresponding monitoring area, wherein the real-time data comprises the cumulative precipitation, the real-time waterway depth of the drainage channel, the real-time area of the confluence area and the initial water depth of the confluence area; the water level monitoring sensor for collecting the real-time waterway depth of the drainage channel is arranged in the drainage channel in each monitoring area;

[0065] S6: monitoring and warning the waterlogging situation of the monitoring area according to the predicted water depth and the preset depth.

[0066] ​In the optional embodiment, the precipitation area and the accumulated precipitation amount are determined according to the ground observation precipitation data; the towns in the precipitation area are identified according to the satellite remote sensing data and the digital elevation model, the confluence area in the town and the real-time area and the initial water depth of the confluence area are determined; the precipitation area is divided according to the terrain according to the digital elevation model, and a plurality of monitoring areas and the terrain confluence factor of each monitoring area are obtained; when the preset condition is reached, the real-time data are updated, the real-time data and the terrain confluence factor in each monitoring area obtained are input into the water depth prediction model, and the predicted water depth of the confluence area in the corresponding monitoring area is obtained, for example, the real-time area of the confluence area is inversely deduced by using the last predicted water depth and the digital elevation model as the input value of the current prediction stage, at this time, the real-time area is obtained without relying on the satellite remote sensing data, the predicted water depth can be continuously obtained, the monitoring of the precipitation aggregation can be continuously and timely performed, and the error between the predicted water depth and the actual value can be controlled within an acceptable range, the waterlogging situation of the monitoring area is monitored and warned according to the predicted water depth and the preset depth, and the monitoring of the flood disaster is facilitated.

[0067] The following will be described in detail.

[0068] S1: Obtain satellite remote sensing data, ground observation precipitation data and a digital elevation model (DEM).

[0069] Specifically, the acquisition of satellite remote sensing data involves multiple satellite platforms, which are commonly used for land use, weather monitoring, environmental analysis, etc. The satellite remote sensing data can be obtained from the EOSDIS platform of NASA, the Copernicus of ESA, the JAXA of Japan, the Earth Explorer of USGS and the data of Fengyun weather satellites, etc. The ground observation precipitation data can be obtained from the Climate Prediction Center or the China Meteorological Data Network, etc. In addition, the local ground observation stations such as weather stations can also provide ground observation precipitation data for real-time acquisition of the current precipitation situation. The DEM data reflects the terrain elevation information, which can be obtained from the OpenDEM data platform and the NASA DEM data platform, and the SRTM and ASTER GDEM are also commonly used digital elevation models.

[0070] S2: Determine the precipitation area and the accumulated precipitation amount according to the ground observation precipitation data.

[0071] Specifically, the precipitation data of a single station can be used to directly accumulate the precipitation value of the station in a specified time period, and the precipitation value of the station is used to calculate the accumulated precipitation amount of the local area. Or rasterize the precipitation area, analyze the precipitation amount of each grid, and then accumulate the precipitation amount of all grids step by step to obtain the accumulated precipitation amount of the entire precipitation area.

[0072] S3: According to satellite remote sensing data and digital elevation model, identify the town in the precipitation area, determine the catchment area in the town and the real-time area and initial water depth of the catchment area.

[0073] Specifically, the data source for water body information extraction using satellite remote sensing data mainly includes radar remote sensing data. Using radar remote sensing data for water body information extraction, according to the characteristics that the surface of the water body is approximately smooth, the scattering value in the SAR image is low, and it appears as a dark area, the extreme point of the image histogram is solved to obtain the water body segmentation threshold, the part less than the threshold in the image is marked as water body, and the part greater than the threshold is marked as background, forming a binary image. The advantages of this method are fast speed, simple principle, small amount of calculation, and it is suitable for water body extraction of low noise and small SAR image. In addition, the town can be quickly identified from the DEM, and the results of the two identifications are integrated to determine the catchment area (i.e. the area where water has formed) in the town and its surrounding area, and further analyze the real-time area of the catchment area. The catchment area is pasted into the digital elevation model, and the catchment area in the digital elevation model is moved until the area of the catchment area pasted into the digital elevation model is equal to the real-time area analyzed, the position of the catchment area is determined, and the water surface height of the catchment area, i.e. the initial water depth, is obtained.

[0074] S4: According to the digital elevation model, the precipitation area is divided according to the terrain to obtain a plurality of monitoring areas and the terrain runoff factor of each monitoring area.

[0075] Specifically, through the digital elevation model, the terrain of the precipitation area can be known, which can be divided according to the terrain of mountain, hill, basin or plain, or divided according to the overall height difference or slope of the whole region, and divided into different slope grades of terrain, so as to obtain a plurality of monitoring areas and the terrain runoff factor of each monitoring area, so that the terrain runoff factor of each monitoring area can be basically maintained at the same or similar level. The terrain runoff factor β can be determined by the slope-runoff relationship experience table extracted from the digital elevation model (DEM), β is between 0 and 1, indicating the runoff loss efficiency caused by the terrain slope. β=0 represents a completely flat terrain, no runoff loss, and the water depth is not adjusted; β tends to 1, indicating a steep terrain, and the runoff is quickly discharged, and the water depth is significantly reduced. Using GIS tools (such as ArcGIS or QGIS) to calculate the terrain slope of each monitoring area, according to the terrain slope s, the terrain runoff factor β is obtained, β=a·tanh(b·s), a and b are determined by nonlinear regression, or determined by experience, which are constants.

[0076] S5: when the preset condition is reached, updating real-time data, inputting real-time data and terrain confluence factors in each monitoring area into a water depth prediction model to obtain a predicted water depth of a confluence area in the corresponding monitoring area, wherein the real-time data includes an accumulated rainfall, a real-time waterway depth of a drainage channel, a real-time area of the confluence area, and an initial water depth of the confluence area; a water level monitoring sensor is arranged in the drainage channel in each monitoring area to collect the real-time waterway depth of the drainage channel.

[0077] Specifically, the water depth prediction model is:

[0078]

[0079] wherein, H(t) represents a predicted water depth in a preset time t; C represents a comprehensive runoff coefficient; P represents an accumulated rainfall in the preset time t; represents a drainage flow limit of the drainage pipeline; represents a real-time area of the confluence area; t represents a preset time, i.e., a specified continuous rainfall duration; represents an impervious ground runoff coefficient; represents an impervious area proportion; represents a pervious ground runoff coefficient; represents a pervious area proportion; represents an initial water depth of the confluence area; h represents a real-time waterway depth in the drainage pipeline, represents a height of the drainage pipeline, and β represents a terrain confluence factor.

[0080] The drainage flow limit can be calculated by the following formula.

[0081]

[0082] wherein, n represents a pipeline roughness coefficient, represents a water passing section area, and R represents a hydraulic radius, represents a slope of the drainage pipeline. Assuming that the drainage system is a rectangular open channel (such as a roadside sewer), the water depth prediction model can be expressed as: , B represents a width of the drainage channel, and H represents a water depth of the drainage channel.

[0083] For example, in the early stage of rainfall and in a monitoring area with a gentle terrain, it can be assumed that is constant, the drainage channel is in the maximum drainage capacity, , the impervious area proportion of the urban roof is = 0.95, the impervious area proportion of the grassland is = 70%, and the pervious area proportion of the grassland is = 0.2. = 30%, then C = 0.95*0.7 + 0.2*0.3 = 0.725, the cumulative precipitation P = 50mm / hr = 13.89*10 -6 m / s, drainage flow limit = 2m³ / s, real-time area of confluence region = 1km 2 = 10 6 m 2 , rainfall duration t = 2 hours = 7200s. The calculation process of the predicted water depth is as follows.

[0084]

[0085] However, in actual application, as the precipitation increases, the real-time area of the confluence region changes with the water level rising, and the real-time area corresponds to the water depth; in addition, as the weather changes, the cumulative precipitation also changes; in addition, as the water depth and the precipitation increase, the water in the drainage pipeline cannot be discharged in time, and part of the precipitation is retained in the drainage pipeline, causing the water level in the drainage pipeline to gradually rise, so in the water depth prediction model, multiple real-time data are not fixed and change in real time, and are related to each other, so the real-time data input into the water depth prediction model need to be updated to reduce the error between the predicted water depth value and the true value. However, real-time data do not need to be updated at all times, but if the real-time data are regularly updated, the updating method is not flexible enough and cannot change in real time according to the precipitation, so a real-time data updating method is set.

[0086] Specifically, the cumulative precipitation is the total precipitation between two real-time data updating time points, and when flood disaster monitoring is needed, the cumulative precipitation is started from the time point of obtaining satellite remote sensing data, and when the precipitation reaches the preset cumulative amount, the real-time data are input into the water depth prediction model, so that the value that the water depth may reach after t (for example, one hour or several hours) in the future is predicted. From the time point of inputting the real-time data into the water depth prediction model, the cumulative precipitation is re-counted, and after the cumulative precipitation reaches the preset cumulative amount again, the water depth predicted at the previous time point is used as the initial water depth, and the water depth is predicted again using the new cumulative precipitation, and the cycle is repeated.

[0087] There are three real-time data updating methods, the first and second real-time data updating methods are executed when new satellite remote sensing data are not obtained, and the third real-time data updating method is executed when new satellite remote sensing data are obtained. The preset conditions corresponding to the three methods are that the statistical duration of the cumulative precipitation is equal to the preset duration, the cumulative precipitation is equal to the preset cumulative amount, and the satellite remote sensing data are obtained again.

[0088] The first real-time data updating method is as follows: when a preset condition is reached, updating the real-time data includes:

[0089] When the statistical length of the accumulated rainfall is equal to the preset length, the predicted water depth of the previous prediction stage is taken as the new initial water depth.

[0090] According to the predicted water depth of the previous prediction stage and the digital elevation model, the real-time area of the confluence area is updated.

[0091] The sum of the rainfall between the prediction time point of the previous prediction stage and the prediction time point of the current prediction stage is taken as the accumulated rainfall, wherein the prediction time point of the current prediction stage is the time point when the statistical length of the accumulated rainfall is equal to the preset length.

[0092] The real-time waterway depth of the drainage channel at the current time point is obtained, wherein the current time point is the prediction time point of the current prediction stage.

[0093] Specifically, the preset length is set to ten minutes, half an hour or one hour, etc. In the absence of special circumstances, the water depth of the confluence area is regularly predicted. In the process of regular prediction, because no new satellite remote sensing data is obtained and a complete preset length is completed, the predicted water depth of the previous prediction stage is taken as the new initial water depth, and the predicted water depth of the previous prediction stage and the digital elevation model are used to determine the real-time area of the current confluence area. The sum of the rainfall between the previous prediction time point and the current prediction time point is taken as the accumulated rainfall. The real-time waterway depth of the drainage channel collected by the water level monitoring sensor in the drainage channel is obtained in time, and these real-time data obtained by reanalysis are input into the water depth prediction model to obtain the prediction result of the current prediction stage, so as to know the water depth after t time from the current time.

[0094] The second real-time data updating method is as follows: when a preset condition is reached, updating the real-time data includes:

[0095] When the accumulated rainfall is equal to the preset accumulated amount, the statistical length of the accumulated rainfall is extracted.

[0096] The statistical length is taken as the preset length of the previous prediction stage and is input into the water depth prediction model to re-determine the predicted water depth of the previous prediction stage.

[0097] The re-determined predicted water depth of the previous prediction stage is taken as the new initial water depth.

[0098] According to the re-determined predicted water depth of the previous prediction stage and the digital elevation model, the real-time area of the confluence area is updated.

[0099] sum the precipitation between the prediction time point of the last prediction stage and the prediction time point of the current prediction stage as the accumulated precipitation, wherein the prediction time point of the current prediction stage is the time point when the accumulated precipitation is equal to the preset accumulated amount;

[0100] obtain the real-time waterway depth of the drainage channel at the current time point, wherein the current time point is the prediction time point of the current prediction stage.

[0101] Specifically, when the accumulated precipitation is equal to the preset accumulated amount, it indicates that the precipitation is relatively serious at this time, and on the basis of the above regular prediction, a new prediction time point can be added, and the subsequent time points are sequentially moved backward based on the newly added prediction time point. When the accumulated precipitation is equal to the preset accumulated amount before the statistical duration reaches the preset duration, it indicates that the duration of the accumulated precipitation is less than the preset duration, and the prediction water depth obtained by using the preset duration for prediction in the last prediction stage cannot be used in the current prediction stage. It is necessary to re-calculate the prediction result of the last prediction stage, to take the statistical duration of the accumulated precipitation as the preset duration of the last prediction stage, to keep the remaining real-time data of the last prediction stage unchanged, to re-calculate using the water depth prediction model, to obtain the prediction water depth of the last prediction stage, and then to use the re-determined prediction water depth of the last prediction stage as the initial water depth of the current prediction stage. The re-determined prediction water depth of the last prediction stage and the digital elevation model are used to determine the real-time area of the confluence area of the current prediction stage, and the sum of the precipitation between the prediction time point of the last prediction stage and the prediction time point of the current prediction stage is taken as the accumulated precipitation, which is equal to the preset accumulated amount. The real-time waterway depth of the drainage channel collected by the water level monitoring sensor in the drainage channel is obtained, and these real-time data obtained by re-analysis are input into the water depth prediction model to obtain the prediction result of the current prediction stage, so as to know the water depth after t time from the current time for flood disaster monitoring.

[0102] The above two real-time data updating methods can adjust the real-time data in time according to the changes of the external weather and environment, so that the real-time data is more consistent with the actual value, and the error between the prediction value and the true value is reduced. In the absence of satellite remote sensing data, flood disaster prediction can still be carried out using multi-source data, and the situation of data delay, prediction delay or excessive prediction deviation can be avoided.

[0103] The third real-time data updating method is as follows: when the preset condition is reached, the real-time data is updated, which comprises:

[0104] When the satellite remote sensing data is re-obtained, the real-time area of the confluence area is updated according to the re-obtained satellite remote sensing data.

[0105] updating the initial waterlogging depth of the confluence area according to the real-time area of the confluence area obtained from the satellite remote sensing data, the digital elevation model and the updated confluence area, and taking the updated initial waterlogging depth as the predicted waterlogging depth of the last prediction stage;

[0106] summing up the precipitation between the prediction time point of the last prediction stage and the prediction time point of the current prediction stage as the cumulative precipitation, wherein the prediction time point of the current prediction stage is the time point when the satellite remote sensing data is reacquired;

[0107] obtaining the real-time waterway depth of the drainage channel at the current time point, wherein the current time point is the prediction time point of the current prediction stage.

[0108] Specifically, when the satellite passes, the satellite reacquires and returns the satellite remote sensing data of the precipitation area. At this time, the real-time area of the confluence area obtained from the satellite remote sensing data is more close to the true value. Therefore, the third data updating method is inserted again in the first data updating method. At this time, the real-time area of the confluence area at the current time point is determined by using the reacquired satellite remote sensing data. Then, the position of the real-time area in the digital elevation model is determined by using the position correspondence between the satellite remote sensing data and the digital elevation model. The waterlogging depth at the current time point is determined by using the real-time area and the digital elevation model. At this time, the obtained waterlogging depth is not much different from the true value. The waterlogging depth obtained at this time is used to replace the predicted waterlogging depth of the last prediction stage, as the initial waterlogging depth of the current prediction stage. The cumulative precipitation of the current prediction stage is the sum of the precipitation between the prediction time point of the last prediction stage and the time point when the satellite remote sensing data is reacquired. The real-time waterway depth of the drainage channel at the current time point is obtained by using the water level monitoring sensor in the drainage channel. The real-time data obtained by reanalysis is input into the water depth prediction model to obtain the prediction result of the current prediction stage, so as to know the waterlogging depth after t time from the current time, which is used for monitoring the flood disaster. In the third real-time data updating method, the predicted waterlogging depth is corrected by using the reacquired satellite remote sensing data. That is, the third real-time data updating method is inserted on the basis of the first or second real-time data updating method. The predicted value is continuously corrected by using the third data updating method, so that the error between the overall prediction result and the true value is kept within an acceptable range. The above method uses the satellite remote sensing data obtained at a long interval, the digital elevation model, the ground observed precipitation data and the water depth prediction model to continuously, timely and controllable-predict the waterlogging depth of the confluence area, which is beneficial to the monitoring of the flood disaster.

[0109] S6: monitoring and warning the waterlogging situation of the monitoring area according to the predicted waterlogging depth and the preset depth.

[0110] Specifically, when the predicted water depth is greater than or equal to the preset depth, comprehensive warning information is generated, and the comprehensive warning information is pushed to each user terminal in the monitoring area; when the predicted water depth is less than the preset depth, no comprehensive warning information is generated; thereby the entire monitoring area is monitored and warned as a whole.

[0111] In addition, the monitoring and warning of the waterlogging condition of the monitoring area according to the predicted water depth and the preset depth further comprises:

[0112] When the predicted water depth is less than the preset depth, the submergence boundary of the confluence area is determined according to the real-time area of the confluence area and the digital elevation model.

[0113] According to the position of the protected object and the submergence boundary marked in the digital elevation model, the transverse distance and the longitudinal distance between the protected object and the submergence boundary are determined. The transverse distance is the shortest straight line distance between the protected object and the submergence boundary in the horizontal direction.

[0114] According to the current predicted water depth and the longitudinal distance, a target water depth is determined.

[0115] According to the current real-time area of the confluence area and the cross-sectional area of the confluence area corresponding to the position of the protected object, an average area is obtained.

[0116] The current cumulative precipitation, the average area and the target water depth are input into the water depth prediction model to reversely determine the prediction time.

[0117] Specifically, in this step, the average area is used as the real-time area, and the target water depth is used as the prediction result in the water depth prediction model. The water depth prediction model is reversely used to obtain the time required to reach the target water depth, which is used as the prediction time.

[0118] According to the satellite remote sensing data, it is judged whether it rains in the prediction time.

[0119] Specifically, using satellite remote sensing data or ground observation precipitation data, analyzing the rainfall amount of clouds in the air or according to the future rainfall condition predicted by the meteorological bureau, it is judged whether it rains in the prediction time.

[0120] When it rains in the prediction time, and the prediction time is less than a time threshold or the transverse distance is less than a preset distance, point-to-point warning information is generated, and the point-to-point warning information is pushed to the protected object.

[0121] Specifically, when it is still raining in the prediction time, it is further judged whether the prediction time is less than the time threshold or the lateral distance is less than the preset distance; when the prediction time is less than the time threshold or the lateral distance is less than the preset distance, it is indicated that the inundation boundary of the accumulated water may reach the location of the protected object in the prediction time, and since the protected object may be a key enterprise or unit, etc., large equipment or machines, etc. are arranged inside, in order to give the protected object some evacuation time, it is necessary to make an early warning to the protected object in advance, generate point-to-point early warning information, and push the point-to-point early warning information to the protected object, so that the protected object has more time to evacuate safely, which is beneficial to the protection of property safety.

[0122] As shown in Figure 2 The flood disaster multi-source information monitoring system provided by the embodiment of the application comprises:

[0123] The data acquisition module 100 is configured to acquire satellite remote sensing data, ground observation precipitation data and a digital elevation model.

[0124] The data analysis module 200 is configured to determine a precipitation area and accumulated precipitation according to the ground observation precipitation data; and identify a town in the precipitation area, determine a confluence area in the town and real-time area and initial accumulated water depth of the confluence area according to the satellite remote sensing data and the digital elevation model; and divide the precipitation area according to terrain to obtain a plurality of monitoring areas and a terrain confluence factor of each monitoring area according to the digital elevation model.

[0125] The water depth prediction module 300 is configured to update real-time data when a preset condition is reached, input the real-time data and the terrain confluence factor in each monitoring area into a water depth prediction model to obtain a predicted accumulated water depth of the confluence area in the corresponding monitoring area, wherein the real-time data comprises the accumulated precipitation, real-time waterway depth of a drainage channel, real-time area of the confluence area and initial accumulated water depth of the confluence area; and an accumulated water level monitoring sensor is arranged in the drainage channel in each monitoring area to collect the real-time waterway depth of the drainage channel.

[0126] The monitoring and early warning module 400 is configured to monitor and early warn the accumulated water condition of the monitoring area according to the predicted accumulated water depth and a preset depth.

[0127] In the optional embodiment of the application, the water depth prediction module 300 further comprises a first updating unit, a second updating unit and a third updating unit.

[0128] The first updating unit is configured to, when the statistical duration of the accumulated rainfall is equal to the preset duration, take the predicted water depth of the previous prediction stage as a new initial water depth, update the real-time area of the confluence area according to the predicted water depth of the previous prediction stage and the digital elevation model, and take the sum of the rainfall between the prediction time point of the previous prediction stage and the prediction time point of the current prediction stage as the accumulated rainfall.

[0129] The second updating unit is configured to, when the accumulated rainfall is equal to the preset accumulated amount, extract the statistical duration of the accumulated rainfall, take the statistical duration as the preset duration of the previous prediction stage, input the statistical duration into the water depth prediction model, and re-determine the predicted water depth of the previous prediction stage. The predicted water depth of the previous prediction stage is taken as a new initial water depth. The real-time area of the confluence area is updated according to the predicted water depth of the previous prediction stage and the digital elevation model. The sum of the rainfall between the prediction time point of the previous prediction stage and the prediction time point of the current prediction stage is taken as the accumulated rainfall.

[0130] The third updating unit is configured to, when the satellite remote sensing data is re-acquired, update the real-time area of the confluence area according to the re-acquired satellite remote sensing data, update the initial water depth of the confluence area according to the satellite remote sensing data, the digital elevation model and the updated real-time area of the confluence area, and take the updated initial water depth as the predicted water depth of the previous prediction stage. The sum of the rainfall between the prediction time point of the previous prediction stage and the time point at which the satellite remote sensing data is re-acquired is taken as the accumulated rainfall of the current prediction stage.

[0131] The electronic device provided in the embodiments of the present application comprises a memory and a processor. The memory is configured to store a computer program. The processor is configured to implement the flood disaster multi-source information monitoring method as described above when the computer program is executed.

[0132] The computer readable storage medium provided in the embodiments of the present application has the computer program stored thereon. When the computer program is executed by the processor, the flood disaster multi-source information monitoring method as described above is implemented.

[0133] In the embodiments, the electronic device and the computer readable storage medium have similar beneficial effects to those of the flood disaster multi-source information monitoring method, which will not be repeated here.

[0134] An electronic device that can be a server or a client of the present application will now be described, which is an example of a hardware device that can be applied to aspects of the present application. The electronic device is intended to represent a wide variety of digital electronic computing devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computing devices. The electronic device can also represent a wide variety of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0135] The electronic device includes a computing unit that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, the ROM, and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0136] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), or a random access memory (RAM). In the present application, the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0137] It is to be noted that, in the present text, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0138] The above examples are merely intended for describing and illustrating the technical solutions of the present application, but not for limiting the same; even though the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing examples, or make equivalent replacements to some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring multi-source information on flood disasters, characterized in that, include: Acquire satellite remote sensing data, ground-based precipitation data, and digital elevation models; Based on ground-based precipitation data, the precipitation areas and cumulative precipitation amounts were determined. Based on satellite remote sensing data and digital elevation models, towns within the precipitation area are identified, and the catchment areas within the towns, as well as the real-time area and initial water depth of the catchment areas, are determined. Based on the digital elevation model, the precipitation area is divided according to the terrain, resulting in multiple monitoring areas and the topographic runoff factor for each monitoring area. The topographic runoff factor represents the runoff loss efficiency due to the terrain slope. When the preset conditions are met, the real-time data is updated, and the real-time data and topographic confluence factor of each monitoring area are input into the water depth prediction model to obtain the predicted water depth of the confluence area in the corresponding monitoring area. The real-time data includes the cumulative precipitation, the real-time waterway depth of the drainage pipe, the real-time area, and the initial water depth. Based on the predicted water depth and the preset depth, the water accumulation situation in the monitoring area is monitored and early warning is issued; The water depth prediction model is as follows: in, H(t) represents the predicted water depth within a preset time period t; C represents the comprehensive runoff coefficient; P represents the cumulative precipitation within a preset time period t; Indicates the drainage flow limit of the drainage pipe; The current area represents the real-time area of ​​the confluence region; t represents the preset duration. This represents the runoff coefficient of impermeable surfaces; Indicates the proportion of impermeable area; Indicates the runoff coefficient of permeable pavement; Indicates the percentage of permeable area; The initial water depth in the catchment area is indicated by h; the real-time water depth within the drainage pipe is indicated by h. β represents the height of the drainage pipe, and β represents the topographic runoff factor. The monitoring and early warning system for water accumulation in the monitoring area based on the predicted water depth and the preset depth also includes: When the predicted water depth is less than the preset depth, the flooding boundary of the catchment area is determined based on the real-time area and digital elevation model of the catchment area. Based on the location of the protected object and the inundation boundary marked in the digital elevation model, determine the lateral and longitudinal distances between the protected object and the inundation boundary. Determine the target water depth based on the current predicted water depth and longitudinal distance; The average area is obtained by combining the current real-time area of ​​the confluence zone with the cross-sectional area of ​​the confluence zone corresponding to the location of the protected object; The current cumulative precipitation, average area, and target water depth are input into the water depth prediction model, and the prediction time is determined in reverse. The average area is used as the real-time area, and the target water depth is used as the prediction result in the water depth prediction model. The water depth prediction model is used in reverse to obtain the time required to reach the target water depth, and this time is used as the prediction time. Based on satellite remote sensing data, determine whether precipitation will continue within the predicted timeframe; If precipitation persists within the predicted time period, and the predicted time is less than the time threshold or the lateral distance is less than the preset distance, a point-to-point early warning message is generated and pushed to the protected object.

2. The method for monitoring multi-source information on flood disasters as described in claim 1, characterized in that, The preset condition is that the statistical duration of the cumulative precipitation is equal to the preset duration; The step of updating real-time data when preset conditions are met includes: When the statistical duration of cumulative precipitation equals the preset duration, the predicted water depth of the previous forecast stage will be used as the new initial water depth. Update the real-time area of ​​the catchment area based on the predicted water depth and digital elevation model from the previous prediction phase. The total precipitation between the forecast time point of the previous forecast stage and the forecast time point of the current forecast stage is taken as the cumulative precipitation. The forecast time point of the current forecast stage is the time point when the statistical duration of the cumulative precipitation equals the preset duration.

3. The method for monitoring multi-source information on flood disasters as described in claim 1, characterized in that, The preset condition is that the cumulative precipitation equals the preset cumulative amount; The step of updating real-time data when preset conditions are met includes: When the cumulative precipitation equals the preset cumulative amount, the statistical duration for extracting the cumulative precipitation is determined. The statistical duration is used as the preset duration of the previous prediction stage and input into the water depth prediction model to redetermine the predicted water depth of the previous prediction stage. Use the predicted water depth from the previous prediction stage as the new initial water depth; Update the real-time area of ​​the catchment area based on the predicted water depth and digital elevation model from the previous prediction phase. The total precipitation between the forecast time point of the previous forecast stage and the forecast time point of the current forecast stage is taken as the cumulative precipitation. The forecast time point of the current forecast stage is the time point when the cumulative precipitation equals the preset cumulative amount.

4. The method for monitoring multi-source information on flood disasters as described in claim 1, characterized in that, The preset condition is to reacquire satellite remote sensing data; The step of updating real-time data when the preset conditions are met includes: When satellite remote sensing data is regained, the real-time area of ​​the confluence region is updated based on the regained satellite remote sensing data; Based on satellite remote sensing data, digital elevation model and updated real-time area of ​​the catchment area, update the initial water depth of the catchment area, and use the updated initial water depth as the predicted water depth of the previous prediction stage, and as the initial water depth of the current prediction stage. The cumulative precipitation is the sum of the precipitation between the forecast time point of the previous forecast stage and the forecast time point of the current forecast stage. The forecast time point of the current forecast stage is the time point when the satellite remote sensing data is obtained again.

5. The method for monitoring multi-source information on flood disasters as described in any one of claims 2-4, characterized in that, The step of updating real-time data when the preset conditions are met also includes: Get the real-time waterway depth of the drainage pipe at the current time point.

6. The method for monitoring multi-source information on flood disasters as described in claim 1, characterized in that, The monitoring and early warning system for water accumulation in the monitored area based on the predicted water depth and the preset depth includes: When the predicted water depth is greater than or equal to the preset depth, a comprehensive early warning message is generated and pushed to every user terminal within the monitoring area.

7. A multi-source information monitoring system for flood disasters, characterized in that, The flood disaster multi-source information monitoring system, as described in any one of claims 1 to 6, comprises: The data acquisition module is used to acquire satellite remote sensing data, ground-based precipitation data, and digital elevation models; The data analysis module is used to determine the precipitation area and cumulative precipitation based on ground-based precipitation observation data; it is also used to identify towns within the precipitation area based on satellite remote sensing data and digital elevation models, determine the catchment area within the town, as well as the real-time area and initial water depth of the catchment area; and it is also used to divide the precipitation area according to the terrain based on the digital elevation model, obtaining multiple monitoring areas and the topographic catchment factor of each monitoring area. The water depth prediction module is used to update real-time data when preset conditions are met. It inputs real-time data and topographic confluence factors from each monitoring area into the water depth prediction model to obtain the predicted water depth of the confluence area in the corresponding monitoring area. The real-time data includes cumulative precipitation, real-time waterway depth of drainage pipes, real-time area of ​​the confluence area, and initial water depth of the confluence area. Each monitoring area is equipped with a water level monitoring sensor in the drainage pipes to collect the real-time waterway depth of the drainage pipes. The monitoring and early warning module is used to monitor and issue early warnings about water accumulation in the monitored area based on the predicted water depth and the preset depth.

8. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the flood disaster multi-source information monitoring method as described in any one of claims 1 to 6 when executing the computer program.

Images