Reservoir basin flood disaster real-time monitoring method and system based on satellite remote sensing

Abstract

The invention discloses a reservoir basin flood disaster real-time monitoring method and system based on satellite remote sensing, and the method comprises the steps: obtaining the satellite remote sensing data of a reservoir basin, the satellite remote sensing data comprises reservoir drainage basin precipitation, reservoir drainage basin temperature, reservoir drainage basin water level, soil saturation, reservoir drainage basin gradient, tree density and house density; a flood risk assessment model is set, an assessment value of flood occurrence is calculated according to the satellite remote sensing data, and the flood risk assessment model comprises an influence function of rainfall on flood ponding; and setting a threshold value of flood occurrence, comparing the evaluation value of the flood occurrence with the threshold value of the flood occurrence, and when the evaluation value of the flood occurrence exceeds the threshold value of the flood occurrence, judging that a flood dangerous case will occur and sending out alarm information.

Description

Technical Field

[0001] The present invention belongs to the technical field of flood monitoring, and more specifically, relates to a real-time monitoring method and system for flood disasters in a reservoir basin based on satellite remote sensing. Background Art

[0002] The occurrence of flood disasters generally has the characteristics of strong suddenness, great harmfulness, and wide spatio-temporal distribution. To monitor, evaluate, rescue, and arrange post-disaster reconstruction of flood disasters, it is necessary to collect and feedback relevant information on flood disasters in a timely, accurate, and reliable manner. However, the traditional information collection means, processes, and levels mainly based on manual work have been difficult to meet the needs of flood control and disaster resistance. The remote sensing technology developed in the 1960s has been increasingly applied in the research of flood disasters due to its advantages such as large observation range, large amount of information obtained, high speed, good real-time performance, and strong dynamics. Many scholars have conducted research on the monitoring and evaluation of flood disasters using remote sensing technology and achieved a series of research results. When a flood disaster occurs, quickly, dynamically, and accurately extracting water body information and determining the flood inundation area and the degree of disaster is of great significance for the government to carry out rescue work. With its high repetition frequency and large-scale observation ability, remote sensing technology can provide a large amount of real-time information on the inundation process in flood areas for decision-making departments. By comprehensively using remote sensing satellite data, realizing large-scale, all-weather, all-day, and dynamic disaster monitoring, rapid disaster emergency response, and accurate disaster assessment can make the civil disaster reduction work in China more scientific and modern, and ensure the sustainable and stable development of the national economy and society.

[0003] However, there is no technical solution in the prior art that can accurately predict flood disasters in a reservoir basin. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a real-time monitoring method for flood disasters in a reservoir basin based on satellite remote sensing, including:

[0005] Obtaining satellite remote sensing data of the reservoir basin, where the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density, and house density;

[0006] Setting up a flood risk assessment model, and calculating an assessment value of flood occurrence according to the satellite remote sensing data, where the flood risk assessment model includes: an influence function of precipitation on flood waterlogging;

[0007] Setting a threshold for flood occurrence, comparing the assessment value of flood occurrence with the threshold for flood occurrence, and when the assessment value of flood occurrence exceeds the threshold for flood occurrence, determining that a flood danger will occur and sending an alarm message.

[0008] Furthermore, the flood risk assessment model includes:

[0009]

[0010] Among them, F(t) is the assessment value of flood occurrence at time t, Z(t) is the influence function of precipitation on floodwater accumulation at time t, λ 1 is the first adjustment factor of the flood risk assessment model, θ 1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ 2 is the third adjustment factor of the flood risk assessment model, λ 3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, t k is the acquisition time point of the k-th historical satellite remote sensing data, σ k is the adjustment factor of the k-th acquisition time point, p′ is the number of historical time periods, C n′ is the weight of the n′-th time period, P n′ (t) is the precipitation of the reservoir basin at time t in the n′-th time period, T n′ (t) is the temperature of the reservoir basin at time t in the n′-th time period, γ n′ is the adjustment factor of the n′-th time period.

[0011] Furthermore, the influence function Z(t) of precipitation on floodwater accumulation at time t includes:

[0012]

[0013] Among them, Factor i (t) is the i-th comprehensive environmental factor at time t, P(t) is the precipitation of the reservoir basin at time t, T(t) is the temperature of the reservoir basin at time t, S(t) is the soil saturation at time t, A j (t) is the j-th regional local factor at time t, μ j is the first adjustment factor of the j-th region, H(t) is the water level of the reservoir basin at time t, λ′ j is the second adjustment factor of the j-th region, α′ is the adjustment factor of the influence function Z(t).

[0014] Furthermore, the water flow rate index V(t) of the reservoir basin at time t includes:

[0015]

[0016] Among them, α1 α, the first adjustment factor for the water flow rate index 2 D, the second adjustment factor for the water flow rate index k′ ξ(t) is the k'-th local hydrogeological factor at time t, ξ is the third adjustment factor for the water flow rate index, and Slope(x, y, t) is the slope at the position (x, y) in the reservoir basin at time t.

[0017] Furthermore, the i-th comprehensive environmental factor Factor i (t) includes:

[0018]

[0019] where Factor 1 (t) is the first comprehensive environmental factor at time t, Slope(x, y) is the slope at the position (x, y) in the reservoir basin, Distance(x, y) is the distance from the center of the reservoir basin to the position (x, y), Factor 2 (t) is the second comprehensive environmental factor at time t, S max is the maximum value of soil saturation, β' is the adjustment factor for the second comprehensive environmental factor, Factor 3 (t) is the third comprehensive environmental factor at time t, Ur(x, y) is the tree density at the position (x, y), and γ'' is the adjustment factor for the third comprehensive environmental factor.

[0020] Furthermore, the j-th regional local factor A j (t) includes:

[0021]

[0022]

[0023] where A 1 (t) is the first regional local factor at time t, H max is the maximum water level of the reservoir basin, α'' is the adjustment factor for the first regional local factor, A 2 (t) is the second regional local factor at time t, β'' is the adjustment factor for the second regional local factor, A 3 (t) is the third regional local factor at time t, θ 1 is the first adjustment factor for the third regional local factor, LU(x, y) is the housing density at the position (x, y), θ 2 is the second adjustment factor for the third regional local factor.

[0024] Further, the k'-th local hydrogeological factor D k′ (t) includes:

[0025]

[0026] Among them, D 1 (t) is the 1st local hydrogeological factor at time t, θ 3 is the adjustment factor of the 1st local hydrogeological factor, D 2 (t) is the 2nd local hydrogeological factor at time t, a″′ is the adjustment factor of the 2nd local hydrogeological factor, Obstacle(x, y) is the volume of the blocking object at the position (x, y), D 3 (t) is the 3rd local hydrogeological factor at time t, γ″′ is the first adjustment factor of the 3rd local hydrogeological factor, θ 4 is the second adjustment factor of the 3rd local hydrogeological factor.

[0027] The present invention also proposes a real-time monitoring system for flood disasters in a reservoir basin based on satellite remote sensing, including:

[0028] A satellite remote sensing data acquisition module for acquiring satellite remote sensing data of the reservoir basin. Among them, the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density, and house density;

[0029] A model setting module for setting a flood risk assessment model and calculating an assessment value of flood occurrence according to the satellite remote sensing data. Among them, the flood risk assessment model includes: an influence function of precipitation on floodwater accumulation;

[0030] An alarm module for setting a threshold for flood occurrence, comparing the assessment value of flood occurrence with the threshold for flood occurrence, and when the assessment value of flood occurrence exceeds the threshold for flood occurrence, determining that a flood risk will occur and sending an alarm message.

[0031] Further, the flood risk assessment model includes:

[0032]

[0033] Among them, F(t) is the assessment value of flood occurrence at time t, Z(t) is the influence function of precipitation on floodwater accumulation at time t, λ 1 is the first adjustment factor of the flood risk assessment model, θ 1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ 2 is the third adjustment factor of the flood risk assessment model, λ3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, and t k is the acquisition time point of the k-th historical satellite remote sensing data, and σ k is the adjustment factor of the k-th acquisition time point, p′ is the number of historical time periods, and C n′ is the weight of the n′-th time period, and P n′ (t) is the precipitation of the reservoir basin at time t in the n′-th time period, and T n′ (t) is the temperature of the reservoir basin at time t in the n′-th time period, and γ n′ is the adjustment factor of the b′-th time period.

[0034] Furthermore, the influence function Z(t) of precipitation on flood ponding at time t includes:

[0035]

[0036] Among them, Factor i (t) is the i-th comprehensive environmental factor at time t, P(t) is the precipitation of the reservoir basin at time t, T(t) is the temperature of the reservoir basin at time t, S(t) is the soil saturation at time t, and A j (t) is the j-th regional local factor at time t, and μ j is the first adjustment factor of the j-th region, H(t) is the water level of the reservoir basin at time t, and λ′ j is the second adjustment factor of the j-th region, and α′ is the adjustment factor of the influence function Z(t).

[0037] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following beneficial effects are obtained:

[0038] The present invention combines satellite remote sensing data and sets up a flood risk assessment model, which can accurately predict flood disasters in the reservoir basin, thereby avoiding flood disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is the flowchart of the method in Embodiment 1 of the present invention;

[0040] Figure 2 is the system structure diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] In order to better understand the above technical solutions, the following will describe the above technical solutions in detail in conjunction with the accompanying drawings of the specification and specific embodiments.

[0042] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a storage medium, and a display screen. Among them, at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0043] The processor may include one or more processing cores. The processor connects various parts within the entire terminal using various interfaces and lines, and by running or executing instructions, programs, code sets, or instruction sets stored in the storage medium, as well as calling data stored in the storage medium, it executes various functions of the terminal and processes data.

[0044] The storage medium may include a random access memory (RAM), and may also include a read-only memory (ROM). The storage medium can be used to store instructions, programs, code, code sets, or instructions.

[0045] The display screen is used to display the user interfaces of various applications.

[0046] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be elaborated here.

[0047] Embodiment 1

[0048] As Figure 1 shown, an embodiment of the present invention proposes a real-time monitoring method for flood disasters in a reservoir basin based on satellite remote sensing, including:

[0049] Step 101, obtaining satellite remote sensing data of the reservoir basin, where the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density, and house density;

[0050] Step 102, setting up a flood risk assessment model, and calculating an assessment value of flood occurrence according to the satellite remote sensing data, where the flood risk assessment model includes: an influence function of precipitation on floodwater accumulation;

[0051] Specifically, the flood risk assessment model includes:

[0052]

[0053] Among them, F(t) is the evaluation value of flood occurrence at time t, Z(t) is the influence function of precipitation on flood waterlogging at time t, λ 1 is the first adjustment factor of the flood risk assessment model, λ 1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ 2 is the third adjustment factor of the flood risk assessment model, λ 3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, t k is the acquisition time point of the k-th historical satellite remote sensing data, σ k is the adjustment factor of the k-th acquisition time point, p′ is the number of historical time periods, C n′ is the weight of the n′-th time period, P n′ (t) is the precipitation of the reservoir basin at time t in the n′-th time period, T n′ (t) is the temperature of the reservoir basin at time t in the n′-th time period, γ n′ is the adjustment factor of the n′-th time period.

[0054] The reflection coefficient R(t) of the reservoir basin at time t is:

[0055]

[0056] Among them, NIR(t) is the reflectance of the near-infrared band at time t, and RED(t) is the reflectance of the red band at time t.

[0057] Specifically, the influence function Z(t) of precipitation on flood waterlogging at time t includes:

[0058]

[0059] Among them, Factor i (t) is the i-th comprehensive environmental factor at time t, P(t) is the precipitation of the reservoir basin at time t, T(t) is the temperature of the reservoir basin at time t, S(t) is the soil saturation at time t, A j (t) is the j-th regional local factor at time t, μ j is the first adjustment factor of the j-th region, H(t) is the water level of the reservoir basin at time t, λ′ j is the second adjustment factor of the j-th region, and α′ is the adjustment factor of the influence function Z(t).

[0060] Specifically, the water flow rate index V(t) of the reservoir basin at time t includes:

[0061]

[0062] Among them, α 1 is the first adjustment factor of the water flow rate index, and α 2 is the second adjustment factor of the water flow rate index. D k′ (t) is the k'-th local hydrogeological factor at time t, ξ is the third adjustment factor of the water flow rate index, and Slope(x, y, t) is the slope at the position (x, y) in the reservoir basin at time t.

[0063] Specifically, the i-th comprehensive environmental factor Factor i (t) at time t includes:

[0064]

[0065] Among them, Factor 1 (t) is the first comprehensive environmental factor at time t, Slope(x, y) is the slope at the position (x, y) in the reservoir basin, Distance(x, y) is the distance from the center of the reservoir basin to the position (x, y), and Factor 2 (t) is the second comprehensive environmental factor at time t, S max is the maximum value of soil saturation, β' is the adjustment factor of the second comprehensive environmental factor, and Factor 3 (t) is the third comprehensive environmental factor at time t, Ur(x, y) is the tree density at the position (x, y), and γ″ is the adjustment factor of the third comprehensive environmental factor.

[0066] Specifically, the j-th regional local factor A j (t) at time t includes:

[0067]

[0068]

[0069] Among them, A 1 (t) is the first regional local factor at time t, H max is the maximum water level of the reservoir basin, α″ is the adjustment factor of the first regional local factor, and A 2 (t) is the second regional local factor at time t, β″ is the adjustment factor of the second regional local factor, and A 3 (t) is the third regional local factor at time t, θ 1 is the first adjustment factor of the third regional local factor, LU(x, y) is the house density at the position (x, y), and θ 2The second adjustment factor for the third regional local factor.

[0070] Specifically, the k'-th local hydrogeological factor D k′ (t) at time t includes:

[0071]

[0072] Among them, D 1 (t) is the first local hydrogeological factor at time t, θ 3 is the adjustment factor of the first local hydrogeological factor, D 2 (t) is the second local hydrogeological factor at time t, a″′ is the adjustment factor of the second local hydrogeological factor, Obstacle(x, y) is the volume of the blocking object at position (x, y), D 3 (t) is the third local hydrogeological factor at time t, γ″′ is the first adjustment factor of the third local hydrogeological factor, θ 4 is the second adjustment factor of the third local hydrogeological factor.

[0073] Step 103: Set the threshold for flood occurrence, compare the flood occurrence evaluation value with the flood occurrence threshold. When the flood occurrence evaluation value exceeds the flood occurrence threshold, it is determined that a flood danger will occur, and an alarm message is sent.

[0074] The following is a specific example of this embodiment, as follows:

[0075] Data collection and processing:

[0076] 1. Data collection

[0077] 1.1 Local data collection

[0078] For the data stored locally in the system deployment, a method of file reading is designed to collect observation data and scan data from the data terminal in real time or at regular intervals. Since the system collects, shares, and distributes the data stored locally in the form of files, the FTP method is used to implement the transmission of file data. FTP allows users to communicate with another host in the form of file operations (such as file addition, deletion, modification, query, transmission, etc.). However, the user does not actually log in to the computer they want to access and become a full user. The FTP program can be used to access remote resources to achieve the two-way transmission of files by the user, even if the two computers may be equipped with different operating systems and file storage methods.

[0079] 1.2 Tianqing data collection

[0080] For the radar-based data product data stored in Tianqing, due to its confidential nature, a standardized API data interface program is designed to implement Tianqing data acquisition. Since Tianqing provides the TCP protocol, the system can request the data interface provided by Tianqing and accept the returned data through HTTP requests, thus achieving the acquisition of Tianqing data without passing through any third-party data acquisition platform.

[0081] 1.3 External Unit Data Acquisition

[0082] For the data acquisition of external network units, the external unit can provide appropriate data interfaces or APIs, enabling the external unit to transmit data to the system in a secure manner. Ensure the use of secure authentication and authorization mechanisms on the API, such as using tokens or keys for authentication, and verify the validity and integrity of the incoming data. Consider using secure data transfer protocols such as HTTPS to encrypt the security of data during transmission.

[0083] 2. Data Processing

[0084] The data used by the system mainly includes meteorological station, hydrological station, satellite station, radar station, and real-time analysis grid product data. The specific data processing methods are as follows:

[0085] 2.1 Satellite Data Processing

[0086] The received raw data needs to be preprocessed, including noise removal, geometric distortion correction, atmospheric correction, etc. These preprocessing steps aim to improve the quality and accuracy of the data. If the satellite captures multiple images, they need to be registered to ensure they are aligned in the same coordinate system. Registration is usually performed using ground control points or feature point matching methods. If the satellite is equipped with multiple sensors, the data obtained by them can be fused to obtain more dimensional and information-rich remote sensing data. In addition, extracting useful ground object information from remote sensing images is one of the important tasks of data processing. Through image processing and computer vision algorithms, operations such as water body feature extraction, target detection and classification can be performed to identify and extract water bodies. The processed remote sensing data can be stored and managed for subsequent analysis and applications. At the same time, data sharing can also be carried out for use by other research institutions, government departments or the public.

[0087] 2.2 Radar Data Processing

[0088] The collected radar-based data is parsed through program decoding means according to the base data storage format, and finally the observed values of the corresponding physical parameters are obtained, which serve as the data support for the application display of radar data. The main physical parameters include: reflectivity factor, velocity, spectral width, etc. In the construction of this system, if radar data is used, the reflectivity factor is mainly used as the physical parameter.

[0089] 2.3 Meteorological and Hydrological Station Data

[0090] The collected data may contain noise, missing values, or outliers. Before further analysis, the data needs to be cleaned. This includes removing invalid data, filling in missing values, correcting outliers, etc. After cleaning, various data analysis tasks can be performed, such as statistical analysis, trend analysis, correlation analysis, etc. These analyses can help understand the variation patterns of meteorological and hydrological parameters, extract useful information, and support prediction and decision-making.

[0091] 2.3 Real-time Analysis Grid Product Data

[0092] Most of the product data is stored in the netCDF format or the grib2 format. Such formats can efficiently store, manage, retrieve, and distribute network data. Another major advantage of this data format is its self-describing feature, which can write information such as data types, creation time, data range, etc. into the file. When data users do not have a data format description, they can obtain the data description by reading the file attributes, which is convenient for use.

[0093] 3. Real-time Monitoring

[0094] The real-time monitoring module is a key component of the system construction. It can integrate rainfall, water level, and underlying surface conditions, and comprehensively display ground-based, airborne, and space-based data on a single map.

[0095] 3.1 Rainfall Monitoring

[0096] Based on the rainfall observation data of ground meteorological stations, satellite remote sensing data, radar observation data, and real-time analysis CLDAS data, multiple data sources are comprehensively utilized to monitor and analyze rainfall conditions, obtain the spatio-temporal distribution characteristics of rainfall, and be able to reflect the cumulative rainfall, rainfall grade, rainfall intensity, etc. And using GIS technology and statistical analysis methods, analyze the spatio-temporal distribution of rainfall, and display the distribution characteristics of rainfall in different regions and time periods by drawing rainfall color patch maps and other forms.

[0097] 3.2 Precipitation Monitoring Based on Meteorological Stations

[0098] Meteorological stations are usually distributed in different geographical locations, including cities, villages, mountains, plains, etc., to cover a wide area. They are divided into national stations and regional stations, and can obtain real-time data, providing the current precipitation situation. By processing and analyzing the precipitation data observed by meteorological stations, statistical characteristics of precipitation can be obtained, such as total precipitation, average precipitation intensity, maximum precipitation, etc. The precipitation in unobserved areas can also be estimated by interpolation methods based on the observed data of multiple meteorological stations. Commonly used interpolation methods include inverse distance weighted interpolation, Kriging interpolation, etc., to generate grid rainfall data with spatial distribution characteristics, and render color-coded precipitation maps at different levels based on GIS maps.

[0099] 3.3 Areal Rainfall Monitoring

[0100] Areal precipitation is the average precipitation of a region, which represents the actual precipitation of the region. It can describe the precipitation of the region more objectively than traditional single-point precipitation. It is also the input field of all flood forecasting systems and has a close relationship with the flow and water level changes of rivers in a specific area. In particular, heavy rain with areal rainfall greater than the critical value is the direct factor leading to floods.

[0101] Currently, there are various methods for estimating areal precipitation. The more commonly used method is to first interpolate the precipitation data of each rain gauge station to obtain the precipitation data of grid points at other ungauged stations, and then use the Thiessen polygon method to calculate the areal precipitation of a certain region. The Thiessen polygon method is also called the perpendicular bisector method or weighted average method. This method first calculates the area weight coefficient of each rain gauge station, and then accumulates the product of the rainfall at each station and the area weight of the station it occupies to obtain the areal rainfall.

[0102] Method for calculating the weight number of rain gauge stations: Connect the adjacent rain gauge stations in the basin with straight lines, draw the perpendicular bisectors of each connection line. These bisectors intersect to divide the basin into several polygons, and there is a rain gauge station in each polygon. Let each rain gauge station use the polygon area where it is located as the control area △A, and the ratio of △A to the total area A of the basin is the weight number of the rain gauge station. The average areal rainfall of the basin is:

[0103]

[0104] In the formula, f 1 , f 2 …f n are the weight numbers calculated by the polygon areas of each rain gauge station respectively;

[0105] P 1 , P 2 …P k , are the rainfall amounts at each measuring station during the same period and the average areal rainfall of the basin respectively.

[0106] This method takes into account the weights of stations when calculating areal precipitation, with relatively high accuracy, and is particularly suitable for river basins with unevenly distributed stations. The technical difficulty in automating the areal precipitation calculation program on a computer lies in determining the uniqueness of the polygon surrounding each station and the area of any polygon.

[0107] 3.4 Precipitation Monitoring Based on Satellite Remote Sensing Data

[0108] The process of estimating surface precipitation from satellite observation data. By analyzing precipitation-related information in satellite remote sensing data, such as cloud images, thermal infrared radiation data, microwave brightness temperature data, etc., the precipitation in different regions can be estimated, thus realizing the monitoring and prediction of precipitation over a large area.

[0109] 3.5 Precipitation Monitoring Based on Radar Data

[0110] Based on radar observation data and precipitation observation data, spatial distribution products of precipitation intensity and cumulative precipitation estimated by the Z-R relationship method are obtained, realizing refined precipitation monitoring with high temporal resolution.

[0111] Z-R relationship: Z = A·Rb

[0112] where R is the estimated rainfall value (unit: mm / h), and Z is the radar reflectivity factor corresponding to the station (unit: mm6·m-3).

[0113] 3.6 Precipitation Monitoring Based on Real-Time Grid Assimilation Analysis Products

[0114] Real-time grid assimilation analysis products (such as: CLDAS) have the characteristics of high spatio-temporal resolution, can provide detailed precipitation distribution information, and the timeliness of their data can ensure the real-time update of precipitation monitoring products.

[0115] 4. Water Regime Monitoring

[0116] Satellite remote sensing images are used for water body identification and extraction, and the changes in water areas are determined by the local water body areas and the water body areas identified by monitoring. At the same time, elevation data and land cover data are combined to further discriminate and analyze flood areas to eliminate errors and improve accuracy. Based on GIS technology, the extracted remote sensing water area data are displayed in real time, and the local water areas and real-time monitoring water areas are distinguished through rendering technology to identify the changes in water areas.

[0117] Water regime monitoring and water level change analysis are carried out according to the real-time water level, water depth, and flow data of the reservoirs in the river basin, and the water storage and outflow of key reservoirs are monitored.

[0118] 5. Underlying Surface Conditions

[0119] The monitoring information of the underlying surface mainly includes surface features and land use information related to flood disasters, that is, it is necessary to realize the monitoring display of land use / land cover data, terrain elevation data (DEM), surface vegetation index (NDVI) data, and reservoir basin distribution.

[0120] 6. Forecast and Early Warning

[0121] The flood forecast and early warning module mainly issues early warnings based on the real-time monitoring and forecasting of rainfall and water conditions in the current reservoir basin. This module needs to display the forecast precipitation and forecast runoff through precipitation forecast data and runoff prediction models to realize the prediction of rainfall and water conditions in the reservoir basin.

[0122] Through the real-time monitoring and prediction of precipitation levels, reservoir basin water levels, and runoff information, flood risk assessment is carried out. Based on historical flood events and threshold standards, flood warning levels and corresponding thresholds are set. When the precipitation exceeds a certain threshold and reaches the rainstorm level or the predicted flood flow exceeds a certain water level, the corresponding warning level is triggered.

[0123] In addition, this module also includes issuing early warnings by calculating the flood area of all basins. When calculating the flood area, mainly according to the occurrence of flood disasters, the pixel point areas of the affected areas are calculated and accumulated in three levels: light, medium, and heavy. Finally, the inundation areas of different levels of flood disasters are obtained, and this area is represented by three colors: red, yellow, and blue according to the different degrees of disaster.

[0124] 7. Early Warning Release

[0125] Based on the flood forecast and early warning information, this module determines the responsible department and release process for early warning information, forms standardized early warning information content or early warning products, and realizes the information management and information release of early warning information or products. Among them, the early warning information needs to be classified and statistically displayed according to the warning level, showing the total number of early warning information for different warning levels, and can be retrieved according to the warning level and warning communication period, and the content of the early warning information can also be viewed.

[0126] The content of the early warning information needs to clarify the following information (including but not limited to):

[0127] Flood occurrence location: Specify the specific geographical location that may be affected by the flood, including the name of the basin, river, or region.

[0128] Flood occurrence time: Predict the date and time period of the flood occurrence to remind the public to make preparations.

[0129] Predicted flood intensity: Describe the intensity of the flood, such as the predicted flood water level, flow rate, or rainfall, etc.

[0130] Potential impacts and risks: Describe the impacts and risks that may be caused to people, property, and infrastructure, such as possible flooded areas, road closures, power outages, etc.

[0131] Suggestions for early warning actions: Provide suggestions for the public to respond to floods, including measures such as evacuation, risk avoidance, enhanced protection, and avoiding wading through water.

[0132] In addition, the determined flood control departments and responsible persons can be selected, and early warning information can be sent, including but not limited to social platforms such as mobile phone text messages, Weibo, WeChat official accounts, as well as meteorological information applications and websites.

[0133] Embodiment 2

[0134] As Figure 2 shown, the embodiment of the present invention also provides a real-time monitoring system for flood disasters in a reservoir basin based on satellite remote sensing, including:

[0135] A module for obtaining satellite remote sensing data, which is used to obtain satellite remote sensing data of the reservoir basin. Among them, the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density, and house density;

[0136] A module for setting a model, which is used to set a flood risk assessment model and calculate an assessment value of flood occurrence according to the satellite remote sensing data. Among them, the flood risk assessment model includes: an influence function of precipitation on flood ponding;

[0137] Specifically, the flood risk assessment model includes:

[0138]

[0139] Among them, F(t) is the assessment value of flood occurrence at time t, Z(t) is the influence function of precipitation on flood ponding at time t, λ 1 is the first adjustment factor of the flood risk assessment model, θ 1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ 2 is the third adjustment factor of the flood risk assessment model, λ 3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, t k is the acquisition time point of the kth historical satellite remote sensing data, σ k is the adjustment factor of the kth acquisition time point, p′ is the number of historical time periods, C n′ is the weight of the n′th time period, P n′Precipitation of the reservoir basin at time t in the n'-th time period is (t), and T n′ Temperature of the reservoir basin at time t in the n'-th time period is (t), and γ n′ is the adjustment factor for the n'-th time period.

[0140] Specifically, the influence function Z(t) of precipitation at time t on floodwater accumulation includes:

[0141]

[0142] Among them, Factor i The i-th comprehensive environmental factor at time t is (t), the precipitation of the reservoir basin at time t is P(t), the temperature of the reservoir basin at time t is T(t), the soil saturation at time t is S(t), and A j The j-th regional local factor at time t is (t), and μ j is the first adjustment factor for the j-th region, the water level of the reservoir basin at time t is H(t), and λ' j is the second adjustment factor for the j-th region, and α' is the adjustment factor for the influence function Z(t).

[0143] Specifically, the flow rate index V(t) of the reservoir basin at time t includes:

[0144]

[0145] Among them, α 1 is the first adjustment factor for the flow rate index, α 2 is the second adjustment factor for the flow rate index, and D k′ The k'-th local hydrogeological factor at time t is (t), ξ is the third adjustment factor for the flow rate index, and Slope(x, y, t) is the slope at the position (x, y) in the reservoir basin at time t.

[0146] Specifically, the i-th comprehensive environmental factor Factor i at time t includes:

[0147]

[0148] Among them, Factor 1 The 1st comprehensive environmental factor at time t is (t), Slope(x, y) is the slope at the position (x, y) in the reservoir basin, Distance(x, y) is the distance from the center of the reservoir basin to the position (x, y), and Factor 2 The 2nd comprehensive environmental factor at time t is (t), and S maxis the maximum value of soil saturation, β′ is the adjustment factor of the second comprehensive environmental factor, Factor 3 (t) is the third comprehensive environmental factor at time t, Ur(x, y) is the tree density at location (x, y), and γ″ is the adjustment factor of the third comprehensive environmental factor.

[0149] Specifically, the jth regional local factor A j (t) at time t includes:

[0150]

[0151]

[0152] Among them, A 1 (t) is the first regional local factor at time t, H max is the maximum water level of the reservoir basin, α″ is the adjustment factor of the first regional local factor, A 2 (t) is the second regional local factor at time t, β″ is the adjustment factor of the second regional local factor, A 3 (t) is the third regional local factor at time t, θ 1 is the first adjustment factor of the third regional local factor, LU(x, y) is the housing density at location (x, y), θ 2 is the second adjustment factor of the third regional local factor.

[0153] Specifically, the k′th local hydrogeological factor D k′ (t) at time t includes:

[0154]

[0155] Among them, D 1 (t) is the first local hydrogeological factor at time t, θ 3 is the adjustment factor of the first local hydrogeological factor, D 2 (t) is the second local hydrogeological factor at time t, a″′ is the adjustment factor of the second local hydrogeological factor, Obstacle(x, y) is the volume of the blocking object at location (x, y), D 3 (t) is the third local hydrogeological factor at time t, γ″′ is the first adjustment factor of the third local hydrogeological factor, θ 4 is the second adjustment factor of the third local hydrogeological factor.

[0156] An alarm module is used to set a threshold for flood occurrence, compare the flood evaluation value with the flood occurrence threshold, and when the flood evaluation value exceeds the flood occurrence threshold, it is determined that a flood danger will occur and an alarm message is sent.

[0157] Embodiment 3

[0158] An embodiment of the present invention also provides a storage medium storing multiple instructions for implementing the real-time monitoring method for reservoir basin flood disasters based on satellite remote sensing.

[0159] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network or in any mobile terminal in a mobile terminal group.

[0160] Optionally, in this embodiment, the storage medium is set to store program codes for executing the steps of Embodiment 1.

[0161] Embodiment 4

[0162] An embodiment of the present invention also provides an electronic device including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions that can be loaded and executed by the processor so that the processor can execute the real-time monitoring method for reservoir basin flood disasters based on satellite remote sensing.

[0163] Specifically, the electronic equipment in this embodiment may be a computer terminal, and the computer terminal may include: one or more processors and a storage medium.

[0164] Among them, the storage medium can be used to store software programs and modules, such as the real-time monitoring method for reservoir basin flood disasters based on satellite remote sensing in the embodiment of the present invention, corresponding program instructions / modules. The processor runs the software programs and modules stored in the storage medium to execute various functional applications and data processing, that is, to implement the above real-time monitoring method for reservoir basin flood disasters based on satellite remote sensing. The storage medium may include a high-speed random storage medium and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely set relative to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0165] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the steps of Embodiment 1.

[0166] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0167] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0168] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0169] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0170] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0171] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks or optical disks and other various media that can store program codes.

[0172] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom still fall within the protection scope of the present invention.

Claims

1. A method for real-time monitoring of flood disasters in reservoir basins based on satellite remote sensing, characterized in that: include: Acquire satellite remote sensing data of the reservoir basin, wherein the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density and house density; A flood risk assessment model is set up, and an assessment value of flood occurrence is calculated based on the satellite remote sensing data, wherein the flood risk assessment model includes: an influence function of precipitation on flood accumulation; A flood threshold is set, and the flood assessment value is compared with the flood threshold. When the flood assessment value exceeds the flood threshold, it is determined that a flood hazard will occur and an alarm message is issued.

2. A method for real-time monitoring of flood disasters in a reservoir basin based on satellite remote sensing as claimed in claim 1, characterized in that: The flood risk assessment model includes: Wherein, F(t) is the assessment value of flood at time t, Z(t) is the influence function of precipitation on flood waterlogging at time t, λ1 is the first adjustment factor of the flood risk assessment model, θ1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ2 is the third adjustment factor of the flood risk assessment model, λ3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, t k is the collection time point of the kth historical satellite remote sensing data, σ k is the adjustment factor of the kth collection time point, p′ is the number of historical time periods, C n′ is the weight of the n′th time period, P n′ (t) is the precipitation in the reservoir basin at time t in the n′th time period, T n′ (t) is the reservoir basin temperature at time t in the n′th time period, γ n′ is the adjustment factor for the n′th time period.

3. A method for real-time monitoring of flood disasters in reservoir basins based on satellite remote sensing as claimed in claim 2, characterized in that: The influence function Z(t) of precipitation on flood water accumulation at time t includes: Among them, Factor i (t) is the i-th comprehensive environmental factor at time t, P(t) is the precipitation in the reservoir basin at time t, T(t) is the temperature in the reservoir basin at time t, S(t) is the soil saturation at time t, A j (t) is the jth regional local factor at time t, μ j is the first adjustment factor of the jth region, H(t) is the reservoir basin water level at time t, λ′ j is the second adjustment factor of the jth region, and α′ is the adjustment factor affecting the function Z(t).

4. A method for real-time monitoring of flood disasters in a reservoir basin based on satellite remote sensing as claimed in claim 3, characterized in that: The water flow rate index V(t) of the reservoir basin at time t includes: Among them, α1 is the first adjustment factor of the water flow rate index, α2 is the second adjustment factor of the water flow rate index, and D k′ (t) is the k′th local hydrogeological factor at time t, ξ is the third adjustment factor of the water flow rate index, and Slope(x, y, t) is the slope at position (x, y) in the reservoir basin at time t.

5. A method for real-time monitoring of flood disasters in a reservoir basin based on satellite remote sensing as claimed in claim 4, characterized in that: The i-th comprehensive environmental factor at time t i (t) include: Among them, Factor1(t) is the first comprehensive environmental factor at time t, Slope(x, y) is the slope at the location (x, y) in the reservoir basin, Distance(x, y) is the distance from the center of the reservoir basin to the location (x, y), Factor2(t) is the second comprehensive environmental factor at time t, S max is the maximum value of soil saturation, β′ is the adjustment factor of the second comprehensive environmental factor, Factor3(t) is the third comprehensive environmental factor at time t, Ur(x, y) is the tree density at position (x, y), and γ″ is the adjustment factor of the third comprehensive environmental factor.

6. A method for real-time monitoring of flood disasters in a reservoir basin based on satellite remote sensing as claimed in claim 5, characterized in that: The jth regional local factor A at time t j (t) include: Among them, A1(t) is the first regional local factor at time t, H max is the maximum water level of the reservoir basin, α″ is the adjustment factor of the first regional local factor, A2(t) is the second regional local factor at time t, β″ is the adjustment factor of the second regional local factor, A3(t) is the third regional local factor at time t, θ1 is the first adjustment factor of the third regional local factor, LU(x, y) is the house density at position (x, y), and θ2 is the second adjustment factor of the third regional local factor.

7. A method for real-time monitoring of flood disasters in a reservoir basin based on satellite remote sensing as claimed in claim 6, characterized in that: The k′th local hydrogeological factor D at time t k′ (t) include: Wherein, D1(t) is the first local hydrogeological factor at time t, θ3 is the adjustment factor of the first local hydrogeological factor, D2(t) is the second local hydrogeological factor at time t, θ″′ is the adjustment factor of the second local hydrogeological factor, Obstacle(x, y) is the volume of the obstruction at position (x, y), D3(t) is the third local hydrogeological factor at time t, γ″′ is the first adjustment factor of the third local hydrogeological factor, and θ4 is the second adjustment factor of the third local hydrogeological factor.

8. A real-time monitoring system for flood disasters in reservoir basins based on satellite remote sensing, characterized in that: include: A module for acquiring satellite remote sensing data is used to acquire satellite remote sensing data of a reservoir basin, wherein the satellite remote sensing data includes: precipitation in the reservoir basin, temperature in the reservoir basin, water level in the reservoir basin, soil saturation, slope of the reservoir basin, tree density and house density; A model module is set up to set up a flood risk assessment model, and to calculate the assessment value of flood occurrence according to the satellite remote sensing data, wherein the flood risk assessment model includes: an influence function of precipitation on flood accumulation; The alarm module is used to set a flood threshold, compare the flood assessment value with the flood threshold, and when the flood assessment value exceeds the flood threshold, determine that a flood hazard will occur and issue an alarm message.

9. A real-time monitoring system for flood disasters in reservoir basins based on satellite remote sensing as claimed in claim 8, characterized in that: The flood risk assessment model includes: Wherein, F(t) is the assessment value of flood at time t, Z(t) is the influence function of precipitation on flood waterlogging at time t, λ1 is the first adjustment factor of the flood risk assessment model, θ1 is the second adjustment factor of the flood risk assessment model, R(t) is the reflection coefficient of the reservoir basin at time t, λ2 is the third adjustment factor of the flood risk assessment model, λ3 is the fourth adjustment factor of the flood risk assessment model, V(t) is the water flow rate index of the reservoir basin at time t, η is the fifth adjustment factor of the flood risk assessment model, m is the number of acquisition time points, t k is the collection time point of the kth historical satellite remote sensing data, σ k is the adjustment factor of the kth collection time point, p′ is the number of historical time periods, C n′ is the weight of the n′th time period, P n′ (t) is the precipitation in the reservoir basin at time t in the n′th time period, T n′ (t) is the reservoir basin temperature at time t in the n′th time period, γ n′ is the adjustment factor for the n′th time period.

10. A real-time monitoring system for flood disasters in reservoir basins based on satellite remote sensing as claimed in claim 9, characterized in that: The influence function Z(t) of precipitation on flood water accumulation at time t includes: Among them, Factor i (t) is the i-th comprehensive environmental factor at time t, P(t) is the precipitation in the reservoir basin at time t, T(t) is the temperature in the reservoir basin at time t, S(t) is the soil saturation at time t, A j (t) is the jth regional local factor at time t, μ j is the first adjustment factor of the jth region, H(t) is the reservoir basin water level at time t, λ′ j is the second adjustment factor of the jth region, and α′ is the adjustment factor affecting the function Z(t).

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