Rain information flood reporting method and system

Abstract

The invention relates to the technical field of rainfall monitoring, in particular to a rainfall flood reporting method and system, and the method comprises the steps: dividing a reservoir and a surrounding region into a plurality of rainfall affected regions according to the first coordinate data of rainfall detection equipment; calculating average rainfall data through rainfall detection data of rainfall detection equipment in the rainfall influence area; drawing rainfall distribution images of the reservoir and the surrounding area; based on the influence relationship between the cloud picture data and the rainfall data, comparing the rainfall distribution image with the meteorological cloud picture, and determining a rainfall data change curve in combination with the meteorological characteristic data and the average rainfall data; calculating a first predicted precipitation amount of each precipitation influence area, and determining water level rising data of the reservoir; and accumulating the water level rising data of the reservoir in each time interval, and determining the accumulated water level rising data and rainfall early warning level of the reservoir in each time interval. The influence of rainfall around the reservoir on the water level of the reservoir is calculated based on the terrain around the reservoir, and the flood control early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rainfall monitoring, and specifically provides a rainfall information reporting method and system. Background Art

[0002] With the intensification of global climate change, extreme weather events occur frequently, posing higher requirements for flood control work. Traditional rainfall information reporting methods mainly rely on manual observations and static water level control strategies, and it is difficult to cope with complex and variable precipitation conditions and basin characteristics.

[0003] In recent years, with the development of water regime forecasting systems and intelligent scheduling algorithms, dynamic monitoring and prediction have become important means to improve flood control capabilities. For example, a Chinese patent discloses a water level monitoring and management method based on multi-source information fusion, application number: 202510117424.4. This patent uses sensors to obtain water level and water level-related data, and obtains information on surface features and meteorological data through GIS data; performs data cleaning and standardization on the collected data; uses a convolutional neural network to analyze infrared cloud image data obtained by satellites to predict precipitation, and uses the weighted average method for multi-source information fusion and water level prediction through a hydrological model; performs real-time water level monitoring and water level management decision-making based on the accurate water level data obtained from data fusion. However, the above method has the following disadvantages: 1. The monitoring area is not divided similarly in detail, and the differences in the impact of precipitation in different regions on the water level are not distinguished. It is difficult to accurately evaluate the impact of precipitation on the water level under complex terrains and different precipitation conditions, resulting in limited accuracy of water level prediction; 2. Although data cleaning and standardization processing are performed, when performing data fusion, the weights are determined only based on the reliability and accuracy of the data sources, and the spatial distribution characteristics of precipitation data are not processed, resulting in insufficient fineness and accuracy in processing precipitation data, affecting the accuracy of subsequent water level prediction; 3. This method uses a convolutional neural network to analyze satellite infrared cloud image data to predict precipitation and uses a distributed hydrological model to simulate water level changes, without fully considering the complex dynamic relationship between cloud image data, precipitation, and water level, and the accuracy and adaptability of the model are relatively weak; 3. The water level risk warning mechanism set by this method issues an alarm only based on the ratio relationship between the water level and the flood water level, without comprehensively considering factors such as precipitation trends and water level change rates, and the warning information is not comprehensive and accurate enough. In actual flood control applications, it may not be able to provide timely and accurate support for decision-making. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a rainfall information reporting method and system, which solves the problem of insufficient warning accuracy in the prior art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A rainfall information reporting method, including the following steps:

[0006] S102. Obtain the first coordinate data of the precipitation detection device;

[0007] S104. Calculate the minimum distance between the precipitation detection device and the reservoir based on the first coordinate data of the precipitation detection device, determine the first device and the second device based on the minimum distance, and divide the reservoir and its surrounding area into multiple precipitation influence regions through the first device and the second device. The precipitation influence regions include the first region and the second region;

[0008] S106. Calculate the average precipitation data of each precipitation influence region through the precipitation detection data of the precipitation detection device within the precipitation influence region;

[0009] S108. Draw a precipitation distribution image of the reservoir and its surrounding area based on the average precipitation data of all precipitation influence regions;

[0010] S110. Based on the influence relationship between the cloud map data and the precipitation data, compare the precipitation distribution image with the meteorological cloud map, and combine the meteorological characteristic data and the average precipitation data of the precipitation influence region to determine the precipitation data change curve of the precipitation influence region;

[0011] S112. Determine the time interval t at preset time intervals n , calculate the first predicted precipitation amount of each precipitation influence region within the time interval t n and calculate the water level rise data of the reservoir within the time interval t n based on the first predicted precipitation amount;

[0012] S114. Accumulate the water level rise data of the reservoir in each time interval to determine the cumulative water level rise data of the reservoir at the end of each time interval;

[0013] S116. Determine the precipitation warning level of the reservoir in each time interval based on the cumulative water level rise data.

[0014] Preferably, the step S104 specifically includes:

[0015] S202. Determine the precipitation detection devices with a minimum distance less than the preset distance as the first device;

[0016] S204. Determine the other precipitation detection devices except the first device as the second device;

[0017] S206. Connect the first coordinate data of adjacent precipitation detection devices pairwise to generate multiple precipitation influence regions;

[0018] S208. Determine the region formed by connecting the first devices as the first region;

[0019] S210. Determine the area formed by connecting the first device and the second device as the second area.

[0020] Preferably, the specific steps for calculating the average precipitation data in step S106 are as follows:

[0021] S302. In the precipitation influence area, connect the intermediate coordinate data of the first coordinate data of each group of adjacent precipitation detection devices with the remaining first coordinate data to generate a plurality of first line segments;

[0022] S304. Determine the central coordinate data of the precipitation influence area as the overlapping coordinate data of all the first line segments;

[0023] S306. Determine the first influence weight of the precipitation data of each precipitation detection device according to the distance ratio between the first coordinate data of each precipitation detection device in the precipitation influence area and the central coordinate data;

[0024] S308. Multiply the precipitation data of each precipitation detection device by its corresponding first influence weight and accumulate to obtain the average precipitation data of the precipitation influence area.

[0025] Preferably, the following steps are further included:

[0026] S0. Obtain historical monitoring data and perform data preprocessing on the historical monitoring data. The preprocessing includes at least data cleaning and format conversion;

[0027] S0a. Analyze the preprocessed historical monitoring data to determine the influence relationship between the cloud map data and the precipitation data. The relationship is expressed by the formula:

[0028]

[0029] where P is the precipitation data, h is the cloud layer height, d is the cloud layer thickness, x a , y a is the offset distance of the meteorological cloud map, and k1, k2, k3 are influence factors.

[0030] Preferably, step S110 includes:

[0031] S1101. Based on the influence relationship between the cloud map data and the precipitation data, compare the precipitation distribution image and the meteorological cloud map, and determine the values of k1, k2, k3, x a and y a in combination with the meteorological characteristic data and the average precipitation data of the precipitation influence area, and determine the precipitation data calculation formula based on the cloud map data;

[0032] S1102. Analyze through the meteorological cloud map and the meteorological characteristic data to predict the moving path of the cloud map;

[0033] S1103. Determine the cloud map data affecting the precipitation influence area through the cloud map movement path, and draw the precipitation data change curve of the precipitation influence area according to the precipitation data calculation formula based on the cloud map data; the cloud map data includes at least cloud height and cloud thickness.

[0034] Preferably, the method for calculating the first predicted precipitation amount in step S112 is as follows:

[0035] By analyzing the precipitation data change curve of the time interval t n and determining the first predicted precipitation amount within the time interval t n based on the initial time, end time of the time interval t n and the area of the region enclosed by the precipitation data change curve and the coordinate axes.

[0036] Preferably, the specific steps for calculating the water level rise data in step S112 are as follows:

[0037] S1121. If the precipitation influence area is the first area, directly use its first predicted precipitation amount as the predicted precipitation amount of the sub-region of the reservoir;

[0038] S1122. If the precipitation influence area is the second area, determine the soil water absorption change curve based on the riverbank soil data and adjust the curve according to the precipitation data;

[0039] S1123. Calculate the area of the region enclosed by the initial time, end time of the time interval t n the precipitation data change curve and the soil absorption change curve to determine the second predicted precipitation amount of the precipitation influence area;

[0040] S1124. Determine the second predicted precipitation amount of the precipitation influence area as the predicted precipitation amount of the sub-region of the reservoir;

[0041] S1125. Accumulate the predicted precipitation amounts of all sub-regions to obtain the predicted total precipitation amount of the reservoir, and determine the water level rise data through the ratio of the total precipitation amount to the area of the reservoir.

[0042] The present invention also provides a rain information reporting system for implementing the above method, including:

[0043] A data acquisition module for acquiring the first coordinate data of the precipitation detection device;

[0044] A region division module for dividing the precipitation influence area;

[0045] A first analysis module for calculating the average precipitation data and drawing the precipitation distribution image;

[0046] A second analysis module for determining a precipitation data change curve in combination with cloud map data;

[0047] A third analysis module for predicting precipitation amounts and calculating water level rise data;

[0048] A precipitation warning module for generating precipitation warning levels.

[0049] Preferably, the area division module is specifically configured to:

[0050] Divide the first device and the second device based on the minimum distance between the precipitation detection device and the reservoir, and generate a first area and a second area by connecting the coordinates of adjacent devices.

[0051] Preferably, when calculating the average precipitation data, the first analysis module performs weighted accumulation on the precipitation data through the central coordinates and distance weights.

[0052] A third aspect of the present invention provides a computer-readable storage medium, which includes a rain information reporting method program. When the rain information reporting method program is executed by a processor, the steps of the above-mentioned rain information reporting method are implemented.

[0053] The beneficial effects of the present invention are:

[0054] (1) By obtaining the first coordinate data of the precipitation detection device, the present invention divides the reservoir and its surrounding area into multiple precipitation influence areas, including a first area that has a direct impact on the water level rise of the reservoir and a second area that has an indirect impact. This division method fully considers the topographical factors around the reservoir. Compared with the general method of traditional rain situation monitoring, it can more accurately reflect the impact of precipitation in different areas on the reservoir water level, providing more accurate basic data for subsequent rain situation analysis and warning.

[0055] (2) When calculating the average precipitation data of the precipitation influence area, the present invention uses a unique method to determine the central coordinates of the area and the first influence weight of the precipitation data of each precipitation detection device, and obtains the average precipitation data through weighted calculation. This calculation method fully considers the position differences of the precipitation detection devices in the area, avoids the errors caused by simple average calculation, and more truly reflects the actual precipitation situation of each precipitation influence area, which helps to accurately grasp the rain situation distribution of the reservoir and its surrounding area.

[0056] (3) Based on the influence relationship between cloud map data and precipitation data, the precipitation distribution image and the meteorological cloud map are compared, and the precipitation data change curve is determined by combining meteorological characteristic data and average precipitation data. The complex relationship between cloud map data and precipitation data is determined through historical monitoring data, and various factors such as cloud layer height, thickness, and the offset distance of the influence of meteorological cloud maps on precipitation are considered, making the precipitation prediction more scientific and reasonable. At the same time, the moving path of the cloud map is predicted, further improving the accuracy of the precipitation trend prediction in the precipitation influence area and providing a reliable basis for the reservoir water level prediction.

[0057] (4) According to the first predicted precipitation in each precipitation influence area, the water level rise data of the reservoir in different time intervals is calculated by combining factors such as riverbank soil data. Different calculation methods are used for different types of precipitation influence areas, fully considering actual situations such as soil water absorption, making the calculated water level rise data more accurate. The accurate water level rise data helps relevant departments more accurately evaluate the change of the reservoir water level, timely adjust the flood control strategy, reasonably arrange operations such as flood discharge and water storage, and improve the scientificity and effectiveness of flood control decision-making.

[0058] (5) By accumulating the water level rise data of the reservoir in each time interval, the cumulative water level rise data is determined, and the precipitation warning level is determined based on this. The clear and definite warning level division enables relevant departments and personnel to quickly understand the degree of rainstorm risk faced by the reservoir and take corresponding preventive measures in a timely manner. Under the guidance of the warning level, work such as personnel evacuation and material transfer can be organized in advance, effectively reducing the possible losses caused by floods and ensuring the safety of people's lives and property and the safe operation of reservoir facilities. Description of the Drawings

[0059] Figure 1 Shows the flowchart of a rainstorm reporting method provided by the present invention;

[0060] Figure 2 Shows the flowchart of the precipitation influence area division method provided by the present invention;

[0061] Figure 3 Shows the flowchart of the average precipitation data calculation method for the precipitation influence area provided by the present invention;

[0062] Figure 4 Shows the block diagram of a rainstorm reporting system provided by the present invention. Detailed Embodiments

[0063] In order to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0064] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0065] Figure 1 The flowchart of a rain information and flood warning method provided by the present invention is shown.

[0066] As Figure 1 shown, the present invention discloses a rain information and flood warning method, including:

[0067] S102. Obtain the first coordinate data of the precipitation detection device;

[0068] S104. Calculate the minimum distance between the precipitation detection device and the reservoir according to the first coordinate data of the precipitation detection device, determine the first device and the second device based on the minimum distance, and divide the reservoir and its surrounding area into multiple precipitation influence areas through the first device and the second device. The precipitation influence areas include the first area and the second area;

[0069] S106. Calculate the average precipitation data of each precipitation influence area through the precipitation detection data of the precipitation detection device in the precipitation influence area;

[0070] S108. Draw the precipitation distribution image of the reservoir and its surrounding area according to the average precipitation data of all precipitation influence areas;

[0071] S110. Based on the influence relationship between the cloud map data and the precipitation data, compare the precipitation distribution image with the meteorological cloud map, and combine the meteorological characteristic data and the average precipitation data of the precipitation influence area to determine the precipitation data change curve of the precipitation influence area;

[0072] S112. Determine the time interval t n at a preset time interval, calculate the first predicted precipitation amount of each precipitation influence area within the time interval t n , and calculate the water level rise data of the reservoir within the time interval t n based on the first predicted precipitation amount;

[0073] S114. Accumulate the water level rise data of the reservoir in each time interval to determine the cumulative water level rise data of the reservoir at the end of each time interval;

[0074] S116. Determine the precipitation warning level of the reservoir in each time interval based on the cumulative water level rise data.

[0075] According to an embodiment of the present invention, the first coordinate data of the precipitation detection device is the installation coordinate of the precipitation detection device, and the precipitation detection device is a device for detecting precipitation, such as a rain gauge. The minimum distance between the precipitation detection device and the reservoir is the shortest distance from the first coordinate data of the precipitation detection device to the reservoir edge. Based on the minimum distance between the precipitation detection device and the reservoir, the precipitation detection devices near the reservoir (such as within 3 meters) are determined as the first devices, and the precipitation detection devices in the surrounding areas such as the littoral zone (such as outside 3 meters) are determined as the second devices. According to the first coordinate data of the first devices and the second devices, the precipitation influence area corresponding to the reservoir or the upstream river is determined as the first area, and the surrounding area within a certain range around the reservoir is determined as the second area. Among them, the reservoir and its surrounding area refer to the construction land of the reservoir and the land within 2 kilometers around it, including the dam area, the reservoir area, the littoral zone, etc., which are the main scope and key objects of reservoir management.

[0076] The precipitation data can be represented by the precipitation rate, that is, the precipitation amount per unit area per unit time, and is determined by the ratio of the precipitation amount obtained by the precipitation detection device to the precipitation collection time. The average precipitation data is determined by calculating the average value of the precipitation data within a continuous plurality of precipitation collection times, and the rainfall conditions in the precipitation influence area are represented by the average precipitation data. After the average precipitation data of all the precipitation influence areas is calculated, the precipitation distribution image of the reservoir and its surrounding area is obtained.

[0077] The influence relationship between the cloud image data and the precipitation data is obtained through the analysis of various detection data collected during the previous monitoring of the rainfall conditions around the reservoir, that is, historical monitoring data analysis, including the corresponding relationship between different meteorological characteristic data and the offset distance of the influence of the meteorological cloud image on precipitation, and the influence relationship between the cloud height and cloud thickness on the precipitation rate and precipitation amount. Based on the precipitation distribution image, the meteorological cloud image and the meteorological characteristic data (temperature, pressure, wind direction, wind speed, humidity, etc.) of the reservoir and its surrounding area, the influencing factors k1, k2, k3 and the offset distances x a , y a are determined, the precipitation data calculation formula based on the cloud image data is determined, and the moving path of the cloud image data is predicted. Substitute the cloud height and cloud thickness of the cloud image data at different times into the precipitation data calculation formula based on the cloud image data to determine the predicted precipitation data of the precipitation influence area at different times, and draw the precipitation data change curve of the precipitation influence area according to the predicted time. Among them, the preset time interval is set by the system, such as 1 hour, etc., and those skilled in the art can adjust the preset time interval according to actual needs. The time interval t nis the nth time interval from the current time interval t0. According to the category of the precipitation influence area, calculate the first predicted precipitation of each precipitation influence area respectively. Accumulate the first predicted precipitation of each precipitation influence area, calculate the ratio of the accumulation result to the reservoir area, and determine the water level rise data of the reservoir in the time interval t n Based on the current time, accumulate the water level rise data of the reservoir in each time interval in turn to determine the cumulative water level rise data of the reservoir at the end of each time interval. For example, the cumulative water level rise data of the reservoir at the end of the time interval t1 is the water level rise data of the time interval t1, and the cumulative water level rise data of the reservoir at the end of the time interval t2 is the sum of the water level rise data of the time intervals t1 and t2.

[0078] According to the water level rise data during precipitation, divide the reservoir precipitation warning level into 5 levels. The higher the water level rise data, the higher the corresponding reservoir precipitation warning level.

[0079] Figure 2 shows the flowchart of the precipitation influence area division method provided by the present invention.

[0080] As Figure 2 shown, according to the embodiment of the present invention, step S104 specifically includes:

[0081] S202. Determine the first device as the precipitation detection device with the minimum distance less than the preset distance;

[0082] S204. Determine other precipitation detection devices other than the first device as the second device;

[0083] S206. Connect the first coordinate data of adjacent precipitation detection devices in pairs to generate multiple precipitation influence areas;

[0084] S208. Determine the area formed by connecting the first devices as the first area;

[0085] S210. Determine the area formed by connecting the first device and the second device as the second area.

[0086] It should be noted that the preset distance is set by the system. When the minimum distance between the precipitation detection device and the reservoir is less than the preset distance of the system, it means that the precipitation detection device is installed near the reservoir; otherwise, it means that the precipitation device is installed in the surrounding area such as the littoral zone. By connecting the first coordinate data of adjacent precipitation detection devices pairwise, connection line segments are obtained, and the area formed by any three connection line segments is determined as the precipitation influence area. Among them, the precipitation influence area formed by pairwise connection of the first devices is the reservoir or the upstream river channel, and the precipitation in this area has a direct impact on the water level rise of the reservoir, which is represented by the first area; the precipitation influence area formed by pairwise connection of the first device and the second device is the surrounding area within a certain range around the reservoir, and the precipitation in this area indirectly affects the water level rise of the reservoir through surface runoff or subsurface runoff, etc., which is represented by the second area.

[0087] Figure 3 The flowchart of the method for calculating the average precipitation data of the precipitation influence area provided by the present invention is shown.

[0088] As Figure 3 shown, according to an embodiment of the present invention, the specific steps for calculating the average precipitation data in step S106 are as follows:

[0089] S302. In the precipitation influence area, connect the intermediate coordinate data of the first coordinate data of each group of adjacent precipitation detection devices with the remaining first coordinate data to generate a plurality of first line segments;

[0090] S304. Determine the central coordinate data of the precipitation influence area as the overlapping coordinate data of all the first line segments;

[0091] S306. Determine the first influence weight of the precipitation data of each precipitation detection device according to the distance ratio between the first coordinate data and the central coordinate data of each precipitation detection device in the precipitation influence area;

[0092] S308. Multiply the precipitation data of each precipitation detection device by its corresponding first influence weight and accumulate them to obtain the average precipitation data of the precipitation influence area.

[0093] It should be noted that the remaining first coordinate data is the first coordinate data of another precipitation detection device other than the first coordinate data of adjacent precipitation detection devices. It is calculated by adding the abscissa and ordinate of the first coordinate data of adjacent precipitation detection devices and then dividing by 2 to determine the intermediate coordinate data. If there are no overlapping coordinate data for all the first line segments, the center coordinate of the largest inscribed circle of the triangle formed by the three first line segments is determined as the overlapping coordinate data. Calculate the coordinate distance from each first coordinate data to the overlapping coordinate data, calculate the ratio of each coordinate distance to the sum of all coordinate distances, determine the distance ratio between each first coordinate data and the center coordinate data, and determine the first influence weight of the precipitation data of each precipitation detection device through the distance ratio between the first coordinate data and the center coordinate data. Among them, the smaller the distance ratio, the higher the first influence weight of the precipitation data of the precipitation detection device, and the sum of all the first influence weights is 1.

[0094] According to an embodiment of the present invention, the following steps are further included:

[0095] S0. Obtain historical monitoring data and perform data preprocessing on the historical monitoring data. The preprocessing includes at least data cleaning and format conversion;

[0096] S0a. Analyze the preprocessed historical monitoring data to determine the influence relationship between the cloud map data and the precipitation data. The relationship is expressed by the formula:

[0097]

[0098] where P is the precipitation data, h is the cloud layer height, d is the cloud layer thickness, x a , y a is the offset distance of the meteorological cloud map, and k1, k2, and k3 are influence factors.

[0099] It should be noted that the historical monitoring data includes various detection data collected during the previous rain situation monitoring around the reservoir, including the precipitation data of each precipitation detection device, the meteorological cloud map and meteorological characteristic data corresponding to the collection time, etc. First, perform data cleaning on the previously collected various detection data, including handling missing values, outliers, and data deduplication, and then perform standardized processing on the data format of the collected data, such as unifying the date into a specific format, unifying the representation method of units, and performing standardized processing on the text (such as case conversion, removing spaces, etc.).

[0100] Analyze the historical detection data through methods such as deep learning and neural network models, calculate the corresponding relationship between different meteorological characteristic data and the offset distance of the influence of the meteorological cloud map on precipitation, and the influence relationship between the cloud layer height and cloud layer thickness on the precipitation rate and precipitation amount, so as to determine the influence relationship between the cloud map data and the precipitation data.

[0101] According to an embodiment of the present invention, step S110 includes:

[0102] S1101. Based on the influence relationship between the cloud map data and the precipitation data, compare the precipitation distribution image and the meteorological cloud map, and determine the values of k1, k2, k3, x a and y a to determine the precipitation data calculation formula based on the cloud map data;

[0103] S1102. Analyze through the meteorological cloud map and meteorological characteristic data to predict the moving path of the cloud map;

[0104] S1103. Determine the cloud map data affecting the precipitation influence area through the moving path of the cloud map, and draw the precipitation data change curve of the precipitation influence area according to the precipitation data calculation formula based on the cloud map data; the cloud map data includes at least the cloud layer height and the cloud layer thickness.

[0105] It should be noted that by comparing the precipitation distribution image and the meteorological cloud map, analyzing in combination with the meteorological characteristic data and the average precipitation data of the precipitation influence area, and comparing with each detection data in the historical monitoring data, the influence factors k1, k2, k3 and the offset distance x a , y a are determined to determine the precipitation data calculation formula based on the cloud map data.

[0106] Among them, the precipitation data calculation formula based on the cloud map data can be expressed by the influence relationship between the cloud map data and the precipitation data. Through the precipitation data based on the cloud map data, when the cloud layer height and the cloud layer thickness in the known cloud map data are known, the precipitation data of the precipitation influence area can be predicted.

[0107] The meteorological characteristic data includes temperature, air pressure, wind direction, wind speed, humidity, etc. By analyzing the meteorological cloud map and the meteorological characteristic data, the change situation and the moving path of the meteorological cloud map within a certain time can be determined. Determine the cloud map data (including the cloud layer height and the cloud layer thickness) of the meteorological cloud map corresponding to the precipitation influence area at different times through the moving path of the cloud map, calculate the predicted precipitation data of the precipitation influence area at different times according to the precipitation data calculation formula based on the cloud map data, and connect the predicted precipitation data at adjacent times to draw the precipitation data change curve of the precipitation influence area.

[0108] According to an embodiment of the present invention, the method for calculating the first predicted precipitation amount in step S112 is:

[0109] By analyzing the precipitation data change curve of the time interval t n and according to the time interval t nThe area enclosed by the initial time, end time, and the precipitation data change curve and the coordinate axes is used to determine the time interval t n The first predicted precipitation within it.

[0110] It should be noted that the horizontal axis of the coordinate axis is time and the vertical axis is precipitation data. According to the precipitation data change curve at the initial time and end time of the time interval t n The time interval t is divided. n The area enclosed by the initial time, end time, precipitation data change curve, and the horizontal axis of the coordinate axis is used to determine the first predicted precipitation of the time interval t n The unit of the first predicted precipitation is mm.

[0111] According to the embodiment of the present invention, the specific steps for calculating the water level rise data in step S112 are as follows:

[0112] S1121: If the precipitation affected area is the first area, directly use its first predicted precipitation as the predicted precipitation of the sub-area of the reservoir;

[0113] S1122: If the precipitation affected area is the second area, determine the soil water absorption change curve based on the riverbank soil data and adjust the curve according to the precipitation data;

[0114] S1123: Calculate the area enclosed by the initial time, end time, precipitation data change curve, and soil absorption change curve of the time interval t n to determine the second predicted precipitation of the precipitation affected area;

[0115] S1124: Determine the second predicted precipitation of the precipitation affected area as the predicted precipitation of the sub-area of the reservoir;

[0116] S1125: Accumulate the predicted precipitations of all sub-areas to obtain the predicted total precipitation of the reservoir, and determine the water level rise data by the ratio of the total precipitation to the area of the reservoir.

[0117] It should be noted that the precipitation in the first region directly falls into the reservoir or the upstream river channel, and the precipitation falling into the upstream river channel can flow into the reservoir through the river channel. Therefore, the precipitation in the first region is equal to the increase in the water source of the reservoir, and the first predicted precipitation in the precipitation influence area is directly determined as the predicted precipitation of the sub-region of the reservoir; the precipitation in the second region falls into the surrounding areas such as the littoral zone. When the water content of the soil in the littoral zone is low, the precipitation is mainly absorbed by the soil in the littoral zone, and the unabsorbed water source flows into the reservoir through surface runoff and other means. The soil water absorption change curve is affected by the soil type, the proportion of each soil type in the river bank, and the soil water content. The soil type and the proportion of each soil type in the river bank can be determined by collecting and analyzing soil samples from the river bank, and the soil water content can be detected by a soil water content sensor. The soil water absorption rate under different soil water contents is determined by the change of soil water content over time under different precipitation data and different soil water contents in the historical monitoring data, and the soil water absorption change curve is drawn. The soil absorption change curve is adjusted according to the precipitation data. When the precipitation data is lower than the soil water absorption rate, all the precipitation is absorbed by the soil; otherwise, the soil absorbs the precipitation at the soil water absorption rate, and the remaining precipitation flows into the reservoir.

[0118] Among them, the precipitation influence area formed by connecting the first devices in pairs is the reservoir or the upstream river channel, and the precipitation in this area has a direct impact on the rise of the reservoir water level, which is represented by the first region; the precipitation influence area formed by connecting the first devices and the second devices in pairs is the surrounding area within a certain range around the reservoir, and the precipitation in this area has an indirect impact on the rise of the reservoir water level through surface runoff or subsurface runoff and other means, which is represented by the second region.

[0119] Figure 4 The block diagram of a rain information reporting system provided by the present invention is shown.

[0120] As Figure 4 shown, the second aspect of the present invention provides a rain information reporting system, including:

[0121] A data acquisition module, configured to acquire the first coordinate data of the precipitation detection device;

[0122] A region division module, configured to divide the precipitation influence area;

[0123] A first analysis module, configured to calculate the average precipitation data and draw a precipitation distribution image;

[0124] A second analysis module, configured to determine the precipitation data change curve in combination with the cloud map data;

[0125] A third analysis module, configured to predict the precipitation and calculate the water level rise data;

[0126] A precipitation warning module, configured to generate a precipitation warning level.

[0127] In a third aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a rain information reporting method program. When the rain information reporting method program is executed by a processor, the steps of a rain information reporting method as described above are implemented.

[0128] The information involved in this application (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between user terminals and other devices, etc.) are all authorized by users or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards in relevant countries and regions. For example, the "first coordinate data of precipitation detection equipment", "precipitation detection data", "historical monitoring data", etc. involved in this disclosure are all obtained under sufficient authorization.

[0129] The present invention discloses a rain information reporting method and system. The method includes: dividing the reservoir and its surrounding area into multiple precipitation influence areas according to the first coordinate data of precipitation detection equipment; calculating the average precipitation data through the precipitation detection data of precipitation detection equipment in the precipitation influence area; drawing a precipitation distribution image of the reservoir and its surrounding area; based on the influence relationship between cloud map data and precipitation data, comparing the precipitation distribution image with the meteorological cloud map, and combining meteorological characteristic data and average precipitation data to determine the precipitation data change curve of the precipitation influence area; calculating the first predicted precipitation amount of each precipitation influence area to determine the water level rise data of the reservoir; accumulating the water level rise data of the reservoir in each time interval to determine the cumulative water level rise data and precipitation warning level of the reservoir in each time interval. The present invention calculates the influence of precipitation around the reservoir on the reservoir water level based on the terrain around the reservoir, improving the accuracy of flood control early warning.

[0130] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device 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, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0131] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed across 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.

[0132] In addition, each functional unit in the embodiments of the present invention may be fully integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0133] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks and other various media that can store program codes.

[0134] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical disks and other various media that can store program codes.

Claims

1. A rainfall forecasting method, characterized in that: The following steps are involved: S102, obtaining first coordinate data of a precipitation detection device; S104, calculating the minimum distance between the precipitation detection device and the reservoir according to the first coordinate data of the precipitation detection device, determining the first device and the second device based on the minimum distance, and dividing the reservoir and the surrounding area into a plurality of precipitation impact areas by the first device and the second device, wherein the precipitation impact areas include the first area and the second area; S106, calculating average precipitation data of each precipitation affected area through precipitation detection data of precipitation detection equipment in the precipitation affected area; S108, drawing a precipitation distribution image of the reservoir and surrounding areas based on the average precipitation data of all precipitation-affected areas; S110, based on the influence relationship between the cloud map data and the precipitation data, comparing the precipitation distribution image with the meteorological cloud map, combining the meteorological characteristic data and the average precipitation data of the precipitation affected area, determining the precipitation data change curve of the precipitation affected area; S112, determine the time interval t according to the preset time interval n , calculate the precipitation impact area in the time interval t n The first predicted precipitation in the time interval t is calculated based on the first predicted precipitation. n Water level rise data within S114, accumulating the water level rise data of the reservoir in each time interval to determine the cumulative water level rise data of the reservoir at the end of each time interval; S116. Determine the precipitation warning level of the reservoir in each time interval based on the accumulated water level rise data.

2. The rainfall forecasting method according to claim 1, characterized in that: The step S104 specifically includes: S202, determining a precipitation detection device whose minimum distance is less than a preset distance as a first device; S204, determining other precipitation detection devices other than the first device as second devices; S206, connecting the first coordinate data of adjacent precipitation detection devices in pairs to generate multiple precipitation impact areas; S208, determining an area formed by connecting the first device as a first area; S210: Determine an area formed by connecting the first device and the second device as a second area.

3. The rainfall forecasting method according to claim 1, characterized in that: The specific steps of calculating the average precipitation data in step S106 are: S302: In the precipitation affected area, the intermediate coordinate data of the first coordinate data of each group of adjacent precipitation detection devices are connected with the remaining first coordinate data to generate a plurality of first line segments; S304, determining the coincident coordinate data of all first line segments as the center coordinate data of the precipitation impact area; S306, determining a first influence weight of precipitation data of each precipitation detection device according to a distance ratio between the first coordinate data and the center coordinate data of each precipitation detection device in the precipitation influence area; S308: Multiply the precipitation data of each precipitation detection device by its corresponding first influence weight and add them up to obtain average precipitation data of the precipitation influence area.

4. The rain forecasting method according to claim 1, characterized in that: The following steps are also included: S0. Acquire historical monitoring data, and perform data preprocessing on the historical monitoring data, wherein the preprocessing at least includes data cleaning and format conversion; S0a, analyzing the pre-processed historical monitoring data, determining the influence relationship between the cloud map data and the precipitation data, the relationship is expressed by the formula: Among them, P is the precipitation data, h is the cloud height, d is the cloud thickness, x a ,y a is the offset distance of the meteorological cloud map, and k1, k2, and k3 are influencing factors.

5. The rainfall forecasting method according to claim 1, characterized in that: The step S110 includes: S1101. Based on the influence relationship between cloud image data and precipitation data, the precipitation distribution image and the meteorological cloud image are compared, and k1, k2, k3, and x are determined by combining the meteorological characteristic data and the average precipitation data of the precipitation-affected area. a and a The value of determines the precipitation data calculation formula based on the cloud image data; S1102, analyzing the meteorological cloud map and meteorological characteristic data to predict the cloud map movement path; S1103. Determine the cloud map data that affects the precipitation impact area through the cloud map moving path, and draw a precipitation data change curve for the precipitation impact area according to a precipitation data calculation formula based on the cloud map data; the cloud map data includes at least cloud layer height and cloud layer thickness.

6. The rainfall forecasting method according to claim 1, characterized in that: The method for calculating the first predicted precipitation in step S112 is: By n The precipitation data change curve is analyzed according to the time interval t n The initial time, the end time, the area enclosed by the precipitation data change curve and the coordinate axis are used to determine the time interval t n The first predicted precipitation within .

7. The rain forecasting method according to claim 1, characterized in that: The specific steps of calculating the water level rise data in step S112 are: S1121. If the precipitation affected area is the first area, directly use the first predicted precipitation as the sub-area predicted precipitation of the reservoir; S1122. If the precipitation affected area is the second area, determine a soil water absorption change curve based on riverbank soil data, and adjust the curve according to the precipitation data; S1123, calculate time interval t n Determine the second predicted precipitation amount of the precipitation impact area based on the initial time, the end time, the precipitation data change curve and the soil absorption change curve; S1124, determining the second predicted precipitation in the precipitation-affected area as the predicted precipitation in the sub-area of ​​the reservoir; S1125. Accumulate the predicted precipitation in all sub-areas to obtain the predicted total precipitation of the reservoir, and determine the water level rise data by the ratio of the total precipitation to the reservoir area.

8. A rain forecasting system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: A data acquisition module, used to acquire first coordinate data of a precipitation detection device; The regional division module is used to divide the precipitation impact area; The first analysis module is used to calculate the average precipitation data and draw a precipitation distribution image; The second analysis module is used to determine the precipitation data change curve in combination with the cloud map data; The third analysis module is used to predict precipitation and calculate water level rise data; The precipitation warning module is used to generate precipitation warning levels.

9. The rain forecasting system according to claim 8, characterized in that: The area division module is specifically used for: The first device and the second device are divided based on the minimum distance between the precipitation detection device and the reservoir, and the first area and the second area are generated by connecting the coordinates of adjacent devices.

10. The rain forecasting system according to claim 8, characterized in that: When the first analysis module calculates the average precipitation data, the precipitation data is weightedly accumulated using the center coordinates and the distance weight.

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