A river flow velocity prediction method, device and medium

Abstract

The embodiment of the present application discloses a river velocity prediction method, device and medium, which belongs to the field of river velocity prediction technology and solves the problem of low accuracy and reliability of velocity monitoring technology. The remote sensing water level data corresponding to the cross section of the river to be measured is obtained, and the associated data is generated based on the remote sensing water level data and the preset geographic information of the river to be measured; the associated data is input into the preset velocity inversion model to obtain the water flow data corresponding to the cross section of the river to be measured; the upstream water level data corresponding to the cross section of the river to be measured is obtained, and the first velocity trend graph corresponding to the cross section of the river to be measured is generated based on the upstream water level data and the water flow data; the water flow data, weather data and associated data are input into the preset velocity prediction model to obtain the second velocity trend graph corresponding to the cross section of the river to be measured; based on the first velocity trend graph and the second velocity trend graph, the reference water flow data is determined, and the reference water flow data is compared with the preset warning threshold value to issue a river velocity warning prompt.

Description

Technical Field

[0001] The present application relates to the technical field of river velocity prediction, and in particular to a river velocity prediction method, device and medium. Background Art

[0002] As global climate change intensifies and extreme weather events occur frequently, hydrological phenomena such as river floods, floodplain floods, plain river network flows, and reservoir floods have an increasingly significant impact on human society. These flow conditions usually have the characteristics of large width-depth ratio, fast flow velocity changes, and complex flow patterns, which poses great technical challenges to the real-time monitoring and accurate prediction of flow velocity.

[0003] Among the existing flow velocity monitoring technologies, most of the common flow velocity monitoring equipment on the market is arranged on both sides of the river bank, and the velocity distribution of the entire river section is estimated by measuring the velocity at a specific point. However, this method often cannot accurately reflect the velocity changes of the entire section in rivers with large width and depth, especially in areas with complex water flow and large velocity gradient, the accuracy and reliability of the monitoring results will be seriously affected. Summary of the invention

[0004] The embodiments of the present application provide a river flow velocity prediction method, device and medium for solving the following technical problem: the existing flow velocity monitoring technology has low accuracy and reliability in monitoring results in areas with complex water flow and large flow velocity gradient.

[0005] The present application embodiment adopts the following technical solutions:

[0006] The embodiment of the present application provides a method for predicting river flow velocity, including obtaining remote sensing water level data corresponding to a cross section of a river to be measured, generating associated data based on the remote sensing water level data and preset geographic information of the river to be measured; inputting the associated data into a preset flow velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured; obtaining upstream water level data corresponding to the cross section of the river to be measured, generating a first flow velocity trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; inputting the water flow data, weather data and associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured; determining reference water flow data based on the first flow velocity trend graph and the second flow velocity trend graph, and comparing the reference water flow data with a preset warning threshold to provide a river flow velocity warning prompt.

[0007] The embodiment of the present application generates more targeted and accurate correlation data by combining remote sensing water level data with the specific location and morphology of the river, thereby more accurately analyzing the flow velocity of the river. The flow velocity inversion model can efficiently and reliably estimate the flow velocity of the river, thereby improving the speed of flow velocity prediction. By generating a flow velocity trend chart through upstream water level data and weather data, the accuracy of river flow velocity prediction can be improved, providing a more comprehensive reference for river flow velocity warning, increasing the real-time and accuracy of flow velocity prediction, and providing strong technical support for flood prevention and emergency management.

[0008] In one implementation of the present application, remote sensing water level data corresponding to a cross-section of a river to be measured is obtained, and associated data is generated based on the remote sensing water level data and preset geographic information of the river to be measured, specifically including: based on the location area corresponding to the remote sensing water level data, querying in the preset geographic information of the river to be measured to determine the current reference geographic information of the river to be measured corresponding to the location area; obtaining historical associated data corresponding to the current reference geographic information to determine historical remote sensing water level data based on the historical associated data; performing similarity calculation on the current remote sensing water level data and the historical remote sensing water level data; when the similarity calculation result does not meet the preset similarity threshold, issuing an abnormal alarm for the current remote sensing water level data; when the similarity calculation result meets the preset similarity threshold, generating associated data based on the current remote sensing water level data and the current reference geographic information.

[0009] In one implementation of the present application, before the associated data is input into a preset flow velocity inversion model to obtain the water flow data corresponding to the cross-section of the river to be measured, the method also includes: constructing a first flow velocity function based on the water depth data, flow velocity data, spatial position data and time data corresponding to the cross-section of the river to be measured; constructing a second flow velocity function based on the water depth data, flow velocity data, gravitational acceleration, spatial position data and time data corresponding to the cross-section of the river to be measured; and constructing a preset flow velocity inversion model based on the first flow velocity function and the second flow velocity function.

[0010] In one implementation of the present application, a first flow velocity trend graph corresponding to a cross-section of a river to be measured is generated based on upstream water level data and water flow data, specifically including: comparing the upstream water level data with historical upstream water level data to determine a first fluctuation value of the upstream water level; obtaining upstream weather data, and determining a second fluctuation value of the upstream water level within a preset time period in the future based on the upstream weather data; inputting the first fluctuation value and the second fluctuation value into a preset flow velocity coefficient prediction model to output an influence coefficient through the preset flow velocity coefficient prediction model; and adjusting the water flow data based on the influence coefficient to obtain a first flow velocity trend graph corresponding to the cross-section of the river to be measured within a preset time period in the future.

[0011] In one implementation of the present application, water flow data, weather data and related data are input into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the river cross-section to be measured, specifically including: inputting water flow data, weather data and related data into a preset flow velocity prediction model to obtain the predicted flow velocity value corresponding to the river cross-section to be measured; arranging the predicted flow velocity values ​​in chronological order, and performing a difference calculation for two adjacent predicted flow velocity values; when the difference calculation result is greater than a preset difference threshold, re-predicting the flow velocity corresponding to the corresponding time point; when the difference calculation results of two adjacent predicted flow velocity values ​​are not greater than the preset difference threshold, generating a second flow velocity trend graph corresponding to the river cross-section to be measured based on the predicted flow velocity value.

[0012] In one implementation of the present application, reference water flow data is determined based on a first flow velocity trend graph and a second flow velocity trend graph, specifically including: determining an increase or decrease in flow velocity within a preset time according to the first flow velocity trend graph; and determining an increase or decrease in flow velocity within a preset time affected by weather data according to the second flow velocity trend graph; calculating an increase in water level within a preset time and an increase or decrease in flow velocity within a preset time affected by weather data to obtain a change in water flow; obtaining reference water flow data corresponding to a cross-section of the river to be measured within a preset time based on the change in water flow and the water flow data.

[0013] In one implementation of the present application, based on the water flow change and the water flow data, reference water flow data corresponding to the cross-section of the river to be measured within a preset time is obtained, specifically including: sorting the water flow change corresponding to the cross-section of the river to be measured within the preset time in chronological order; and sorting the water flow data corresponding to the cross-section of the river to be measured within the preset time in chronological order; determining the water flow change and the water flow data corresponding to the same time; calculating the water flow change and the water flow data corresponding to the same time to obtain the reference water flow data corresponding to the cross-section of the river to be measured within the preset time.

[0014] In one implementation of the present application, the reference water flow data is compared with a preset warning threshold to provide a river flow velocity warning prompt, specifically including: comparing the reference water flow data within a preset time with a preset flow velocity warning table to determine the warning level corresponding to the reference water flow data within the preset time; dividing the reference water flow data within the preset time based on the warning level; and providing a warning prompt based on the different warning levels after the division, and the preset times corresponding to the different warning levels.

[0015] An embodiment of the present application provides a river flow rate prediction device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain remote sensing water level data corresponding to a cross-section of a river to be measured, and generate associated data based on the remote sensing water level data and preset geographic information of the river to be measured; input the associated data into a preset flow rate inversion model to obtain water flow data corresponding to the cross-section of the river to be measured; obtain upstream water level data corresponding to the cross-section of the river to be measured, and generate a first flow rate trend graph corresponding to the cross-section of the river to be measured based on the upstream water level data and the water flow data; input the water flow data, weather data and associated data into the preset flow rate prediction model to obtain a second flow rate trend graph corresponding to the cross-section of the river to be measured; determine reference water flow data based on the first flow rate trend graph and the second flow rate trend graph, and compare the reference water flow data with a preset warning threshold to provide a river flow rate warning prompt.

[0016] A non-volatile computer storage medium provided in an embodiment of the present application stores computer executable instructions, wherein the computer executable instructions are configured to: obtain remote sensing water level data corresponding to a cross section of a river to be measured, and generate associated data based on the remote sensing water level data and preset geographic information of the river to be measured; input the associated data into a preset flow velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured; obtain upstream water level data corresponding to the cross section of the river to be measured, and generate a first flow velocity trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; input the water flow data, weather data, and associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured; determine reference water flow data based on the first flow velocity trend graph and the second flow velocity trend graph, and compare the reference water flow data with a preset warning threshold to provide a river flow velocity warning prompt.

[0017] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: The embodiments of the present application generate more targeted and accurate correlation data by combining remote sensing water level data with the specific location and morphology of the river, thereby more accurately analyzing the flow rate of the river. The flow rate inversion model can efficiently and reliably estimate the flow rate of the river, thereby improving the speed of flow rate prediction. By generating a flow rate trend chart through upstream water level data and weather data, the accuracy of river flow rate prediction can be improved, and the river flow rate warning provides a more comprehensive reference, which increases the real-time and accuracy of flow rate prediction, and provides strong technical support for flood prevention and emergency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0019] In the figure:

[0020] Figure 1 A flow chart of a river flow velocity prediction method provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the structure of a river flow velocity prediction device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] Embodiments of the present application provide a river flow velocity prediction method, device, and medium.

[0023] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0024] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A flow chart of a river velocity prediction method provided in an embodiment of the present application is as follows: Figure 1 As shown in FIG. 1 , the river flow velocity prediction method includes the following steps:

[0026] S101, obtaining remote sensing water level data corresponding to a cross section of a river to be measured, and generating associated data based on the remote sensing water level data and preset geographic information of the river to be measured.

[0027] In one embodiment of the present application, based on the location area corresponding to the remote sensing water level data, a query is performed in the preset geographic information of the river to be measured to determine the current reference geographic information of the river to be measured corresponding to the location area. The historical associated data corresponding to the current reference geographic information is obtained to determine the historical remote sensing water level data based on the historical associated data. The current remote sensing water level data and the historical remote sensing water level data are calculated for similarity. When the similarity calculation result does not meet the preset similarity threshold, an abnormal alarm is issued for the current remote sensing water level data. When the similarity calculation result meets the preset similarity threshold, associated data is generated based on the current remote sensing water level data and the current reference geographic information.

[0028] Specifically, firstly, the remote sensing water level data of a certain location is obtained, which includes the water level information of the location, and the detailed geographic information corresponding to the location area is queried in the preset geographic information database. Such geographic information may include the name, location, flow direction, length, width, etc. of the river. According to the queried geographic information, the current reference geographic information of the river to be measured corresponding to the current location area is determined, and the historical associated data corresponding to the current reference geographic information is further obtained, wherein the historical associated data includes the water level data, weather data, flow data, etc. of the same location or the same river section in the past.

[0029] Furthermore, based on the acquired historical correlation data, the historical remote sensing water level data is determined, and the similarity between the current remote sensing water level data and the historical remote sensing water level data is calculated, wherein the similarity calculation in the embodiment of the present application can be calculated by a distance metric, such as Euclidean distance. If the similarity calculation result does not meet the preset similarity threshold, an abnormal alarm is issued for the current remote sensing water level data. If the similarity calculation result meets the preset similarity threshold, correlation data is generated based on the current remote sensing water level data and the current reference geographic information.

[0030] S102, inputting the associated data into a preset velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured.

[0031] In one embodiment of the present application, a first velocity function is constructed based on water depth data, velocity data, spatial position data, and time data corresponding to the cross section of the river to be measured. A second velocity function is constructed based on water depth data, velocity data, gravity acceleration, spatial position data, and time data corresponding to the cross section of the river to be measured. A preset velocity inversion model is constructed based on the first velocity function and the second velocity function.

[0032] Specifically, the preset flow velocity inversion model in the embodiment of the present application is as follows:

[0033] First flow rate function:

[0034] Second flow rate function:

[0035] Among them, h is the water depth, u is the flow velocity, g is the gravitational acceleration, x is the spatial position, and t is the time.

[0036] After the preset velocity inversion model is constructed, the associated data is input into the preset velocity inversion model, so as to output the water flow data corresponding to the cross section of the river to be measured through the preset velocity inversion model.

[0037] S103, obtaining upstream water level data corresponding to the cross-section of the river to be measured, and generating a first flow velocity trend graph corresponding to the cross-section of the river to be measured based on the upstream water level data and the water flow data.

[0038] In one embodiment of the present application, the upstream water level data is compared with the historical upstream water level data to determine the first fluctuation value of the upstream water level. The upstream weather data is obtained, and the second fluctuation value of the upstream water level in a future preset time period is determined based on the upstream weather data. The first fluctuation value and the second fluctuation value are input into a preset flow velocity coefficient prediction model to output an influence coefficient through the preset flow velocity coefficient prediction model. The water flow data is adjusted based on the influence coefficient to obtain a first flow velocity trend graph corresponding to the cross section of the river to be measured in the future preset time period.

[0039] Specifically, the current upstream water level data is collected, the upstream water level data under the same period or similar conditions in history are retrieved, the current data is compared with the historical data, and the water level change or deviation, i.e., the first fluctuation value, is calculated. The weather forecast data of the upstream area is obtained, and the weather forecast data is input into the upstream water level prediction model to output the change value of the upstream water level affected by the weather, i.e., the second fluctuation value of the upstream water level in the future preset time period is output.

[0040] Furthermore, the first fluctuation value and the second fluctuation value are used as input data, and the pre-trained velocity coefficient prediction model is used for calculation to output the influence coefficient. By inputting the first fluctuation value and the second fluctuation value, the influence coefficient is predicted, which reflects the influence of the upstream water level change on the downstream flow velocity. The basic water flow data of the cross section to be measured is obtained, and the influence coefficient is applied to adjust the basic water flow data to draw the adjusted first velocity trend graph.

[0041] Assume that the current upstream water level is 2 meters higher than the same period in history (the first fluctuation value), and the weather forecast shows that in the next 24 hours, there will be a heavy to torrential rain in the upstream area, and the water level is expected to rise by another 3 meters (the second fluctuation value). These two values ​​are input into the velocity coefficient prediction model, and the model outputs an influence coefficient of 1.5, which means that for every 1 meter change in the upstream water level, the downstream flow velocity will change by 1.5 times. Then, based on this influence coefficient, the water flow data of the downstream cross section to be measured is adjusted, and the first velocity trend graph is drawn, showing that the flow velocity of the cross section will gradually increase from the current 2 meters / second to 3.5 meters / second in the next 24 hours.

[0042] S104, inputting the water flow data, weather data and associated data into a preset flow velocity prediction model to obtain a second flow velocity trend diagram corresponding to the cross section of the river to be measured.

[0043] In one embodiment of the present application, water flow data, weather data and associated data are input into a preset flow velocity prediction model to obtain a predicted flow velocity value corresponding to a cross section of the river to be measured. The predicted flow velocity values ​​are arranged in chronological order, and a difference calculation is performed for two adjacent predicted flow velocity values. When the difference calculation result is greater than a preset difference threshold, the flow velocity corresponding to the corresponding time point is re-predicted. When the difference calculation results of two adjacent predicted flow velocity values ​​are not greater than the preset difference threshold, a second flow velocity trend graph corresponding to the cross section of the river to be measured is generated based on the predicted flow velocity value.

[0044] Specifically, the collected water flow data such as current flow velocity, weather data such as rainfall, wind speed, wind direction, etc., and related data are input as input and input into a preset flow velocity prediction model. The training method of the preset flow velocity prediction model in the embodiment of the present application is to take the preset water flow data, weather data and related data sample set as input, take the flow velocity corresponding to the input sample set as output, and train the preset neural network model to obtain the preset flow velocity prediction model.

[0045] Furthermore, the velocity prediction model calculates a series of predicted velocity values ​​corresponding to the cross-section of the river to be measured based on the input data. These predicted velocity values ​​are usually arranged in chronological order, reflecting the changing trend of the velocity in the future. The difference between two adjacent predicted velocity values ​​is calculated to evaluate the magnitude of the velocity change. If the difference calculation result is greater than the preset difference threshold, it means that the velocity has changed significantly in a short period of time. This may be due to inaccurate model prediction or a sudden change in external conditions. In this case, the velocity corresponding to the corresponding time point is re-predicted. If the difference calculation results of two adjacent predicted velocity values ​​are not greater than the preset difference threshold, it means that the velocity change is within a reasonable range and the prediction result is relatively reliable. A second velocity trend graph corresponding to the cross-section of the river to be measured is generated based on the predicted velocity value.

[0046] S105. Determine reference water flow data based on the first flow velocity trend graph and the second flow velocity trend graph, and compare the reference water flow data with a preset warning threshold to provide a river flow velocity warning prompt.

[0047] In one embodiment of the present application, the amount of increase or decrease in flow velocity within a preset time is determined based on the first flow velocity trend graph. And, the amount of increase or decrease in flow velocity within a preset time affected by weather data is determined based on the second flow velocity trend graph. The amount of water level increase within a preset time and the amount of increase or decrease in flow velocity within a preset time affected by weather data are calculated to obtain a water flow change. Based on the water flow change and the water flow data, reference water flow data corresponding to the cross section of the river to be measured within the preset time is obtained.

[0048] Specifically, the amount of increase or decrease in flow velocity within a preset time can be determined by obtaining the first flow velocity trend graph, and the amount of increase or decrease in flow velocity within a preset time affected by weather data can be determined by obtaining the second flow velocity trend graph. The amount of change corresponding to the first flow velocity trend graph and the amount of change corresponding to the second flow velocity trend graph are calculated to obtain the amount of water flow change. Based on the calculated amount of water flow change and the original water flow data, reference water flow data corresponding to the cross section of the river to be measured within the preset time can be generated.

[0049] In one embodiment of the present application, the water flow changes corresponding to the cross-section of the river to be measured within the preset time are sorted in chronological order. Also, the water flow data corresponding to the cross-section of the river to be measured within the preset time are sorted in chronological order. The water flow changes and water flow data corresponding to the same time are determined. The water flow changes and water flow data corresponding to the same time are calculated to obtain reference water flow data corresponding to the cross-section of the river to be measured within the preset time.

[0050] Specifically, the water flow changes within a preset time (for example, the next week) are sorted in chronological order, and after sorting, a list of water flow changes arranged in chronological order is obtained. Similarly, the water flow data corresponding to the cross-section of the river to be measured within the preset time is sorted in chronological order, and after sorting, a list of water flow data corresponding to the actual time is obtained. After having two lists sorted by time, find the corresponding water flow changes and actual water flow data at the same time point. Calculate the corresponding water flow changes and actual water flow data at the same time point to obtain reference water flow data.

[0051] In one embodiment of the present application, the reference water flow data within a preset time is compared with a preset flow rate warning table to determine the warning level corresponding to the reference water flow data within the preset time. The reference water flow data within the preset time is divided based on the warning level. Based on the different warning levels after the division and the preset times corresponding to the different warning levels, a warning prompt is given.

[0052] Specifically, the preset flow rate warning table in the embodiment of the present application includes different water flow data, and also includes warning levels corresponding to different water flow data. The reference water flow data within the preset time is compared with this flow rate warning table to determine the warning level corresponding to each reference water flow data point. After determining the warning level of each reference water flow data point, these data can be divided according to the warning level. According to the different warning levels after division and the preset time corresponding to each warning level, warning prompts can be formulated and issued. These prompts can help relevant departments and individuals prepare in advance to deal with possible flood risks.

[0053] Figure 2 This is a schematic diagram of the structure of a river flow velocity prediction device provided in an embodiment of the present application. Figure 2 As shown, a river flow rate prediction device is characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain remote sensing water level data corresponding to a cross section of a river to be measured, and generate associated data based on the remote sensing water level data and preset geographic information of the river to be measured; input the associated data into a preset flow rate inversion model to obtain water flow data corresponding to the cross section of the river to be measured; obtain upstream water level data corresponding to the cross section of the river to be measured, and generate a first flow rate trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; input the water flow data, weather data and associated data into the preset flow rate prediction model to obtain a second flow rate trend graph corresponding to the cross section of the river to be measured; determine reference water flow data based on the first flow rate trend graph and the second flow rate trend graph, and compare the reference water flow data with a preset warning threshold to provide a river flow rate warning prompt.

[0054] The embodiment of the present application also provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: obtain remote sensing water level data corresponding to a cross-section of a river to be measured, and generate associated data based on the remote sensing water level data and preset geographic information of the river to be measured; input the associated data into a preset flow velocity inversion model to obtain water flow data corresponding to the cross-section of the river to be measured; obtain upstream water level data corresponding to the cross-section of the river to be measured, and generate a first flow velocity trend graph corresponding to the cross-section of the river to be measured based on the upstream water level data and the water flow data; input the water flow data, weather data and associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross-section of the river to be measured; determine reference water flow data based on the first flow velocity trend graph and the second flow velocity trend graph, and compare the reference water flow data with a preset warning threshold to provide a river flow velocity warning prompt.

[0055] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0056] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various modifications and changes. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for predicting river flow velocity, characterized in that: The method comprises: Acquire remote sensing water level data corresponding to the cross section of the river to be measured, and generate correlation data based on the remote sensing water level data and preset geographic information of the river to be measured; Inputting the associated data into a preset velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured; Acquire upstream water level data corresponding to the cross section of the river to be measured, and generate a first flow velocity trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; Inputting the water flow data, weather data and the associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured; Based on the first flow velocity trend graph and the second flow velocity trend graph, reference water flow data is determined, and the reference water flow data is compared with a preset warning threshold value to provide a river flow velocity warning prompt; Based on the upstream water level data and the water flow data, generating a first flow velocity trend graph corresponding to the cross section of the river to be measured specifically includes: Comparing the upstream water level data with historical upstream water level data to determine a first fluctuation value of the upstream water level data; Acquire upstream weather data, and determine a second fluctuation value of upstream water level data within a future preset time period based on the upstream weather data; Inputting the first fluctuation value and the second fluctuation value into a preset flow rate coefficient prediction model to output an influence coefficient through the preset flow rate coefficient prediction model; The water flow data is adjusted based on the influence coefficient to obtain a first flow velocity trend diagram corresponding to the cross section of the river to be measured within a future preset time period.

2. A river flow velocity prediction method according to claim 1, characterized in that: The step of acquiring remote sensing water level data corresponding to the cross section of the river to be measured and generating associated data based on the remote sensing water level data and preset geographic information of the river to be measured specifically includes: Based on the location area corresponding to the remote sensing water level data, querying in the preset geographic information of the river to be measured to determine the current reference geographic information of the river to be measured corresponding to the location area; Acquire historical associated data corresponding to the current reference geographic information, so as to determine historical remote sensing water level data based on the historical associated data; Calculate the similarity between the current remote sensing water level data and the historical remote sensing water level data; When the similarity calculation result does not meet the preset similarity threshold, an abnormal alarm is issued for the current remote sensing water level data; When the similarity calculation result meets the preset similarity threshold, associated data is generated based on the current remote sensing water level data and the current reference geographic information.

3. A river flow velocity prediction method according to claim 1, characterized in that: Before inputting the associated data into a preset velocity inversion model to obtain the water flow data corresponding to the cross section of the river to be measured, the method further includes: Constructing a first velocity function based on water depth data, velocity data, spatial position data and time data corresponding to the cross section of the river to be measured; Constructing a second velocity function based on the water depth data, velocity data, gravity acceleration, spatial position data and time data corresponding to the cross section of the river to be measured; A preset flow velocity inversion model is constructed based on the first flow velocity function and the second flow velocity function.

4. A river flow velocity prediction method according to claim 1, characterized in that: The step of inputting the water flow data, weather data and the associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured specifically includes: Inputting the water flow data, the weather data and the associated data into the preset flow velocity prediction model to obtain the predicted flow velocity value corresponding to the cross section of the river to be measured; Arranging the predicted flow velocity values ​​in chronological order, and performing a difference calculation between two adjacent predicted flow velocity values; When the difference calculation result is greater than the preset difference threshold, the flow velocity corresponding to the corresponding time point is re-predicted; When the calculated difference results of two adjacent predicted flow velocity values ​​are not greater than the preset difference threshold, a second flow velocity trend graph corresponding to the cross section of the river to be measured is generated based on the predicted flow velocity values.

5. A river flow velocity prediction method according to claim 1, characterized in that: The determining of reference water flow data based on the first flow velocity trend graph and the second flow velocity trend graph specifically includes: According to the first flow velocity trend graph, determining the increase or decrease of the flow velocity within a preset time; and, according to the second flow velocity trend graph, determining the increase or decrease in flow velocity within a preset time period affected by weather data; Calculate the increase or decrease of the flow velocity within the preset time and the increase or decrease of the flow velocity within the preset time affected by the weather data to obtain the water flow change; Based on the water flow variation and the water flow data, reference water flow data corresponding to the cross section of the river to be measured within a preset time is obtained.

6. A river flow velocity prediction method according to claim 5, characterized in that: Based on the water flow variation and the water flow data, reference water flow data corresponding to the cross section of the river to be measured within a preset time is obtained, specifically including: Sort the water flow changes corresponding to the cross-sections of the river to be measured within a preset time in chronological order; And, sorting the water flow data corresponding to the cross-section of the river to be measured within a preset time in chronological order; Determine the water flow change and the water flow data corresponding to the same time; The water flow change corresponding to the same time is calculated with the water flow data to obtain reference water flow data corresponding to the cross section of the river to be measured within a preset time.

7. A river flow velocity prediction method according to claim 1, characterized in that: The comparing the reference water flow data with a preset warning threshold to provide a river flow velocity warning prompt specifically includes: Comparing the reference water flow data within a preset time with a preset flow rate warning table to determine the warning level corresponding to the reference water flow data within the preset time; Dividing the reference water flow data within the preset time based on the warning level; Based on the different warning levels after classification and the preset times corresponding to the different warning levels, warning prompts are given.

8. A river flow velocity prediction device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire remote sensing water level data corresponding to the cross section of the river to be measured, and generate correlation data based on the remote sensing water level data and preset geographic information of the river to be measured; Inputting the associated data into a preset velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured; Acquire upstream water level data corresponding to the cross section of the river to be measured, and generate a first flow velocity trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; Inputting the water flow data, weather data and the associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured; Based on the first flow velocity trend graph and the second flow velocity trend graph, reference water flow data is determined, and the reference water flow data is compared with a preset warning threshold value to provide a river flow velocity warning prompt; Based on the upstream water level data and the water flow data, generating a first flow velocity trend graph corresponding to the cross section of the river to be measured specifically includes: Comparing the upstream water level data with historical upstream water level data to determine a first fluctuation value of the upstream water level data; Acquire upstream weather data, and determine a second fluctuation value of upstream water level data within a future preset time period based on the upstream weather data; Inputting the first fluctuation value and the second fluctuation value into a preset flow rate coefficient prediction model to output an influence coefficient through the preset flow rate coefficient prediction model; The water flow data is adjusted based on the influence coefficient to obtain a first flow velocity trend diagram corresponding to the cross section of the river to be measured within a future preset time period.

9. A non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to: Acquire remote sensing water level data corresponding to the cross section of the river to be measured, and generate correlation data based on the remote sensing water level data and preset geographic information of the river to be measured; Inputting the associated data into a preset velocity inversion model to obtain water flow data corresponding to the cross section of the river to be measured; Acquire upstream water level data corresponding to the cross section of the river to be measured, and generate a first flow velocity trend graph corresponding to the cross section of the river to be measured based on the upstream water level data and the water flow data; Inputting the water flow data, weather data and the associated data into a preset flow velocity prediction model to obtain a second flow velocity trend graph corresponding to the cross section of the river to be measured; Based on the first flow velocity trend graph and the second flow velocity trend graph, reference water flow data is determined, and the reference water flow data is compared with a preset warning threshold value to provide a river flow velocity warning prompt; Based on the upstream water level data and the water flow data, generating a first flow velocity trend graph corresponding to the cross section of the river to be measured specifically includes: Comparing the upstream water level data with historical upstream water level data to determine a first fluctuation value of the upstream water level data; Acquire upstream weather data, and determine a second fluctuation value of upstream water level data within a future preset time period based on the upstream weather data; Inputting the first fluctuation value and the second fluctuation value into a preset flow rate coefficient prediction model to output an influence coefficient through the preset flow rate coefficient prediction model; The water flow data is adjusted based on the influence coefficient to obtain a first flow velocity trend diagram corresponding to the cross section of the river to be measured within a future preset time period.

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