A method, device, equipment and medium for predicting reservoir water surface line

Abstract

The present invention relates to the field of data processing, and its purpose is to provide a method, device, equipment and medium for predicting the water surface line of a reservoir, the method comprising: obtaining the inflow information, prediction time, current water level and cross-sectional information and historical flow and water level information of each main and tributary river channel of the target reservoir; establishing a hydrodynamic model; arranging points for each main and tributary river channel according to the inflow information, prediction time, current water level and hydrodynamic model to obtain multiple prediction points; predicting the water level of the prediction point according to the inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range; and generating predicted water surface lines at different times within the prediction time range through the water level prediction data. The present invention arranges multiple prediction points for each main and tributary river channel in combination with multiple conditions, thereby reducing the number of spatial points compared to a large-scale average point arrangement, and at the same time being able to ensure prediction accuracy, effectively improving the utilization efficiency of each prediction point, and improving the prediction simulation efficiency of the water surface line.

Description

Technical Field

[0001] The invention relates to the field of data processing, and in particular to a method, a device, equipment and a medium for predicting a reservoir water surface line. Background Art

[0002] Large reservoirs have the functions of flood control, irrigation, power generation, water supply, and shipping. The change of water surface line is an important basis for the scientific dispatch of reservoirs. Predicting the water surface line plays a positive role in realizing the flood control and benefit-promoting functions of reservoirs. Especially under different water inflow conditions during the flood season, if the water surface line of the reservoir can be accurately predicted, the reservoir capacity can be used to intercept and store floods, while not causing the risk of flooding upstream, and reducing the peak flow entering the downstream river channel, the purpose of reducing flood disasters can be achieved. However, in the prior art, the existing data is calculated based on historical conditions through manual observation and experience judgment, and the prediction results have large errors, are time-consuming and inefficient. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method for predicting a reservoir water surface line, so as to solve the problem of large error and low efficiency in water surface line prediction in the prior art.

[0004] In order to achieve the above object, the present invention provides the following technical solutions:

[0005] An embodiment of the present invention provides a method for predicting a reservoir water surface line, comprising:

[0006] Obtaining water inflow information, forecast duration, current water level of the target reservoir, and cross-sectional information and historical flow and water level information of the main and tributary rivers of the target reservoir;

[0007] Establishing a hydrodynamic model based on the cross-section information and historical flow and water level information;

[0008] Arrange points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain a plurality of prediction points corresponding to each river channel;

[0009] Predicting the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range;

[0010] The predicted water surface lines at different times within the predicted time range are generated using the water level prediction data.

[0011] Optionally, establishing a hydrodynamic model based on the cross-section information and historical flow and water level information includes:

[0012] Dividing the main and tributary river channels into multiple river sections according to preset distances;

[0013] Extracting flow data and water level data corresponding to each river section from the historical flow and water level information;

[0014] A hydrodynamic model for each river section is established based on the cross-section information, flow data and water level data.

[0015] Optionally, the arranging points for each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain a plurality of prediction points corresponding to each river channel includes:

[0016] Points are distributed in each river section according to the preset distribution interval to obtain the initial prediction points of each river section;

[0017] Analyze the water level change rate of the initial prediction point based on the water inflow information, the prediction duration, the hydrodynamic model and the current water level;

[0018] Analyze the supplementary prediction points of each river section according to the water level change rate, the prediction duration and the number of points corresponding to the preset time length;

[0019] Points are arranged for each river channel based on the initial prediction points and the supplementary prediction points corresponding to each river section to obtain a plurality of prediction points corresponding to each river channel.

[0020] Optionally, analyzing the water level change rate of the initial prediction point based on the water inflow information, the prediction duration, the hydrodynamic model and the current water level includes:

[0021] Obtaining the current time, and obtaining the predicted time when the predicted duration ends based on the current time and the predicted duration;

[0022] The water inflow information and the prediction time are input into the hydrodynamic model to obtain the predicted water level of each initial prediction point;

[0023] The water level change rate of the initial prediction point is calculated according to the predicted water level and the current water level.

[0024] Optionally, the analyzing of the supplementary prediction points for each river section according to the water level change rate, the prediction duration and the point quantity corresponding to the preset time length includes:

[0025] The total number of supplementary distribution points is obtained according to the predicted duration, the number of river sections and the distribution point quantity corresponding to the preset time length;

[0026] Analyze the distribution ratio of each river section based on the water level change rate of each initial prediction point in each river section;

[0027] The supplementary prediction points for each river section are obtained according to the distribution point ratio and the total number of supplementary distribution points.

[0028] Optionally, the point distribution ratio of each river section is analyzed based on the water level change rate of each initial prediction point in each river section, including:

[0029] Perform logarithmic transformation on the water level change rate of each initial prediction point to obtain the initial coefficient of each initial prediction point;

[0030] The initial coefficients corresponding to the initial prediction points in each river section are superimposed and calculated to obtain the first coefficient of each river section;

[0031] Calculate the total coefficient of the river channel based on the first coefficient of each river section;

[0032] The distribution ratio of each river section is obtained by calculating the proportion of the first coefficient in the total coefficient.

[0033] Optionally, the method further includes:

[0034] Compare the predicted water surface line at each moment with the preset elevation data of the river channel to obtain the risk prediction point of the water surface overflowing the river channel, wherein the preset elevation data is the elevation data of the two ends of the river channel section corresponding to the predicted point;

[0035] Analyzing the risk prediction point to obtain the river channel position, overflow direction and overflow water level corresponding to the risk prediction point;

[0036] The river channel location, overflow direction, overflow water level and preset elevation data corresponding to the risk prediction point are summarized to obtain a risk prediction result, and the risk prediction result is reported.

[0037] The embodiment of the present invention also provides a device for predicting a reservoir water surface line, comprising:

[0038] An acquisition module is used to obtain the water inflow information, prediction duration, current water level of the target reservoir, and the cross-sectional information and historical flow and water level information of the main and tributary rivers of the target reservoir;

[0039] A model building module, used to build a hydrodynamic model based on the cross-section information and historical flow and water level information;

[0040] A point distribution module is used to distribute points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain multiple prediction points corresponding to each river channel;

[0041] A prediction module, used to predict the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range;

[0042] A generation module is used to generate predicted water surface lines at different times within the prediction time range through the water level prediction data.

[0043] An embodiment of the present invention further provides an electronic device, including:

[0044] A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting the water surface line of a reservoir provided in an embodiment of the present invention by executing the computer instructions.

[0045] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting a reservoir water surface line provided by an embodiment of the present invention.

[0046] The technical solution of the present invention has the following advantages:

[0047] The present invention provides a method for predicting the water surface line of a reservoir, by obtaining the inflow information, prediction time, current water level of the target reservoir and the cross-sectional information and historical flow and water level information of each main and tributary river channel of the target reservoir; establishing a hydrodynamic model based on the cross-sectional information and historical flow and water level information; arranging points for each main and tributary river channel according to the inflow information, prediction time, current water level and hydrodynamic model to obtain multiple prediction points corresponding to each river channel; predicting the water level of each prediction point according to the inflow information and hydrodynamic model to obtain water level prediction data within the prediction time range; and generating predicted water surface lines at different times within the prediction time range through the water level prediction data. The present invention arranges multiple prediction points for each main and tributary river channel in combination with multiple conditions, thereby reducing the number of spatial points compared to a large-scale average point arrangement, and at the same time being able to ensure prediction accuracy, effectively improving the utilization efficiency of each prediction point, and improving the prediction simulation efficiency of the water surface line. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 It is a flow chart of a method for predicting a reservoir water surface line in an embodiment of the present invention;

[0050] Figure 2 A flow chart of establishing a hydrodynamic model based on cross-section information and historical flow and water level information according to an embodiment of the present invention;

[0051] Figure 3This is a flow chart of arranging points for each main and tributary river channel according to an embodiment of the present invention;

[0052] Figure 4 Schematic diagram of the number of points corresponding to water level changes according to an embodiment of the present invention;

[0053] Figure 5 A flow chart of analyzing the water level change rate of an initial prediction point according to an embodiment of the present invention;

[0054] Figure 6 A flowchart of analyzing supplementary prediction points for each river section according to an embodiment of the present invention;

[0055] Figure 7 A flow chart of analyzing the distribution ratio of each river section according to an embodiment of the present invention;

[0056] Figure 8 A flowchart of risk prediction according to an embodiment of the present invention;

[0057] Fig. 9 It is a structural schematic diagram of a device for predicting a reservoir water surface line in an embodiment of the present invention;

[0058] Fig.10 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0060] According to an embodiment of the present invention, a method embodiment for predicting a reservoir water surface line is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0061] In this embodiment, a method for predicting the water surface line of a reservoir is provided, which can be used in the above-mentioned terminal equipment, such as a computer, etc. Figure 1 As shown, the method for predicting the reservoir water surface line includes the following steps:

[0062] Step S1: Obtain the inflow information, prediction time, current water level, and cross-sectional information and historical flow and water level information of each main and tributary river of the target reservoir. Specifically, the inflow information includes: inflow time, inflow water level, inflow flow, inflow location, etc.; the prediction time is the length of time for prediction based on demand; the current water level is the current water level height at each location of the reservoir; the cross-sectional information includes the cross-sectional dimensions of the river section perpendicular to the water flow direction and the cross-sectional area of ​​the water flow.

[0063] Step S2: Establish a hydrodynamic model based on cross-sectional information and historical flow and water level information. Specifically, by establishing a distributed hydrodynamic model, accurate analysis can be performed on each river section in the subsequent analysis process, thereby determining the water level changes at each prediction point in each river channel, effectively improving the accuracy of the analysis.

[0064] Step S3: According to the water inflow information, prediction time, current water level and hydrodynamic model, points are arranged in each main and tributary river channel to obtain multiple prediction points corresponding to each river channel. Specifically, by analyzing the water inflow information, prediction time and current water level, different numbers of prediction points are arranged in different sections of different river channels. Compared with the traditional uniform point arrangement method, this solution reduces the number of points while ensuring the prediction accuracy, effectively improves the utilization efficiency of each prediction point, and thus improves the prediction simulation efficiency of the water surface line.

[0065] Step S4: predict the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range. Specifically, by predicting the water level of each prediction point, it is convenient to generate an accurate prediction water surface line based on the prediction data.

[0066] Step S5: Generate predicted water surface lines at different times within the prediction time range through water level prediction data. Specifically, by generating predicted water surface lines at different times, it can provide decision-making references for reservoir dispatching departments, facilitate early understanding and deployment of reservoir dispatching plans, and especially provide timely and effective support for flood control and disaster reduction dispatching.

[0067] Through the above steps S1 to S5, the method for predicting the water surface line of a reservoir provided by an embodiment of the present invention arranges multiple prediction points for each main and tributary river channel by combining multiple conditions, thereby reducing the number of spatial points compared to a large-scale average distribution of points, while ensuring the prediction accuracy, effectively improving the utilization efficiency of each prediction point, and improving the prediction simulation efficiency of the water surface line.

[0068] Specifically, in one embodiment, the above step S2, such as Figure 2 As shown, the specific steps include:

[0069] Step S21: Divide each main and tributary river channel into multiple river sections according to a preset distance. Specifically, by dividing, the calculation accuracy is improved, and positioning and subsequent search are facilitated.

[0070] Step S22: extracting the flow data and water level data corresponding to each river section from the historical flow and water level information.

[0071] Step S23: Establish a hydrodynamic model for each river section based on the cross-section information, flow data and water level data.

[0072] Specifically, the hydrodynamic model includes the water flow continuity equation and the water flow motion equation:

[0073] Continuity equation for water flow:

[0074] The equation of motion for water flow is:

[0075] Among them, t represents time, x represents space, B represents water surface width, Z represents water level, Q represents flow rate, and q represents water volume. L is the lateral inflow flow on the river section, u is the average flow velocity of the section, g is the gravitational acceleration, A is the cross-sectional area of ​​the water flow, and n i is the roughness coefficient of the ith river section, and R is the hydraulic radius.

[0076] By analyzing the historical data of each river section and establishing a distributed hydrodynamic model, accurate analysis can be performed on each river section in the subsequent analysis process, thereby determining the water level changes at each prediction point in each river channel, effectively improving the accuracy of the analysis.

[0077] Specifically, in one embodiment, the above step S3, as Figure 3 As shown, the specific steps include:

[0078] Step S31: arranging points for each river section according to a preset arrangement interval to obtain initial prediction points for each river section. Specifically, for example, a river channel is divided into 6 river sections, and each river section has 6 initial prediction points, then the total number of initial prediction points for the river channel is 36.

[0079] Step S32: Analyze the water level change rate of the initial prediction point based on the water inflow information, prediction duration, hydrodynamic model and current water level. Specifically, based on the water inflow information and prediction duration, the water levels of all initial prediction points at the end of the prediction duration are predicted, and the water level change rate of each initial prediction point can be analyzed based on the predicted end water level and the current water level.

[0080] Step S33: Analyze the additional prediction points for each river section according to the water level change rate, prediction duration, and the number of points corresponding to the preset time length. Figure 4As shown, more prediction points are added to river sections with large water level change rates, and fewer or no prediction points are added to river sections with small water level change rates. In this way, the number of points can be reduced, the amount of calculation can be reduced, and the efficiency can be improved while meeting the accuracy.

[0081] Step S34: Based on the initial prediction points and supplementary prediction points corresponding to each river section, each river channel is distributed to obtain multiple prediction points corresponding to each river channel. Specifically, if the accuracy of this scheme is achieved by using the ordinary uniform distribution method, it is necessary to distribute the same prediction points as those in the river sections with large water level change rates in this scheme in all river sections. However, in actual situations, the distribution of a large number of prediction points in river sections with small water level change rates does not significantly improve the accuracy, but greatly increases the amount of calculation and reduces the overall calculation efficiency. If the ordinary uniform distribution method is used to reduce the distribution density and the number of distribution points, the simulation accuracy of this scheme cannot be achieved. Therefore, this scheme distributes the number of prediction points according to the change amplitude of the water surface line, which can reduce the number of distribution points and the amount of calculation while ensuring accuracy, thereby improving efficiency.

[0082] Specifically, in one embodiment, the above step S32 is as follows: Figure 5 As shown, the specific steps include:

[0083] Step S321: Acquire the current time, and obtain the predicted time when the predicted duration ends based on the current time and the predicted duration.

[0084] Step S322: The predicted water level of each initial prediction point is obtained by inputting the water inflow information and the prediction time into the hydrodynamic model.

[0085] Step S323: Calculate the water level change rate of the initial prediction point according to the predicted water level and the current water level.

[0086] Specifically, the water level Z at the predicted time T at the end of the prediction period T With the current water level Z 0 For comparison, the ratio is the water level change rate r = (Z T -min(Z 0 )) / Z 0 , where min(Z 0 ) is the minimum value of the current water level corresponding to the position of the initial prediction point at the current moment. The water level change rate for the jth prediction point is r j By calculating the water level change rate, it is convenient to allocate the number of supplementary prediction points according to the water level change amplitude.

[0087] Specifically, in one embodiment, the above step S33 is as follows: Figure 6 As shown, the specific steps include:

[0088] Step S331: The total number of supplementary points is obtained according to the prediction duration, the number of river sections and the number of points corresponding to the preset time length. Specifically, the number of points corresponding to the preset time length is, for example, 12 points are distributed on average to each river section every hour. If the required prediction time is shorter, it can be reduced to 3-6 points. If the above-mentioned river channel is divided into 6 river sections, the total number of supplementary points for the river channel predicted for 1 hour is 72; if the prediction is 0.5 hours, the total number of supplementary points for the river channel is 36.

[0089] Step S332: Analyze the distribution point ratio of each river section based on the water level change rate of each initial prediction point in each river section. Specifically, since each river section contains multiple initial prediction points, by analyzing the water level change rate of each initial prediction point, the proportion coefficient of each river section can be comprehensively calculated, thereby obtaining the distribution point ratio of each river section.

[0090] Step S333: Obtain the supplementary prediction points for each river section according to the distribution point ratio and the total number of supplementary distribution points. Specifically, more prediction points are added to river sections with large water level change rates, and fewer prediction points or no prediction points are added to river sections with small water level change rates. In this way, the number of distribution points can be reduced, the amount of calculation can be reduced, and the efficiency can be improved while meeting the accuracy.

[0091] Specifically, in one embodiment, the above step S332 is as follows: Figure 7 As shown, the specific steps include:

[0092] Step S3321: Perform logarithmic transformation on the water level change rate of each initial prediction point to obtain the initial coefficient of each initial prediction point.

[0093] Step S3322: The initial coefficients corresponding to the initial prediction points in each river section are superimposed and calculated to obtain the first coefficient of each river section.

[0094] Step S3323: Calculate the total coefficient of the river channel based on the first coefficient of each river section.

[0095] Step S3324: The distribution ratio of each river section is obtained by calculating the proportion of the first coefficient in the total coefficient.

[0096] Specifically, for each prediction point, the water level change rate r j Perform logarithmic transformation to obtain the corresponding initial coefficient rs j =lg(r j ), the first coefficient of the i-th river section is Among them, Ni 1 is the order of the first initial prediction point of the river section, Ni 6 is the order of the sixth initial prediction point of the river section. The total coefficient RS of the above river channel is the sum of the first coefficients of each river section. Therefore, the distribution ratio of the supplementary prediction points of the ith river section is RSi / RS.

[0097] Specifically, in one embodiment, the above method, such as Figure 8 As shown, the specific steps include:

[0098] Step S51: Compare the predicted water surface line at each moment with the preset elevation data of the river channel to obtain the risk prediction point of the water surface overflowing the river channel. The preset elevation data is the elevation data of the two ends of the river channel section corresponding to the prediction point.

[0099] Step S52: Analyze the risk prediction point to obtain the river position, overflow direction and overflow water level corresponding to the risk prediction point. Specifically, by comparison, when the water level of a certain prediction point exceeds the elevation of both ends of the river section, overflow will occur, causing flooding risk.

[0100] Step S53: Summarize the river channel location, overflow direction, overflow water level and preset elevation data corresponding to the risk prediction point to obtain the risk prediction result, and report the risk prediction result.

[0101] Specifically, through prediction comparison, the location of the river channel where risks exist, as well as the overflow direction and overflow water level can be determined. Especially in the flood season, predictions can be made based on different water inflow information, so that the risk prediction results can be reported, making it easy to take measures in advance to use the reservoir capacity to intercept floods. While not causing the risk of flooding upstream, the peak flow entering the downstream river channel can be reduced, thereby achieving the purpose of reducing flood disasters.

[0102] In the present embodiment, a device for predicting the water surface line of a reservoir is also provided, and the device is used to implement the above-mentioned embodiment and the preferred embodiment, and the description thereof will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and contemplated.

[0103] This embodiment provides a device for predicting the water surface line of a reservoir, such as Fig. 9 As shown, including:

[0104] The acquisition module 101 is used to obtain the water inflow information, prediction time, current water level and cross-sectional information and historical flow and water level information of each main and tributary river channel of the target reservoir. For details, please refer to the relevant description of step S1 in the above method embodiment, which will not be repeated here.

[0105] The model building module 102 is used to build a hydrodynamic model based on the cross-sectional information and the historical flow and water level information. For details, please refer to the relevant description of step S2 in the above method embodiment, which will not be repeated here.

[0106] The point distribution module 103 is used to distribute points on each main and tributary river channel according to the water inflow information, prediction time, current water level and hydrodynamic model to obtain multiple prediction points corresponding to each river channel. For details, please refer to the relevant description of step S3 in the above method embodiment, which will not be repeated here.

[0107] The prediction module 104 is used to predict the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range. For details, please refer to the relevant description of step S4 in the above method embodiment, which will not be repeated here.

[0108] The generation module 105 is used to generate predicted water surface lines at different times within the prediction time range through the water level prediction data. For details, please refer to the relevant description of step S5 in the above method embodiment, which will not be repeated here.

[0109] The device for predicting the water surface line of a reservoir in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0110] The further functional description of each of the above modules is the same as that of the above corresponding embodiments and will not be repeated here.

[0111] According to an embodiment of the present invention, there is also provided an electronic device, such as Fig.10 As shown, the electronic device may include a processor 1001 and a memory 1002, wherein the processor 1001 and the memory 1002 may be connected via a bus or other means. Fig.10 The example of connecting through bus is taken in the following.

[0112] The processor 1001 may be a central processing unit (CPU). The processor 1001 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0113] The memory 1002 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the methods in the method embodiments of the present invention. The processor 1001 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 1002, that is, implementing the methods in the above method embodiments.

[0114] The memory 1002 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created by the processor 1001, etc. In addition, the memory 1002 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 1002 may optionally include a memory remotely arranged relative to the processor 1001, and these remote memories may be connected to the processor 1001 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0115] One or more modules are stored in the memory 1002 , and when executed by the processor 1001 , the method in the above method embodiment is executed.

[0116] The specific details of the above electronic device can be understood by referring to the corresponding related descriptions and effects in the above method embodiments, and will not be repeated here.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0118] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for predicting a reservoir water surface line, characterized in that: include: Obtaining water inflow information, forecast duration, current water level of the target reservoir, and cross-sectional information and historical flow and water level information of the main and tributary rivers of the target reservoir; Establishing a hydrodynamic model based on the cross-section information and historical flow and water level information; Arrange points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain a plurality of prediction points corresponding to each river channel; Predicting the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range; Generate predicted water surface lines at different times within the predicted time range through the water level prediction data; The method of distributing points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain a plurality of prediction points corresponding to each river channel includes: Points are distributed in each river section according to the preset distribution interval to obtain the initial prediction points of each river section; Analyze the water level change rate of the initial prediction point based on the water inflow information, the prediction duration, the hydrodynamic model and the current water level; Analyze the supplementary prediction points of each river section according to the water level change rate, the prediction duration and the number of points corresponding to the preset time length; Arranging points for each river channel based on the initial prediction points corresponding to each river section and the supplementary prediction points to obtain multiple prediction points corresponding to each river channel; The method of analyzing the supplementary prediction points of each river section according to the water level change rate, the prediction duration and the point quantity corresponding to the preset time length includes: The total number of supplementary distribution points is obtained according to the predicted duration, the number of river sections and the distribution point quantity corresponding to the preset time length; Analyze the distribution ratio of each river section based on the water level change rate of each initial prediction point in each river section; The supplementary prediction points for each river section are obtained according to the distribution point ratio and the total number of supplementary distribution points.

2. The method for predicting a reservoir water surface line according to claim 1, characterized in that: The step of establishing a hydrodynamic model based on the cross-section information and the historical flow and water level information includes: Dividing the main and tributary river channels into multiple river sections according to preset distances; Extracting flow data and water level data corresponding to each river section from the historical flow and water level information; A hydrodynamic model for each river section is established based on the cross-section information, flow data and water level data.

3. The method for predicting a reservoir water surface line according to claim 1, characterized in that: The analyzing the water level change rate of the initial prediction point based on the water inflow information, the prediction duration, the hydrodynamic model and the current water level includes: Obtaining the current time, and obtaining the predicted time when the predicted duration ends based on the current time and the predicted duration; The water inflow information and the prediction time are input into the hydrodynamic model to obtain the predicted water level of each initial prediction point; The water level change rate of the initial prediction point is calculated according to the predicted water level and the current water level.

4. The method for predicting a reservoir water surface line according to claim 1, characterized in that: The point distribution ratio of each river section is analyzed based on the water level change rate of each initial prediction point in each river section, including: Perform logarithmic transformation on the water level change rate of each initial prediction point to obtain the initial coefficient of each initial prediction point; The initial coefficients corresponding to the initial prediction points in each river section are superimposed and calculated to obtain the first coefficient of each river section; Calculate the total coefficient of the river channel based on the first coefficient of each river section; The distribution ratio of each river section is obtained by calculating the proportion of the first coefficient in the total coefficient.

5. The method for predicting a reservoir water surface line according to claim 1, characterized in that: The method further comprises: Compare the predicted water surface line at each moment with the preset elevation data of the river channel to obtain the risk prediction point of the water surface overflowing the river channel, wherein the preset elevation data is the elevation data of the two ends of the river channel section corresponding to the predicted point; Analyzing the risk prediction point to obtain the river channel position, overflow direction and overflow water level corresponding to the risk prediction point; The river channel location, overflow direction, overflow water level and preset elevation data corresponding to the risk prediction point are summarized to obtain a risk prediction result, and the risk prediction result is reported.

6. A device for predicting the water surface line of a reservoir, characterized in that: include: An acquisition module is used to obtain the water inflow information, prediction duration, current water level of the target reservoir, and the cross-sectional information and historical flow and water level information of the main and tributary rivers of the target reservoir; A model building module, used to build a hydrodynamic model based on the cross-section information and historical flow and water level information; A point distribution module is used to distribute points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain multiple prediction points corresponding to each river channel; A prediction module, used to predict the water level of each prediction point according to the water inflow information and the hydrodynamic model to obtain water level prediction data within the prediction time range; A generating module, used for generating predicted water surface lines at different times within the predicted time range according to the water level prediction data; The method of distributing points on each main and tributary river channel according to the water inflow information, the prediction duration, the current water level and the hydrodynamic model to obtain a plurality of prediction points corresponding to each river channel includes: Points are distributed in each river section according to the preset distribution interval to obtain the initial prediction points of each river section; Analyze the water level change rate of the initial prediction point based on the water inflow information, the prediction duration, the hydrodynamic model and the current water level; Analyze the supplementary prediction points of each river section according to the water level change rate, the prediction duration and the number of points corresponding to the preset time length; Arranging points for each river channel based on the initial prediction points corresponding to each river section and the supplementary prediction points to obtain multiple prediction points corresponding to each river channel; The method of analyzing the supplementary prediction points of each river section according to the water level change rate, the prediction duration and the point quantity corresponding to the preset time length includes: The total number of supplementary distribution points is obtained according to the predicted duration, the number of river sections and the distribution point quantity corresponding to the preset time length; Analyze the distribution ratio of each river section based on the water level change rate of each initial prediction point in each river section; The supplementary prediction points for each river section are obtained according to the distribution point ratio and the total number of supplementary distribution points.

7. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for predicting the water surface line of a reservoir as described in any one of claims 1-5 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for predicting a reservoir water surface line according to any one of claims 1 to 5.

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