A short-term hydrological forecasting method and system for a dam site section

Abstract

The present application discloses a short-term hydrological forecasting method and system for a dam site section, relating to data processing and hydrological forecasting technologies, including: pre-establishing a dam area terrain model; obtaining video monitoring data of the historical dam area, rainfall data of the area covered by the dam, and historical water level monitoring data of the dam area; finding out the water level floating range corresponding to the water level monitoring data according to the historical water level monitoring data of the dam area; constructing a training data set and training a short-term hydrological prediction model; performing prediction by using the trained short-term hydrological prediction model according to the current video monitoring data and water level monitoring data; and, simulating the result in the pre-established dam area terrain model according to the predicted result, the current video monitoring data and water level monitoring data. On the one hand, the present application can improve the accuracy of short-term hydrological forecasting of the dam site section, and on the other hand, improve the auxiliary effect of short-term hydrological forecasting of the dam site section on users.

Description

Technical Field

[0001] The present application relates to the fields of data processing and hydrological forecasting, and particularly to a short-term hydrological forecasting method and system for a dam site cross-section. Background Art

[0002] Hydrological forecasting is of great significance in the fields of flood control, drought resistance, and rational utilization of water resources. Hydrologic forecasting refers to making qualitative or quantitative predictions about the hydrological conditions of a certain water body, a certain area, or a certain hydrological station within a certain period of time in the future based on previous or current hydrometeorological data. In actual production and life, basin hydrological forecasting can provide important decision-making support for basin flood and drought disaster prevention and control, safe and economic operation of reservoir groups, scientific allocation of water resources, and social sustainable development. At present, hydrological forecasting is often realized by using hydrological models. A hydrological model refers to an approximate scientific model obtained by generalizing complex hydrological phenomena and processes through simulation methods.

[0003] On the one hand, existing forecasting models have problems with forecasting accuracy, and the forecasting accuracy is affected by many uncertain factors, such as model input uncertainty and model structure uncertainty. On the other hand, the forecasting results cannot be intuitively reflected, which reduces the auxiliary effect of the forecasting results on users. Summary of the Invention

[0004] The embodiments of the present application provide a short-term hydrological forecasting method and system for a dam site cross-section. By combining local monitoring videos and water level data and constructing a dam area terrain model, on the one hand, the accuracy of short-term hydrological forecasting for the dam site cross-section is improved, and on the other hand, the auxiliary effect of short-term hydrological forecasting for the dam site cross-section on users is improved.

[0005] The embodiments of the present application propose a short-term hydrological forecasting method for a dam site cross-section, including:

[0006] Pre-establish a dam area terrain model, and establish a cross-section based on the distribution of the units behind the dam according to the dam area terrain model;

[0007] Obtain video monitoring data of the historical dam area, rainfall data of the area covered by the dam area, and historical water level monitoring data of the dam area;

[0008] Determine relevant video frames from the video monitoring data according to the historical water level monitoring data of the dam area;

[0009] Find out the water level floating range corresponding to the water level monitoring data in the relevant video frames;

[0010] Use the water level monitoring data and the corresponding fluctuation range as a data group to construct a training data set;

[0011] Adding labels to the data groups in the training dataset based on the rainfall data to train a short-term hydrological prediction model using the labeled training dataset;

[0012] Performing prediction using the trained short-term hydrological prediction model according to the current video monitoring data and water level monitoring data; and,

[0013] Performing result simulation on the pre-established dam area terrain model and cross-section according to the predicted results, the current video monitoring data, and the water level monitoring data.

[0014] Optionally, establishing a cross-section based on the dam area terrain model according to the distribution of the units behind the dam includes:

[0015] Obtaining the historical power generation data of the units behind the dam to determine the power generation utilization rate of each unit;

[0016] Determining the positions corresponding to the units with utilization rates higher than the preset threshold in the dam area terrain model and establishing a cross-section.

[0017] Optionally, determining relevant video frames from the video monitoring data according to the historical water level monitoring data of the dam area includes:

[0018] Determining the time stamp of any water level monitoring data;

[0019] Determining the associated video segment in the video monitoring data according to the time stamp;

[0020] Extracting several video frames at a set interval from the associated video segment as the relevant video frames.

[0021] Optionally, finding the water level fluctuation range corresponding to the water level monitoring data in the relevant video frames includes:

[0022] Selecting one of the extracted multiple video frames as the base frame;

[0023] Selecting multiple reference points in the base frame and establishing a coordinate system according to one of the reference points; and,

[0024] Identifying the boundaries of the dam body and water area regions from each video frame and enhancing the lines of the identified boundaries of the water area regions;

[0025] Aligning the dam body in the coordinate system for the other video frames according to the remaining reference points;

[0026] Adjusting the transparency within the boundaries of the water area regions in each aligned video frame to present the enhanced water area boundaries in any video frame;

[0027] Determine the pixel fluctuation range corresponding to the water level monitoring data according to the maximum and minimum values of the water area boundary after adjusting the transparency.

[0028] Optionally, determining the pixel fluctuation range corresponding to the water level monitoring data according to the maximum and minimum values of the water area boundary after adjusting the transparency includes:

[0029] Based on the aligned dam body, determine several horizontal reference lines based on the enhanced water area boundary of each presented line;

[0030] Select a horizontal reference line with the most maximum and minimum values of the enhanced water area boundary as the corresponding pixel fluctuation range.

[0031] Optionally, determining the water level floating range corresponding to the water level monitoring data according to the maximum and minimum values of the water area boundary after adjusting the transparency further includes:

[0032] Pre-determine the mapping relationship between the water level and each horizontal reference line according to the water level scale included in the video monitoring data;

[0033] According to the mapping relationship and the determined pixel fluctuation range of the corresponding water level monitoring data, determine the water level floating range of the water level monitoring data.

[0034] Optionally, simulating the results in the pre-established dam area terrain model and cross-section according to the predicted results, the current video monitoring data, and the water level monitoring data includes:

[0035] Pre-map each horizontal reference line to the dam area terrain model and control each horizontal reference line to be hidden;

[0036] Present the current water level monitoring data in the dam area terrain model;

[0037] According to the water level prediction data and the fluctuation range included in the predicted results, dynamically present the water level prediction data in the predicted results based on the current water level monitoring data, and highlight the horizontal reference line closest to the predicted fluctuation range.

[0038] Optionally, simulating the results in the pre-established dam area terrain model and cross-section according to the predicted results, the current video monitoring data, and the water level monitoring data further includes:

[0039] Make markings on the cross-section based on each horizontal reference line; and,

[0040] Synchronously with the dam area terrain model, dynamically present the water level prediction data in the predicted results based on the current water level monitoring data.

[0041] An embodiment of the present application also provides a short-term hydrological forecasting system for a dam site cross-section, including a processor and a memory. A computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the short-term hydrological forecasting method for the dam site cross-section as described above are implemented.

[0042] The forecasting method of the embodiment of the present application combines local monitoring videos and water level data. By constructing a dam area terrain model, on the one hand, the accuracy of short-term hydrological forecasting for the dam site cross-section is improved, and on the other hand, the auxiliary effect of short-term hydrological forecasting for the dam site cross-section on users is improved.

[0043] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present invention are specifically described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0045] Figure 1 It is a schematic diagram of the basic process of the short-term hydrological forecasting method for the dam site cross-section of this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0047] An embodiment of the present application provides a short-term hydrological forecasting method for a dam site cross-section, as Figure 1 shown, including the following steps:

[0048] In step S101, a dam area terrain model is established in advance, and a cross-section is established based on the distribution of the units behind the dam according to the dam area terrain model. In some examples, only the establishment of the dam area terrain model and the cross-section view can be presented on the same interface.

[0049] In step S102, video monitoring data of the historical dam area, rainfall data of the area covered by the dam area, and historical water level monitoring data of the dam area are obtained. In some embodiments, power generation data of the units behind the dam can also be obtained, and the power generation data of the units and the rainfall data of the area covered by the dam area can be used to add data tags.

[0050] In step S103, according to the historical water level monitoring data of the dam area, relevant video frames are determined from the video monitoring data. In some embodiments, the historical water level monitoring data of the dam area are discrete data without continuity, while the video monitoring data have a certain continuity. In the embodiments of the present application, based on the water level monitoring data, corresponding video frames in a corresponding time period are further determined from the video monitoring data, so as to judge the water level fluctuation in the corresponding time period.

[0051] In step S104, the water level fluctuation range corresponding to the water level monitoring data is found in the relevant video frames.

[0052] In step S105, the water level monitoring data and the corresponding fluctuation range are used as a data group to construct a training data set.

[0053] In step S106, based on the rainfall data, marks are added to the data groups in the training data set to train a short-term hydrological prediction model using the marked training data set. In some embodiments, the short-term hydrological prediction model can be a network model based on the Encoder+Decoder+LSTM architecture.

[0054] In step S107, predictions are performed using the trained short-term hydrological prediction model according to the current video monitoring data and water level monitoring data.

[0055] In step S108, according to the prediction results, the current video monitoring data and water level monitoring data, result simulations are performed on the pre-established dam area terrain model and cross-section.

[0056] The prediction method of the embodiments of the present application combines local monitoring videos and water level data. By constructing a dam area terrain model, on the one hand, the accuracy of short-term hydrological prediction of the dam site section is improved, and on the other hand, the auxiliary effect of short-term hydrological prediction of the dam site section on users is improved.

[0057] In some embodiments, establishing a cross-section based on the dam area terrain model according to the distribution of the units behind the dam includes:

[0058] Obtain the historical power generation data of the units behind the dam to determine the power generation utilization rate of each unit;

[0059] Determine the units with utilization rates higher than the preset threshold at the corresponding positions in the terrain model of the dam area, and establish cross-sections. For example, in some examples, the area of the intake of the units in front of the dam of the dam site section is taken as the key area of concern, and the units with high power generation utilization rates are determined according to the power generation utilization rates of each unit in the historical data, and the cross-sections are accurately determined.

[0060] In some embodiments, according to the historical water level monitoring data of the dam area, determining the relevant video frames from the video monitoring data includes:

[0061] Determine the time stamp of any water level monitoring data;

[0062] According to the time stamp, determine the associated video segment in the video monitoring data. For example, according to the generation time of any water level monitoring data, query in the video monitoring data to obtain the associated video segment at this generation time. In some examples, the associated video segment can be a video segment with a certain duration before and after this generation time.

[0063] Extract several video frames at a set interval from the associated video segment as the relevant video frames.

[0064] In some embodiments, finding the water level floating range corresponding to the water level monitoring data in the relevant video frames includes:

[0065] Select one of the extracted multiple video frames as the base frame. The base frame can be randomly selected from the multiple video frames.

[0066] In the base frame, select multiple reference points and establish a coordinate system based on one of the reference points. In some embodiments, the reference points can be fixed identifiers or objects on the dam body or the river bank, so that the dam bodies in multiple video frames can be aligned based on the reference points.

[0067] Identify the boundaries of the dam body and the water area in each video frame, and enhance the lines of the identified boundaries of the water area. In some embodiments, the boundaries of the dam body and the water area can be determined based on an edge detection algorithm, and enhancing the lines of the identified boundaries of the water area can be achieved by deepening the color values of the boundary pixels and other methods.

[0068] Align the dam bodies of other video frames in the coordinate system according to the remaining reference points.

[0069] Adjust the transparency within the boundaries of the water area in each aligned video frame so as to present the enhanced water area boundaries of each line in any video frame. That is, after adjusting the transparency, the boundaries of the water areas of multiple video frames can be prominently presented based on the dam body. After changing the transparency based on the enhanced line color values, the boundaries are also clearly retained.

[0070] The maximum and minimum values of the water area boundary after adjusting the transparency are determined as the pixel fluctuation range corresponding to the water level monitoring data.

[0071] In some embodiments, determining the maximum and minimum values of the water area boundary after adjusting the transparency as the pixel fluctuation range corresponding to the water level monitoring data includes:

[0072] Based on the aligned dam body, several horizontal reference lines are determined based on the enhanced water area boundary of each presented line. In some embodiments, the several horizontal reference lines can be determined based on the water level scale behind the dam. For example, several corresponding horizontal reference lines are established on the dam body image and the dam area terrain model in a way of every 3 - 5 cm interval. Specifically, the mapping relationship can be determined according to the monitored pixel distance when determining several horizontal reference lines on the dam body image.

[0073] Select a horizontal reference line with the most maximum and minimum values of the enhanced water area boundary as the corresponding pixel fluctuation range. In some examples, for the mixed image after changing the transparency within the boundary of the water area, if the maximum and minimum points fall on the horizontal reference line with the most points, it is considered as the maximum and minimum fluctuation range of the corresponding time range. In this way, local maximum and minimum noises are also filtered out, improving the recognition accuracy.

[0074] In some embodiments, determining the water level floating range corresponding to the water level monitoring data based on the maximum and minimum values of the water area boundary after adjusting the transparency further includes:

[0075] Pre - determine the mapping relationship between the water level and each horizontal reference line according to the water level scale included in the video monitoring data;

[0076] According to the mapping relationship and the determined pixel fluctuation range corresponding to the water level monitoring data, determine the water level floating range of the water level monitoring data. That is, in this example, according to the position of the water level scale in the video monitoring, the relationship with the horizontal reference line is determined, and thus the fluctuation range of the water level monitoring data can be obtained.

[0077] In some embodiments, according to the predicted result, the current video monitoring data and the water level monitoring data, the result simulation in the pre - established dam area terrain model and cross - section includes:

[0078] Pre - map each horizontal reference line to the dam area terrain model and control each horizontal reference line to be hidden. In this example, each determined horizontal reference line is further mapped to the dam area terrain model, and by controlling the hiding, a clean dam area terrain model can be provided.

[0079] Present the current water level monitoring data in the dam area terrain model.

[0080] Based on the water level prediction data and the fluctuation range included in the prediction result, the water level prediction data in the prediction result is dynamically presented based on the current water level monitoring data, and the horizontal reference line closest to the predicted fluctuation range is highlighted. In this way, the water level prediction data and the fluctuation range can be visually presented on the dam area terrain model, thereby improving the auxiliary effect on the staff.

[0081] In some embodiments, according to the prediction result, the current video monitoring data and the water level monitoring data, the result simulation on the pre-established dam area terrain model and the cross-section further includes:

[0082] Marking on the cross-section based on each horizontal reference line; and,

[0083] Synchronously with the dam area terrain model, the water level prediction data in the prediction result is dynamically presented based on the current water level monitoring data. Further, the data display efficiency can be improved through the cross-section, thereby assisting in improving the hydrological scheduling effect of the dam area.

[0084] The embodiment of the present application also proposes a short-term hydrological forecasting system for the dam site cross-section, including a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the steps of the short-term hydrological forecasting method for the dam site cross-section as described above are implemented.

[0085] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure having equivalent elements, modifications, omissions, combinations (e.g., solutions that cross various embodiments), adaptations or changes. It is not limited to the examples described in this specification or during the implementation of this application, and the examples will be construed as non-exclusive.

[0086] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description.

[0087] The above embodiments are only exemplary embodiments of the present disclosure. Those skilled in the art can make various modifications or equivalent replacements to the present invention within the essence and protection scope of the present disclosure, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present invention.

Claims

1. A short-term hydrological forecasting method for a dam site section, characterized in that: include: Pre-establishing a dam area terrain model, and establishing a truncation surface according to the dam area terrain model based on the distribution of units behind the dam; Obtain historical video monitoring data of the dam area, rainfall data of the area covered by the dam area, and historical water level monitoring data of the dam area; Determining relevant video frames from the video monitoring data based on historical water level monitoring data of the dam area; Finding the water level floating range of the corresponding water level monitoring data in the relevant video frame; Using the water level monitoring data and the corresponding fluctuation range as a data group to construct a training data set; adding labels to the data groups in the training data set based on the rainfall data, so as to train a short-term hydrological prediction model using the labeled training data set; Perform forecasts using the trained short-term hydrological forecast model based on current video monitoring data and water level monitoring data; as well as, Based on the predicted results and the current video monitoring data and water level monitoring data, the results are simulated on the pre-established dam area terrain model and cross-section; Finding the water level floating range of the corresponding water level monitoring data in the relevant video frame includes: Select one of the extracted multiple video frames as a base frame; In the base frame, multiple reference points are selected, and a coordinate system is established according to one of the reference points; and, Identify the boundary of the dam and the water area from each video frame, and perform line enhancement on the boundary of the identified water area; According to the remaining reference points, aligning the dam body in the coordinate system for other video frames; Adjusting the transparency within the boundary range of the water area in each aligned video frame to present the enhanced water boundary of each line in any video frame; According to the aligned dam body, several horizontal reference lines are determined based on the enhanced water boundaries of the presented lines; A horizontal reference line with the most maximum values ​​including the enhanced water area boundary is selected as the corresponding pixel fluctuation interval.

2. The short-term hydrological forecasting method for a dam site section as claimed in claim 1, characterized in that: Establishing the truncation surface based on the distribution of units behind the dam and the terrain model of the dam area includes: Obtain historical power generation data of the units behind the dam to determine the power generation utilization rate of each unit; The units with utilization rates higher than a preset threshold are determined at positions corresponding to the terrain model of the dam area, and a truncation surface is established.

3. The short-term hydrological forecasting method for a dam site section according to claim 1, characterized in that: According to the historical water level monitoring data of the dam area, determining the relevant video frames from the video monitoring data includes: Determine the time stamp of any water level monitoring data; Determining, according to the time stamp, an associated video segment in the video monitoring data; From the associated video segments, several video frames are extracted at set intervals as related video frames.

4. The short-term hydrological forecasting method for a dam site section according to claim 1, characterized in that: The water level floating range corresponding to the water level monitoring data determined according to the maximum value of the water area boundary after adjusting the transparency also includes: Determine in advance the mapping relationship between the water level and each horizontal reference line based on the water level scale included in the video monitoring data; The water level floating range of the water level monitoring data is determined according to the mapping relationship and the determined pixel fluctuation interval corresponding to the water level monitoring data.

5. The short-term hydrological forecasting method for a dam site section as claimed in claim 4, characterized in that: Based on the predicted results and the current video monitoring data and water level monitoring data, the results are simulated on the pre-established dam area terrain model and cross-section, including: Pre-mapping each horizontal reference line to the dam area terrain model and controlling each horizontal reference line to be hidden; Presenting current water level monitoring data on the dam area terrain model; According to the water level prediction data and fluctuation range contained in the prediction results, the water level prediction data in the prediction results are dynamically presented based on the current water level monitoring data, and the horizontal reference line closest to the predicted fluctuation range is highlighted.

6. The short-term hydrological forecasting method for a dam site section as claimed in claim 5, characterized in that: Based on the predicted results and the current video monitoring data and water level monitoring data, the result simulation is carried out on the pre-established dam area terrain model and cross-section, including: Marking the section surface based on each horizontal reference line; and, The water level prediction data in the prediction results are dynamically presented based on the current water level monitoring data, in synchronization with the dam area terrain model.

7. A short-term hydrological forecasting system for a dam site section, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the short-term hydrological forecasting method for a dam site section as described in any one of claims 1 to 6 are implemented.

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