Lake silt automatic measurement visual model processing method and system

Abstract

The application relates to the technical field of underwater detection, and discloses a lake silt automatic measurement visual model processing method and system, which comprises the following steps: S1, acquiring geographical data of a target lake through remote sensing, and setting n target measurement points according to the geographical data; S2, importing the target measurement points into a measurement device, driving the measurement device to the corresponding target measurement points for measurement, and acquiring measurement data; and S3, transmitting the measurement data corresponding to each target measurement point to a data analysis end, analyzing the measurement data of each target measurement point through the data analysis end, and generating a real-time layered image of the target lake. The application can obtain a predicted layered image of the target lake in a first time period in the future at low cost.

Description

Technical Field

[0001] This invention relates to the field of underwater detection technology, specifically to a method and system for processing an automatic measurement visualization model of lake silt. Background Technology

[0002] Silt refers to cohesive soil deposited in flowing water environments with a natural water content greater than the liquid limit and a natural porosity greater than 1.0, with a particle size of less than 0.03 mm. Long-term silt accumulation not only affects the water quality of lakes but also adversely impacts the lake and its surrounding ecosystem, severely damaging the local environment. Therefore, dredging is typically necessary to improve the lake's ecological environment. Before dredging, the extent of silt accumulation is measured.

[0003] Currently, existing technologies involve measuring before dredging, but measuring the silt accumulation only provides results at that specific moment. Generally, the process begins with measurement, continues with the planning and design of the dredging work based on the measurements, and only then does the dredging operation commence. Furthermore, dredging is typically carried out gradually, addressing each sub-area. This results in lengthy operations for each dredging session. Since the density of the silt layer is easily affected by factors such as weather, wind speed, and water flow, it is a fluctuating process. This adds new uncertainties to the dredging work, potentially leading to longer and more time-consuming operations, resulting in low dredging efficiency and increased construction costs.

[0004] To address this issue, engineers typically place fixed measuring buoys within designated areas to obtain real-time and accurate data on the silt layer. However, the measuring buoys have a small measurement radius, requiring the deployment of multiple buoys in lakes or reservoirs to achieve comprehensive measurements, which increases the cost of the measurement. Summary of the Invention

[0005] The present invention aims to provide a first solution, which is an automatic measurement and visualization model processing method for lake silt, which can obtain a predicted layered image of the target lake in the first time period in the future at low cost.

[0006] To achieve the above objectives, the following technical solution is adopted: a method for processing an automatic measurement visualization model of lake silt, comprising: a database constructed in a data analysis terminal, the database storing historical measurement information and corresponding historical weather characteristics of each lake; S1, acquiring geographical data of the target lake through remote sensing, and setting n target measurement points based on the geographical data; S2, importing the n target measurement points into a measurement device, the measurement device traveling to the corresponding target measurement points to perform measurements, and acquiring measurement data of each target measurement point; S3, transmitting the measurement data corresponding to each target measurement point to the data analysis terminal, analyzing the measurement data of each target measurement point through the data analysis terminal, and generating a real-time layered image of the target lake; S4, acquiring the weather characteristics of the target lake within a first time period, comparing the weather characteristics with the historical weather characteristics stored in the database, selecting the lake with the highest similarity as a reference lake, and using the historical measurement information corresponding to the historical weather characteristics of the reference lake within the first time period as a change parameter; S5, adjusting the real-time layered image of the target lake according to the change parameter, and obtaining a predicted layered image of the target lake within a future first time period.

[0007] The beneficial effects of this solution are as follows: Compared with the existing technology of measuring first and then carrying out dredging work, this embodiment is different in that, after obtaining measurement data through ultrasonic measurement, the weather characteristics of the target lake in a certain period of time in the future are compared with the historical weather characteristics of lakes in the same city-level area in the database. The lake with the highest similarity is matched, and the historical measurement information of the lake in this period is analyzed as the analysis of the silt layer changes of the target lake in a certain period of time in the future. This is not only simple to calculate and low in cost, but also can accurately predict the changes of the silt layer in this period of time.

[0008] Preferably, the measuring device uses ultrasonic technology to measure the lake.

[0009] Beneficial effects: Ultrasonic measurement can accurately measure the thickness of the silt layer and can adapt to different water quality conditions, including turbid water, and is not affected by the turbidity of the water.

[0010] Preferably, the reference lake is the target lake or other lakes in the same city-level region as the target lake.

[0011] Beneficial effects: Expanding the number of reference lakes allows for more comparative analysis; however, limiting the geographical scope of the reference lakes is problematic because different regions have different topography and climate. Even if the weather characteristics are similar within a certain period, the changes in the silt layer are affected by the topography. Expanding the geographical scope of the reference lakes can lead to the influence of geographical and topographical factors other than weather characteristics, which may compromise the accuracy.

[0012] Preferably, the first time period is fourteen days.

[0013] Beneficial effect: Currently, weather forecasts are generally more accurate for the next fourteen days.

[0014] Preferably, the weather characteristics include wind speed and direction, precipitation, season, and temperature.

[0015] Beneficial effects: The thickness of the silt layer is greatly affected by wind speed and direction, precipitation and temperature. By identifying the most significant factors affecting the thickness of the silt layer and eliminating other parameters such as humidity that have a smaller impact on the silt layer, the thickness of the silt layer can be calculated more accurately and efficiently.

[0016] Preferably, the method further includes step S6, where the data analysis terminal is connected to the first model and obtains the location and amount of dredging; the location and amount of dredging are used as adjustment parameters, and the location of dredging is used as the center point, extending outward from the center point by a first length as the adjustment area; the adjustment parameters and the predicted layered image are imported into the first model; the silt change in the adjustment area is calculated and analyzed by the first model; and the silt change in the adjustment area is covered onto the corresponding area of ​​the predicted layered image to obtain the target layered image.

[0017] Beneficial effects: By using the location and amount of dredging as adjustment parameters, and limiting the adjustment target to a region within a first numerical range of the dredging location, the thickness of the silt layer in the predicted layered image is adjusted using the first model to obtain the target layered image. This eliminates the need to remeasure the changes in the silt layer in the nearby dredging area each time partial dredging is performed. This not only reduces the number of measurements but also accurately predicts changes in the silt layer, improving dredging efficiency. Furthermore, limiting the region reduces the computational load of the model, increasing its calculation speed and saving time in the first model's processing and analysis.

[0018] Preferably, the first model is the Pangu model.

[0019] Beneficial effect: It has stronger reasoning ability when dealing with calculations of more complex changes in silt layer thickness.

[0020] Preferably, the first length is 500m.

[0021] Beneficial effect: By limiting the scope, the computational load of the model can be effectively reduced.

[0022] Preferably, the real-time layered image, the predicted layered image, and the target layered image are all three-dimensional images.

[0023] Beneficial effect: 3D images can more intuitively show the changes in the silt layer.

[0024] The second solution is an automatic measurement and visualization model processing system for lake silt, used to execute the automatic measurement and visualization model processing method for lake silt in the first solution. The automatic measurement and visualization model processing system for lake silt includes: a target measurement point setting module, including remote sensing, for acquiring geographic data of the target lake, and setting n target measurement points based on the geographic data, sending the target measurement points to a measurement module; a measurement module, including a measurement device, for receiving the target measurement points, performing measurements at each target measurement point, acquiring measurement data, and sending the measurement data to a data analysis module; and a data analysis module, including a data analysis terminal, for receiving the measurement data, analyzing the measurement data, and generating a real-time layered image of the target lake.

[0025] Beneficial effects: Compared to measurements using fixed buoys or drones, this embodiment provides an automatic measurement and visualization model processing system for lake silt. By employing a measuring device, it offers high flexibility and is less affected by open airflow in lakes compared to drone measurements. This allows for precise measurements at the designated points, improving the accuracy of results such as silt distribution and thickness. Furthermore, the measuring device can automatically travel to the measurement points, resulting in higher measurement efficiency. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of Embodiment 1 of the present invention. Detailed Implementation

[0027] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 As shown, a method for processing an automatic measurement and visualization model of lake silt includes:

[0030] S1. Obtain geographical data of the target lake through remote sensing, and set n target measurement points based on the geographical data.

[0031] Specifically, the geographic data of the target lake can be imported into a GIS system to more efficiently determine measurement points. Measurement points can be determined based on the selected measurement accuracy.

[0032] S2, the n target measurement points are imported into the measuring device. The measuring device travels to the corresponding target measurement point to perform the measurement and acquire the measurement data of each target measurement point. The measurement accuracy can be selected as follows: 1m, 2m, 3m, 4m, 5m, 6m, 7m, 8m, 9m, 10m, 11m, 12m, 13m, 14m, 15m, etc., with a tolerance of 1m. If a measurement accuracy of 1m is selected, that is, according to the shape of the lake, the distance between two measurement points is 1m, covering the shoreline of the lake, and according to the size of the lake, every 1m from the shoreline towards the center of the lake is used as a measurement point.

[0033] S3 transmits the measurement data corresponding to each target measurement point to the data analysis terminal. The data analysis terminal analyzes the measurement data of each target measurement point to generate a real-time layered image of the target lake.

[0034] Specifically, in this embodiment, the measuring device is an unmanned measuring device. The measuring device automatically moves to the selected measuring point. It uses ultrasonic waves to measure the lake and obtain measurement data. In this embodiment, the measurement accuracy is set at 5 meters between two target measuring points. After obtaining the measurement data for each target measuring point, the data includes the depths of the clear water layer and the silt layer. The measurement data is then aggregated to form a real-time layered image of the target lake. In the real-time layered image, from high to low, it includes the clear water layer, the silt layer, and the bottom solid layer.

[0035] The natural sedimentation process of solid matter reveals that the sediment at the bottom will stratify into two distinct layers: a solid bottom layer and a silt layer. In other words, the entire lake can be divided into three layers: a clear water layer, a silt layer, and a solid bottom layer. Since the density of the silt layer varies, this variation is closely related to weather and climate. By measuring the difference in the propagation speed of ultrasound waves in the silt layer and seawater, the thickness of the clear water layer and the silt layer at a given measurement point can be determined.

[0036] A database has been built in the data analysis terminal, which stores historical measurement information and corresponding historical weather characteristics for each lake. Weather characteristics include wind speed and direction, precipitation, season, and temperature.

[0037] S4. Obtain the weather characteristics of the target lake in the first time period, compare the weather characteristics with the historical weather characteristics stored in the database, select the lake with the highest similarity as the reference lake, and use the historical measurement information corresponding to the historical weather characteristics of the reference lake in the first time period as the change parameter.

[0038] Specifically, since the thickness of the silt layer is a variable process and is greatly affected by the weather, predicting the changes in the silt layer through weather requires considering the impact of future wind speed and direction, precipitation, and temperature on the silt layer. If a precise measurement of the silt layer thickness is required, a very large amount of data is needed, which is too costly.

[0039] In this embodiment, a database is constructed that stores historical measurement information and corresponding historical weather characteristics of the target lake, as well as historical measurement information and corresponding weather conditions of other lakes located in the same city-level area as the target lake. When it is necessary to predict the changes in the silt layer of the target lake over a future period, the weather characteristics of the target lake during a first future time period are obtained. In this embodiment, the first time period is fourteen days. The weather characteristics of the target lake during the first future time period are compared with the historical weather characteristics stored in the database.

[0040] The weather characteristics described are compared with historical weather characteristics stored in the database. Specifically, the comparison includes whether the two belong to the same season; if one belongs to summer and the other to winter, the comparison will fail. This is because there are significant differences between different silt layers, making variations in silt density unreliable as a reference standard. Secondly, if the wind direction is consistent, the comparison will be successful. Furthermore, the minimum and maximum wind speeds of the two lakes must not differ by more than three wind speed levels. The total precipitation of the two lakes within the first time period must differ by less than 5 millimeters. The average temperature of the two lakes within the first time period must differ by less than 3 degrees Celsius. After meeting the above conditions, the smaller the difference, the higher the similarity. The lake with the highest similarity will be selected as the reference lake.

[0041] Historical measurement information corresponding to historical weather characteristics of the reference lake within the first time period is retrieved from the database and used as variation parameters. Specifically, the first time period with the highest similarity between the reference lake and the target lake within the first time period is determined. Historical measurement information for the reference lake at the beginning and end of the first time period is obtained. If the first time period is fourteen days, then historical measurement information for the reference lake on the first and fourteenth days of the first time period is obtained. From the first day to the fourteenth day, based on its historical measurement information, the average variation thickness of the silt layer of the reference lake is used as a variation parameter.

[0042] For example, when predicting the silt layer thickness of Reservoir A in Region A over the next fourteen days, the weather conditions for Reservoir A over the next fourteen days are obtained. The database is searched for the precipitation data of lakes in Region A over the past fourteen days. The lake with the highest consistency between the precipitation data of those lakes and the weather conditions for Reservoir A over the next fourteen days is designated as Lake B. The average change in silt layer thickness in Lake B over the next fourteen days is then used as the silt layer thickness change for Reservoir A over the next fourteen days, thus obtaining the silt layer thickness of Reservoir A in Region A for the next fourteen days. Region A refers to a city-level area.

[0043] Predicting the thickness of a lake's silt layer based on weather conditions is computationally intensive, complex, time-consuming, and costly to achieve accurate measurements. A better approach is to construct a database to identify other lakes in the same region with weather conditions closely matching those of the lake requiring prediction over a future period. By analyzing the silt layer thickness changes of these other lakes during the corresponding period, the predicted silt layer thickness can be predicted for the lake in the future. This method is not only computationally simpler and faster, but also more cost-effective, enabling accurate measurement of silt layer thickness.

[0044] S5, adjust the real-time layered image of the target lake according to the changed parameters, and obtain the predicted layered image of the target lake in the first time period in the future.

[0045] Specifically, real-time layered imaging involves processing measurement data from each measurement point to obtain a 3D model of the lake. In this model, the bottom solid layer is the lowest point, and the lake's surface is the highest point. Lines are connected to the measurement data from each point in the lake, and lines are also connected to the data from the silt layer, the clear water layer, and the bottom solid layer. After further processing, a 3D model of the entire lake is formed, resulting in a visualized image of the lake. By changing parameters to modify the thickness of the silt layer in the target lake, a predicted layered image of the target lake for the first time period in the future can be obtained.

[0046] It should be noted that lakes include both lakes and reservoirs. Water level changes in a target lake can be obtained through remote sensing satellites, allowing for analysis of future water level variations. Combined with these water level changes, the thickness of the clear water layer can be determined. In this embodiment, the measuring device can be a survey vessel. The measurement data primarily includes the location of the silt layer, its thickness at different locations, the height of the silt layer above the water surface, and the lake's depth.

[0047] Beneficial effects of this embodiment

[0048] Compared to existing technologies that either require re-measuring to adjust the original real-time layered image or setting up fixed measuring buoys on the lake, this embodiment differs in that after obtaining a real-time layered image through ultrasonic measurement, the weather characteristics of the target lake over a certain period of time are compared with the historical weather characteristics of lakes in the same city-level region in the database. The lake with the highest similarity is matched, and the historical measurement information of the reference lake during this period is analyzed as the basis for analyzing the silt layer changes of the target lake over a certain period of time in the future. This yields a predicted layered image of the target lake for the first time period in the future, eliminating the need for costly fixed measuring buoys on the lake.

[0049] Secondly, compared to calculating and predicting the layered image of a target lake over a future period using various algorithms and models, this method is not only computationally complex but also lacks guaranteed accuracy. In this embodiment, the weather characteristics are compared with historical weather characteristics stored in a database. Specifically, the comparison includes whether the two belong to the same season; if one is summer and the other winter, the comparison fails because significant differences exist between different silt layers, making changes in silt density difficult to use as a reference standard. Secondly, if the wind direction is consistent, the comparison is successful. Furthermore, the minimum and maximum wind speeds of the two lakes must not differ by more than three wind speed levels. The total precipitation of the two lakes within the first time period must differ by less than 5 millimeters. The average temperature of the two lakes within the first time period must differ by less than 3 degrees Celsius. After meeting these conditions, the smaller the difference, the higher the similarity. The lake with the highest similarity is selected as the reference lake. By comparing these lakes, a predicted layered image for a future period is obtained. This method is not only computationally simple but also accurately predicts changes in the silt layer during this period.

[0050] Example 2

[0051] Unlike Embodiment 1, the automatic measurement and visualization model processing method for lake silt further includes S6, where a data analysis terminal is connected to a first model to acquire the location and amount of dredging. The location and amount of dredging can be selected by the operator, used as adjustment parameters. The location of the dredging is taken as the center point, and a first length extending outward from the center point is defined as the adjustment area. The adjustment parameters and the predicted layered image are imported into the first model. The first model calculates and analyzes the silt changes in the adjustment area, and the silt changes in the adjustment area are then overlaid onto the corresponding area of ​​the predicted layered image to obtain the target layered image.

[0052] Specifically, when workers are dredging a certain area of ​​a lake, the flowing water carries silt from nearby areas into that area, reducing the thickness of the silt. Simultaneously, because the dredging work has been ongoing for some time, the silt layer thickness in other areas also changes due to weather conditions and the flow of silt from nearby areas into the dredged areas after large-scale dredging. This causes the previously measured silt layer thickness to become inaccurate, and the method described in Example 1 is also inaccurate in determining the thickness of other silt layers.

[0053] To improve the efficiency of dredging, when dredging other areas of the lake, it is necessary to measure the thickness of the silt layer in those areas multiple times, depending on the progress of the dredging work, to ensure the smooth and efficient implementation of the dredging operation. However, repeatedly measuring the location and thickness of the silt layer requires assigning specific personnel to conduct the measurements, which not only increases costs but also reduces efficiency because dredging work in other areas can only begin after these measurements are taken.

[0054] After dredging work is carried out in a certain area, the staff imports the dredging location and volume into the data analysis terminal. The data analysis terminal is connected to the Pangu Model, and the dredging location and volume are input into the Pangu Model as adjustment parameters. An area with a radius of 500 meters centered on the dredging location is designated as the adjustment zone. Through analysis of the Pangu Model, the thickness and distribution of the silt layer in the adjustment zone are calculated, and this adjustment zone is overlaid on the corresponding position of the original predicted layered image to obtain the target layered image. This corresponding position refers to the area with a radius of 500 meters centered on the dredging location. In this embodiment, the predicted layered image refers to the image at the beginning of the construction phase, while the real-time layered image in Embodiment 1 refers to the image during the measurement phase.

[0055] Staff utilized historical measurement information and corresponding historical weather characteristics stored in the database for each lake, as well as how each dredging volume affected sediment changes in the surrounding area. They have already trained a corresponding sediment layer analysis model within the existing Pangu model. Analysis can then be performed simply by importing the dredging location, volume, and adjustment area into the Pangu model. Dredging refers to the removal of sediment. Dredging volume refers to the volume of dredged sediment and bottom mud.

[0056] Beneficial effects of this embodiment

[0057] Current technologies involve dredging a specific area of ​​a lake, then measuring the silt thickness in nearby areas to obtain a precise silt thickness before proceeding with the dredging work. This process is time-consuming.

[0058] This embodiment uses the location and amount of dredging as adjustment parameters, and limits the adjustment target to an area within 500 meters of the dredging location. By using the Pangu model to analyze and adjust the predicted layered image, the target layered image is obtained. This eliminates the need to remeasure the changes in the silt layer in the nearby dredging area each time partial dredging is performed. This not only reduces the number of measurements but also accurately predicts changes in the silt layer, improving dredging efficiency. Furthermore, by limiting the area, the computational load of the model is reduced, the calculation speed of the model is increased, and the processing and analysis time of the Pangu model is saved.

[0059] The automatic measurement and visualization model processing method for lake silt provided in Embodiment 2 of the present invention is described in a brief manner. For any parts not mentioned in the embodiment section, please refer to the corresponding content in Embodiment 1 above.

[0060] Example 3

[0061] Currently, to ensure accuracy, most silt measurements in lakes are conducted using fixed buoys. However, this method only allows for measurement of a single area, limiting its flexibility. Another method involves using drones, but due to the openness of lakes and the presence of large waves, drones are prone to swaying, causing their measuring instruments to obtain inaccurate data. Furthermore, the open air currents of lakes make it difficult for drones to precisely align with the measurement points, resulting in significant errors. After multiple measurements and subsequent data analysis, the errors become even greater, leading to inaccurate results regarding silt distribution and thickness.

[0062] This embodiment provides an automatic measurement and visualization model processing system for lake silt, specifically including:

[0063] The target measurement point setting module includes remote sensing, which is used to acquire geographic data of the target lake, and set n target measurement points based on the geographic data, and send the target measurement points to the measurement module.

[0064] Specifically, geographic data of the target lake is acquired through remote sensing, including the lake's shape and size. To facilitate and efficiently set target measurement points, the geographic data can be imported into a GIS system, and the measurement accuracy can be set using the GIS system, thereby establishing the target measurement points.

[0065] The measurement module includes a measurement device for receiving target measurement points, performing measurements at each target measurement point, acquiring measurement data, and sending the measurement data to the data analysis module.

[0066] Specifically, the measuring device is equipped with radar, GPS, ultrasonic measuring instruments, and wireless transceivers. The device receives relevant information about the target measurement point via the wireless transceiver and then uses GPS and radar to autonomously navigate to the target measurement point. The device measures the lake using ultrasonic technology, obtaining measurement data through the ultrasonic measuring instrument, and then transmitting the data to a data analysis unit via the wireless transceiver.

[0067] The data analysis module includes a data analysis terminal, which is used to receive the measurement data, analyze the measurement data, and generate a real-time layered image of the target lake.

[0068] Specifically, the data analysis terminal can typically be located on the computer processors of the staff working in the lakeside area, or it can be a cloud platform. Meanwhile, the data analysis terminal on the computer processors in the lakeside working area can generate real-time layered images of the target lake. If the goal is to obtain a predicted layered image or a target layered image of the target lake for the first time period in the future, the processing power of the computer processing devices in the temporary working area set up along the lakeside is limited, so the corresponding processing and analysis can be performed by the cloud platform.

[0069] The beneficial effects of this embodiment:

[0070] Compared to measurements using fixed buoys or drones, this embodiment provides an automated measurement and visualization model processing system for lake silt. By employing a measuring device, it offers greater flexibility and is less susceptible to the influence of open airflow in lakes compared to drone measurements. This allows for precise measurements at the designated points, improving the accuracy of results such as silt distribution and thickness. Furthermore, the measuring device can automatically travel to the measurement points, resulting in higher measurement efficiency.

[0071] The automatic measurement and visualization model processing system for lake silt provided in Embodiment 3 of the present invention is described in a brief manner. For any parts not mentioned in the embodiment section, please refer to the corresponding content in Embodiment 1 above.

[0072] Numerous specific details are set forth in this specification. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, systems, and techniques have not been shown in detail so as not to obscure the understanding of this specification. In the description of this specification, references to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., mean that a specific feature, method, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this specification.

[0073] In this specification, the illustrative expressions of the terms used do not necessarily refer to the same embodiments or examples. Furthermore, the specific features, systems, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of those different embodiments or examples, without contradiction.

[0074] The above descriptions are merely embodiments of the present invention, and common knowledge such as specific technical solutions and / or characteristics are not described in detail here. It should be noted that those skilled in the art can make various modifications and improvements without departing from the technical solutions of the present invention, and these should also be considered within the scope of protection of the present invention. These modifications and improvements will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for processing an automatic measurement and visualization model of lake silt, characterized in that, include: The data analysis terminal contains a database that stores historical measurement information and corresponding historical weather characteristics for each lake; S1, acquire geographical data of the target lake through remote sensing, and set n target measurement points based on the geographical data; S2, the n target measurement points are imported into the measurement device, the measurement device travels to the corresponding target measurement point to perform measurement, and the measurement data of each target measurement point are obtained; S3, transmit the measurement data corresponding to each target measurement point to the data analysis terminal, analyze the measurement data of each target measurement point through the data analysis terminal, and generate a real-time layered image of the target lake; S4, obtain the weather characteristics of the target lake in the first time period, compare the weather characteristics with the historical weather characteristics stored in the database, select the lake with the highest similarity as the reference lake, and use the historical measurement information corresponding to the historical weather characteristics of the reference lake in the first time period as the change parameter; S5, adjust the real-time layered image of the target lake according to the changed parameters, and obtain the predicted layered image of the target lake in the first time period in the future.

2. A visualization measurement and processing method for hydrological detection according to claim 1, characterized in that, The measuring device uses ultrasonic technology to measure the lake.

3. A visualization measurement and processing method for hydrological detection according to claim 1, characterized in that, The reference lake is either the target lake or other lakes within the same municipal area as the target lake.

4. A visualization measurement and processing method for hydrological detection according to claim 1, characterized in that, The first time period is fourteen days.

5. A visualization measurement and processing method for hydrological detection according to claim 1, characterized in that, The weather characteristics include wind speed and direction, precipitation, season, and temperature.

6. A visualization measurement and processing method for hydrological detection according to claim 1, characterized in that, It also includes S6, where the data analysis terminal is connected to the first model and obtains the location and amount of dredging; the location and amount of dredging are used as adjustment parameters, and the location of dredging is used as the center point, extending outward from the center point by a first length as the adjustment area. The adjustment parameters and the predicted layered image are imported into the first model, and the silt change in the adjustment area is calculated and analyzed by the first model. The silt change in the adjustment area is then overlaid onto the corresponding area of ​​the predicted layered image to obtain the target layered image.

7. A visualization measurement and processing method for hydrological detection according to claim 6, characterized in that, The first model is the Pangu model.

8. A visualization measurement and processing method for hydrological detection according to claim 6, characterized in that, The first length is 500m.

9. A visualization measurement and processing method for hydrological detection according to claim 6, characterized in that, The real-time layered image, the predicted layered image, and the target layered image are all three-dimensional images.

10. An automatic measurement and visualization model processing system for lake silt, characterized in that, A visualization measurement processing method for hydrological detection, performed according to any one of claims 1-9; The automatic measurement and visualization model processing system for lake silt includes: The target measurement point setting module includes remote sensing, which is used to acquire geographic data of the target lake, and set n target measurement points according to the geographic data, and send the target measurement points to the measurement module; The measurement module includes a measurement device for receiving target measurement points, performing measurements at each target measurement point, acquiring measurement data, and sending the measurement data to the data analysis module. The data analysis module includes a data analysis terminal, which is used to receive the measurement data, analyze the measurement data, and generate a real-time layered image of the target lake.

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