A channel flow measurement method and device based on big data deduction of flow rate

Abstract

This application discloses a channel flow measurement method and apparatus based on big data-based velocity estimation, relating to the field of channel water flow measurement. The method includes acquiring the surface depth of water, surface depth of silt, surface velocity of water, and intra-water velocity measured using an automatic current meter at different times and locations of various measuring points under different operating conditions in a target channel; inferring the matching relationship between surface depth, silt surface depth, and intra-water velocity, or deriving the matching relationship between surface depth, silt surface depth, surface velocity, and intra-water velocity, thereby determining the flow rate of the channel under test. In this application, the matching relationship between surface depth, silt surface depth, and velocity, or the matching relationship between surface depth, silt surface depth, surface velocity, and intra-water velocity, enables accurate measurement of flow rate under extreme sediment conditions.

Description

Technical Field

[0001] This application relates to the field of channel water flow measurement, and in particular to a channel flow measurement method and device based on big data-based flow velocity estimation. Background Technology

[0002] Generally, water flow is measured by multiplying the flow velocity by the cross-sectional area of ​​the water passage. However, in regions like Xinjiang where the water flow contains a high amount of sediment, measuring flow velocity using a propeller-driven anemometer can be accurate initially. After a period of time, fine sediment enters the anemometer, causing malfunctions in the rotor and rendering the measurement ineffective. Similarly, aquatic plants entangled in the anemometer can also cause malfunctions. Furthermore, regular maintenance and repair of the anemometer significantly increase labor and material costs. Additionally, propeller-driven anemometers require periodic calibration and verification of the flow velocity calculation formula, which is also a considerable expense. In recent years, Doppler technology has been introduced for flow measurement. However, in sediment-laden water flows, the attenuation of Doppler ultrasound waves is severe when penetrating the water, making velocity measurements inaccurate and affecting the accuracy of flow rate calculations. Therefore, a method for measuring flow in channels under extreme sediment conditions is urgently needed. Summary of the Invention

[0003] The purpose of this application is to provide a channel flow measurement method and device based on big data-driven flow velocity estimation, which can accurately measure the flow rate under extreme sediment conditions.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a channel flow measurement method based on big data-driven flow velocity projection, including: The system acquires the surface depth of water, the surface depth of silt, and the flow velocity in the water measured by an automatic current meter at different times at various measuring points under different operating conditions in the target channel. The measuring points are arranged along the width of the channel. The automatic current meter intermittently detects the flow velocity in the water. For each measuring point, the surface depth of the water body, the surface depth of the silt, and the corresponding flow velocity in the water body at different times are summarized, and the matching relationship between the surface depth of the water body, the surface depth of the silt, and the flow velocity is inferred. The flow rate of the channel to be tested is determined based on the matching relationship between the water surface depth, the silt surface depth, and the flow velocity.

[0005] Secondly, this application provides a channel flow measurement method based on big data-driven flow velocity projection, including: The system acquires the surface depth of water, surface depth of silt, surface velocity of water, and intra-water velocity measured by an automatic current meter at different times and locations at various measuring points under different operating conditions in the target channel. The automatic current meter intermittently detects the intra-water velocity. For each measuring point, the surface depth of the water body, the surface depth of the silt, the corresponding surface velocity of the water body and the corresponding velocity in the water body at different times are summarized, and the matching relationship between the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body and the velocity in the water body is inferred. When the variation in the surface depth of the target channel is greater than the preset variation, the flow rate of the channel to be tested is determined based on the matching relationship between the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body, and the velocity in the water body.

[0006] Thirdly, this application provides a channel flow measurement system based on big data-driven flow velocity projection, comprising: The first data acquisition module is used to acquire the surface depth of the water, the surface depth of the silt, and the flow velocity in the water measured by an automatic flow meter at different times under different operating conditions of the target channel; the measuring points are arranged along the width of the channel; the automatic flow meter intermittently detects the flow velocity in the water. The first data matching module is used to summarize the water surface depth, silt surface depth and corresponding water flow velocity at different times for each measuring point, and to infer the matching relationship between water surface depth, silt surface depth and flow velocity. The first flow rate determination module is used to determine the flow rate of the channel to be tested based on the matching relationship between the water surface depth, the silt surface depth, and the flow velocity.

[0007] Fourthly, this application provides a channel flow measurement system based on big data-driven flow velocity projection, comprising: The second data acquisition module is used to acquire the surface depth of water, surface depth of silt, surface velocity of water, and velocity in the water measured by an automatic current meter at different times under different operating conditions of the target channel; the automatic current meter intermittently detects the velocity in the water. The second data matching module is used to summarize the silt surface depth, the corresponding water surface velocity, and the corresponding water flow velocity at different times for each measuring point, and to deduce the matching relationship between the water surface depth, silt surface depth, water surface velocity, and water flow velocity. The second flow rate determination module is used to determine the flow rate of the channel under test based on the matching relationship between the water surface depth, silt surface depth, water surface velocity, and water velocity when the change in the water surface depth of the target channel is greater than the preset change.

[0008] Fifthly, this application provides a channel flow measurement device based on big data-driven flow velocity estimation, comprising: a cloud platform, a mobile mechanism, a retractable probe mounted on the mobile mechanism, and a radar detection mechanism; one end of the retractable probe is connected to the mobile mechanism, and the other end is equipped with a mud level sensor; an automatic flow velocity meter is also mounted on the retractable probe. The moving mechanism is used to move along the width of the target channel to drive the radar detection mechanism to detect the surface depth and surface velocity of the water at each measuring point. It also drives the automatic flow meter and mud level sensor on the telescopic probe to extend into the water in the channel for intermittent detection, so as to obtain the flow velocity and surface depth of the silt at each measuring point at different times.

[0009] The cloud platform is equipped with the aforementioned channel flow measurement system based on big data-driven flow rate projection.

[0010] Sixthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned channel flow measurement method based on big data-based flow rate estimation.

[0011] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a channel flow measurement method and apparatus based on big data-based velocity estimation. It acquires the surface depth of water, surface depth of silt, and flow velocity measured by a current meter at different times at various measuring points under extreme sediment conditions in a target channel. It then infers the matching relationship between water surface depth, silt surface depth, and flow velocity. Based on this matching relationship, it determines the flow rate of the channel under test. This application avoids the problems of current meter failure, inaccurate velocity measurement, and high maintenance costs caused by continuous use of a current meter under extreme sediment conditions, as well as the problem of inaccurate flow rate calculation due to severe ultrasonic attenuation in Doppler technology. Therefore, this application can accurately measure the flow rate under extreme sediment conditions. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating a channel flow measurement method based on big data-based flow velocity estimation, provided as an embodiment of this application; Figure 2A schematic diagram illustrating the detection of water surface depth and silt surface depth according to an embodiment of this application; Figure 3 A schematic diagram illustrating the detection of water surface depth, silt surface depth, and channel sidewall topography provided in an embodiment of this application; Figure 4 A flowchart illustrating a channel flow measurement method based on big data-driven flow velocity estimation, provided as another embodiment of this application; Figure 5 A schematic diagram of the functional modules of a channel flow measurement system based on big data-based flow velocity estimation, provided as an embodiment of this application; Figure 6 A schematic diagram of the functional modules of a channel flow measurement system based on big data-based flow velocity estimation provided in another embodiment of this application; Figure 7 This is a schematic diagram of the structure of a mud level sensor provided in one embodiment of this application; Figure 8 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application.

[0014] Reference numerals: 1-Main frame; 2-Slide rod; 3-Slide rod retaining ring; 4-Mud contact plate; 5-Metal sensor; 6-Top cover; 7-Sensing plate; 8-Sensor bracket; 9-Bolt. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] In one exemplary embodiment, such as Figure 1 As shown, a channel flow measurement method based on big data-driven flow velocity estimation is provided, applicable to channels under normal operating conditions and channels under extreme sediment conditions. This embodiment uses channel flow measurement under extreme sediment conditions as an example for illustration. The method is executed by computer equipment and specifically includes the following steps 101 to 103.

[0018] Step 101: Obtain the surface depth of the water body, the surface depth of the silt, and the flow velocity in the water body measured by an automatic current meter at different times under different working conditions (such as extreme sediment conditions) of the target channel; the positions of each measuring point are arranged along the width of the channel; the automatic current meter intermittently detects the flow velocity in the water body.

[0019] Step 102: For each measuring point, summarize the water surface depth, silt surface depth and corresponding water flow velocity at different times, and deduce the matching relationship between water surface depth, silt surface depth and flow velocity.

[0020] Step 103: Determine the flow rate of the channel to be tested based on the matching relationship between water surface depth, silt surface depth, and flow velocity.

[0021] By implementing steps 101 to 103 above, this application only requires intermittent flow rate measurements using a current meter to infer the matching relationship between water surface depth, silt surface depth, and flow rate. It does not require continuous placement of the current meter in water with high sediment content. Subsequently, the flow rate data for the channel under test is estimated solely through the matching relationship between water surface depth, silt surface depth, and flow rate. This avoids the problems of current meter failure, inaccurate flow rate measurement, and high maintenance costs caused by continuous use of the current meter under extreme sediment conditions, as well as the problem of inaccurate flow rate calculation due to severe ultrasonic attenuation in Doppler technology. Therefore, this application can accurately measure the flow rate under extreme sediment conditions. After a period of verification of flow measurements, this application allows for automatic matching of previously detected data for flow rate calculation during subsequent operation, eliminating the need to use a current meter to measure flow rate and avoiding the impact of current meter malfunctions.

[0022] In an exemplary embodiment of this application, step 101, which involves obtaining the water surface depth, silt surface depth, and flow velocity measured by an automatic current meter at different times at various measuring points under extreme sediment conditions in the target channel, specifically includes: (1) Control the moving mechanism to move along the width of the target channel; the moving mechanism is equipped with a telescopic probe and a radar detection mechanism; one end of the telescopic probe is connected to the moving device, and the other end is equipped with a mud level sensor; the telescopic probe is also equipped with an automatic flow rate meter.

[0023] (2) At each measuring point, the surface depth of the water is detected using a radar detection mechanism. An automatic flow velocity meter and a mud level sensor on a retractable probe are used to intermittently probe the water in the channel, obtaining the flow velocity and mud surface depth at each measuring point at different times. For example... Figure 2 As shown.

[0024] In one exemplary embodiment of this application, step 103, determining the flow rate of the channel to be measured based on the matching relationship between water surface depth, silt surface depth, and flow velocity, specifically includes: (1) Measure the surface depth of the water and the surface depth of the silt in the channel to be tested at the current moment.

[0025] (2) Based on the matching relationship between the water surface depth, silt surface depth and flow velocity at each measuring point, calculate the flow velocity in the water corresponding to the water surface depth and silt surface depth at each measuring point in the channel to be measured at the current time.

[0026] By utilizing the mud level sensor mounted on the retractable probe of the aforementioned moving mechanism to monitor the surface depth of silt in the water, the silt topography in the water can be accurately detected. Even for channel sidewalls without silt, the topographic structure can be accurately detected, avoiding the impact of channel sidewall deformation on the accuracy of water flow cross-section calculations. For example... Figure 3 As shown.

[0027] (3) Determine the cross section corresponding to each measuring point based on the water surface depth and silt surface depth at each measuring point location in the channel to be measured at the current time.

[0028] (4) Based on the calculated water velocity at each measuring point location in the channel to be measured at the current time and the corresponding cross-section of the water flow at each measuring point location, the flow rate of the channel to be measured at the current time can be obtained.

[0029] In another exemplary embodiment of this application, the channel flow measurement method based on big data-induced flow velocity further includes: When the difference between the current water surface depth and silt surface depth of the channel under test and the previously measured historical water surface depth and historical silt surface depth is less than the preset difference level, the flow rate calculated from the flow velocity corresponding to the historical water surface depth and historical silt surface depth is taken as the current flow rate of the channel under test.

[0030] In another exemplary embodiment of this application, such as Figure 4 As shown, a channel flow measurement method based on big data-driven flow velocity projection is provided, including: Step 201: Obtain the surface depth of water, surface depth of silt, surface velocity of water, and velocity in the water measured by an automatic current meter at different times for each measuring point under different operating conditions of the target channel; the automatic current meter intermittently detects the velocity in the water.

[0031] Step 202: For each measuring point, summarize the water surface depth, silt surface depth, corresponding water surface velocity, and corresponding water mid-flow velocity at different times, and deduce the matching relationship between water surface depth, silt surface depth, water surface velocity, and water mid-flow velocity.

[0032] Step 203: When the change in the water surface depth of the target channel is greater than the first preset change or the change in the silt surface depth is greater than the second preset change, the flow rate of the channel to be tested is determined according to the matching relationship between the water surface depth, the silt surface depth, the water surface velocity, and the water velocity.

[0033] In an exemplary embodiment of this application, step 203, determining the flow rate of the channel to be measured based on the matching relationship between water surface depth, silt surface depth, water surface velocity, and water velocity, specifically includes: (1) Measure the surface depth of the water body, the surface depth of the silt, and the surface velocity of the water body at the current moment in the target channel.

[0034] (2) When the change in the surface depth of the target channel is greater than the first preset change or the silt surface depth is greater than the second preset change, the silt surface depth and the silt surface velocity at each measuring point of the target channel at the current moment are calculated based on the matching relationship between the surface depth of the water body, the silt surface depth, the surface velocity of the water body, and the velocity in the water body.

[0035] (3) Determine the cross section corresponding to each measuring point based on the water surface depth and silt surface depth at each measuring point location in the target channel at the current time.

[0036] (4) Based on the water velocity calculated at each measuring point of the target channel at the current time and the cross-section of the water flow corresponding to each measuring point, the flow rate of the target channel at the current time is obtained.

[0037] In another exemplary embodiment of this application, the channel flow measurement method based on big data-induced flow velocity further includes: When the differences between the current water surface depth, silt surface depth, water surface velocity, and water in-vessel velocity of the channel under test and their corresponding historical water surface depth, historical silt surface depth, historical water surface velocity, and historical water in-vessel velocity are all less than the preset difference level, the flow rate calculated from the historical water in-vessel velocity corresponding to the historical water surface depth, historical silt surface depth, and historical water surface velocity is taken as the current flow rate of the channel under test.

[0038] In another exemplary embodiment of this application, such as Figure 5 As shown, a channel flow measurement system based on big data-driven flow velocity projection is provided, including: The first data acquisition module M1 is used to acquire the surface depth of the water, the surface depth of the silt, and the flow velocity in the water measured by an automatic flow meter at different times under different operating conditions of the target channel. The measuring points are arranged along the width of the channel. The automatic flow meter intermittently detects the flow velocity in the water.

[0039] The first data matching module M2 is used to summarize the water surface depth, silt surface depth and corresponding water flow velocity at different times for each measuring point, and to deduce the matching relationship between water surface depth, silt surface depth and flow velocity. The first flow rate determination module M3 is used to determine the flow rate of the channel to be tested based on the matching relationship between the water surface depth, the silt surface depth, and the flow velocity.

[0040] In another exemplary embodiment of this application, such as Figure 6 As shown, a channel flow measurement system based on big data-driven flow velocity projection is provided, including: The second data acquisition module N1 is used to acquire the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body, and the velocity in the water body measured by an automatic current meter at different times under different operating conditions of the target channel; the automatic current meter intermittently detects the velocity in the water body.

[0041] The second data matching module N2 is used to summarize the surface depth of the water body, the surface depth of the silt, the corresponding surface velocity of the water body and the corresponding velocity in the water body at different times for each measuring point, and to deduce the matching relationship between the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body and the velocity in the water body.

[0042] The second flow rate determination module N3 is used to determine the flow rate of the channel under test based on the matching relationship between the water surface depth, silt surface depth, water surface velocity, and water velocity when the change in the water surface depth of the target channel is greater than the preset change.

[0043] In another exemplary embodiment of this application, a channel flow measurement device for extreme sediment conditions based on big data-based flow velocity estimation is provided. The device is characterized by comprising: a cloud platform, a mobile mechanism, a retractable probe rod mounted on the mobile mechanism, and a radar detection mechanism; one end of the retractable probe rod is connected to the mobile mechanism, and the other end is provided with a mud level sensor; an automatic flow velocity meter is also provided on the retractable probe rod.

[0044] The moving mechanism is used to move along the width of the target channel to drive the radar detection mechanism to detect the surface depth and surface velocity of the water at each measuring point. It also drives the automatic flow meter and mud level sensor on the telescopic probe to extend into the water in the channel for intermittent detection, so as to obtain the flow velocity and surface depth of the silt at each measuring point at different times.

[0045] At least one of the aforementioned channel flow measurement systems based on big data-driven flow velocity projection is deployed on the cloud platform.

[0046] In another exemplary embodiment of this application, such as Figure 7 As shown, the mud level sensor includes: main frame 1, slide bar 2, mud contact plate 4, metal sensor 5, and sensing plate 7.

[0047] The main frame 1 is fixedly connected to the telescopic probe rod. Specifically, the telescopic probe rod is connected to the top cover 6 at the upper end of the main frame 1. The main frame 1 is slidably connected to the outside of the slide rod 2. One end of the slide rod 2 is fixedly connected to the mud contact plate 4. The sensing plate 7 is located on the mud contact plate 4. The metal sensor 5 is located on the main frame 1. The metal sensor 5 and the sensing plate 7 are arranged opposite to each other.

[0048] As the telescopic probe extends downward, when the mud contact plate 4 comes into contact with the silt, the main frame 1 continues to slide downward along the outside of the slide bar 2 under the action of the telescopic probe. The distance between the metal sensor 5 and the sensing plate 7 shortens. When the metal sensor 5 senses the sensing plate 7, the metal sensor 5 outputs a sensing signal to the controller. The controller receives the sensing signal and identifies it as a contact with the silt signal, which is considered as sensing the depth of the silt surface.

[0049] The mud level sensor also includes: a sliding rod retaining ring 3, a top cover 6, a sensor bracket 8, and bolts 9. The mud contact plate 4 and the sliding rod 2 are fixedly connected by bolts 9; in order to ensure that the main frame 1 is stably slidably connected to the outside of the sliding rod 2, a bolt 9 is also fixed at the upper end of the sliding rod 2 so that the main frame 1 and the sliding rod 2 do not separate.

[0050] The main frame 1 provides overall support for all components of the mud level sensor; the slide bar 2 provides guidance and support for the mud contact plate 4; the slide bar retaining ring 3 limits the sliding stroke; the mud contact plate 4 is used to contact the silt; the metal sensor 5 is used to detect the sensing element 7; the top cover 6 is used to protect the internal components of the mud level sensor; the sensing element 7 is used to provide a sensing source for the metal sensor 5; and the sensor bracket 8 is used to install the metal sensor 5.

[0051] Working principle of the mud level sensor: The mud level sensor is installed on a telescopic probe, which has the ability to precisely control its stroke. The controller controls the telescopic probe to move downward. When the mud level sensor contacts the silt, and the metal sensor 5 senses the sensing element 7, the telescopic probe stops moving downward. The controller then starts calculating the stroke of the telescopic probe, and the silt depth can be obtained through the calculation.

[0052] As an example, the mud-contact plate 4 can be configured as a hollow columnar structure, such as a cylindrical structure, with an opening at the lower end for contacting the silt surface, and the upper end of the cylinder fixedly connected to the slide rod 2. The overall shape of the main frame 1 and the top cover 6 can also be configured as a columnar structure.

[0053] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores channel flow measurement data under extreme sediment conditions based on large-scale data-driven flow velocity estimation. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a channel flow measurement method based on large-scale data-driven flow velocity estimation.

[0054] Those skilled in the art will understand that Figure 8 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0055] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0057] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0058] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0059] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A channel flow measurement method based on big data-driven velocity projection, characterized in that, include: The system acquires the surface depth of water, the surface depth of silt, and the flow velocity in the water measured by an automatic current meter at different times at various measuring points under different operating conditions in the target channel. The measuring points are arranged along the width of the channel. The automatic current meter intermittently detects the flow velocity in the water. For each measuring point, the surface depth of the water body, the surface depth of the silt, and the corresponding flow velocity in the water body at different times are summarized, and the matching relationship between the surface depth of the water body, the surface depth of the silt, and the flow velocity is inferred. The flow rate of the channel to be tested is determined based on the matching relationship between the water surface depth, the silt surface depth, and the flow velocity.

2. The channel flow measurement method based on big data-driven flow velocity estimation according to claim 1, characterized in that, The flow rate of the channel under test is determined based on the matching relationship between water surface depth, silt surface depth, and flow velocity. Specifically, this includes: Measure the surface depth of the water and the surface depth of the silt in the channel under test at the current moment; Based on the matching relationship between the water surface depth, silt surface depth and flow velocity at each measuring point, the flow velocity in the water corresponding to the water surface depth and silt surface depth at each measuring point in the channel under test at the current moment can be calculated. The cross-section of water flow at each measuring point is determined based on the current water surface depth and silt surface depth at each measuring point in the channel to be measured. Based on the calculated water velocity at each measuring point in the channel under test at the current moment and the corresponding cross-sectional area of ​​the water flow at each measuring point, the flow rate of the channel under test at the current moment can be obtained.

3. The channel flow measurement method based on big data-driven velocity estimation according to claim 2, characterized in that, The channel flow measurement method based on big data-driven flow velocity projection also includes: When the difference between the current water surface depth and silt surface depth of the channel under test and the previously measured historical water surface depth and historical silt surface depth is less than the preset difference level, the flow rate calculated from the flow velocity corresponding to the historical water surface depth and historical silt surface depth is taken as the current flow rate of the channel under test.

4. A channel flow measurement method based on big data-driven velocity projection, characterized in that, include: The system acquires the surface depth of water, surface depth of silt, surface velocity of water, and intra-water velocity measured by an automatic current meter at different times and locations at various measuring points under different operating conditions in the target channel. The automatic current meter intermittently detects the intra-water velocity. For each measuring point, the surface depth of the water body, the surface depth of the silt, the corresponding surface velocity of the water body and the corresponding velocity in the water body at different times are summarized, and the matching relationship between the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body and the velocity in the water body is inferred. When the change in the surface depth of the target channel is greater than the first preset change or the change in the silt surface depth is greater than the second preset change, the flow rate of the channel to be tested is determined according to the matching relationship between the surface depth of the water body, the silt surface depth, the surface velocity of the water body, and the velocity in the water body.

5. The channel flow measurement method based on big data-driven flow velocity estimation according to claim 4, characterized in that, The flow rate of the channel to be measured is determined based on the matching relationship between water surface depth, silt surface depth, water surface velocity, and water velocity in the body. Specifically, this includes: Measure the current water surface depth, silt surface depth, and water surface velocity in the target channel; When the change in the surface depth of the target channel is greater than the first preset change or the silt surface depth is greater than the second preset change, the silt surface depth and the corresponding silt surface velocity at each measuring point in the target channel at the current moment are calculated based on the matching relationship between the surface depth of the water body, the silt surface depth, the surface velocity of the water body, and the silt velocity in the water body. Determine the cross-section of each measuring point based on the current water surface depth and silt surface depth at each measuring point location in the target channel. Based on the calculated water velocity at each measuring point location in the target channel at the current moment and the corresponding cross-sectional area of ​​the water flow at each measuring point location, the flow rate of the target channel at the current moment can be obtained.

6. A channel flow measurement system based on big data-driven flow velocity projection, characterized in that, include: The first data acquisition module is used to acquire the surface depth of the water, the surface depth of the silt, and the flow velocity in the water measured by an automatic flow meter at different times under different operating conditions of the target channel; the measuring points are arranged along the width of the channel; the automatic flow meter intermittently detects the flow velocity in the water. The first data matching module is used to summarize the water surface depth, silt surface depth and corresponding water flow velocity at different times for each measuring point, and to deduce the matching relationship between water surface depth, silt surface depth and flow velocity. The first flow rate determination module is used to determine the flow rate of the channel to be tested based on the matching relationship between the water surface depth, the silt surface depth, and the flow velocity.

7. A channel flow measurement system based on big data-driven flow velocity projection, characterized in that, include: The second data acquisition module is used to acquire the surface depth of water, surface depth of silt, surface velocity of water, and velocity in the water measured by an automatic current meter at different times under different operating conditions of the target channel; the automatic current meter intermittently detects the velocity in the water. The second data matching module is used to summarize the surface depth of the water body, the surface depth of the silt, the corresponding surface velocity of the water body and the corresponding velocity in the water body at different times for each measuring point, and to deduce the matching relationship between the surface depth of the water body, the surface depth of the silt, the surface velocity of the water body and the velocity in the water body. The second flow rate determination module is used to determine the flow rate of the channel under test based on the matching relationship between the water surface depth, silt surface depth, water surface velocity, and water velocity when the change in the water surface depth of the target channel is greater than the preset change.

8. A channel flow measurement device based on big data-driven flow velocity projection, characterized in that, include: The cloud platform, mobile mechanism, retractable probe rod and radar detection mechanism mounted on the mobile mechanism; one end of the retractable probe rod is connected to the mobile mechanism, and the other end is equipped with a mud level sensor; an automatic flow rate meter is also mounted on the retractable probe rod. The moving mechanism is used to move along the width of the target channel to drive the radar detection mechanism to detect the surface depth and surface velocity of the water at each measuring point. It also drives the automatic flow meter and mud level sensor on the telescopic probe to extend into the water in the channel for intermittent detection, so as to obtain the flow velocity and surface depth of the silt at each measuring point at different times. The cloud platform is equipped with the channel flow measurement system based on big data-driven flow velocity estimation as described in claim 6 or claim 7.

9. The channel flow measurement device based on big data-driven flow velocity estimation according to claim 8, characterized in that, The mud level sensor includes: main frame, mud contact plate, slide bar, metal sensor and sensing plate; The main frame is fixedly connected to the telescopic probe rod, and the main frame is slidably connected to the outside of the slide rod. One end of the slide rod is fixedly connected to the mud contact plate, the sensing plate is set on the mud contact plate, and the metal sensor is set on the main frame; the metal sensor and the sensing plate are arranged opposite to each other. As the telescopic probe extends downwards, when the mud contact plate comes into contact with the silt, the main frame continues to slide downwards along the outside of the slide bar under the action of the telescopic probe. The distance between the metal sensor and the sensing plate shortens. When the metal sensor senses the sensing plate, the metal sensor outputs a sensing signal to the controller, which is considered as sensing the depth of the silt surface.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the channel flow measurement method based on big data-based flow velocity estimation as described in any one of claims 1-3, or the channel flow measurement method based on big data-based flow velocity estimation as described in any one of claims 4-5.

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