Double-core adaptive open channel flow measurement device based on machine deep learning algorithm

Abstract

The invention relates to a double-core self-adaptive open channel flow measuring device based on a machine deep learning algorithm, which comprises a first base and a second base mounted on two sides of an open channel, a first lifting seat is slidably mounted on the first base, a mounting seat is mounted on the first lifting seat, and a second lifting seat is mounted on the mounting seat. An ultrasonic sensor, a limiting switch, a sediment content monitoring sensor, a liquid level sensor, a salinity sensor and a temperature sensor are mounted on the mounting seat; multi-layer water flow velocity measurement is carried out through a limit switch and a sediment content monitoring sensor, the sediment deposition depth is measured at the same time, measurement is carried out through the sediment content monitoring sensor, a liquid level sensor, a salinity sensor and a temperature sensor, and a second controller is responsible for arrangement of all data. And the liquid level, the average instantaneous flow velocity, the instantaneous flow rate, the accumulated flow rate, the sediment content, the temperature and the salinity of the channel are calculated through a machine deep learning algorithm according to the measured data.

Description

Technical Field

[0001] The present invention relates to the technical field of open channel flow measurement, and in particular to a dual-core adaptive open channel flow measurement device based on a machine deep learning algorithm. Background Art

[0002] The significance of implementing open channel flow monitoring is that water measurement in irrigation areas is a basic condition for irrigation water management and an effective means to promote water conservation.

[0003] During the measurement process, the existing devices only use a single measurement of data and are unable to perform calculations and summaries based on the data. At the same time, during the measurement process, the measurement data at different depths and locations in the open channel are different, and multi-point measurement is required. The data from a single point has certain deviations, which affects the overall data of the open channel. Summary of the invention

[0004] The problem solved by the present invention is to provide a dual-core adaptive open channel flow measuring device based on a machine deep learning algorithm, which solves the technical problem that the existing devices only use a single measurement of data during the measurement process and cannot perform calculations and inductions based on the data. At the same time, during the measurement process, the measurement data of different depths and different positions of the open channel are different, and multi-point measurement is required. The data of a single point has a certain deviation, which affects the overall data of the open channel.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A dual-core adaptive open channel flow measuring device based on a machine deep learning algorithm comprises a first base and a second base installed on both sides of the open channel, a first lifting seat is slidably installed on the first base, a mounting seat is installed on the first lifting seat, an ultrasonic sensor, a limit switch, a sediment content monitoring sensor, a liquid level sensor, a salt sensor and a temperature sensor are installed on the mounting seat, a second lifting seat is slidably installed on the second base, a plurality of guide steel cables are installed between the first lifting seat and the second lifting seat, and the guide steel cables penetrate the mounting seat, a conveying steel cable is installed between the first lifting seat and the second lifting seat for transmission, and the conveying steel cable drives the mounting seat to move.

[0007] Preferably, an inner groove is provided in the first lifting seat, and guide wheels are installed at the top and bottom of the inner groove.

[0008] Preferably, a wheel seat is installed on the second lifting seat, and a driving wheel is installed between the two wheel seats. A first motor is installed on the outer side of the wheel seat, and an output end of the first motor is connected to the driving wheel.

[0009] Preferably, a conveying steel cable is installed on the outer side of the guide wheel and the driving wheel, and the conveying steel cable passes through the mounting seat, and the top end penetrating portion of the conveying steel cable is fixedly connected to the mounting seat, and the bottom end penetrating portion of the conveying steel cable is slidably connected to the mounting seat.

[0010] Preferably, a first slide groove is provided on the first base, a reducer is installed on the top of the first base, the reducer input end is connected to the second motor output end, the reducer output end is located in the first slide groove and a first threaded rod is installed, and the first threaded rod is threadedly connected to the first lifting seat.

[0011] Preferably, a second slide groove is provided on the second base, a third motor is installed on the top of the second base, a second threaded rod is installed in the second slide groove at the output end of the third motor, and the second threaded rod is threadedly connected to the second lifting seat.

[0012] Preferably, a support frame is installed on the bank of the open channel, a solar panel is installed on the support frame, an electric control box is installed on the support frame, and a first controller, a second controller and a battery are installed in the electric control box.

[0013] Preferably, the first controller is used to control the opening and closing of the first motor, the second motor and the third motor and the data collection of the ultrasonic sensor and the limit switch.

[0014] Preferably, the second controller is used for data collection of sediment content monitoring sensors, liquid level sensors, salinity sensors and temperature sensors and for total flow measurement calculation of detection data of the first controller.

[0015] Preferably, the specific operating steps of the device are as follows:

[0016] Through the control of the first controller, the second motor works, and the speed reducer adjusts the speed to realize the rotation of the first threaded rod, thereby driving the first lifting seat connected by thread to rise and fall, and realize the lifting and falling of the mounting seat. The second threaded rod is driven to rotate by the second motor, thereby driving the second lifting seat connected by thread to rise and fall, and the lifting height of the first lifting seat and the second lifting seat are the same. The driving wheel is driven to rotate by the first motor, thereby driving the conveying steel cable to transmit between the guide wheel and the driving wheel, thereby driving the mounting seat to translate along the guide steel cable. At this time, multi-layer water flow velocity is measured through limit switches and sediment content monitoring sensors, and the sediment accumulation depth is measured at the same time. The sediment content monitoring sensor, liquid level sensor, salt sensor and temperature sensor are used for measurement. The second controller is responsible for sorting all data, and calculates the liquid level, average instantaneous flow velocity, instantaneous flow, cumulative flow, sediment content, temperature, salinity, etc. of the channel through the machine deep learning algorithm based on the measured data.

[0017] The beneficial effects of the present invention are: multi-layer water flow velocity measurement is performed through limit switches and sediment content monitoring sensors, and sediment accumulation depth is measured at the same time, and the measurement is performed through sediment content monitoring sensors, liquid level sensors, salinity sensors and temperature sensors, and the second controller is responsible for sorting all data, and according to the measurement data, the liquid level, average instantaneous flow velocity, instantaneous flow, cumulative flow, sediment content, temperature, salinity, etc. of the channel are calculated through a machine deep learning algorithm;

[0018] The first lifting seat and the mounting seat are lifted and lowered by the second motor, and the second lifting seat is lifted and lowered by the third motor, thereby realizing measurement of data at different depths of the open channel. The first motor drives the driving wheel to rotate, thereby driving the conveying steel cable to be transmitted between the guide wheel and the driving wheel, thereby driving the mounting seat to translate along the guide steel cable, thereby realizing measurement of data at different positions of the open channel, realizing multi-point measurement, and ensuring the accuracy of the measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the first structure of the present invention as a whole;

[0020] Figure 2 It is a schematic diagram of the second structure of the present invention as a whole;

[0021] Figure 3 It is a schematic diagram of the third structure of the present invention as a whole;

[0022] Figure 4 It is a cross-sectional view of the first lifting seat and the mounting seat of the present invention;

[0023] Figure 5 It is a schematic diagram of the internal structure of the electric control box of the present invention.

[0024] Legend:

[0025] 1. First base; 2. First lifting seat; 3. Mounting seat; 4. Ultrasonic sensor; 5. Limit switch; 6. Sediment content monitoring sensor; 7. Liquid level sensor; 8. Salt sensor; 9. Temperature sensor; 10. Second base; 11. Second lifting seat; 12. Guide steel cable; 13. Conveying steel cable; 14. Inner groove; 15. Guide wheel; 16. Wheel seat; 17. First motor; 18. Driving wheel; 19. Support frame; 20. Solar panel; 21. Electric control box; 22. First controller; 23. Second controller; 24. Battery; 25. First slide; 26. First threaded rod; 27. Reducer; 28. Second motor; 29. ​​Second slide; 30. Second threaded rod; 31. Third motor. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Specific examples are given below.

[0028] See also Figure 1 to Figure 5 A dual-core adaptive open channel flow measuring device based on a machine deep learning algorithm comprises a first base 1 and a second base 10 installed on both sides of the open channel, a first lifting seat 2 is slidably installed on the first base 1, a mounting seat 3 is installed on the first lifting seat 2, an ultrasonic sensor 4, a limit switch 5, a sediment content monitoring sensor 6, a liquid level sensor 7, a salt sensor 8 and a temperature sensor 9 are installed on the mounting seat 3, a second lifting seat 11 is slidably installed on the second base 10, a plurality of guide steel cables 12 are installed between the first lifting seat 2 and the second lifting seat 11, and the guide steel cables 12 pass through the mounting seat 3, a conveying steel cable 13 is installed between the first lifting seat 2 and the second lifting seat 11, and the conveying steel cable 13 drives the mounting seat 3 to move.

[0029] An inner groove 14 is provided in the first lifting seat 2, and guide wheels 15 are installed on the top and bottom of the inner groove 14. A wheel seat 16 is installed on the second lifting seat 11, and a driving wheel 18 is installed between the two wheel seats 16. A first motor 17 is installed on the outer side of the wheel seat 16, and the output end of the first motor 17 is connected to the driving wheel 18. When the first motor 17 works, it drives the driving wheel 18 to rotate, thereby driving the conveying cable 13 to transmit between the guide wheel 15 and the driving wheel 18, thereby driving the mounting seat 3 to translate along the guide cable 12.

[0030] A conveying steel cable 13 is installed on the outer side of the guide wheel 15 and the driving wheel 18. The conveying steel cable 13 passes through the mounting seat 3, and the top penetration part of the conveying steel cable 13 is fixedly connected to the mounting seat 3, and the bottom penetration part of the conveying steel cable 13 is slidably connected to the mounting seat 3.

[0031] A first slide groove 25 is provided on the first base 1, and a reducer 27 is installed on the top of the first base 1. The input end of the reducer 27 is connected to the output end of the second motor 28. The output end of the reducer 27 is located in the first slide groove 25 and a first threaded rod 26 is installed, and the first threaded rod 26 is threadedly connected to the first lifting seat 2. When the second motor 28 is working, the reducer 27 adjusts the speed to realize the rotation of the first threaded rod 26, thereby driving the threaded first lifting seat 2 to rise and fall, and realizing the lifting and falling of the mounting seat 3. A second slide groove 29 is provided on the second base 10, and a third motor 31 is installed on the top of the second base 10. The output end of the third motor 31 is located in the second slide groove 29 and a second threaded rod 30 is installed, and the second threaded rod 30 is threadedly connected to the second lifting seat 11. The second motor 28 works to drive the second threaded rod 30 to rotate, thereby driving the threaded second lifting seat 11 to rise and fall.

[0032] A support frame 19 is installed on the bank of the open channel, a solar panel 20 is installed on the support frame 19, an electric control box 21 is installed on the support frame 19, and a first controller 22, a second controller 23 and a battery 24 are installed in the electric control box 21. The electricity generated by the solar panel 20 is stored in the battery 24 for use by the device. The first controller 22 is used to control the opening and closing of the first motor 17, the second motor 28 and the third motor 31, and the data collection of the ultrasonic sensor 4 and the limit switch 5. The second controller 23 is used for data collection of the sediment content monitoring sensor 6, the liquid level sensor 7, the salt sensor 8 and the temperature sensor 9, and the sum of the flow measurement calculations of the detection data of the first controller 22.

[0033] The specific operation steps of the device are as follows:

[0034] The first controller 22 controls the second motor 28 to work, and the reducer 27 adjusts the speed to realize the rotation of the first threaded rod 26, thereby driving the first lifting seat 2 connected by the thread to lift and lower, and realize the lifting and lowering of the mounting seat 3. The second motor 28 drives the second threaded rod 30 to rotate, thereby driving the second lifting seat 11 connected by the thread to lift and lower, and the lifting heights of the first lifting seat 2 and the second lifting seat 11 are the same. The first motor 17 drives the driving wheel 18 to rotate, thereby driving the conveying steel cable 13 to transmit between the guide wheel 15 and the driving wheel 18, thereby driving the mounting seat 3 to translate along the guide steel cable 12. At this time, the limit switch 5 and the sediment content monitoring sensor 6 are used to measure the multi-layer water flow velocity, and the sediment accumulation depth is measured at the same time. The sediment content monitoring sensor 6, the liquid level sensor 7, the salt sensor 8 and the temperature sensor 9 are used for measurement. The second controller 23 is responsible for sorting all data, and the liquid level, average instantaneous flow velocity, instantaneous flow, cumulative flow, sediment content, temperature, salinity, etc. of the channel are calculated through the machine deep learning algorithm based on the measured data.

[0035] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A dual-core adaptive open channel flow measurement device based on machine deep learning algorithm, characterized in that: The invention comprises a first base (1) and a second base (10) installed on both sides of an open channel, a first lifting base (2) being slidably installed on the first base (1), a mounting base (3) being installed on the first lifting base (2), an ultrasonic sensor (4), a limit switch (5), a sediment content monitoring sensor (6), a liquid level sensor (7), a salt sensor (8) and a temperature sensor (9) being installed on the mounting base (3), a second lifting base (11) being slidably installed on the second base (10), a plurality of guide steel cables (12) being installed between the first lifting base (2) and the second lifting base (11), and the guide steel cables (12) passing through the mounting base (3), a conveying steel cable (13) being transmission-installed between the first lifting base (2) and the second lifting base (11), and the conveying steel cable (13) driving the mounting base (3) to move.

2. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 1 is characterized in that: An inner groove (14) is provided in the first lifting seat (2), and guide wheels (15) are installed at the top and bottom of the inner groove (14).

3. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 2 is characterized in that: A wheel seat (16) is installed on the second lifting seat (11), and a driving wheel (18) is installed between the two wheel seats (16). A first motor (17) is installed outside the wheel seat (16), and an output end of the first motor (17) is connected to the driving wheel (18).

4. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 3 is characterized in that: A conveying steel cable (13) is installed on the outer sides of the guide wheel (15) and the driving wheel (18). The conveying steel cable (13) passes through the mounting seat (3), and the top end of the conveying steel cable (13) is fixedly connected to the mounting seat (3), and the bottom end of the conveying steel cable (13) is slidably connected to the mounting seat (3).

5. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 4 is characterized in that: A first slide groove (25) is provided on the first base (1), a reducer (27) is installed on the top of the first base (1), an input end of the reducer (27) is connected to an output end of a second motor (28), a first threaded rod (26) is installed in the output end of the reducer (27) located in the first slide groove (25), and the first threaded rod (26) is threadedly connected to the first lifting seat (2).

6. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 5 is characterized in that: A second slide groove (29) is provided on the second base (10), a third motor (31) is installed on the top of the second base (10), an output end of the third motor (31) is located in the second slide groove (29) and a second threaded rod (30) is installed therein, and the second threaded rod (30) is threadedly connected to the second lifting base (11).

7. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 6 is characterized in that: A support frame (19) is installed on the bank of the open channel, a solar panel (20) is installed on the support frame (19), an electric control box (21) is installed on the support frame (19), and a first controller (22), a second controller (23) and a storage battery (24) are installed in the electric control box (21).

8. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 7, characterized in that: The first controller (22) is used to control the start and stop of the first motor (17), the second motor (28) and the third motor (31) and to collect data from the ultrasonic sensor (4) and the limit switch (5).

9. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 8, characterized in that: The second controller (23) is used for data collection of the sediment content monitoring sensor (6), the liquid level sensor (7), the salinity sensor (8) and the temperature sensor (9), and for total flow measurement calculation of the detection data of the first controller (22).

10. The dual-core adaptive open channel flow measurement device based on machine deep learning algorithm according to claim 9, characterized in that: The specific operation steps of the device are as follows: The first controller (22) controls the second motor (28) to operate, and the speed reducer (27) adjusts the speed to realize the rotation of the first threaded rod (26), thereby driving the first lifting seat (2) connected by the thread to be lifted and lowered, thereby realizing the lifting and lowering of the mounting seat (3). The second motor (28) drives the second threaded rod (30) to rotate, thereby driving the second lifting seat (11) connected by the thread to be lifted and lowered, and the lifting heights of the first lifting seat (2) and the second lifting seat (11) are the same. The first motor (17) drives the driving wheel (18) to rotate, thereby driving the conveying steel cable (13) between the guide wheel (15) and the driving wheel (18). The transmission is carried out between the wheels (18), thereby driving the mounting seat (3) to translate along the guide steel cable (12). At this time, the multi-layer water flow velocity is measured through the limit switch (5) and the sediment content monitoring sensor (6), and the sediment accumulation depth is measured at the same time. The sediment content monitoring sensor (6), the liquid level sensor (7), the salt sensor (8) and the temperature sensor (9) are used for measurement. The second controller (23) is responsible for sorting all the data, and according to the measured data, the liquid level, average instantaneous flow velocity, instantaneous flow, cumulative flow, sediment content, temperature, salinity, etc. of the channel are calculated through the machine deep learning algorithm.

Images