A portable flow measuring device for hydrological testing

Abstract

The present invention discloses a portable flow measuring device for hydrological testing, which belongs to the field of detection technology. The device includes a box A, a box B and a drive assembly. Radar flowmeters and acoustic Doppler flowmeters are installed on both sides of box B, and box A moves along a rod or rope through the drive assembly. The detection and balancing system collects environmental parameters in real time, such as water surface roughness, wind speed, particle content, water depth, etc., constructs radar and acoustic confidence models through normalization processing, dynamically allocates sensor weights in combination with the height change function, and finally outputs high-precision detection results through the flow rate model. The present invention solves the problem of insufficient reliability of a single sensor in a complex environment through multi-source data fusion and adaptive algorithms. It has the advantages of portability, robustness and high precision, and is suitable for river, flood and field hydrological monitoring.

Description

Technical Field

[0001] The invention belongs to the field of detection technology, and in particular relates to a portable flow measuring device for hydrological testing. Background Art

[0002] Traditional hydrological flow measurement devices mostly use a single sensor (such as a propeller current meter or a fixed ADCP), which has problems such as large size, poor portability, and insufficient environmental adaptability. Under complex hydrological conditions (such as high turbidity, strong winds and waves, or drastic water level fluctuations), a single sensor is susceptible to interference, resulting in increased measurement errors. For example, radar current meters experience severe signal attenuation during heavy rain or mirror-like water surfaces, while acoustic equipment loses reliability in clear water or extreme pH environments. Existing technologies lack a dynamic fusion mechanism for multiple sensors and are unable to adjust weights based on real-time environmental parameters, making it difficult to strike a balance between accuracy and robustness. In addition, traditional devices are highly fixed and difficult to adapt to mobile detection needs. Therefore, there is an urgent need for a lightweight, adaptive, and highly accurate portable flow measurement device. Summary of the Invention

[0003] In view of the deficiencies in the prior art, the present invention provides a portable flow measuring device for hydrological testing, which solves the above-mentioned problems.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: A portable flow measuring device for hydrological testing, comprising a box A and a box B, wherein the box B is mounted on the box A via a support rod, and the box A passes through a rod or a rope through a through hole provided in the box A, and further comprising:

[0005] Detection components, including a radar flow meter and an acoustic Doppler flow meter installed on both sides of box B;

[0006] The driving assembly is installed on the box A and is used to drive the box A to drive the box B to move along the length direction of the rod or rope;

[0007] The detection and balancing system is used to analyze the detection results of radar current meters and acoustic Doppler current meters and output the final detection results, including:

[0008] A data acquisition module is used to obtain detection condition information A of the radar current meter, detection condition information B of the acoustic Doppler current meter, and changes in the height of the detection point;

[0009] A data analysis module is used to perform normalization processing on the detection condition information A and the detection condition information B to output detection condition index information A and detection condition index information B;

[0010] The radar flow measurement confidence analysis module is used to construct a radar detection confidence model based on the detection condition index information A and output the radar flow measurement confidence;

[0011] The acoustic flow measurement confidence analysis module is used to construct an acoustic detection confidence model based on the detection condition index information B and output the acoustic flow measurement confidence;

[0012] The weight distribution module builds a weight distribution model based on the acoustic flow measurement confidence and radar flow measurement confidence under height changes, and outputs the respective weights of the radar current meter and acoustic Doppler current meter detection results in the pre-built flow detection model;

[0013] The flow velocity determination module imports the detection results of the radar flow meter and the acoustic Doppler flow meter and the weights of their respective detection results in the flow velocity detection model into the flow velocity detection model and outputs the final detected flow velocity.

[0014] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] Further technical solution: The flow rate detection model is expressed as:

[0016]

[0017] in, Indicates the final detection flow rate, It means the surface velocity measured by radar velocity meter. It indicates the surface velocity measured by the acoustic Doppler flowmeter. represents the weight of the surface velocity measured by the radar flow meter, It represents the weight of the surface velocity measured by the acoustic Doppler flow meter. .

[0018] Further technical solution: The operation steps of the weight distribution module are:

[0019] Perform minimum-maximum normalization processing on the current radar wave frequency and radar power to obtain the radar frequency adaptability factor and radar power attenuation factor;

[0020] Performing minimum-maximum normalization processing on the current sound wave frequency and sound wave pulse to obtain the sound wave frequency adaptability factor and the sound wave pulse optimization factor;

[0021] The radar frequency adaptability factor and the radar power attenuation factor are introduced into the radar comprehensive reliability model to obtain the radar comprehensive reliability, and the acoustic wave frequency adaptability factor and the acoustic wave pulse optimization factor are introduced into the acoustic comprehensive reliability model to obtain the acoustic comprehensive reliability;

[0022] The radar comprehensive reliability and acoustic comprehensive reliability under the current height change are imported into the weight distribution model, and the respective weights of the radar current meter and acoustic Doppler current meter detection results in the pre-built velocity detection model are output;

[0023] The weight distribution model is expressed as:

[0024]

[0025] in, represents the weight of the surface velocity measured by the radar flow meter, represents the weight of the surface velocity measured by the acoustic Doppler flow meter, represents the comprehensive reliability of the radar, represents the comprehensive acoustic reliability, represents the radar height attenuation function, represents the acoustic height stability function.

[0026] Further technical solution: The radar comprehensive reliability model is expressed as:

[0027]

[0028] in, represents the comprehensive reliability of the radar, represents the confidence of radar flow measurement, represents the radar frequency adaptability factor, represents the radar power attenuation factor;

[0029] The acoustic comprehensive reliability model is expressed as:

[0030]

[0031] in, represents the comprehensive acoustic reliability, represents the confidence of acoustic flow measurement, represents the acoustic frequency adaptability factor, Represents the acoustic pulse optimization factor.

[0032] Further technical solution: The radar height attenuation function is expressed as:

[0033]

[0034] in, represents the radar height attenuation function, Indicates the height change of the detection point. represents the radar altitude attenuation coefficient;

[0035] The acoustic height stability function is expressed as:

[0036]

[0037] in, represents the acoustic height stability function, Indicates the height change of the detection point. Indicates the upper limit of the tolerance for acoustic detection height changes.

[0038] Further technical solution: the detection condition information A includes water surface roughness, water surface wind speed and environmental visibility, and the detection condition information B includes water body particle content, water depth of target water area and water body pH value.

[0039] Further technical solution: The working steps of the data analysis module are:

[0040] The water surface roughness and water surface wind speed are respectively compared with the corresponding maximum allowable values ​​to obtain the water surface roughness index and water surface wind speed index;

[0041] Importing ambient visibility into the formula Obtain visibility index ,in, Indicates the current environment visibility. Indicates the maximum permissible environmental visibility;

[0042] Importing water particle content into the formula The particle content index is obtained from represents the attenuation coefficient, Indicates the current particle content in the water body;

[0043] Importing water pH into the formula The pH deviation index is obtained from Indicates the pH value of water;

[0044] Import the water depth of the target water area into the formula Get water depth index ,in, Indicates the depth of the target water area. Indicates the maximum applicable water depth of the equipment.

[0045] Further technical solution: The operation steps of the radar flow measurement confidence analysis module are as follows:

[0046] According to the water surface roughness index, water surface wind speed index and visibility index, a radar detection confidence model is constructed and the water surface roughness index, water surface wind speed index and visibility index are imported to output the radar flow measurement confidence;

[0047] The radar detection confidence model is expressed as:

[0048]

[0049] in, represents the confidence of radar flow measurement, represents the water surface roughness index, represents the surface wind speed index, represents the visibility index, represents the weight coefficient and .

[0050] Further technical solution: The operation steps of the acoustic flow measurement confidence analysis module are as follows:

[0051] According to the particle content index, pH value deviation index and water depth index, an acoustic detection confidence model is constructed and the particle content index, pH value deviation index and water depth index are imported to output the acoustic flow measurement confidence;

[0052] The acoustic detection confidence model is expressed as:

[0053]

[0054] in, represents the confidence of acoustic flow measurement, represents the particle content index, represents the water depth index, Indicates pH deviation index, represents the weight coefficient and .

[0055] Further technical solution: The drive assembly includes a drive box, rollers, a placement box, a gear pair and a motor. The two drive boxes and the placement box are symmetrically installed on the box body A. The four groups of rollers are fixedly connected to the rotating shafts connected to the drive box and the placement box by rotation. One of the rotating shafts in each group is fixedly connected to the output shaft of the motor detachably installed on the drive box, and each group of rotating shafts is connected through a gear pair.

[0056] The present invention provides a portable flow measuring device for hydrological testing, which has the following advantages compared with the prior art:

[0057] The present invention adopts a modular box design and combines a drive component to move along a rope. It is suitable for field and emergency monitoring scenarios. It also integrates radar and acoustic current meter. It dynamically allocates weights through the detection balance system, significantly improving the measurement reliability in complex environments (such as floods and turbid water). It has adaptive optimization capabilities and can build a confidence model based on multiple parameters such as water surface roughness, wind speed, visibility, and particle content. It can adjust sensor weights in real time to reduce the impact of environmental interference. By introducing radar height attenuation function and acoustic stability function, it can effectively compensate for measurement deviations caused by height fluctuations or water level fluctuations at the detection point. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Schematic diagram of the structure of the detection and balancing system of the present invention.

[0059] Figure 2 It is a schematic diagram of the three-dimensional structure of the present invention.

[0060] Figure 3 Schematic diagram of the structure of the driving component in the present invention.

[0061] Figure 4 It is a structural diagram of the box B in the present invention.

[0062] Notes on the accompanying drawings: 1. Box A; 101. Through hole; 2. Box B; 3. Radar flowmeter; 4. Acoustic Doppler flowmeter; 5. Drive assembly; 501. Drive box; 502. Roller; 503. Placement box; 504. Gear pair; 505. Motor; 6. Support rod; 7. Detection and balancing system; 701. Data acquisition module; 702. Data analysis module; 703. Radar flow measurement confidence analysis module; 704. Acoustic flow measurement confidence analysis module; 705. Weight distribution module; 706. Flow rate determination module; 8. Power supply placement box. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0065] See also Figures 1 to 4 According to one embodiment of the present invention, a portable flow measuring device for hydrological testing is provided, comprising a box A1 and a box B2. The box B2 is mounted on the box A1 via a support rod 6. The box A1 is passed through a rod or rope through a through hole 101 provided in the box A1 (to ensure that the entire device is suspended above the water surface). The device further comprises:

[0066] The detection component includes a radar current meter 3 and an acoustic Doppler current meter 4 installed on both sides of the box B2;

[0067] The driving assembly 5 is installed on the box A1 and is used to drive the box A1 to drive the box B2 to move along the length direction of the rod or rope;

[0068] The detection and equalization system 7 is used to analyze the detection results of the radar current meter 3 and the acoustic Doppler current meter 4 and output the final detection results, including:

[0069] Data acquisition module 701, used to obtain detection condition information A of radar current meter 3, detection condition information B of acoustic Doppler current meter 4, and detection point height change (the difference between the current height and the ideal detection height, which can be obtained by the displacement sensor installed on box A1);

[0070] The data analysis module 702 is configured to perform normalization processing on the detection condition information A and the detection condition information B to output detection condition index information A and detection condition index information B;

[0071] The radar flow measurement confidence analysis module 703 is used to construct a radar detection confidence model based on the detection condition index information A and output the radar flow measurement confidence;

[0072] Acoustic flow measurement confidence analysis module 704, used to construct an acoustic detection confidence model based on the detection condition index information B and output the acoustic flow measurement confidence;

[0073] The weight allocation module 705 constructs a weight allocation model based on the acoustic flow measurement confidence and the radar flow measurement confidence under height variation, and outputs the respective weights of the detection results of the radar current meter 3 and the acoustic Doppler current meter 4 in the pre-established flow detection model;

[0074] The flow velocity determination module 706 imports the detection results of the radar current meter 3 and the acoustic Doppler current meter 4 and the weights of the respective detection results in the flow velocity detection model into the flow velocity detection model, and outputs the final detected flow velocity.

[0075] This technical solution achieves lightweight and mobile functionality through the modular design of enclosures A1 and B2, along with a drive assembly 5 that moves along a rod or rope, overcoming the limitations of traditional fixed devices. The coordinated arrangement of a radar current meter and an acoustic Doppler current meter within the detection assembly enhances adaptability in complex environments through complementary dual-sensor detection.

[0076] Data acquisition module 701 simultaneously acquires detection condition information and altitude change data from radar and acoustic sensors, providing multi-dimensional input for dynamic fusion. Data analysis module 702 normalizes the detection condition information from the two sensor types, unifying the dimensional differences between the different parameters and ensuring comparability in subsequent confidence analysis.

[0077] The radar flow measurement confidence analysis module 703 constructs a confidence model based on the normalized detection condition index, which can quantify the reliability of the radar current meter under different water surface roughness, wind speed, and visibility conditions. The acoustic flow measurement confidence analysis module evaluates the applicability of acoustic sensors in turbid or acidic or alkaline environments using normalized indices of water particle content, water depth, and pH value.

[0078] The weight assignment module incorporates an altitude variation function, combining the dynamic characteristics of radar and acoustic confidence levels to adjust the fusion weights in real time. For example, radar signals decay exponentially with altitude, while acoustic signals decay linearly. This module uses mathematical modeling to reflect the differences in sensor performance at different altitudes, ensuring the rationality of weight assignment. Ultimately, the flow velocity determination module outputs weighted fusion results, preserving the advantages of dual sensors while addressing the limitations of a single sensor in specific environments. This significantly improves measurement accuracy and robustness in complex hydrological conditions.

[0079] Preferably, the detection condition information A includes water surface roughness (which can be obtained by wave height obtained by a pressure wave sensor), water surface wind speed and environmental visibility, and the detection condition information B includes water particle content (which can be detected by a turbidity sensor), water depth of the target water area, and water pH value.

[0080] The roughness of the water surface directly affects the intensity of radar wave reflection, while the wind speed on the water surface and the environmental visibility jointly determine the stability of electromagnetic wave propagation. These three factors form quantitative constraints on radar flow measurement from a physical perspective. The particle content in the water body is related to the attenuation characteristics of the acoustic signal, the water depth determines the length of the sound wave propagation path, and the pH value affects the surface corrosion rate of the acoustic transducer. These three factors construct the reliability boundary of acoustic detection from the perspective of medium characteristics. By independently modeling the core interference factors of the two types of sensors, the model complexity caused by parameter cross-coupling is avoided, and accurate input dimensions are provided for subsequent confidence calculations. This parameter classification mechanism enables the system to automatically identify the dominant interference factors for different environmental scenarios, thereby dynamically adjusting the multi-sensor fusion strategy.

[0081] Preferably, the working steps of the data analysis module 702 are:

[0082] The water surface roughness and water surface wind speed are respectively compared with the corresponding maximum allowable values ​​to obtain the water surface roughness index and water surface wind speed index;

[0083] Importing ambient visibility into the formula Obtain visibility index ,in, Indicates the current environment visibility. Indicates the maximum permissible environmental visibility;

[0084] Importing water particle content into the formula The particle content index is obtained from Represents the attenuation coefficient (used to control the effect of particle concentration on acoustic detection reliability, unit is L / mg), Indicates the current particle content in the water body;

[0085] Importing water pH into the formula The pH deviation index is obtained from Indicates the pH value of water;

[0086] Import the water depth of the target water area into the formula Get water depth index ,in, Indicates the depth of the target water area. Indicates the maximum applicable water depth of the equipment.

[0087] This technical solution transforms raw detection condition information into unified evaluation metrics by constructing a standardized index system for multidimensional environmental parameters. The surface roughness index quantifies the degree of interference from water surface fluctuations on radar signals by calculating the ratio of the actual measured value to the maximum value. The surface wind speed index uses linear normalization to reflect the impact of wind speed on radar wave scattering. The visibility index uses inverse normalization to convert the negative impact of low visibility on optical signals into a positive attenuation coefficient. The particle content index uses an exponential function to emphasize the nonlinear response of suspended particles in highly turbid water to acoustic signal attenuation. The pH deviation index, calculated as the difference between the absolute value and the threshold range, captures the corrosive effects of pH deviations from neutral on acoustic sensors. The water depth index uses progressive normalization to reflect the differential impact of different water depths on the acoustic wave propagation path. These indexes provide integrated quantitative inputs for subsequent confidence models, addressing the technical limitation of traditional methods that cannot accurately quantify the impact of environmental parameters on multiple sensors.

[0088] Preferably, the operation steps of the radar flow measurement confidence analysis module 703 are:

[0089] According to the water surface roughness index, water surface wind speed index and visibility index, a radar detection confidence model is constructed and the water surface roughness index, water surface wind speed index and visibility index are imported to output the radar flow measurement confidence;

[0090] The radar detection confidence model is expressed as:

[0091]

[0092] in, represents the confidence of radar flow measurement, represents the water surface roughness index, represents the surface wind speed index, represents the visibility index, represents the weight coefficient and .

[0093] Preferably, the operation steps of the acoustic flow measurement confidence analysis module 704 are:

[0094] According to the particle content index, pH value deviation index and water depth index, an acoustic detection confidence model is constructed and the particle content index, pH value deviation index and water depth index are imported to output the acoustic flow measurement confidence;

[0095] The acoustic detection confidence model is expressed as:

[0096]

[0097] in, represents the confidence of acoustic flow measurement, represents the particle content index, represents the water depth index, Indicates pH deviation index, represents the weight coefficient and .

[0098] Preferably, the operation steps of the weight allocation module 705 are:

[0099] Perform minimum-maximum normalization processing on the current radar wave frequency and radar power to obtain the radar frequency adaptability factor and radar power attenuation factor;

[0100] Performing minimum-maximum normalization processing on the current sound wave frequency and sound wave pulse to obtain the sound wave frequency adaptability factor and the sound wave pulse optimization factor;

[0101] The radar frequency adaptability factor and the radar power attenuation factor are introduced into the radar comprehensive reliability model to obtain the radar comprehensive reliability, and the acoustic wave frequency adaptability factor and the acoustic wave pulse optimization factor are introduced into the acoustic comprehensive reliability model to obtain the acoustic comprehensive reliability;

[0102] The radar comprehensive reliability and acoustic comprehensive reliability under the current height change are imported into the weight distribution model, and the respective weights of the detection results of the radar current meter 3 and the acoustic Doppler current meter 4 in the pre-built velocity detection model are output;

[0103] The weight distribution model is expressed as:

[0104]

[0105] in, represents the weight of the surface velocity measured by the radar velocity meter 3, represents the weight of the surface velocity measured by the acoustic Doppler flowmeter 4, represents the comprehensive reliability of the radar, represents the comprehensive acoustic reliability, represents the radar altitude attenuation function (representing the effect of altitude change on the radar), represents the acoustic altitude stability function (the effect of altitude changes on acoustic equipment);

[0106] The radar comprehensive reliability model is expressed as:

[0107]

[0108] in, represents the comprehensive reliability of the radar, represents the confidence of radar flow measurement, represents the radar frequency adaptability factor, represents the radar power attenuation factor;

[0109] The acoustic comprehensive reliability model is expressed as:

[0110]

[0111] in, represents the comprehensive acoustic reliability, represents the confidence of acoustic flow measurement, represents the acoustic frequency adaptability factor, represents the acoustic pulse optimization factor;

[0112] The radar height attenuation function is expressed as:

[0113]

[0114] in, represents the radar height attenuation function, Indicates the height change of the detection point. Indicates the radar height attenuation coefficient (unit: m -1 , generally take 0.1m -1 );

[0115] The acoustic height stability function is expressed as:

[0116]

[0117] in, represents the acoustic height stability function, Indicates the height change of the detection point. Indicates the upper limit of the acoustic detection altitude change tolerance (maximum allowable altitude change).

[0118] Specifically, as the device moves along a pole or rope, or as its height changes due to strong winds while stationary, the height change at the detection point is input into the weight distribution model in real time. The radar height attenuation function uses an exponential form to simulate the attenuation of signal strength caused by atmospheric absorption and scattering when electromagnetic waves propagate through the air. The acoustic height stability function uses a linear relationship to represent the energy dissipation caused by the extension of the sound wave propagation path in water. When the detection height exceeds the upper tolerance limit, the acoustic detection result is automatically excluded. These two functions are multiplied by the radar and acoustic comprehensive reliability, respectively, and then normalized to generate dynamic weights.

[0119] Compared to existing technologies, traditional methods fail to consider the impact of altitude variations on multi-sensor data fusion and rely solely on fixed weights or a single attenuation model. This results in an inability to accurately reflect the performance differences between radar and acoustic equipment during altitude variations. This solution establishes an attenuation function that matches physical properties. For example, an exponential model is used to account for the nonlinear attenuation of electromagnetic wave propagation, and a reduction function with an upper tolerance limit is used to account for the approximate linear relationship of acoustic path loss. This ensures that the weight distribution more closely matches the actual signal attenuation pattern, overcoming the limited applicability of a single model in complex altitude scenarios.

[0120] Through the above technical solution, this application solves the problem of dynamic fluctuations in sensor reliability caused by altitude changes during hydrological measurement equipment, and realizes adaptive adjustment of radar and acoustic flow measurement weights. In low-altitude rainstorms or mirror-like water surface scenarios, the radar weight is reduced through exponential decay to suppress signal interference; in high-altitude clear water environments close to the upper limit of acoustic detection, the acoustic weight is increased through linear decay to avoid Doppler effect errors, thereby maintaining flow measurement accuracy and stability throughout the full range of the device's altitude movement.

[0121] Preferably, the flow velocity detection model is expressed as:

[0122]

[0123] in, Indicates the final detection flow rate, It indicates that the surface velocity is measured by the radar velocity meter 3. It indicates the surface velocity measured by the acoustic Doppler flowmeter 4, represents the weight of the surface velocity measured by the radar velocity meter 3, represents the weight of the surface velocity measured by the acoustic Doppler flowmeter 4, .

[0124] See also Figure 2 as well as Figure 3Preferably, the driving assembly 5 includes a driving box 501, a roller 502, a placement box 503, a gear pair 504 and a motor 505. The two driving boxes 501 and the placement box 503 are symmetrically mounted on the box body A1. The four groups of rollers 502 are fixedly connected to the rotating shafts connected to the driving box 501 and the placement box 503 by rotation. One of the rotating shafts in each group is fixedly connected to the output shaft of the motor 505 detachably mounted on the driving box 501. Each group of rotating shafts is connected by a gear pair 504. The purpose of this arrangement is to The motor 505 is used to drive the rotating shaft to drive the roller 502 fixedly connected to it to rotate. At this time, the rotating shaft drives another rotating shaft through the gear pair 504 to drive another roller 502 to rotate in the opposite direction relative to the movement direction of the first roller 502, that is, each group of two rollers 502 is prompted to roll in opposite directions along the rod or rope passing through the box A1, thereby prompting the box A1 to move linearly along the length direction of the rod or rope, and prompting the radar current meter 3 and the acoustic Doppler current meter 4 located on the box A1 to reach above the target water area.

[0125] See also Figure 4 Preferably, the box body B2 is provided with a power supply placement box 8 for installing the device power supply.

[0126] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0127] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A portable flow measuring device for hydrological testing, comprising a box A and a box B, wherein the box B is mounted on the box A via a support rod, and the box A passes through a rod or rope through a through hole in the box A, characterized in that: Also includes: Detection components, including a radar flow meter and an acoustic Doppler flow meter installed on both sides of box B; The driving assembly is installed on the box A and is used to drive the box A to drive the box B to move along the length direction of the rod or rope; The detection and balancing system is used to analyze the detection results of radar current meters and acoustic Doppler current meters and output the final detection results, including: A data acquisition module is used to obtain detection condition information A of the radar current meter, detection condition information B of the acoustic Doppler current meter, and changes in the height of the detection point; A data analysis module is used to perform normalization processing on the detection condition information A and the detection condition information B to output detection condition index information A and detection condition index information B; The radar flow measurement confidence analysis module is used to construct a radar detection confidence model based on the detection condition index information A and output the radar flow measurement confidence; The acoustic flow measurement confidence analysis module is used to construct an acoustic detection confidence model based on the detection condition index information B and output the acoustic flow measurement confidence; The weight distribution module builds a weight distribution model based on the acoustic flow measurement confidence and radar flow measurement confidence under height changes, and outputs the respective weights of the radar current meter and acoustic Doppler current meter detection results in the pre-built flow detection model; The flow velocity determination module imports the detection results of the radar flow meter and the acoustic Doppler flow meter and the weights of their respective detection results in the flow velocity detection model into the flow velocity detection model and outputs the final detected flow velocity; The weight distribution model is expressed as: in, represents the weight of the surface velocity measured by the radar flow meter, represents the weight of the surface velocity measured by the acoustic Doppler flow meter, represents the comprehensive reliability of the radar, represents the comprehensive acoustic reliability, represents the radar height attenuation function, represents the acoustic height stability function.

2. The portable flow measuring device for hydrological testing according to claim 1, characterized in that: The flow rate detection model is expressed as: in, Indicates the final detection flow rate, It means the surface velocity measured by radar velocity meter. It indicates the surface velocity measured by the acoustic Doppler flowmeter. represents the weight of the surface velocity measured by the radar flow meter, It represents the weight of the surface velocity measured by the acoustic Doppler flow meter. .

3. The portable flow measuring device for hydrological testing according to claim 2, characterized in that: The operation steps of the weight distribution module are: Perform minimum-maximum normalization processing on the current radar wave frequency and radar power to obtain the radar frequency adaptability factor and radar power attenuation factor; Performing minimum-maximum normalization processing on the current sound wave frequency and sound wave pulse to obtain the sound wave frequency adaptability factor and the sound wave pulse optimization factor; The radar frequency adaptability factor and the radar power attenuation factor are introduced into the radar comprehensive reliability model to obtain the radar comprehensive reliability, and the acoustic wave frequency adaptability factor and the acoustic wave pulse optimization factor are introduced into the acoustic comprehensive reliability model to obtain the acoustic comprehensive reliability; The radar comprehensive reliability and acoustic comprehensive reliability under the current height change are imported into the weight distribution model, and the respective weights of the radar current meter and acoustic Doppler current meter detection results in the pre-built velocity detection model are output.

4. The portable flow measuring device for hydrological testing according to claim 3, characterized in that: The radar comprehensive reliability model is expressed as: in, represents the comprehensive reliability of the radar, represents the confidence of radar flow measurement, represents the radar frequency adaptability factor, represents the radar power attenuation factor; The acoustic comprehensive reliability model is expressed as: in, represents the comprehensive acoustic reliability, represents the confidence of acoustic flow measurement, represents the acoustic frequency adaptability factor, Represents the acoustic pulse optimization factor.

5. The portable flow measuring device for hydrological testing according to claim 3, characterized in that: The radar height attenuation function is expressed as: in, represents the radar height attenuation function, Indicates the height change of the detection point. represents the radar altitude attenuation coefficient; The acoustic height stability function is expressed as: in, represents the acoustic height stability function, Indicates the height change of the detection point. Indicates the upper limit of the tolerance for acoustic detection height changes.

6. The portable flow measuring device for hydrological testing according to claim 1 or 3, characterized in that: The detection condition information A includes water surface roughness, water surface wind speed and environmental visibility, and the detection condition information B includes water body particle content, water depth of target water area and water body pH value.

7. The portable flow measuring device for hydrological testing according to claim 6, characterized in that: The working steps of the data analysis module are: The water surface roughness and water surface wind speed are respectively compared with the corresponding maximum allowable values ​​to obtain the water surface roughness index and water surface wind speed index; Importing ambient visibility into the formula Obtain visibility index ,in, Indicates the current environment visibility. Indicates the maximum permissible ambient visibility; Importing water particle content into the formula The particle content index is obtained from represents the attenuation coefficient, Indicates the current particle content in the water body; Importing water pH into the formula The pH deviation index is obtained from Indicates the pH value of water; Import the water depth of the target water area into the formula Get water depth index ,in, Indicates the depth of the target water area. Indicates the maximum applicable water depth of the equipment.

8. The portable flow measuring device for hydrological testing according to claim 7, characterized in that: The operation steps of the radar flow measurement confidence analysis module are as follows: According to the water surface roughness index, water surface wind speed index and visibility index, a radar detection confidence model is constructed and the water surface roughness index, water surface wind speed index and visibility index are imported to output the radar flow measurement confidence; The radar detection confidence model is expressed as: in, represents the confidence of radar flow measurement, represents the water surface roughness index, represents the surface wind speed index, represents the visibility index, represents the weight coefficient and .

9. The portable flow measuring device for hydrological testing according to claim 7, characterized in that: The operation steps of the acoustic flow measurement confidence analysis module are as follows: According to the particle content index, pH value deviation index and water depth index, an acoustic detection confidence model is constructed and the particle content index, pH value deviation index and water depth index are imported to output the acoustic flow measurement confidence; The acoustic detection confidence model is expressed as: in, represents the confidence of acoustic flow measurement, represents the particle content index, represents the water depth index, Indicates pH deviation index, represents the weight coefficient and .

10. The portable flow measuring device for hydrological testing according to claim 1, characterized in that: The drive assembly includes a drive box, rollers, a placement box, a gear pair and a motor. The two drive boxes and the placement box are symmetrically installed on the box body A. The four groups of rollers are fixedly connected to the rotating shafts connected to the drive box and the placement box by rotation. One of the rotating shafts in each group is fixedly connected to the output shaft of the motor detachably installed on the drive box, and each group of rotating shafts is connected through a gear pair.

Images