Fluid self-adaptive flow velocity testing system and method for real-time liquid viscosity coefficient detection

Abstract

The invention belongs to the field of fluid flow velocity testing, and provides a fluid self-adaptive flow velocity testing system and method for real-time liquid viscosity coefficient detection, and the method comprises the steps: obtaining the section width, depth and pressure of a to-be-tested fluid; the fluid dynamic viscosity coefficient is detected in real time by adopting a quartz crystal oscillation frequency change principle and combining a temperature gradient compensation algorithm; calculating the optimal measurement depth of the average flow velocity according to the section width, depth and viscosity coefficient; the average flow velocity and flow are calculated based on multiple groups of data measured by the flow velocity measurement module at the optimal measurement depth; according to the optimal measurement depth calculated by the calculation control system, the flow velocity measurement module is moved to the optimal measurement depth, and the average flow velocity and flow are calculated based on multiple sets of data measured at the optimal measurement depth. According to the invention, the functions of real-time viscosity coefficient detection, intelligent deep optimization and full-automatic adjustment are integrated, the measurement precision can be obviously improved, and the operation complexity is reduced.

Description

Technical Field

[0001] This invention belongs to the field of fluid velocity testing, specifically relating to a fluid adaptive velocity testing system and method for real-time detection of liquid viscosity coefficient. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In existing fluid measurement technologies, flow velocity measurement is generally performed by fixing the position of the flow velocity measuring device in the fluid using a support. However, this existing measurement method has the following technical problems and limitations: (1) Limited measurement accuracy: Traditional supports cannot automatically measure fluid cross-sectional parameters and rely on manual measurement of width and depth, with measurement errors typically reaching 5-15%. Manual measurement is easily limited by operator experience, environmental conditions and the accuracy of measuring tools, especially in harsh weather or complex terrain conditions, where measurement accuracy further declines.

[0004] (2) Limitations of fixed depth coefficient: Existing measurement methods generally use an empirical fixed depth coefficient (usually 0.6 times the water depth) when calculating the average flow velocity. This method ignores the differences in viscosity characteristics of different liquid materials. According to fluid mechanics theory, the viscosity coefficient of a liquid has a decisive influence on the velocity profile distribution. The fixed coefficient method can have an error of 20-30% in high viscosity or special media.

[0005] (3) Insufficient application of velocity profile theory: According to Prandtl boundary layer theory and Reynolds number Re criterion, there are significant differences in velocity profiles under different flow states: Laminar flow (Re<2000): the velocity profile is parabolic, with the maximum velocity located at 0.5 times the depth below the water surface; Turbulent flow (Re>4000): the velocity profile is logarithmically distributed, and the location of the average velocity varies depending on the roughness of the substrate, generally between 0.368 and 0.6 times the depth; Transitional flow (2000≤Re≤4000): the velocity profile is between the two, requiring interpolation calculation. The existing testing system fails to automatically adjust the measurement depth according to the Reynolds number characteristics of the actual fluid, resulting in systematic deviations in the measurement results.

[0006] (4) Lack of real-time viscosity coefficient detection capability: In engineering practice, complex fluids with varying viscosity coefficients are frequently encountered, such as surface runoff containing sediment. ), industrial wastewater containing additives ( ), mineral-rich groundwater ( ), petrochemical fluids ( Existing measurement systems cannot detect these changes in real time and can only use preset parameters, resulting in the accumulation of measurement errors.

[0007] (4) Low level of automation: Most existing measurement systems are semi-automatic or manual, requiring technicians to adjust them on-site. This is not only inefficient, but also poses safety hazards in dangerous environments (such as floods and chemically polluted areas). The lack of remote monitoring and automatic adjustment capabilities limits their application in unattended scenarios. Summary of the Invention

[0008] To address the aforementioned problems, this invention proposes a fluid adaptive flow velocity testing system and method for real-time liquid viscosity coefficient detection. This invention integrates real-time viscosity coefficient detection, intelligent depth optimization, and fully automatic adjustment functions, and can significantly improve measurement accuracy (25-40%), greatly reduce operational complexity, and achieve truly unmanned intelligent measurement.

[0009] According to some embodiments, the present invention adopts the following technical solution: A real-time fluid viscosity coefficient detection adaptive flow velocity testing system includes a support main system, a multi-parameter cross-section measurement system, an intelligent viscosity coefficient detection system, a computational control system, and an adaptive adjustment system, wherein: The main support system includes a support and an adjustable arm connected thereto, for positioning within a set distance range of the fluid to be measured and for supporting other systems; The multi-parameter cross-section measurement system includes a housing suspended at the end of the adjustable arm. The housing is equipped with a laser rangefinder, an ultrasonic detection module, a pressure sensor group, and a flow velocity measurement module, which are used to acquire the cross-sectional width, depth, and pressure of the fluid to be measured, respectively. The intelligent viscosity coefficient detection system is installed on the housing and is used to detect the fluid dynamic viscosity coefficient in real time by using the principle of quartz crystal oscillation frequency change and combined with temperature gradient compensation algorithm. The computational control system receives data from the multi-parameter cross-sectional measurement system and the intelligent viscosity coefficient detection system, calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient, and calculates the average flow velocity and flow rate based on multiple sets of data measured by the flow velocity measurement module at the optimal measurement depth. An adaptive adjustment system is used to adjust the adjustable arm to move the flow rate measurement module to the optimal measurement depth based on the optimal measurement depth calculated by the calculation control system.

[0010] As an alternative implementation, the bracket includes a base with a counterweight or a weight greater than a set value. The bracket includes two movably connected support rods with an adjustable arm between them. By adjusting the length of the adjustable arm, the angle between the two support rods can be changed, thereby changing the height of the housing on which the sensor is mounted.

[0011] As a further step, the adjustable arm is equipped with a ball screw drive mechanism, and the base is equipped with a shock absorption mechanism.

[0012] As an alternative implementation, the end of the adjustable arm is provided with a fixing mechanism, and the housing is connected to the fixing mechanism by a rope, so that the position / height of the housing can be adjusted by the adjustable arm.

[0013] As an alternative implementation, the laser rangefinder includes two units, symmetrically arranged on both sides of the housing; the pressure sensor group includes several pressure sensors, which are sequentially arranged at the lower end of the housing; and the ultrasonic detection module is arranged at the lower end of the housing. The multi-parameter cross-section measurement system also includes a position feedback sensor for detecting its position in the fluid being measured.

[0014] As an alternative implementation, the multi-parameter cross-sectional measurement system is located at the fluid end face when measuring the cross-sectional width, depth, and pressure of the fluid under test.

[0015] As an alternative implementation, the intelligent viscosity coefficient detection system includes a quartz crystal oscillating viscosity sensor, a multi-point temperature compensation network, and a liquid density detection module. The quartz crystal oscillating viscosity sensor is used to detect viscosity. The multi-point temperature compensation network is used to perform temperature compensation based on real-time temperature data to eliminate the influence of temperature on the oscillation frequency of the quartz crystal and correct the physical variation law of fluid viscosity with temperature. The density detection module and the dielectric constant analysis unit are used to detect fluid density and dielectric constant. Based on temperature, density, and dielectric constant, the final viscosity coefficient is output through a data fusion algorithm.

[0016] As an alternative implementation, the adaptive adjustment system includes an AC servo motor drive group, a photoelectric encoder, a multi-axis synchronous controller, and an intelligent anti-collision system. The multi-axis synchronous controller is used to receive the calculated optimal measurement depth and plan the motion trajectory. The AC servo motor drive group drives the adjustable arm to move according to the planned motion trajectory. The photoelectric encoder is used to detect the position of the adjustable arm and feed back real-time position information to the multi-axis synchronous controller to form closed-loop control. The intelligent anti-collision system is used to detect the distance between the support and the adjustable arm to prevent collisions.

[0017] As an alternative implementation, the system further includes a communication system comprising multiple communication modules, each with a different communication method, for connecting to the cloud.

[0018] The working method of the above system includes the following steps: The main support system is positioned within a set distance range of the fluid to be measured and supports other systems. Control the adjustable arm to adjust the height of the housing so that the multi-parameter cross-sectional measurement system is positioned on the surface of the fluid to be measured, with at least a portion extending into the fluid to obtain the cross-sectional width, depth, and pressure of the fluid to be measured; Real-time detection of fluid dynamic viscosity coefficient using an intelligent viscosity coefficient detection system; The computational control system calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient. An adaptive adjustment system is used to adjust the adjustable arm and adjust the height of the housing according to the optimal measurement depth calculated by the calculation and control system, so as to move the flow velocity measurement module on the housing to the optimal measurement depth. The average flow velocity and flow rate are calculated based on multiple sets of data obtained by the flow velocity measurement module at the optimal measurement depth.

[0019] As an alternative implementation, the process of calculating the optimal measurement depth for the average flow velocity based on the cross-sectional width, depth, and viscosity coefficient includes: using a Reynolds number calculation algorithm, where the Reynolds number Re is: Re = (ρ × uest × H) / μ; Where ρ is the liquid density, kg / m³ 3 u_est is the estimated flow velocity in m / s, H is the water depth in m, and μ is the dynamic viscosity in Pa·s; After calculating the Reynolds number Re, the optimal measurement depth D is calculated using a depth adaptive algorithm based on the Reynolds number Re. Laminar flow region, Re≤2000: ; Transition zone, 2000 < Re < 4000: ; Turbulent region, Re≥4000: ; in , , This is a correction factor for the liquid type. Let H be the surface roughness function, and H be the water depth. It is an interpolation coefficient that lies between the laminar flow coefficient and the turbulent flow coefficient.

[0020] As a further step, the liquid type correction coefficient is determined using a machine learning optimization algorithm. After training on historical data, the algorithm is used to determine the liquid type correction coefficient based on the real-time value.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention innovatively integrates laser ranging, ultrasonic array and pressure sensing technologies to achieve high-precision cross-sectional measurement in complex environments; This invention calculates the optimal measurement depth for average flow velocity based on cross-sectional width, depth, and viscosity coefficient, and then performs multiple measurements at the optimal measurement depth, effectively improving the accuracy and precision of the measurement.

[0022] In the calculation process, this invention considers the real-time viscosity coefficient and the differences in velocity profiles under different flow states. It uses machine learning optimization algorithms to optimize parameters and automatically optimizes measurement parameters by combining historical data, thereby improving long-term measurement stability and further enhancing the accuracy and adaptability of the measurement results.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0025] Figure 1 A schematic diagram of the system structure of one embodiment; Figure 2 This is a schematic diagram of a system fluid cross-section measurement according to one embodiment; Figure 3 This is a velocity profile distribution and optimal measurement depth map under different viscosity coefficients in one embodiment; Figure 4 This is a flowchart illustrating the system workflow and data processing of one embodiment.

[0026] The components include: 1. Fixed base; 2. Adjustable arm; 3. Laser rangefinder; 4. Ultrasonic depth detector; 5. Pressure sensor; 6. Viscosity sensor; 7. Temperature compensation module; 8. Microcontroller; 9. Data processing unit; 10. Storage module; 11. Stepper motor; 12. Position feedback sensor; 13. Flow meter fixing device; and 14. Display screen. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0030] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0031] Example 1 A fluid adaptive flow velocity testing system for real-time detection of liquid viscosity coefficient, such as Figure 1 As shown, it includes a support main system, a multi-parameter cross-section measurement system, an intelligent viscosity coefficient detection system, a calculation control system, and an adaptive adjustment system, wherein: The main support system includes a support and an adjustable arm connected thereto, for positioning within a set distance range of the fluid to be measured and for supporting other systems; The multi-parameter cross-section measurement system includes a housing suspended at the end of the adjustable arm. The housing is equipped with a laser rangefinder, an ultrasonic detection module, a pressure sensor group, and a flow velocity measurement module, which are used to acquire the cross-sectional width, depth, and pressure of the fluid to be measured, respectively. The intelligent viscosity coefficient detection system is installed on the housing and is used to detect the fluid dynamic viscosity coefficient in real time by using the principle of quartz crystal oscillation frequency change and combined with temperature gradient compensation algorithm. The computational control system receives data from the multi-parameter cross-sectional measurement system and the intelligent viscosity coefficient detection system, calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient, and calculates the average flow velocity and flow rate based on multiple sets of data measured by the flow velocity measurement module at the optimal measurement depth. An adaptive adjustment system is used to adjust the adjustable arm to move the flow rate measurement module to the optimal measurement depth based on the optimal measurement depth calculated by the calculation control system.

[0032] In this embodiment, the bracket includes a base, the base is provided with a counterweight or the weight is greater than a set value, the end of the adjustable arm is provided at the upper end of the base, and the adjustable arm is a multi-section guide rail type adjustable arm, with each section of the arm being movably connected to the other.

[0033] The base is made of aluminum alloy and equipped with an anti-vibration / damping system. Existing equipment can be selected. The adjustable arm is equipped with a precision ball screw transmission mechanism, with a working stroke of 0-50m, positioning accuracy of ±0.5mm, and load capacity of 500kg. The structural design has been optimized through finite element analysis.

[0034] The bracket includes two movably connected support rods with an adjustable arm between them. By adjusting the length of the adjustable arm, the angle between the two support rods can be changed, thereby changing the height of the housing on which the sensor is mounted.

[0035] In this embodiment, the adjustable arm is equipped with a ball screw drive mechanism, and the base is equipped with a shock absorption mechanism. A fixing mechanism is provided at the end of the adjustable arm, and the housing is connected to the fixing mechanism via a rope, allowing the position / height of the housing to be adjusted via the adjustable arm.

[0036] like Figure 2 As shown, the laser rangefinder includes two, symmetrically arranged on both sides of the housing; the pressure sensor group includes several pressure sensors, which are sequentially arranged at the lower end of the housing. The pressure magnitude helps to confirm the depth of the flow meter, and the pressure change can be used to determine whether the bottom has been reached. The ultrasonic detection module is located at the lower end of the housing; The multi-parameter cross-section measurement system also includes a position feedback sensor for detecting its position in the fluid being measured.

[0037] When measuring the cross-sectional width, depth, and pressure of the fluid under test, the multi-parameter cross-sectional measurement system is located at the fluid end face, meaning that part of the shell is inside the fluid.

[0038] In this embodiment, the intelligent viscosity coefficient detection system includes a quartz crystal oscillating viscosity sensor, a multi-point temperature compensation network, a liquid density detection module, and a dielectric constant analysis unit. The quartz crystal oscillating viscosity sensor is used to detect viscosity. The multi-point temperature compensation network is used to perform temperature compensation based on real-time temperature data to eliminate the influence of temperature on the oscillation frequency of the quartz crystal and correct the physical variation law of fluid viscosity with temperature. The density detection module and the dielectric constant analysis unit are used to detect fluid density and dielectric constant, and output the final viscosity coefficient by comprehensively considering temperature, density, and dielectric constant through a data fusion algorithm.

[0039] A quartz crystal oscillating viscosity sensor, a multi-point temperature compensation network, a liquid density detection module, and a dielectric constant analysis unit synchronously acquire all sensor data at a set frequency (e.g., 100Hz), and output the final viscosity coefficient through a data fusion algorithm.

[0040] The mechanism of temperature compensation consists of three parts. The first is quartz crystal frequency temperature compensation, which eliminates the influence of temperature on the oscillation frequency of the quartz crystal and ensures the measurement accuracy of the sensor itself. This is the basic compensation in terms of hardware.

[0041] The compensation formula is:

[0042] Secondly, viscosity-temperature compensation corrects for the inherent physical change in fluid viscosity with temperature. The viscosity of all fluids is strongly dependent on temperature; for example, water has a viscosity of approximately 1.0 at 20 degrees Celsius. However, it increases to 1.8 at 0 degrees Celsius. . The equation describes this exponential relationship; without compensation, a 20-degree Celsius temperature change would result in an 80% viscosity error. This is a compensation at the physical property level, reflecting the true viscosity of the fluid. The compensation formula is:

[0043] Thirdly, there is comprehensive temperature compensation, which addresses the multiple temperature coupling effects present in actual measurements: sensor temperature, fluid temperature, and ambient temperature may differ and influence each other. Comprehensive compensation achieves accurate system-level compensation through a multi-parameter fusion algorithm, combined with density and dielectric constant corrections.

[0044] The final compensation formula is:

[0045] in, The original frequency, This is the temperature value used as a reference, typically a standard operating temperature is chosen as the reference point. The temperature compensation algorithm calculates the difference between the actual temperature and the reference temperature. - This is used to compensate for the effect of temperature changes on the oscillation frequency of the quartz crystal. The temperature of quartz crystals. For fluid temperature, , Where B is the temperature coefficient and B is the activation energy parameter. Density correction factor This is the dielectric constant correction factor. This is the raw, unprocessed output data.

[0046] The purpose of dielectric constant analysis is to identify fluid type and provide viscosity correction factors. The measurement principle involves measuring the relative dielectric constant using the capacitance method. The analysis process involves matching the measured dielectric constant with a built-in database to identify the fluid type (e.g., pure water). ≈81, Petroleum =2-5), output the corresponding viscosity correction factor. This improves the accuracy of the final viscosity coefficient calculation, and its formula is:

[0047] in, This is the viscosity correction factor. The reference capacitance is typically the capacitance value in air or a vacuum. This is the capacitance value obtained from actual measurement.

[0048] In this embodiment, the adaptive adjustment system includes an AC servo motor drive group, a photoelectric encoder, a multi-axis synchronous controller, and an intelligent anti-collision system. During operation, the calculation and control system sends the target depth to the multi-axis synchronous controller. The controller plans the motion trajectory and drives the AC servo motor. The motor drives the adjustable arm to move through a precision ball screw. The photoelectric encoder provides real-time feedback of position information to form a closed-loop control. The intelligent anti-collision system monitors the entire process to ensure safety. In conjunction with the support system, a fixed base provides a stable platform, a shock-absorbing mechanism eliminates environmental interference, and a counterweight ensures a stable center of gravity. The servo motor, installed inside the base, drives the adjustable arm to achieve precise three-dimensional spatial adjustment with a positioning accuracy of ±1mm. The entire process employs PID control algorithms and multi-axis synchronous control algorithms to ensure that the sensor housing can move quickly and smoothly to the optimal measurement depth position.

[0049] The intelligent collision avoidance system includes multiple distance detection modules, which are respectively mounted on the bracket and the adjustable arm to perform distance detection, or other existing collision avoidance components can be selected.

[0050] In some embodiments, the system further includes a communication system, which comprises multiple communication modules, each with a different communication method, for connecting to the cloud.

[0051] In this embodiment, the intelligent viscosity coefficient detection system employs the principle of quartz crystal oscillation frequency variation, combined with a temperature gradient compensation algorithm, to detect the fluid dynamic viscosity coefficient μ in real time (measurement range 0.0001-10 Pa·s, accuracy ±0.5%). The calculation and control system integrates an advanced Reynolds number intelligent discrimination algorithm and a velocity profile model based on CFD simulation verification. Based on the measured cross-sectional width W (accuracy ±2 mm), depth H (accuracy ±5 mm), and viscosity coefficient μ, it automatically determines the optimal measurement depth D for the average flow velocity. A dual-beam laser rangefinder uses phase difference measurement technology to achieve accurate width measurement, a multi-frequency ultrasonic detection array eliminates the influence of water surface fluctuations, a high-precision pressure sensor provides cross-validation of depth data, and a floating-point coprocessor ensures the real-time performance and accuracy of complex fluid dynamics calculations.

[0052] The adaptive adjustment system receives signals from the optimized control algorithm and drives a high-precision servo motor to achieve precise three-dimensional positioning of the flow meter (positioning accuracy ±1mm). Users can obtain real-time cross-sectional parameters, viscosity coefficient distribution, optimal measurement depth, flow velocity profile, and flow rate data through a touch screen or mobile APP. It also supports historical data query, trend analysis, and anomaly alarm functions.

[0053] The system integrates machine learning optimization algorithms, which can automatically optimize measurement parameters based on historical measurement data, achieving full intelligence and unmanned operation of fluid measurement.

[0054] Example 2 Based on the working method of the system provided in Embodiment 1, such as Figure 3 , Figure 4 As shown, it includes the following steps: The main support system is positioned within a set distance range of the fluid to be measured and supports other systems. Control the adjustable arm to adjust the height of the housing so that the multi-parameter cross-sectional measurement system is positioned on the surface of the fluid to be measured, with at least a portion extending into the fluid to obtain the cross-sectional width, depth, and pressure of the fluid to be measured; Real-time detection of fluid dynamic viscosity coefficient using an intelligent viscosity coefficient detection system; The computational control system calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient. An adaptive adjustment system is used to adjust the adjustable arm and adjust the height of the housing according to the optimal measurement depth calculated by the calculation and control system, so as to move the flow velocity measurement module on the housing to the optimal measurement depth. The average flow velocity and flow rate are calculated based on multiple sets of data obtained by the flow velocity measurement module at the optimal measurement depth.

[0055] The process of calculating the optimal measurement depth for average flow velocity based on cross-sectional width, depth, and viscosity coefficient includes: using the Reynolds number calculation algorithm, where the Reynolds number Re is: Re = (ρ × uest × H) / μ; Where ρ is the liquid density, kg / m³ 3 u_est is the estimated flow velocity in m / s, H is the water depth in m, and μ is the dynamic viscosity in Pa·s; After calculating the Reynolds number Re, the optimal measurement depth D is calculated using a depth adaptive algorithm based on the Reynolds number Re. Laminar flow region, Re≤2000: ; Transition zone, 2000 < Re < 4000: ; Turbulent region, Re≥4000: ; in , , This is a correction factor for the liquid type. Let H be the surface roughness function, and H be the water depth. It is an interpolation coefficient that lies between the laminar flow coefficient (0.5) and the turbulent flow coefficient (0.6).

[0056] As a further step, the liquid type correction coefficient is determined using a machine learning optimization algorithm. After training on historical data, the algorithm is used to determine the liquid type correction coefficient based on the real-time value.

[0057] For example, Long Short-Term Memory (LSTM) networks or Support Vector Machines (SVMs) can be used for parameter optimization learning. The trained model can then be used for trend analysis and prediction based on historical data, adaptive parameter optimization, and automatic outlier detection and filtering.

[0058] The following is an example of flow measurement at a certain river cross-section.

[0059] Step 1: Install and fix the support equipment on the riverbank. After the system is started, it will perform a self-test. Step 2: The laser rangefinder 3 scanned and measured the river width W=12.5m, the ultrasonic detector 4 measured the maximum depth H=3.2m, and the pressure sensor 5 provided depth verification; Step 3: Viscosity sensor 6 detects the viscosity coefficient of the water. (20 degrees Celsius clean water), temperature compensation module 7 performs temperature correction; Step 4: Microcontroller 8 calculation: Initial flow rate estimation =1.2m / s, Re=(1000×1.2×3.2) / 0.00089≈4,314,000>4000 (turbulent flow), therefore D=0.6×3.2=1.92m; Step 5: Stepper motor 11 drives adjustment arm 2 to adjust the flow meter to a depth of 1.92m, and position feedback sensor 12 confirms that it is in place; Step 6: The average flow velocity at this depth was measured to be V = 1.35 m / s. The flow rate was calculated to be Q = 1.35 × 12.5 × 3.2 = 54.0 m³ / s. 3 / s.

[0060] During the self-test process after system startup, sensor calibration is performed. Other sensors can be used to detect environmental parameters (such as temperature data). Communication with the cloud is established and the network is configured to ensure smooth data upload and storage.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fluid adaptive flow velocity testing system for real-time detection of liquid viscosity coefficient, characterized in that, It includes the main support system, a multi-parameter cross-section measurement system, an intelligent viscosity coefficient detection system, a computational control system, and an adaptive adjustment system, among which: The main support system includes a support and an adjustable arm connected thereto, for positioning within a set distance range of the fluid to be measured and for supporting other systems; The multi-parameter cross-section measurement system includes a housing suspended at the end of the adjustable arm. The housing is equipped with a laser rangefinder, an ultrasonic detection module, a pressure sensor group, and a flow velocity measurement module, which are used to acquire the cross-sectional width, depth, and pressure of the fluid to be measured, respectively. The intelligent viscosity coefficient detection system is installed on the housing and is used to detect the fluid dynamic viscosity coefficient in real time by using the principle of quartz crystal oscillation frequency change and combined with temperature gradient compensation algorithm. The computational control system receives data from the multi-parameter cross-sectional measurement system and the intelligent viscosity coefficient detection system, calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient, and calculates the average flow velocity and flow rate based on multiple sets of data measured by the flow velocity measurement module at the optimal measurement depth. An adaptive adjustment system is used to adjust the adjustable arm to move the flow rate measurement module to the optimal measurement depth based on the optimal measurement depth calculated by the calculation control system.

2. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The bracket includes a base, the base is provided with a counterweight or the weight is greater than a set value, the bracket includes two movably connected support rods, and an adjustable arm is provided between the two. By adjusting the length of the adjustable arm, the angle between the two support rods can be changed, thereby changing the height of the housing on which the sensor is installed. The adjustable arm is equipped with a ball screw drive mechanism, and the base is equipped with a shock absorption mechanism.

3. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The adjustable arm is equipped with a fixing mechanism at its end. The housing is connected to the fixing mechanism by a rope, and the position / height of the housing can be adjusted by the adjustable arm.

4. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The laser rangefinder includes two units, symmetrically arranged on both sides of the housing; the pressure sensor group includes several pressure sensors, which are sequentially arranged at the lower end of the housing; the ultrasonic detection module is located at the lower end of the housing. The multi-parameter cross-section measurement system also includes a position feedback sensor for detecting its position in the fluid being measured.

5. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The multi-parameter cross-sectional measurement system is located at the fluid end face when measuring the cross-sectional width, depth, and pressure of the fluid under test.

6. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The intelligent viscosity coefficient detection system includes a quartz crystal oscillating viscosity sensor, a multi-point temperature compensation network, and a liquid density detection module. The quartz crystal oscillating viscosity sensor is used to detect viscosity. The multi-point temperature compensation network is used to perform temperature compensation based on real-time temperature data to eliminate the influence of temperature on the oscillation frequency of the quartz crystal and correct the physical variation law of fluid viscosity with temperature. The density detection module and dielectric constant analysis unit are used to detect fluid density and dielectric constant. Based on temperature, density, and dielectric constant, the final viscosity coefficient is output through a data fusion algorithm.

7. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The adaptive adjustment system includes an AC servo motor drive group, a photoelectric encoder, a multi-axis synchronous controller, and an intelligent anti-collision system. The multi-axis synchronous controller is used to receive the calculated optimal measurement depth and plan the motion trajectory. The AC servo motor drive group drives the adjustable arm to move according to the planned motion trajectory. The photoelectric encoder is used to detect the position of the adjustable arm and feed back real-time position information to the multi-axis synchronous controller to form closed-loop control. The intelligent anti-collision system is used to detect the distance between the support and the adjustable arm to prevent collisions.

8. The fluid adaptive flow velocity testing system for real-time liquid viscosity coefficient detection as described in claim 1, characterized in that, The system also includes a communication system, which comprises multiple communication modules, each with a different communication method, for connecting to the cloud.

9. A method of operating the system based on any one of claims 1-8, characterized in that, Includes the following steps: The main support system is positioned within a set distance range of the fluid to be measured and supports other systems. Control the adjustable arm to adjust the height of the housing so that the multi-parameter cross-sectional measurement system is positioned on the surface of the fluid to be measured, with at least a portion extending into the fluid to obtain the cross-sectional width, depth, and pressure of the fluid to be measured; Real-time detection of fluid dynamic viscosity coefficient using an intelligent viscosity coefficient detection system; The computational control system calculates the optimal measurement depth for average flow velocity based on the cross-sectional width, depth, and viscosity coefficient. An adaptive adjustment system is used to adjust the adjustable arm and adjust the height of the housing according to the optimal measurement depth calculated by the calculation and control system, so as to move the flow velocity measurement module on the housing to the optimal measurement depth. The average flow velocity and flow rate are calculated based on multiple sets of data obtained by the flow velocity measurement module at the optimal measurement depth.

10. The working method as described in claim 9, characterized in that, The process of calculating the optimal measurement depth for average flow velocity based on cross-sectional width, depth, and viscosity coefficient includes: using the Reynolds number calculation algorithm, where the Reynolds number Re is: Re = (ρ × uest × H) / μ; Where ρ is the liquid density, kg / m³ 3 u_est is the estimated flow velocity in m / s, H is the water depth in m, and μ is the dynamic viscosity in Pa·s; After calculating the Reynolds number Re, the optimal measurement depth D is calculated using a depth adaptive algorithm based on the Reynolds number Re. Laminar flow region, Re≤2000: ; Transition zone, 2000 < Re < 4000: ; Turbulent region, Re≥4000: ; in , , This is a correction factor for the liquid type. Let H be the surface roughness function, and H be the water depth. It is an interpolation coefficient that lies between the laminar flow coefficient and the turbulent flow coefficient; Alternatively, the liquid type correction coefficient can be determined using a machine learning optimization algorithm trained on historical data, and then based on the real-time value.

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