Flow metering method for a non-full pipe electromagnetic flow meter

Abstract

This application relates to the field of flow measurement technology, specifically to a flow measurement method for a non-full-pipe electromagnetic flowmeter. The method includes: acquiring laser pulse signals emitted by a laser level gauge in the non-full-pipe electromagnetic flowmeter and receiving echo signals; obtaining fluid velocity in real time through electrodes at preset heights on the inner wall of the pipe; acquiring initial matching echo signal points for each pulse signal point in the laser pulse signal, and acquiring the Reynolds number of the fluid in the pipe at the acquisition time of each pulse signal point; acquiring the disorder degree of each pulse signal point; acquiring the local disorder clustering degree of each pulse signal point and the correlation degree of each pulse signal point through the disorder degree distribution of preset nearest neighbor pulse signal points, and optimizing the disorder degree; screening mismatched pulse signal points and rematching them with echo signal points; and acquiring the fluid flow rate in the pipe. This application aims to improve the accuracy of flow measurement using a non-full-pipe electromagnetic flowmeter.

Description

Technical Field

[0001] This application relates to the field of flow measurement technology, specifically to a flow measurement method for a non-full-pipe electromagnetic flowmeter. Background Technology

[0002] In practical applications, liquids in urban drainage systems, industrial wastewater discharge pipes, and agricultural irrigation channels are often in a non-full-pipe flow state. However, traditional electromagnetic flowmeters are mainly designed for full-pipe flow. Electrodes are installed along the horizontal diameter of the pipe. When a conductive fluid moves in a magnetic field, cutting magnetic lines of force, an induced electromotive force is generated in the direction perpendicular to both the magnetic field and the flow direction. The current flow rate is then calculated. In a non-full-pipe state, the upper part of the pipe is filled with air, and the lower part is filled with liquid. The liquid flow is irregular, making it impossible to accurately measure the relationship between the induced electromotive force and the flow velocity.

[0003] Traditional electromagnetic flowmeters are widely used in industrial fluid measurement, but their operating principle cannot meet the measurement requirements of conductive liquids that are not fully filled in the pipe. In the case of a partially filled pipe, the measurement accuracy drops significantly due to changes in liquid level and complex flow patterns, failing to meet the demands for precise flow monitoring in industrial production. Summary of the Invention

[0004] In view of the above, it is necessary to provide a flow measurement method for non-full-pipe electromagnetic flowmeters, which improves the flow measurement accuracy of non-full-pipe electromagnetic flowmeters compared with traditional flow measurement methods.

[0005] The flow measurement method of a non-full-pipe electromagnetic flowmeter proposed in this application adopts the following technical solution:

[0006] One embodiment of this application provides a flow measurement method for a non-full-pipe electromagnetic flowmeter, the method comprising the following steps:

[0007] The laser pulse signal emitted by the laser level gauge in the non-full pipe electromagnetic flowmeter and the echo signal received are collected within a preset time period; the fluid velocity is obtained in real time through electrodes at preset heights on the inner wall of the pipe.

[0008] In terms of timing, echo signal points with the same sequence number as each pulse signal point in the laser pulse signal are recorded as initial matched echo signal points. The initial liquid level height in the pipeline at the time of acquisition of each pulse signal point is obtained by using the time difference between each pulse signal point and its initial matched echo signal point. The Reynolds number of the fluid in the pipeline at the time of acquisition of each pulse signal point is obtained by using the flow velocity collected by the electrode at the initial liquid level height.

[0009] By analyzing the relationship between the signal amplitude of each pulse signal point and the transmission distance, the attenuated signal amplitude of each pulse signal point is obtained. By comparing the signal amplitude of each pulse signal point with its initial matched echo signal point and the attenuated signal amplitude, the disorder of each pulse signal point is obtained.

[0010] By analyzing the disorder distribution of preset nearest neighbor pulse signal points for each pulse signal point, the local disorder clustering degree of each pulse signal point is obtained. By comparing the initial liquid level height difference of each pulse signal point with that of its neighboring pulse signal points and the Reynolds number, the correlation degree of each pulse signal point is obtained. Combined with the local disorder clustering degree, the disorder degree is optimized. By analyzing the distribution of the optimized disorder degree of all pulse signal points, mismatched pulse signal points are selected from all pulse signal points. By combining the optimized disorder degree with the particle swarm optimization algorithm, the matching echo signal points of each mismatched pulse signal point are re-acquired. Finally, by analyzing each pulse signal point and its matched echo signal points, the fluid flow rate in the pipeline at the acquisition time of each pulse signal point is obtained.

[0011] In one embodiment, when obtaining the Reynolds number of the fluid in the pipe at the time of each pulse signal point acquisition, the fluid velocity in the Reynolds number calculation formula is: the average value of the flow velocities collected by all electrodes at the initial liquid level height corresponding to each pulse signal point.

[0012] In one embodiment, obtaining the attenuated signal amplitude of each pulse signal point includes:

[0013] The expression for the attenuated signal amplitude at each pulse signal point is:

[0014] In the formula, This represents the signal amplitude after attenuation at the q-th pulse signal point; This represents the signal amplitude of the q-th pulse signal point; It represents an exponential function with the natural constant as the base; α represents the attenuation coefficient of the pulse signal in the medium; This represents the distance between the liquid surface and the laser level gauge, calculated using the q-th pulse signal point and its initial matched echo signal point.

[0015] The method for obtaining α is as follows: substituting the signal amplitude of the first pulse signal point and its initial matched echo signal point, and the distance between the liquid surface and the laser level gauge calculated using the first pulse signal point and its initial matched echo signal point, into the equation. .

[0016] In one embodiment, the process of obtaining the disorder level is as follows:

[0017] Calculate the difference between the signal amplitude of the initial matched echo signal point and the attenuated signal amplitude of each pulse signal point; the disorder is the ratio of the difference to the signal amplitude of each pulse signal point.

[0018] In one embodiment, the expression for the local disorder clustering degree is:

[0019] In the formula, U represents the local disorder clustering degree of the q-th pulse signal point; U represents the number of preset nearest neighbor pulse signal points of the q-th pulse signal point; , ε represents the preset disorder of the u-th and (u+1)-th nearest neighbor pulse signal points of the q-th pulse signal point, respectively; ε represents a preset positive number. This indicates the absolute value operation.

[0020] In one embodiment, the process of obtaining the correlation degree is as follows:

[0021] Calculate the deviation of the initial liquid level height between each pulse signal point and each of its adjacent pulse signal points, and count the maximum value among the deviations between each pulse signal point and all its adjacent pulse signal points;

[0022] Calculate the ratio of the normalized value of the Reynolds number to the normalized value of the maximum value, and calculate the difference between the ratio and 1;

[0023] The correlation degree is the reciprocal of the sum of the difference value and a preset positive number.

[0024] In one embodiment, the process of optimizing the disorder is as follows:

[0025] The disorder optimization factor for each pulse signal point is obtained by combining the correlation degree and the local disorder clustering degree.

[0026] Calculate the sum of the disorder optimization factor and 1, and use the product of the sum and the disorder of each pulse signal point as the optimized disorder of each pulse signal point.

[0027] In one embodiment, the disorder optimization factor is a normalized value of the product of the correlation degree and the local disorder clustering degree.

[0028] In one embodiment, the process of filtering each mismatched pulse signal point from all pulse signal points is as follows:

[0029] Based on the optimized disorder of all pulse signal points, all pulse signal points are divided into two categories. The mean of the optimized disorder of all pulse signal points in each category is calculated. Each pulse signal point in the category with the largest mean is taken as a mismatched pulse signal point.

[0030] In one embodiment, during the process of reacquiring the matching echo signal points of each mismatched pulse signal point, the fitness value of each particle in the particle swarm optimization algorithm is the reciprocal of the sum of the optimized disorder of all pulse signal points under each particle.

[0031] This application has at least the following beneficial effects:

[0032] This application considers the different fluid velocities at different heights inside the pipe. By installing transverse electrodes at different heights inside the pipe, the fluid velocity can be measured more accurately, which is beneficial to improving the accuracy of flow measurement. By calculating the Reynolds number, the flow state of the fluid can be determined. By calculating the disorder degree, the matching error between the pulse signal points in the laser pulse signal and the echo signal points in the echo signal can be quantified, which helps to identify mismatched pulse signal points, so as to dynamically adjust the signal matching strategy according to the magnitude of the disorder degree. By calculating the distribution of the disorder degree of the nearest pulse signal points of each pulse signal point, the local disorder clustering degree can be obtained, which helps to identify mismatch phenomena caused by liquid surface fluctuations. Through local feature recognition, it is possible to... This method more accurately identifies mismatched pulse signal points, reducing misjudgments caused by environmental interference. By analyzing the correlation between liquid level changes and Reynolds number, the correlation degree of each pulse signal point is obtained, effectively avoiding abnormal error rates caused by environmental interference. Optimizing the error rate through local error clustering and correlation can more accurately reflect signal mismatch situations. The optimized error rate distribution allows for more accurate identification of mismatched pulse signal points. Combined with the optimized error rate and particle swarm optimization algorithm, each mismatched pulse signal point is re-matched with its echo signal point, improving signal matching effectiveness. Flow measurement is then performed using the optimized matching results, improving the accuracy of flow measurement for non-full-pipe electromagnetic flowmeters. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating the steps of a flow measurement method for a non-full-pipe electromagnetic flowmeter provided in this application;

[0035] Figure 2 This is a schematic diagram showing the installation position of the lateral electrode;

[0036] Figure 3 This is a schematic diagram illustrating the optimization process for error level.

[0037] Figure 4 This is a schematic diagram of the matching process between pulse signal points and echo signal points. Detailed Implementation

[0038] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0039] Unless otherwise defined, 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 application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0040] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0041] The following description, in conjunction with the accompanying drawings, details a specific scheme for a flow measurement method for a non-full-pipe electromagnetic flowmeter provided in this application.

[0042] This application provides a flow measurement method for a non-full-pipe electromagnetic flowmeter in one embodiment. Specifically, it provides the following flow measurement method for a non-full-pipe electromagnetic flowmeter. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0043] Step 1: Collect the laser pulse signal emitted by the laser level gauge in the non-full pipe electromagnetic flowmeter and the echo signal received within a preset time period; obtain the fluid flow rate in real time through electrodes at preset heights on the inner wall of the pipe.

[0044] The structure of the non-full-pipe electromagnetic flowmeter used in this application is as follows: a high-precision laser level gauge is installed above the vertical center of the electromagnetic flowmeter sensor at the integrated inductor coil, with its emitting end vertically downwards precisely aligned with the inside of the pipe. Simultaneously, at preset heights on the inner wall of the pipe, a pair of corrosion-resistant, highly conductive transverse electrodes are symmetrically embedded horizontally, with the material optionally being platinum-plated stainless steel.

[0045] In this embodiment, the preset heights refer to 1 / 2, 1 / 4, and 1 / 8 of the inner wall height of the pipe, with a total of 3 pairs of transverse electrodes embedded. The installation height and number of transverse electrodes are preset manually, and the implementer can set the installation height and number of transverse electrodes according to the actual situation. This application does not impose any special restrictions. A schematic diagram of the transverse electrode installation position is shown below. Figure 2 As shown, Figure 2 In the diagram, 1 represents a laser level gauge, 2 represents a pair of horizontal electrodes at 1 / 2 height, 3 represents a pair of horizontal electrodes at 1 / 4 height, 4 represents a pair of horizontal electrodes at 1 / 8 height, and 5 represents a ground electrode.

[0046] A laser level gauge is a smart sensor, essentially an instrument that uses a laser beam to measure the distance to a liquid surface. It employs advanced laser pulse ranging technology, calculating the distance from the laser level gauge to the liquid surface by measuring the time difference between the emitted laser beam and the received reflected light, and then converting this distance into the liquid level height. Within a preset time period, the laser pulse signal emitted by the laser level gauge and the echo signal received in a non-full-pipe electromagnetic flowmeter are collected.

[0047] In this embodiment, the length of the preset time period is 1 second, and the emission frequency of the laser level gauge is 100 Hz. The length of the preset time period and the emission frequency of the laser level gauge are preset by humans, and the implementer can set them according to the actual situation. This application does not impose any special restrictions.

[0048] When liquid flows in a pipe, the transverse electrodes located below the liquid level capture the induced electromotive force generated by the fluid cutting magnetic lines of force, based on the principle of electromagnetic induction. Since the fluid velocity varies at different heights, the measurements from multiple pairs of transverse electrodes can reflect the vertical distribution characteristics of the flow velocity. Therefore, in this application, the transverse electrodes deployed in a non-full-pipe electromagnetic flowmeter are used to collect the induced electromotive force in real time. The fluid velocity is obtained based on the induced electromotive force and Faraday's law of electromagnetic induction. At the acquisition time of each pulse signal point in the laser pulse signal, each pair of transverse electrodes below the liquid level can obtain the fluid velocity. The method of obtaining the fluid velocity based on the induced electromotive force and Faraday's law of electromagnetic induction is a known technique and will not be elaborated upon here. Each laser pulse in the laser pulse signal is recorded as a pulse signal point, and each laser pulse in the echo signal is recorded as an echo signal point.

[0049] Step 2: Obtain the initial matched echo signal points of each pulse signal point in the laser pulse signal; obtain the Reynolds number of the fluid in the pipe at the acquisition time of each pulse signal point; obtain the disorder degree of each pulse signal point; optimize the disorder degree by comparing the initial liquid level height difference of each pulse signal point with the Reynolds number through the preset disorder degree distribution of the nearest neighbor pulse signal points of each pulse signal point; filter out each mismatched pulse signal point from all pulse signal points, and re-obtain the matched echo signal points of each mismatched pulse signal point.

[0050] Traditional algorithms typically use the time difference between the laser pulse signal emitted by the smart sensor and the received echo signal as the echo time of the laser pulse signal. Combined with the propagation speed of the laser pulse signal, the propagation distance is calculated. This process achieves accurate measurement for solids that do not change. However, in flow measurement scenarios using non-full-pipe electromagnetic flowmeters, the characteristics of fluid flow cause fluctuations in the liquid level, resulting in an unpredictable echo time. This affects the accuracy of the smart sensor in measuring the liquid level height, thus impacting the accuracy and efficiency of subsequent flow measurement.

[0051] Step 2.1: In terms of timing, the echo signal points with the same sequence number as the pulse signal points in the laser pulse signal are recorded as initial matched echo signal points. The initial liquid level height in the pipeline at the time of acquisition of each pulse signal point is obtained by using the time difference between each pulse signal point and its initial matched echo signal point. The Reynolds number of the fluid in the pipeline at the time of acquisition of each pulse signal point is obtained by using the flow rate collected by the electrode at the initial liquid level height.

[0052] The echo signal points in the echo signal that have the same sequence number as each pulse signal point in the laser pulse signal are recorded as the initial matching echo signal points for each pulse signal point. The pulse signal points in the laser pulse signal and the echo signal points in the echo signal are numbered sequentially. The time difference between each pulse signal point and its initial matching echo signal point is recorded as the initial echo time. The initial propagation distance of each pulse signal point is obtained by comparing the initial echo time with the propagation speed of the laser pulse signal. The difference between the pipe's inner diameter and each initial propagation distance is used as the initial liquid level height in the pipe at the time of pulse signal point acquisition, which is then used to determine which transverse electrodes are activated. The key to laser level gauge ranging lies in accurately locating the echo signal points corresponding to the pulse signal points in the laser pulse signal. The propagation speed of the laser pulse signal can be obtained through prior knowledge.

[0053] For fluids, this application uses a density meter and a viscometer deployed in a laser level gauge to acquire the fluid density and dynamic viscosity in real time. It also acquires the average flow velocity obtained from all pairs of transverse electrodes below the initial liquid level at each pulse signal acquisition point. The Reynolds number of the fluid in the pipe at each pulse signal acquisition point is obtained using the fluid density, average flow velocity, pipe characteristic length, dynamic viscosity, and kinematic viscosity. The pipe characteristic length is the pipe's inner diameter. Kinematic viscosity can be obtained from dynamic viscosity and density. The specific method for obtaining kinematic viscosity from dynamic viscosity and density is well-known and will not be elaborated upon here. The Reynolds number is a dimensionless number in fluid mechanics that characterizes the fluid flow state, such as laminar, transitional, or turbulent flow. It is used to quantify the relative strength of inertial and viscous forces in a fluid. A larger Reynolds number indicates a stronger dominance of inertial forces in the fluid, and the flow tendency is more towards turbulence, i.e., chaotic and intensely mixed. A smaller Reynolds number indicates a more laminar flow, i.e., smooth and stratified flow. The specific method for calculating the Reynolds number is a well-known technique and will not be elaborated upon in this application.

[0054] Step 2.2: Obtain the attenuated signal amplitude of each pulse signal point by using the relationship between the signal amplitude of each pulse signal point and the transmission distance. Obtain the disorder of each pulse signal point by comparing the signal amplitude of each pulse signal point with the initial matched echo signal point and the attenuated signal amplitude.

[0055] By relating the signal amplitude at each pulse signal point to the transmission distance, the attenuated signal amplitude at each pulse signal point can be obtained using the following expression:

[0056] In the formula, This represents the signal amplitude after attenuation at the q-th pulse signal point; This represents the signal amplitude of the q-th pulse signal point; It represents an exponential function with the natural constant as the base; α represents the attenuation coefficient of the pulse signal in the medium; This represents the distance between the liquid surface and the laser level gauge, calculated using the q-th pulse signal point and its initial matched echo signal point. Specifically, it is obtained through the time difference between the q-th pulse signal point and its initial matched echo signal point, as well as the propagation speed of the laser pulse signal. In this embodiment, α is calculated by substituting the signal amplitude of the 1st pulse signal point into... Substitute the signal amplitude of the initial matched echo signal point of the first pulse signal point into the input... The distance between the liquid level and the laser level gauge, calculated using the first pulse signal point and its initial matched echo signal point, is substituted into the equation. ,pass The value of α is calculated.

[0057] Furthermore, by comparing the signal amplitude of each pulse signal point with its initial matched echo signal point and the attenuated signal amplitude, the disorder degree of each pulse signal point is obtained, expressed as:

[0058] In the formula, This indicates the disorder of the q-th pulse signal point; This represents the signal amplitude of the initial matched echo signal point of the q-th pulse signal point; This represents the signal amplitude after attenuation at the q-th pulse signal point; This represents the signal amplitude of the q-th pulse signal point; This indicates the absolute value operation.

[0059] It should be noted that the greater the difference between the amplitude of the initial matched echo signal of the q-th pulse signal point and the amplitude of the attenuated signal, the lower the degree of matching between the q-th pulse signal point and its initial matched echo signal point. A higher degree of misalignment indicates a lower degree of matching between the q-th pulse signal point and its initial matched echo signal point, resulting in lower accuracy of the calculated initial echo time, and necessitating re-matching of the pulse signal point and echo signal point. Conversely, a lower degree of misalignment indicates a higher degree of matching between the pulse signal point and the echo signal point, resulting in higher accuracy of the calculated initial echo time.

[0060] Step 2.3: Obtain the local disorder clustering degree of each pulse signal point by using the disorder distribution of preset neighboring pulse signal points; obtain the correlation degree of each pulse signal point by comparing the difference in initial liquid level height between each pulse signal point and its neighboring pulse signal points with the Reynolds number; and optimize the disorder degree by combining the local disorder clustering degree.

[0061] The disorder level reflects the matching status between the pulse signal point and the echo signal point by the attenuation of the signal amplitude at the pulse signal point. However, since the disorder level reflects the matching status of a single pulse signal point, its robustness is low when directly using the disorder level to measure the matching effect of the pulse signal point. This is because when the echo signal point acquisition process is affected by environmental interference, causing the signal amplitude of the echo signal to deviate from the predicted amplitude, the disorder level of the pulse signal point is high, but the matching between the pulse signal point and the echo signal point may not show any disorder.

[0062] Based on the above analysis, this application optimizes the disorder of each pulse signal point by using the preset disorder distribution of neighboring pulse signal points and comparing the difference in initial liquid level height between each pulse signal point and its neighboring pulse signal points with the Reynolds number. The specific process is as follows:

[0063] (1) Obtain the local disorder clustering degree of each pulse signal point by the disorder distribution of the preset neighboring pulse signal points of each pulse signal point.

[0064] Regarding the phenomenon of mismatch between pulse signal points and echo signal points caused by liquid surface fluctuations, since a mismatch often occurs at a certain pulse signal point, its adjacent pulse signal points tend to also mismatch, the distribution of mismatch degree exhibits a certain degree of local concentration. Therefore, the local mismatch concentration degree of each pulse signal point can be obtained by using the mismatch degree distribution of its preset nearest neighbor pulse signal points. The expression is as follows:

[0065] In the formula, U represents the local disorder clustering degree of the q-th pulse signal point; U represents the number of preset nearest neighbor pulse signal points of the q-th pulse signal point; , These represent the preset disorder of the u-th and u+1-th nearest neighbor pulse signal points of the q-th pulse signal point, respectively; ε represents a preset positive number used to avoid the denominator being 0. The value of ε is preset by the user and can be set by the implementer. In this embodiment, the value of ε is 0.01. This indicates the absolute value operation.

[0066] In this embodiment, the value of U is 10. The value of U is preset by the user and can be set by the implementer. This application does not impose any special restrictions.

[0067] (2) The correlation of each pulse signal point is obtained by comparing the difference in initial liquid level height between each pulse signal point and the adjacent pulse signal point with the Reynolds number.

[0068] Furthermore, by comparing the difference in initial liquid level height between each pulse signal point and its adjacent pulse signal points with the Reynolds number, the correlation degree of each pulse signal point is obtained, expressed as:

[0069] In the formula, This represents the correlation degree of the q-th pulse signal point; This represents the normalized Reynolds number of the fluid in the pipe at the time of acquisition of the q-th pulse signal point; the deviation of the initial liquid level height between the q-th pulse signal point and each of its adjacent pulse signal points is calculated. σ represents the normalized value of the maximum value among the deviation values ​​between the q-th pulse signal point and all its adjacent pulse signal points; σ represents a preset positive number used to avoid the denominator being 0. The value of σ is preset by the user and can be set by the implementer. In this embodiment, the value of σ is 0.01. This indicates the absolute value operation.

[0070] In this embodiment, the deviation between the initial liquid level heights is the absolute value of the difference.

[0071] In this embodiment, the Sigmoid function is used to obtain the normalized value of the Reynolds number and the normalized value of the maximum value. The Sigmoid function is a well-known technology and will not be described in detail in this application.

[0072] It should be noted that a higher Reynolds number indicates a stronger dominance of inertial forces in the fluid, leading to a more turbulent flow pattern, meaning greater fluid flow fluctuations and surface level fluctuations. If the normalized Reynolds number is closer to the normalized value of the maximum surface height difference, it suggests that the surface level change is due to the fluid's inherent flow properties rather than interference with the echo signal, and the actual distortion should be greater. Optimizing the distortion by calculating the correlation degree can avoid situations where environmental interference with the echo signal leads to excessive distortion.

[0073] (3) The disorder of each pulse signal point is optimized by the correlation degree and the local disorder clustering degree of each pulse signal point.

[0074] Furthermore, by using the correlation degree and local disorder clustering degree of each pulse signal point, the disorder optimization factor of each pulse signal point is obtained. Specifically, the normalized value of the product of the correlation degree and local disorder clustering degree of each pulse signal point is used as the disorder optimization factor of each pulse signal point.

[0075] In this embodiment, the Min-Max normalization method is used to obtain the normalized value of the product of the correlation degree and the local disorder clustering degree of each pulse signal point. The Min-Max normalization method is a well-known technology and will not be described in detail in this application.

[0076] Furthermore, the disorder of each pulse signal point is optimized by using an error disorder optimization factor, resulting in the optimized disorder of each pulse signal point, expressed as:

[0077] In the formula, This represents the optimized disorder of the q-th pulse signal point; This indicates the disorder of the q-th pulse signal point; This represents the error optimization factor for the q-th pulse signal point. A schematic diagram of the error optimization process is shown below. Figure 3 As shown.

[0078] Step 2.4: Based on the distribution of the optimized results of the disorder of all pulse signal points, each mismatched pulse signal point is selected from all pulse signal points; by combining the optimized disorder with the particle swarm algorithm, the matching echo signal points of each mismatched pulse signal point are re-acquired.

[0079] Based on the optimized disorder of all pulse signal points, all pulse signal points are divided into two categories. The mean of the optimized disorder of all pulse signal points in each category is calculated. Each pulse signal point in the category with the largest mean is taken as a mismatched pulse signal point.

[0080] In this embodiment, the K-Means algorithm is used to divide all pulse signal points into two categories. The absolute value of the difference between the optimized disorder of the pulse signal points is used as the distance metric in the K-Means algorithm. The K-Means algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to divide all pulse signal points into two categories, implementers can use other existing technologies, such as the Otsu threshold segmentation algorithm, iterative threshold segmentation, etc. This application does not impose any special restrictions.

[0081] Furthermore, this application employs a particle swarm optimization (PSO) algorithm to re-match echo signal points for each mismatched pulse signal point. In the PSO algorithm, the number of particles in the swarm is 100, the particle dimension is equal to the number of mismatched pulse signal points, the inertia weight is 0.9, and both the cognitive and social coefficients are 1.5. The fitness value of each particle is the reciprocal of the sum of the optimized disorder values ​​of all pulse signal points under that particle. The higher the fitness value, the better the matching effect between the pulse signal point and the echo signal point. In this application, the particle with the highest fitness value is selected as the final matching result, obtaining the matched echo signal points for each mismatched pulse signal point. The particle number of 100, the inertia weight of 0.9, and the cognitive and social coefficients of 1.5 are merely one embodiment of this application; implementers can set specific values ​​according to actual conditions, and this application does not impose any special restrictions. A schematic diagram of the matching process between pulse signal points and echo signal points is shown below. Figure 4 As shown.

[0082] Step 3: Obtain the fluid flow rate in the pipeline at the acquisition time of each pulse signal point by matching each pulse signal point with its corresponding echo signal point.

[0083] Based on the pulse signal points in the laser pulse signal sequence and their finally matched echo signal points, the liquid level height in the pipe at the time of acquisition of each pulse signal point can be obtained. The cross-sectional area of ​​the fluid in the pipe at the time of acquisition of each pulse signal point can be calculated using the liquid level height and the inner diameter of the pipe. The calculation of the cross-sectional area of ​​the fluid in the pipe at the time of acquisition of each pulse signal point using the liquid level height and the inner diameter of the pipe is a well-known technique and will not be described in detail in this application.

[0084] Furthermore, the average value of the fluid velocity obtained by all pairs of transverse electrodes at the liquid level height in the pipe at each pulse signal acquisition time is extracted. The product of the average value and the cross-sectional area of ​​the fluid in the pipe at each pulse signal acquisition time is taken as the fluid flow rate in the pipe at each pulse signal acquisition time.

[0085] In summary, this application considers the different fluid velocities at different heights inside the pipe. By installing transverse electrodes at different heights inside the pipe, the fluid velocity can be measured more accurately, which is beneficial to improving the accuracy of flow measurement. By calculating the Reynolds number, the flow state of the fluid can be determined. By calculating the disorder degree, the matching error between the pulse signal points in the laser pulse signal and the echo signal points in the echo signal can be quantified, which helps to identify mismatched pulse signal points, so as to dynamically adjust the signal matching strategy according to the magnitude of the disorder degree. By calculating the distribution of the disorder degree of the nearest pulse signal points of each pulse signal point, the local disorder clustering degree can be obtained, which helps to identify mismatch phenomena caused by liquid surface fluctuations, and the local feature identification can be used to identify This method can more accurately filter out mismatched pulse signal points, reducing misjudgments caused by environmental interference. By obtaining the correlation between liquid level changes and Reynolds number, the correlation degree of each pulse signal point can be obtained, effectively avoiding abnormal error degree caused by environmental interference. Optimizing the error degree by local error clustering degree and correlation degree can more accurately reflect the signal mismatch situation. Through the optimized error degree distribution, mismatched pulse signal points can be more accurately filtered out. By combining the optimized error degree with the particle swarm optimization algorithm, the echo signal point is rematched for each mismatched pulse signal point, improving the signal matching effect. Flow measurement is performed using the optimized matching results, improving the flow measurement accuracy of non-full-pipe electromagnetic flowmeters.

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0087] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A flow measurement method for a non-full-pipe electromagnetic flowmeter, characterized in that, The method includes the following steps: The laser pulse signal emitted by the laser level gauge in the non-full pipe electromagnetic flowmeter and the echo signal received are collected within a preset time period; the fluid velocity is obtained in real time through electrodes at preset heights on the inner wall of the pipe. In terms of timing, echo signal points with the same sequence number as each pulse signal point in the laser pulse signal are recorded as initial matched echo signal points. The initial liquid level height in the pipeline at the time of acquisition of each pulse signal point is obtained by using the time difference between each pulse signal point and its initial matched echo signal point. The Reynolds number of the fluid in the pipeline at the time of acquisition of each pulse signal point is obtained by using the flow velocity collected by the electrode at the initial liquid level height. By analyzing the relationship between the signal amplitude of each pulse signal point and the transmission distance, the attenuated signal amplitude of each pulse signal point is obtained. By comparing the signal amplitude of each pulse signal point with its initial matched echo signal point and the attenuated signal amplitude, the disorder of each pulse signal point is obtained. By analyzing the disorder distribution of preset nearest neighbor pulse signal points for each pulse signal point, the local disorder clustering degree of each pulse signal point is obtained. The correlation degree of each pulse signal point is obtained by comparing the initial liquid level height difference between each pulse signal point and its neighboring pulse signal points with the Reynolds number. The disorder degree is then optimized based on the local disorder clustering degree. Mismatched pulse signal points are selected from all pulse signal points based on the distribution of the optimized disorder degrees. The matched echo signal points of each mismatched pulse signal point are re-acquired using the optimized disorder degree combined with a particle swarm optimization algorithm. Finally, the fluid flow rate in the pipeline at the acquisition time of each pulse signal point is obtained by comparing each pulse signal point with its matched echo signal points. The correlation degree of each pulse signal point is obtained by the following expression: In the formula, This represents the correlation degree of the q-th pulse signal point; This represents the normalized Reynolds number of the fluid in the pipe at the time of acquisition of the q-th pulse signal point; the deviation of the initial liquid level height between the q-th pulse signal point and each of its adjacent pulse signal points is calculated. This represents the normalized value of the maximum deviation among the q-th pulse signal point and all its neighboring pulse signal points; σ represents a preset positive number. This indicates the absolute value operation; The process of obtaining the disorder level is as follows: Calculate the difference between the signal amplitude of the initial matched echo signal point and the attenuated signal amplitude of each pulse signal point; the disorder is the ratio of the difference to the signal amplitude of each pulse signal point; The expression for the local disorder clustering degree is: In the formula, U represents the local disorder clustering degree of the q-th pulse signal point; U represents the number of preset nearest neighbor pulse signal points of the q-th pulse signal point; , ε represents the preset disorder of the u-th and (u+1)-th nearest neighbor pulse signal points of the q-th pulse signal point, respectively; ε represents a preset positive number. This indicates the absolute value operation.

2. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 1, characterized in that, When obtaining the Reynolds number of the fluid in the pipe at the time of each pulse signal point acquisition, the fluid velocity in the Reynolds number calculation formula is: the average value of the flow velocities collected by all electrodes at the initial liquid level height corresponding to each pulse signal point.

3. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 1, characterized in that, The process of obtaining the attenuated signal amplitude of each pulse signal point includes: The expression for the attenuated signal amplitude at each pulse signal point is: In the formula, This represents the signal amplitude after attenuation at the q-th pulse signal point; This represents the signal amplitude of the q-th pulse signal point; It represents an exponential function with the natural constant as the base; α represents the attenuation coefficient of the pulse signal in the medium; This represents the distance between the liquid surface and the laser level gauge, calculated using the q-th pulse signal point and its initial matched echo signal point. The method for obtaining α is as follows: substituting the signal amplitude of the first pulse signal point and its initial matched echo signal point, and the distance between the liquid surface and the laser level gauge calculated using the first pulse signal point and its initial matched echo signal point, into the equation. .

4. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 1, characterized in that, The process of optimizing the disorder is as follows: The disorder optimization factor for each pulse signal point is obtained by combining the correlation degree and the local disorder clustering degree. Calculate the sum of the disorder optimization factor and 1, and use the product of the sum and the disorder of each pulse signal point as the optimized disorder of each pulse signal point.

5. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 4, characterized in that, The disorder optimization factor is the normalized value of the product of the correlation degree and the local disorder clustering degree.

6. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 1, characterized in that, The process of filtering out each mismatched pulse signal point from all pulse signal points is as follows: Based on the optimized disorder of all pulse signal points, all pulse signal points are divided into two categories. The mean of the optimized disorder of all pulse signal points in each category is calculated, and each pulse signal point in the category with the largest mean is taken as a mismatched pulse signal point.

7. The flow measurement method of a non-full-pipe electromagnetic flowmeter as described in claim 1, characterized in that, In the process of reacquiring the matching echo signal points of each mismatched pulse signal point, the fitness value of each particle in the particle swarm algorithm is the reciprocal of the sum of the optimized disorder of all pulse signal points under each particle.

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