Liquid suspended matter concentration measurement method, device, equipment and storage medium

Abstract

The present application belongs to the field of measurement and control, and proposes a method, device, equipment and storage medium for measuring the concentration of liquid suspension. The method includes: obtaining a two-dimensional matrix of the echo signal intensity of the ultrasonic wave reflected by the liquid suspension; determining the abnormal data in the two-dimensional matrix based on a preset abnormal value judgment factor; regressing and calculating the echo signal intensity of the abnormal data according to a predetermined multivariate regression model of the two-dimensional matrix; interpolating the echo signal intensity after regression calculation by a cubic spline interpolation method; determining the optimal polynomial fitting curve model of concentration, temperature and echo signal intensity according to a pre-set optimal polynomial curve, and determining the liquid suspension concentration based on the polynomial fitting curve model. Since the model is obtained by regression calculation of abnormal data and fitting of data after interpolation, the accuracy of the liquid suspension concentration determined by the model is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of measurement and control, and in particular to a method, device, equipment and storage medium for measuring the concentration of suspended solids in liquids. Background Art

[0002] The suspended matter content in a liquid is a key parameter characterizing liquid-solid two-phase flow, and its accurate measurement is of great significance. Two-phase flow phenomena are widely present in fields such as oil extraction, hydraulic engineering, and aerospace. Determining the water content of crude oil, maintaining the underwater portion of hydraulic structures, jet impact of liquid mixing, and evaporative cycle refrigeration systems on aircraft all fall under the rubric of two-phase flow. In-depth research on characteristics such as suspended matter concentration and particle size is crucial for a comprehensive understanding of two-phase flow, understanding flow conditions, rationally utilizing either phase, and exploring its impact on geographical and ecological environments.

[0003] Currently, the main methods for measuring suspended solids concentration in liquids include filtration weighing, spectroscopy, and acoustic attenuation. These methods are primarily direct methods, and due to the potential for measurement errors during the measurement process, the accuracy of the calculated suspended solids concentration is low. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a liquid suspended matter concentration measurement method, device, equipment and storage medium to solve the problem in the prior art that measurement errors may exist when measuring suspended matter concentration, resulting in low accuracy of the calculated suspended matter concentration.

[0005] A first aspect of an embodiment of the present application provides a method for measuring the concentration of suspended matter in a liquid, the method comprising:

[0006] obtaining a two-dimensional matrix of echo signal intensities of ultrasonic waves reflected by the liquid suspension;

[0007] Determining abnormal data in the two-dimensional matrix based on a preset abnormal value determination factor;

[0008] Regression-calculate the echo signal intensity of the abnormal data according to a predetermined multiple regression model of the two-dimensional matrix;

[0009] The echo signal intensity after regression calculation is interpolated by using the cubic spline interpolation method;

[0010] An optimal polynomial fitting curve model of concentration, temperature and echo signal intensity is determined according to a preset optimal polynomial curve, and the concentration of the liquid suspension is determined based on the polynomial fitting curve model.

[0011] In conjunction with the first aspect, in a first possible implementation of the first aspect, determining the multiple regression model of the two-dimensional matrix includes:

[0012] Extracting a predetermined number of normal data from the two-dimensional matrix, wherein one of the extracted normal data is adjacent to the other data;

[0013] The extracted data are substituted into a predetermined multivariate fitting polynomial, and the coefficients of the multivariate fitting polynomial are calculated to obtain the multivariate regression model.

[0014] In conjunction with the first possible implementation of the first aspect, in a second possible implementation of the first aspect, regressively calculating the echo signal strength of the abnormal data includes:

[0015] Determining normal data adjacent to the abnormal data to be calculated according to the position of the abnormal data to be calculated;

[0016] The echo signal strength of the abnormal data is regressively calculated based on the normal data and the multivariate regression model.

[0017] In combination with the first aspect, in a third possible implementation of the first aspect, interpolation processing is performed on the echo signal strength after regression calculation using a cubic spline interpolation method, including:

[0018] Generating a sequence of the echo signal strength after the regression calculation according to time, and dividing the generated sequence into a predetermined number of subintervals;

[0019] According to the normal distribution characteristics of the measured data, the fitting polynomial of each subinterval is determined.

[0020] In combination with the first aspect, in a fourth possible implementation manner of the first aspect, before performing interpolation processing on the echo signal strength after regression calculation using a cubic spline interpolation method, the method further includes:

[0021] The collected non-stationary data are weighted preprocessed using the entropy weight method.

[0022] In combination with the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, weighted preprocessing is performed on the collected non-stationary data using an entropy weight method, including:

[0023] Calculate the entropy value of each group of data according to the entropy weight method, and determine the weight coefficient of each group of data according to the entropy value;

[0024] The weighted preprocessed data obtained from the non-stationary data is determined according to the original data mean of the measurement data and the weight coefficient.

[0025] In combination with the first aspect, in a sixth possible implementation of the first aspect, determining an optimal polynomial fitting curve model of concentration, temperature, and echo signal intensity based on a pre-set optimal polynomial curve includes:

[0026] Determine the suspension liquid with different concentrations at different temperatures and perform polynomial curve fitting to establish a scattered sound intensity-temperature polynomial fitting curve model;

[0027] Determine the concentration-scattering sound intensity polynomial fitting curve model by curve fitting at different temperatures and different concentrations of suspended liquid;

[0028] According to the scattered sound intensity-temperature polynomial fitting curve model and the concentration-scattered sound intensity polynomial fitting curve model, a concentration-temperature-scattered sound intensity polynomial fitting curve model constituting a three-dimensional space is obtained.

[0029] A second aspect of the embodiments of the present application provides a liquid suspended matter concentration measuring device, the device comprising:

[0030] an echo signal intensity acquisition unit, configured to acquire a two-dimensional matrix of echo signal intensities of the ultrasonic waves reflected by the liquid suspension;

[0031] an abnormal data determination unit, configured to determine abnormal data in the two-dimensional matrix based on a preset abnormal value determination factor;

[0032] A regression calculation unit, configured to regressively calculate the echo signal strength of the abnormal data according to a predetermined multiple regression model of the two-dimensional matrix;

[0033] An interpolation unit, used for performing interpolation processing on the echo signal strength after regression calculation by using a cubic spline interpolation method;

[0034] The concentration determination unit is used to determine an optimal polynomial fitting curve model of concentration, temperature and echo signal intensity according to a preset optimal polynomial curve, and determine the liquid suspended matter concentration based on the polynomial fitting curve model.

[0035] A third aspect of an embodiment of the present application provides a liquid suspended matter concentration measuring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in any one of the first aspects are implemented.

[0036] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any one of the first aspects are implemented.

[0037] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: the present application obtains a two-dimensional matrix of ultrasonic echo signal intensities, determines abnormal data in the two-dimensional matrix based on a preset outlier judgment factor, regresses and calculates the echo signal intensities of the abnormal data through a predetermined multivariate regression model of the two-dimensional matrix, interpolates the regression-calculated echo signal intensities through a cubic spline interpolation method, fits the interpolated data, and selects the optimal polynomial fitting curve model of concentration, temperature and echo signal intensity. Since the model is obtained based on the regression calculation of abnormal data and the fitting of the interpolated data, the accuracy of the liquid suspended matter concentration determined by the model is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0039] Figure 1 This is a schematic diagram of a flow chart of a method for measuring the concentration of suspended solids in a liquid provided in an embodiment of the present application;

[0040] Figure 2 1 is a schematic diagram of a two-dimensional matrix of echo signal strength provided in an embodiment of the present application;

[0041] Figure 3 This is a schematic diagram of a liquid suspended matter concentration measurement system provided in an embodiment of the present application;

[0042] Figure 4 This is a schematic diagram comparing concentration measurement results provided in an embodiment of the present application;

[0043] Figure 5 Schematic diagram of a liquid suspended matter concentration measuring device provided in an embodiment of the present application;

[0044] Figure 6 Schematic diagram of a liquid suspended matter concentration measuring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0046] In order to illustrate the technical solution described in this application, specific embodiments are provided below.

[0047] Figure 1 A schematic diagram of a method for measuring the concentration of suspended solids in a liquid provided in an embodiment of the present application is described in detail as follows:

[0048] In S101 , a two-dimensional matrix of echo signal intensities of ultrasonic waves reflected by the liquid suspension is obtained.

[0049] In the embodiment of the present application, an ultrasonic transmitter can transmit an ultrasonic signal into a liquid containing suspended matter, and an ultrasonic receiver can collect the ultrasonic signal reflected by the suspended matter in the liquid. The greater the concentration of the suspended matter, the greater the intensity of the echo signal reflected by the suspended matter.

[0050] When determining the polynomial fitting curve model in the embodiments of the present application, the effects of different suspended solids concentrations on echo signal strength can be compared and tested. Based on industry experience, the echo signal strength thresholds corresponding to different concentrations can be determined. For example, the echo signal strength thresholds corresponding to suspended solids concentration y1 can be determined to be i1 and i2, with i1 being less than i2. If the echo signal strength detected is greater than i2 or less than i1, it can be considered that the suspended solids concentration has changed to a concentration other than y1.

[0051] Based on the predetermined echo signal strength thresholds corresponding to different suspended matter concentrations, by changing the suspended matter concentration, when the detected echo signal strength is compared with the predetermined echo signal strength threshold and it is determined that the suspended matter concentration has changed, the changed suspended matter concentration, temperature and echo signal strength are recorded.

[0052] In the embodiment of the present application, the echo signal strength can be acquired by the receiver and then the envelope spectrum or power spectrum can be obtained by Fourier transform. The autocorrelation function of the signal and the power spectrum density can be used as a pair of Fourier transform pairs to calculate the autocorrelation function of the signal:

[0053]

[0054] Where S(ω) is the power spectrum of the signal and ω is the frequency of the signal. The echo signal strength is determined by the amplitude of the echo signal and is calculated by energy, i.e., the echo intensity I = ∑20log(A i ). Among them, A i After Fourier transform, the spectrum diagram includes the spectrum energy intensity corresponding to the signal energy peak point.

[0055] This application measures the time series of data signals {X i}, i = 1, 2, ..., N, use spectrum analysis to obtain the echo signal strength (for example, the spectrum analysis method can be used to use 512 measurement data signals to calculate the echo signal strength once), and reconstruct the strength data into an m*n matrix SS (m represents the number of data sequence groups, and n represents the number of groupings):

[0056]

[0057] In order to facilitate subsequent calculations, the echo signal intensity in the two-dimensional matrix, that is, the ultrasonic echo signal intensity, may be normalized:

[0058] Get the normalized echo signal strength.

[0059] In S102 , abnormal data in the two-dimensional matrix is ​​determined based on a preset abnormal value determination factor.

[0060] When collecting echo signal strength, due to system or environmental reasons, the collected data may contain abnormal data, that is, data with distorted echo signal strength. To improve the accuracy of the data used, abnormal data can be identified and regression calculation can be performed on the identified abnormal data. The regression data can then be used for model fitting to obtain a more accurate fitting model, facilitating more accurate determination of suspended matter concentration in the liquid.

[0061] The abnormality judgment factor may be: That is, the judgment factor is related to the ratio of useful signal to total power. s f s : is the sampling rate of AD. R(0) is the sum of the noise power and signal power of the sampled echo signal. And:

[0062] R(0)=∑x(k)x*(k) / (m), k=1,2,...m. In the actual signal processing process, |R(ι)| can be the modulus value of the complex autocorrelation operation result of the received echo signal strength, which has the same dimension as R(0) and can be called useful power.

[0063] In S103 , the echo signal strength of the abnormal data is regressively calculated according to a predetermined multiple regression model of the two-dimensional matrix.

[0064] Based on the two-dimensional matrix of echo signal intensities determined in S101 and the abnormal data determined in S102, normal data included in the two-dimensional matrix can be determined. Based on the normal data, a multivariate linear regression model can be determined. Based on the multivariate linear regression model, the echo signal intensities at the locations where the abnormal data are located are regressively calculated, i.e., the echo signal intensities closer to the true values.

[0065] like Figure 2 The figure shows a two-dimensional matrix diagram of echo signal strength. The normal data group extracted from the two-dimensional matrix includes in, The central data of the data group, and other data Adjacent. Through this set of normal data, the coefficients k0, k1, k2, k3, k4 of the multivariate fitting polynomial can be calculated to obtain the multivariate regression model:

[0066] I ij =k0+k1I i-1j +k2I i+1j +k3I ij-1 +k4I ij+1

[0067] Among them, I ij ,I i-1j ,I i+1j ,I ij-1 ,I ij+1 is the echo signal intensity in the two-dimensional matrix.

[0068] After the multiple regression model is determined, abnormal data is determined according to the abnormal value decision factor, and then the echo signal strength corresponding to the abnormal data can be calculated one by one according to the multiple regression model.

[0069] It is understandable that it is not necessary to be limited to selecting 5 adjacent data to calculate the multiple regression model. It is also possible to select 9 adjacent data or any other number of data to calculate the parameters of the multiple regression model.

[0070] In S104, the echo signal strength obtained after regression calculation is interpolated using a cubic spline interpolation method.

[0071] In order to increase the amount of data required for fitting, after performing regression calculation on the abnormal data, interpolation processing can be further performed based on the data after regression calculation.

[0072] Before performing the interpolation process, the embodiment of the present application may further perform weighted preprocessing on the non-stationary data in the data after the regression calculation process by using the entropy weight method, thereby reducing the impact of nonlinear measurement errors.

[0073] Among them, during preprocessing, the entropy value of each group of data can be calculated according to the entropy weight method:

[0074] Among them, I ij Indicates the normalized echo signal strength.

[0075] Weight coefficient of each group of data Where:

[0076]

[0077] After regression calculation, the mean of each group of data is in, Represents the matrix i-th row data X 11 、X 21 To X N1 The average value of the data preprocessing result is:

[0078]

[0079] Among them, s i is the data obtained after preprocessing of echo signal intensity.

[0080] In an embodiment of the present application, the pre-processed data or the data processed by regression calculation can be interpolated by cubic spline interpolation to provide more fitting data points for subsequent polynomial surface fitting and improve the goodness of curve fitting.

[0081] The pre-processed data can be generated into a sequence {S(i)}i=1,2..,n in chronological order, and the sequence can be divided into a predetermined number of sub-intervals. For example, it can be divided into N sub-intervals, each of which:

[0082] [S j ,S j+1 ](j=0,1,...,N-1) all satisfy the cubic polynomial:

[0083] S j (x) = a j0 +a j1 x+a j2 x 2 +a j3 x 3 .

[0084] In order to ensure that the fitting curve nodes of the subinterval are smooth, let:

[0085] S(x j -0)=S(x j +0)

[0086] S′(x j-0)=S′(x j +0)

[0087] S″(x j -0)=S″(x j +0)

[0088] S(x j )=f(x j )=y j

[0089] h j =x j -x j-1

[0090] S″(x j )=M j ,j=0,1,..,N

[0091] In the subinterval S(x)=S j (x), according to the Lagrange interpolation formula:

[0092]

[0093] Comprehensive deduction can get the relationship:

[0094]

[0095] According to the characteristics of the measured data generally satisfying the normal distribution, the fitting curves at both ends are in a horizontal state, and S(x j ) satisfies the first type of boundary conditions:

[0096] S′(x1)=S′(x N )=0

[0097] Through the above formula, the specific fitting polynomial of each sub-interval is obtained.

[0098] In S105 , an optimal polynomial fitting curve model of concentration, temperature and echo signal intensity is determined according to a preset optimal polynomial curve, and the concentration of the liquid suspension is determined based on the polynomial fitting curve model.

[0099] The series of scattered data obtained after interpolation processing are subjected to curve fitting using polynomials, and a fitting function set consisting of multiple fitting functions can be constructed. The coefficient R 2 Select the optimal curve fitting function type. 2 The closer it is to 1, the better the fitting degree of the fitting curve is.

[0100] in,

[0101] Xs : original data points, Forecast data, The original data mean.

[0102] Based on the established polynomial curve fitting model, the scattered sound intensity-temperature polynomial fitting curve model established by polynomial curve fitting of different suspended solids concentrations at different temperatures can be obtained. The models of each fitting curve are as follows:

[0103]

[0104] Curve fitting was performed under different temperatures and different concentrations of suspended liquids to establish a concentration-scattering sound intensity polynomial fitting curve model:

[0105]

[0106] The fitting curves obtained by the polynomial fitting curve models of scattered sound intensity-temperature and concentration-scattered sound intensity in the above formula can form a polynomial fitting curve model of concentration-temperature-scattered sound intensity in three-dimensional space. According to the calculated coefficient R 2 The function form of the optimal surface fitting is selected to obtain a polynomial fitting curve model of concentration-temperature-scattering sound intensity in three-dimensional space.

[0107] After fitting the polynomial fitting curve model of concentration-temperature-scattered sound intensity in three-dimensional space based on the data correction and interpolation processing, the suspended matter concentration of the liquid corresponding to the detected echo signal intensity at different temperatures can be found according to the model, thereby effectively improving the accuracy of the determined concentration.

[0108] like Figure 3 The figure shows a schematic diagram of a liquid suspended solids concentration measurement system provided in an embodiment of the present application. The system can detect the transmission signal emitted by the ultrasonic transmission system and receive the echo signal of the ultrasonic wave reflected by the suspended solids. After analog-to-digital conversion of the collected signal, a processor such as a DSP can calculate the suspended solids concentration corresponding to the signal intensity according to the polynomial fitting curve model determined by the liquid suspended solids concentration measurement method described in this application. The calculated concentration is then transmitted to a back-end display device such as a PC via a single-chip microcomputer. Alternatively, the DSP can calculate the echo signal intensity, and the back-end device can calculate the concentration based on the calculated echo signal intensity.

[0109] For example, this system can transmit a 1MHz sinusoidal wave excitation signal, propagate the sound wave in the liquid to be tested through the ultrasonic transmitting transducer, and the receiving transducer receives the delayed sinusoidal wave signal. The scattering intensity measurement system uses a two-channel AD sampling circuit to collect the transmitting signal and the receiving signal respectively, and performs signal processing in the DSP. Then, the microcontroller accesses the DSP-processed data to calculate the suspended matter echo scattering intensity.

[0110] In order to verify the actual effect of the method proposed in the embodiment of this application, Figure 4 As shown in the figure, the suspended solids concentration measurement results described in the examples of this application are compared with an algorithm that directly uses mean scatter points for surface fitting. The maximum absolute error of the suspended solids concentration measured by this application is only 0.128%, while the maximum absolute error measured by the least squares surface fitting method is 0.2%. The accuracy of each data point measured using the improved fitting algorithm is superior to the measurement accuracy of scatter point fitting, which can reduce the impact of large error points on the overall measurement error, thereby improving the measurement accuracy of suspended solids concentration.

[0111] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0112] Figure 5 A schematic diagram of a liquid suspended matter concentration measuring device provided in an embodiment of the present application is shown as follows: Figure 5 As shown, the device includes:

[0113] an echo signal strength acquisition unit 501 for acquiring a two-dimensional matrix of echo signal strengths of ultrasonic waves reflected by the liquid suspension;

[0114] An abnormal data determination unit 502 is used to determine abnormal data in the two-dimensional matrix based on a preset abnormal value determination factor;

[0115] A regression calculation unit 503 is configured to regressively calculate the echo signal strength of the abnormal data according to a predetermined multiple regression model of the two-dimensional matrix;

[0116] An interpolation unit 504 is configured to perform interpolation processing on the echo signal strength after regression calculation using a cubic spline interpolation method;

[0117] The concentration determination unit 505 is configured to determine an optimal polynomial fitting curve model of concentration, temperature and echo signal intensity according to a preset optimal polynomial curve, and determine the liquid suspended matter concentration based on the polynomial fitting curve model.

[0118] Figure 5 The liquid suspension concentration measuring device shown is Figure 1 The liquid suspension concentration measurement method shown corresponds to.

[0119] Figure 6 Schematic diagram of a liquid suspended matter concentration measuring device provided in one embodiment of the present application. Figure 6 As shown, the liquid suspended matter concentration measuring device 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, such as a liquid suspended matter concentration measurement program. When the processor 60 executes the computer program 62, the steps of the aforementioned liquid suspended matter concentration measurement method embodiments are implemented. Alternatively, when the processor 60 executes the computer program 62, the functions of the modules / units in the aforementioned device embodiments are implemented.

[0120] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 62 in the liquid suspended matter concentration measuring device 6.

[0121] The liquid suspended matter concentration measuring device may include, but is not limited to, a processor 60 and a memory 61. It will be understood by those skilled in the art that Figure 6 It is only an example of the liquid suspended matter concentration measuring device 6 and does not constitute a limitation of the liquid suspended matter concentration measuring device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the liquid suspended matter concentration measuring device may also include input and output devices, network access devices, buses, etc.

[0122] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0123] The memory 61 can be an internal storage unit of the liquid suspended matter concentration measuring device 6, such as a hard disk or memory of the liquid suspended matter concentration measuring device 6. The memory 61 can also be an external storage device of the liquid suspended matter concentration measuring device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the liquid suspended matter concentration measuring device 6. Furthermore, the memory 61 can also include both an internal storage unit of the liquid suspended matter concentration measuring device 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the liquid suspended matter concentration measuring device. The memory 61 can also be used to temporarily store data that has been output or is about to be output.

[0124] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0125] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0129] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0130] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0131] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for measuring the concentration of suspended solids in a liquid, characterized in that: The method comprises: obtaining a two-dimensional matrix of echo signal intensities of ultrasonic waves reflected by the liquid suspension; Based on a preset outlier judgment factor, the abnormal data in the two-dimensional matrix is ​​determined, and the outlier judgment factor is: , is the sum of the noise power and signal power of the sampled echo signal, In the actual signal processing process, it is the modulus of the complex autocorrelation operation result of the received echo signal strength; Regression-calculate the echo signal intensity of the abnormal data according to a predetermined multiple regression model of the two-dimensional matrix; The echo signal intensity after regression calculation is interpolated by using the cubic spline interpolation method; Curve fitting is performed on the scattered data obtained after interpolation processing, multiple polynomial curves are constructed and the optimal polynomial curve is selected, the optimal polynomial fitting curve model of concentration, temperature and echo signal intensity is determined according to the optimal polynomial curve, and the liquid suspended matter concentration is determined based on the polynomial fitting curve model.

2. The method according to claim 1, characterized in that Determining a multiple regression model of the two-dimensional matrix includes: Extracting a predetermined number of normal data from the two-dimensional matrix, wherein one of the extracted normal data is adjacent to the other data; The extracted data are substituted into a predetermined multivariate fitting polynomial, and the coefficients of the multivariate fitting polynomial are calculated to obtain the multivariate regression model.

3. The method according to claim 2, characterized in that Regression calculation of the echo signal strength of the abnormal data includes: Determining normal data adjacent to the abnormal data to be calculated according to the position of the abnormal data to be calculated; The echo signal strength of the abnormal data is regressively calculated based on the normal data and the multivariate regression model.

4. The method according to claim 1, wherein The echo signal strength after regression calculation is interpolated using the cubic spline interpolation method, including: Generating a sequence of the echo signal strength after the regression calculation according to time, and dividing the generated sequence into a predetermined number of subintervals; According to the normal distribution characteristics of the measured data, the fitting polynomial of each subinterval is determined.

5. The method according to claim 1, wherein Before performing interpolation processing on the echo signal strength after regression calculation by using the cubic spline interpolation method, the method further includes: The collected non-stationary data are weighted preprocessed using the entropy weight method.

6. The method according to claim 5, characterized in that The collected non-stationary data is weighted preprocessed using the entropy weight method, including: Calculate the entropy value of each group of data according to the entropy weight method, and determine the weight coefficient of each group of data according to the entropy value; The weighted preprocessed data obtained from the non-stationary data is determined according to the original data mean of the measurement data and the weight coefficient.

7. The method according to claim 1, characterized in that Determining an optimal polynomial fitting curve model of concentration, temperature and echo signal intensity according to the optimal polynomial curve includes: Determine the suspension liquid with different concentrations at different temperatures and perform polynomial curve fitting to establish a scattered sound intensity-temperature polynomial fitting curve model; Determine the concentration-scattering sound intensity polynomial fitting curve model by curve fitting at different temperatures and different concentrations of suspended liquid; According to the scattered sound intensity-temperature polynomial fitting curve model and the concentration-scattered sound intensity polynomial fitting curve model, a concentration-temperature-scattered sound intensity polynomial fitting curve model constituting a three-dimensional space is obtained.

8. A liquid suspended matter concentration measuring device, characterized in that: The device comprises: an echo signal intensity acquisition unit, configured to acquire a two-dimensional matrix of echo signal intensities of the ultrasonic waves reflected by the liquid suspension; The abnormal data determination unit is used to determine the abnormal data in the two-dimensional matrix based on a preset abnormal value judgment factor, where the abnormal judgment factor is: , is the sum of the noise power and signal power of the sampled echo signal, In the actual signal processing process, it is the modulus of the complex autocorrelation operation result of the received echo signal strength; A regression calculation unit, configured to regressively calculate the echo signal strength of the abnormal data according to a predetermined multiple regression model of the two-dimensional matrix; An interpolation unit, used for performing interpolation processing on the echo signal strength after regression calculation by using a cubic spline interpolation method; A concentration determination unit is used to perform curve fitting on the scattered data obtained after interpolation processing, construct multiple polynomial curves and select the optimal polynomial curve, determine the optimal polynomial fitting curve model of concentration, temperature and echo signal intensity according to the optimal polynomial curve, and determine the liquid suspended matter concentration based on the polynomial fitting curve model.

9. A liquid suspended matter concentration measuring device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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