Method for calculating turbidity of low-range turbidity meter

Abstract

The invention relates to the technical field of water turbidity monitoring, and provides a method for calculating the turbidity of a low-range turbidity meter, which comprises the following steps of: enabling signals collected by the turbidity meter to enter a judgment array until the number of the signals in the judgment array reaches n; judging a signal collected first in the judgment array, if the signal is normal data, enabling the signal to enter a normal data array, and if the signal is bubble data, directly rejecting the signal; the average value of m newly confirmed normal fluctuation signals is calculated to serve as a scattering signal value; and calculating and outputting the current turbidity measured and output by the turbidity meter according to the scattering signal value. Single bubbles and continuous bubbles are identified, bubble data, normal data fluctuation and actual turbidity change of a solution are distinguished, and obvious abnormal fluctuation of signals caused by refraction of the bubbles to a measurement light path is avoided, so that stability and accuracy of measurement are realized. As the calculated scattering signal value is the average value of the normal signals, a certain smoothing effect is achieved on the result.

Description

Technical Field

[0001] The invention relates to the technical field of water turbidity monitoring, and in particular to a method for calculating the turbidity of a low-range turbidity meter. Background Art

[0002] Turbidity is used to indicate the clarity or turbidity of water. It is a term used to express the optical properties of water samples. It is related to the mass concentration of suspended matter, particle size, shape, refractive index, etc. The more suspended matter there is, the greater the turbidity. Turbidity can reflect the degree of water pollution to a considerable extent and is currently one of the important monitoring indicators for measuring water quality.

[0003] However, in turbidity detection, there are bubbles in the solution, and the refraction of the measuring light path by the bubbles causes obvious abnormal fluctuations in the signal, resulting in large changes in the turbidity value and affecting the measurement stability. Therefore, it is necessary to design a measurement method that can reduce the impact of bubbles on the refraction of the measuring light path and improve the data stability of turbidity measurement. Summary of the invention

[0004] In order to achieve the above object, the present invention provides the following technical solutions: The present invention provides a method for calculating the turbidity of a low-range turbidity meter, the calculation method comprising: (1) The signals collected by the turbidity meter are entered into a judgment array until the number of signals in the judgment array reaches n, wherein the signals in the judgment array are updated in real time, and the judgment array is the n signals most recently collected by the turbidity meter, n 3; (2) Determine the first signal collected in the judgment array. If the signal is normal data, the signal is added to the normal data array. If the signal is bubble data, the signal is directly removed. The signals in the normal data array are updated in real time. The normal data array is a collection of signals confirmed to be normal fluctuations. (3) Calculate the average value of the most recently confirmed m normal fluctuation signals as the scattering signal value AD, where m>3; (4) Calculate and output the turbidity T measured by the current turbidity meter based on the scattered signal value AD; (5) Repeat steps (1) to (4) to calculate and output the turbidity of the next turbidity meter measurement output.

[0005] Furthermore, the criterion for determining the first signal collected in the array in step (2) is to determine whether there is a linear relationship between the signal and the remaining n-1 signals, or whether there is a linear relationship between the signal and the latest n-1 confirmed normal fluctuation signals in the normal data array; If there is a linear relationship, it may be caused by normal changes in water concentration. If there is a linear relationship and the deviation between the first signal collected in the judgment array and the latest confirmed normal fluctuation signal in the normal data array is greater than k1, or the deviation from the last calculated scattering signal value AD is greater than k1, it means that the linearity may also be that three consecutive bubble data are just linear. In this case, the first signal collected in the judgment array is bubble data, otherwise it is normal data; k1 is obtained based on actual test normal measurement and simulated bubble measurement, which is related to instrument performance; If there is no linear relationship, it may be normal data fluctuation, but normal data fluctuation should have a reasonable range, that is, deviation > k2. If there is no linear relationship, and the deviation between the first signal collected in the array and the most recently confirmed normal fluctuation signal in the normal data array is > k2, or the deviation with the last calculated scattering signal value AD is > k2, then the first signal collected in the array is judged to be bubble data, otherwise it is normal data; among them, k2 is obtained based on actual test normal measurement and simulated bubble measurement, and is related to instrument performance.

[0006] Furthermore, the bubble identification algorithm also includes: setting a monitoring array, the data in the monitoring array is updated in real time, and the monitoring array is q original signals newly collected by the turbidity meter, where qn>1; Calculate the stability of q original signals in the monitoring array, If the stability meets the conditions, it is judged that the current solution concentration is stable, the data is normal and there are no bubbles, then the q original signals will replace the n signals in the judgment array and the latest confirmed qn normal fluctuation signals in the normal data array in chronological order; If the stability condition is not met, go directly to step (3).

[0007] Furthermore, when in step (2), it is determined that the first signal collected in the array is bubble data and is eliminated, in step (3), the random value obtained by multiplying the average value of the most recently confirmed m normal fluctuation signals by a coefficient k3 is used as the scattering signal value AD, where the value range of k3 can be: 0.99< k3<1.01.

[0008] Furthermore, in step (4), the turbidity T measured by the current turbidity meter is:

[0009] Among them, K is the coefficient determined by user calibration.

[0010] The present invention has the following beneficial effects: The present invention obtains the signal waveform when bubbles appear through actual testing, performs linear judgment on each newly collected signal and combines the fluctuation threshold judgment to realize the recognition of single bubbles and continuous bubbles, distinguishes bubble data, normal data fluctuations and actual turbidity changes of the solution, avoids the refraction of the bubble on the measurement light path to cause obvious abnormal fluctuations in the signal, thereby achieving measurement stability and accuracy. In addition, the present invention calculates the turbidity output of the current turbidity meter measurement based on the average value of the latest confirmed m normal fluctuation signals as the scattered signal value AD. Since the output scattered signal value is the average value of the normal signal, it also has a certain smoothing effect on the result. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of the calculation method of the present invention.

[0012] Figure 2 It is a schematic diagram of the position of each data in the judgment array, normal data array and monitoring array in the embodiment.

[0013] Figure 3 It is a calculation flow chart of the embodiment.

[0014] Figure 4 It is a diagram showing the effect of identifying and correcting a single bubble using the present invention in an embodiment.

[0015] Figure 5 It is a diagram showing the effect of identifying and correcting continuous bubbles using the present invention in an embodiment.

[0016] Figure 6 This is a diagram showing the effect of data correction on actual turbidity change of a solution using the present invention in an embodiment. DETAILED DESCRIPTION

[0017] The specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. It should be pointed out that the embodiments are only specific explanations of the invention and should not be regarded as limitations of the invention. The purpose of the embodiments is to enable those skilled in the art to better understand and reproduce the technical solutions of the present invention. The protection scope of the present invention shall still be based on the scope defined by the claims.

[0018] like Figure 1 As shown, the present invention provides a method for calculating the turbidity of a low-range turbidity meter, the calculation method comprising: S1, the signals collected by the turbidity meter are entered into the judgment array until the number of signals in the judgment array reaches n, wherein the signals in the judgment array are updated in real time, and the judgment array is the n signals collected by the turbidity meter the most recently, n 3; S2, judging the first signal collected in the judgment array, if the signal is normal data, the signal is put into the normal data array, if the signal is bubble data, the signal is directly eliminated; wherein the signal in the normal data array is updated in real time, and the normal data array is a collection of signals confirmed to be normal fluctuations; The criterion for determining the first signal collected in the array is to determine whether there is a linear relationship between the signal and the remaining n-1 signals, or whether there is a linear relationship between the signal and the latest n-1 confirmed normal fluctuation signals in the normal data array; If there is a linear relationship, it may be caused by normal changes in water concentration. If there is a linear relationship and the deviation between the first signal collected in the judgment array and the most recently confirmed normal fluctuation signal in the normal data array is greater than k1, or the deviation from the last calculated scattering signal value AD is greater than k1, it means that the linearity may also be that three consecutive bubble data are just linear. In this case, the first signal collected in the judgment array is bubble data, otherwise it is normal data; k1 is obtained based on actual test normal measurement and simulated bubble measurement, and is related to instrument performance; If there is no linear relationship, it may be normal data fluctuation, but normal data fluctuation should have a reasonable range, that is, the deviation > k2. If there is no linear relationship, and the deviation between the first signal collected in the array and the most recently confirmed normal fluctuation signal in the normal data array is > k2, or the deviation with the last calculated scattering signal value AD is > k2, then the first signal collected in the array is judged to be bubble data, otherwise it is normal data; wherein K2 is obtained based on actual test normal measurement and simulated bubble measurement, and is related to instrument performance.

[0019] In order to prevent the turbidity of the solution from changing suddenly without undergoing a slow change process, the sensor is directly placed in a water body of another concentration, and a monitoring array is set. The data in the monitoring array is updated in real time. The monitoring array is the q original signals collected by the turbidity meter the most recently. Calculate the stability of q original signals in the monitoring array, where the stability can be obtained by calculating the relative standard deviation; If the stability meets the conditions, the current solution concentration is judged to be stable, the data is normal and there are no bubbles, then the q original signals are replaced in chronological order with the n signals in the judgment array and the qn most recently confirmed normal fluctuation signals in the normal data array; If the stability does not meet the condition, directly go to step S3; S3, calculating the average value of the m most recently confirmed normal fluctuation signals as the scattering signal value AD, where m>3; When in step S2, it is determined that the first signal collected in the array is bubble data and is eliminated, in step S3, the random value obtained by multiplying the average value of the most recently confirmed m normal fluctuation signals by a coefficient k3 is used as the scattering signal value AD, where 0.99< k3<1.01.

[0020] S4, calculating and outputting the turbidity T of the current turbidity meter measurement output according to the scattering signal value AD; , where K is the coefficient determined by user calibration.

[0021] S5, repeating steps S1 to S4, calculating and outputting the turbidity of the next turbidity meter measurement output. Example

[0022] like Figure 2 As shown, it is set that there are 3 signals in the judgment array 1, 6 signals in the normal data array 2, and 6 data in the monitoring array 3. At present, the 3 signals in the judgment array are A1, A2 and A3 in the order of collection; at present, the 6 signals in the normal data array are B1, B2, B3, B4, B5 and B6 in the order of determination; at present, the 6 signals in the monitoring array are C1, C2, C3, C4, C5 and C6 in the order of collection; A method for calculating the turbidity of a low-range turbidity meter, the method comprising: like Figure 3 As shown, S1, the signal collected by the turbidity meter enters the judgment array. When there are 3 signals in the judgment array, the next step is performed. S2, if the linearity of A1 with A2 and A3, or A1 with B5 and B6 is greater than 0.9, it may be caused by normal changes in water concentration. When the deviation between A1 and B6 is > 1000, or the deviation from the last calculated scattering signal value AD is > 1000, it means that the linearity may also be that three consecutive bubble data are just linear, then A1 is bubble data; otherwise, it is normal data; If there is no linear relationship, it may be normal data fluctuation, but normal data fluctuation should have a reasonable range, that is, deviation>k2. If there is no linear relationship, and the deviation between A1 and B6 is > 300, or the deviation between A1 and the last calculated scattering signal value AD is > 300, then A1 is judged to be bubble data; otherwise, it is normal data. In order to prevent the turbidity of the solution from changing suddenly without a slow change process, if the sensor is directly placed in a water body of another concentration, a monitoring array is set, and the stability of C1, C2, C3, C4, C5 and C6 in the monitoring array is calculated. If the stability is <5%, it is judged that the current solution concentration is stable, the data is normal and there are no bubbles, then C1, C2, C3, C4, C5 and C6 are replaced in chronological order with A1, A2, A3, B4, B5 and B6 in the judgment array; specifically, C1 replaces B4, C2 replaces B5, C3 replaces B6, C4 replaces A1, C5 replaces A2, and C6 replaces A3; If the stability does not meet the condition, go directly to step S3.

[0023] S3, if A1 is determined to be normal data, the six most recently confirmed normal fluctuation signals are A1, B6, B5, B4, B3 and B2 in the order of determination time, and the average value of the six most recently confirmed normal fluctuation signals is calculated as the scattering signal value AD; If A1 is determined to be bubble data and is eliminated, the six most recently confirmed normal fluctuation signals are still B6, B5, B4, B3, B2 and B1. The random value obtained by multiplying the average value of B6, B5, B4, B3, B2 and B1 by a coefficient k3 is used as the scattering signal value AD, where 0.99< k3<1.01.

[0024] S4, calculating and outputting the turbidity T of the current turbidity meter measurement output according to the scattering signal value AD; , where K is the coefficient determined by user calibration.

[0025] S5, the signal A4 collected by the turbidity meter enters the judgment array, continues to judge A2, and calculates and outputs the corresponding turbidity at this time.

[0026] like Figure 4 As shown in FIG. , this is the recognition and correction effect of a single bubble by this scheme; Figure 5 As shown in FIG. , the recognition and correction effect of continuous bubbles by this scheme is shown; Figure 6 As shown, the identification and correction effects of the data change when the actual turbidity of the solution changes.

[0027] The present invention obtains the signal waveform when bubbles appear through actual tests, performs linear judgment on each newly collected signal, realizes the identification of single bubbles and continuous bubbles, distinguishes bubble data, normal data fluctuations and actual turbidity changes of the solution, avoids the refraction of the bubble on the measurement light path to cause obvious abnormal fluctuations in the signal, thereby achieving measurement stability and accuracy.

[0028] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0029] It should be noted that the technical features not described in detail in the present invention can be implemented by any existing technology.

Claims

1. A method for calculating the turbidity of a low-range turbidity meter, characterized in that: The calculation method includes: (1) The signals collected by the turbidity meter are entered into a judgment array until the number of signals in the judgment array reaches n, wherein the signals in the judgment array are updated in real time, and the judgment array is the n signals most recently collected by the turbidity meter, n 3; (2) Determine the first signal collected in the judgment array. If the signal is normal data, the signal is added to the normal data array. If the signal is bubble data, the signal is directly removed. The signals in the normal data array are updated in real time. The normal data array is a collection of signals confirmed to be normal fluctuations. (3) Calculate the average value of the most recently confirmed m normal fluctuation signals as the scattering signal value AD, where m>3; (4) Calculate and output the turbidity T measured by the current turbidity meter based on the scattered signal value AD; (5) Repeat steps (1) to (4) to calculate and output the turbidity of the next turbidity meter measurement output.

2. The method for calculating turbidity of a low-range turbidity meter according to claim 1, characterized in that: The criterion for determining the first signal collected in the array in step (2) is to determine whether there is a linear relationship between the signal and the remaining n-1 signals, or whether there is a linear relationship between the signal and the latest n-1 confirmed normal fluctuation signals in the normal data array; If there is a linear relationship, and the deviation between the first signal collected in the judgment array and the most recently confirmed normal fluctuation signal in the normal data array is greater than k1, or the deviation between the first signal collected in the judgment array and the last calculated scattering signal value AD is greater than k1, then the first signal collected in the judgment array is bubble data, otherwise it is normal data; If there is no linear relationship, and the deviation between the first signal collected in the judgment array and the most recently confirmed normal fluctuation signal in the normal data array is greater than k2, or the deviation from the last calculated scattering signal value AD is greater than k2, then the first signal collected in the judgment array is bubble data, otherwise it is normal data.

3. The method for calculating turbidity of a low-range turbidity meter according to claim 1, characterized in that: The bubble identification algorithm further includes: setting a monitoring array, the data in the monitoring array is updated in real time, the monitoring array is q original signals newly collected by the turbidity meter, where qn>1; Calculate the stability of q original signals in the monitoring array, If the stability meets the conditions, it is judged that the current solution concentration is stable, the data is normal and there are no bubbles, then the q original signals will replace the n signals in the judgment array and the latest confirmed qn normal fluctuation signals in the normal data array in chronological order; If the stability condition is not met, go directly to step (3).

4. The method for calculating turbidity of a low-range turbidity meter according to claim 1 or 3, characterized in that: When in step (2), it is determined that the first signal collected in the array is bubble data and is discarded, in step (3), the random value obtained by multiplying the average value of the most recently confirmed m normal fluctuation signals by a coefficient k3 is used as the scattering signal value AD.

5. The method for calculating turbidity of a low-range turbidity meter according to claim 4, characterized in that: In step (4), the turbidity T measured by the current turbidity meter output is: Among them, K is the coefficient determined by user calibration.

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