Intelligent dosing device for sludge dewatering based on flocculation fractal dimension

Abstract

The application discloses a sludge dewatering intelligent dosing device based on floc fractal dimension, which comprises: a model construction module, which is based on the sludge image of the sludge water of a water supply plant, calculates the floc fractal dimension, and the model of the floc performance value; an analysis module, which obtains the real-time floc fractal dimension D, inputs the floc fractal dimension D into the floc performance curve in the model construction module, outputs the floc performance value ZX based on the floc performance curve, and analyzes whether to dose, to obtain a dosing signal and a non-dosing signal; and a dosing module, which, when the dosing signal is obtained, adds appropriate and accurate flocculating reagents according to the shape characteristics of the sludge floc, accurately and reasonably outputs the amount of the reagents according to the sludge concentration of the sludge water of the water supply plant, and guarantees the efficiency of the reagent treatment; and when no reagent is added, adjusts the frequency of the collection and monitoring according to the floc condition, so that the collection and treatment times are effectively reduced on the premise of guaranteeing the timeliness of the dosing, and the efficiency of the dosing is improved.

Description

Technical Field

[0001] This invention relates to the field of floc fractal dimension technology, specifically to an intelligent dosing device for sludge dewatering based on floc fractal dimension. Background Technology

[0002] Chinese patent CN108830429B discloses an optimization method for a wastewater treatment plant based on fractal dimension, comprising: (1) diluting the sludge and placing a drop on a glass slide; (2) taking an image of the sludge using a microscope; (3) processing the sludge image using image processing software to obtain the geometric parameters of the projected area A and perimeter P of a single sludge floc, and obtaining the two-dimensional fractal dimension D of the floc based on fractal theory; (4) through calculation and analysis, deriving the sludge's settling performance, adsorption performance, biological activity, and physical structure from the fractal dimension, thereby evaluating the operating status of the sludge discharge water;

[0003] In existing technologies, the sludge thickening and dewatering process in water treatment plants mainly relies on manual experience and certain technical parameters to select the dosage of flocculants and coagulants, resulting in waste of chemicals and preventing them from achieving their maximum effect. Currently, the sludge thickening and dewatering process in water treatment plants is relatively crude, lacking intelligent analysis and processing capabilities, making it impossible to dynamically understand the thickening and sedimentation process, and causing a lag in the adjustment of chemical addition. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned above in the background technology and to propose an intelligent dosing device for sludge dewatering based on the fractal dimension of flocs.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A smart dosing device for sludge dewatering based on the fractal dimension of flocs includes:

[0007] The model building module calculates the fractal dimension of flocs and the model with floc performance values ​​based on sludge images of sludge discharged from the water supply plant.

[0008] The specific working process of this model building module is as follows:

[0009] Step 1: Take sludge from the wastewater discharge of the water treatment plant during a historical period as a sample, and divide it into several equal portions; after diluting the sludge discharge, take one drop and place it on a glass slide, and cover it with a coverslip; use a microscope to take images of the sludge discharge on the covered slide, taking at least 5 photos of each sample from different fields of view; use image processing software to process the images of the sludge discharge, obtain the projected area A and perimeter P of a single activated sludge floc, and obtain the fractal dimension D of the floc based on fractal theory;

[0010] Step 2: Analyze the operating status of sludge during the dewatering process using sedimentation and adsorption performance values ​​to obtain floc performance values;

[0011] Step 3: Construct a rectangular coordinate system with the fractal dimension of the floc as the abscissa and the floc performance value as the ordinate. Substitute the fractal dimension and floc performance value of the floc at the same time into the coordinate system to obtain the floc performance curve.

[0012] The analysis module obtains the real-time fractal dimension D of the flocs, substitutes the fractal dimension D into the floc performance curve in the model building module, outputs the floc performance value ZX based on the floc performance curve, and performs a drug-dosing analysis to obtain the drug-dosing signal and the drug-not-dosing signal.

[0013] The dosing module, upon receiving a dosing signal, adds appropriate and precise flocculants based on the shape characteristics of the sludge flocs.

[0014] As a further aspect of the present invention: the settlement performance value is obtained by the following method:

[0015] Set the data acquisition time node as T, where the data acquisition time node T includes the initial acquisition time Tc, the midpoint acquisition time Tz, and the end acquisition time Tj.

[0016] The settlement value ZCTc at the initial acquisition time Tc, the midpoint settlement value ZCTz at the midpoint acquisition time Tz, and the final settlement value ZCTj at the end acquisition time Tj are obtained.

[0017] The maximum settlement value ZCTcmax and minimum settlement value ZCTcmin within the acquisition time node are obtained, as well as the acquisition time Tmax corresponding to the maximum settlement value ZCTcmax and the acquisition time Tmin corresponding to the minimum settlement value ZCTcmin.

[0018] Through formula The settlement performance value ZXC was calculated; where a1 and a2 are both proportionality coefficients, with a1 taking a value of 0.51 and a2 taking a value of 0.63.

[0019] As a further aspect of the present invention, the adsorption performance value is obtained by the following method:

[0020] Set the data acquisition time node as T, where the data acquisition time node T includes the initial acquisition time Tc, the midpoint acquisition time Tz, and the end acquisition time Tj.

[0021] The adsorption value ZXTc at the initial collection time Tc, the midpoint adsorption value ZXTz at the midpoint collection time Tz, and the final adsorption value ZXTj at the end collection time Tj are obtained.

[0022] The maximum adsorption value ZXTcmax and the minimum adsorption value ZXTcmin within the collection time node are obtained, as well as the collection time Tmax corresponding to the maximum adsorption value ZXTcmax and the collection time Tmin corresponding to the minimum adsorption value ZXTcmin.

[0023] Through formula The adsorption performance value ZXX was calculated; where a3 and a4 are proportionality coefficients.

[0024] As a further aspect of the present invention: through the formula The floc performance value ZX was calculated.

[0025] As a further aspect of the present invention, the specific working process of the analysis module is as follows:

[0026] Step 1: Compare the obtained floc performance value ZX with the floc performance threshold;

[0027] If the floc performance value ZX ≤ floc performance threshold, then a no-drug-addition signal is generated;

[0028] If the floc performance value ZX > the floc performance threshold, then a dosing signal is generated.

[0029] As a further aspect of the present invention, the specific working process of the dosing module is as follows:

[0030] Obtain the maximum floc particle size value within detection period i and label it as ZL i. Sum the maximum floc particle size values ​​ZL i within detection period i and take the average to obtain the maximum average floc particle size ZLp. Compare the maximum average floc particle size ZLp with the maximum average floc particle size threshold.

[0031] If the maximum average value of floc particle size ZLp is greater than the maximum average threshold of floc particle size, then the floc particle size influence signal is determined. A rectangular coordinate system is established with time as the X-axis and floc particle size as the Y-axis. Based on the start time of detection period i and the maximum floc particle size value within detection period i, n particle size points are marked in the rectangular coordinate system. The two particle size points with the largest ordinate values ​​are marked as high diameter points, and the two particle size points with the smallest ordinate values ​​are marked as low diameter points. A quadrilateral is obtained by connecting the two high diameter points and the low diameter points in sequence. The area value of the quadrilateral is marked as the particle size influence coefficient ZHx.

[0032] As a further aspect of the present invention: the maximum floc dispersion value within the detection period i is obtained and marked as ZP i; the maximum floc dispersion value ZP i within the detection period i is summed and averaged to obtain the maximum average floc dispersion value ZPp; and the maximum average floc dispersion value ZPp is compared with the maximum average floc dispersion threshold.

[0033] If the maximum average value of floc dispersion ZPp is greater than the maximum average threshold of floc dispersion, then the pump influence signal is determined. A rectangular coordinate system is established with time as the X-axis and floc dispersion as the Y-axis. Based on the start time of detection period i and the maximum floc dispersion value within detection period i, n dispersion points are marked in the rectangular coordinate system. The two dispersion points with the largest ordinate values ​​are marked as high dispersion points, and the two dispersion points with the smallest ordinate values ​​are marked as low dispersion points. A quadrilateral is obtained by connecting the two high dispersion points and the two low dispersion points in sequence. The area value of the quadrilateral is marked as the dispersion influence coefficient ZBx.

[0034] As a further aspect of the present invention: The particle size influence coefficient ZHx and the dispersion influence coefficient ZBx are obtained, along with the corresponding particle size influence standard coefficient ZHxb and dispersion influence standard coefficient ZBxb; This is achieved through the formula... The dosage of the drug LY was calculated; where b1 and b2 are both proportionality coefficients.

[0035] As a further aspect of the present invention, it also includes:

[0036] The monitoring module, when it receives a signal that no pesticide has been added, monitors and judges the floc structure based on its performance value.

[0037] The specific working process of this monitoring module is as follows:

[0038] Step 1: Calculate the floc performance value ZX using the formula ZG=ln(d1*ZX) to obtain the floc monitoring value ZG; where d1 is the proportional coefficient, and d1 takes a value of 1.35;

[0039] Step 2: Compare the obtained floc monitoring value ZG with the floc monitoring threshold;

[0040] When the floc monitoring value ZG≥X2, a low-frequency monitoring signal is generated;

[0041] When X2 > floc monitoring value ZG ≥ X1, a medium-frequency monitoring signal is generated;

[0042] When the floc monitoring value ZG < X1, a high-frequency monitoring signal is generated; where X1 and X2 are set thresholds, and X2 > X1 > 0.

[0043] The beneficial effects of this invention are:

[0044] This invention relates to an intelligent sludge dewatering dosing device. Based on the fractal dimension of flocs, it performs real-time analysis of the treatment data in the sludge discharged from the water supply plant and obtains the floc performance value. Then, it determines whether to add chemicals based on the floc performance value. When adding chemicals, it accurately and reasonably outputs the amount of chemicals according to the sludge treatment status to ensure the high efficiency of chemical treatment. When not adding chemicals, it adjusts the frequency of data collection and monitoring according to the floc condition, thereby effectively reducing the number of data collection and processing times while ensuring the timeliness of chemical addition, thus improving the efficiency of chemical addition. Attached Figure Description

[0045] The invention will now be further described with reference to the accompanying drawings.

[0046] Figure 1 This is a system block diagram of the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1 As shown, the present invention is an intelligent dosing device for sludge dewatering based on the fractal dimension of flocs, comprising:

[0049] The model building module calculates the fractal dimension of flocs based on sludge images of sludge discharged from the water supply plant.

[0050] The specific working process of this model building module is as follows:

[0051] Step 1: Take sludge from the wastewater discharge of the water treatment plant during a historical period as a sample, and divide it into several equal portions; after diluting the sludge discharge, take one drop and place it on a glass slide, and cover it with a coverslip; use a microscope to take images of the sludge discharge on the covered slide, taking at least 5 photos of each sample from different fields of view; use image processing software to process the images of the sludge discharge, obtain the projected area A and perimeter P of a single activated sludge floc, and obtain the fractal dimension D of the floc based on fractal theory;

[0052] Step 2: Analyze the operating status of sludge during the dewatering process using sedimentation and adsorption performance values ​​to obtain floc performance values;

[0053] Settlement performance values ​​are obtained in the following ways:

[0054] Set the data acquisition time node as T, where the data acquisition time node T includes the initial acquisition time Tc, the midpoint acquisition time Tz, and the end acquisition time Tj.

[0055] The settlement value ZCTc at the initial acquisition time Tc, the midpoint settlement value ZCTz at the midpoint acquisition time Tz, and the final settlement value ZCTj at the end acquisition time Tj are obtained.

[0056] The maximum settlement value ZCTcmax and minimum settlement value ZCTcmin within the acquisition time node are obtained, as well as the acquisition time Tmax corresponding to the maximum settlement value ZCTcmax and the acquisition time Tmin corresponding to the minimum settlement value ZCTcmin.

[0057] Through formula The settlement performance value ZXC was calculated; where a1 and a2 are proportionality coefficients, with a1 taking a value of 0.51 and a2 taking a value of 0.63.

[0058] Adsorption performance values ​​are obtained in the following way:

[0059] Set the data acquisition time node as T, where the data acquisition time node T includes the initial acquisition time Tc, the midpoint acquisition time Tz, and the end acquisition time Tj.

[0060] The adsorption value ZXTc at the initial collection time Tc, the midpoint adsorption value ZXTz at the midpoint collection time Tz, and the final adsorption value ZXTj at the end collection time Tj are obtained.

[0061] The maximum adsorption value ZXTcmax and the minimum adsorption value ZXTcmin within the collection time node are obtained, as well as the collection time Tmax corresponding to the maximum adsorption value ZXTcmax and the collection time Tmin corresponding to the minimum adsorption value ZXTcmin.

[0062] Through formula The adsorption performance value ZXX was calculated; where a3 and a4 are proportionality coefficients, with a3 taking a value of 0.85 and a4 taking a value of 0.47.

[0063] Through formula The floc performance value ZX was calculated.

[0064] Step 3: Construct a rectangular coordinate system with the fractal dimension of the floc as the abscissa and the floc performance value as the ordinate. Substitute the fractal dimension and floc performance value of the floc at the same time into the coordinate system to obtain the floc performance curve.

[0065] The analysis module obtains the real-time fractal dimension D of the flocs, substitutes the fractal dimension D into the floc performance curve in the model building module, outputs the floc performance value ZX based on the floc performance curve, and performs analysis on whether to add medicine.

[0066] The specific working process of this analysis module is as follows:

[0067] Step 1: Compare the obtained floc performance value ZX with the floc performance threshold;

[0068] If the floc performance value ZX ≤ floc performance threshold, then a no-drug-addition signal is generated;

[0069] If the floc performance value ZX > the floc performance threshold, then a dosing signal is generated;

[0070] In this invention, the "no chemical addition" signal indicates that the current wastewater treatment process has good settling and adsorption effects, resulting in good wastewater purification, and therefore no chemical addition is needed; the "chemical addition" signal indicates that the current wastewater treatment process does not have good settling and adsorption effects, resulting in poor wastewater purification, and therefore chemical addition is needed.

[0071] The dosing module, upon receiving a dosing signal, adds appropriate and precise flocculants based on the shape characteristics of the sludge flocs.

[0072] The specific working process of this dosing module is as follows:

[0073] Step 1: Obtain the maximum floc particle size value within detection period i and label it as ZL i. Sum the maximum floc particle size values ​​ZL i within detection period i and take the average to obtain the maximum average floc particle size ZLp. Compare the maximum average floc particle size ZLp with the maximum average floc particle size threshold.

[0074] If the maximum average value of floc particle size ZLp is greater than the maximum average threshold of floc particle size, then the floc particle size influence signal is determined. A rectangular coordinate system is established with time as the X-axis and floc particle size as the Y-axis. The start time of detection period i and the maximum floc particle size value within detection period i are used to mark n particle size points in the rectangular coordinate system. The two particle size points with the largest ordinate values ​​are marked as high diameter points, and the two particle size points with the smallest ordinate values ​​are marked as low diameter points. The two high diameter points and the two low diameter points are connected sequentially to obtain a quadrilateral. The area value of the quadrilateral is marked as the particle size influence coefficient ZHx.

[0075] Step 2: Obtain the maximum floc dispersion value within detection period i and label it as ZP i. Sum the maximum floc dispersion values ​​ZP i within detection period i and take the average to obtain the maximum average floc dispersion value ZPp. Compare the maximum average floc dispersion value ZPp with the maximum average floc dispersion threshold.

[0076] If the maximum average value of floc dispersion ZPp is greater than the maximum average value threshold of floc dispersion, then the pump influence signal is determined. A rectangular coordinate system is established with time as the X-axis and floc dispersion as the Y-axis. The start time of detection period i and the maximum floc dispersion value within detection period i are used to mark n dispersion points in the rectangular coordinate system. The two dispersion points with the largest ordinate values ​​are marked as high dispersion points, and the two dispersion points with the smallest ordinate values ​​are marked as low dispersion points. The two high dispersion points and the two low dispersion points are connected sequentially to obtain a quadrilateral. The area value of the quadrilateral is marked as the dispersion influence coefficient ZBx.

[0077] Step 3: Obtain the particle size influence coefficient ZHx and dispersion influence coefficient ZBx, as well as the corresponding standard coefficients ZHxb and ZBxb for particle size influence and dispersion influence; use the formula... The dosage of the reagent LY was calculated; where b1 and b2 are proportionality coefficients, with b1 and b2 both set to 0.84; LYb is the standard value for reagent addition in the process.

[0078] The monitoring module, when it receives a signal that no pesticide has been added, monitors and judges the floc structure based on its performance value.

[0079] The specific working process of this monitoring module is as follows:

[0080] Step 1: Calculate the floc performance value ZX using the formula ZG=ln(d1*ZX) to obtain the floc monitoring value ZG; where d1 is the proportional coefficient, and d1 takes a value of 1.35;

[0081] Step 2: Compare the obtained floc monitoring value ZG with the floc monitoring threshold;

[0082] When the floc monitoring value ZG≥X2, a low-frequency monitoring signal is generated;

[0083] When X2 > floc monitoring value ZG ≥ X1, a medium-frequency monitoring signal is generated;

[0084] When the floc monitoring value ZG < X1, a high-frequency monitoring signal is generated; where X1 and X2 are set thresholds, and X2 > X1 > 0;

[0085] In this invention, the low-frequency monitoring signal, medium-frequency monitoring signal, and high-frequency monitoring signal represent the acquisition time node intervals that decrease sequentially.

[0086] The working principle of this invention: The intelligent sludge dewatering dosing device of this invention analyzes the treatment data of the sludge discharge water from the water supply plant in real time based on the fractal dimension of the flocs and obtains the floc performance value. Then, it determines whether to add chemicals based on the floc performance value. When adding chemicals, it outputs the dosage of chemicals accurately and reasonably according to the sludge treatment situation to ensure the high efficiency of chemical treatment. When not adding chemicals, it adjusts the frequency of data collection and monitoring according to the floc condition, thereby effectively reducing the number of data collection and processing times while ensuring the timeliness of chemical addition, thus improving the efficiency of chemical addition.

[0087] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A sludge dewatering intelligent dosing device based on floc fractal dimension, characterized in that, The model construction module specifically works in the following process: Step 1: Take the sludge of the effluent water in the historical time period of the water supply treatment plant as a sample, and divide it into several equal parts; dilute the effluent water sludge, take a drop and put it on a glass slide, and cover it with a cover glass; use a microscope to take pictures of the effluent water on the glass slide, at least 5 different views for each sample; use image processing software to process the effluent water images to obtain the projection area A and perimeter P of a single activated sludge floc, and obtain the floc fractal dimension D based on fractal theory; Step 2: Analyze the running state of the sludge during the dewatering process by the settling performance value and the adsorption performance value to obtain the floc performance value; The settling performance value is obtained in the following way: Set the collection time node as T, which includes the collection initial time Tc, the collection midpoint time Tz, and the collection end time Tj; Get the settling value ZCTc at the collection initial time Tc, the midpoint settling value ZCTz at the collection midpoint time Tz, and the end settling value ZCTj at the collection end time Tj; Get the maximum settling value ZCTcmax and the minimum settling value ZCTcmin within the collection time node, as well as the collection time Tmax corresponding to the maximum settling value ZCTcmax and the collection time Tmin corresponding to the minimum settling value ZCTcmin; The adsorption performance value is obtained in the following way: Set the collection time node as T, which includes the collection initial time Tc, the collection midpoint time Tz, and the collection end time Tj; The settlement performance value ZXC is calculated by the formula ; wherein, a1 and a2 are both proportional coefficients, a1 has a value of 0.51, and a2 has a value of 0.

63. Get the adsorption value ZXTc at the collection initial time Tc, the midpoint adsorption value ZXTz at the collection midpoint time Tz, and the end adsorption value ZXTj at the collection end time Tj; Get the maximum adsorption value ZXTcmax and the minimum adsorption value ZXTcmin within the collection time node, as well as the collection time Tmax corresponding to the maximum adsorption value ZXTcmax and the collection time Tmin corresponding to the minimum adsorption value ZXTcmin; Step 3: Construct a rectangular coordinate system with the floc fractal dimension as the abscissa and the floc performance value as the ordinate, and substitute the floc fractal dimension and the floc performance value at the same time into the coordinate system to obtain the floc performance curve; The analysis module obtains the real-time floc fractal dimension D and substitutes it into the floc performance curve in the model construction module, and outputs the floc performance value ZX based on the floc performance curve, and analyzes whether to add chemicals to obtain the chemical addition signal and the no-chemical addition signal; The adsorption performance value Zxx is calculated by the formula ; wherein a3 and a4 are proportional coefficients. The chemical addition module adds appropriate and accurate flocculating agents according to the shape characteristics of the sludge flocs when the chemical addition signal is obtained. The analysis module specifically works in the following process: Step 1: Compare the obtained floc performance value ZX with the floc performance threshold value; 2. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 1, characterized in that, The floc performance value ZX is calculated by the formula ZX = 1000 - 1000 x (1 - 0.5 x ( 3. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 2, characterized in that, If the floc performance value ZX is less than or equal to the floc performance threshold value, a no-chemical addition signal is generated; If the floc performance value ZX is greater than the floc performance threshold value, a chemical addition signal is generated. The chemical addition module specifically works in the following process: ​ 4. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 1, characterized in that, ​ The maximum floc particle size value in the detection period i is obtained and marked as ZLi, the maximum floc particle size value ZLi in the detection period i is summed and averaged to obtain the maximum average floc particle size ZLp, and the maximum average floc particle size ZLp is compared with the maximum average floc particle size threshold: If the maximum average floc particle size ZLp is greater than the maximum average floc particle size threshold, the floc particle size influence signal is determined, a rectangular coordinate system is established with time as the X-axis and floc particle size as the Y-axis, the starting time of the detection period i and the maximum floc particle size value in the detection period i are marked in the rectangular coordinate system, the two particle size points with the maximum vertical coordinate values are marked as high points, the two particle size points with the minimum vertical coordinate values are marked as low points, and a quadrilateral is obtained by connecting the two high points and the two low points in turn, and the area value of the quadrilateral is marked as the particle size influence coefficient ZHx.

5. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 4, characterized in that, The maximum floc dispersion value in the detection period i is obtained and marked as ZPi, the maximum floc dispersion value ZPi in the detection period i is summed and averaged to obtain the maximum average floc dispersion ZPp, and the maximum average floc dispersion ZPp is compared with the maximum average floc dispersion threshold: If the maximum average floc dispersion ZPp is greater than the maximum average floc dispersion threshold, the material pump influence signal is determined, a rectangular coordinate system is established with time as the X-axis and floc dispersion as the Y-axis, the starting time of the detection period i and the maximum floc dispersion value in the detection period i are marked in the rectangular coordinate system, the two dispersion points with the maximum vertical coordinate values are marked as high points, the two dispersion points with the minimum vertical coordinate values are marked as low points, and a quadrilateral is obtained by connecting the two high points and the two low points in turn, and the area value of the quadrilateral is marked as the dispersion influence coefficient ZBx.

6. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 5, characterized in that, The particle size influence coefficient ZHx and the dispersion influence coefficient ZBx are obtained, and the corresponding particle size influence standard coefficient ZHxb and dispersion influence standard coefficient ZBxb are obtained. The dosage LY of the medicament is calculated; wherein, b1 and b2 are proportional coefficients.

7. The sludge dewatering intelligent dosing device based on the fractal dimension of floc according to claim 1, characterized in that, Further comprising: A monitoring module that, when the no-dosing signal is obtained, monitors and judges the floc performance value according to the situation of the floc performance value; The specific working process of the monitoring module is as follows: Step 1: The obtained floc performance value ZX is calculated to obtain a floc supervision value ZG through the formula ZG = ln(d1*ZX); wherein d1 is a proportional coefficient, and d1 takes a value of 1.35; Step 2: The obtained floc supervision value ZG is compared with a floc supervision threshold; When the floc supervision value ZG is greater than or equal to X2, a low-frequency monitoring signal is generated; When X2 is greater than the floc supervision value ZG and the floc supervision value ZG is greater than or equal to X1, a medium-frequency monitoring signal is generated; When the floc supervision value ZG is less than X1, a high-frequency monitoring signal is generated; wherein X1 and X2 are set thresholds, and X2>X1>0.

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