Intelligent control system for a fluid mixing fine cutter for pulp preparation

Abstract

This invention discloses an intelligent control system for a fluid mixing fine cutter in pulp preparation, relating to the field of pulp preparation technology. The system includes a data acquisition module that collects and preprocesses concentration and differential pressure signals in real time; a blockage analysis module that calculates the fusion fluctuation coefficient and triggers discreteness and continuity detection to obtain the detection value; and a water injection module that determines whether to inject water based on the detection value. If water injection is needed, it repeats until no further injection is required; otherwise, it returns to the acquisition process or terminates the process. This invention simultaneously detects concentration dispersion and differential pressure continuity through the fusion fluctuation coefficient, proactively identifying signs of soft blockage. Based on the detection value, it automatically injects water intermittently, precisely diluting the pulp in the early stages of resistance increase, maintaining pulp flow stability, and preventing further blockage.

Description

Technical Field

[0001] This invention relates to the field of pulp preparation technology, specifically to an intelligent control system for a fluid mixing fine cutter for pulp preparation. Background Technology

[0002] Pulp is a network-like suspension composed of long, thin fibers. When the pulp passes through a narrowing channel, if the pulp concentration increases, the interwoven structure that was originally broken up by the turbulence will reconnect and clump together. If these soft clumps are not dispersed and peeled off by the subsequent high-speed fluid, they will begin to accumulate at the narrowing point of the channel, forming a situation where the channel is not completely blocked, but the flow resistance is already increasing. At the same time, the surface of the channel becomes rough due to wear, making it easier for fibers to stick and become the starting point for accumulation. When the operator notices an abnormality by observing a slight change in the ammeter, it means that in the fluid mixing mill, the pulp is transported by a high-pressure pump. When fiber clumps begin to accumulate in the narrow channel and form a slight blockage, the effective flow cross-sectional area of ​​the channel decreases instantly, and the flow resistance increases sharply. In order to maintain the set flow rate, the motor must output more torque to overcome this additional reaction force, resulting in a noticeable slight increase in the operating current. However, by this time, the blockage has often been developing for some time, missing the best window of opportunity for precise adjustment when the resistance has only increased slightly. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent control system for a fluid mixing fine cutter for pulp preparation. This system solves the problem that pulp fibers flocculate and gradually accumulate at the narrowing point of the flow channel due to concentration fluctuations, leading to a contraction of the flow cross-section, a slight increase in resistance and motor current. When this change is manually detected by an ammeter, soft blockage has already formed, missing the optimal time for precise intervention when resistance first appears.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control system for a fluid mixing fine cutter for pulp preparation, comprising the following specific modules: a data acquisition module: acquiring concentration signals and differential pressure signals in real time and performing preprocessing; a blockage analysis module: calculating the fusion fluctuation coefficient based on the concentration signal and differential pressure signal, triggering discrete detection of the concentration signal and continuous detection of the differential pressure signal based on the fusion fluctuation coefficient, obtaining detection values, and sending the detection values ​​to the water injection module; a water injection module: analyzing whether to inject water based on the detection values, if water injection is required, then injecting water and continuing to analyze whether to inject water until it is determined that water injection is not required; if water injection is determined not to be required, then returning to the data acquisition module or directly ending the process.

[0005] Furthermore, the method for obtaining the fusion fluctuation coefficient is as follows: set a concentration fluctuation threshold and a pressure difference fluctuation threshold, and set a fusion fluctuation coefficient, initially assigning it a value of zero; calculate the variance of the concentration signal in the time series to obtain the concentration fluctuation value; calculate the variance of the pressure difference signal in the time series to obtain the pressure difference fluctuation value; compare the concentration fluctuation value with the concentration fluctuation threshold, and compare the pressure difference fluctuation value with the pressure difference fluctuation threshold; if the concentration fluctuation value is greater than the concentration fluctuation threshold and the pressure difference fluctuation value is greater than the pressure difference fluctuation threshold, then assign the fusion fluctuation coefficient a value of one; otherwise, keep the initial value.

[0006] Furthermore, the specific method for obtaining the concentration fluctuation value is as follows: let M be the number of concentration amplitudes in the concentration signal, calculate the average value of each concentration amplitude according to M, then calculate the square of the difference between each concentration amplitude and the average value of the concentration amplitude and sum them up, and divide the sum by M to obtain the concentration fluctuation value.

[0007] Furthermore, the specific method for obtaining the concentration fluctuation value is as follows: ;in, Indicates the concentration fluctuation value. This indicates the number of concentration amplitude values ​​in the concentration signal. Indicates the first Each concentration amplitude, This represents the average concentration amplitude.

[0008] Furthermore, the specific method for obtaining the differential pressure fluctuation value is as follows: assuming that the number of differential pressure amplitude values ​​in the differential pressure signal is N, calculate the average value of each differential pressure amplitude value according to N, then calculate the squared difference between each differential pressure amplitude value and the average value of the differential pressure amplitude value and sum them up, and divide the sum by N to obtain the differential pressure fluctuation value.

[0009] Furthermore, the specific method for obtaining the differential pressure fluctuation value is as follows: ;in, This indicates the pressure difference fluctuation value. This indicates the number of differential pressure amplitude values ​​in the differential pressure signal. Indicates the first Each pressure differential amplitude, This represents the average value of the pressure difference amplitude.

[0010] Furthermore, the specific method for obtaining the detection value is as follows: when the fusion fluctuation coefficient is one, the sampling points of the concentration signal are calculated to obtain discrete values, and the sampling points of the pressure difference signal are calculated to obtain continuous values. The ratio of the continuous value to the discrete value is calculated to obtain the detection value.

[0011] Furthermore, the specific method for obtaining the discrete value is as follows: count the number of sampling points of the concentration signal to obtain the concentration sampling quantity, and calculate the ratio between the concentration sampling quantity and the number of concentration amplitudes in the concentration signal to obtain the discrete value.

[0012] Furthermore, the specific method for obtaining the continuous value is as follows: count the number of sampling points of the differential pressure signal to obtain the number of differential pressure samples, and calculate the ratio of the number of differential pressure samples to the number of differential pressure amplitudes in the differential pressure signal to obtain the continuous value.

[0013] Furthermore, the specific method for analyzing whether to inject water based on the detection value is as follows: set a detection threshold and assign a value of one to the detection threshold, compare the detection value with the detection threshold, and if the detection value is greater than the detection threshold, it indicates that water needs to be injected; otherwise, water does not need to be injected.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. By integrating the discrete detection of concentration signals and the continuous detection of differential pressure signals through the fusion of fluctuation coefficients, it is possible to simultaneously capture the discrete characteristics of concentration pulses and the continuous fluctuation characteristics of differential pressure in the early stage of a slight increase in flow channel resistance caused by fiber flocculation, thereby achieving advanced identification of blockage symptoms and avoiding the delayed response of soft blockages that have already formed when relying on ammeters manually.

[0015] 2. The detection value is calculated based on the ratio of discrete value to continuous value, and the water injection module is automatically controlled to inject water intermittently until the detection value falls back below the threshold. This achieves precise automatic dilution intervention when the flow channel cross section just shows an upward trend in resistance, maintaining the stability of pulp flow and preventing further blockage.

[0016] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0017] Figure 1 This is a flowchart of the intelligent control method for the fluid mixing fine cutter for pulp preparation according to the present invention. Detailed Implementation

[0018] 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.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0020] Example 1: like Figure 1 As shown, this embodiment of the invention provides an intelligent control system for a fluid mixing fine cutter for pulp preparation, comprising the following specific modules: Data acquisition module: The instantaneous solids content of the pulp is measured at millisecond rates using a microwave concentration sensor, which is a Valmet MCA-2000 microwave concentration transmitter. At the same time, high-frequency dynamic differential pressure sensors are installed at both ends of this narrow opening, which are KuliteXTL-190M high-frequency dynamic pressure sensors. The instantaneous pressure difference between the inlet and outlet of the flow channel is recorded in real time to simultaneously acquire the pulp concentration signal and pressure difference signal. The reason for needing a concentration signal is that the fiber agglomerate is a localized dense aggregate in space. It moves with the mainstream in the flow channel. When it passes the concentration measurement point, the solid content sensed by the probe spikes instantly, and then immediately drops back as the agglomerate flows away. The time it takes for the agglomerate to pass through is its spatial size divided by the flow velocity. Once the agglomerate leaves, the concentration signal returns to calm until the next agglomerate passes through, at which point it spikes again. Therefore, the concentration signal is a discrete signal in the time domain. The reason for the differential pressure signal is that when the pulp concentration is too high, it will clump together and form an elastic porous structure in a local area. This structure is continuously compressed, deformed, torn and re-clumped by the high-speed fluid. Each process of clumping, tearing and falling off generates pressure waves in the wall and fluid. As long as the clumps are still near the narrow opening, it will not stop. Therefore, the differential pressure signal is a continuous signal in the time domain. The pulp concentration signal and differential pressure signal are cleaned to remove redundant values ​​and improve the data quality of the concentration signal and differential pressure signal. The concentration signal and differential pressure signal are then timestamped and the time unit is seconds.

[0021] Blockage analysis module: Calculates the fusion fluctuation coefficient based on the concentration signal and the differential pressure signal, triggers the discreteness detection of the concentration signal and the continuity detection of the differential pressure signal based on the fusion fluctuation coefficient, obtains the detection value, and sends the detection value to the water injection module; Water injection module: Based on the detection value, analyze whether water injection is needed. If water injection is needed, the PLC programmable logic controller drives the solenoid valve of the dilution water nozzle in an intermittent pulse manner to inject a small amount of dilution water upstream of the narrowing section of the flow channel to disperse the high-concentration fiber clumps. The system continues to analyze whether water injection is needed until it is determined that water injection is not needed. If water injection is determined that water injection is not needed, the PLC programmable logic controller closes the solenoid valve of the dilution water nozzle and returns to the data acquisition module or terminates directly.

[0022] Example 2 differs from Example 1 in that: The method for obtaining the fusion volatility coefficient is as follows: Set concentration fluctuation thresholds and differential pressure fluctuation thresholds. The concentration fluctuation threshold is the average of normal concentration fluctuation values ​​in historical data, and the differential pressure fluctuation threshold is the average of normal differential pressure fluctuation values ​​in historical data. A fusion fluctuation coefficient is set and initially set to zero. The variance of the concentration signal over time is calculated to obtain the concentration fluctuation value, and the variance of the differential pressure signal over time is calculated to obtain the differential pressure fluctuation value. The concentration fluctuation value is compared with the concentration fluctuation threshold, and the differential pressure fluctuation value is compared with the differential pressure fluctuation threshold. If both the concentration fluctuation value and the differential pressure fluctuation value are greater than the concentration fluctuation threshold and the differential pressure fluctuation threshold, then the fusion fluctuation coefficient is adjusted. The initial value is set to one; otherwise, the initial value is retained. By simultaneously monitoring whether the concentration fluctuation value and the differential pressure fluctuation value both exceed their respective thresholds, it is initially determined whether there is a state in the pulp that simultaneously meets the two conditions of the appearance of flocculent agglomerates and the continuous pulsation of flow resistance. Only when the fusion fluctuation coefficient is set to one will the system enter the subsequent discrete and continuous value calculation process, and then obtain the detection value to determine whether water injection is needed. If the fusion fluctuation coefficient remains zero, it means that the concentration dispersion and the continuous differential pressure fluctuation have not occurred simultaneously, and the system will not initiate further blockage analysis and water injection intervention, thereby avoiding false triggering caused by a single signal fluctuation.

[0023] The specific method for obtaining concentration fluctuation values ​​is as follows: Let M be the number of concentration amplitudes in the concentration signal. The average of each concentration amplitude is calculated based on M to obtain the average concentration amplitude. Then, the difference between each concentration amplitude and the average concentration amplitude is squared and summed. The sum is divided by M to obtain the concentration fluctuation value. The concentration fluctuation value alone can identify the frequency and intensity of flocculent agglomerates, but it cannot determine whether these agglomerates have become stuck at the narrowing part of the flow channel and caused a continuous increase in resistance. ; in, Indicates the concentration fluctuation value. This indicates the number of concentration amplitude values ​​in the concentration signal. Indicates the first Each concentration amplitude, This represents the average concentration amplitude.

[0024] The specific method for obtaining differential pressure fluctuation values ​​is as follows: Let N be the number of differential pressure amplitude values ​​in the differential pressure signal. The average value of each differential pressure amplitude is calculated based on N. Then, the difference between each differential pressure amplitude value and the average value of the differential pressure amplitude is squared and summed. The sum is divided by N to obtain the differential pressure fluctuation value. The differential pressure fluctuation value alone can be used to determine whether the flow resistance has an abnormal unstable trend, but it cannot distinguish whether such fluctuation is caused by true flocculation and blockage. ; in, This indicates the pressure difference fluctuation value. This indicates the number of differential pressure amplitude values ​​in the differential pressure signal. Indicates the first Each pressure differential amplitude, This represents the average value of the pressure difference amplitude.

[0025] The specific method for obtaining the detection value is as follows: When the fusion fluctuation coefficient is one, the sampling points of the concentration signal are calculated to obtain discrete values, and the sampling points of the differential pressure signal are calculated to obtain continuous values. The ratio of the continuous value to the discrete value is calculated to obtain the detection value. ; in, Indicates the detected value. Indicates continuous values. It represents a discrete value and is not zero.

[0026] The specific method for obtaining discrete values ​​is as follows: The number of sampling points for the concentration signal is counted to obtain the concentration sampling quantity. The ratio of the concentration sampling quantity to the number of concentration amplitudes in the concentration signal is calculated to obtain the discrete value. Since the differential pressure signal exhibits continuous fluctuations during flocculation and blockage, and the sampling points cover every signal amplitude, the number of differential pressure samples is equal to the number of differential pressure amplitudes, and the continuous value is equal to 1. However, the concentration signal only generates pulse spikes when the fiber clumps pass through the measuring point, and most of the sampling time is in a smooth interval without pulses. Therefore, the number of concentration samples is less than the number of concentration amplitudes, and the discrete value is less than 1. Dividing the continuous value by the discrete value will result in a detection value greater than 1.

[0027] The specific method for obtaining continuous values ​​is as follows: The number of sampling points of the differential pressure signal is counted to obtain the number of differential pressure samples. The ratio of the number of differential pressure samples to the number of differential pressure amplitude values ​​in the differential pressure signal is calculated to obtain the continuous value.

[0028] The specific method for analyzing whether water injection is needed based on the test values ​​is as follows: Set a detection threshold and assign it a value of 1. Compare the detected value with the detection threshold. If the detected value is greater than the detection threshold, it means that water needs to be injected; otherwise, no water needs to be injected.

[0029] The preferred embodiments disclosed in this invention are merely illustrative examples of feasible implementation methods and are not intended to exhaustively cover all technical details of the invention, nor do they constitute a limitation on the scope of protection of this invention. In practical applications, those skilled in the art can make appropriate adjustments, combinations, or substitutions to the methods or systems in these embodiments based on specific production conditions, equipment configurations, and process requirements, without departing from the core concept of this invention. For example, the acquisition method, data processing algorithm, control threshold, or specific implementation form of the execution unit can all be reasonably changed according to the actual situation.

[0030] Specifically, the microwave concentration sensor used in the data acquisition module is not limited to the Valmet MCA-2000 model; it can be replaced with other online concentration transmitters based on the dielectric constant measurement principle, such as the Endress+Hauser Liquidant series or the VEGACAP series from VEGA. Its sampling rate can be adjusted within the range of 10Hz to 1kHz according to the pulp flow rate and channel cross-sectional dimensions to ensure the capture of concentration pulse spikes when fiber clumps pass through. The differential pressure sensor is not limited to the Kulite XTL-190M model; any high-frequency response differential pressure transmitter can be used, such as the Rosemount 3051S or the Honeywell STD800 series. Its dynamic response frequency should not be lower than the sampling rate of the concentration sensor, and the pressure taps on both sides of the narrow channel opening should be symmetrical and appropriately spaced to eliminate phase delay caused by fluid inertia.

[0031] In addition to removing redundant values, cleaning algorithms in data preprocessing can also include a denoising step based on Kalman filtering or moving median filtering to suppress high-frequency noise introduced by electromagnetic interference or fluid turbulence.

[0032] In the blockage analysis module, the calculation method for the fusion fluctuation coefficient can employ variance as a fluctuation measure, or alternative statistical measures such as standard deviation, mean absolute deviation, or high-frequency energy ratio based on wavelet transform. The methods for setting concentration fluctuation thresholds and differential pressure fluctuation thresholds can utilize adaptive threshold generation algorithms based on historical operating data, such as dynamic thresholds based on exponentially weighted moving averages or anomaly detection boundaries based on Gaussian mixture models. In the calculation of discrete values, the statistical method for counting the number of concentration sampling points can introduce a sliding time window (window length can be set to 0.5 to 5 seconds) to enhance sensitivity to transient pulses; in the calculation of continuous values, the statistical method for counting the number of differential pressure sampling points can also use window integration, with a minimum continuous duration criterion set to avoid misjudgments caused by single pressure spikes. The calculation of the ratio of the detected value JC can further incorporate logarithmic transformation or saturated nonlinear mapping to expand the dynamic range of the detected value or suppress interference from abnormally large values.

[0033] In the water injection module, the intermittent pulse mode of the PLC-driven dilution water nozzle is not limited to a fixed duty cycle; it can be designed as a closed-loop proportional control: the greater the magnitude by which the detected value JC exceeds the detection threshold, the shorter the water injection pulse cycle or the longer the single water injection pulse width; when JC falls back to near the threshold, the water injection intensity is gradually reduced to achieve variable gain control, preventing excessive dilution that leads to excessively low pulp concentration. The dilution water nozzle should be installed approximately 5 to 10 times the hydraulic diameter upstream of the narrowing section of the flow channel to ensure that the injected water is fully mixed with the pulp before entering the narrow opening. The water source can be process white water or clean water, and its conductivity and pH value should match the chemical environment of the pulp system to avoid changes in fiber surface charge that could lead to increased flocculation.

[0034] Furthermore, the technical concepts disclosed in this invention possess universal scalability and adaptability. They are not only applicable to the fluid mixing and fine-tuning machine scenario in the embodiments, but can also be applied in similar technical fields or related industrial processes through analogy, transplantation, or improvement. For example, in situations such as blockage warning in mineral slurry pipeline transportation, sludge transport pipeline anti-blockage in sewage treatment, and high-viscosity fluid filtration systems in the food industry, wherever there is an increase in flow resistance due to the accumulation of solid particles or fiber flocs at the constriction of the flow channel, a control strategy of concentration-pressure difference dual-signal fusion fluctuation detection and adaptive dilution intervention can be adopted. In these extended applications, the physical dimensions of the concentration signal (such as turbidity, density, or solid percentage) need to be adjusted accordingly, the range and sampling rate of the pressure difference signal need to be recalibrated according to the fluid viscosity and flow rate, and the baseline value of the detection threshold JC is no longer fixed at 1, but is obtained through experimental calibration to achieve the corresponding system's blockage critical ratio.

[0035] Any technical solution formed by making logically equivalent substitutions, reasonable adjustments to the order of steps, or recombination of module functions based on the principles, ideas, or framework disclosed in this specification should be considered to fall within the spirit and scope of this invention. It should be further noted that the specific descriptions and drawings in the patent documents are only for assisting in understanding this invention, and their details should not be interpreted as limitations on the claims. The true scope of protection of this invention should be determined by the claims recorded in the authorized text, and includes all equivalent technical solutions that comply with the provisions of patent law under these claims. Any implementation method that has the same or similar functions and achieves similar effects through reasonable transformation of technical means under the guidance of the concept of this invention falls within the scope of protection sought by this invention.

[0036] Therefore, the descriptions in this specification are merely illustrative. Any adjustments to implementation methods, equivalent substitutions of technical features, or further applications based on the concept of this invention, as long as they do not depart from the overall technical idea of ​​this invention, should be included within the scope of protection of this invention. We encourage those skilled in the art to innovate and optimize based on their understanding of the core of this invention and in conjunction with specific practices, so as to jointly promote the progress and development of related technologies.

Claims

1. An intelligent control system for a fluid mixing fine cutter for pulp preparation, characterized in that: Includes the following specific modules: Data acquisition module: Real-time acquisition of concentration and differential pressure signals, and preprocessing of these signals; Blockage analysis module: Calculates the fusion fluctuation coefficient based on the concentration signal and the differential pressure signal, triggers the discreteness detection of the concentration signal and the continuity detection of the differential pressure signal based on the fusion fluctuation coefficient, obtains the detection value, and sends the detection value to the water injection module; Water injection module: Analyzes whether water injection is needed based on the detection value. If water injection is needed, it is injected and the analysis continues until it is determined that water injection is not needed. If water injection is not needed, it returns to the data acquisition module or terminates directly.

2. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 1, characterized in that: The method for obtaining the fusion fluctuation coefficient is as follows: Set concentration fluctuation threshold and differential pressure fluctuation threshold, and set a fusion fluctuation coefficient, initially set to zero. Calculate the variance of the concentration signal in the time series to obtain the concentration fluctuation value, and calculate the variance of the differential pressure signal in the time series to obtain the differential pressure fluctuation value. Compare the concentration fluctuation value with the concentration fluctuation threshold and the differential pressure fluctuation value with the differential pressure fluctuation threshold. If both the concentration fluctuation value and the differential pressure fluctuation value are greater than the concentration fluctuation threshold and the differential pressure fluctuation threshold, then set the fusion fluctuation coefficient to one; otherwise, keep the initial value.

3. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 2, characterized in that: The specific method for obtaining the concentration fluctuation value is as follows: Let M be the number of concentration amplitudes in the concentration signal. Calculate the mean of each concentration amplitude based on M to obtain the average concentration amplitude. Then, calculate the squared difference between each concentration amplitude and the average concentration amplitude and sum them up. Divide the sum by M to obtain the concentration fluctuation value.

4. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 3, characterized in that: The specific method for obtaining the concentration fluctuation value is as follows: ； in, Indicates the concentration fluctuation value. This indicates the number of concentration amplitude values ​​in the concentration signal. Indicates the first Each concentration amplitude, This represents the average concentration amplitude.

5. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 4, characterized in that: The specific method for obtaining the differential pressure fluctuation value is as follows: Let N be the number of differential pressure amplitude values ​​in the differential pressure signal. Calculate the average value of each differential pressure amplitude based on N. Then, calculate the squared difference between each differential pressure amplitude value and the average value of the differential pressure amplitude, and sum them up. Divide the sum by N to obtain the differential pressure fluctuation value.

6. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 5, characterized in that: The specific method for obtaining the differential pressure fluctuation value is as follows: ； in, This indicates the pressure difference fluctuation value. This indicates the number of differential pressure amplitude values ​​in the differential pressure signal. Indicates the first Each pressure differential amplitude, This represents the average value of the pressure difference amplitude.

7. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 6, characterized in that: The specific method for obtaining the detection value is as follows: When the fusion fluctuation coefficient is one, the sampling points of the concentration signal are calculated to obtain discrete values, and the sampling points of the differential pressure signal are calculated to obtain continuous values. The ratio of the continuous value to the discrete value is then calculated to obtain the detection value.

8. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 7, characterized in that: The specific method for obtaining the discrete values ​​is as follows: The number of sampling points in the concentration signal is counted to obtain the concentration sampling quantity. The ratio of the concentration sampling quantity to the number of concentration amplitudes in the concentration signal is calculated to obtain the discrete value.

9. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 8, characterized in that: The specific method for obtaining the continuous values ​​is as follows: The number of sampling points of the differential pressure signal is counted to obtain the number of differential pressure samples. The ratio of the number of differential pressure samples to the number of differential pressure amplitude values ​​in the differential pressure signal is calculated to obtain the continuous value.

10. The intelligent control system for the fluid mixing fine cutter for pulp preparation according to claim 9, characterized in that: The specific method for analyzing whether water needs to be injected based on the detection value is as follows: Set a detection threshold and assign it a value of 1. Compare the detected value with the detection threshold. If the detected value is greater than the detection threshold, it means that water needs to be injected; otherwise, no water needs to be injected.

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