Intelligent operation control system for automatic batching all-in-one machine

Abstract

The invention discloses an intelligent operation control system for an automatic batching all-in-one machine, relates to the technical field of intelligent operation, and can quickly and accurately identify and deal with the blockage condition in a pipeline of the batching all-in-one machine through real-time data acquisition, accurate analysis and automatic judgment, thereby effectively improving the stability and safety of equipment. The system not only realizes dynamic monitoring of material flow by collecting key data such as pressure and flow speed, but also establishes a steady-state model based on historical data, so that the system can evaluate and quantify the blockage risk in real time, and can send out an early warning signal at the initial stage of blockage, thereby reducing the maintenance intervention frequency and downtime; in addition, the automatic continuous monitoring and alarming functions integrated in the system can intelligently trigger the unblocking operation, the scouring force is dynamically adjusted according to real-time monitoring feedback, and it is ensured that the unblocking process is efficient and the pipeline is safe.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation technology, and in particular to an intelligent operation control system for an automatic batching integrated machine. Background Art

[0002] In the process of industrial automation production, automatic batching machines are widely used in various industries to achieve efficient and accurate material proportioning and transmission. Traditional automatic batching machines usually rely on mechanical transmission and control circuits to complete the precise measurement and transmission of materials. These devices can deliver various raw materials to the production line according to the set ratio through auxiliary devices such as sensors, control valves, and agitators.

[0003] At present, there are blockage problems in the batching process of automatic batching machines, especially in the pipeline. Due to the poor fluidity of the material or the easy accumulation between particles, the blockage phenomenon is particularly common. Once the blockage occurs, it will directly lead to the inability of the material to pass through the pipeline smoothly, and even affect the continuity of the production line, causing equipment shutdown, reduced output and increased production costs. Pipeline blockage is often not completely formed in a short period of time, but is aggravated as the material gradually accumulates in the pipeline. In the case that the equipment is not equipped with a real-time monitoring system, it is difficult for operators to detect signs of blockage in time. Usually, they can only start to deal with it when the blockage has formed and affects production, which seriously delays the batching progress. However, the method of triggering the alarm only by the preset upper pressure limit in the existing equipment is not completely reliable, and it is easy to cause false alarms or missed alarms.

[0004] Therefore, the existing automatic batching machines need to be improved in terms of intelligence, fault detection, and automatic adjustment. In order to ensure the efficiency and continuity of the automatic batching process, there is an urgent need for an intelligent operation control system that can instantly identify and judge the degree of blockage at the early stage of material blockage and automatically initiate countermeasures, so that the automatic batching machine can automatically clear the blockage without manual intervention and dynamically adjust the batching speed and flow rate, thereby effectively solving the shutdown problem caused by blockage in the existing technology. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent operation control system for an automatic batching integrated machine, which solves the problems in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent operation control system for an automatic batching machine, comprising:

[0007] The data acquisition module is used to collect the real-time movement data of materials in the integrated machine pipeline and send the real-time movement data to the subsequent analysis module;

[0008] The real-time movement data includes real-time pressure data and real-time flow rate data;

[0009] The data analysis module is used to establish a steady-state model based on historical movement data, compare the real-time movement data with the steady-state model, obtain the real-time deviation value through calculation, and generate the congestion coefficient based on the real-time deviation data;

[0010] A judgment module is used to judge whether there is a potential congestion situation according to the congestion coefficient, and if it is determined that there is a potential congestion situation, a warning signal is triggered;

[0011] The adjustment module is used to automatically reduce the feeding rate when the warning signal is obtained; at the same time, the blocking coefficient is obtained in real time, and if the blocking warning signal is removed, the normal feeding rate is automatically restored;

[0012] The continuous monitoring module is used to determine whether the blockage persists. If the blockage persists, material transportation is stopped and the reverse flushing device is automatically started. The reverse flushing device adjusts the flushing force according to the real-time pressure data in the pipeline.

[0013] As a further solution of the present invention: in the data analysis module, the steady-state model is established according to the historical movement data, the real-time movement data is compared with the steady-state model, and the real-time deviation value is obtained by calculation. The specific method of generating the congestion coefficient according to the real-time deviation data is:

[0014] The historical pressure data and historical flow rate data of the pipeline under steady state are denoted as P hist (t) and V hist (t), a steady-state model of pressure and flow rate is constructed through regression analysis. The steady-state model is expressed by the following formula:

[0015]

[0016] In the formula, is the steady-state pressure value in the steady-state model, and f(V(t)) is the function fitted by the historical movement data;

[0017] The real-time pressure data and real-time flow rate data at the current moment are recorded as P real (t) and V real (t), and calculate the deviation values ​​ΔP(t) and ΔV(t) by the following formula:

[0018]

[0019] ΔV(t)=|V real (t)-V hist (t)|

[0020] Wherein, the deviation values ​​ΔP(t) and ΔV(t) represent the deviations between the current pressure data and the current flow rate data and the historical pressure data and the historical flow rate data;

[0021] Then, according to the specific values ​​of the deviation values ​​ΔP(t) and ΔV(t), the blocking coefficient CI(t) is determined by the following formula:

[0022]

[0023] In the formula, w p and w v is the weight coefficient, P threshold and V threshold Expressed as thresholds for real-time pressure data and real-time flow rate data.

[0024] As a further solution of the present invention: in the judgment module, the judgment of whether there is a potential congestion situation based on the congestion coefficient, if it is determined that there is a potential congestion situation, the specific way of triggering the early warning signal is:

[0025] The congestion coefficient CI(t) is compared with the preset value CI alert Make comparisons;

[0026] When CI(t)>CI alert When it is determined that there may be potential congestion, an early warning signal is triggered. In other cases, no action is taken.

[0027] As a further solution of the present invention: in the regulating module, when the warning signal is obtained, the feeding rate is automatically reduced; at the same time, the blocking coefficient is obtained in real time, and if the blocking warning signal is released, the specific manner of automatically restoring the normal feeding rate is as follows:

[0028] When the trigger warning signal is obtained, the current blocking coefficient CI(t) is obtained, and the feeding rate is adjusted according to the current blocking coefficient CI(t), wherein the feeding rate is determined by the feeding rate adjustment function Q(t), and the feeding rate adjustment function Q(t) is embodied by the following formula:

[0029]

[0030] In the formula, Q max is the maximum feed rate under normal conditions, k is the adjustment coefficient, and 0 <k≤1;CI max is the upper limit of the blocking coefficient;

[0031] Then, the specific value of the blocking coefficient CI(t) is obtained in real time, and the feeding rate is adjusted in real time using the following formula:

[0032] As a further solution of the present invention: in the continuous monitoring module, the specific method of determining whether the blockage situation persists is:

[0033] The cumulative congestion coefficient sum C(T,ts) in the past T time is calculated at each time step Δt, where the time step Δt is a preset value. The cumulative congestion coefficient sum C(T,ts) is expressed by the following formula:

[0034]

[0035] Where C(T,ts) represents the cumulative congestion coefficient sum in the past T time at time ts; ts-T to ts is the time range of the current rolling window;

[0036] Then, the continuous congestion determination within the rolling window is performed: each time the sum of the product congestion coefficients C(T, t) is updated, it is compared with the cumulative threshold C threshold Compare:

[0037] If C(T,t)≥C threshold , it is determined that the congestion persists, and no action is taken in other cases.

[0038] As a further solution of the present invention: in the continuous monitoring module, the reverse flushing device adjusts the flushing force according to the pressure in the pipeline in a specific manner as follows:

[0039] The flushing force F(t) is used as a function of the current pressure deviation; the flushing force is expressed by the following formula:

[0040]

[0041] Where F(t) is the current scouring intensity; F max is the maximum permissible flushing force; k is the adjustment coefficient, which is used to control the sensitivity of the flushing force to pressure changes, with a value range of 0 <k≤1;P min It is the minimum pressure value to ensure pipeline safety; P max It is the highest pressure value to ensure pipeline safety;

[0042] During the clearing process, each time real-time pressure data P is collected real (t), adjust the flushing force F(t) to ensure that the pressure P in the pipeline real (t) Not exceeding the safety range:

[0043]

[0044] As a further solution of the present invention: it also includes: an alarm module, which is used to stop flushing after the reverse flushing device has worked for a preset time, start material transportation, continue to obtain the blockage coefficient, and determine whether there is a potential blockage through a judgment module. If not, it means that the blockage is cleared. If so, it means that the blockage is not cleared thoroughly, and an alarm message is issued to notify the staff to handle it.

[0045] The present invention provides an intelligent operation control system for an automatic batching machine. Compared with the prior art, it has the following beneficial effects:

[0046] The present invention can quickly and accurately identify and respond to blockages in the batching machine pipeline through real-time data collection, precise analysis and automated judgment, effectively improving the stability and safety of the equipment. The system not only realizes dynamic monitoring of material flow by collecting key data such as pressure and flow rate, but also establishes a steady-state model based on historical data, enabling the system to evaluate and quantify blockage risks in real time, and issue early warning signals at the early stage of blockage, thereby reducing the frequency of maintenance interventions and downtime.

[0047] In addition, the system's integrated automated continuous monitoring and alarm functions can intelligently trigger the clearing operation and dynamically adjust the flushing force based on real-time monitoring feedback to ensure that the clearing process is efficient and the pipeline is safe. The design of the alarm module further enables continuous tracking of blockage conditions to ensure stable operation of the system after the blockage is removed. Overall, the control system enables the batching machine to self-monitor, self-regulate and self-clean blockages without human supervision, significantly improving batching efficiency and equipment operation reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Figure 1 The present invention is a structural framework diagram of an intelligent operation control system for an automatic batching integrated machine. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Embodiment 1

[0052] See also Figure 1 , the present invention provides an intelligent operation control system for an automatic batching machine, comprising;

[0053] The data acquisition module is used to collect the real-time movement data of materials in the integrated machine pipeline and send the real-time movement data to the subsequent analysis module;

[0054] The real-time movement data includes real-time pressure data and real-time flow rate data, wherein the real-time pressure data is represented by the pressure exerted on the pipe wall by the material when it moves in the pipe, and the real-time flow rate data is represented by the speed of the material when it moves in the pipe;

[0055] Specifically, real-time movement data is collected by installing pressure sensors and flow rate sensors at appropriate locations in the pipeline. The specific installation locations are selected by professional staff and sensor protection measures are taken. Pressure sensors and flow rate sensors are installed to monitor the material movement status in real time.

[0056] The data analysis module is used to establish a steady-state model based on historical movement data, compare the real-time movement data with the steady-state model, obtain the real-time deviation value through calculation, and generate the congestion coefficient based on the real-time deviation data;

[0057] The specific method of establishing a steady-state model based on historical movement data, comparing the real-time movement data with the steady-state model, obtaining a real-time deviation value through calculation, and generating a congestion coefficient based on the real-time deviation data is as follows:

[0058] The historical pressure data and historical flow rate data of the pipeline under steady state are denoted as P hist (t) and V hist (t), a steady-state model of pressure and flow rate is constructed through regression analysis (such as polynomial regression or exponential smoothing method). The steady-state model is expressed by the following formula:

[0059] P(t)=f(V(t))

[0060] Where P(t) is the steady-state pressure value in the steady-state model, and f(V(t)) is a function fitted by historical movement data, which is used to characterize the relationship between pressure and flow velocity under normal flow conditions;

[0061] The real-time pressure data and real-time flow rate data at the current moment are recorded as P real (t) and V real (t), and calculate the deviation values ​​ΔP(t) and ΔV(t) by the following formula:

[0062]

[0063] ΔV(t)=|V real (t)-V hist (t)|

[0064] Wherein, the deviation values ​​ΔP(t) and ΔV(t) represent the deviations between the current pressure data and the current flow rate data and the historical pressure data and the historical flow rate data;

[0065] Then, according to the specific values ​​of the deviation values ​​ΔP(t) and ΔV(t), the blocking coefficient CI(t) is determined by the following formula:

[0066]

[0067] In the formula, w p and w v is the weight coefficient, reflecting its influence on congestion, P threshold and V threshold It is expressed as the threshold of real-time pressure data and real-time flow rate data, which is used to normalize the deviation. It is usually taken as the maximum allowable value of the deviation under normal movement state. The specific parameter value is set by professional staff;

[0068] The data analysis module establishes a steady-state model based on historical movement data, and compares and analyzes the real-time collected data with this steady-state model to calculate the deviation value and generate the blockage coefficient. Specifically, through the regression analysis of the historical pressure and flow rate data in the pipeline, a pressure and flow rate relationship model under normal flow conditions is established, and the blockage trend of the pipeline is accurately evaluated by quantifying the deviation data, avoiding the errors caused by relying solely on experience or manual judgment.

[0069] A judgment module is used to judge whether there is a potential congestion situation according to the congestion coefficient. If it is determined that there is a potential congestion situation, an early warning signal is triggered to trigger the anti-congestion mechanism in advance;

[0070] The specific method of judging whether there is a potential congestion situation according to the congestion coefficient and triggering a warning signal if there is a potential congestion situation is:

[0071] The congestion coefficient CI(t) is compared with the preset value CI alert For comparison, the default value CI alert Set up by professional staff;

[0072] When CI(t)>CI alert When a potential blockage occurs, it is determined that a warning signal may be triggered. In other cases, no action is taken.

[0073] The adjustment module is used to automatically reduce the feeding rate and feed volume when an early warning signal is received to prevent excessive accumulation of materials and aggravate blockage. At the same time, the blockage coefficient is obtained in real time. If the blockage early warning signal is removed, the normal feeding rate is automatically restored to ensure efficient and stable feeding process.

[0074] When the warning signal is obtained, the feeding rate is automatically reduced; at the same time, the blocking coefficient is obtained in real time, and the judgment module is used to judge that if the blocking warning signal is removed, the normal feeding rate is automatically restored. The specific method is:

[0075] When the trigger warning signal is obtained, the current blocking coefficient CI(t) is obtained, and the feeding rate is adjusted according to the current blocking coefficient CI(t), wherein the feeding rate is determined by the feeding rate adjustment function Q(t), and the feeding rate adjustment function Q(t) is embodied by the following formula:

[0076]

[0077] In the formula, Q max is the maximum feed rate under normal conditions, k is the adjustment coefficient, which is used to control the sensitivity of the feed rate, and 0 <k≤1;CI max It is the upper limit of the blocking coefficient, which is usually the maximum blocking risk that the system can withstand;

[0078] Then, the specific value of the blocking coefficient CI(t) is obtained in real time, and the feeding rate is adjusted in real time using the following formula:

[0079]

[0080] Explanation: When CI(t) increases (blockage risk increases), Q(t) will decrease, thereby reducing the feed rate; conversely, when CI(t) decreases, the feed rate Q(t) will gradually return to Q max ;

[0081] The continuous monitoring module is used to determine whether the blockage persists. If the blockage persists, the material conveying is stopped and the reverse flushing device is automatically started. The reverse flushing device adjusts the flushing force according to the real-time pressure data in the pipeline to ensure that the clearing process is effective and does not damage the pipeline.

[0082] It should be noted that the reverse flushing device is equipped with a two-way air valve and a high-pressure gas storage unit at a designated position in the pipeline, which can apply reverse airflow to the blocked area and flush the material through high pressure, thereby effectively clearing the blockage; at the same time, the flushing force will be adjusted according to the real-time pressure data in the pipeline to ensure that the clearing process is effective and does not damage the pipeline;

[0083] The specific method of determining whether the congestion situation persists is:

[0084] Through the rolling window monitoring mechanism, the past T time window is continuously detected in each time step, so as to achieve real-time and continuous congestion judgment;

[0085] The cumulative congestion coefficient sum C(T,ts) in the past T time is calculated at each time step Δt. The cumulative congestion coefficient sum C(T,ts) is expressed by the following formula:

[0086]

[0087] Where C(T,ts) represents the cumulative congestion coefficient sum in the past T time at time ts; ts-T to ts is the time range of the current rolling window;

[0088] It should be noted that the time step is expressed as the frequency of updating the data. For example, if the value of the congestion coefficient is collected once per second, then Δt=1, and the window time T is usually longer than the time step Δt; for example, the window time can be 10 seconds, which means that all changes in the congestion coefficient are accumulated within 10 seconds to determine whether the congestion is continuous;

[0089] Then, the continuous congestion determination within the rolling window is performed: each time the sum of the product congestion coefficients C(T, t) is updated, it is compared with the cumulative threshold C threshold Compare:

[0090] If C(T,t)≥C threshold , it is determined that the congestion situation persists, and no action is taken in other situations;

[0091] The specific method of adjusting the flushing force of the reverse flushing device according to the pressure in the pipeline is:

[0092] The flushing force F(t) is used as a function of the current pressure deviation; the flushing force can be expressed by the following formula:

[0093]

[0094] Where F(t) is the current scouring intensity; F max is the maximum permissible flushing force; k is the adjustment coefficient, which is used to control the sensitivity of the flushing force to pressure changes, with a value range of 0 <k≤1;P min It is the minimum pressure value to ensure pipeline safety, usually the effective lower limit for clearing blockage; P max It is the maximum pressure value to ensure pipeline safety. Exceeding this value may cause damage to the pipeline.

[0095] During the clearing process, each time real-time pressure data P is collected real (t), adjust the flushing force F(t) to ensure that the pressure P in the pipeline real (t) Not exceeding the safety range:

[0096]

[0097] Explanation: Low pressure increases scour (P real (t)≤P min ): When the pressure is lower than P min When the flushing force is insufficient, the maximum flushing force F is used. max To effectively clear the blockage;

[0098] Medium pressure automatic regulation (Pmin <P real (t) <P max ) Within this range, the flushing force F(t) is dynamically adjusted according to the real-time deviation of the pressure to ensure that the blockage can be effectively cleared without excessive pressure;

[0099] High pressure stop flushing (P real (t)≥P max ) If the pressure reaches P max or higher, stop flushing immediately to avoid damage to the pipeline;

[0100] The continuous monitoring module continuously monitors the blockage status of the pipeline through a rolling window mechanism, realizing effective judgment of the real-time blockage situation; the module calculates the cumulative blockage coefficient in the past time window at each time step, and judges whether the blockage situation persists by the cumulative sum; when the blockage situation persists, the module will automatically stop material transportation and start the reverse flushing device, and dynamically adjust the flushing force according to the real-time pressure data to ensure that the clearing process is both effective and will not damage the pipeline structure; this automated and dynamically adjusted clearing method helps the system to autonomously handle blockage situations without relying on manual labor, reduce the risk of equipment damage, and shorten the time required for the clearing process; the design of the continuous monitoring module enables the system to take clearing measures before the blockage has a serious impact, greatly improving the operating efficiency and reliability of the automatic batching system;

[0101] The alarm module is used to stop flushing after the reverse flushing device has worked for a preset time, start material transportation, continue to obtain the blockage coefficient, and use the judgment module to determine whether there is potential blockage. If not, it means that the blockage is cleared. If so, it means that the blockage is not cleared thoroughly, and an alarm message is issued to notify the staff to handle it;

[0102] The preset time for the work is determined by professional staff based on experience;

[0103] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0104] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent operation control system for an automatic batching machine, characterized in that: include: The data acquisition module is used to collect the real-time movement data of materials in the integrated machine pipeline and send the real-time movement data to the subsequent analysis module; The real-time movement data includes real-time pressure data and real-time flow rate data; The data analysis module is used to establish a steady-state model based on historical movement data, compare the real-time movement data with the steady-state model, obtain the real-time deviation value through calculation, and generate the congestion coefficient based on the real-time deviation data; A judgment module is used to judge whether there is a potential congestion situation according to the congestion coefficient, and if it is determined that there is a potential congestion situation, a warning signal is triggered; The adjustment module is used to automatically reduce the feeding rate when the warning signal is obtained; at the same time, the blocking coefficient is obtained in real time, and if the blocking warning signal is removed, the normal feeding rate is automatically restored; The continuous monitoring module is used to determine whether the blockage persists. If the blockage persists, material transportation is stopped and the reverse flushing device is automatically started. The reverse flushing device adjusts the flushing force according to the real-time pressure data in the pipeline.

2. The intelligent operation control system for an automatic batching machine according to claim 1, characterized in that: In the data analysis module, the steady-state model is established based on the historical movement data, the real-time movement data is compared with the steady-state model, and the real-time deviation value is obtained by calculation. The specific method of generating the congestion coefficient based on the real-time deviation data is as follows: The historical pressure data and historical flow rate data of the pipeline under steady state are denoted as P hist (t) and V hist (t), a steady-state model of pressure and flow rate is constructed through regression analysis. The steady-state model is expressed by the following formula: In the formula, is the steady-state pressure value in the steady-state model, and f(V(t)) is the function fitted by the historical movement data; The real-time pressure data and real-time flow rate data at the current moment are recorded as P real (t) and V real (t), and calculate the deviation values ​​ΔP(t) and ΔV(t) by the following formula: ΔV(t)=|V real (t)-V hist (t)| Wherein, the deviation values ​​ΔP(t) and ΔV(t) represent the deviations between the current pressure data and the current flow rate data and the historical pressure data and the historical flow rate data; Then, according to the specific values ​​of the deviation values ​​ΔP(t) and ΔV(t), the blocking coefficient CI(t) is determined by the following formula: In the formula, w p and w v is the weight coefficient, P threshold and V threshold Expressed as thresholds for real-time pressure data and real-time flow rate data.

3. The intelligent operation control system for an automatic batching machine according to claim 2, characterized in that: In the judgment module, the judgment of whether there is a potential congestion situation based on the congestion coefficient, if it is determined that there is a potential congestion situation, the specific method of triggering the warning signal is: The congestion coefficient CI(t) is compared with the preset value CI alert Make comparisons; When CI(t)>CI alert When it is determined that there may be potential congestion, an early warning signal is triggered. In other cases, no action is taken.

4. The intelligent operation control system for an automatic batching machine according to claim 1, characterized in that: In the adjustment module, when the warning signal is obtained, the feeding rate is automatically reduced; at the same time, the blocking coefficient is obtained in real time. If the blocking warning signal is removed, the specific method of automatically restoring the normal feeding rate is as follows: When the trigger warning signal is obtained, the current blocking coefficient CI(t) is obtained, and the feeding rate is adjusted according to the current blocking coefficient CI(t), wherein the feeding rate is determined by the feeding rate adjustment function Q(t), and the feeding rate adjustment function Q(t) is embodied by the following formula: In the formula, Q max is the maximum feed rate under normal conditions, k is the adjustment coefficient, and 0 <k≤1;CI max is the upper limit of the blocking coefficient; Then, the specific value of the blocking coefficient CI(t) is obtained in real time, and the feeding rate is adjusted in real time using the following formula:

5. The intelligent operation control system for an automatic batching machine according to claim 3 is characterized in that: In the continuous monitoring module, the specific method of determining whether the congestion situation persists is: The cumulative congestion coefficient sum C(T,ts) in the past T time is calculated at each time step Δt, where the time step Δt is a preset value. The cumulative congestion coefficient sum C(T,ts) is expressed by the following formula: Where C(T,ts) represents the cumulative congestion coefficient sum in the past T time at time ts; ts-T to ts is the time range of the current rolling window; Then, the continuous congestion determination within the rolling window is performed: each time the sum of the product congestion coefficients C(T, t) is updated, it is compared with the cumulative threshold C threshold Compare: If C(T,t)≥C threshold , it is determined that the congestion persists, and no action is taken in other cases.

6. The intelligent operation control system for an automatic batching machine according to claim 5, characterized in that: In the continuous monitoring module, the reverse flushing device adjusts the flushing force according to the pressure in the pipeline in the following specific manner: The flushing force F(t) is used as a function of the current pressure deviation; the flushing force is expressed by the following formula: Where F(t) is the current scouring intensity; F max is the maximum permissible flushing force; k is the adjustment coefficient, which is used to control the sensitivity of the flushing force to pressure changes, with a value range of 0 <k≤1;P min It is the minimum pressure value to ensure pipeline safety; P max It is the highest pressure value to ensure pipeline safety; During the clearing process, each time real-time pressure data P is collected real (t), adjust the flushing force F(t) to ensure that the pressure P in the pipeline real (t) Not exceeding the safety range:

7. The intelligent operation control system for an automatic batching machine according to claim 1, characterized in that: Also includes: The alarm module is used to stop flushing after the reverse flushing device has worked for a preset time, start material transportation, continue to obtain the blockage coefficient, and determine whether there is potential blockage through the judgment module. If not, it means that the blockage is cleared. If so, it means that the blockage is not cleared thoroughly, and an alarm message is issued to notify the staff to handle it.

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