An intelligent operation control system for automatic batching machine

Abstract

The present invention discloses an intelligent operation and control system for an automatic batching machine, which relates to the field of intelligent operation technology. 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, thereby 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, so that the system can evaluate and quantify the blockage risk in real time, and can issue an early warning signal at the early stage of blockage, thereby reducing the frequency of maintenance intervention and downtime; in addition, the system's integrated automated continuous monitoring and alarm functions can intelligently trigger clearing operations, and dynamically adjust the flushing force according to real-time monitoring feedback, to ensure that the clearing 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 all-in-one machine. Background Art

[0002] In industrial automated production, automatic batching machines are widely used across a variety of industries to achieve efficient and precise material proportioning and delivery. Traditional automatic batching machines typically rely on mechanical transmission and control circuits to accurately measure and deliver materials. These devices utilize auxiliary devices such as sensors, control valves, and agitators to deliver various raw materials to the production line in the desired proportions.

[0003] Currently, automatic batching machines have clogging problems during the batching process, especially in the pipelines. Due to the poor fluidity of the materials or the easy accumulation between particles, the clogging phenomenon is particularly common. Once a blockage occurs, it will directly lead to the inability of materials to pass through the pipeline smoothly, and may even affect the continuity of the production line, resulting in equipment downtime, 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 absence of a real-time monitoring system for the equipment, 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 already formed and affects production, which seriously delays the batching progress. However, the method of triggering the alarm only by the preset pressure upper limit in existing equipment is not completely reliable and is prone to false alarms or missed alarms.

[0004] Therefore, existing automatic batching machines still need to be improved in terms of intelligence, fault detection, and automated adjustment. To ensure the efficiency and continuity of the automatic batching process, there is an urgent need for an intelligent operation and control system that can immediately identify the initial stage of material blockage, determine the degree of blockage, and automatically initiate countermeasures. This allows the automatic batching machine to automatically clear blockages without human intervention and dynamically adjust the batching speed and flow rate, effectively solving the problem of downtime caused by blockage in existing technologies. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent operation and control system for an automatic batching 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 and 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 mobility data, compare the real-time mobility data with the steady-state model, calculate the real-time deviation value, 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 based on the congestion coefficient, and trigger an early warning signal if it is determined that there is a potential congestion situation;

[0011] The adjustment module is used to automatically reduce the feeding rate when a warning signal is received; at the same time, the blockage coefficient is obtained in real time, and if the blockage warning signal is lifted, 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, a steady-state model is established based on historical movement data, and real-time movement data is compared with the steady-state model to obtain a real-time deviation value through calculation. The specific method for generating the congestion coefficient based on the real-time deviation data is as follows:

[0014] The historical pressure data and historical flow rate data of the pipeline under steady state are respectively 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] Where, 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 current real-time pressure data and real-time flow rate data 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] Where 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 is performed. If it is determined that there is a potential congestion situation, the specific method 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, the regulating module automatically reduces the feeding rate when a warning signal is obtained; at the same time, the blockage coefficient is obtained in real time, and if the blockage warning signal is released, the normal feeding rate is automatically restored in the following specific manner:

[0028] When a trigger warning signal is obtained, the current blockage coefficient CI(t) is obtained, and the feeding rate is adjusted according to the current blockage coefficient CI(t). The feeding rate is determined by the feeding rate adjustment function Q(t), and the feeding rate adjustment function Q(t) is expressed by the following formula:

[0029]

[0030] Where 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 blockage coefficient CI(t) is obtained in real time, and the feed 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 over the past T time period 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 the following specific manner:

[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 force; 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, real-time pressure data P is collected each time. real (t), adjust the flushing force F(t) to ensure that the pressure in the pipeline P 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 use the judgment module to judge 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.

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

[0046] Through real-time data collection, precise analysis, and automated judgment, this invention can quickly and accurately identify and address blockages in the batching machine pipeline, effectively improving the stability and safety of the equipment. The system not only dynamically monitors 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 assess and quantify blockage risks in real time. Early warning signals can be issued at the earliest stages of blockage, thereby reducing the frequency of maintenance interventions and downtime.

[0047] Furthermore, the system's integrated automated continuous monitoring and alarm functions intelligently trigger blockage clearing operations and dynamically adjust flushing intensity based on real-time monitoring feedback, ensuring efficient clearing and pipeline safety. The alarm module design further enables continuous tracking of blockage conditions, ensuring stable system operation after the blockage is cleared. Overall, this control system enables the batching machine to self-monitor, self-regulate, and self-clear blockages without human intervention, significantly improving batching efficiency and equipment reliability. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0049] Figure 1 This is a structural framework diagram of an intelligent operation and control system for an automatic batching machine according to the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0051] Example 1

[0052] See also Figure 1 , the present invention provides an intelligent operation and 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 represents the pressure exerted by the material on the pipe wall when the material moves in the pipe, and the real-time flow rate data represents the speed of the material when the material 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. By installing pressure sensors and flow rate sensors, the material movement status is monitored in real time.

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

[0057] The steady-state model is established based on historical movement data, and the real-time movement data is compared with the steady-state model. 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:

[0058] The historical pressure data and historical flow rate data of the pipeline under steady state are respectively 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 current real-time pressure data and real-time flow rate data 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] Where w p and w v is the weight coefficient, reflecting the degree of its impact on congestion, P threshold and V threshold It is expressed as the threshold of real-time pressure data and real-time flow rate data, used to normalize the deviation. It is usually taken as the maximum allowable value of the deviation under normal movement. 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 real-time data with this steady-state model to calculate deviation values and generate a blockage coefficient. Specifically, through regression analysis of historical pressure and flow rate data in the pipeline, a pressure and flow rate relationship model under normal flow conditions is established. By quantifying deviation data, the blockage trend of the pipeline is accurately assessed, avoiding 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 based on 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 based on the congestion coefficient and triggering a warning signal and triggering the anti-congestion mechanism in advance is as follows:

[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 it is determined that there may be potential congestion, an early warning signal is 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 aggravated blockage. At the same time, the blockage coefficient is obtained in real time. If the blockage warning signal is lifted, the normal feeding rate is automatically restored to ensure an efficient and stable feeding process.

[0074] The specific method of automatically reducing the feeding rate when the warning signal is obtained and obtaining the blockage coefficient in real time and judging it through the judgment module is as follows:

[0075] When a trigger warning signal is obtained, the current blockage coefficient CI(t) is obtained, and the feeding rate is adjusted according to the current blockage coefficient CI(t). The feeding rate is determined by the feeding rate adjustment function Q(t), and the feeding rate adjustment function Q(t) is expressed by the following formula:

[0076]

[0077] Where 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 congestion coefficient, which is usually the maximum congestion risk that the system can withstand;

[0078] Then, the specific value of the blockage coefficient CI(t) is obtained in real time, and the feed 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 feeding rate; conversely, when CI(t) decreases, the feeding 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, material transportation is stopped and the reverse flushing device is automatically activated. 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 location in the pipeline. It can apply reverse airflow to the blocked area, flushing 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, thus achieving 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 over the past T time period 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 represents the frequency of updating data. For example, if the congestion coefficient value 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 congestion persists.

[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 force; 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, real-time pressure data P is collected each time. real (t), adjust the flushing force F(t) to ensure that the pressure in the pipeline P 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 pipeline's blockage status through a rolling window mechanism, effectively determining real-time blockage conditions. At each time step, the module calculates the cumulative blockage coefficient within the past time window and uses the cumulative sum to determine whether the blockage persists. If a blockage persists, the module automatically stops material delivery and activates a reverse flushing device, dynamically adjusting the flushing force based on real-time pressure data to ensure that the clearing process is both effective and harmless to the pipeline structure. This automated, dynamically adjusted clearing method helps the system handle blockages independently, reducing the risk of equipment damage and shortening the clearing process. The design of the continuous monitoring module enables the system to take clearing measures before a blockage becomes a serious problem, significantly improving the efficiency and reliability of the automatic batching system.

[0101] The alarm module is used to stop the reverse flushing device after the 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 it is, 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 their 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 and are not intended to limit the present invention. 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 and 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 mobility data, compare the real-time mobility data with the steady-state model, calculate the real-time deviation value, 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 based on the congestion coefficient, and trigger an early warning signal if it is determined that there is a potential congestion situation; The adjustment module is used to automatically reduce the feeding rate when a warning signal is received; at the same time, the blockage coefficient is obtained in real time, and if the blockage warning signal is lifted, the normal feeding rate is automatically restored; 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 activated. The reverse flushing device adjusts the flushing force according to the real-time pressure data in the pipeline; In the data analysis module, a steady-state model is established based on historical movement data, and real-time movement data is compared with the steady-state model to obtain a real-time deviation value through calculation. The specific method for generating a congestion coefficient based on the real-time deviation data is as follows: The historical pressure data and historical flow rate data under the steady state of the pipeline are recorded as and , a steady-state model of pressure and flow rate is constructed through regression analysis. The steady-state model is expressed by the following formula: Where, is the steady-state pressure value in the steady-state model, is the function fitted by the historical movement data; The current real-time pressure data and real-time flow rate data are recorded as and , and calculate the deviation value by the following formula and : In the formula, the deviation value and Indicates the deviation 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 deviation value and The specific value of the blockage coefficient is determined by the following formula : Where, and is the weight coefficient, and Expressed as thresholds for real-time pressure data and real-time flow rate data; In the judgment module, the judgment module judges 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 early warning signal is: The clogging coefficient With the default value Make comparisons; when When it is determined that there may be potential congestion, an early warning signal is triggered. In other cases, no action is taken.

2. The intelligent operation and control system for an automatic batching machine according to claim 1, characterized in that: In the regulation module, when an early warning signal is obtained, the feeding rate is automatically reduced; 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 in the following specific manner: When the trigger warning signal is obtained, the current congestion coefficient is obtained , according to the current congestion coefficient The feed rate is adjusted, wherein the feed rate is adjusted by the feed rate adjustment function To determine the feed rate adjustment function It is reflected by the following formula: Where, is the maximum feeding rate under normal conditions, is the adjustment coefficient, and ; is the upper limit of the blocking coefficient; Then get the congestion coefficient in real time The specific value of the feed rate is adjusted in real time using the following formula: .

3. The intelligent operation and control system for an automatic batching machine according to claim 1, characterized in that: In the continuous monitoring module, the specific method of determining whether the congestion situation persists is: Get at each time step Calculate the past The total cumulative congestion coefficient over time , where the time step is the preset value, the total cumulative blocking coefficient It is reflected by the following formula: Where, Indicates time Moment, past The total cumulative congestion coefficient over time; arrive The time range of the current rolling window; Then, the continuous congestion determination within the rolling window is performed: whenever the sum of the product congestion coefficients is updated Then, compare it with the cumulative threshold Compare: like , it is determined that the congestion persists, and no action is taken in other cases.

4. The intelligent operation and control system for an automatic batching machine according to claim 3, 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: Through the flushing force As a function of the current pressure deviation, the flushing force is expressed by the following formula: Where, is the current scour intensity; is the maximum permissible scouring force; It is the adjustment coefficient, which is used to control the sensitivity of the flushing force to the pressure change. ; It is the minimum pressure value to ensure pipeline safety; It is the highest pressure value to ensure pipeline safety; During the clearing process, real-time pressure data is collected each time Afterwards, adjust the flushing force To ensure the pressure in the pipeline Do not exceed the safety range: 。 5. The intelligent operation and 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 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.

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