Intelligent control method and system for production and processing of water-soluble fertilizer

Abstract

The invention discloses an intelligent control method and system for production and processing of a water-soluble fertilizer, and relates to the technical field of intelligent control of production and processing, and the system can quickly respond to any deviation occurring in the production process through real-time data monitoring of a data acquisition module and accurate calculation of a data processing and analysis module. Through comprehensive adjustment of the PID control module, production parameters can be corrected in time, and it is ensured that key quality indexes, such as the concentration Cf of the water-soluble fertilizer, the liquid flow rate Qf and the pH value pHf, of production of the water-soluble fertilizer are kept within a target range. Therefore, the quality problem caused by fluctuation of production conditions can be effectively avoided, and the consistency and the stability of products are improved. And the real-time monitoring function of the quality feedback module ensures that the quality index is always close to the target value under the adjusted production condition. When the system detects that the target quality requirement is achieved, further adjustment can be automatically stopped, and resource waste and unnecessary fluctuation caused by excessive adjustment are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control in production and processing, and specifically to an intelligent control method and system for the production and processing of water-soluble fertilizers. Background Art

[0002] The intelligent control system for the production and processing of water-soluble fertilizers belongs to intelligent manufacturing. It is widely used in agricultural production, especially in the production and processing of fertilizers. In the production of water-soluble fertilizers, this field requires precise control of various parameters in the production process to ensure the consistency of product quality and the improvement of production efficiency.

[0003] In the current production process of water-soluble fertilizers, although some automated equipment has been put into use, most production controls still rely on manual experience or simple automatic control systems. These traditional methods often have difficulty in realizing real-time monitoring and adjustment of key parameters when dealing with complex and changeable production environments, resulting in large fluctuations in product quality.

[0004] The disadvantages of the existing systems mainly stem from the inability to obtain and process key data in the production process in real time. When the production environment changes, or the performance of production equipment fluctuates, the system cannot adjust control parameters in time, resulting in the deviation of fertilizer concentration, flow rate, and pH value from the target range. This deviation may lead to a decline in product quality, the emergence of non-standard fertilizer products, and ultimately affect the yield and quality of crops.

[0005] In view of the deficiencies of the existing technology, the present invention provides an intelligent control method and system for the production and processing of water-soluble fertilizers, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent control system for the production and processing of water-soluble fertilizers includes a data acquisition module, a data processing and analysis module, a PID control module, a quality feedback module, a historical data storage and learning module, and a user interface and alarm module;

[0007] The data acquisition module is responsible for collecting real-time data from various production equipment, specifically including the concentration of water-soluble fertilizers, liquid flow rate, ambient temperature, and pH value;

[0008] The data processing and analysis module preprocesses and analyzes the data collected by the data acquisition module. The preprocessing specifically includes filtering the data and calculating the quality index deviation, including the concentration deviation value ΔC f (t) of water-soluble fertilizers, the liquid flow rate deviation value ΔQ f (t), and the pH value deviation value ΔpH f (t);

[0009] The PID control module generates a total control output u(t) based on the comprehensive deviation e(t) between the filtered data and the target value, which is used to correct the production parameters;

[0010] The quality feedback module monitors the adjusted quality indicators in real time, including the real-time value of the concentration of the water-soluble fertilizer The real-time value of the liquid flow rate And the real-time value of the pH value Judge the status of the quality indicators. When the target quality requirements are met, stop the adjustment measures. When the target quality requirements are not met, continue the adjustment measures;

[0011] The historical data storage and learning module stores the data, quality indicator deviations, and adjustment measures during the production process, and continuously adjusts the control strategy through a machine learning model;

[0012] The user interface and alarm module provides a user operation interface, displays real-time data, control parameters, and quality indicator deviations, and issues an alarm when the comprehensive deviation e(t) exceeds the threshold deviation e max (t).

[0013] Preferably, the data acquisition module includes a sensor unit and a data acquisition and transmission unit;

[0014] The sensor unit collects data on the concentration of the water-soluble fertilizer, liquid flow rate, mixing ratio, ambient temperature, and pH value through various sensors installed inside the mixing tank and fluid pipeline. The sensors include a concentration sensor, a flow meter, a temperature sensor, and a pH sensor;

[0015] The concentration sensor is used to measure the concentration C of the water-soluble fertilizer f ;

[0016] The flow meter is used to measure the liquid flow rate Q f ;

[0017] The temperature sensor is used to monitor the ambient temperature T e ;

[0018] The pH sensor is used to measure the pH value pH of the water-soluble fertilizer f ;

[0019] The data acquisition and transmission unit packs and encapsulates the real-time data collected by the sensors, and transmits it through the network to the data processing and analysis module for analysis and storage.

[0020] Preferably, the data processing and analysis module includes a data preprocessing unit and a quality deviation calculation unit;

[0021] The data preprocessing unit is responsible for filtering the data; including preprocessing the data using a low-pass filter to remove high-frequency noise in the data;

[0022] The concentration C of the water-soluble fertilizer is processed through a low-pass filter f , the liquid flow rate Q is measured f and the pH value pH of the water-soluble fertilizer f are processed to obtain the concentration of the water-soluble fertilizer after filtering at time t the liquid flow rate after filtering at time t and after filtering at time t

[0023] The mass deviation calculation unit calculates the deviation between the filtered data and the target mass value, and the obtained deviation through calculation provides a decision basis for the control system; specifically, the deviation calculator calculates the concentration target value C of the water-soluble fertilizer fget , the liquid flow rate target value Q fget and the target value pH of the pH value fget to calculate the mass index deviation, and obtains the concentration deviation value ΔC f (t), the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t);

[0024] The concentration deviation value ΔC of the water-soluble fertilizer f (t), the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t) are summarized and combined into a comprehensive deviation e(t);

[0025] The comprehensive deviation e(t) is obtained by adding the weight coefficient ω of the calculation result of the concentration deviation value ΔC f (t) of the water-soluble fertilizer and the concentration target value C of the water-soluble fertilizer fget , the weight coefficient ω of the calculation result of the liquid flow rate deviation value ΔQ C (t) and the liquid flow rate target value Q f (t), fget the weight coefficient ω of the calculation result Q and the weight coefficient ω of the pH value deviation value ΔpH f (t) and the pH value target value pH fget of the pH value pH added together.

[0026] Preferably, the PID control module includes a PID calculation unit and a control execution unit;

[0027] The PID calculation unit calculates the output of the PID controller based on the comprehensive deviation e(t) obtained by the mass deviation calculation unit, and generates the total control output u(t) for adjusting the production parameters; it includes proportional control, integral control, and derivative control, and obtains the total control output u(t) through the fitting of proportional control, integral control, and derivative control;

[0028] The total control output u(t) is obtained by adding the final control output u(t), the output ui(t) of the integral control, and the output ud(t) of the derivative control;

[0029] The control execution unit is responsible for converting the total control output u(t) generated by the PID calculation unit into actual production equipment operation instructions for adjusting the production parameters of the equipment, specifically including control signal conversion and actuator drive;

[0030] The control signal conversion converts the total control output u(t) generated by the PID calculation unit into a control signal through a converter; the actuator drive controls the actual operation of the actuator, specifically including adjusting the valve opening, the pump speed, and the heater power to adjust the parameters in the production process.

[0031] Preferably, the quality feedback module includes a quality monitoring unit and an adjustment decision unit;

[0032] The quality monitoring unit monitors the adjusted quality indicators, including the real-time value of the concentration of the water-soluble fertilizer The real-time value of the liquid flow rate And the real-time value of the pH value And compares the quality indicators with the target quality indicators through a target value comparator to obtain a deviation judgment result;

[0033] The target value comparator compares the currently monitored real-time value of the concentration of the water-soluble fertilizer With the target value C of the concentration of the water-soluble fertilizer fget To obtain the real-time value of the concentration of the water-soluble fertilizer And the target value C of the concentration of the water-soluble fertilizer fget The concentration difference ΦC f (t) between them;

[0034] If|ΦC f (t)|≤θc, the quality target is achieved, and the deviation judgment result is obtained as qualified, and the adjustment measure is stopped;

[0035] If|ΦC f (t)|>θc, the quality target is not achieved, and the deviation judgment result is obtained as unqualified, and the adjustment measure is continued;

[0036] The adjustment decision-making unit determines whether to adjust the operating parameters of the production equipment or stop adjusting the operation of the production equipment according to the deviation judgment result of the quality monitoring unit.

[0037] Preferably, the historical data storage and learning module includes a data storage unit and a machine learning unit;

[0038] The historical data storage stores the data collected during the production process, including the concentration C of the water-soluble fertilizer f , the liquid flow rate Q f , the pH value pH of the water-soluble fertilizer f and the total control output u(t) to form a historical data set Dst.

[0039] Preferably, the machine learning unit adjusts the control strategy during the production process through a machine learning algorithm according to the historical data set Dst to improve the control accuracy of the system;

[0040] Train a machine learning model based on historical data, learn the relationship between the total control output u(t) and the quality index during the production process, adjust the control strategy in real time, and obtain a new total control output u new (t);

[0041] After obtaining the new total control output u new (t), the control execution unit is responsible for converting the new total control output u new (t) into an actual production equipment operation instruction to adjust the production parameters of the equipment.

[0042] Preferably, the user interface and alarm module includes a display unit and an alarm unit;

[0043] The display unit displays the real-time data, control parameters, and quality index deviations during the production process through a graphical interface, enabling operators to intuitively view the various indicators of the current production process;

[0044] Among them, the real-time data includes the real-time value of the concentration of the water-soluble fertilizer the real-time value of the liquid flow rate and the real-time value of the pH value

[0045] The control parameters include the opening degree of the regulating valve, the rotation speed of the pump, and the power of the heater;

[0046] The quality index deviations include the concentration deviation value ΔC f (t), the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t).

[0047] Preferably, the alarm unit is responsible for monitoring the comprehensive deviation e(t) during the production process and issuing an alarm when the deviation exceeds the threshold deviation e max (t);

[0048] When an alarm is issued, the alarm unit sends an audible and visual alarm signal to the operator, and at the same time, the alarm information is prominently displayed on the user interface.

[0049] An intelligent control method for the production and processing of water-soluble fertilizers includes the following steps:

[0050] Step 1: The data acquisition module is responsible for collecting real-time data from various production equipment, specifically including the concentration of water-soluble fertilizers, liquid flow rate, ambient temperature, and pH value;

[0051] Step 2: The data processing and analysis module preprocesses and analyzes the data collected by the data acquisition module. The preprocessing specifically includes filtering the data and calculating the quality index deviation, including the concentration deviation value ΔC f (t), the liquid flow rate deviation value ΔQ f (t), and the pH value deviation value ΔpH f (t);

[0052] Step 3: The PID control module generates the total control output u(t) according to the comprehensive deviation e(t) between the filtered data and the target value, and is used to correct the production parameters;

[0053] Step 4: The quality feedback module monitors the adjusted quality indicators in real time, including the real-time value of the concentration of water-soluble fertilizers the real-time value of the liquid flow rate and the real-time value of the pH value Judge the quality index status. When the target quality requirement is met, stop the adjustment measures. When the target quality requirement is not met, continue the adjustment measures;

[0054] Step 5: The historical data storage and learning module stores the data, quality index deviation, and adjustment measures during the production process, and continuously adjusts the control strategy through the machine learning model;

[0055] Step 6: The user interface and alarm module provide a user operation interface, display real-time data, control parameters, and quality index deviation, and issue an alarm when the comprehensive deviation e(t) exceeds the threshold deviation e max (t).

[0056] The present invention provides an intelligent control method and system for the production and processing of water-soluble fertilizers, which have the following beneficial effects:

[0057] (1) During system operation, through the real-time data monitoring of the data acquisition module and the precise calculation of the data processing and analysis module, the system can quickly respond to any deviation that occurs during the production process. Through the comprehensive adjustment of the PID control module, the production parameters can be corrected in a timely manner to ensure the key quality indicators of water-soluble fertilizer production, such as the concentration C of water-soluble fertilizer f , the liquid flow rate Q f , and the pH value pH f are maintained within the target range. This can effectively avoid quality problems caused by fluctuations in production conditions and improve the consistency and stability of products. The real-time monitoring function of the quality feedback module ensures that under the adjusted production conditions, the quality indicators always move closer to the target values. When the system detects that the target quality requirements have been met, it will automatically stop further adjustment to avoid resource waste and unnecessary fluctuations caused by over-regulation.

[0058] (2) Through the precise cooperation of the sensor unit and the data acquisition and transmission unit, key data during the production process of water-soluble fertilizer, such as the concentration C of water-soluble fertilizer f , the liquid flow rate Q f , the ambient temperature T e , and the pH value pH of water-soluble fertilizer f are collected in real time, and high-frequency noise is removed through filtering. This data preprocessing greatly improves the accuracy and stability of the collected data, ensuring the accuracy of subsequent control decisions. The quality deviation calculation unit calculates the deviation between the filtered data and the target value, and summarizes the concentration deviation value ΔC f (t), the liquid flow rate deviation value ΔQ f (t), and the pH value deviation value ΔpH f (t) into a comprehensive deviation e(t), enabling the system to accurately evaluate the deviation degree of each parameter during the production process.

[0059] (3) By fitting the formula, the output accuracy of the PID controller is ensured, making the adjustment of production parameters more precise and effectively improving the stability and consistency of the production process. In this way, the system can quickly respond to deviations that occur during the production process and achieve precise process control. The quality monitoring unit in the quality feedback module can monitor the adjusted quality indicators in real time, such as the concentration of water-soluble fertilizer, the liquid flow rate, and the pH value, and compare these real-time data with the target values to determine whether the quality requirements are met. This real-time monitoring mechanism can detect deviations in a timely manner and, through the dynamic decision-making function of the adjustment decision unit, quickly execute corresponding adjustment measures to ensure that the production process always remains in the optimal state.

[0060] (4) Optimize the control strategy during the production process through the machine learning unit in the historical data storage and learning module. By analyzing the historical dataset Dst, the machine learning algorithm can identify and learn the complex relationship between the total control output u(t) and the quality indicators, and adjust the control strategy in real time. This data-driven learning method significantly improves the control accuracy of the system and ensures that all indicators during the production process are always in the best state.

[0061] The new total control output u new (t) is obtained by combining the historical average term with the dynamic adjustment formula of the real-time control output, enabling the system to flexibly respond to various changes that occur during the production process. By considering the difference between the average trend of historical data and the current production status, the system can adjust parameters more accurately. Description of the Drawings

[0062] Figure 1 It is a schematic diagram of the block diagram process of an intelligent control system for the production and processing of a water-soluble fertilizer according to the present invention;

[0063] Figure 2 It is a schematic diagram of the steps of an intelligent control method for the production and processing of a water-soluble fertilizer according to the present invention. Detailed Embodiments

[0064] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] Embodiment 1

[0066] The present invention provides an intelligent control system for the production and processing of a water-soluble fertilizer. Please refer to Figure 1 , including a data acquisition module, a data processing and analysis module, a PID control module, a quality feedback module, a historical data storage and learning module, and a user interface and alarm module;

[0067] The data acquisition module is responsible for collecting real-time data from various production equipment, specifically including the concentration of water-soluble fertilizer, liquid flow rate, ambient temperature, and pH value;

[0068] The data processing and analysis module preprocesses and analyzes the data collected by the data acquisition module. The preprocessing specifically includes filtering the data and calculating the quality index deviation, including the concentration deviation value ΔC f (t), the liquid flow rate deviation value ΔQ f (t), and the pH value deviation value ΔpHf (t);

[0069] The PID control module generates a total control output u(t) according to the comprehensive deviation e(t) between the filtered data and the target value, for correcting production parameters;

[0070] The quality feedback module monitors the adjusted quality indicators in real time, including the real-time value of the concentration of the water-soluble fertilizer The real-time value of the liquid flow rate And the real-time value of the pH value Judge the status of the quality indicators. When the target quality requirements are met, stop the adjustment measures. When the target quality requirements are not met, continue the adjustment measures;

[0071] The historical data storage and learning module stores the data, quality indicator deviations and adjustment measures in the production process, and continuously adjusts the control strategy through a machine learning model;

[0072] The user interface and alarm module provides a user operation interface, displays real-time data, control parameters and quality indicator deviations, and issues an alarm when the comprehensive deviation e(t) exceeds the threshold deviation e max (t).

[0073] In this embodiment, through the real-time data monitoring of the data acquisition module and the accurate calculation of the data processing and analysis module, the system can quickly respond to any deviation occurring in the production process. Through the comprehensive adjustment of the PID control module, the production parameters can be corrected in time to ensure that the key quality indicators of the water-soluble fertilizer production, such as the concentration C f Of the water-soluble fertilizer, the liquid flow rate Q f And the pH value pH f Are maintained within the target range. This can effectively avoid quality problems caused by fluctuations in production conditions, and improve the consistency and stability of the product. The real-time monitoring function of the quality feedback module ensures that under the adjusted production conditions, the quality indicators always approach the target value. When the system detects that the target quality requirements have been met, it will automatically stop further adjustment to avoid waste of resources and unnecessary fluctuations caused by over-regulation. If the quality does not meet the standard, the system will continue to execute the adjustment measures to ensure that the final product meets the expected standards. This feedback control greatly improves the efficiency and accuracy of quality control.

[0074] Through the combination of the data acquisition module and the data processing and analysis module, the present invention realizes real-time data acquisition and precise processing in the production process. Through filtering processing and deviation calculation, the system can quickly detect changes in quality indicators and make corresponding adjustments. This has higher real-time performance and accuracy compared to traditional quality control methods, significantly improving production efficiency and product quality. By introducing the closed-loop feedback mechanism of the PID control module and the quality feedback module, the system can automatically adjust and optimize control parameters during the production process to ensure that the quality indicators always remain within the target range. This mechanism effectively reduces production fluctuations caused by untimely or inaccurate manual adjustment. At the same time, through the accumulation and learning of historical data, the intelligent adjustment ability of the system is continuously improved, further optimizing the production process.

[0075] Example 2

[0076] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: The data acquisition module includes a sensor unit and a data acquisition and transmission unit;

[0077] The sensor unit collects data on the concentration of water-soluble fertilizer, liquid flow rate, mixing ratio, ambient temperature, and pH value through various sensors installed inside the mixing tank and fluid pipeline. The sensors include a concentration sensor, a flow meter, a temperature sensor, and a pH sensor;

[0078] The concentration sensor is used to measure the concentration C of the water-soluble fertilizer f ;

[0079] The flow meter is used to measure the liquid flow rate Q f ;

[0080] The temperature sensor is used to monitor the ambient temperature T e ;

[0081] The pH sensor is used to measure the pH value pH of the water-soluble fertilizer f ;

[0082] The data acquisition and transmission unit packs and encapsulates the real-time data collected by the sensors and transmits it through the network to the data processing and analysis module for analysis and storage;

[0083] The data processing and analysis module includes a data preprocessing unit and a quality deviation calculation unit;

[0084] The data preprocessing unit is responsible for filtering the data; including using a low-pass filter to preprocess the data to remove high-frequency noise in the data;

[0085] Among them, the filtering processing formula is:

[0086]

[0087] In the formula, C f (t) represents the concentration of the water-soluble fertilizer collected at time t, represents the concentration of the water-soluble fertilizer after filtering at time t, α represents the filtering coefficient, and 0 < α ≤ 1;

[0088]

[0089] In the formula, Q f (t) represents the liquid flow rate collected at time t, represents the liquid flow rate after filtering at time t;

[0090]

[0091] In the formula, pH f (t) represents the pH value of the water-soluble fertilizer collected at time t, represents the pH value of the water-soluble fertilizer after filtering at time t;

[0092] The mass deviation calculation unit calculates the deviation between the filtered data and the target mass value, and the obtained deviation through calculation provides a decision-making basis for the control system; specifically, the deviation calculator calculates the concentration target value C of the water-soluble fertilizer fget , the liquid flow rate target value Q fget and the target value pH of the pH value fget to calculate the mass index deviation and obtain the deviation value:

[0093]

[0094] In the formula, ΔC f (t), ΔQ f (t) and ΔpH f (t) respectively represent the concentration deviation value, liquid flow rate deviation value and pH value deviation value of the water-soluble fertilizer at time t, and respectively represent the concentration, liquid flow rate and pH value of the water-soluble fertilizer after filtering, C fget , Q fget and pH fget respectively represent the concentration target value, liquid flow rate target value and pH value target value of the water-soluble fertilizer;

[0095] The concentration deviation value ΔC of the water-soluble fertilizer f (t), the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t) are summarized and combined into a comprehensive deviation e(t);

[0096] The comprehensive deviation e(t) is obtained through the following formula:

[0097]

[0098] In the formula, ω C represents the weight coefficient of the calculation result of the concentration deviation value ΔC f (t) of the water-soluble fertilizer and the target concentration value C fget of the water-soluble fertilizer; ω Q represents the weight coefficient of the calculation result of the liquid flow rate deviation value ΔQ f (t) and the target liquid flow rate value Q fget ; ω pH represents the weight coefficient of the deviation value ΔpH f (t) of the pH value and the target pH value fget of the pH value.

[0099] In this embodiment, through the precise cooperation of the sensor unit and the data acquisition and transmission unit, key data such as the concentration C f of the water-soluble fertilizer, the liquid flow rate Q f , the ambient temperature T e , and the pH value pH f of the water-soluble fertilizer during the production process of the water-soluble fertilizer are collected in real time, and high-frequency noise is removed through filtering processing. This data preprocessing greatly improves the accuracy and stability of the collected data, ensures the accuracy of subsequent control decisions, calculates the deviation between the filtered data and the target value through the quality deviation calculation unit, and sums up the concentration deviation value ΔC f (t) of the water-soluble fertilizer, the liquid flow rate deviation value ΔQ f (t), and the deviation value ΔpH f (t) of the pH value into the comprehensive deviation e(t), enabling the system to accurately evaluate the deviation degree of each parameter in the production process. This comprehensive deviation provides a more accurate adjustment basis for the PID control module, ensures that the adjustment of production parameters is more targeted, and thus effectively improves the quality control effect of the entire production process. By introducing the weight coefficient, the system can flexibly adjust the calculation of the comprehensive deviation e(t) according to the importance of each quality index in the production process. This means that the key parameters in the production process can be preferentially processed and adjusted, thereby further enhancing the flexibility and adaptability of quality control.

[0100] Embodiment 3

[0101] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically: The PID control module includes a PID calculation unit and a control execution unit;

[0102] The PID calculation unit calculates the output of the PID controller based on the comprehensive deviation e(t) obtained by the mass deviation calculation unit, and generates the total control output u(t) for adjusting the production parameters; it includes proportional control, integral control, and derivative control, and obtains the total control output u(t) through the fitting of proportional control, integral control, and derivative control;

[0103] The total control output u(t) is obtained through the following formula:

[0104] u(t) = up(t) + ui(t) + ud(t);

[0105] up(t) = K p e(t);

[0106]

[0107] In the formula, u(t) represents the final control output, specifically used to adjust the operating parameters of the production equipment. up(t) represents the output of proportional control, ui(t) represents the output of integral control, ud(t) represents the output of derivative control, e(t) represents the comprehensive deviation, and K p 、K i and K d respectively represent the proportional coefficient, integral coefficient, and derivative coefficient of the comprehensive deviation e(t), represents the cumulative value of the comprehensive deviation, represents the change rate of the comprehensive deviation;

[0108] The control execution unit is responsible for converting the total control output u(t) generated by the PID calculation unit into actual production equipment operation instructions for adjusting the production parameters of the equipment, specifically including control signal conversion and actuator drive;

[0109] The quality feedback module includes a quality monitoring unit and an adjustment decision unit;

[0110] The quality monitoring unit monitors the adjusted quality indicators, including the real-time value of the concentration of the water-soluble fertilizer the real-time value of the liquid flow rate and the real-time value of the pH value and compares the quality indicators with the target quality indicators through a target value comparator to obtain a deviation judgment result;

[0111] The target value comparator compares the currently monitored real-time value of the concentration of the water-soluble fertilizer with the target value C fget of the concentration of the water-soluble fertilizer:

[0112]

[0113] In the formula, ΦCf (t) represents the real-time value of the concentration of the water-soluble fertilizer and the target value C of the concentration of the water-soluble fertilizer fget The concentration difference between them, θc represents the concentration threshold;

[0114] If|ΦC f (t)|≤θc, the quality target is achieved, and the deviation judgment result is obtained as qualified, and the adjustment measure is stopped;

[0115] If|ΦC f (t)|>θc, the quality target is not achieved, and the deviation judgment result is obtained as unqualified, and the adjustment measure is continued;

[0116] The adjustment decision-making unit determines whether to adjust the operating parameters of the production equipment or stop adjusting the operation of the production equipment according to the deviation judgment result of the quality monitoring unit.

[0117] In this embodiment, through the PID calculation unit in the PID control module, the comprehensive deviation e(t) is calculated through proportional control, integral control and derivative control to generate the total control output u(t). The output accuracy of the PID controller is ensured through the fitting formula, making the adjustment of production parameters more accurate, effectively improving the stability and consistency of the production process. In this way, the system can quickly respond to the deviations that occur in the production process and achieve precise process control. The quality monitoring unit in the quality feedback module can monitor the quality indicators after adjustment in real time, such as the concentration of water-soluble fertilizer, liquid flow rate and pH value, and compare these real-time data with the target values to judge whether the quality requirements are met. This real-time monitoring mechanism can detect deviations in a timely manner, and through the dynamic decision-making function of the adjustment decision-making unit, quickly execute the corresponding adjustment measures to ensure that the production process always remains in the optimal state. By introducing the adjustment decision-making unit in this embodiment, the intelligence level of the system is greatly improved. Compared with the traditional system that requires frequent manual intervention, the intelligent decision-making mechanism of this embodiment can automatically judge and execute the necessary adjustment measures to ensure that the production process always maintains the best state. This automated improvement not only improves production efficiency, but also reduces the workload of operators, while reducing the errors of manual operations.

[0118] Embodiment 4

[0119] This embodiment is an explanatory description based on Embodiment 3, please refer to Figure 1 , specifically: The historical data storage and learning module includes a data storage unit and a machine learning unit;

[0120] The historical data storage stores the data collected during the production process, including the concentration C of the water-soluble fertilizer f , liquid flow rate Q f , pH value pH of the water-soluble fertilizerf Together with the total control output u(t), they form the historical data set Dst.

[0121] The machine learning unit adjusts the control strategy in the production process according to the historical data set Dst through machine learning algorithms to improve the control accuracy of the system.

[0122] Train a machine learning model based on historical data, learn the relationship between the total control output u(t) and the quality index in the production process, adjust the control strategy in real time, and obtain a new total control output u new (t);

[0123] The new total control output u new (t) is obtained through the following formula:

[0124]

[0125] In the formula, represents the historical average term of the total control output u(t), k t represents the weight coefficient of the historical average term, Δt represents the time interval, and N represents the total number of time points;

[0126] After obtaining the new total control output u new (t), the control execution unit is responsible for converting the new total control output u new (t) into actual production equipment operation instructions to adjust the production parameters of the equipment.

[0127] The user interface and alarm module include a display unit and an alarm unit;

[0128] The display unit displays the real-time data, control parameters, and quality index deviations in the production process through a graphical interface, and the operator can intuitively view the various indicators of the current production process;

[0129] Among them, the real-time data includes the real-time value of the concentration of water-soluble fertilizer the real-time value of the liquid flow rate and the real-time value of the pH value

[0130] The control parameters include the opening degree of the regulating valve, the rotation speed of the pump, and the power of the heater;

[0131] The quality index deviation includes the concentration deviation value ΔC of the water-soluble fertilizer f (t), the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t).

[0132] The alarm unit is responsible for monitoring the comprehensive deviation e(t) in the production process and when the deviation exceeds the threshold emax An alarm is issued at time (t);

[0133] When the alarm is issued, the alarm unit sends out an audible and visual alarm signal to the operator, and at the same time, the alarm information is prominently displayed on the user interface.

[0134] In this embodiment, through the machine learning unit in the historical data storage and learning module, the control strategy in the production process is optimized. By analyzing the historical data set Dst, the machine learning algorithm can identify and learn the complex relationship between the total control output u(t) and the quality index, and adjust the control strategy in real time. This data-driven learning method significantly improves the control accuracy of the system and ensures that all indicators in the production process are always in the best state.

[0135] The new total control output u new (t) is obtained by combining the historical average term and the dynamic adjustment formula of the real-time control output, so that the system can flexibly respond to various changes occurring in the production process. By considering the difference between the average trend of historical data and the current production state, the system can adjust parameters more accurately. This dynamic adjustment mechanism greatly improves the stability and response speed of the production process. The user interface and the display unit in the alarm module clearly and intuitively display the real-time data, control parameters and quality index deviation in the production process through a graphical interface. The operator can monitor the production status in real time and make a quick response. In addition, the alarm unit sends out an audible and visual alarm signal when the comprehensive deviation exceeds the threshold, ensuring that abnormal situations can be detected and processed in time, thereby improving the safety and reliability of the system.

[0136] Embodiment 5

[0137] An intelligent control method for the production and processing of water-soluble fertilizers, please refer to Figure 2 , specifically: including the following steps:

[0138] Step 1: The data acquisition module is responsible for collecting real-time data from each production device, specifically including the concentration of water-soluble fertilizer, liquid flow rate, ambient temperature and pH value;

[0139] Step 2: The data processing and analysis module preprocesses and analyzes the data collected by the data acquisition module. The preprocessing specifically includes filtering the data and calculating the quality index deviation, including the concentration deviation value ΔC f (t) of the water-soluble fertilizer, the liquid flow rate deviation value ΔQ f (t) and the pH value deviation value ΔpH f (t);

[0140] Step 3: The PID control module generates the total control output u(t) based on the comprehensive deviation e(t) between the filtered data and the target value, which is used to correct the production parameters;

[0141] Step 4: The quality feedback module monitors the adjusted quality indicators in real time, including the real-time value of the concentration of the water-soluble fertilizer The real-time value of the liquid flow rate and the real-time value of the pH value Judge the status of the quality indicators. When the target quality requirements are met, stop the adjustment measures. When the target quality requirements are not met, continue the adjustment measures;

[0142] Step 5: The historical data storage and learning module stores the data, quality indicator deviations, and adjustment measures during the production process, and continuously adjusts the control strategy through a machine learning model;

[0143] Step 6: The user interface and alarm module provides a user operation interface, displays real-time data, control parameters, and quality indicator deviations, and issues an alarm when the comprehensive deviation e(t) exceeds the threshold deviation e max (t).

[0144] In this embodiment, in Steps 1 to 6, by deploying a concentration sensor, a flow meter, a temperature sensor, and a pH sensor, the production status of the water-soluble fertilizer is monitored and recorded in real time. The data acquisition and transmission unit is responsible for encapsulating the data collected by the sensors and transmitting it to the next link through the network to ensure the timeliness and accuracy of the data. The data of the concentration of the water-soluble fertilizer, the liquid flow rate, and the pH value are filtered by a filter to remove unnecessary noise and obtain more accurate measurement values. Then, the difference between the actual measurement value and the target value is calculated, that is, the quality indicator deviation, and these deviation values will be used for the next PID control. The PID calculation unit receives the comprehensive deviation e(t), calculates the proportional control term, the integral control term, and the derivative control term through the PID algorithm, and finally obtains the total control output u(t). It is converted into specific device operation instructions through the control execution unit, such as adjusting the rotation speed of the pump, adjusting the opening of the valve, etc., so as to realize the real-time correction of the production parameters.

[0145] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for the production and processing of water-soluble fertilizers, characterized in that: It includes data acquisition module, data processing and analysis module, PID control module, quality feedback module, historical data storage and learning module and user interface and alarm module; The data acquisition module is responsible for collecting real-time data from various production equipment; The data processing and analysis module preprocesses and analyzes the data collected by the data collection module, and the preprocessing specifically includes filtering the data and calculating the quality index deviation; The PID control module generates a total control output u(t) based on the comprehensive deviation e(t) between the filtered data and the target value, which is used to correct the production parameters; The quality feedback module monitors the adjusted quality index in real time and determines the state of the quality index; The historical data storage and learning module stores quality indicator deviations and adjustment measures; The user interface and alarm module provide a user operation interface, display real-time data, control parameters and quality index deviations, and alarm when the comprehensive deviation e(t) exceeds the threshold deviation e max (t) The alarm is issued.

2. The intelligent control system for production and processing of water-soluble fertilizers according to claim 1 is characterized by: The data acquisition module includes a sensor unit and a data acquisition and transmission unit; The sensor unit collects data on water-soluble fertilizer concentration, liquid flow rate, mixing ratio, ambient temperature and pH value through various sensors installed inside the mixing tank and the fluid pipeline, and the sensors include concentration sensors, flow meters, temperature sensors and pH sensors; The concentration sensor is used to measure the concentration C of water-soluble fertilizer. f ; The flow meter is used to measure the liquid flow rate Q f ; The temperature sensor is used to monitor the ambient temperature T e ; The pH sensor is used to measure the pH value of water-soluble fertilizers. f ; The data acquisition and transmission unit packages and encapsulates the real-time data collected by the sensor, and transmits it to the data processing and analysis module through the network for analysis and storage.

3. The intelligent control system for production and processing of water-soluble fertilizers according to claim 1 is characterized by: The data processing and analysis module includes a data preprocessing unit and a quality deviation calculation unit; The data preprocessing unit is responsible for filtering the data, including preprocessing the data using a low-pass filter to remove high-frequency noise in the data; The concentration of water-soluble fertilizer C is measured by low-pass filter f , measure liquid flow rate Q f and pH of water-soluble fertilizers f Processing is performed to obtain the concentration of water-soluble fertilizer after filtering at time t Liquid flow rate after filtering at time t and filtered at time t The mass deviation calculation unit calculates the deviation between the filtered data and the target mass value, and provides a decision basis for the control system through the deviation obtained by calculation; specifically, the concentration target value C of the water-soluble fertilizer is calculated by the deviation calculator. fget , Liquid flow rate target value Q fget and the target pH fget Calculate the quality index deviation and obtain the concentration deviation value ΔC f (t), liquid flow rate deviation ΔQ f (t) and pH deviation ΔpH f (t); Concentration deviation ΔC for water-soluble fertilizers f (t), liquid flow rate deviation ΔQ f (t) and pH deviation ΔpH f (t) are summarized and combined into a comprehensive deviation e(t); The comprehensive deviation e(t) is calculated by the concentration deviation ΔC of the water-soluble fertilizer. f (t) and the target concentration of water-soluble fertilizer C fget The weight coefficient ω of the calculation result C , Liquid flow rate deviation ΔQ f (t) and the target value of liquid flow rate Q fget The weight coefficient ω of the calculation result Q The deviation from pH value is ΔpH f (t) and the target pH value fget The weight coefficient ω pH Add to obtain.

4. The intelligent control system for production and processing of water-soluble fertilizers according to claim 3 is characterized by: The PID control module includes a PID calculation unit and a control execution unit; The PID calculation unit calculates the PID controller output according to the comprehensive deviation e(t) obtained by the quality deviation calculation unit, and generates a total control output u(t) for adjusting the production parameters; including proportional control, integral control and differential control, and the total control output u(t) is obtained by fitting the proportional control, integral control and differential control; The total control output u(t) is obtained by adding the final control output u(t), the integral control output ui(t) and the differential control output ud(t); The control execution unit is responsible for converting the total control output u(t) generated by the PID calculation unit into actual production equipment operation instructions for adjusting the production parameters of the equipment, specifically including control signal conversion and actuator driving.

5. The intelligent control system for production and processing of water-soluble fertilizers according to claim 1 is characterized by: The quality feedback module includes a quality monitoring unit and an adjustment decision unit; The quality monitoring unit monitors the adjusted quality indicators, including the real-time value of the concentration of the water-soluble fertilizer. Real-time value of liquid flow rate and pH value in real time and comparing the quality index with the target quality index through a target value comparator to obtain a deviation judgment result; The target value comparator converts the currently monitored real-time concentration value of the water-soluble fertilizer into The target value C of the concentration of water-soluble fertilizer fget Compare and obtain the real-time concentration value of water-soluble fertilizer The target value C of the concentration of water-soluble fertilizer fget The concentration difference between f (t); If |ΦC f (t)|≤θc, the quality target is achieved, the deviation is judged as qualified, and the adjustment measures are stopped; If |ΦC f When (t)|>θc, the quality target is not achieved, the deviation judgment result is unqualified, and the adjustment measures are continued; The adjustment decision unit decides to adjust the operating parameters of the production equipment or stop adjusting the operation of the production equipment according to the deviation judgment result of the quality monitoring unit.

6. The intelligent control system for production and processing of water-soluble fertilizers according to claim 1 is characterized by: The historical data storage and learning module includes a data storage unit and a machine learning unit; The historical data storage includes the data collected during the production process, including the concentration of water-soluble fertilizer C f , Liquid flow rate Q f , pH value of water-soluble fertilizer f and the total control output u(t) constitute the historical data set Dst.

7. The intelligent control system for production and processing of water-soluble fertilizers according to claim 6 is characterized by: The machine learning unit adjusts the control strategy in the production process according to the historical data set Dst through a machine learning algorithm to improve the control accuracy of the system; Train the machine learning model based on historical data, learn the relationship between the total control output u(t) and quality indicators in the production process, adjust the control strategy in real time, and obtain the new total control output u new (t); Get the new total control output u new (t), the control execution unit is responsible for the new total control output u new (t) Convert into actual production equipment operation instructions to adjust the production parameters of the equipment.

8. The intelligent control system for production and processing of water-soluble fertilizers according to claim 1 is characterized by: The user interface and alarm module includes a display unit and an alarm unit; The display unit displays real-time data, control parameters and quality index deviations in the production process through a graphical interface, and the operator can intuitively view various indicators of the current production process; Among them, real-time data includes the real-time value of the concentration of water-soluble fertilizers Real-time value of liquid flow rate and pH value in real time Control parameters include adjusting valve opening, pump speed and heater power; The quality index deviation includes the concentration deviation value ΔC of water-soluble fertilizers f (t), liquid flow rate deviation ΔQ f (t) and pH deviation ΔpH f (t).

9. The intelligent control system for production and processing of water-soluble fertilizers according to claim 8, characterized in that: The alarm unit is responsible for monitoring the comprehensive deviation e(t) during the production process and alarming when the deviation exceeds the threshold value e(t). max (t) an alarm is issued; When an alarm is sounded, the alarm unit sends an audible and visual alarm signal to the operator, and the alarm information is highlighted on the user interface.

10. An intelligent control method for the production and processing of water-soluble fertilizers, applied to the intelligent control method and system for the production and processing of water-soluble fertilizers according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: The data acquisition module is responsible for collecting real-time data from various production equipment, including water-soluble fertilizer concentration, liquid flow rate, ambient temperature and pH value; Step 2: The data processing and analysis module preprocesses and analyzes the data collected by the data acquisition module. The preprocessing specifically includes filtering the data and calculating the quality index deviation, including the concentration deviation value ΔC of the water-soluble fertilizer. f (t), liquid flow rate deviation ΔQ f (t) and pH deviation ΔpH f (t); Step 3: The PID control module generates a total control output u(t) based on the comprehensive deviation e(t) between the filtered data and the target value, which is used to correct the production parameters; Step 4: The quality feedback module monitors the adjusted quality indicators in real time, including the real-time concentration value of water-soluble fertilizers Real-time value of liquid flow rate and pH value in real time Determine the status of quality indicators. When the target quality requirements are met, stop adjusting measures. When the target quality requirements are not met, continue adjusting measures. Step 5: The historical data storage and learning module stores the data, quality indicator deviations and adjustment measures of the production process, and continuously adjusts the control strategy through the machine learning model; Step 6: The user interface and alarm module provides a user operation interface to display real-time data, control parameters and quality index deviations, and alarms when the comprehensive deviation e(t) exceeds the threshold deviation e max (t) The alarm is issued.

Images