Automatic batching and stirring control system based on industrial computer

Abstract

The invention discloses an automatic batching and stirring control system based on an industrial computer, belongs to the field of new wall material production control systems, and aims to solve the problems that the real-time performance is poor, and the influence of raw material characteristic changes on a fall value cannot be pre-judged. Weighing, humidity, infrared flow velocity, temperature, pressure, torque and airflow sensors are integrated, multi-dimensional parameters such as material weight, flow velocity, humidity and air pressure in a bin are monitored in real time, the influence of environmental interference on the sensors is dynamically compensated through multi-parameter collaborative feedback, the real-time performance and reliability of data acquisition are improved, a fuzzy PID control algorithm and a machine learning model are fused, and the real-time performance of the system is improved. A nonlinear mapping relation is established through a training data set, the initial valve opening degree and the fall value are predicted, errors are corrected in real time, dependence on historical data is reduced, the first-time use error of a new formula is reduced, the response time for coping with sudden environment changes is shortened, a single adjustable-caliber electromagnetic valve is adopted, the inner wall is coated with a nano anti-sticking coating, and the service life of the valve is prolonged. And the opening degree is dynamically adjusted in combination with a PID algorithm.

Description

Technical Field

[0001] The present invention relates to the field of new wall material production control systems, and in particular to an automatic batching and stirring control system based on an industrial computer. Background Art

[0002] As one of the core directions of green building development, new wall materials have attracted much attention in recent years in terms of preparation technology using industrial solid waste such as fly ash, coal gangue, stone powder, slag and bamboo charcoal as the main raw materials. This type of material can not only absorb a large amount of industrial waste, but also significantly reduce the energy consumption and carbon emissions of traditional sintered bricks. When preparing new wall materials, the raw materials need to be batched and mixed.

[0003] Published patent: A dynamic control system and method for batching accuracy in a concrete mixing plant (publication number: CN104260207A), consisting of a batching device, a weighing sensor, a batching instrument (PLC), and an industrial control computer. The system compares real-time dynamic weighing data with the expected batching setpoints (i.e., mix ratio parameters). The batching device uses a two-stage batching method, coarse weighing and fine weighing, to control the discharge volume in a step-by-step manner. A loop control algorithm is used to automatically correct the real-time drop and achieve precise batching using inching control. The system has the outstanding advantages of high batching accuracy and good stability, and can therefore be applied to concrete and asphalt concrete mixing plants in the civil engineering field. It can achieve real-time automatic control, improve the automation level of the mixing plant, enhance the accuracy and reliability of batching, and ensure the quality of concrete production.

[0004] The above patents have poor real-time performance and lack real-time comprehensive feedback on humidity, temperature, and flow rate. They cannot predict the impact of changes in raw material characteristics on the head value, frequently adjust valve openings or pipeline configurations, increase manual intervention, reduce the degree of automation, and are difficult to adapt to diverse material flow rate requirements. The control algorithm is based on fixed rules or simple feedback and lacks multi-parameter collaborative optimization capabilities. It is impossible to predict the optimal combination of raw material characteristics and ratios through machine learning, resulting in low resource utilization and reliance on historical data to correct the head value. Dust is easily generated during transportation, and the sensor is easily affected by high humidity and high dust, resulting in zero drift or decreased sensitivity. Sticky material residues aggravate sensor contamination, and the cumulative effect of weighing errors is significant. The system stability is insufficient in extreme environments. The system does not integrate humidity detection or drying modules. Fluctuations in the moisture content of the raw materials directly affect the weighing accuracy. Waste determination relies on manual labor, and substandard mixtures are directly discarded, resulting in resource waste and increased costs. Summary of the Invention

[0005] The purpose of the present invention is to provide an automatic batching and mixing control system based on an industrial computer, which solves the problem in the background art that product innovation and service optimization lack systematic methods and tools and are difficult to meet the needs of enterprises.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: an automatic batching and stirring control system based on an industrial computer, comprising: a multi-parameter dynamic monitoring module, an intelligent prediction and correction module, an adaptive valve system, a self-cleaning and waste recovery module, a recipe adaptive management module, an environmental compensation unit, a stirring control module, a feeding control module, and a bamboo charcoal moisture content pretreatment unit;

[0007] Multi-parameter dynamic monitoring module: integrated with weighing sensor, humidity sensor, infrared flow rate detector, temperature sensor, pressure sensor, torque sensor and airflow sensor;

[0008] Intelligent prediction and correction module: Based on fuzzy PID control algorithm and machine learning model, it adjusts dynamic parameters such as drop value, valve opening and closing degree, material flow rate, etc.

[0009] Adaptive valve system: uses a single adjustable caliber electromagnetic valve, and dynamically adjusts the valve opening and closing degree through the PID algorithm of the industrial computer;

[0010] Self-cleaning and waste recovery module: including a high-frequency vibrator at the bottom of the weighing hopper, an air blowing device, a screw conveyor, a laser scanner, an RFID tag system, a near-infrared spectrometer, and a waste storage bin;

[0011] Recipe adaptive management module: automatically generates initial ingredient ratio parameters based on raw material density and humidity characteristics, stores recipe parameters, and associates supplier information;

[0012] Environmental compensation unit: The environmental compensation unit collects ambient temperature and dust concentration data in real time through temperature sensors and dust concentration sensors;

[0013] Mixing control module: speed adjustment unit and intermittent mixing unit, automatically adjust the speed of the mixing blade according to the raw material ratio;

[0014] Feeding control module: Generates feeding priority sequence based on raw material density and viscosity parameters;

[0015] The bamboo charcoal moisture content pretreatment unit comprises a humidity sensor, a drying device and an infrared flow velocity detector.

[0016] Furthermore, the airflow sensor in the multi-parameter dynamic monitoring module is installed on the top of the fly ash bin, the weighing sensor adopts a cantilever beam structure, the surface of which is covered with a polytetrafluoroethylene anti-stick film, and the humidity sensor is a capacitive probe.

[0017] Furthermore, the inner wall of the valve of the adaptive valve system is coated with a nano anti-stick coating, the opening and closing degree of the valve is dynamically adjusted by an industrial computer through a PID algorithm, the infrared flow rate detector feeds back the material flow rate to the industrial computer in real time, the target opening and closing degree is dynamically calculated based on the fuzzy PID algorithm, and a pulse signal is output to adjust the solenoid valve. During the fly ash batching stage, the weighing deviation caused by the fluffy material is compensated according to the air pressure data in the bin.

[0018] Furthermore, the working process of the self-cleaning and waste recovery module is as follows: after the ingredients are mixed, the high-frequency vibrator oscillates, and the air blowing device is started synchronously to spray compressed air to remove the residue on the bucket wall. After the quality of the substandard mixture is judged, it is returned to the corresponding raw material bin by the screw conveyor.

[0019] Furthermore, the feeding control module generates a feeding priority sequence according to the raw material density and viscosity parameters, gives priority to feeding materials with poor fluidity, dynamically adjusts the valve opening and closing degree according to the density difference, controls the opening interval of each raw material bin valve through an industrial computer, and synchronously starts the air blowing device during the bamboo charcoal feeding stage.

[0020] Furthermore, the humidity sensor of the bamboo charcoal moisture content pretreatment unit is used to monitor the moisture content of the bamboo charcoal raw material in real time, and is automatically activated when it is detected that the moisture content exceeds a preset threshold. After drying is completed, the fluidity of the bamboo charcoal is verified by an infrared flow rate detector, and the subsequent ingredient parameters are adjusted, and the pretreated bamboo charcoal is weighed.

[0021] Furthermore, the process of the automatic batching and mixing control system based on industrial computers is as follows:

[0022] S1 Raw material pretreatment

[0023] Moisture content control: The humidity sensor detects the moisture content of the bamboo charcoal. If the moisture content exceeds the threshold, high-temperature drying is triggered. After reaching the standard, the fluidity is verified.

[0024] Anti-sticking pretreatment: Nano-coated valves reduce fly ash adhesion, and infrared flow rate detection calibrates feeding parameters in real time;

[0025] S2 Intelligent Recipe Generation

[0026] Dynamic proportioning: Automatically generate the initial formula based on the physical properties of raw materials;

[0027] Encrypted storage: Blockchain records recipe parameters, links suppliers and quality inspection data, and ensures traceability;

[0028] S3 dynamic batching optimization

[0029] Coordinated feeding: Raw materials with high viscosity are fed first, and the air blowing device prevents accumulation;

[0030] Algorithm collaboration:

[0031] Machine learning: predicting initial valve opening and drop value;

[0032] Fuzzy PID: real-time correction of errors caused by environmental changes, reducing the initial error of new formulas;

[0033] S4 Mixing and Quality Inspection

[0034] Intelligent mixing: select intermittent mixing mode according to the proportion of fly ash;

[0035] AI quality inspection: Multispectral imaging detects particle distribution, and AI vision identifies agglomerates;

[0036] S5 self-cleaning and waste recycling

[0037] Cleaning control: high-frequency vibration + high-pressure air blowing to remove residues, laser scanning to trigger secondary cleaning;

[0038] Waste recycling: intelligently sort out unqualified materials, add new raw materials at a ratio of ≤10%, and monitor the composition using near-infrared spectroscopy;

[0039] S6 data closed-loop management

[0040] Blockchain traceability: Production data is encrypted and stored, and customers can scan the code to trace batch information;

[0041] Predictive maintenance: Vibration and air pressure data are used to analyze equipment health status and dynamically optimize process parameters.

[0042] The beneficial effects of the present invention are:

[0043] 1. The present invention integrates weighing, humidity, infrared flow rate, temperature, pressure, torque and airflow sensors to monitor multi-dimensional parameters such as material weight, flow rate, humidity, and air pressure in the warehouse in real time. Through multi-parameter collaborative feedback, it dynamically compensates for the impact of environmental interference on the sensor, improves the real-time and reliability of data acquisition, integrates the fuzzy PID control algorithm and the machine learning model, establishes a nonlinear mapping relationship through the training data set, predicts the initial valve opening and drop value, and corrects the error in real time, reducing dependence on historical data, reducing the error when a new formula is used for the first time, and shortening the response time to sudden environmental changes.

[0044] 2. The present invention adopts a single adjustable caliber electromagnetic valve with a nano-anti-stick coating on the inner wall. It dynamically adjusts the opening and closing degree in combination with the PID algorithm. Based on the fluffy characteristics of fly ash, it compensates for the weighing deviation through the air pressure data in the silo, replaces the traditional structure, simplifies the mechanical complexity, integrates a high-frequency vibrator, a high-pressure air blowing device and a laser scanner, and combines multispectral imaging with the YOLOv5s+ResNet50 model to sort waste. Unqualified materials are reused in proportion, reducing maintenance costs, improving the residue removal rate, and enhancing the waste recycling rate.

[0045] 3. The present invention automatically generates an initial formula based on the physical properties of the raw materials, and uses blockchain to encrypt and store formula parameters, quality inspection data, and supplier information, achieving full process traceability, shortening formula generation time, reducing the risk of data tampering, supporting dynamic adaptation of multiple formulas, and correcting weighing zero drift in real time through temperature and dust sensors. It triggers a three-level alarm and cooling mechanism in extreme environments, improves system stability, extends sensor life, uses infrared flow rate detection to calibrate the fluidity of bamboo charcoal, and dynamically adjusts the feeding priority sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a system structure block diagram of the present invention;

[0047] Figure 2 It is a flow chart of the system algorithm of the present invention. DETAILED DESCRIPTION

[0048] 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.

[0049] like Figure 1-2 As shown, the following preferred technical solutions are provided:

[0050] An automatic batching and mixing control system based on an industrial computer, comprising: a multi-parameter dynamic monitoring module, an intelligent prediction and correction module, an adaptive valve system, a self-cleaning and waste recovery module, a recipe adaptive management module, an environmental compensation unit, a mixing control module, a feeding control module, and a bamboo charcoal moisture content pretreatment unit;

[0051] Multi-parameter dynamic monitoring module: integrated weighing sensor, humidity sensor, infrared flow rate detector, temperature sensor, pressure sensor, dust concentration sensor and airflow sensor;

[0052] Intelligent Prediction and Correction Module: The intelligent prediction algorithm uses a fuzzy PID control algorithm and a machine learning model to dynamically calculate the head gap value and correct the batching parameters based on real-time monitoring data. By integrating the fuzzy PID control algorithm with the machine learning model, this module uses a training data set of raw material physical properties, environmental parameters, and historical batching errors to establish a nonlinear mapping relationship between raw material flow rate and valve opening, generating initial batching prediction parameters. Based on real-time data from flow rate, air pressure, temperature and humidity sensors, the fuzzy PID algorithm is used to dynamically adjust the head gap correction value. During the production process, dynamic parameter optimization of the head gap value, valve opening, and material flow rate is performed. Based on real-time data from flow rate, air pressure, temperature and humidity sensors, the machine learning model predicts initial batching parameters, and the fuzzy PID algorithm corrects errors through feedback control, reducing reliance on historical data. This addresses the challenges of first-time use of new recipes or sudden environmental changes such as humidity fluctuations or excessive dust levels, reducing initial errors for new recipes while improving batching efficiency and ensuring feeding accuracy. Its core goal is to solve real-time control issues in production through the collaboration of dynamic response and algorithms, ensuring high-precision and highly robust automated production.

[0053] Adaptive valve system: uses a single adjustable caliber electromagnetic valve, and dynamically adjusts the valve opening and closing degree through the PID algorithm of the industrial computer;

[0054] Self-cleaning and waste recovery module: including a high-frequency vibrator at the bottom of the weighing hopper, an air blowing device, a screw conveyor, a laser scanner, an RFID tag system, a near-infrared spectrometer, and a waste storage bin;

[0055] Recipe Adaptive Management Module: The module automatically generates initial batching parameters based on raw material density and moisture characteristics, and builds a closed-loop system from static generation to dynamic optimization, dynamic optimization to data feedback, and data feedback to static generation. During the recipe design phase, the module statically generates raw material ratios, process thresholds, and supplier association information based on raw material physical properties to ensure process standardization and data integrity. Blockchain technology is used to encrypt and store recipe data, process parameters, and quality inspection results, generating tamper-proof hash values to achieve distributed storage and production batch traceability. After optimizing parameters through the intelligent prediction and correction module, the feedback data is iterated back to the static generation stage to form a continuously optimized production closed loop. With zero-redundant storage and tamper-proof data chain as its core goals, it reduces manual intervention and provides a benchmark parameter framework for the production process. At the same time, the full-process data closed loop significantly improves production reliability and traceability.

[0056] Initial batching parameter generation: Assume there are n kinds of raw materials, i represents the i-th raw material, and the raw material density is ρ i , humidity is H i , through the function f(ρ i ,H i) to generate the initial batching parameters P i , the formula can be expressed as: P i =f(ρ i ,H i ), P i =aρ i +bH i +c, the values of coefficients a, b, and c are determined by data analysis, and function f is a mathematical model established based on the relationship between raw material characteristics and ingredients;

[0057] Data feedback and iterative optimization: Assume that the optimized batching parameters of the intelligent prediction and correction module are P i new , the feedback data is F, the feedback data is iterated to the static generation stage, and the adjustment function is g(P i new ,F), then the new initial batching parameter P i next , the calculation formula is: P i next =g(P i new ,F), the adjustment function g is designed based on the actual production situation and optimization objectives. It can adjust the optimized batching parameters P according to the feedback data F of batching error and product quality index in production. i new Adjust to generate initial batching parameters P that are more in line with production requirements i next , to achieve continuous optimization of the production closed loop;

[0058] Environmental compensation unit: The environmental compensation unit collects ambient temperature and dust concentration data in real time through temperature sensors and dust concentration sensors. The temperature sensor monitors the ambient temperature at a frequency of 1Hz, and the dust sensor updates the concentration data every 0.5 seconds. When the temperature fluctuation is greater than ±5℃, the zero drift of the weighing sensor is corrected. The unit automatically calibrates and encrypts the data and stores it in the blockchain every 24 hours. When the dust concentration is greater than 50mg / m 3 When the positive pressure sealing mode is activated, the air pressure in the chamber is +50Pa, and the self-cleaning module is triggered synchronously with high-frequency vibration 25kHz and enhanced air blowing 0.7MPa. When the temperature is greater than 70℃ or the dust is greater than 100mg / m 3 A three-level alarm is triggered, with sound and light + remote notification, starting the cooling fan or suspending feeding. In high humidity environments, a humidity correction factor is introduced; in low temperature environments, sensor self-heating and dust peak scenarios are enabled, and the sealing mode is delayed for 5 minutes. Data management uses blockchain to encrypt and store temperature and dust records. Smart contracts automatically trigger maintenance instructions and generate analysis reports daily to ensure production traceability and reliability.

[0059] Mixing control module: The mixing control module realizes automatic operation based on industrial computers. Its core functions include parameter initialization, dynamic adjustment and intelligent optimization. According to the raw material ratio provided by the recipe adaptive management module, the ratio of fly ash to bamboo charcoal is set, the initial speed of the mixing blade is set, and the torque sensor is used to monitor the mixing shaft load in real time to ensure that the operation is within the rated range. The mixing mode is intelligently switched according to the fly ash ratio in the raw material. When the fly ash ratio is less than or equal to 50%, the speed is dynamically adjusted in continuous mode; if the fly ash ratio exceeds 50%, it switches to an intermittent mode of running for 10 seconds and pausing for 2 seconds to avoid agglomeration of high fly ash materials due to frictional heat generation. Combined with the temperature and humidity data of the environmental compensation unit, the speed is reduced in a high temperature environment to reduce heat accumulation, and the parameters are optimized in real time. When the ambient temperature exceeds 70°C or the dust concentration exceeds 100mg / m 3 A three-level alarm mechanism, including audible and visual alarms and remote notifications, is triggered when mixing is complete, pausing mixing and starting the cooling fan. After mixing is complete, the waste recovery module is triggered to check the uniformity of the mixture by detecting whether the mixing shaft load is less than 10% of the rated value. Multispectral imaging technology is used to analyze particle distribution using visible light and the moisture content of bamboo charcoal using near-infrared imaging. A deep learning model fused with YOLOv5s and ResNet50 is used to locate unqualified agglomerates. Feedback is provided to optimize subsequent parameters, including a discrete coefficient of less than 5% for lightweight materials, a discrete coefficient of less than 3% for high-strength materials, and a grayscale variance of less than 10%. All operating data, including parameter adjustment records, alarm events, and optimization results, is encrypted and stored using blockchain technology, supporting production batch traceability and smart contract-based automated maintenance. A lubrication instruction is triggered after the high-frequency vibrator has operated for 1,000 hours. The module also evaluates the impact of mixing strategies on finished product performance based on an ANSYS digital twin simulation model, enabling continuous process optimization. The module ensures optimal material mixing uniformity, equipment operational safety, and production efficiency through multi-sensor collaborative control, dynamic algorithm adjustment, and closed-loop data management throughout the entire process.

[0060] Feeding control module: Generates feeding priority sequence based on raw material density and viscosity parameters;

[0061] The bamboo charcoal moisture content pretreatment unit comprises a humidity sensor, a drying device and an infrared flow velocity detector.

[0062] The airflow sensor in the multi-parameter dynamic monitoring module is installed on the top of the fly ash silo. The weighing sensor adopts a cantilever beam structure and is covered with a polytetrafluoroethylene anti-stick film on the surface. The humidity sensor is a capacitive probe. The multi-parameter dynamic monitoring module is mainly responsible for real-time collection of key physical parameters related to the production process to ensure the accurate operation of the batching and mixing control system. Before starting the system, each sensor is calibrated to ensure measurement accuracy, and the monitoring range and sampling frequency of each sensor are set. The weighing sensor monitors the material weight in real time to ensure the accuracy of the feed amount. The humidity sensor monitors the humidity of the material and the environment to prevent humidity fluctuations from affecting production. The infrared flow rate detector measures the flow rate of the material to provide data support for the intelligent prediction and correction module. The temperature sensor monitors the ambient temperature and is used in the environmental compensation unit to correct the zero drift of the weighing sensor. The airflow sensor is installed in the fly ash silo. At the top, the air pressure in the warehouse is monitored to prevent changes in air pressure from affecting weighing accuracy. The collected data is preliminarily denoised and smoothed to improve data quality. The processed data is transmitted to all modules in real time. The data generated by other modules are shared with all modules through industrial computers for further analysis and decision-making. The real-time monitoring data exceeds the preset threshold. Once abnormal data is found, the alarm mechanism is immediately triggered to notify the operator to process it. All collected data and abnormal alarm records are stored for subsequent analysis and traceability. Blockchain technology can be used to encrypt and store data to ensure data security and non-tamperability, so that the multi-parameter dynamic monitoring module can provide accurate and real-time data support for the automatic batching and mixing control system based on industrial computers, ensuring the stability and reliability of the production process. The dust concentration sensor can detect dust concentration.

[0063] The inner wall of the valve of the adaptive valve system is coated with a nano-anti-stick coating to reduce the adhesion of sticky materials such as fly ash. The opening and closing degree of the valve is dynamically adjusted by an industrial computer using a PID algorithm to adapt to the flow rate requirements of different materials. The control process of the adaptive valve system includes: real-time feedback of material flow rate from an infrared flow rate detector to the industrial computer, dynamic calculation of the target opening and closing degree based on a fuzzy PID algorithm, output of a pulse signal to adjust the solenoid valve, and compensation for weighing deviation caused by fluffy materials based on the air pressure data in the silo during the fly ash batching stage.

[0064] Dynamic adjustment of valve opening and closing: Adjust valve opening and closing based on the density difference of raw materials. Let raw material density be ρ i , the valve opening degree is O i , establish the linear formula O i =k·ρ i +b, k and b are coefficients determined according to actual conditions. This formula indicates that the higher the density of the raw material, the larger the corresponding valve opening and closing degree, so as to ensure that different materials are evenly distributed in the mixing bin.

[0065] The working process of the self-cleaning and waste recycling module is as follows: after the weighing hopper completes the feeding of raw materials into the mixer, the position sensor confirms that the valve is completely closed, triggering the fully automatic cleaning process, using a high-frequency vibrator, which is divided into standard mode and enhanced mode. The frequency, amplitude and working time of the high-frequency vibrator are adaptively switched according to the viscosity of the material. The enhanced mode is used for materials with high viscosity, and the standard mode is used for materials with low viscosity. The standard mode: 20kHz frequency, 0.1mm amplitude for 5 seconds, enhanced mode: 25kHz frequency, 0.2mm amplitude intermittent pulse vibration for 10 seconds, and the inner wall of the weighing hopper is scratched. It is divided into 6 areas, and the residual distribution is detected by the pressure sensor, and the vibrator in the corresponding area is activated in a direction to achieve precise cleaning. The air blowing is started with a delay of 0.5 seconds after the vibration is started. 8 groups of 45° angled nozzles spray in an alternating spray mode for 0.2 seconds and cover the entire surface of the bucket wall at intervals of 0.1 seconds. The air pressure is dynamically adjusted according to the ambient humidity. When the humidity is ≤70%, the air pressure is 0.5MPa. When the humidity is greater than 70%, the air pressure is increased to 0.7MPa and the spraying is extended to 7 seconds to effectively remove the residual wet materials. Combined with the residual weight of the weighing sensor >0.1% of the rated value and the laser scanner 3D point cloud recognition residual area >5cm 2 , triggering up to 3 iterative cleanings, the delivery pipeline is embedded with a PTFE lining to reduce friction, an integrated ultrasonic anti-adhesion module, and real-time monitoring of abnormal flow and blockage risk > 80% triggers reverse pulse cleaning to ensure smooth pipeline operation;

[0066] After the mixture is stirred, the torque sensor detects that the stirring shaft load is less than 10% of the rated value and starts intelligent recycling. Through the multispectral imaging system, the visible light channel analyzes the uniformity of particle distribution, and the near-infrared channel detects the moisture content of bamboo charcoal; the YOLOv5s+ResNet50 fusion model is used to locate the agglomerates with mAP ≥ 95%, and the discrete coefficient of lightweight materials is calculated to be less than 5%, high-strength materials to be less than 3%, and grayscale variance to be less than 10% to dynamically determine the waste. The pneumatic baffle array has 12 groups of solenoid valves with a response time of less than 50ms. The waste is sorted into the intermediate buffer bin according to the unqualified area, and new raw materials are added at a ratio of ≤ 10% after secondary stirring; RFID tags record the source ratio of the waste, mixing time, and reasons for failure. The near-infrared spectrometer monitors the consistency of the recycled material composition in real time to ensure the quality of recycling. The impact of waste recycling on the performance of the finished product is evaluated based on ANSYS digital twin simulation. NSGAI The I algorithm balances recovery rate and energy consumption. When XRF detects excessive heavy metals, the waste is isolated in the hazardous materials bin and an alarm is issued. If a pipeline is blocked, it automatically switches to a backup pipeline and clears it with a high-pressure water jet at 20 MPa to ensure continuous production.

[0067] Waste determination: In the multispectral imaging system analysis, the dispersion coefficient is calculated to determine the uniformity of particle distribution. The dispersion coefficient formula is: Where σ is the sample standard deviation, x is the sample mean, for lightweight materials, when C v轻质材料<5%; For high-strength materials, when C v高强材料 <3% and grayscale variance V<10%, it is judged as waste. Here the grayscale variance calculation formula is Where n is the number of samples, x i is the i-th sample value, x is the sample mean;

[0068] Cleaning records, waste data, and optimization parameters are encrypted and written to the Hyperledger Fabric blockchain, generating an unalterable hash value. Smart contracts automatically execute quality traceability, allowing customers to scan a QR code to retrieve test reports and equipment maintenance reminders. A high-frequency vibrator triggers a lubrication instruction after 1,000 hours of operation. Vibration signal time-frequency analysis predicts piezoelectric ceramic lifespan, and air pressure and flow curve fitting assesses pipeline wear. An industrial computer generates a failure probability × impact level matrix to guide manual inspections based on priority. Recycled data is fed back to the recipe management module in real time to dynamically adjust valve opening and stirring time.

[0069] Feeding control module: Generates a feeding priority sequence based on the density and viscosity parameters of the raw materials, prioritizes materials with poor fluidity, and sorts the raw materials from high to low according to the viscosity coefficient. The materials with higher viscosity coefficients are fed earlier. Based on the density difference of the raw materials, dynamically adjusts the opening and closing degree of each raw material bin valve to ensure that different materials are evenly distributed in the mixing bin. The opening interval of each raw material bin valve is controlled by an industrial computer to ensure that the feeding time difference between fly ash and coal gangue is 2 to 5 seconds. During the bamboo charcoal feeding stage, the air blowing device is started synchronously to prevent the accumulation of materials in the bin. The feeding strategy is adjusted based on the temperature and dust concentration data from the environmental compensation unit. The multi-parameter dynamic monitoring module monitors the feeding amount and flow rate in real time. If a feeding blockage or abnormal flow rate is detected, an alarm is immediately triggered and feeding is suspended. The self-cleaning module is activated to clean the pipeline. Feeding parameters, valve adjustment records, and abnormal events are encrypted and stored in the blockchain for the recipe adaptive management module to optimize subsequent feeding strategies. Combined with the mixing uniformity feedback from the waste recovery module, the viscosity coefficient sorting rules are dynamically adjusted to ensure feeding efficiency and mixing quality, providing reliable protection for automated production.

[0070] Generate the feeding priority sequence and set the viscosity coefficient of the raw material to μ i , i=1,2,…,n, n is the number of raw material types, sorted from high to low according to the viscosity coefficient, the sorting formula can be simply expressed as: μ i1 ≥μ i2 ≥…≥μ in , where i1,i2,…,i n is the number of the raw material. The sorting result determines the priority of the material feeding, that is, raw material i1 is fed first, i2 is second, and so on. The viscosity coefficient sorting rule is dynamically adjusted: let the numerical index of the mixing uniformity feedback from the waste recycling module be S, and the current viscosity coefficient sorting is μ i1 ≥μi2 ≥…≥μ in , the formula for adjusting the viscosity coefficient based on mixing uniformity feedback can be expressed as: j=1,2,…,n, where is the adjusted viscosity coefficient, is the viscosity coefficient before adjustment, Δμ is the adjustment step size, f(S) is a function of the mixing uniformity index S, and the direction and amplitude of the adjustment are determined according to the value of S. If the mixing uniformity is poor, the value of f(S) may cause a significant change in the viscosity coefficient ranking to optimize the feeding sequence and improve the mixing quality; if the mixing uniformity is good, the value of f(S) will cause a small change in the viscosity coefficient ranking or keep it unchanged.

[0071] The bamboo charcoal moisture content pretreatment unit includes a humidity sensor, a drying device and an infrared flow rate detector. The humidity sensor of the bamboo charcoal moisture content pretreatment unit is used to monitor the moisture content of the bamboo charcoal raw material in real time, and is automatically activated when it is detected that the moisture content exceeds a preset threshold. The activation logic of the drying device is: if the moisture content of the bamboo charcoal is greater than 8%, the high-temperature drying mode is started at a temperature of 120-150°C and lasts for 5 to 10 minutes. If the moisture content is between 5% and 8%, the low-temperature dehumidification mode is started at a temperature of 60-80°C and lasts for 8 to 15 minutes. After the drying is completed, the fluidity of the bamboo charcoal is verified by the infrared flow rate detector, and the subsequent ingredient parameters are adjusted. The pretreated bamboo charcoal is then weighed to ensure that the moisture content error is ≤1%.

[0072] The process of automatic batching and mixing control system based on industrial computer is as follows:

[0073] S1 Raw material pretreatment

[0074] Moisture content control: The humidity sensor detects the moisture content of the bamboo charcoal. If the moisture content exceeds the threshold, high-temperature drying is triggered. After reaching the standard, the fluidity is verified.

[0075] Anti-sticking pretreatment: Nano-coated valves reduce fly ash adhesion, and infrared flow rate detection calibrates feeding parameters in real time;

[0076] S2 Intelligent Recipe Generation

[0077] Dynamic proportioning: Automatically generate the initial formula based on the physical properties of raw materials;

[0078] Encrypted storage: Blockchain records recipe parameters, links suppliers and quality inspection data, and ensures traceability;

[0079] S3 dynamic batching optimization

[0080] Coordinated feeding: Raw materials with high viscosity are fed first, and the air blowing device prevents accumulation;

[0081] Algorithm collaboration:

[0082] Machine learning: predicting initial valve opening and drop value;

[0083] Fuzzy PID: real-time correction of errors caused by environmental changes, reducing the initial error of new formulas;

[0084] S4 Mixing and Quality Inspection

[0085] Intelligent mixing: select intermittent mixing mode according to the proportion of fly ash;

[0086] AI quality inspection: Multispectral imaging detects particle distribution, and AI vision identifies agglomerates;

[0087] S5 self-cleaning and waste recycling

[0088] Cleaning control: high-frequency vibration + high-pressure air blowing to remove residues, laser scanning to trigger secondary cleaning;

[0089] Waste recycling: intelligently sort out unqualified materials, add new raw materials at a ratio of ≤10%, and monitor the composition using near-infrared spectroscopy;

[0090] S6 data closed-loop management

[0091] Blockchain traceability: Production data is encrypted and stored, and customers can scan the code to trace batch information;

[0092] Predictive maintenance: Vibration and air pressure data are used to analyze equipment health status and dynamically optimize process parameters.

[0093] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0094] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An automatic batching and mixing control system based on industrial computers, characterized in that: include: Multi-parameter dynamic monitoring module, intelligent prediction and correction module, adaptive valve system, self-cleaning and waste recovery module, formula adaptive management module, environmental compensation unit, stirring control module, feeding control module, bamboo charcoal moisture content pretreatment unit; Multi-parameter dynamic monitoring module: integrated with weighing sensor, humidity sensor, infrared flow rate detector, temperature sensor, pressure sensor, torque sensor and airflow sensor; Intelligent prediction and correction module: Based on fuzzy PID control algorithm and machine learning model, it adjusts dynamic parameters such as drop value, valve opening and closing degree, material flow rate, etc. Adaptive valve system: uses a single adjustable caliber electromagnetic valve, and dynamically adjusts the valve opening and closing degree through the PID algorithm of the industrial computer; Self-cleaning and waste recovery module: including a high-frequency vibrator at the bottom of the weighing hopper, an air blowing device, a screw conveyor, a laser scanner, an RFID tag system, a near-infrared spectrometer, and a waste storage bin; Recipe adaptive management module: automatically generates initial ingredient ratio parameters based on raw material density and humidity characteristics, stores recipe parameters, and associates supplier information; Environmental compensation unit: The environmental compensation unit collects ambient temperature and dust concentration data in real time through temperature sensors and dust concentration sensors; Mixing control module: speed adjustment unit and intermittent mixing unit, automatically adjust the speed of the mixing blade according to the raw material ratio; Feeding control module: Generates feeding priority sequence based on raw material density and viscosity parameters; The bamboo charcoal moisture content pretreatment unit comprises a humidity sensor, a drying device and an infrared flow velocity detector.

2. The automatic batching and mixing control system based on an industrial computer according to claim 1, characterized in that: The airflow sensor in the multi-parameter dynamic monitoring module is installed on the top of the fly ash bin, the weighing sensor adopts a cantilever beam structure, the surface of which is covered with a polytetrafluoroethylene anti-stick film, and the humidity sensor is a capacitive probe.

3. The automatic batching and mixing control system based on an industrial computer according to claim 1, characterized in that: The inner wall of the valve of the adaptive valve system is coated with a nano-anti-stick coating. The opening and closing degree of the valve is dynamically adjusted by an industrial computer through a PID algorithm. An infrared flow rate detector feeds back the material flow rate to the industrial computer in real time. The target opening and closing degree is dynamically calculated based on the fuzzy PID algorithm, and a pulse signal is output to adjust the solenoid valve. During the fly ash batching stage, the weighing deviation caused by the fluffy material is compensated according to the air pressure data in the bin.

4. The automatic batching and mixing control system based on an industrial computer according to claim 1, characterized in that: The working process of the self-cleaning and waste recovery module is as follows: after the batching is completed, the high-frequency vibrator oscillates, and the air blowing device is started synchronously to spray compressed air to remove the residue on the bucket wall. After the quality of the substandard mixed material is judged, it is returned to the corresponding raw material bin by the screw conveyor.

5. The automatic batching and mixing control system based on an industrial computer according to claim 5, characterized in that: The feeding control module generates a feeding priority sequence based on the raw material density and viscosity parameters, gives priority to feeding materials with poor fluidity, dynamically adjusts the valve opening and closing degree according to the density difference, controls the opening interval of each raw material bin valve through an industrial computer, and synchronously starts the air blowing device during the bamboo charcoal feeding stage.

6. The automatic batching and mixing control system based on an industrial computer according to claim 1, characterized in that: The humidity sensor of the bamboo charcoal moisture content pretreatment unit is used to monitor the moisture content of the bamboo charcoal raw material in real time. It is automatically activated when it is detected that the moisture content exceeds a preset threshold. After drying is completed, the fluidity of the bamboo charcoal is verified by an infrared flow rate detector, and the subsequent ingredient parameters are adjusted, and the pretreated bamboo charcoal is weighed.

7. The automatic batching and mixing control system based on an industrial computer according to claim 1, characterized in that: The process of automatic batching and mixing control system based on industrial computer is as follows: S1 Raw material pretreatment Moisture content control: The humidity sensor detects the moisture content of the bamboo charcoal. If the moisture content exceeds the threshold, high-temperature drying is triggered. After reaching the standard, the fluidity is verified. Anti-sticking pretreatment: Nano-coated valves reduce fly ash adhesion, and infrared flow rate detection calibrates feeding parameters in real time; S2 Intelligent Recipe Generation Dynamic proportioning: Automatically generate the initial formula based on the physical properties of raw materials; Encrypted storage: Blockchain records recipe parameters, links suppliers and quality inspection data, and ensures traceability; S3 dynamic batching optimization Coordinated feeding: Raw materials with high viscosity are fed first, and the air blowing device prevents accumulation; Algorithm collaboration: Machine learning: predicting initial valve opening and drop value; Fuzzy PID: real-time correction of errors caused by environmental changes, reducing the initial error of new formulas; S4 Mixing and Quality Inspection Intelligent mixing: select intermittent mixing mode according to the proportion of fly ash; AI quality inspection: Multispectral imaging detects particle distribution, and AI vision identifies agglomerates; S5 self-cleaning and waste recycling Cleaning control: high-frequency vibration + high-pressure air blowing to remove residues, laser scanning to trigger secondary cleaning; Waste recycling: intelligently sort out unqualified materials, add new raw materials at a ratio of ≤10%, and monitor the composition using near-infrared spectroscopy; S6 data closed-loop management Blockchain traceability: Production data is encrypted and stored, and customers can scan the code to trace batch information; Predictive maintenance: Vibration and air pressure data are used to analyze equipment health status and dynamically optimize process parameters.

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