A plate stable conveying control method and system

Abstract

The present invention belongs to the technical field of sheet conveying, and specifically relates to a method and system for controlling the smooth conveying of sheets. The present invention effectively suppresses speed fluctuations through real-time monitoring and dynamic adjustment, ensures high precision and smoothness of the conveying process, and reduces sheet damage and equipment wear. Automatically adjust the tension of the conveyor belt to adapt to sheets of different materials and thicknesses, thereby improving the conveying efficiency and flexibility of sheet processing. Automatically identify and correct sheet position deviations using machine vision technology, reduce manual intervention, and improve the automation level and accuracy of conveying. Early warning of potential faults reduces sudden downtime, extends the service life of equipment, and ensures the continuity and stability of production. By integrating key technologies such as speed control, tension adjustment, automatic deviation correction, predictive maintenance, and intelligent scheduling, the problems of insufficient stability and adaptability of existing conveying systems are solved, and the overall intelligence level and production efficiency of the production line are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of plate conveying, and in particular relates to a plate smooth conveying control method and system. Background Art

[0002] In modern manufacturing, the sheet conveying system is one of the key links in the production line, which is of great significance for improving production efficiency, ensuring product quality and reducing production costs. Traditional sheet conveying methods often rely on fixed parameter control strategies, lacking real-time response and adaptability to dynamic changing factors in the conveying process. This leads to problems such as large speed fluctuations, position offset, and unstable conveying in the conveying process when facing sheets of different materials and thicknesses. In severe cases, it may even cause accelerated equipment wear and production stagnation. The sheet conveying systems in the prior art often have the following problems:

[0003] Speed ​​stability issues: Traditional conveying systems have difficulty in effectively controlling small fluctuations in conveying speed, especially when conveying thick or special-textured plates. Speed ​​fluctuations directly affect the smoothness and accuracy of conveying. Insufficient adaptability: Plates of different materials and thicknesses have different requirements for conveyor belt tension. Fixed tension settings cannot meet diverse conveying requirements, which can easily cause the plates to slip or be conveyed poorly. Position deviation and correction difficulties: There is a lack of efficient automatic correction mechanisms. Once the plates deviate from the target position, they often rely on manual intervention, which increases production costs and reduces efficiency. Low maintenance predictability: The maintenance of key components such as motors is often based on responses after a fault occurs. There is a lack of effective predictive maintenance methods, which increases the risk of unexpected downtime. Inefficient scheduling: The matching of production plans with actual conveying status is not intelligent enough, and the conveying sequence and rhythm cannot be flexibly adjusted, affecting overall production efficiency and the ability to respond to market changes.

[0004] In summary, the existing conveying system has problems with stability and adaptability, which affects the intelligence level and production efficiency of the overall production line. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for controlling the smooth conveying of plates, aiming to solve the problems of insufficient stability and adaptability of existing conveying systems by integrating key technologies such as speed control, tension adjustment, automatic correction, predictive maintenance and intelligent scheduling, so as to improve the intelligence level and production efficiency of the overall production line.

[0006] To achieve the above object, the present invention adopts the following technical solution: a method for controlling the smooth conveying of a plate, comprising the following steps:

[0007] a. Use at least two speed sensors installed at different positions on the conveyor belt to collect real-time speed data during the sheet conveying process V t , where t is time;

[0008] b. Calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process;

[0009] c. According to the speed fluctuation coefficient ΔV, dynamically adjust the current output of the conveyor belt drive motor I(t) = I o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations;

[0010] d. Implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold;

[0011] e. Adjust the conveyor belt tension F based on the material and thickness parameter D of the conveying plate t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the thickness of the plate, which is used to ensure the smooth transportation of plates of different materials and thicknesses;

[0012] f. Use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position through the servo control system according to ΔX, X target is the desired system output position, X actual is the actual output value of the system;

[0013] g. Realize intelligent scheduling, optimize the sorting and delivery rhythm of plates according to the production plan and current delivery status, and the scheduling algorithm is based on T opt =argmin T ∑(T i -T) 2 , where T i is the time required for each production link, and T is the target shortest total time.

[0014] Preferably, the speed sensor is a laser Doppler velocimeter.

[0015] Preferably, it also includes: according to the vibration frequency f detected during the conveying process V , adjust the damping coefficient of the conveyor belt support structure, including:

[0016] Vibration monitoring and frequency extraction: Install acceleration sensors at the support points of the conveyor belt to continuously monitor the vibration signals generated during the conveying process;

[0017] The vibration signal collected by the sensor is converted from a time domain signal to a frequency domain signal, and the vibration frequency f is extracted. V ; Based on historical data analysis, establish the relationship between frequency and damping requirements and determine the frequency correlation coefficient k f ; Adjust parameter settings and determine adjustment parameter n;

[0018] The damping coefficient is calculated by the formula According to the real-time monitored vibration frequency f V and the determined k f ,n value, calculate the current required damping coefficient C d ;

[0019] Dynamic damping adjustment is implemented by equipping the support position of the conveyor belt with a variable damper according to the calculated damping coefficient C d , automatically adjust the working state of the damper, increase or decrease the damping force in real time to suppress vibration during the conveying process.

[0020] Preferably, the predictive maintenance analysis further includes monitoring motor voltage fluctuations and performing multivariate analysis in combination with current and temperature data, including:

[0021] Install voltage sensors, current sensors and temperature sensors in the motor drive system of the conveyor belt to collect the voltage U(t), current I(t) and temperature T when the motor is running. motor (t) data;

[0022] Preprocess the collected data, extract key features from the preprocessed data, and then screen out the feature combination that has the greatest impact on fault prediction;

[0023] Based on the selected features, a multivariate analysis model is constructed. When training the model, historical fault records are used as label data and fault-free operation data are used as negative samples. The model is trained through supervised learning methods to identify fault precursor patterns.

[0024] Input the real-time collected data into the trained multivariate analysis model to evaluate the health of the motor in real time;

[0025] Set a warning threshold. When the model predicts that the probability of failure exceeds the preset threshold, an alarm is triggered immediately.

[0026] Preferably, the acquisition of the material and thickness parameter D of the plate includes:

[0027] For each plate, a unique RFID tag is pre-encoded according to its material and thickness information, and the encoded RFID tag is firmly attached to the non-processing surface or designated position of the plate;

[0028] An integrated RFID reading system is installed at the entrance of the sheet into the conveyor line. When the sheet passes through the RFID reading area, it automatically activates and transmits a radio frequency signal to activate the RFID tag on the sheet;

[0029] The reader receives the unique ID and stored parameter information returned by the tag, and converts the received RF signal into readable data, including material code and thickness value D, through decoding software;

[0030] According to the material and thickness parameters D obtained by decoding, the preset parameter database is queried to match the corresponding tension adjustment instructions. The database stores the optimal tension setting values ​​corresponding to different materials and thicknesses. According to the matching results, the actual required conveyor belt tension adjustment amount is calculated to generate specific adjustment instructions.

[0031] Preferably, the servo control system adopts a PID controller to dynamically adjust the control parameters according to the offset ΔX, using the formula U(t)=K p ·e(t)+K i ∫e(t)dt+K d (de(t) / dt) adjusts the output, where e(t) = X target -X actual , K p ,K i ,K d are the proportional, integral, and differential gains of the PID controller, Xtarget is the desired system output position, Xactual is the current actual output value of the system, and U(t) is the voltage of the drive motor.

[0032] Preferably, the intelligent scheduling dynamically adjusts T according to the raw material inventory status. opt To optimize the supply chain.

[0033] Preferably, the method further comprises: before the conveying starts, using an ultrasonic sensor to detect the surface flatness of the plate, and adjusting the initial tension of the conveyor belt according to the detection result, including:

[0034] An ultrasonic sensor array is installed at the starting end of the conveyor line to transmit and receive ultrasonic pulses to detect the flatness of the plate surface;

[0035] Before conveying begins, the ultrasonic sensor array is activated to emit ultrasonic pulses to the surface of the sheet.

[0036] Collect and process the echo data from the sensor, analyze the surface height distribution, and calculate the flatness index;

[0037] According to the test results of the flatness of the plate, the corresponding tension adjustment instructions are formulated and sent to the tension control system of the conveyor belt to automatically adjust the tension device to achieve the adjustment of the conveyor belt tension.

[0038] On the other hand, the present invention provides a plate smooth conveying control system, a real-time speed monitoring module, which is used to collect real-time speed data V during the plate conveying process using at least two speed sensors installed at different positions of the conveyor belt. t , where t is time;

[0039] Speed ​​stability evaluation module, used to calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process;

[0040] Dynamic current adjustment module, used to dynamically adjust the current output I(t)=I of the conveyor belt drive motor according to the speed fluctuation coefficient ΔV o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations;

[0041] The predictive maintenance early warning module is used to implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt. motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold;

[0042] The tension adjustment module is used to adjust the conveyor belt tension F based on the material and thickness parameters D of the conveyed plate. t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the thickness of the plate, which is used to ensure the smooth transportation of plates of different materials and thicknesses;

[0043] The correction module is used to use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position through the servo control system according to ΔX, X target is the desired system output position, X actual is the actual output value of the system;

[0044] The scheduling optimization module is used to realize intelligent scheduling. According to the production plan and the current conveying status, the sorting and conveying rhythm of the plates are optimized. The scheduling algorithm is based on T opt =argmin T ∑(T i -T) 2 , where T i is the time required for each production link, and T is the target shortest total time;

[0045] Big data analysis module, used to collect and analyze historical transportation data, used to optimize the adjustment coefficient k ΔV ,K D ,k f .

[0046] Technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a method and system for controlling the smooth conveyance of plates, which has the following advantages:

[0047] The present invention effectively suppresses speed fluctuations through real-time monitoring and dynamic adjustment, ensures high precision and stability of the conveying process, and reduces plate damage and equipment wear. The conveyor belt tension is automatically adjusted to adapt to plates of different materials and thicknesses, thereby improving conveying efficiency and flexibility in plate handling. Machine vision technology is used to automatically identify and correct plate position deviations, reducing manual intervention and improving the automation level and accuracy of conveying. Early warning of potential faults reduces sudden downtime, extends the service life of equipment, and ensures the continuity and stability of production. By integrating key technologies such as speed control, tension adjustment, automatic deviation correction, predictive maintenance, and intelligent scheduling, the problems of insufficient stability and adaptability of existing conveying systems are solved, and the overall intelligence level and production efficiency of the production line are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a method for controlling the smooth conveyance of plates according to the present invention;

[0049] Figure 2 The present invention is a block diagram of a control system for smooth plate transportation. DETAILED DESCRIPTION

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

[0051] The present invention provides a method for controlling the smooth conveying of plates. Figure 1 As shown, the following steps are included:

[0052] a. Use at least two speed sensors installed at different positions on the conveyor belt to collect real-time speed data during the sheet conveying process V t , where t is time; preferably, the speed sensor is a laser Doppler velocimeter.

[0053] In this embodiment, in order to accurately collect the real-time speed data Vt during the plate conveying process, at least two high-performance laser Doppler velocimeters are used and installed at the head and tail ends of the conveyor belt or key monitoring points respectively.

[0054] The model of the laser Doppler speed measurement system is PolytecPDV-100. The laser Doppler speed meter is a non-contact device for measuring the speed of high-speed moving objects. Its principle is based on the Doppler effect. When a laser beam is irradiated onto the surface of a moving object or passes through a fluid, the reflected or scattered light waves will undergo a frequency shift, and this frequency shift is proportional to the relative speed of the object relative to the light source. The PDV-100 system emits a beam of laser to the surface of the plate on the conveyor belt, and part of the laser is reflected back to the detector. The movement of the plate causes a slight change in the frequency of the reflected light, which is accurately measured and converted into speed data V by the built-in optical components and high-speed data processor. t The system has extremely high measurement accuracy (up to micrometer / second level) and response speed, and is very suitable for speed monitoring in dynamically changing environments.

[0055] Installation and configuration: Two PDV-100 laser Doppler velocimeters are installed at the starting and end points of the conveyor belt, or at key monitoring points on the conveying path, to ensure full coverage of the entire conveying process of the plate. Ensure that the laser beam forms an appropriate angle with the conveyor belt surface to obtain the best reflection signal, while avoiding direct irradiation on surfaces that may cause strong reflections to reduce measurement errors.

[0056] By connecting the system to a computer through its own software, real-time data collection, analysis and display can be achieved, providing reliable data support for subsequent speed fluctuation coefficient calculation and control strategy adjustment.

[0057] The advantages of using a laser Doppler velocimeter as a speed sensor are its high precision, fast response and non-contact measurement characteristics. It can accurately monitor speed changes without affecting the conveying process, providing a solid data basis for dynamically adjusting the current output of the conveyor belt drive motor, thereby effectively improving the stability and efficiency of the conveying system.

[0058] b. Calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process;

[0059] In the evaluation of the stability of the sheet conveying process, calculating the speed fluctuation coefficient ΔV is a key step. This step requires not only accurate data collection, but also reasonable algorithm processing to accurately reflect the stability of the conveying process. The following is a detailed implementation of this process:

[0060] Data collection cycle definition: First, the time period of data collection needs to be clarified, such as collecting speed data once every second or every minute, to ensure that the collection frequency can capture the details of speed changes without adding unnecessary processing burden due to too dense data. Assume that the data is collected once per second, that is, the sampling frequency is 1Hz.

[0061] Data processing algorithm:

[0062] Data preprocessing: De-noise a series of speed data obtained in each acquisition cycle. Sliding average or median filtering can be used to remove accidental outliers to ensure V max and V min accuracy.

[0063] Calculate the maximum, minimum and average values:

[0064] V max : The maximum value of the speed data is selected in each cycle, which represents the fastest speed of the plate conveying in this time period.

[0065] V min : Accordingly, the minimum value in the speed data is selected, representing the slowest speed.

[0066] V avg :Calculate the arithmetic mean of all speed data within a period to reflect the overall speed level of the period.

[0067] Calculate the speed fluctuation coefficient: According to the above definition, calculate the speed fluctuation coefficient ΔV = (Vmax -V min ) / V avg The smaller this value is, the smaller the speed fluctuation is during the conveying process and the higher the stability is.

[0068] Monitoring and feedback mechanism: The calculated ΔV value is displayed in real time on the operation interface of the control room and compared with the preset stability standard. If ΔV exceeds the preset range, the system automatically triggers an alarm and dynamically adjusts the current output of the conveyor belt drive motor according to the speed fluctuation coefficient mentioned above to restore the stability of the conveyor.

[0069] Through the above implementation, a quantitative evaluation of the stability of the conveying process is achieved, which provides a scientific basis for the subsequent real-time control strategy and ensures that the conveying operation is carried out efficiently and stably.

[0070] c. According to the speed fluctuation coefficient ΔV, dynamically adjust the current output of the conveyor belt drive motor I(t) = I o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations;

[0071] d. Implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold;

[0072] Predictive maintenance analysis also includes monitoring motor voltage fluctuations and combining current and temperature data for multivariate analysis to more accurately predict potential failures. The specific steps are as follows:

[0073] Step 1: Multi-source data collection: Install high-precision voltage sensors, current sensors, and temperature sensors in the motor drive system to ensure that the voltage U(t), current I(t), and temperature T of the motor can be collected in real time and accurately. motor (t) Data.

[0074] Step 2: Data preprocessing: filter the collected data to remove noise interference and ensure data quality. Apply time series analysis technology to smooth the fluctuation curves of voltage, current and temperature for subsequent analysis. Align the time series data synchronously to ensure that the voltage, current and temperature data are compared and analyzed at the same time resolution.

[0075] Step 3: Feature extraction and selection: extract key features from the preprocessed data, such as the mean, variance, harmonic content of voltage and current, as well as the maximum temperature and temperature rise rate. Use feature selection algorithms (such as recursive feature elimination and principal component analysis PCA) to screen out the most influential feature combinations for fault prediction, reduce the dimension while maintaining the validity of the information.

[0076] Step 4: Multivariate analysis model construction, based on the selected features, build a multivariate analysis model. You can choose machine learning algorithms such as support vector machine (SVM), random forest (RF), neural network (NN), or use deep learning methods such as long short-term memory network (LSTM) to process time series data. When training the model, use historical fault records as label data and fault-free operation data as negative samples, and train the model to identify fault precursor patterns through supervised learning methods.

[0077] Step 5: Real-time monitoring and fault warning: Input the real-time collected data into the trained multivariate analysis model to evaluate the health of the motor in real time. Set the warning threshold. When the model predicts that the probability of failure exceeds the preset threshold, an alarm is triggered immediately to prompt the operator to take preventive measures or arrange maintenance.

[0078] Step 6: Model maintenance and optimization, regularly collect new operating data and fault cases, iteratively train and verify the model to ensure that the prediction accuracy continues to improve over time. Analyze false positives and false negatives, adjust model parameters or introduce new features, optimize prediction performance, and form a closed-loop mechanism of continuous improvement to achieve more accurate predictive maintenance.

[0079] e. Adjust the conveyor belt tension F based on the material and thickness parameter D of the conveying plate t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the plate thickness, which is used to ensure the smooth transportation of plates of different materials and thicknesses; the acquisition of material and thickness parameters D is completed by automatically identifying the plate label information through the integrated RFID reading system. The specific steps are as follows:

[0080] Step 1: RFID tag encoding and attachment. Pre-encode a unique RFID tag for each plate according to its material and thickness information. The label content should include but not be limited to key parameters such as material type code, precise thickness value, batch number, etc. Attach the encoded RFID tag firmly to the non-processing surface or designated location of the plate to ensure that it is not easy to fall off during transportation and is easy to be identified by the RFID reader.

[0081] Step 2: RFID reading system deployment: Install an integrated RFID reading system at the entrance of the conveyor line, including RFID antenna, reader and supporting control software. Ensure that the reading range covers the path of the plate and can quickly and accurately capture the tag information.

[0082] Step 3: Real-time data capture and decoding, when the plate passes through the RFID reading area, the system automatically activates and transmits the radio frequency signal to activate the RFID tag on the plate. The reader receives the unique ID and stored parameter information returned by the tag, and converts the received radio frequency signal into readable data, including the material code and thickness value D, through the decoding software.

[0083] Step 4: Parameter matching and tension adjustment instruction generation. The control system queries the preset parameter database based on the decoded material and thickness parameters D and matches the corresponding tension adjustment instructions. The database stores the optimal tension setting values ​​corresponding to different materials and thicknesses. Based on the matching results, the actual required conveyor belt tension adjustment amount is calculated and a specific adjustment instruction is generated.

[0084] Step 5: Execute tension adjustment. The adjustment command is sent to the tension control system of the conveyor system, driving the actuator to automatically adjust the tensioning device of the conveyor belt to ensure the tension F of the conveyor belt. t It meets the best requirements for current sheet metal conveying. Real-time monitoring of the tension adjustment process ensures smooth and rapid adjustment to avoid unstable conveying caused by sudden changes in tension.

[0085] Step 6: Data recording and system optimization, record the plate information read each time, the tension adjustment operation performed and the related effects in the database for subsequent analysis and optimization. Review the data regularly, evaluate the stability of conveying plates of different materials and thicknesses, fine-tune the tension setting value in the database according to the actual operation situation, and continuously optimize the adaptability and efficiency of the entire system.

[0086] f. Use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position according to ΔX through the servo control system; the specific steps are as follows:

[0087] Step 1: System identification and initial parameter determination. First, perform system identification on the servo control system and collect response data under different operating conditions to determine the dynamic characteristics of the system. Initially set the proportional K of the PID controller. p , integral K i and differential K d Gain parameter. This is usually based on experience or a rough estimate from system identification results.

[0088] Step 2: Target value setting and error calculation to determine the control target X target , that is, the desired system output position or state. Real-time measurement of the current actual output value X of the system actual , calculate the deviation e(t) = X target -X actual , as the input of the PID controller.

[0089] Step 3: PID calculation and output adjustment, applying the PID control algorithm formula U(t)=K p ·e(t)+K i ∫e(t)dt+K d (de(t) / dt), calculate the control signal U(t) in real time.

[0090] Among them, the proportional term K p e(t) directly reflects the current deviation; the integral term ∫e(t)dt accumulates historical errors and is used to eliminate static deviations; the differential term de(t) / dt predicts future trends and improves response speed.

[0091] Step 4: Output limiting processing, limit the calculated control signal U(t) to prevent excessive control action from causing system oscillation or hardware damage. Set upper and lower limits to ensure that U(t) remains within a safe and effective range.

[0092] Step 5: Feedback control is executed, and the processed control signal U(t) is output to the servo driver to adjust the voltage or current of the drive motor, thereby changing the position or speed of the actuator. The state of the actuator is monitored in real time to ensure that it is accurately adjusted according to the control signal, gradually reducing the deviation e(t) until it reaches or remains near the target value.

[0093] Step 6: Parameter adjustment and performance optimization, observe the system response, and fine-tune the PID parameter K according to the actual operation effect p ,K i ,K d , it may be necessary to iterate testing and adjustment, such as trial and error, Ziegler-Nichols law, or more advanced self-tuning algorithms. Monitor and record system performance, such as response time, overshoot, steady-state error, and other indicators, and continuously optimize until the predetermined control accuracy and stability requirements are met.

[0094] g. Realize intelligent scheduling, optimize the sorting and delivery rhythm of plates according to the production plan and current delivery status, and the scheduling algorithm is based on T opt =argmin T Σ(T i -T) 2 , where T iis the time required for each production link, and T is the target shortest total time. In intelligent scheduling, T is dynamically adjusted according to the inventory status of raw materials. opt To achieve the optimization of the supply chain. The specific steps are as follows:

[0095] Step 1: Data integration and real-time monitoring, integrating ERP system, inventory management system and production scheduling system to ensure real-time update and sharing of key data such as raw material inventory status, production progress, order requirements, etc. Real-time monitoring of raw material inventory levels, including total amount, remaining amount of various materials and expected consumption rate.

[0096] Step 2: Demand forecasting and inventory analysis: Use time series analysis, machine learning and other methods to forecast market demand in the near future and in the future. Analyze the gap between current inventory levels and forecasted demand, and identify potential risks of inventory shortages or excesses.

[0097] Step 3: Optimize production plan. Dynamically adjust production plan based on demand forecast results and current inventory status. Prioritize production tasks related to raw materials with tight inventory and balance the workload of each production line. opt , ensuring that production scheduling not only meets market demand, but also takes into account the optimal use of inventory, reducing backlogs and emergency procurement costs.

[0098] Step 4: Material Requirements Planning (MRP) adjustment: Update the material requirements plan according to the optimized production plan, issue purchase or internal transfer instructions in a timely manner, and ensure that the supply of raw materials matches the production schedule. For raw materials with long delivery periods, start the procurement process in advance to avoid affecting the production schedule due to insufficient inventory.

[0099] Step 5: Dynamically adjust inventory strategies, analyze historical inventory turnover data, and dynamically adjust safety stock levels and reorder points based on market fluctuations. Implement lean inventory management to reduce unnecessary inventory holding costs and improve capital turnover efficiency.

[0100] Step 6: Performance monitoring and feedback loop, regularly evaluate the effect of intelligent scheduling algorithms, including key performance indicators such as inventory cost savings, on-time order delivery rate, and production efficiency improvement. According to the evaluation results, adjust and optimize the algorithm, introduce new data sources or improve the prediction model, and form a closed-loop management mechanism for continuous improvement.

[0101] Through the above steps, the intelligent scheduling algorithm can not only flexibly respond to changes in market demand, but also ensure the efficient operation of the supply chain, minimize inventory occupancy, and improve overall operational efficiency.

[0102] Furthermore, according to the vibration frequency f detected during the conveying process v , adjust the damping coefficient of the conveyor belt support structure, including:

[0103] Step 1: Vibration monitoring and frequency extraction: Install acceleration sensors at key support points of the conveying system to continuously monitor the vibration signals generated during the conveying process.

[0104] Perform fast Fourier transform on the vibration signal collected by the sensor to convert it from time domain signal to frequency domain signal, so as to extract the main vibration frequency f V , this frequency reflects the current vibration characteristics of the conveying system;

[0105] Step 2: Determine the frequency correlation coefficient. Based on historical data analysis or experimental tests, establish a relationship model between frequency and damping requirements and determine the frequency correlation coefficient k. f , which reflects the degree of damping adjustment required for effective vibration reduction at a specific frequency;

[0106] Step 3: Adjust the parameter settings and determine the adjustment parameter n. This parameter determines the slope of the curve of the damping coefficient changing with frequency, and affects the sensitivity and effect of the damping adjustment. Through system simulation or field tests, optimize the n value to achieve the best vibration reduction effect and system stability.

[0107] Step 4: Damping coefficient calculation, apply formula C d =k f ·f V n , according to the real-time monitored vibration frequency f V and the determined k f ,n value, calculate the current required damping coefficient C d ;

[0108] Step 5: Dynamic damping adjustment is implemented by equipping the key support positions of the conveyor belt with variable dampers according to the calculated damping coefficient C d , automatically adjust the working state of the damper, increase or decrease the damping force in real time to suppress vibration during transportation; through a closed-loop control system, ensure that the damping adjustment responds quickly to changes in vibration frequency and maintains the stable operation of the conveying system.

[0109] Step 6: Effect monitoring and feedback adjustment. Real-time monitoring of the vibration level of the system after adjusting the damping. Use the feedback signal of the acceleration sensor to evaluate the vibration reduction effect. If the vibration level still exceeds the preset threshold, perform steps 1 to 5 again. You may need to fine-tune k according to the actual situation. f ,n value until the ideal smooth conveying state is achieved. Collect data regularly, evaluate and optimize the damping adjustment strategy to ensure high efficiency and stability in long-term operation.

[0110] Furthermore, before the conveying starts, an ultrasonic sensor is used to detect the surface flatness of the plate, and the initial tension of the conveyor belt is adjusted according to the detection result, including:

[0111] Step 1: Ultrasonic sensor deployment and calibration, install the ultrasonic sensor array at the beginning of the conveyor line to ensure that the entire width of the plate is covered. These sensors are used to transmit and receive ultrasonic pulses to detect the flatness of the plate surface. The sensors are precisely calibrated, including angle, distance and time delay corrections, to ensure the accuracy of the measurement results.

[0112] Step 2: Start the surface flatness detection program. Before the conveying starts, start the ultrasonic detection program, and the sensor array emits ultrasonic pulses to the surface of the plate. The pulses are reflected by the surface of the plate, and the sensor records the echo time. The unevenness of the plate surface is determined by calculating the echo time difference.

[0113] Step 3: Data processing and flatness assessment, collect and process the echo data of the sensor, apply algorithms to analyze the surface height distribution, and calculate flatness indicators, such as peak-to-valley difference, flatness deviation, etc. Set thresholds to determine whether the plate meets the conveying requirements. If the flatness is lower than the preset standard, an alarm is triggered or unqualified plates are automatically excluded.

[0114] Step 4: Formulate a tension adjustment strategy. According to the flatness test results of the plate, formulate a corresponding tension adjustment strategy. If the plate is relatively uneven, it may be necessary to increase the initial tension of the conveyor belt to ensure stability during the conveying process. Determine the adjustment parameters, such as the increased basic tension value or the tension adjustment coefficient. These parameters should be directly related to the flatness index and can be obtained through algorithms or table lookup.

[0115] Step 5: Dynamically adjust the conveyor belt tension, send the calculated adjustment command to the conveyor belt tension control system, automatically adjust the tensioning device, and achieve precise adjustment of the conveyor belt tension. Ensure that the adjustment process is smooth and fast to avoid affecting the overall start-up time of the conveyor line.

[0116] Step 6: Conveying process monitoring and feedback optimization. During the conveying process, continuously monitor the running status of the conveyor belt and the stability of the plate conveying, collect data and feed it back to the management system. Compare the conveying effect before and after adjustment, analyze the effectiveness of the tension adjustment strategy, and continuously optimize and adjust the algorithm and parameter settings according to the actual operation situation to achieve the best conveying stability.

[0117] Through the above steps, the solution can effectively detect and adjust the initial tension of the conveyor belt according to the flatness of the plate surface before transportation begins, thereby ensuring the smooth progress of the entire transportation process and reducing the failure rate and plate damage during transportation.

[0118] On the other hand, the present invention provides a plate stable conveying control system, such as Figure 2 As shown, it includes: real-time speed monitoring module, speed stability evaluation module, dynamic current adjustment module, predictive maintenance warning module, tension adjustment module, deviation correction module, scheduling optimization module and big data analysis module.

[0119] The real-time speed monitoring module is used to collect real-time speed data during the conveying process of the plate using at least two speed sensors installed at different positions of the conveyor belt. t , where t is time;

[0120] Speed ​​stability evaluation module, used to calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process;

[0121] Dynamic current adjustment module, used to dynamically adjust the current output I(t)=I of the conveyor belt drive motor according to the speed fluctuation coefficient ΔV o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations;

[0122] The predictive maintenance early warning module is used to implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt. motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold;

[0123] The tension adjustment module is used to adjust the conveyor belt tension F based on the material and thickness parameters D of the conveyed plate. t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the thickness of the plate, which is used to ensure the smooth transportation of plates of different materials and thicknesses;

[0124] The correction module is used to use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position through the servo control system according to ΔX, Xtarget is the desired system output position, X actual is the actual output value of the system;

[0125] The scheduling optimization module is used to realize intelligent scheduling. According to the production plan and the current conveying status, the sorting and conveying rhythm of the plates are optimized. The scheduling algorithm is based on T opt =argmin T Σ(T i -T) 2 , where T i is the time required for each production link, and T is the target shortest total time.

[0126] Specifically, the big data analysis module is used to collect and analyze historical transportation data to optimize the adjustment coefficient k ΔV ,K D ,k f , in order to achieve self-learning and continuous improvement of the transportation control strategy. The specific implementation steps are as follows:

[0127] Step 1: Data collection and integration. Design and deploy a data collection system to ensure that all relevant data (such as sensor data, control parameters, environmental conditions, etc.) are accurately recorded and stored in each link of the sheet conveying (such as speed monitoring, vibration detection, tension adjustment, fault warning, etc.).

[0128] The data is integrated into a unified big data platform, and the format is standardized and cleaned to remove outliers and invalid data to ensure data quality.

[0129] Step 2: Feature engineering and model building. Perform feature engineering on the integrated data to extract features that are closely related to the optimization of the transportation control strategy, such as the distribution of historical adjustment coefficients, adjustment frequency, system response time, and changes in transportation efficiency.

[0130] Based on the feature set, build machine learning or deep learning models, such as regression models, neural networks, reinforcement learning models, etc., to predict and optimize the adjustment coefficient k ΔV ,K D ,k f wait.

[0131] Step 3: Historical data analysis and pattern recognition, using big data analysis tools to analyze historical transportation data, identify the relationship between transportation efficiency, stability and adjustment coefficient, and the optimal parameter configuration under different working conditions. Through clustering, association rule analysis and other methods, special needs and potential problems under different materials, thicknesses or specific working conditions are discovered.

[0132] Step 4: Dynamically optimize the adjustment coefficients, feed back the analysis results to the control system, and dynamically adjust the coefficients kΔV, kD, kf, etc. to adapt to the specific requirements of the current transportation task and historical learning results. Implement an online update mechanism for the adjustment coefficients to ensure that the transportation strategy can be continuously optimized based on the latest data and analysis results.

[0133] Step 5: Performance evaluation and feedback loop, set performance evaluation indicators, such as transportation stability, efficiency improvement percentage, failure rate reduction, etc., and regularly evaluate the actual effect of the optimized transportation control strategy. Collect evaluation feedback, compare with the expected goals, identify optimization space, and form a data-driven iterative improvement mechanism.

[0134] Step 6: Knowledge base construction and self-learning, establish a conveying control knowledge base, store successful cases, failure lessons, best practices, etc. in the optimization process, as a resource for subsequent self-learning and decision support. Integrate automated learning algorithms to enable the system to self-adjust and optimize model parameters based on new data, and achieve continuous self-learning and intelligent upgrading of conveying control strategies. Through the above steps, the big data analysis module can make full use of historical data, continuously optimize conveying control strategies, promote the development of intelligent and adaptive capabilities of the conveying system, and thus improve overall production efficiency and stability.

[0135] In addition, the above-mentioned real-time speed monitoring module, speed stability evaluation module, dynamic current adjustment module, predictive maintenance warning module, tension adjustment module, correction module and scheduling optimization module are also used to implement the other steps of the above-mentioned plate smooth conveying control method when executed, which will not be elaborated here one by one.

[0136] In summary, the present invention effectively suppresses speed fluctuations through real-time monitoring and dynamic adjustment, ensures high precision and stability of the conveying process, and reduces plate damage and equipment wear. Automatically adjust the tension of the conveyor belt to adapt to plates of different materials and thicknesses, thereby improving conveying efficiency and flexibility in plate handling. Automatically identify and correct plate position deviations using machine vision technology, reducing manual intervention and improving the level of automation and accuracy in conveying. Early warning of potential faults reduces sudden downtime, extends the service life of equipment, and ensures the continuity and stability of production. By integrating key technologies such as speed control, tension adjustment, automatic deviation correction, predictive maintenance, and intelligent scheduling, the problems of insufficient stability and adaptability of existing conveying systems are solved, and the overall intelligence level and production efficiency of the production line are improved.

[0137] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling the smooth conveying of plates, characterized in that: The following steps are involved: a. Use at least two speed sensors installed at different positions on the conveyor belt to collect real-time speed data during the sheet conveying process V t , where t is time; b. Calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process; c. According to the speed fluctuation coefficient ΔV, dynamically adjust the current output of the conveyor belt drive motor I(t) = I o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations; d. Implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold; e. Adjust the conveyor belt tension F based on the material and thickness parameter D of the conveying plate t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the thickness of the plate, which is used to ensure the smooth transportation of plates of different materials and thicknesses; f. Use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position through the servo control system according to ΔX, X target is the desired system output position, X actual is the actual output value of the system; g. Realize intelligent scheduling, optimize the sorting and delivery rhythm of plates according to the production plan and current delivery status, and the scheduling algorithm is based on T opt =argmin T ∑(T i -T) 2 , where T i is the time required for each production link, and T is the target shortest total time.

2. A plate smooth conveying control method according to claim 1, characterized in that: The speed sensor is a laser Doppler velocimeter.

3. A plate smooth conveying control method according to claim 1, characterized in that: Also includes: According to the vibration frequency f detected during the conveying process V , adjust the damping coefficient of the conveyor belt support structure, including: Vibration monitoring and frequency extraction: Install acceleration sensors at the support points of the conveyor belt to continuously monitor the vibration signals generated during the conveying process; The vibration signal collected by the sensor is converted from a time domain signal to a frequency domain signal, and the vibration frequency f is extracted. V ; Based on historical data analysis, establish the relationship between frequency and damping requirements and determine the frequency correlation coefficient k f ; Adjust parameter settings and determine adjustment parameter n; The damping coefficient is calculated by the formula C d =k f ·f V n , according to the real-time monitored vibration frequency f V and the determined k f ,n value, calculate the current required damping coefficient C d ; Dynamic damping adjustment is implemented by equipping the support position of the conveyor belt with a variable damper according to the calculated damping coefficient C d , automatically adjust the working state of the damper, increase or decrease the damping force in real time to suppress vibration during the conveying process.

4. A plate smooth conveying control method according to claim 1, characterized in that: The predictive maintenance analysis also includes monitoring motor voltage fluctuations and combining current and temperature data to perform multivariate analysis, including: Install voltage sensors, current sensors and temperature sensors in the motor drive system of the conveyor belt to collect the voltage U(t), current I(t) and temperature T when the motor is running. motor (t) data; Preprocess the collected data, extract key features from the preprocessed data, and then screen out the feature combination that has the greatest impact on fault prediction; Based on the selected features, a multivariate analysis model is constructed. When training the model, historical fault records are used as label data and fault-free operation data are used as negative samples. The model is trained through supervised learning methods to identify fault precursor patterns. Input the real-time collected data into the trained multivariate analysis model to evaluate the health of the motor in real time; Set a warning threshold. When the model predicts that the probability of failure exceeds the preset threshold, an alarm is triggered immediately.

5. A plate smooth conveying control method according to claim 1, characterized in that: The acquisition of the material and thickness parameters D of the plate includes: For each plate, a unique RFID tag is pre-encoded according to its material and thickness information, and the encoded RFID tag is firmly attached to the non-processing surface or designated position of the plate; An integrated RFID reading system is installed at the entrance of the sheet into the conveyor line. When the sheet passes through the RFID reading area, it automatically activates and transmits a radio frequency signal to activate the RFID tag on the sheet; The reader receives the unique ID and stored parameter information returned by the tag, and converts the received RF signal into readable data, including material code and thickness value D, through decoding software; According to the material and thickness parameters D obtained by decoding, the preset parameter database is queried to match the corresponding tension adjustment instructions. The database stores the optimal tension setting values ​​corresponding to different materials and thicknesses. According to the matching results, the actual required conveyor belt tension adjustment amount is calculated to generate specific adjustment instructions.

6. A plate smooth conveying control method according to claim 1, characterized in that: The servo control system adopts a PID controller to dynamically adjust the control parameters according to the offset ΔX, using the formula U(t)=K p ·e(t)+K i ∫e(t)dt+K d (de(t) / dt) adjusts the output, where e(t) = X target -X actual , K p ,K i ,K d are the proportional, integral, and differential gains of the PID controller, respectively. target is the desired system output position, X actual is the actual output value of the system, and U(t) is the voltage of the driving motor.

7. A plate smooth conveying control method according to claim 1, characterized in that: In the intelligent scheduling, T is dynamically adjusted according to the raw material inventory status. opt To optimize the supply chain.

8. A plate smooth conveying control method according to claim 1, characterized in that: Also includes: Before conveying begins, an ultrasonic sensor is used to detect the surface flatness of the plate, and the initial tension of the conveyor belt is adjusted according to the detection results, including: An ultrasonic sensor array is installed at the starting end of the conveyor line to transmit and receive ultrasonic pulses to detect the flatness of the plate surface; Before the conveying starts, the ultrasonic sensor array is started to emit ultrasonic pulses to the surface of the plate; Collect and process the echo data from the sensor, analyze the surface height distribution, and calculate the flatness index; According to the test results of the flatness of the plate, the corresponding tension adjustment instructions are formulated and sent to the tension control system of the conveyor belt to automatically adjust the tension device to achieve the adjustment of the conveyor belt tension.

9. A plate smooth conveying control system for executing the method as claimed in claim 3, characterized in that: include: The real-time speed monitoring module is used to collect real-time speed data during the conveying process of the plate using at least two speed sensors installed at different positions of the conveyor belt. t , where t is time; Speed ​​stability evaluation module, used to calculate the speed fluctuation coefficient ΔV = (V max -V min ) / V avg , where V max and V min are the maximum and minimum speed values ​​during the acquisition period, V avg is the average speed during the cycle, which is used to evaluate the stability of the conveying process; Dynamic current adjustment module, used to dynamically adjust the current output I(t)=I of the conveyor belt drive motor according to the speed fluctuation coefficient ΔV o +k ΔV ΔV, where I o is the basic current, k ΔV It is a proportional coefficient adjusted according to the system to achieve instant compensation for speed fluctuations; The predictive maintenance early warning module is used to implement predictive maintenance analysis by monitoring the motor operating temperature T of the conveyor belt. motor Compared with historical data, when T motor >T thres When the warning is triggered, T thres is the preset temperature threshold; The tension adjustment module is used to adjust the conveyor belt tension F based on the material and thickness parameters D of the conveyed plate. t =F b +K D ·D 2 , F b is the basic tension, K D It is an adjustment coefficient related to the thickness of the plate, which is used to ensure the smooth transportation of plates of different materials and thicknesses; The correction module is used to use machine vision to identify the edge position of the plate and calculate the offset ΔX = X target -X actual , and automatically correct the conveying position through the servo control system according to ΔX, X target is the desired system output position, X actual is the actual output value of the system; The scheduling optimization module is used to realize intelligent scheduling. According to the production plan and the current conveying status, the sorting and conveying rhythm of the plates are optimized. The scheduling algorithm is based on T opt =argmin T ∑(T i -T) 2 , where T i is the time required for each production link, and T is the target shortest total time.

10. The plate smooth conveying control system according to claim 9, characterized in that: Also includes: Big data analysis module, used to collect and analyze historical transportation data, used to optimize the adjustment coefficient k ΔV ,K D ,k f .

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