A method for intelligent control of loading

Abstract

The application provides a kind of intelligent control method of loading, belongs to the technical field of loading control.This method includes: constructing the material quantity prediction model of bunker, predicting the material quantity of bunker based on the high-low signal of bunker, material level meter data and the numerical value of inlet and outlet material belt scale;Based on the predicted bunker material quantity, the preset material quantity threshold is combined to output the bunker material quantity state in real time, and the belt conveyor is controlled to start and stop based on the bunker material quantity state;With preset sampling frequency, the instantaneous flow value of belt scale is collected;Based on the instantaneous flow value of belt scale, determine the transport threshold;According to the transport threshold to which the instantaneous flow value set of belt scale belongs, judge whether the transport is abnormal, and trigger alarm when abnormal occurs.The material quantity prediction model is used to monitor the bunker material quantity in real time, and the belt conveyor is intelligently adjusted to start and stop, effectively avoiding material stacking or equipment idle damage;At the same time, based on the transport prediction model, the instantaneous flow of belt scale is detected in real time, and the alarm is triggered when the transport is abnormal, which improves the safety and stability of the production process.

Description

Technical Field

[0001] This invention relates to the field of material feeding control technology, and in particular to an intelligent control method for material feeding. Background Technology

[0002] Material handling control systems are a core component of industrial automated production, widely used in machining, assembly lines, and logistics warehousing. By controlling material conveying equipment (such as conveyor belts, robotic arms, and AGVs), this system achieves precise material supply and seamless process coordination, directly impacting production efficiency and product quality.

[0003] Currently, most material feeding control systems adopt a hierarchical architecture, with logic control implemented by PLCs or industrial computers, combined with detection devices such as photoelectric sensors and encoders to make preliminary judgments on the material's position and status. Existing technologies typically rely on fixed-programmable control strategies, requiring manual parameter adjustments to adapt to production changes. They suffer from low levels of intelligence, unable to autonomously learn production patterns or dynamically optimize scheduling strategies; and insufficient real-time performance, with traditional communication protocols and centralized processing unable to meet the demands of high concurrency and low latency control.

[0004] Therefore, an intelligent control method for material handling is needed. Summary of the Invention

[0005] In view of this, the present invention provides an intelligent control method for material conveying, which predicts the material quantity in real time by constructing a material quantity prediction model, and controls the start and stop of the belt conveyor in real time by judging the material quantity situation, so as to prevent material accumulation or equipment damage.

[0006] Therefore, the present invention provides the following technical solution: A method for intelligent control of material conveying includes: A silo quantity prediction model is constructed based on the silo's high and low level signals, level gauge data, and inlet and outlet belt scale values ​​to predict the silo quantity. Based on the predicted material quantity in the silo and the preset material quantity threshold, the material quantity status of the silo is output in real time, and the start and stop of the belt conveyor are controlled based on the material quantity status of the silo. The instantaneous flow rate of the belt scale is collected at a preset sampling frequency; The transport volume threshold is determined based on the instantaneous flow rate value of the belt scale; the transport volume is judged to be abnormal according to the transport volume threshold to which the set of instantaneous flow rate values ​​of the belt scale belongs, and an alarm is triggered when an abnormality occurs.

[0007] Furthermore, the silo quantity prediction model:

[0008] in, Let be the amount of material in the silo at time t. For feed flow rate, For the discharge flow rate, This indicates the rate of change in material quantity.

[0009] Furthermore, the material level gauge readings are used to provide feedback correction to the silo quantity prediction model:

[0010] in, This is the corrected material quantity value. These are weighting coefficients. and This is the material level-to-material quantity conversion coefficient; This is the value measured by the level gauge.

[0011] Furthermore, the method of controlling the start and stop of the belt conveyor based on the material quantity status of the silo includes: Preset upper and lower limits of silo material quantity are ; like If this happens, the feeding belt will automatically stop. like If so, the upstream material supply equipment will be started automatically.

[0012] Further, determining the transport capacity threshold based on the instantaneous flow rate value of the belt scale includes: Calculate the average flow rate and standard deviation within the sliding time window:

[0013]

[0014] in, Let T be the average flow rate and T be the sliding time window. Standard deviation The instantaneous flow rate of the belt scale; N is the total number of samples taken within the sliding time window T, through... Perform indexing.

[0015] Furthermore, the transport volume threshold: Capacity threshold upper limit (UCL) and capacity threshold (LCL):

[0016] .

[0017] Furthermore, determining whether the traffic volume is abnormal based on the instantaneous traffic volume prediction value combined with a preset traffic volume threshold includes: If the current traffic continues to exceed the range [LCL, UCL], or satisfies:

[0018] This will trigger an abnormal transport volume alarm; among which, This represents the sensitivity coefficient for mutation detection. , indicating the sampling time interval.

[0019] Advantages and positive effects of the present invention: This method achieves intelligent control of the material conveying system by constructing a material quantity prediction model and a transport volume judgment model. The material quantity prediction model monitors the material quantity in the silo in real time and intelligently adjusts the start and stop of the belt conveyor, effectively avoiding damage from material accumulation or equipment idling. Simultaneously, the transport volume prediction model detects the instantaneous flow rate of the belt scale in real time and triggers an alarm when the transport volume is abnormal, improving the safety and stability of the production process. This solution significantly improves the automation level and adaptability of the feeding system, reduces manual intervention, lowers energy consumption, and meets the needs of modern industrial high-efficiency and flexible production. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the intelligent control method for material feeding in Embodiment 1 of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] This invention provides an intelligent control method for material conveying, which realizes intelligent control of the material conveying system by constructing a material quantity prediction model and a conveying volume prediction model.

[0025] Example 1 A method for intelligent control of material conveying includes: S1. Construct a material trough quantity prediction model to predict the material quantity: Using real-time high and low level signals from the silo, level gauge data, and values ​​from the inlet and outlet belt scales as inputs, a material quantity prediction model based on dynamic differential and state observation is constructed. The model learns online based on historical data to predict the real-time material quantity in the silo, and continuously improves its accuracy through feedback correction using actual weighing data.

[0026] 1) The formula for the material quantity prediction model is expressed as:

[0027] Let be the amount of material in the silo at time t. For feed flow rate, The discharge flow rate is, This indicates the rate of change in material quantity.

[0028] 2) Discretize the material quantity prediction model, using the sampling period. By approximation, we obtain the recursive prediction formula:

[0029] in, Indicates the first Each sampling time.

[0030] 3) Simultaneously, the level gauge readings are used for feedback correction:

[0031] in, This is the corrected material quantity value. These are weighting coefficients. and This is the material level-to-material quantity conversion coefficient. By continuously comparing the model with real-time data, the coefficient is automatically updated to improve prediction accuracy.

[0032] S2. Construct a belt conveyor capacity early warning model to issue alarms for abnormal situations: The system collects the instantaneous flow rate and cumulative volume of the belt scale; sets a reasonable fluctuation range for the transport volume using statistical process control methods; and when abnormal deviations are detected, including continuous low flow rate and sudden changes, it combines the changes in the material quantity in the silo to determine whether there are problems such as material leakage, scale malfunction, or material blockage, and issues a warning in real time.

[0033] 1) Collect the instantaneous flow rate value of the belt scale at a preset sampling frequency; 2) Calculate the average flow rate and standard deviation within the sliding time window:

[0034]

[0035] in, Let T be the average flow rate and T be the sliding time window. Standard deviation The instantaneous flow rate of the belt scale; N is the total number of samples taken within the sliding time window T, through... Perform indexing.

[0036] 3) Determine the upper control limit (UCL) and lower control limit (LCL) based on the average and standard deviation of the instantaneous flow rate of the belt scale:

[0037]

[0038] Based on the upper and lower control limits combined with a preset mutation detection sensitivity coefficient, the transport volume is determined: If the current traffic continues to exceed the range [LCL, UCL], or satisfies:

[0039] This will trigger an abnormal transport volume alarm; among which, This represents the sensitivity coefficient for mutation detection. , indicating the sampling time interval.

[0040] S3. Control the start and stop of the belt conveyor based on the predicted material quantity in the silo: Preset upper and lower limits of silo material quantity are ; like If this happens, the feeding belt will automatically stop. like If so, the upstream material supply equipment will be started automatically.

[0041] Meanwhile, it supports interlocking control between multiple conveyor belts. When downstream equipment fails, it automatically stops the relevant upstream equipment to prevent material accumulation or equipment damage.

[0042] S4. Obtain the expected arrival time of the material using the material expected arrival time prediction model: A mathematical model for material transport time is established by considering the belt conveyor speed, belt conveyor length, start-stop acceleration time, and flapper switching time.

[0043] in, Where v is the buffer time, v is the overall conveyor speed, and L is the conveyor length. For start-stop acceleration time, For the time of flip-board switching, This represents the total time from material start-up to arrival.

[0044] Example 2 The intelligent control method for material handling and feeding is implemented in the system, including: Server-side: Deploy industrial-grade servers, configuration: Dual Intel Xeon Silver 4310 processors; 64GB DDR4 ECC memory; 1TB SSD system disk + 10TB HDD data storage disk; Dual Gigabit Ethernet cards.

[0045] Client: Multiple industrial computers, running Windows 10 IoT system, connected to the server via a switch to form a local area network.

[0046] Network topology: A star topology is adopted, with the server as the core node, and clients and PLCs connected via industrial Ethernet (Profinet / CIP).

[0047] Software module implementation 1. Establishing a communication connection Using TCP / IP long connections, heartbeat detection maintains the link, and data encryption and identity authentication ensure security; Protocol selection: The client and server use a TCP / IP Socket long connection, and the heartbeat packet is used every 30 seconds to check the link status.

[0048] Security mechanisms: Communication data is encrypted using AES-256; The client needs to authenticate with an account and password.

[0049] 2. PLC heterogeneous device access By abstracting device interfaces through an object tree framework, we can develop PLC adapters for various brands and achieve unified read and write operations. Object tree framework implementation: Create a generic device interface IDevice and define the ReadTag() / WriteTag() methods; The adapter DLL is dynamically loaded at runtime, and objects are instantiated according to the device model and attached to the object tree.

[0050] 3. Equipment status acquisition and control Status acquisition and control: Real-time reading of various sensors and status signals, output results based on client operation commands or model, and write to the PLC for execution via server; Data processing and storage: Time-series databases are used to store real-time data, and relational databases are used to store event records and process parameters; 4. Data Visualization Data visualization: The client uses chart controls to dynamically display real-time data, historical curves, and alarm information; Client-side interface components: Trend chart control: Developed based on ECharts, dynamically retrieves historical data from the server; Device status panel: Color coding indicates operating status (green - normal / red - fault); Parameter settings window: Input boxes are bound to data validation rules (such as traffic threshold ≥ 0).

[0051] 5. Intelligent Model Execution: Each predictive model is deployed as a service on the server, executed periodically or event-driven, and the output results are used for interface display and control command generation. Event Log Table (SQL Server): Stores operation logs and alarm events (operator, time, action type).

[0052] After deploying this method in the raw material conveying system of a steel plant: Equipment compatibility: Supports three brands of PLCs (Siemens / Rockwell / Omron), reducing integration time by 60%; Control real-time performance: Command response latency <100ms, meeting the requirements of high-speed production line operation; Maintenance efficiency: By analyzing belt scale fluctuations through trend charts, material blockage faults can be accurately located, reducing downtime by 40%.

[0053] The material quantity prediction error is less than 5%, and the accuracy rate of abnormal transport volume warning is over 90%, which significantly improves the automation level and operating efficiency of the conveying system and reduces downtime due to malfunctions.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent control of material conveying, characterized in that, include: A silo quantity prediction model is constructed based on the silo's high and low level signals, level gauge data, and inlet and outlet belt scale values ​​to predict the silo quantity. Based on the predicted material quantity in the silo and the preset material quantity threshold, the material quantity status of the silo is output in real time, and the start and stop of the belt conveyor are controlled based on the material quantity status of the silo. The instantaneous flow rate of the belt scale is collected at a preset sampling frequency; The transport volume threshold is determined based on the instantaneous flow rate value of the belt scale; the transport volume is judged to be abnormal based on the instantaneous flow rate value of the belt scale and the transport volume threshold, and an alarm is triggered when an abnormality occurs; The silo quantity prediction model: in, Let be the amount of material in the silo at time t. For feed flow rate, For the discharge flow rate, Indicates the rate of change in material quantity; The material quantity prediction model is discretized, with a sampling period of... By approximation, we obtain the recursive prediction formula: in, Indicates the first Each sampling time; The material level gauge readings are used to provide feedback correction to the silo quantity prediction model. in, This is the corrected material quantity value. These are weighting coefficients. and This is the material level-to-material quantity conversion coefficient; This is the value measured by the level gauge; The method of controlling the start and stop of the belt conveyor based on the material quantity status of the silo includes: Preset upper and lower limits of silo material quantity are ; like If this happens, the feeding belt will automatically stop. like If so, the upstream feeding equipment will start automatically; The process of determining the transport capacity threshold based on the instantaneous flow rate value of the belt scale includes: Calculate the average flow rate and standard deviation within the sliding time window: in, Let T be the average flow rate and T be the sliding time window. Standard deviation The instantaneous flow rate of the belt scale is N; N is the total number of samples taken within the sliding time window T. Perform indexing; The transport volume threshold includes: Upper limit of capacity threshold (UCL) and lower limit of capacity threshold (LCL): 。 2. The method according to claim 1, characterized in that, Based on the instantaneous flow rate value of the belt scale and the aforementioned transport volume threshold, determine whether the transport volume is abnormal, including: If the current traffic continues to exceed the range [LCL, UCL], or satisfies: This will trigger an abnormal transport volume alarm; among which, This represents the sensitivity coefficient for mutation detection. , indicating the sampling period.

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