Intelligent control method for material conveying and feeding

Abstract

The invention provides an intelligent conveying and feeding control method, and belongs to the technical field of feeding control. The method comprises the following steps: constructing a stock bin material quantity prediction model, and predicting the stock bin material quantity based on high and low position signals of a stock bin, data of a level gage and numerical values of feeding and discharging belt weighers; outputting a stock bin material quantity state in real time on the basis of the predicted stock bin material quantity in combination with a preset material quantity threshold value, and controlling a belt conveyor to start and stop on the basis of the stock bin material quantity state; collecting an instantaneous flow value of the belt weigher at a preset sampling frequency; determining a transportation volume threshold value based on the instantaneous flow value of the belt weigher; and judging whether the transportation volume is abnormal or not according to the transportation volume threshold value to which the belt weigher instantaneous flow value set belongs, and triggering an alarm when the transportation volume is abnormal. The stock bin stock quantity is monitored in real time through the stock quantity prediction model, starting and stopping of the belt conveyor are intelligently adjusted, and stockpiling or equipment idling damage is effectively avoided; and meanwhile, the instantaneous flow of the belt weigher is detected in real time based on the transportation volume prediction model, and an alarm is triggered when the transportation volume is abnormal, so that the safety and stability of the production process are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of feeding control, in particular to a feeding intelligent control method. BACKGROUND

[0002] The feeding control system is a core link in industrial automation production, widely used in mechanical processing, assembly line, logistics and warehousing fields. The system realizes the precise supply of materials and process connection by controlling material conveying equipment (such as conveyor belt, mechanical arm, AGV, etc.), directly affecting production efficiency and product quality.

[0003] At present, most feeding control systems adopt hierarchical architecture, and PLC or industrial computer is used to realize logic control, combined with photoelectric sensor, encoder and other detection devices to complete the preliminary judgment of material position and state. The existing technology usually relies on fixed programming control strategy, which needs manual parameter adjustment to adapt to production changes. The intelligent level is low, which cannot learn production rules or dynamically optimize scheduling strategy; the real-time performance is insufficient, and traditional communication protocol and centralized processing cannot meet the control requirements of high concurrency and low delay.

[0004] Therefore, a feeding intelligent control method is needed. SUMMARY

[0005] Therefore, the present application provides a feeding intelligent control method, which constructs a material quantity prediction model, predicts the material quantity in real time, and controls the start and stop of the belt conveyor in real time according to the material quantity to prevent material stacking or equipment damage.

[0006] Therefore, the present application provides the following technical solutions: A feeding intelligent control method, comprising: Constructing a material quantity prediction model of the material bin, which predicts the material quantity of the material bin based on the high and low signals of the material bin, the material level meter data, and the input and output material belt scale values; Real-time outputting the material quantity state of the material bin based on the predicted material quantity and a preset material quantity threshold, and controlling the start and stop of the belt conveyor based on the material quantity state of the material bin; Collecting the instantaneous flow value of the belt scale at a preset sampling frequency; Determining the transport quantity threshold based on the instantaneous flow value of the belt scale; judging whether the transport quantity is abnormal according to the transport quantity threshold to which the set of instantaneous flow values of the belt scale belongs, and triggering an alarm when an abnormality occurs.

[0007] Further, the material quantity prediction model of the material bin comprises:

[0008] Wherein, is the material quantity of the material bin at time t, is the input flow, is the output flow, represents the material quantity change rate.

[0009] Further, the measured value of the level gauge is introduced to feedback correct the material quantity prediction model of the silo:

[0010] wherein, is the corrected material quantity value, is the weighting coefficient, and is the level-material quantity conversion coefficient. is the measured value of the level gauge.

[0011] Further, the silo material quantity state is used to control the start and stop of the belt conveyor, including: The upper and lower limits of the silo material quantity are preset as ; If , the feeding belt is automatically stopped; If , the upstream feeding equipment is automatically started.

[0012] Further, the instantaneous flow value of the belt scale is used to determine the transport quantity threshold, including: The average flow and standard deviation in the sliding time window are calculated:

[0013]

[0014] wherein, is the average flow, T is the sliding time window, is the standard deviation, is the instantaneous flow of the belt scale; N is the total number of samples in the sliding time window T, and is indexed by .

[0015] Further, the transport quantity threshold: The upper limit of the transport quantity threshold UCL and the lower limit of the transport quantity threshold LCL:

[0016] .

[0017] Further, based on the instantaneous flow prediction value and the preset transport quantity threshold, it is determined whether the transport quantity is abnormal, including: If the current flow continuously exceeds the range of [LCL, UCL], or satisfies:

[0018] an abnormal transport quantity alarm is triggered; wherein, Sensitivity coefficient for mutation detection denotes a sampling time interval.

[0019] Advantages and positive effects of the present application: The method realizes intelligent control of the material conveying system by constructing a material quantity prediction model and a conveying quantity judgment model. The material quantity prediction model is used to monitor the material quantity of the stock bin in real time, and the belt conveyor is intelligently started and stopped to effectively avoid material stacking or damage caused by equipment idling. Meanwhile, the instantaneous flow of the belt scale is detected in real time based on the conveying quantity prediction model, and an alarm is triggered when the conveying quantity is abnormal, thereby improving the safety and stability of the production process. The scheme significantly improves the automation level and adaptability of the feeding system, reduces manual intervention, reduces energy consumption, and meets the needs of modern industrial efficient and flexible production. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0021] Figure 1 The flow chart of the intelligent control method for feeding in the embodiment 1 of the present application. DETAILED DESCRIPTION

[0022] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] The application provides an intelligent control method for material loading, which realizes intelligent control of a material conveying system by constructing a material quantity prediction model and a conveying quantity prediction model.

[0025] Embodiment 1 An intelligent control method for material loading, comprising: S1, constructing a material tank material quantity prediction model to predict the material quantity: The high and low signals of the material bin, the material level meter data and the input and output material belt scale values are collected in real time as inputs to construct a material quantity prediction model based on dynamic difference and state observation. The material quantity prediction model learns online according to historical data to realize real-time prediction of the material quantity of the material bin, and feedback correction is performed through actual scale data in the process to continuously improve the estimation accuracy.

[0026] 1) The formula of the material quantity prediction model is:

[0027] Let the material quantity of the material bin at time t be Let the input flow be Let the output flow be Let the material quantity change rate be represented by

[0028] 2) Discretization is performed on the material quantity prediction model to obtain a recursive estimation formula by sampling period Wherein,

[0029] Let the k-th sampling time be represented by

[0030] 3) At the same time, the material level meter measurement value is introduced for feedback correction:

[0031] Wherein, Let the corrected material quantity value be Let the weighting coefficient be And Let the material level-material quantity conversion coefficient be. The coefficients are automatically updated by continuously comparing the model with real-time data to improve the estimation accuracy.

[0032] S2, constructing a belt conveyor conveying quantity early warning model to issue an alarm for abnormal conditions: The instantaneous flow and cumulative quantity of the belt scale are collected; the reasonable fluctuation range of the conveying quantity is set through the statistical process control method, and when abnormal deviation is detected, including continuous low flow and sudden change; combined with the material quantity change of the material bin, it is judged whether there are problems such as material leakage, scale body abnormality or blockage, and real-time early warning is issued. ​​

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

[0034]

[0035] wherein, is the average flow, T is the sliding time window, is the standard deviation, is the instantaneous flow of the belt scale; N is the total number of samples within the sliding time window T, indexed by

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

[0037]

[0038] Determine the traffic situation according to the control upper limit and the control lower limit combined with a preset mutation detection sensitivity coefficient: If the current flow continuously exceeds the range of [LCL, UCL], or satisfies:

[0039] Trigger the traffic anomaly alarm; wherein, is the mutation detection sensitivity coefficient; , indicates the sampling time interval.

[0040] S3, according to the bunker material quantity prediction value, control the start and stop of the belt conveyor: The preset upper and lower limits of the bunker material quantity are ; If , the feeding belt is automatically stopped; If , the upstream feeding equipment is automatically started.

[0041] At the same time, interlocking control between multiple belt conveyors is supported, and when the downstream equipment fails, the upstream related equipment is automatically stopped to prevent material stacking or equipment damage.

[0042] S4, obtain the expected arrival time of the material through the material expected arrival time prediction model: Establish a mathematical model of material transmission time by comprehensively considering the belt conveyor speed, belt conveyor length, start-stop acceleration time, and flap switching time:

[0043] wherein,​ is the buffer time, v is the comprehensive belt speed, L is the length of the belt, is the start-stop acceleration time, is the flap switching time, is the total time from starting to arriving.

[0044] Example 2 The intelligent control method for loading material realizes a system, which comprises: Server side: deploy an industrial server and configure: Dual-channel Intel Xeon Silver 4310 processor; 64GB DDR4 ECC memory; 1TB SSD system disk + 10TB HDD data storage disk; Dual 10 / 100 / 1000 Mbps Ethernet card.

[0045] Client side: multiple industrial computers, installed with Windows 10 IoT system, and connected to the server through a switch to form a LAN.

[0046] Network topology: star topology, with the server as the core node, and the client and PLC connected through industrial Ethernet (Profinet / CIP).

[0047] Software module implementation 1. Communication connection establishment TCP / IP long connection is used, heartbeat detection is used to maintain the link, data encryption and identity authentication are used to ensure security; Protocol selection: TCP / IP Socket long connection between client and server, heartbeat packet interval 30 seconds to detect link state.

[0048] Security mechanism: Communication data is encrypted using AES-256; The client needs to be authenticated by account and password.

[0049] 2. Access to PLC heterogeneous devices Abstract device interface through object tree framework, develop adapters for multiple brands of PLCs, and realize unified read-write operation; Object tree framework implementation: Create a general device interface IDevice, define ReadTag() / WriteTag() methods; Runtime dynamically loads adapter DLL, instantiates objects according to device models and mounts them to the object tree.

[0050] 3. Device state acquisition and control State acquisition and control: Real-time reading of various sensors and state signals, according to the client's operation instructions or model output results, through the server to write to PLC execution; Data processing and storage: Use time series database to store real-time data, relational database to store event records and process parameters; 4. Data visualization Data visualization: Client dynamically displays real-time data, historical curves and alarm information through chart controls; Client interface components: Trend chart control: Based on ECharts development, dynamically pulling server historical data; Device status panel: Color coding indicates running status (green-normal / red-fault); Parameter setting window: Input box binds data validation rules (such as flow threshold ≥ 0).

[0051] 5. Intelligent model running: Each prediction model is deployed as a service on the server, executing at regular intervals or event-driven, and the output results are used for interface display and control instruction generation. Event record table (SQL Server): Store operation logs, alarm events (operator, time, action type).

[0052] After deploying this method in the raw material conveying system of a steel plant: Device compatibility: Access to 3 brands of PLC (Siemens / Rockwell / Omron), integration time reduced by 60%; Control real-time: Instruction response delay <100ms, meeting the needs of high-speed production line operation; Maintenance efficiency: Analyze the belt scale fluctuation through trend chart, accurately locate the blockage fault, reduce downtime by 40%.

[0053] Material quantity estimation error is less than 5%, and the accuracy rate of abnormal volume early warning is more than 90%, which significantly improves the automation level and operation efficiency of the conveying system and reduces the downtime.

[0054] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

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 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.

2. The method according to claim 1, characterized in that, 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, This indicates the rate of change in material quantity.

3. The method according to claim 1, characterized in that, 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.

4. The method according to claim 1, characterized in that, 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 for 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.

5. The method according to claim 1, characterized in that, 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; N is the total number of samples taken within the sliding time window T, through... Perform indexing.

6. The method according to claim 1, characterized in that, The transport volume threshold: Capacity threshold upper limit (UCL) and capacity threshold (LCL): 。 7. The method according to claim 1, characterized in that, 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: This will trigger an abnormal transport volume alarm; among which, This represents the sensitivity coefficient for mutation detection. , indicating the sampling time interval.

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