Color-coated sheet full-process intelligent production logistics integrated information integration system and method

Abstract

The invention provides a color-coated sheet full-process intelligent production logistics integrated information integration system and method, and relates to the field of color-coated sheet intelligent manufacturing. The color-coated sheet full-process intelligent production logistics integrated information integration system comprises a production line sensing network, an intelligent production scheduling engine, a dynamic logistics scheduling module and a quality tracing database, and the production line sensing network integrates high-frequency RFID tags of a galvanizing line and a coating line, temperature and humidity sensors and machine detection equipment. The sensors are respectively used for collecting coiled material position information, monitoring the working condition of a curing oven and detecting coating uniformity; and the intelligent production scheduling engine generates a galvanization-color coating linkage production plan through historical production data, real-time order demands and a dynamic time sequence optimization algorithm. According to the solution integrating sensing monitoring, intelligent decision making, simulation optimization and credible tracing, a new efficient, accurate and reliable intelligent manufacturing mode is provided for the color-coated sheet industry, and remarkable economic benefits and application and popularization values are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent manufacturing of color coated steel sheets, and particularly to a full-process intelligent production and logistics integrated information system and method for color coated steel sheets. BACKGROUND

[0002] Color coated steel sheets and strips are a kind of composite materials formed by applying one or more layers of organic coating on a metal substrate through a color roller coating unit. The main purpose of the coating is to provide protection, insulation and decorative effects. In the manufacturing process of color coated steel sheets, accurate control of coating thickness is crucial.

[0003] The current color coated steel sheet production and logistics system has the following main technical defects and pain points:

[0004] 1. The production and logistics system is fragmented, forming an information island, resulting in delayed material distribution and backlog in the buffer area;

[0005] 2. The logistics system uses fixed path planning, and the dynamic adjustment response is slow, which cannot adapt to production fluctuations;

[0006] 3. The intelligent foundation is weak, the key process sensor coverage is insufficient, and the production scheduling relies on experience rules, resulting in low equipment utilization, high logistics cost and high quality rework rate;

[0007] Therefore, it is urgent to build an intelligent and collaborative integrated solution to achieve a breakthrough. Therefore, the present application provides a full-process intelligent production and logistics integrated information system and method for color coated steel sheets. SUMMARY

[0008] (I) Technical problems solved

[0009] In view of the deficiencies of the prior art, the present application provides a full-process intelligent production and logistics integrated information system and method for color coated steel sheets, which solves the problems raised in the background art.

[0010] (II) Technical solutions

[0011] To achieve the above purpose, the present application realizes the following technical solutions: a full-process intelligent production and logistics integrated information system for color coated steel sheets, comprising:

[0012] Production line sensor network: high-frequency RFID tags, temperature and humidity sensors and machine detection devices integrated in the galvanizing line and the coating line are used to collect coil position information, monitor the working condition of the curing furnace and detect the uniformity of the coating, respectively;

[0013] Intelligent production scheduling engine: through historical production data and real-time order demand, a dynamic time sequence optimization algorithm generates a galvanizing-color coating linkage production plan;

[0014] Dynamic logistics scheduling module: integrate ant colony optimization algorithm and digital twin simulation platform to generate AGV distribution path and pre-act abnormal handling plan;

[0015] Quality traceability database: store galvanized layer thickness, coating batch and process parameters to support product life cycle quality traceability.

[0016] Preferably, the dynamic logistics scheduling module comprises:

[0017] Logistics AGV cluster: equipped with laser navigation sensor and 5G communication module, real-time receive production line material demand instructions;

[0018] Digital twin sub-module: real-time collect AGV coordinate data, environmental obstacle distribution and goods storage location through Internet of Things sensors, and construct warehouse environment state matrix;

[0019] Target optimization sub-module: comprehensive AGV motion energy consumption coefficient, task weight coefficient and equipment load parameter, establish path optimization objective function, generate flexible distribution path.

[0020] Preferably, the intelligent production scheduling engine adopts deep reinforcement learning model, the input parameters include order profit priority, equipment remaining capacity and coating inventory state, and the output is dynamic adjusted strip switching strategy and replenishment instruction.

[0021] Preferably, the production line sensor network comprises an automatic buffer zone device and an online coating thickness detection device, the automatic buffer zone device is arranged between the galvanizing line and the coating line, equipped with an RFID reader and a mechanical arm collaborative sorting device, according to the production scheduling plan, automatically sorting and temporarily storing galvanized coiled material.

[0022] Preferably, the automatic buffer zone device comprises:

[0023] RFID reader array: real-time identify the identification information and storage location of coiled material in the buffer zone;

[0024] Mechanical arm collaborative sorting system: automatically complete coiled material taking and placing operation according to the instruction of intelligent production scheduling engine;

[0025] Temperature and humidity control unit: maintain the buffer zone environment parameters within the galvanized coiled material storage standard range.

[0026] Preferably, the online coating thickness detection device,

[0027] Adopt magnetic thickness measurement method and eddy current thickness measurement method double-mode detection system, respectively applicable to coating measurement of magnetic substrate and non-magnetic substrate;

[0028] Integrate edge computing node, real-time analyze thickness data and link adjustment of glue coating device parameters;

[0029] The detection data is automatically uploaded to a quality traceability database.

[0030] Preferably, the quality traceability database comprises:

[0031] Distributed storage unit: the galvanizing layer thickness detection data, coating batch information and curing furnace process parameters are stored in multiple nodes according to time stamp, forming a chain data structure;

[0032] Smart contract verification unit: automatically check the legality of sensor ID and the compliance of data format when data is written, and reject abnormal data of unauthorized equipment;

[0033] Cross-chain traceability interface: support data intercommunication with supplier coating batch blockchain and downstream customer quality system, realize supply chain full-link traceability.

[0034] The method comprises the following steps:

[0035] The coil position, working condition parameters and coating data are collected in real time through the production line sensor;

[0036] The galvanizing-coating linkage production plan is dynamically generated based on deep reinforcement learning, and replenishment is triggered when the coating is insufficient;

[0037] The coating parameters are adjusted in real time through edge computing, and the data is uploaded to the blockchain database;

[0038] The AGV dynamic path planning is realized by using digital twinning and optimization algorithm;

[0039] The coil intelligent sorting and storage are realized through the automatic buffer area;

[0040] The full-process quality traceability is realized based on the blockchain.

[0041] (Three) beneficial effects

[0042] The present application provides a full-process intelligent production and logistics integrated information system and method for color coated sheets, which has the following beneficial effects:

[0043] The full-process intelligent production and logistics integrated information system for color coated sheets provided by the present application has the following beneficial effects in terms of efficiency improvement: the system optimizes the production line through the cooperation of the intelligent production scheduling engine and the dynamic logistics scheduling module, improves the production line utilization rate, shortens the logistics response speed, reduces the AGV empty load rate, and in terms of quality guarantee, the coating defect rate is reduced by combining the double-mode thickness measurement technology and machine vision detection, and the thickness precision is better controlled.

[0044] The system integrates sensor monitoring, intelligent decision-making, simulation optimization and trusted traceability into one solution, providing the color-coated plate industry with a new model of efficient, accurate and reliable intelligent manufacturing, with significant economic benefits and application promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system block diagram of the present invention;

[0046] Figure 2 This is a flow chart of the intelligent production scheduling engine of the present invention;

[0047] Figure 3 This is a schematic diagram of the process of the coating thickness online detection device of the present invention;

[0048] Figure 4 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Example:

[0051] like Figures 1-4 As shown, an embodiment of the present invention provides an integrated information integration system for the full-process intelligent production logistics of color-coated plates, including a production line sensor network, an intelligent production scheduling engine, a dynamic logistics scheduling module and a quality traceability database. The production line sensor network integrates high-frequency RFID tags, temperature and humidity sensors and machine detection equipment of the galvanizing line and the coating line, which are respectively used to collect coil position information, monitor the working conditions of the curing furnace and detect the uniformity of the coating; the intelligent production scheduling engine generates a galvanizing-color coating linkage production plan through a dynamic timing optimization algorithm based on historical production data and real-time order requirements; the dynamic logistics scheduling module integrates an ant colony optimization algorithm and a digital twin simulation platform to generate an AGV delivery path and rehearse an exception handling solution; the quality traceability database stores the galvanizing layer thickness, paint batches and process parameters, and supports product quality traceability throughout its life cycle.

[0052] System architecture (hardware layer + software layer):

[0053] Hardware layer: Production line sensor network: deploying high-frequency RFID tags (collecting coil location information), temperature and humidity sensors (monitoring curing oven conditions), and machine vision equipment (detecting coating uniformity);

[0054] Logistics AGV cluster: combined with laser navigation and 5G communication module, real-time response to production line material demand.

[0055] Software layer: intelligent scheduling engine: based on historical data and real-time orders, optimize galvanizing-painting linkage production timing (such as dynamically adjust strip steel switching strategy).

[0056] Dynamic logistics scheduling algorithm: apply ant colony optimization algorithm + digital twin simulation, generate the optimal distribution path and pre-exception scenario handling scheme.

[0057] Quality traceability module: record galvanizing layer thickness, coating batch and other key parameters through blockchain technology, support full life cycle traceability.

[0058] Production line sensor network includes automatic buffer zone device and coating thickness online detection device. The automatic buffer zone device is located between the galvanizing line and the painting line, equipped with RFID reader and mechanical arm sorting device, automatically sorts and temporarily stores galvanized coils according to the production plan.

[0059] The production line sensor network includes high-frequency RFID tags, temperature and humidity sensors, and machine vision equipment. High-frequency RFID tags are installed on the coil transport tray to collect real-time coil position information and ensure the accuracy of logistics tracking. Temperature and humidity sensors are placed inside the curing oven to monitor the temperature and humidity conditions inside the oven and ensure coating curing quality. Machine vision equipment uses industrial cameras to detect coating uniformity and adjusts painting parameters in real time using image algorithms.

[0060] The automatic buffer zone device includes an RFID reader array, a mechanical arm sorting system, and a temperature and humidity control unit. The RFID reader array identifies the identification information and storage location of the coils in the buffer zone in real time, and the mechanical arm sorting system automatically completes the coil taking and placing operations according to the intelligent scheduling engine instructions. The temperature and humidity control unit maintains the buffer zone environmental parameters within the galvanized coil storage standard range.

[0061] The automatic buffer zone device is located between the galvanizing line and the painting line. The RFID reader array in it can automatically identify the identification and storage location of the coils in the buffer zone. The mechanical arm sorting system in it automatically completes the coil taking and placing according to the intelligent scheduling engine instructions. The temperature and humidity control unit in it maintains the buffer zone environment to meet the galvanized coil storage standards (such as temperature 20-30℃, humidity ≤60%).

[0062] The coating thickness online detection device uses a dual-mode detection system of magnetic thickness measurement and eddy current thickness measurement, suitable for coating measurement of magnetic and non-magnetic substrates respectively. The coating thickness online detection device integrates edge computing nodes, analyzes thickness data in real time and adjusts the parameters of the glue application device. The detection data of the coating thickness online detection device is automatically uploaded to the quality traceability database.

[0063] The coating thickness online detection device adopts magnetic thickness measurement method (for magnetic substrate) and eddy current thickness measurement method (for non-magnetic substrate) dual-mode detection to ensure measurement accuracy, and integrates edge computing nodes to analyze thickness data in real time and adjust the parameters of the gluing machine (such as roller speed and coating flow). The detection data can be automatically uploaded to the quality traceability database.

[0064] The intelligent scheduling engine uses a deep reinforcement learning model, and the input parameters include order profit priority, device remaining capacity, and coating inventory status. The output is a dynamically adjusted strip switching strategy and replenishment instruction.

[0065] The intelligent scheduling engine uses a deep reinforcement learning (DRL) model, and the input parameters include order profit priority (high value-added order priority), device remaining capacity (real-time load of galvanizing line / coating line), and coating inventory status (warning inventory threshold triggers replenishment). The output is a dynamically optimized galvanizing-painting linkage production plan, and a strip switching strategy and replenishment instruction is generated.

[0066] The dynamic logistics scheduling module includes a logistics AGV cluster, a digital twin sub-module, and a target optimization sub-module. The logistics AGV cluster is equipped with laser navigation sensors and 5G communication modules, and can receive real-time production line material demand instructions. The digital twin sub-module collects AGV coordinate data, environmental obstacle distribution, and cargo storage location in real time through Internet of Things sensors, and constructs a warehouse environment state matrix. The target optimization sub-module establishes a path optimization objective function by integrating AGV motion energy consumption coefficients, task weight coefficients, and device load parameters, and generates an elastic distribution path.

[0067] The logistics AGV cluster of the dynamic logistics scheduling module is equipped with laser navigation and 5G communication modules, and can receive real-time material demand instructions. The digital twin sub-module collects AGV coordinates, environmental obstacles, and cargo storage locations through Internet of Things sensors, constructs a three-dimensional warehouse state matrix, simulates AGV paths, and optimizes abnormal handling schemes (such as backup routes in case of device failure). The target optimization sub-module establishes a multi-objective path optimization function (integrating AGV energy consumption, task priority, and device load). An elastic distribution path is generated using an ant colony optimization algorithm to reduce the empty load rate.

[0068] The quality traceability database includes a distributed storage unit, an intelligent contract verification unit, and a cross-chain traceability interface. The distributed storage unit stores galvanizing layer thickness detection data, coating batch information, and curing furnace process parameters in multiple nodes according to time stamp, forming a chain data structure. The intelligent contract verification unit automatically checks the legality of sensor ID and the compliance of data format when data is written, and rejects abnormal data from unauthorized devices. The cross-chain traceability interface supports data exchange with supplier coating batch blockchains and downstream customer quality systems, realizing supply chain full-link traceability.

[0069] The whole-process quality traceability is realized based on blockchain technology. The distributed storage unit stores the galvanized layer thickness, coating batch, and curing furnace process parameters in blocks according to timestamps, forming tamper-proof chain data. The smart contract verification unit checks the legality of sensor ID, rejects data on-chain from unauthorized devices, and automatically triggers an abnormal alarm (such as suspending production when the coating thickness exceeds the standard). The cross-chain traceability interface is connected with the supplier coating batch blockchain and the downstream customer quality system, realizing whole-link traceability of the supply chain.

[0070] The full-process intelligent production and logistics integrated information integration method of color-coated sheets comprises:

[0071] The production line sensor is used to collect the coil position, working condition parameters, and coating data in real time;

[0072] The galvanizing-color-coating linkage production plan is dynamically generated based on deep reinforcement learning, and the replenishment is triggered when the coating is insufficient;

[0073] The edge computing is used to real-time regulate the coating parameters, and the data is uploaded to the blockchain database;

[0074] The AGV dynamic path planning is realized by using digital twinning and optimization algorithm;

[0075] The coil intelligent sorting and storage are realized by using the automatic buffer area;

[0076] The whole-process quality traceability is realized based on blockchain.

[0077] System operation process:

[0078] Data acquisition: The production line sensor is used to collect the coil position, working condition parameters, and coating data in real time.

[0079] Intelligent production scheduling: The DRL model dynamically generates the production plan, and the automatic replenishment is triggered when the coating is insufficient.

[0080] Logistics scheduling: The digital twinning simulation optimizes the AGV path, and the ant colony algorithm generates the lowest energy consumption distribution scheme.

[0081] Quality control: The edge computing is used to real-time adjust the coating parameters, and the detection data is uploaded to the blockchain database.

[0082] Traceability query: The quality problem batch (such as the galvanized layer thickness history data of a coil) is quickly located through the blockchain interface.

[0083] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements, and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. The integrated information system for the whole process of intelligent production and logistics of color-coated plates is characterized by: include: Production line sensor network: High-frequency RFID tags, temperature and humidity sensors, and machine detection equipment are integrated and deployed in the galvanizing and coating lines to collect coil position information, monitor curing oven conditions, and detect coating uniformity. Intelligent production scheduling engine: Based on historical production data and real-time order requirements, dynamic timing optimization algorithm generates galvanizing and color coating linkage production plan; Dynamic logistics scheduling module: Integrates ant colony optimization algorithms with digital twin simulation platforms to generate AGV delivery routes and preview exception handling solutions; Quality traceability database: stores galvanized layer thickness, coating batches, and process parameters, supporting quality traceability throughout the product life cycle.

2. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 1 is characterized by: The dynamic logistics scheduling module includes: Logistics AGV cluster: equipped with laser navigation sensors and 5G communication modules, it receives real-time instructions on production line material requirements; Digital twin module: This module collects AGV coordinate data, environmental obstacle distribution, and cargo storage locations in real time through IoT sensors to build a warehouse environment status matrix. Target optimization submodule: Comprehensively considers the AGV motion energy consumption coefficient, task weight coefficient and equipment load parameters, establishes the path optimization objective function, and generates a flexible delivery path.

3. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 2 is characterized by: The intelligent production scheduling engine adopts a deep reinforcement learning model, with input parameters including order profit priority, equipment remaining capacity and coating inventory status, and outputs dynamically adjusted strip steel switching strategies and replenishment instructions.

4. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 3 is characterized by: The production line sensor network includes an automated buffer area device and a coating thickness online detection device. The automated buffer area device is located between the galvanizing line and the coating line, and is equipped with an RFID reader and a robotic arm collaborative sorting device to automatically sort and temporarily store galvanized coils according to the production schedule.

5. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 4 is characterized by: The automated buffer area device comprises: RFID reader array: real-time identification of the coiled material’s identification information and storage location in the buffer area; Robotic arm collaborative sorting system: automatically completes coil picking and placing operations according to the instructions of the intelligent production scheduling engine; Temperature and humidity control unit: maintains the environmental parameters of the buffer area within the standard range for galvanized coil storage.

6. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 5 is characterized by: The coating thickness online detection device, The dual-mode detection system uses magnetic thickness measurement and eddy current thickness measurement, which are suitable for coating measurement on magnetic substrates and non-magnetic substrates respectively; Integrate edge computing nodes to analyze thickness data in real time and adjust coating device parameters in a coordinated manner; The test data is automatically uploaded to the quality traceability database.

7. The full-process intelligent production logistics integrated information integration system for color-coated plates according to claim 6 is characterized by: The quality traceability database includes: Distributed storage unit: Galvanizing layer thickness detection data, coating batch information and curing furnace process parameters are stored in multiple nodes in blocks according to timestamps, forming a chain data structure; Smart contract verification unit: automatically verifies the legitimacy of the sensor ID and the compliance of the data format when writing data, and rejects abnormal data from unauthorized devices from being uploaded to the chain; Cross-chain traceability interface: supports data interoperability with the supplier's paint batch blockchain and downstream customer quality systems, enabling full-chain traceability of the supply chain.

8. A method for integrating information on the integrated logistics of the entire intelligent production process of color-coated steel sheets, based on the integrated information on the integrated logistics of the entire intelligent production process of color-coated steel sheets according to any one of claims 1 to 7, characterized in that: The method comprises: Real-time collection of coil position, working parameters and coating data through production line sensors; Dynamically generate galvanizing and color coating linkage production plans based on deep reinforcement learning, and trigger replenishment when coating is insufficient; Use edge computing to control coating parameters in real time and upload data to the blockchain database; Use digital twins and optimization algorithms to achieve AGV dynamic path planning; Intelligent sorting and storage of coils through automated buffer areas; Realize full-process quality traceability based on blockchain.

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