Tray warehouse production line system based on intelligent FMS

Abstract

The invention relates to the technical field of flexible manufacturing systems, in particular to a tray warehouse production line system based on an intelligent FMS. In order to solve the challenges of the FMS in the aspects of real-time operation, function expansion and the like in the prior art, the invention provides a tray warehouse production line system based on the intelligent FMS. The data processing and analysis module analyzes production data to obtain an optimal tray warehouse real-time scheduling scheme, and the tray transportation module carries out path planning on an AGV and realizes intelligent storage and automatic transportation of trays. The production line simulation module carries out virtual debugging, fault prediction and maintenance strategy optimization on the intelligent FMS tray library production line through a digital twin model of the production line, and the man-machine cooperation module provides real-time data display and operation guidance for a user. By adopting a multi-module cooperation architecture, the production efficiency and the response capability are greatly improved, and the requirements of multi-variety and small-batch production in the modern manufacturing industry are met.

Description

Technical Field

[0001] The present invention relates to the technical field of flexible manufacturing systems, and in particular to a pallet warehouse production line system based on intelligent FMS. Background Art

[0002] The current manufacturing industry is developing towards intelligence and flexibility. Enterprises need to cope with the needs of multi-variety and small-batch production and improve production efficiency and responsiveness. The traditional production line model faces problems such as mismatch between product variety and production line adaptability, low manual operation efficiency, and improper inventory management.

[0003] The flexible manufacturing system (FMS) is a highly automated manufacturing system that can realize small-batch production of multiple varieties and adapt to changes in market demand and engineering design. However, in the design and real-time process of intelligent FMS, there are various links from project establishment, decision-making to equipment arrival. The relevant parties are in a point-to-point file operation process to solve the problem, and there is no visual, interactive operation deduction process. Therefore, the performance of the formed intelligent FMS needs to be continuously adjusted and improved in production, and in terms of redundant expansion of functions, it is limited by the product family considered in the system design, and is always faced with the problem of whether it can produce low-cost, high-quality and different varieties of products in a very short development cycle.

[0004] Therefore, there is an urgent need to develop a pallet warehouse production line system based on intelligent FMS to overcome the shortcomings of the existing technology. Summary of the invention

[0005] In order to solve the challenges of real-time operation and function expansion of FMS in the prior art, thereby improving production efficiency and response capability and meeting the needs of multi-variety small batch production, the present invention provides such a pallet warehouse production line system based on intelligent FMS, which is characterized by including a data acquisition module, a data processing and analysis module, a pallet transportation module, a production line simulation module and a human-machine collaboration module;

[0006] The data acquisition module installs multiple sensors in the intelligent FMS pallet warehouse production line to obtain real-time data in the production line;

[0007] The data processing and analysis module is used to pre-process and extract features of various production data collected by the data acquisition module; and analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse;

[0008] The pallet transport module is used to control the AGV to perform autonomous path planning according to the real-time dispatching plan of the pallet library in the data processing and analysis module, so as to realize the intelligent storage and automatic transportation of the pallet;

[0009] The production line simulation module is used to establish a digital twin model of the intelligent FMS pallet warehouse production line, and to perform virtual debugging, fault prediction and maintenance strategy optimization on the intelligent FMS pallet warehouse production line through the digital twin model;

[0010] The human-machine collaboration module is used to provide a human-machine interaction interface to the operator, and based on AR technology, provide the operator with real-time data and operation instructions.

[0011] Furthermore, the data collection module also uses RFID technology and QR code recognition technology to identify and track each pallet and material throughout the process;

[0012] The data acquisition module is provided with an independent unique identifier on each sensor for identifying and distinguishing the data collected by the sensor;

[0013] The data acquisition module combines the RFID and QR code with the unique identifier of the sensor to form a unique identification for each pallet and material, and associates the identification information with the sensor data;

[0014] The data processing and analysis module obtains the identification information in the pallet database, analyzes and obtains the position changes and status updates of materials during the production process.

[0015] Furthermore, the specific execution steps of the data processing and analysis module to analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse are:

[0016] S101: Building digital models of pallets and materials based on RFID and QR code information stored in a database, wherein the digital models are used to describe the position and state characteristics of pallets and materials in the production process, and extracting features of the dynamic behaviors of pallets and materials in combination with sensor data, wherein the extracted features include process time, storage time, and transportation path;

[0017] S102: Real-time monitoring of material flow on the production line, establishing a dynamic production model based on the real-time status information of pallets and materials, and predicting key factors in the production process, including abnormal conditions and blocking points;

[0018] S103: integrating the predicted key factors in the production process to form an overall production process model, and obtaining an optimal real-time scheduling solution for the pallet warehouse by calculating the production process model;

[0019] S104: Implementing the optimal real-time scheduling plan for the pallet warehouse to the actual production line, monitoring the execution effect of the production line, inputting the monitoring data into the production process model, and continuously optimizing the real-time scheduling plan for the pallet warehouse.

[0020] Furthermore, the dynamic production model is constructed based on discrete event simulation DES; and the production process model is constructed based on a state space model, wherein the state equation of the production process model is: , The observation equation is: , in, represents the potential state of the production process, represents the dynamic evolution of the underlying state, represents the effect of external input on the potential state, Indicates the scheduling status of the pallet on the production line. represents the key factors of external input, It refers to random interference factors that cannot be fully determined and predicted during the production process. represents the random noise generated during production process monitoring and data collection. Represents observable production process data, Indicates the influence of various states in the production process on the final observed quantity, Indicates the impact of production line equipment failure on the production process.

[0021] Furthermore, the pallet transport module dynamically plans a path for the AGV according to the arrival time and location in the real-time scheduling plan of the pallet warehouse; the pallet carrying the product is carried by the AGV, and the pallet and the product carried by the pallet are put into storage;

[0022] When the AGV trolley is operating according to the optimal real-time scheduling plan for the pallet warehouse, the pallet transport module monitors whether the AGV trolley has any emergency problems, such as insufficient power and damaged parts; when the AGV trolley has an emergency problem, the pallet transport module initiates an emergency response and replaces the pallet warehouse production line task being executed by the AGV trolley to ensure that the pallet warehouse production line task is completed within the planned time of the real-time scheduling plan for the pallet warehouse.

[0023] Furthermore, the pallet transport module controls the AGV with the pallet to arrive at the specified location at the specified time according to the real-time dispatching plan of the pallet warehouse output by the data processing and analysis module. The pallet transport module specifically adopts the A* algorithm to plan the path of the AGV. The specific execution steps of the pallet transport module to control the AGV are:

[0024] S201: The pallet transportation module receives the real-time scheduling plan of the pallet warehouse output by the data processing and analysis module, and the real-time scheduling plan of the pallet warehouse includes the designated destination and arrival time requirement of the pallet;

[0025] S202: According to the designated destination and arrival time requirement of the pallet, combined with the real-time production line status and the current location of the AGV, the optimal transportation path of the AGV is planned using the A* algorithm; S203: The following factors are considered when planning the path: The operating speed and load capacity of the AGV; The road environment and drivable paths inside the production line; Avoid collision and interference with other AGVs; Meet the pallet's designated destination and arrival time requirements;

[0026] S204: Sending the planned AGV path to the control system of the designated AGV, and the AGV autonomously drives along the planned path to complete the task of transporting the pallet;

[0027] S205: monitoring the transportation process of the AGV and the pallet in real time, and promptly discovering and resolving abnormal situations that occur during the transportation process;

[0028] S206: Feedback the actual transportation status of the AGV to the data processing and analysis module, and the data processing and analysis module continuously optimizes the real-time scheduling plan of the pallet warehouse.

[0029] Furthermore, the pallet transport module detects and resolves abnormal situations that occur during the transport process to ensure that the AGV arrives at the designated location on time or in advance. The execution steps are:

[0030] S2051: monitor the running status of the AGV and the status information of the pallet in real time, wherein the running status includes position, speed, load and power, and the status information includes position, quality and integrity;

[0031] S2052: Compare and analyze the real-time monitored AGV operation status and pallet status information with the predetermined normal working range to determine the type and severity of the existing faults, so as to determine the abnormal conditions of the AGV and the pallet, wherein the abnormal conditions include AGV fault, collision, obstruction and insufficient power;

[0032] S2053: after determining the actual information of the abnormal situation, taking corresponding countermeasures, the countermeasures including triggering a backup plan and adjusting a path planning plan;

[0033] S2054: Feedback the feedback information of abnormal situations and treatment measures to the data processing and analysis module to optimize the real-time scheduling plan of the pallet warehouse.

[0034] Furthermore, the data processing and analysis module determines the optimal storage location of each pallet based on a pallet storage planning algorithm optimized for space, according to the specific layout and size of the pallet warehouse and the storage requirements of the pallets, with the pallet space utilization rate as the optimization target, and the constraints that each pallet must be placed in a designated storage location, that there must be no overlap between pallets, that the pallets must not exceed the boundaries defined by the pallet warehouse, and that the size and storage requirements of each pallet must be met as the constraint targets;

[0035] The pallet transport module formulates storage instructions based on the optimal storage position of each pallet; when the AGV needs to store or retrieve a pallet, it autonomously drives into the designated area based on the storage instructions and path planning, and loads and unloads the pallet through robotic arms and sensors, completing automated handling and storage and retrieval of the pallet.

[0036] Furthermore, the specific process of the production line simulation module for virtual debugging, fault prediction and maintenance strategy optimization of the intelligent FMS pallet warehouse production line according to the digital twin model of the production line is as follows:

[0037] S301: Using digital twin technology to establish a digital twin model of the intelligent FMS pallet warehouse production line, wherein the digital twin model includes equipment layout, process flow and material flow path;

[0038] S302: simulating the production process and equipment configuration on the digital twin model to test the feasibility and effectiveness of real-time scheduling solutions for different pallet warehouses;

[0039] When testing the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses, first set different real-time scheduling schemes for pallet warehouses, then determine the test indicators and evaluation criteria, then perform simulation on the data twin model, run different real-time scheduling schemes for pallet warehouses, monitor the simulation results, compare the running results of different real-time scheduling schemes for pallet warehouses, and judge the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses based on the running results;

[0040] S303: Simulate the failure conditions of equipment in the intelligent FMS pallet warehouse production line through historical failure data, and predict the impact of different failure conditions on the production line;

[0041] Based on historical fault data, simulate various equipment fault conditions and the impact of faults on production line capacity, production efficiency and resource utilization. Then, based on the simulation results, use quantitative analysis methods to quantify the impact of different faults on the intelligent FMS pallet warehouse production line.

[0042] S304: Determine corresponding fault handling solutions and emergency plans based on the prediction results of the impact of different fault situations on the production line, and evaluate the effects and costs of different maintenance strategies by comparing the production efficiency and resource utilization of different maintenance strategies;

[0043] S305: Optimize maintenance plans and cycles through simulation analysis, and continuously monitor the output results of the digital twin model and actual production data to continuously optimize maintenance strategies.

[0044] Furthermore, the human-machine collaboration module provides real-time information and operation instructions to operators on the production line based on AR technology; the real-time position and status information of pallets and materials are superimposed on the real scene of the production line through AR, and operators understand the production progress by observing the real scene of the production line; at the same time, the human-machine collaboration module provides AR animation delay and text instructions for operation tasks including pallet loading and unloading and equipment maintenance, thereby reducing the difficulty of operation.

[0045] Beneficial effects:

[0046] 1. The present invention adopts a multi-module collaborative architecture to comprehensively track the information of pallets and materials in the production process. By integrating various key factors in the production process to generate a production process model, and calculating the production process model, the optimal real-time scheduling solution for the pallet warehouse is obtained, which greatly improves production efficiency and adaptability, and meets the needs of multi-variety small-batch production in the modern manufacturing industry;

[0047] 2. The present invention establishes a digital twin model of the production line through digital twin technology, and performs virtual debugging, fault prediction and maintenance strategy optimization on the digital twin model. It can discover possible problems in advance, automatically optimize and adjust the production plan, reduce production interruptions, improve the system's autonomous management and robustness, and enhance the system's ability to cope with various production abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 A flow chart for analyzing production data according to the present invention;

[0050] Figure 3 A flow chart for the present invention to discover and resolve abnormal situations that occur during transportation;

[0051] Figure 4 Flowchart for virtual commissioning, fault prediction and maintenance strategy optimization for the present invention. DETAILED DESCRIPTION

[0052] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0053] The present invention provides a pallet warehouse production line system based on intelligent FMS, as shown in the attached Figure 1As shown in the figure, it is specifically composed of data acquisition module, data processing and analysis module, pallet transportation module, production line simulation module and human-machine collaboration module. The functions of each module are as follows:

[0054] In this embodiment, the data acquisition module installs several sensors in the intelligent FMS pallet warehouse production line to obtain real-time production data in the production line.

[0055] At the same time, the data collection module also uses RFID technology and QR code recognition technology to identify and track each pallet and material throughout the process;

[0056] The data acquisition module is provided with an independent unique identifier on each sensor, which is used to identify and distinguish the data collected by the sensor;

[0057] The data acquisition module combines the RFID and QR code with the unique identifier of the sensor to form a unique identification for each pallet and material. At the same time, the identification information is associated with the sensor data and stored in the database;

[0058] The data processing and analysis module obtains the data after associating the identification information and sensor data in the database, and uses the unique identification formed by the combination of RFID tags and QR code sensors to completely record and analyze the location changes and status updates of materials during the production process.

[0059] In this embodiment, the data processing and analysis module is used to pre-process and extract features of various data collected by the data acquisition module; and to analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse.

[0060] Specifically, as attached Figure 2 As shown in the figure, the specific execution steps of the data processing and analysis module to analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse are:

[0061] S101: A digital model of the pallet and the material is established based on the RFID and QR code information stored in the database, wherein the digital model is used to describe the characteristics of the pallet and the material during the production process, such as the position and state, and is combined with the sensor data to extract the characteristics of the dynamic behavior of the pallet and the material, and the extracted characteristics include process time, storage time and transfer path, etc.

[0062] S102: Monitor the material flow of the production line in real time, establish a dynamic production model based on the real-time status information of pallets and materials, and predict key factors in the production process including abnormal situations and blocking points.

[0063] Specifically, the dynamic production model is built based on discrete event simulation DES, and the specific construction process is as follows:

[0064] S1021: Divide the process sites of the production line: According to the process flow of the production line, the entire production line is divided into a series of discrete process sites, including the raw material storage area, production process area and finished product storage area.

[0065] S1022: Establish a site model: For each process site, determine the physical objects and equipment resources of the corresponding site as well as the logical relationships and interaction rules therein, where the physical objects include pallets and materials, etc., and the equipment resources include AGV carts and robotic arms, etc.

[0066] S1023: Build logical relationships between sites: Analyze the material flow relationship between each process site in the production line, determine the logical connection relationship between sites, define the transfer time and transfer capacity of materials between sites as constraints, and simulate dynamic changes such as bottlenecks and time consumption in the actual production process.

[0067] S1024: Build a simulation model: Integrate the dynamic behavior models of each site to form an overall simulation model, and add real-time monitoring data and equipment failure probability and other factors to the overall simulation model to enhance the model's ability to respond to actual production and form a dynamic production model.

[0068] S1025: Model verification and optimization: Use historical production data to verify the dynamic production model and correct the model parameters until the output results of the dynamic production model are consistent with the historical production data.

[0069] S103: The predicted key factors in the production process, such as the production rhythm, inventory limit, and energy consumption, are integrated to form an overall production process model. The optimal real-time scheduling solution for the pallet warehouse is obtained by calculating the production process model.

[0070] Specifically, the production process model is constructed based on the state space model, and the state equation of the production process model is: ; The observation equation is: ; in, Represents the potential status of the production process, including but not limited to material inventory status, pallet location status and production equipment status. These status variables can fully reflect the operation of the pallet warehouse production line system; It represents the dynamic evolution of the potential state, that is, the dynamic evolution law of each state variable over time in the production process; Represents the influence of external input on the state, that is, external input For state variables The impact of Indicates the scheduling status of the pallet on the production line, including but not limited to pallet position changes and scheduling priority; Represents key factors of external input, including but not limited to anomalies and choke points; It refers to random interference factors that cannot be fully determined and predicted in the production process. These random interference factors may come from uncertainties in raw material characteristics, equipment status, environmental conditions, etc.; It refers to the random noise generated during the production process monitoring and data collection process. These random noises may come from measurement equipment, environmental interference, data transmission and other links; Represents observable production process data, including real-time status information of pallets and materials; Indicates the influence of various states in the production process on the final observed quantity; It represents the impact of production line equipment failure on the production process. Taking production line equipment failure into consideration helps to improve the adaptability of the production process model to complex on-site conditions.

[0071] S104: Implement the optimal real-time scheduling plan for the pallet warehouse to the actual production line, monitor the execution effect of the production line, input the monitoring data into the production process model, and continuously optimize the real-time scheduling plan for the pallet warehouse.

[0072] In this embodiment, the pallet transport module is used to control the AGV to perform autonomous path planning according to the real-time scheduling plan of the pallet library in the data processing and analysis module, so as to realize the intelligent storage and automatic transportation of pallets. At the same time, the pallet transport module will adjust and optimize the path planning plan of the AGV in real time according to the actual situation of the production line, and is responsible for coordinating the real-time control tasks such as the scheduling of the AGV and path conflict avoidance.

[0073] Specifically, the pallet transport module dynamically plans a path for the AGV according to the specified arrival time and the specified arrival location in the real-time scheduling plan of the pallet warehouse; the pallet carrying the product is carried by the AGV, so that the pallet and the product carried by the pallet are put into storage;

[0074] When the AGV operates according to the optimal real-time dispatch plan for the pallet warehouse, monitor whether the AGV has any emergency problems, including but not limited to insufficient power and damaged parts. When an emergency problem occurs in the AGV, the pallet transport module initiates an emergency response, allowing the spare AGV to replace the current AGV and complete the pallet warehouse task that the current AGV is performing.

[0075] Specifically, the pallet transport module controls the AGV with pallets to arrive at the specified location at the specified time according to the real-time dispatching plan of the pallet warehouse output by the data processing and analysis module. The pallet transport module uses the A* algorithm to plan the path of the AGV. The specific execution steps of the pallet transport module to control the AGV are:

[0076] S201: The pallet transportation module receives the real-time scheduling plan of the pallet library output by the data processing and analysis module, wherein the scheduling plan includes the designated destination and arrival time requirement of the pallet;

[0077] S202: According to the designated destination and arrival time requirement of the pallet, combined with the real-time production line status and the current location of the AGV, the optimal transportation path of the AGV is planned using the A* algorithm;

[0078] S203: The following factors are considered when planning the path:

[0079] The operating speed and load capacity of the AGV;

[0080] The road environment and drivable paths inside the production line;

[0081] Avoid collision and interference with other AGVs;

[0082] Meet the specified arrival time requirements of the pallet;

[0083] S204: Send the planned AGV transportation path to the designated AGV, and the AGV drives autonomously along the planned path to complete the pallet transportation task;

[0084] S205: Monitor the running status of the AGV and the transportation process of the pallet in real time, and promptly discover and resolve abnormal situations that occur during the transportation process;

[0085] S206: The actual transportation status of the AGV is fed back to the data processing and analysis module, and the data processing and analysis module continuously optimizes the real-time scheduling plan of the pallet warehouse.

[0086] Specifically, after the pallet transport module completes the path planning of the AGV, it needs to determine how the AGV automatically stores and retrieves pallets; the data processing and analysis module will determine the optimal storage location for each pallet based on the specific layout and size of the pallet warehouse and the storage requirements of the pallets, and based on the pallet storage planning algorithm optimized for space, with pallet space utilization as the optimization goal, and each pallet must be placed in a designated storage location, there must be no overlap between pallets, the pallets cannot exceed the boundaries of the pallet warehouse, and the size and storage requirements of each pallet must be met as constraint goals.

[0087] The pallet transport module formulates storage instructions based on the optimal storage position of each pallet, and directs the AGV to autonomously navigate to the designated pallet storage position in the pallet library according to the planned path and sequence; when the AGV needs to store or retrieve a pallet, it uses automated equipment such as robotic arms and sensors to transport the pallet to the target storage position, realize the loading and unloading of the pallet, and achieve automated transportation and retrieval of the pallet.

[0088] During the operation of the AGV, the operating status of the AGV and the pallet storage and retrieval process are monitored in real time to promptly detect and respond to possible abnormal situations.

[0089] Specifically, the pallet transport module also needs to ensure that there is no conflict between the real-time scheduling plan of the pallet warehouse and the path planning of the AGV, taking into account the influence of various situations: ensuring that the AGV arrives on time or in advance

[0090] As attached Figure 3 As shown in the figure, the pallet transport module detects and resolves abnormal situations during the transport process to ensure that the AGV arrives at the designated location on time or in advance. The execution steps are as follows:

[0091] S2051: The pallet transport module monitors the operating status of the AGV in real time through the sensors installed on the AGV, where the operating status includes but is not limited to key parameters such as position, speed, load and power. At the same time, the sensor installed on the pallet monitors the status information of the pallet, where the status information of the pallet includes but is not limited to position, quality and integrity.

[0092] S2052: Compare and analyze the real-time monitored AGV and pallet status information with the predetermined normal working range to determine the type and severity of the fault, so as to determine the abnormal conditions of the pallet and AGV, which include AGV failure, collision, blockage and insufficient power.

[0093] S2053: After determining the abnormal situation information, the pallet transport module takes corresponding response measures, including but not limited to triggering the backup plan, adjusting the path planning plan, and initiating the emergency plan. At the same time, the pallet transport module will notify relevant personnel to coordinate resources to jointly deal with the emergency problem.

[0094] S2054: Feedback the feedback information of abnormal situations and treatment measures to the data processing and analysis module to optimize the real-time scheduling plan of the pallet warehouse.

[0095] At the same time, the data processing and analysis module will obtain the actual storage and access status of the AGV, analyze and optimize the real-time scheduling plan of the pallet warehouse, and dynamically adjust the storage layout of the pallet warehouse and the operation strategy of the AGV according to changes in production needs, continuously improving storage density and transportation efficiency.

[0096] In this embodiment, the production line simulation module is used to establish a digital twin model of the production line. The digital twin model is used to perform virtual debugging, fault prediction, and maintenance strategy optimization on the entire intelligent FMS pallet warehouse production line, thereby improving production efficiency and flexibility.

[0097] Specifically, as attached Figure 4As shown in the figure, the specific process of the production line simulation module to perform virtual debugging, fault prediction and maintenance strategy optimization for the entire intelligent FMS pallet warehouse production line based on the digital twin model of the production line is as follows:

[0098] S301: Use the digital twin model to simulate the actual operating environment and process flow of the production line, including AGV transportation, pallet storage, and material delivery; then introduce different production scenarios and tasks into the digital twin model to simulate the operation of the production line.

[0099] S302: The production line simulation module simulates data such as production process and equipment configuration on the digital twin model to test the feasibility and effectiveness of real-time scheduling solutions for different pallet warehouses.

[0100] Specifically, when testing the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses, the production line simulation module first sets different real-time scheduling schemes for pallet warehouses, and then determines the test indicators and evaluation criteria, including transportation efficiency, space utilization, and task completion time; then, it performs simulation on the data twin model, runs different real-time scheduling schemes for pallet warehouses, and monitors the simulation results, including the running path of the AGV cart and the storage and retrieval process of the pallet; by comparing the running results of different real-time scheduling of pallets, the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses are judged according to the running results.

[0101] S303: Simulate equipment failure conditions through historical failure data, specifically simulate the possibility and impact of equipment failure, and predict the impact of different failure conditions on the production line, including production capacity reduction and transportation interruption, by analyzing the simulation results.

[0102] Specifically, the production line simulation module simulates various equipment failure conditions and the impact of failures on the production line's capacity, production efficiency, and resource utilization based on historical failure data. Then, based on the simulation results, a quantitative analysis method is used to quantify the impact of different failures on the production line using production downtime, capacity loss, and maintenance costs as indicators. According to the predicted failure conditions and impact assessment results, corresponding maintenance and preventive measures are formulated.

[0103] S304: Determine corresponding fault handling solutions and emergency plans based on the impact of different fault situations on the production line, and evaluate the effects and costs of different maintenance strategies by comparing the production efficiency and resource utilization of different maintenance strategies.

[0104] S305: Optimize maintenance plans and cycles through simulation analysis, and continuously monitor the output results of the digital twin model and actual production data to continuously optimize maintenance strategies.

[0105] In this embodiment, the human-machine collaboration module is used to provide the operator with a human-machine interaction interface, and based on AR technology, provide the operator with real-time data and operation instructions.

[0106] Specifically, the human-machine collaboration module provides real-time information and operation instructions to operators on the production line based on AR technology; the real-time location and status information of pallets and materials are superimposed on the real scene of the production line through AR, and operators can understand the production progress by observing the real scene of the production line; at the same time, the human-machine collaboration module provides AR animation delay and text instructions for characteristic operation tasks, including pallet loading and unloading and equipment maintenance, to reduce the difficulty of operation; at the same time, it can also provide immersive training for new employees, simulate the production site, and familiarize themselves with the process flow and equipment operation.

[0107] The human-machine collaboration module also includes a centralized visualization interface for showing operators the operation status of the entire pallet warehouse production line system; and on the visualization interface, key production indicators are displayed, including production plan completion and pallet transportation efficiency.

[0108] The above-mentioned embodiments only express the preferred implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person skilled in the art, several modifications, improvements and substitutions can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A pallet warehouse production line system based on intelligent FMS, characterized in that: It includes data acquisition module, data processing and analysis module, pallet transportation module, production line simulation module and human-machine collaboration module; The data acquisition module installs multiple sensors in the intelligent FMS pallet warehouse production line to obtain real-time data in the production line; The data processing and analysis module is used to pre-process and extract features of various production data collected by the data acquisition module; and analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse; The pallet transport module is used to control the AGV to perform autonomous path planning according to the real-time dispatching plan of the pallet library in the data processing and analysis module, so as to realize the intelligent storage and automatic transportation of the pallet; The production line simulation module is used to establish a digital twin model of the intelligent FMS pallet warehouse production line, and to perform virtual debugging, fault prediction and maintenance strategy optimization on the intelligent FMS pallet warehouse production line through the digital twin model; The human-machine collaboration module is used to provide a human-machine interaction interface to the operator, and based on AR technology, provide the operator with real-time data and operation instructions.

2. According to claim 1, a pallet warehouse production line system based on intelligent FMS is characterized in that: The data collection module also uses RFID technology and QR code recognition technology to identify and track each pallet and material throughout the process; The data acquisition module is provided with an independent unique identifier on each sensor for identifying and distinguishing the data collected by the sensor; The data acquisition module combines the RFID, QR code and unique identifier of the sensor to form a unique identifier for each pallet and material, and associates the identification information with the sensor data; The data processing and analysis module obtains identification information in the pallet database, analyzes and obtains position changes and status updates of materials during the production process.

3. According to the intelligent FMS-based pallet warehouse production line system of claim 1, it is characterized in that: The specific execution steps of the data processing and analysis module to analyze the production data to obtain the optimal real-time scheduling solution for the pallet warehouse are as follows: S101: Building digital models of pallets and materials based on RFID and QR code information stored in a database, wherein the digital models are used to describe the position and state characteristics of pallets and materials in the production process, and extracting features of the dynamic behaviors of pallets and materials in combination with sensor data, wherein the extracted features include process time, storage time, and transportation path; S102: Real-time monitoring of material flow on the production line, establishing a dynamic production model based on the real-time status information of pallets and materials, and predicting key factors in the production process, including abnormal conditions and blocking points; S103: integrating the predicted key factors in the production process to form an overall production process model, and obtaining an optimal real-time scheduling solution for the pallet warehouse by calculating the production process model; S104: Implementing the optimal real-time scheduling plan for the pallet warehouse to the actual production line, monitoring the execution effect of the production line, inputting the monitoring data into the production process model, and continuously optimizing the real-time scheduling plan for the pallet warehouse.

4. According to claim 3, a pallet warehouse production line system based on intelligent FMS is characterized in that: The dynamic production model is constructed based on discrete event simulation DES; and the production process model is constructed based on a state space model, wherein the state equation of the production process model is: ； The observation equation is: ; in, represents the potential state of the production process, represents the dynamic evolution of the underlying state, represents the effect of external input on the potential state, Indicates the scheduling status of the pallet on the production line. represents the key factors of external input, It refers to random interference factors that cannot be fully determined and predicted during the production process. represents the random noise generated during production process monitoring and data collection. Represents observable production process data, Indicates the influence of various states in the production process on the final observed quantity, Indicates the impact of production line equipment failure on the production process.

5. According to the intelligent FMS-based pallet warehouse production line system of claim 1, it is characterized in that: The pallet transport module dynamically plans a path for the AGV according to the arrival time and location in the real-time scheduling plan of the pallet warehouse; the pallet carrying the product is carried by the AGV, and the pallet and the product carried by the pallet are put into storage; When the AGV trolley is operating according to the optimal real-time scheduling plan for the pallet warehouse, the pallet transport module monitors whether the AGV trolley has any emergency problems, such as insufficient power and damaged parts; when the AGV trolley has an emergency problem, the pallet transport module initiates an emergency response and replaces the pallet warehouse production line task being executed by the AGV trolley to ensure that the pallet warehouse production line task is completed within the planned time of the real-time scheduling plan for the pallet warehouse.

6. The intelligent FMS-based pallet warehouse production line system according to claim 5 is characterized in that: The pallet transport module controls the AGV to arrive at the specified location at the specified time according to the real-time dispatching plan of the pallet warehouse output by the data processing and analysis module. The pallet transport module uses the A* algorithm to plan the path of the AGV. The specific execution steps of the pallet transport module to control the AGV are: S201: The pallet transportation module receives the real-time scheduling plan of the pallet warehouse output by the data processing and analysis module, and the real-time scheduling plan of the pallet warehouse includes the designated destination and arrival time requirement of the pallet; S202: According to the designated destination and arrival time requirement of the pallet, combined with the real-time production line status and the current location of the AGV, the optimal transportation path of the AGV is planned using the A* algorithm; S203: The following factors are considered when planning the path: The operating speed and load capacity of the AGV; The road environment and drivable paths inside the production line; Avoid collision and interference with other AGVs; Meet the pallet's designated destination and arrival time requirements; S204: Sending the planned AGV path information to the control system of the designated AGV, and the AGV autonomously drives along the planned path to complete the task of transporting the pallet; S205: monitoring the transportation process of the AGV and the pallet in real time, and promptly discovering and resolving abnormal situations that occur during the transportation process; S206: Feedback the actual transportation status of the AGV to the data processing and analysis module, and the data processing and analysis module continuously optimizes the real-time scheduling plan of the pallet warehouse.

7. The intelligent FMS-based pallet warehouse production line system according to claim 6 is characterized in that: The pallet transport module detects and resolves abnormal situations that occur during transportation to ensure that the AGV arrives at the designated location on time or in advance. The execution steps are as follows: S2051: monitor the running status of the AGV and the status information of the pallet in real time, wherein the running status includes the position, speed, load and power, and the status information includes the position, quality and integrity; S2052: Compare and analyze the real-time monitored AGV operation status and pallet status information with the predetermined normal working range to determine the type and severity of the existing faults, so as to determine the abnormal conditions of the AGV and the pallet, which include AGV fault, collision, obstruction and insufficient power; S2053: after determining the actual information of the abnormal situation, taking corresponding countermeasures, the countermeasures including triggering a backup plan and adjusting a path planning plan; S2054: Feedback the feedback information of abnormal situations and treatment measures to the data processing and analysis module to optimize the real-time scheduling plan of the pallet warehouse.

8. The intelligent FMS-based pallet warehouse production line system according to claim 6 is characterized in that: The data processing and analysis module determines the optimal storage location of each pallet based on a pallet storage planning algorithm optimized for space, according to the specific layout and size of the pallet warehouse and the storage requirements of the pallets, with the pallet space utilization rate as the optimization target, and the constraints that each pallet must be placed in a designated storage location, that there must be no overlap between pallets, that the pallets must not exceed the boundaries defined by the pallet warehouse, and that the size and storage requirements of each pallet must be met as the constraint targets; The pallet transport module formulates storage instructions based on the optimal storage position of each pallet; when the AGV needs to store or retrieve a pallet, it autonomously drives into the designated area based on the storage instructions and path planning, and loads and unloads the pallet through robotic arms and sensors, completing automated handling and storage and retrieval of the pallet.

9. The intelligent FMS-based pallet warehouse production line system according to claim 1 is characterized in that: The specific process of the production line simulation module to perform virtual commissioning, fault prediction and maintenance strategy optimization on the intelligent FMS pallet warehouse production line based on the digital twin model of the production line is as follows: S301: Using digital twin technology to establish a digital twin model of the intelligent FMS pallet warehouse production line, wherein the digital twin model includes equipment layout, process flow and material flow path; S302: simulating the production process and equipment configuration on the digital twin model to test the feasibility and effectiveness of real-time scheduling solutions for different pallet warehouses; When testing the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses, first set different real-time scheduling schemes for pallet warehouses, then determine the test indicators and evaluation criteria, and then perform simulation on the data twin model, run different real-time scheduling schemes for pallet warehouses, monitor the simulation results, compare the running results of different real-time scheduling schemes for pallet warehouses, and judge the feasibility and effectiveness of different real-time scheduling schemes for pallet warehouses based on the running results; S303: Simulate the failure conditions of equipment in the intelligent FMS pallet warehouse production line through historical failure data, and predict the impact of different failure conditions on the production line; Based on historical fault data, simulate various equipment fault conditions and the impact of faults on production line capacity, production efficiency and resource utilization. Then, based on the simulation results, use quantitative analysis methods to quantify the impact of different faults on the intelligent FMS pallet warehouse production line. S304: Determine corresponding fault handling solutions and emergency plans based on the prediction results of the impact of different fault situations on the production line, and evaluate the effects and costs of different maintenance strategies by comparing the production efficiency and resource utilization of different maintenance strategies; S305: Optimize maintenance plans and cycles through simulation analysis, and continuously monitor the output results of the digital twin model and actual production data to continuously optimize maintenance strategies.

10. The intelligent FMS-based pallet warehouse production line system according to claim 1 is characterized in that: The human-machine collaboration module provides real-time information and operation instructions for operators on the production line based on AR technology; the real-time location and status information of pallets and materials are superimposed on the real scene of the production line through AR, and operators understand the production progress by observing the real scene of the production line; at the same time, the human-machine collaboration module provides AR animation delay and text instructions for operation tasks including pallet loading and unloading and equipment maintenance to reduce the difficulty of operation.

Images