Furniture production workshop logistics information intelligent monitoring system

Abstract

The invention discloses a furniture production workshop logistics information intelligent monitoring system, and relates to the technical field of furniture production, and the system comprises a material identification module which is used for distributing a unique identity identifier for a material in process and tracking the circulation path of the material in process between processing stations in real time; the station sensing module is used for collecting the circulation state information of each station and identifying the identity label of the in-process material arriving at the station; the abnormity identification module is used for judging the circulation state according to the circulation time, the code scanning record and the processing state of the manufactured material among the processing procedures; according to the order priority and the load condition of the predicted station, dynamically adjusting the scheduling path of the in-process materials; a unique identity label is distributed for each in-process material, real-time tracking of a circulation path of the in-process materials is realized through the material identification module and the station sensing module, a scheduling path of the in-process materials is dynamically adjusted, and a production schedule is optimized.

Description

Technical Field

[0001] This invention relates to the field of furniture manufacturing technology, and more specifically to an intelligent monitoring system for logistics information in a furniture production workshop. Background Technology

[0002] In the invention patent application entitled "A Furniture Production Workshop Logistics Information Monitoring System" (Publication No.: CN108408312A, Publication Date: 2018-08-17), the system includes multiple mobile shelves, a handheld scanning device, and a server. The mobile shelves are equipped with identification codes, signal receiving controllers, signal lights, and / or a first display screen. These shelves are used to place workpieces and receive remote data from the signal receiving controllers to control the signal lights and / or the first display screen. The scanning device scans and identifies preset barcodes or QR codes on the workpiece sheets, displays or queries relevant information about the current workpiece in the current process, and can also scan and update the identification codes of the mobile shelves. The server stores all workpiece data, pushes relevant information about the current workpiece and the identification codes of the mobile shelves to the scanning device, and also sends this information as remote data to the signal receiving controller of the mobile shelves. This invention enables real-time monitoring of the workpiece production process through information management, reducing production costs and shortening the production cycle, while also optimizing production scheduling and increasing capacity.

[0003] While the aforementioned patent employs a combination of mobile shelves, handheld scanning devices, and servers, achieving real-time monitoring of the workpiece production process through the identification codes of the mobile shelves, signal receiving controllers, and display screens, it only enables tracking of in-process materials and basic information feedback. It fails to deeply integrate and optimize multiple dimensions such as production processes, workstation load, and logistics status, resulting in inefficient collaboration and dynamic adjustment between logistics information monitoring and production scheduling management. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring system for logistics information in furniture production workshops to address the aforementioned shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A smart monitoring system for logistics information in a furniture production workshop includes a material identification module, which is used to assign a unique identifier to materials in production and to track the flow path of materials in production between various processing stations in real time. The workstation sensing module is used to collect the flow status information of each workstation and identify the identity of the work-in-process materials arriving at the workstation. The anomaly detection module is used to determine the status of the material in production based on the flow time, barcode scanning records, and processing status of the material between each processing step. The scheduling module is used to detect when the flow path of work-in-process materials is abnormal or when the workstation is predicted to be congested, and to dynamically adjust the scheduling path of work-in-process materials according to the order priority and the predicted workstation load. The information interaction module is used to push the anomaly identification results and scheduling information to the operation interface, so as to realize real-time monitoring and flexible scheduling of the flow status of materials in production.

[0006] Preferably, the material identification module uses radio frequency identification, QR code or ultra-wideband to assign a unique identifier to the work-in-process material. The unique identifier is used to mark the order number, processing information and current workstation status of the work-in-process material.

[0007] Preferably, the material identification module is also used to collect the location change information of the material in production in real time, and link with the workstation sensing module to realize the tracking and location recording of the material in production between each processing workstation.

[0008] Preferably, the anomaly identification module includes a judgment rule, which is used to detect whether the turnover time of the work-in-process material exceeds the cycle time threshold set by the target process, and to determine whether there is a logistics anomaly. The judgment rules also include identifying situations where in-process materials are not scanned and registered at a certain workstation, are continuously missed during scanning, or are abnormally delayed for an extended period of time, and generating an abnormal flag accordingly.

[0009] Preferably, the anomaly identification module compares the processing status data of the in-process materials uploaded by the workstation sensing module with the identification information of the in-process materials to achieve collaborative anomaly judgment on the flow status of the in-process materials and the workstation status.

[0010] Preferably, the scheduling module dynamically adjusts the scheduling paths of multiple work-in-process materials based on an optimized scheduling algorithm. The optimized scheduling algorithm adopts a path optimization strategy based on genetic algorithm, particle swarm optimization, or heuristic algorithm to achieve the optimal path for multi-task scheduling and minimize the delay in the flow of work-in-process materials, resource conflicts, and production stoppages.

[0011] Preferably, the scheduling optimization algorithm weights each factor by adjusting the weights. The factors considered by the scheduling optimization algorithm include, but are not limited to: the order priority of the work-in-process materials, the workstation load, the processing time of the work-in-process materials, the delivery deadline, and the current status of the work-in-process materials.

[0012] Preferably, the weight adjustment adopts an adaptive adjustment algorithm, which dynamically adjusts the weight ratio of each scheduling factor according to the current production environment and workshop operation status to cope with the production demand and workstation load under different working conditions. The weight adjustment includes: order urgency, workstation load, current status of work-in-process materials, and processing time required for the process, and dynamically adjusts the weights according to real-time production data to optimize path decision-making.

[0013] Preferably, the weighting adjustment assigns higher weights to order priorities based on factors such as the urgency of workshop production tasks and the timeliness of handling work-in-process materials, and adjusts the weights of workstation load and processing time based on real-time monitoring data to achieve optimal matching of production tasks and resources.

[0014] Preferably, the information interaction module is used to feed back the abnormal information generated by the abnormality identification module to the workshop operation interface in real time; Preferably, the information interaction module is also used to push the work-in-process material scheduling path adjustment information generated by the scheduling module to the operation interface or mobile terminal, so that operators can make real-time scheduling adjustments and resource allocation in the workshop.

[0015] In the above technical solution, the intelligent monitoring system for logistics information in a furniture production workshop provided by the present invention assigns a unique identification to each work-in-process material and achieves real-time tracking of the flow path of the work-in-process material through the material identification module and the workstation sensing module, ensuring accurate tracking of each work-in-process material; when the flow time of the work-in-process material between each process exceeds the preset cycle time, or when abnormal situations such as missed scanning or excessive dwell time occur, it can identify in real time and automatically trigger abnormal feedback; when production abnormalities are identified or workstation congestion is predicted, it can dynamically adjust the scheduling path of the work-in-process material based on information such as order priority and workstation load, and optimize the production schedule.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and illustrative only, and are not intended to limit this disclosure.

[0017] This application provides an overview of various implementations or examples of the technology described in this disclosure, and is not a full disclosure of the entire scope or all features of the disclosed technology. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a schematic diagram of an intelligent monitoring system for logistics information in a furniture production workshop, provided as an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0021] Reference Figure 1 As shown, the present invention provides an intelligent monitoring system for logistics information in a furniture production workshop, including a material identification module, which is used to assign a unique identifier to the materials in production and to track the flow path of the materials in production between each processing station in real time. The workstation sensing module is used to collect the flow status information of each workstation and identify the identity of the work-in-process materials arriving at the workstation. The anomaly detection module is used to determine the status of the material in production based on the flow time, barcode scanning records, and processing status of the material between each processing step. The scheduling module is used to detect when the flow path of work-in-process materials is abnormal or when the workstation is predicted to be congested, and to dynamically adjust the scheduling path of work-in-process materials according to the order priority and the predicted workstation load. The information interaction module is used to push the anomaly identification results and scheduling information to the operation interface, so as to realize real-time monitoring and flexible scheduling of the flow status of materials in production.

[0022] Specifically, the material identification module assigns a unique identifier, such as an RFID tag, QR code, or UWB tag, to each piece of work-in-process material to facilitate real-time tracking and status monitoring. Each identifier is linked to relevant information such as the material's production task, process progress, and processing requirements, ensuring the system can update and query the accurate location and processing status of each material in real time. The function of the material identification module is to ensure the system's accurate identification and traceability of each work-in-process material, thereby providing basic data support for subsequent logistics monitoring and production scheduling.

[0023] The workstation sensing module is responsible for collecting real-time status information from each processing workstation. Using sensors, barcode scanners, and RFID readers, this module automatically identifies the work-in-process materials arriving at each workstation and simultaneously records their processing status, such as started, in progress, or completed. Through real-time monitoring of workstation status, the workstation sensing module ensures comprehensive control over the load, processing progress, and status of each workstation, thereby providing timely data support for anomaly identification and scheduling decisions.

[0024] The anomaly detection module is used to identify abnormalities in the flow of work-in-process materials between different processes, ensuring the smooth operation of production. This module dynamically assesses the material flow status based on information such as the material's transit time, barcode scanning records, and processing status, identifying potential anomalies, for example: In-process material flow timeout: The time that in-process materials remain in a certain process exceeds the predetermined duration; Missed scan or no scan record: In-process materials were not scanned and registered at the designated workstation; Process skipping: Work-in-process materials are not processed according to the predetermined process; Workstation stalled: A workstation failed to complete its task on time.

[0025] The anomaly detection module automatically triggers an early warning mechanism based on pre-set judgment rules, promptly reminding operators to intervene, thereby effectively avoiding production bottlenecks and waste of workstation resources.

[0026] When the scheduling module detects anomalies in the flow path of work-in-process materials or workstation congestion, it can dynamically schedule tasks based on multi-dimensional data such as order priority and workstation load. Specific functions include: Dynamic path adjustment: When materials in production encounter congested workstations, the scheduling module can reallocate the materials to workstations with lighter loads based on real-time feedback from the system.

[0027] Priority optimization: Based on factors such as the urgency of order delivery, the type of work-in-process materials, and processing requirements, the system automatically assigns appropriate scheduling priorities to each task to ensure that critical orders are processed with priority.

[0028] Flexible scheduling: Based on the workstation load and production scheduling needs, the scheduling module enables flexible parallel production scheduling during the production process, optimizes resource utilization, and shortens the production cycle.

[0029] The scheduling module enables highly flexible production scheduling, ensuring that work-in-process materials flow along the optimal path, thereby improving workshop production efficiency and reducing production cycles and resource waste.

[0030] The information interaction module, through close collaboration with other modules, promptly feeds back anomaly identification results and scheduling information to the operator's interface or mobile terminal. This module implements the following functions: Real-time early warning feedback: When the system detects abnormal situations or adjustments to the scheduling path, the information interaction module promptly pushes relevant information to operators through graphical interfaces, voice broadcasts, mobile terminals, and other means.

[0031] Task Adjustment Reminder: When the material path in production is readjusted, the information interaction module notifies the operator through a visual interface or mobile terminal, enabling them to respond quickly and make corresponding production adjustments.

[0032] Comprehensive monitoring: The information interaction module also supports comprehensive real-time monitoring of the production workshop, helping managers to uniformly schedule and manage the entire production process, the status of work-in-process materials, and resource allocation.

[0033] In another embodiment of the present invention, the material identification module uses radio frequency identification, QR code or ultra-wideband to assign a unique identifier to the work-in-process material. The unique identifier is used to mark the order number, processing procedure information and current workstation status of the work-in-process material.

[0034] The material identification module is also used to collect real-time information on the location changes of materials in production along their flow path, and to link with the workstation sensing module to track and record the flow path and location of materials in production between various processing workstations.

[0035] Specifically, the material identification module and the workstation sensing module are linked in the following ways: 1) Automatic identification when materials arrive at the workstation When the work-in-process material is sent to the processing station, the unique identification of the work-in-process material is transferred along with the work-in-process material. When the material in production arrives at the designated workstation, the scanning device or RFID reader of the workstation sensing module will automatically scan the identification information of the material in production. The material identification module reads the identification information to confirm that the work-in-process material has arrived at the designated workstation, and uploads the flow information to the information interaction module in real time, thereby realizing real-time updates of the position of the work-in-process material in the production process.

[0036] 2) Update workstation status in real time When the material in production completes the current processing state or enters the next processing state, the workstation sensing module will record the current processing workstation status, such as whether processing has started or whether processing has been completed. At the same time, the workstation sensing module will bind the processing status of the material to its corresponding identity identifier and feed the information back to the material identification module. The material identification module compares the received feedback information with the status information of the workstation sensing module and updates the latest status of the material in production, such as "awaiting processing", "processing", "processing completed", etc.

[0037] 3) The status of work-in-process materials is synchronized with the production schedule. During the production process, the material identification module and the workstation sensing module exchange data in real time to ensure that the status update of the materials in production is synchronized with the processing progress. Whenever a work-in-process material completes a certain process at the current processing station, the station sensing module updates the station status and sends it to the information interaction module. The information interaction module then transmits the information to the material identification module, thereby updating the status of the work-in-process material and recording its completion progress.

[0038] For example, if the material in production has been processed, the workstation sensing module will update the processing status of the workstation and feed this information back to the material identification module, so that the identification information of the material in production will be displayed as "processing completed".

[0039] 4) Anomaly Feedback and Path Adjustment If the workstation sensing module detects anomalies such as the material not arriving according to the predetermined process, processing time exceeding the limit, or the material being lost, the workstation sensing module will immediately send the anomaly information back to the material identification module. Based on the feedback information, the material identification module determines the status of the materials in production through the identification data, thereby triggering the dynamic scheduling mechanism of the scheduling module. The material identification module adjusts the scheduling path of work-in-process materials in a timely manner based on the feedback of abnormal information, so as to avoid bottlenecks or workstation congestion in the production process and thus ensure the efficient flow of the production process.

[0040] In another embodiment of the present invention, the anomaly identification module is provided with a judgment rule, which is used to detect whether the turnover time of the work-in-process material exceeds the cycle time threshold set by the target process, and to determine whether there is a logistics anomaly. The judgment rules also include identifying situations where materials in production are not scanned and registered at a certain workstation, are continuously missed during scanning, or are abnormally delayed for an extended period of time, and generating an anomaly flag accordingly.

[0041] The anomaly identification module compares the current processing status data of the materials in production uploaded by the workstation sensing module with the identification information of the materials in production, in order to achieve collaborative anomaly judgment on the flow status of the materials in production and the status of the current processing workstation.

[0042] Specifically, the cycle time threshold set for the target process is a key parameter used to measure the execution time of a process in production. It represents the standard time required for each process to complete one operation during production and is used to set the time limit for the production process, meaning that the current processing step must be completed within the specified time. When this cycle time threshold is exceeded, the anomaly detection module will consider the current processing step to be abnormal and trigger the corresponding anomaly feedback mechanism or make scheduling adjustments.

[0043] 1. Definition of beat time Cycle time is the time required for each process in a production line to complete one unit of product. During production, the cycle time for each process is generally set based on factors such as product design, equipment capacity, and worker operating speed. Setting reasonable cycle times ensures the smoothness and efficiency of the production line.

[0044] 2. The meaning of the beat time threshold The cycle time threshold set for the target process refers to the maximum time that a specific process should take to complete under normal circumstances. If the process fails to complete its task within this time range, it is considered abnormal, which may be due to equipment failure, manual operation problems, material shortages, or other reasons that cause the process to take too long.

[0045] Threshold Setting: The cycle time threshold is set based on the production line's efficiency, order delivery deadlines, and workstation capacity. If this threshold is exceeded, the production line management system will activate an early warning mechanism, notifying operators or schedulers to take countermeasures, such as adjusting production scheduling or conducting equipment checks.

[0046] 3. The role of cycle time threshold in intelligent monitoring systems for logistics information In intelligent monitoring systems, the cycle time threshold of a target process plays several important roles: Production process monitoring: Cycle time thresholds help the system monitor the progress of processes in real time and identify production bottlenecks in a timely manner.

[0047] Anomaly Detection: If the execution time of a certain process exceeds the set cycle time threshold, the system will determine that the process is in a "timeout" or "abnormal" state and trigger the anomaly feedback or alarm system.

[0048] Dynamically adjust production scheduling: If the current processing step times out, the scheduling module can automatically replan the scheduling path and adjust the execution order of subsequent production tasks to ensure that production tasks are completed on time.

[0049] Improving production efficiency: By setting reasonable cycle time thresholds, production rhythm can be optimized, production delays can be avoided, and production efficiency can be improved.

[0050] In another embodiment of the present invention, the scheduling module dynamically adjusts the scheduling paths of multiple in-process materials based on an optimized scheduling algorithm. The optimized scheduling algorithm adopts a path optimization strategy based on genetic algorithm, particle swarm optimization or heuristic algorithm to achieve the optimal path for multi-task scheduling and minimize the delay in the flow of in-process materials, resource conflicts and production stagnation.

[0051] The scheduling optimization algorithm weights various factors by adjusting the weights. The factors considered by the scheduling optimization algorithm include, but are not limited to: the order priority of the work-in-process materials, the workstation load, the processing time of the work-in-process materials, the delivery deadline, and the current status of the work-in-process materials.

[0052] The weight adjustment adopts an adaptive scheduling optimization algorithm, which dynamically adjusts the weight ratio of each scheduling factor according to the current production environment and workshop operation status to cope with the production demand and workstation load under different working conditions. The weight adjustment includes: order urgency, workstation load, current status of work-in-process materials, and processing time required for the process. The weights are dynamically adjusted according to real-time production data to optimize path decision-making.

[0053] The weighting adjustment assigns higher weights to order priorities based on factors such as the urgency of workshop production tasks and the timeliness of handling work-in-process materials. It also adjusts the weights of workstation load and processing time based on real-time monitoring data to achieve optimal matching of production tasks and resources.

[0054] Specifically, 1. Overview of the scheduling module and optimization algorithm The core function of the scheduling module is to dynamically adjust the scheduling paths of multiple work-in-process materials through scheduling optimization algorithms to ensure the efficiency, flexibility, and real-time performance of the production process. The scheduling optimization algorithm employs one or more of the following methods in combination: genetic algorithm, particle swarm optimization (PSO), or heuristic algorithm. Its main purpose is to minimize the flow delay of work-in-process materials, avoid resource conflicts, and reduce production downtime.

[0055] Genetic algorithms (GA) can simulate natural selection and genetic mechanisms to find optimal solutions in large-scale task scheduling.

[0056] Particle Swarm Optimization (PSO) algorithm finds the optimal path by simulating particles gradually updating their positions and velocities in the search space.

[0057] Heuristic algorithms, on the other hand, optimize using experience-based rules and are suitable for scheduling problems with many constraints and complex paths.

[0058] The common goal of these algorithms is to ensure that multi-task production scheduling can reach the optimal path under the constraints of various decision rules, making the flow of each material more efficient and avoiding production stagnation.

[0059] 2. The role and function of the weight adjustment mechanism The scheduling module not only relies on optimization algorithms but also uses a weight adjustment mechanism to weight multiple factors in the scheduling decision. The factors considered in the scheduling optimization algorithm include, but are not limited to: Work-in-process material order priority: Different orders are assigned different priorities based on their delivery urgency. Urgent orders have higher priority to ensure timely completion of production tasks.

[0060] Workstation load status: Based on the load status of each workstation, such as whether the workstation is idle or occupied, it is determined whether materials can enter the workstation for processing.

[0061] In-process material processing time: Based on the processing time of materials at each workstation, determine whether the materials need to be processed faster or relocated to an idle workstation.

[0062] Delivery time: In order to meet the delivery time requirements of orders, production scheduling needs to arrange routes according to the delivery time of materials to ensure the shortest production cycle.

[0063] Current status of materials in production: For example, if a material has been stuck for too long or has encountered an anomaly, the scheduling optimization algorithm will prioritize processing that material to avoid further delays.

[0064] The weight adjustment is achieved through a dynamic adaptive adjustment algorithm, which dynamically adjusts the weight ratio of each factor in real time according to the current production environment and workstation operation status in the workshop, thereby flexibly responding to production demands and resource load.

[0065] 3. Adaptive Mechanism and Optimization Strategy for Weight Adjustment The scheduling optimization algorithm employs an adaptive adjustment algorithm, allowing the weight of each factor to change according to the production status of the workshop. The weight ratios of order urgency, workstation load, current material status, and required processing time for each process—for example, 40% for order urgency, 30% for workstation load, 20% for the current status of work-in-process materials, and 10% for the required processing time—are set and adjusted through the adaptive adjustment algorithm. The specific methods for deriving these weight ratios typically depend on multiple factors, including the production environment, task requirements, and actual production management needs.

[0066] For example: Order urgency: When the system detects that the delivery time of an order is approaching, the algorithm will automatically increase the priority weight of the order to ensure that the order is processed in a timely manner.

[0067] Workstation load status: If a workstation has received multiple tasks, the system will reduce the load weight of that workstation to avoid overloading it and automatically transfer the tasks to a workstation with a lighter load.

[0068] The current processing status of materials in production: The processing status of materials, such as pending processing, in progress, or completed, should also be considered in scheduling optimization. If a material has not started processing for a long time or remains stuck in a certain process step, the system will increase its scheduling priority to complete the processing of that material as soon as possible. This factor is usually given a high weight (e.g., 20%) to ensure that materials in production do not remain idle for too long.

[0069] If materials remain unprocessed for an extended period or stagnate at a workstation for more than a threshold, the system will increase the scheduling weight of the materials and prioritize their scheduling to prevent them from becoming stuck at the workstation. A workstation stagnation exceeding the threshold means that the materials or tasks at a certain processing workstation have not been processed or completed within a predetermined time. The dwell time exceeds the system's preset maximum allowable process dwell time threshold (Cycle Time Threshold). This could mean production bottlenecks, workstation congestion, equipment malfunctions, or operational delays.

[0070] For example: target cycle time: 30 minutes, threshold: target cycle time + safety redundancy of 5 minutes = 35 minutes. If the material stays at the workstation for more than 35 minutes, the system will determine it as abnormal.

[0071] The processing time required for each process also affects the overall production scheduling. Generally, processes that can be completed quickly are prioritized, while processes that take a long time to complete may delay other tasks. Because this factor has a relatively small impact, it can be assigned a low weight (e.g., 10%). However, this still helps ensure that the timeliness of processes does not cause unnecessary delays to other tasks. If a process has a long processing time, the adjustment module will postpone or adjust the scheduling of that process according to the weight adjustment strategy to avoid affecting the overall production progress.

[0072] By continuously adjusting weight ratios based on real-time data, the scheduling system can make flexible optimization decisions based on factors such as the urgency of production tasks and resource availability, ensuring the maximization of production resources and production progress.

[0073] 4. Methods for dynamically adjusting weights Real-time monitoring data input: The system continuously receives real-time data from the workstation sensing module, material identification module, production management module, etc. This data provides a real-time basis for dynamically adjusting weights.

[0074] Dynamic path optimization: Based on real-time weight adjustments, path decisions are optimized so that each step in the production scheduling process can maximize efficiency under multiple constraints, ensuring that each production task is completed on the expected time.

[0075] 5. System scheduling optimization effect The optimized paths provided by the scheduling module effectively avoid resource conflicts and workstation congestion during production. Through dynamic scheduling, potential bottlenecks in production can be quickly resolved, allowing the production line to return to high efficiency in the shortest possible time. The final results are as follows: Minimize workflow delays: Through precise scheduling decisions, the waiting time for materials in production is reduced, ensuring that each task is completed on the optimal path.

[0076] Resource conflict avoidance: The scheduling module will dynamically adjust task allocation based on the load of each workstation to prevent multiple materials from entering a busy workstation at the same time.

[0077] Minimize production downtime: By adjusting scheduling paths in real time, production can be kept from being halted due to material congestion or abnormal conditions at workstations, thereby maximizing production capacity.

[0078] In another embodiment of the present invention, the information interaction module is used to feed back the abnormal information generated by the abnormal identification module to the workshop operation interface in real time; The information interaction module is also used to push the work-in-process material scheduling path adjustment information generated by the scheduling module to the operation interface or mobile terminal, so that operators can make real-time scheduling adjustments and resource allocation in the workshop.

[0079] Among them, the information interaction module is a crucial part of the system. It is responsible for feeding back the core information of the anomaly identification module and the scheduling module to the workshop operators in real time, ensuring that any problems in the production process can be resolved in a timely manner, and promoting the flexibility and efficiency of production scheduling.

[0080] 1.1 Real-time feedback function for abnormal information: The information interaction module primarily serves to provide feedback on abnormal information generated by the abnormal identification module. When the abnormal identification module detects workstation stagnation, timeouts, or other production abnormalities, the information interaction module will push this abnormal status information to the visual interface of the workshop operation interface in real time or send notifications to the operator's equipment via mobile terminal. Specifically, this includes: If the material stagnation time exceeds the threshold, such as when the stagnation time at the workstation exceeds the set maximum cycle time, the system will determine it as abnormal if the material stagnates at the workstation for more than 35 minutes. Missed or unscanned barcodes; materials not fully registered by barcode scanning. When an abnormal workstation load is detected, the relevant modules will respond through the following steps: 1) Detection by the anomaly detection module Abnormal workstation load is automatically identified by the anomaly detection module. This module determines whether an abnormal load has occurred by monitoring the workload, task completion progress, and workstation status (such as processing time, task volume, etc.) of each workstation.

[0081] If the workload at a workstation exceeds its processing capacity, or if a task is not completed on time, the anomaly detection module will automatically generate an anomaly event.

[0082] 2) Dynamically adjust task scheduling Once an abnormal workstation load is detected, the scheduling module will immediately and dynamically adjust the tasks. Possible adjustment methods include: Reassign tasks to lighter workstations: Reassign workstation tasks to workstations with lower workloads to ensure continued smooth production. Delay non-urgent tasks: Adjust the execution order of workstation tasks to ensure that urgent tasks are completed first; Temporarily increase workstation resources: For example, temporarily increase the working hours of a certain workstation or add temporary operators to alleviate abnormal load.

[0083] 3) Feedback from the information interaction module The information interaction module provides feedback on abnormal information to operators through a graphical interface, voice notification, or mobile terminal push, informing them of abnormal workstation load and scheduling adjustment information.

[0084] Operators can respond quickly to feedback information and make necessary adjustments to ensure the normal operation of production tasks.

[0085] 4) Anomaly logging and monitoring The system records detailed information about abnormal workstation load events, including the type of abnormality, workstation number, overloaded workload, and processing time. This not only facilitates real-time response by operators but also provides data support for subsequent production optimization.

[0086] With real-time feedback, operators can quickly identify problems in the production line and take measures to adjust or intervene, preventing further production stagnation or delays.

[0087] 1.2 Dispatch Path Adjustment Information Push Function The information interaction module also pushes material scheduling path adjustment information generated by the scheduling module in real time, ensuring that operators can flexibly allocate resources and adjust workstations based on real-time scheduling optimization. When anomalies occur during production (such as workstation congestion, process timeouts, etc.), the scheduling module will dynamically adjust the production path based on multiple factors (such as material priority, workstation load, order urgency, etc.).

[0088] The information interaction module will push these adjustment information to the user interface or mobile terminal in real time. The specific content includes: New material scheduling path: Displays the path of materials from the current workstation to the new workstation; Adjusted production plan: Production scheduling adjusted based on real-time information such as workstation load and resource utilization; Priority Adjustment: Adjust the production priority of materials based on order delivery dates and urgency.

[0089] Through the information interaction module, operators can quickly obtain the adjusted scheduling plan and make timely adjustments based on the pushed information, such as allocating new workstations, adjusting production tasks, and optimizing resource allocation.

[0090] 1.3 Multi-channel information push mechanism To adapt to different production environments and operational needs, the information interaction module not only supports information display through traditional graphical interfaces but also supports multiple communication methods such as voice broadcasting and mobile terminal push notifications. Through this multi-channel push mechanism, operators can receive real-time anomaly and scheduling information from any location, ensuring rapid response to system feedback whether on the production floor or in the office.

[0091] Graphical interface display: The workshop operation interface clearly displays production progress, abnormal status, scheduling adjustment information, etc. Voice broadcast: Operators can receive notifications of production line abnormalities or scheduling adjustments via voice broadcast; Mobile terminal push: Receive production progress and abnormal warning information via mobile terminals such as smartphones or tablets, which facilitates management and control by operators in different locations.

[0092] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A smart monitoring system for logistics information in a furniture production workshop, characterized in that, It includes a material identification module, which is used to assign a unique identifier to materials in production and to track the flow path of materials in production between various processing stations in real time; The workstation sensing module is used to collect the flow status information of each workstation and identify the identity of the work-in-process materials arriving at the workstation. The anomaly detection module is used to determine the status of the material in production based on the flow time, barcode scanning records, and processing status of the material between each processing step. The scheduling module is used to detect when the flow path of work-in-process materials is abnormal or when the workstation is predicted to be congested, and to dynamically adjust the scheduling path of work-in-process materials according to the order priority and the predicted workstation load. The information interaction module is used to push the anomaly identification results and scheduling information to the operation interface, so as to realize real-time monitoring and flexible scheduling of the flow status of materials in production.

2. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1, characterized in that, The material identification module uses radio frequency identification, QR code or ultra-wideband to assign a unique identifier to the material in production. The unique identifier is used to mark the order number, processing information and current workstation status of the material in production.

3. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1 or 2, characterized in that, The material identification module is also used to collect real-time location change information of the flow path of the materials in production, and link with the workstation sensing module to realize the tracking and location recording of the flow path of the materials in production between each processing workstation.

4. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1, characterized in that, The anomaly identification module is equipped with a judgment rule, which is used to detect whether the turnover time of the work-in-process material exceeds the cycle time threshold set by the target process, and to determine whether there is a logistics anomaly. The judgment rules also include identifying situations where in-process materials are not scanned and registered at a certain workstation, are continuously missed during scanning, or are abnormally delayed for an extended period of time, and generating an abnormal flag accordingly.

5. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1, characterized in that, The anomaly identification module compares the processing status data of the materials in production uploaded by the workstation sensing module with the identification information of the materials in production, in order to achieve collaborative anomaly judgment on the flow status of the materials in production and the workstation status.

6. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1, characterized in that, The scheduling module dynamically adjusts the scheduling paths of multiple in-process materials based on an optimized scheduling algorithm. The optimization algorithm adopts a path optimization strategy based on genetic algorithm, particle swarm optimization, or heuristic algorithm to achieve the optimal path for multi-task scheduling and minimize the delay in the flow of in-process materials, resource conflicts, and production stoppages.

7. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 6, characterized in that, The scheduling optimization algorithm weights each factor by adjusting the weights. The factors considered by the scheduling optimization algorithm include, but are not limited to: the order priority of the work-in-process materials, the workstation load, the processing time of the work-in-process materials, the delivery deadline, and the current status of the work-in-process materials.

8. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 7, characterized in that, The weight adjustment adopts an adaptive adjustment algorithm, which dynamically adjusts the weight ratio of each scheduling factor according to the current production environment and workshop operation status to cope with the production demand and workstation load under different working conditions. The weight adjustment includes: order urgency, workstation load, current status of work-in-process materials, and processing time required for the process, and dynamically adjusts the weights according to real-time production data to optimize path decision-making.

9. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 8, characterized in that, The weighting adjustment assigns higher weights to order priorities based on factors such as the urgency of workshop production tasks and the timeliness of handling work-in-process materials. It also adjusts the weights of workstation load and processing time based on real-time monitoring data to achieve optimal matching of production tasks and resources.

10. The intelligent monitoring system for logistics information in a furniture production workshop according to claim 1, characterized in that, The information interaction module is used to feed back the abnormal information generated by the abnormality identification module to the workshop operation interface in real time. The information interaction module is also used to push the work-in-process material scheduling path adjustment information generated by the scheduling module to the operation interface or mobile terminal, so that operators can make real-time scheduling adjustments and resource allocation in the workshop.

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