Large-scale customized furniture manufacturing workshop information acquisition and management method
By parsing customized order fields to build personalized task models, collecting processing status in real time and performing dynamic scheduling and optimization, the problem of rigid task models in large-scale customized furniture manufacturing is solved, and efficient and flexible production is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING HEADWAY FURNITURE CO LTD
- Filing Date
- 2025-12-03
- Publication Date
- 2026-05-12
AI Technical Summary
In large-scale customized furniture manufacturing workshops, existing technologies lack a dynamic task model construction mechanism, making it impossible to intelligently identify processing anomalies. This leads to rigid production processes, delayed responses, and affects manufacturing efficiency and quality control.
By parsing customized order fields, a personalized task model is built, processing status data is collected in real time, anomalies are identified, and dynamic scheduling optimization is performed in conjunction with the scheduling module through an intelligent early warning feedback mechanism, thereby achieving flexible parallel production scheduling.
It improves the flexibility and responsiveness of the production process, enhances manufacturing efficiency and quality control, and adapts to the production needs of multi-variety, small-batch customized scenarios.
Smart Images
Figure CN122018383A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of furniture manufacturing technology, specifically to a method for information collection and management in a large-scale customized furniture manufacturing workshop. Background Technology
[0002] The invention patent application, titled "Method and System for Monitoring and Controlling Dust Concentration in Intelligent Furniture Production Workshops," published on May 30, 2025 (publication date: CN120069838A), relates to the field of intelligent furniture technology. It involves collecting dust concentration data in the workshop by deploying sensors, constructing a three-dimensional model of dust concentration distribution using a convolutional neural network, predicting dust concentrations in different areas and at different times, and classifying dust hazard levels based on the prediction results. When the dust concentration exceeds the standard, an early warning message is generated and sent to the management terminal. The control center determines the control priority based on the three-dimensional dust concentration distribution model, controls the ventilation system and mobile dust removal devices for graded filtration, and optimizes the three-dimensional model online based on real-time dust concentration data, adjusting the control strategy accordingly. This invention achieves accurate monitoring and efficient control of dust concentration in the workshop, reducing dust hazards and protecting worker health.
[0003] In existing technologies, including the aforementioned patents, environmental monitoring and control measures are implemented in smart furniture production workshops. However, these measures do not address the crucial issues of information collection, task generation, anomaly identification, and closed-loop scheduling management that are closely related to the mass-market customized furniture manufacturing process. Especially in manufacturing scenarios involving multiple parallel orders and frequent switching between personalized products, traditional smart furniture production workshops lack dynamic task model construction mechanisms and intelligent anomaly identification capabilities. They are unable to adaptively optimize task execution paths and iterate scheduling strategies, leading to rigid production processes, delayed responses, and impacting overall manufacturing efficiency and quality control. Summary of the Invention
[0004] The purpose of this invention is to provide a method for information collection and management in a large-scale customized furniture manufacturing workshop, so as to solve the above-mentioned shortcomings in the prior art.
[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for information collection and management in a mass-produced customized furniture manufacturing workshop, comprising the following steps: S1 order acquisition involves receiving user-customized order data through the enterprise resource planning system, parsing the fields, and then extracting the required processing steps and their execution order. The S2 dynamic task model is generated based on the required processing steps and their execution order. Then, the task model generation module pushes the model to the execution terminal of the corresponding workstation in the order of the processing steps. The S3 anomaly identification and judgment system collects data on the processing status of each workstation in real time through information acquisition devices set up at each workstation, and uploads the data to the central control module. The central control module then performs anomaly identification and judgment based on the preset task model and judgment rules. When the S4 early warning module is triggered, if the central control module detects that the collected data meets any abnormal condition, the early warning module will be triggered immediately to generate an abnormal information record and feed the abnormal information back to the operator and the dynamic scheduling module through the human-machine interface or communication module. S5 Dynamic Scheduling and Optimization: The dynamic scheduling module dynamically schedules the current task model based on anomaly information records, enabling flexible parallel scheduling and continuous optimization of multiple order data.
[0006] Preferably, step S1 includes the following sub-steps: S11 performs field parsing on order data received from the Enterprise Resource Planning system, including extracting and parsing product model, structural configuration parameters, panel size, material type, and customization options; S12 will match the parsed order data parameters with the preset process template library, and dynamically adjust the order or insert additional processes according to the rules based on the customization items, and select the corresponding processing process sequence. S13 Extracts the processing steps and their execution order based on the matching results, which are then used to construct the subsequent task model.
[0007] Preferably, the task model generation module in S2 includes a rule engine unit. The rule engine unit is used to customize parameters for user-customized order data and dynamically adjust standard process templates. Dynamic adjustments include, but are not limited to, inserting additional processes, deleting unnecessary processes, or adjusting the execution order of processes to adapt to specific structural configuration requirements and processing requirements.
[0008] Preferably, the preset task model in the central control module in S3 includes the execution sequence of each processing step, status parameters, allowable time threshold, quality qualification standard and equipment correspondence; Preferably, the judgment rule is used to compare the collected data with the preset task model. The judgment rule includes abnormal types such as process timeout, data missing, data abnormality, process skipping, equipment offline and continuous non-conformity.
[0009] Preferably, the abnormal conditions include at least one of the following: the process execution time exceeds a preset time threshold, no data is received within the set time, the data collected exceeds the allowable range, the process execution sequence is abnormal, the equipment is offline, or the continuous processing result is unqualified; When the central control module detects any abnormal condition, it triggers the early warning module to generate an abnormal information record, and then feeds it back to the operator and the dynamic scheduling module through the human-machine interface or the communication module.
[0010] Preferably, the early warning module provides visual alarms through a graphical interface and pushes information about abnormal conditions to the dynamic scheduling module via a communication bus, thereby enabling real-time response and dynamic optimization to abnormal conditions.
[0011] Preferably, the dynamic scheduling module in S5 employs a scheduling optimization algorithm when performing dynamic scheduling, and the scheduling optimization algorithm includes at least one of heuristic algorithm, genetic algorithm, particle swarm algorithm or reinforcement learning algorithm.
[0012] Preferably, the scheduling optimization algorithm is used to optimize the production scheduling of the task model under conditions such as multiple orders, resource constraints, and priority conflicts, so as to minimize the order delay rate and improve equipment utilization.
[0013] Preferably, after receiving abnormal information, the dynamic scheduling module optimizes the current task model through priority adjustment and resource rescheduling strategies; The priority adjustment includes reordering task priorities based on order urgency, task lag, and equipment idle time; The resource rescheduling includes reallocating tasks to be executed to idle or low-load workstations or devices, thereby achieving dynamic reconstruction of local task paths.
[0014] Preferably, the dynamic scheduling module constructs a multi-order task pool and combines it with resource status information to achieve flexible parallel scheduling and continuous optimization of multiple order tasks; The flexible parallel scheduling and continuous optimization includes the following steps: S51 dynamically sorts tasks based on order priority, delivery time, and historical execution status; S52 allocates executable tasks to multiple workstations in parallel while ensuring that resources do not conflict. S53 evaluates production scheduling results in real time based on indicators such as task completion rate and equipment load rate, and triggers scheduling optimization strategies to continuously optimize system execution efficiency and responsiveness.
[0015] In the above technical solution, the present invention provides a method for information collection and management in a large-scale customized furniture manufacturing workshop. By parsing customized order fields and combining preset process templates and rule engines, it realizes the automatic construction of personalized task models, adapting to multi-variety, small-batch customization scenarios. It identifies anomalies through multi-source real-time data collection and model comparison: collecting processing status data of each workstation and combining it with task models to identify anomalies, including multiple types of anomalies such as process timeouts, skipped sequences, equipment offline, and illegal data, to achieve closed-loop quality and process monitoring. Furthermore, through an intelligent early warning feedback mechanism: when an abnormal event occurs, the system can automatically generate an anomaly record and push early warnings through multiple channels such as human-machine interfaces and mobile terminals, improving response speed and management efficiency. Finally, through the scheduling module, it can dynamically adjust the task execution path according to anomaly information, resource status, and task priority using algorithm strategies, realizing task reordering, resource reallocation, and path reconstruction.
[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 the data acquisition and management method flow structure provided in 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, a method for information collection and management in a large-scale customized furniture manufacturing workshop includes the following steps: S1 order acquisition involves receiving user-customized order data through the Enterprise Resource Planning (ERP) system, parsing the fields, and then extracting the required processing steps and their execution order. The orders received from the ERP system include: product code, structural parameters (such as the number of doors / drawers), panel dimensions, material type, and customization requirements (color, edge banding type, etc.). The ERP system uses a structured data parser (such as a JSON / XML parsing module) to read the fields. S1 includes the following sub-steps: S11 performs field parsing on order data received from the Enterprise Resource Planning system, including extracting and parsing product model, structural configuration parameters, panel size, material type, and customization options; S12 will parse the order data parameters and match them with the preset process template library. Based on the judgment rules, the order will be dynamically adjusted or additional processes will be inserted to match the rules and select the corresponding processing process sequence. The preset process template library refers to a set of standardized processing flow models pre-built in the system, which is used to match process paths and generate tasks for furniture products of different categories, models and structures.
[0022] S13 extracts the processing steps and their execution order based on the matching results, which are used to construct the subsequent task model. Here, "processing step" refers to the individual processing steps that a product needs to go through in the manufacturing process; "execution order" refers to the execution sequence logic that these processing steps must follow in terms of time, which can also be understood as: "the arrangement of a series of standard processing steps to guide the entire manufacturing process from raw materials to finished products in the workshop." The S2 dynamic task model is generated based on the required processing steps and their execution order. Then, the task model generation module pushes the model to the execution terminal of the corresponding workstation in the order of the processing steps. The task model generation module in S2 includes a rule engine unit. The rule engine unit is used to customize parameters for user-customized order data and dynamically adjust standard process templates. Dynamic adjustments include, but are not limited to, inserting additional processes, deleting unnecessary processes, or adjusting the execution order of processes to adapt to specific structural configuration requirements and processing requirements.
[0023] A rules engine unit is a system component used to extract "business logic / decision rules" from code and define and execute rules through configuration. Essentially, it's a conditional decision-making system suitable for handling "when...then..." type logic, making it ideal for dynamic decisions such as process selection and workflow adjustment in manufacturing scenarios.
[0024] The core role of the rule engine unit in this invention is shown in the table below. Point of application illustrate For example ① Dynamic process path generation The process content and sequence are dynamically determined based on the customized items in the order. If the number of drawers is greater than 3 → insert the "Structural Reinforcement" process. ② Process Variation Selection Different process schemes can be selected for the same product based on the configuration options. For plates less than 15mm thick, use the "Quick Assembly" solution; otherwise, use "Standard Machining". ③ Insert or exclude optional procedures The presence or absence of door panels and handles will be assessed to determine whether to add a "pre-installed handle" step. No handle → Skip the "drilling" process ④ Maintain flexibility and scalability In the future, new products and structures can be adapted by modifying rules rather than procedures. Low maintenance cost and strong adaptability The configuration rules are applicable to the following key business scenarios: such as order field parsing rule definition: users can customize the parsing template to match the structural parameters and customized fields of different products in the order with the process template.
[0025] Anomaly identification rule definition: used to determine whether the actual processed data deviates from the expected value and to decide whether to trigger an anomaly.
[0026] Task scheduling policy rules: used to define how to adjust tasks when anomalies occur or resources change.
[0027] The S3 anomaly identification and judgment system collects data on the processing status of each workstation in real time through information acquisition devices set up at each workstation, and uploads the data to the central control module. The central control module then performs anomaly identification and judgment based on the preset task model and judgment rules. Specifically, the preset task model in the central control module of S3 includes the execution sequence of each processing step, status parameters, allowable time threshold, quality qualification standards, and equipment correspondence. The judgment rules are used to compare the collected data with the preset task model. The judgment rules include anomaly types such as process timeout, data missing, data anomaly, process skipping, equipment offline, and continuous non-conformity.
[0028] Abnormal conditions include at least one of the following: the process execution time exceeds the preset time threshold, no data is received within the set time, the collected data exceeds the allowable range, the process execution sequence is abnormal, the equipment is offline, or the continuous processing result is unqualified; When the central control module detects any abnormal condition, it triggers the early warning module to generate an abnormal information record, and then feeds it back to the operator and the dynamic scheduling module through the human-machine interface or the communication module. The relationship (logical chain) between abnormal conditions and the early warning module. Real-time data collection ↓ The task model provides baseline parameters. ↓ Judgment rules detect anomalies → "Abnormal condition" is hit. ↓ Trigger the early warning module: → Generate exception records (exception type, time, workstation number) → Push exception information to the operation interface and scheduling module → The scheduling module adjusts the current task model. When the S4 early warning module is triggered, if the central control module detects that the collected data meets any abnormal condition, the early warning module will be triggered immediately to generate an abnormal information record and feed the abnormal information back to the operator and the dynamic scheduling module through the human-machine interface or communication module. The early warning module provides visual alarms through a graphical interface and pushes information about abnormal conditions to the dynamic scheduling module via a communication bus, enabling real-time response and dynamic optimization to abnormal conditions.
[0029] S5 Dynamic Scheduling and Optimization: The dynamic scheduling module dynamically schedules the current task model based on anomaly information records, enabling flexible parallel scheduling and continuous optimization of multiple order data.
[0030] Specifically, the dynamic scheduling module in S5 uses a scheduling optimization algorithm when performing dynamic scheduling. The scheduling optimization algorithm includes at least one of the following: heuristic algorithm, genetic algorithm, particle swarm optimization algorithm, or reinforcement learning algorithm.
[0031] Scheduling optimization algorithms are used to optimize task models and schedule production under conditions such as multiple orders, resource constraints, and priority conflicts, in order to minimize order delay rates and improve equipment utilization.
[0032] Upon receiving abnormal information, the dynamic scheduling module optimizes the current task model through priority adjustment and resource rescheduling strategies. The priority adjustment includes reordering task priorities based on order urgency, task lag, and equipment idle time; The resource rescheduling includes reallocating tasks to be executed to idle or low-load workstations or devices, thereby achieving dynamic reconstruction of local task paths.
[0033] The dynamic scheduling module constructs a multi-order task pool and combines it with resource status information to achieve flexible parallel scheduling and continuous optimization of multiple order tasks; Flexible parallel scheduling and continuous optimization include the following steps: S51 dynamically sorts tasks based on order priority, delivery time, and historical execution status; S52 allocates executable tasks to multiple workstations in parallel while ensuring that resources do not conflict. S53 evaluates production scheduling results in real time based on indicators such as task completion rate and equipment load rate, and triggers scheduling optimization strategies to continuously optimize system execution efficiency and responsiveness.
[0034] Example A furniture manufacturing company supports various customized products (such as wardrobes, bookcases, TV cabinets, etc.) and has multiple automated workstations, including CNC panel saws, edge banding machines, drilling machines, and assembly stations. To achieve efficient and flexible manufacturing, the company has deployed the intelligent information acquisition and management system described in this invention.
[0035] The system process is as follows: S1 Order Acquisition and Field Parsing: The workshop received a batch of customized customer orders through the Enterprise Resource Planning (ERP) system. The order data includes: Product Type: TV Stand Board material information: Multi-layer board, 18mm thick Structural parameters: Two single-layer drawers, recessed door panels Quantity: 5 sets The system inputs the above data into the field parsing module, and uses the rule engine and product structure parsing template to parse it, identifying the following processing steps: cutting → edge sealing → drilling → installation → packaging.
[0036] Execution order: generated by matching from the abstract process template library (template ID: TV001).
[0037] S2 Dynamic Task Model Generation and Push: The system generates a task model based on the process template (TV001) matched to the product. The model structure includes: The task model structure is sequentially sent to the execution terminals of each workstation through the model push module, and CNC-01 is the first to receive the task.
[0038] S3 Anomaly Detection and Judgment: Status acquisition devices (such as processing status reading modules, scanning sensors, etc.) are deployed at each workstation to collect the following data: current task number at the workstation, processing progress status (not started / in progress / completed), actual processing time, and process parameter detection values (such as edge sealing thickness, hole diameter). The central control module compares and judges the expected values in the task model with the judgment rules. If "actual edge sealing thickness = 0.4mm ≠ expected value of 1mm in the model", then a process deviation anomaly is triggered. If the drilling process execution time exceeds the model's maximum value of 5 minutes, then the process is considered to have timed out. The central control module immediately records the exception type, workstation number, task number, and timestamp.
[0039] S4 warning module triggered: Once any abnormal condition is met, the central control module system immediately uses the early warning module to: pop up an abnormal prompt window on the operator interface; simultaneously send abnormal data to the scheduling module and production management center via the MQTT communication module; and automatically generate an abnormal record (e.g., abnormal type = edge sealing thickness abnormal, workstation = EB-02, time = 10:12:43).
[0040] S5 Dynamic Scheduling and Optimization: The scheduling module starts a scheduling optimization algorithm (an improved genetic algorithm is used in this example) based on the anomaly record and the current resource status, and executes the following strategy: the 5 orders originally bound to the EB-02 border sealing task are split up; Normal tasks were transferred to the available EB-03 workstation; The workstation with the malfunction has suspended operations and is awaiting repair. The production scheduling system reassessed the execution time of all orders and updated delivery forecasts; A new task model path is constructed for this batch of orders and issued to subsequent processes.
[0041] The entire scheduling process is completed adaptively by the system, and it is continuously optimized based on feedback from task completion rate and equipment utilization rate to achieve continuous optimization.
[0042] Additional notes: Rules engine configuration example The system has the following pre-defined rules for task model construction and anomaly detection: IF Board thickness ≤ 18mm AND Product type = “TV cabinet” THEN Template ID = TV001 If actual processing time > model maximum time, then mark as abnormal = timeout. If edge sealing thickness < model value - 0.3mm, then the anomaly type is process deviation. The rules engine allows operations and maintenance personnel to add or delete rules as needed without modifying the core system code, and has good scalability.
[0043] 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 method for information collection and management in a large-scale customized furniture manufacturing workshop, characterized in that, The method includes the following steps: S1 order acquisition involves receiving user-customized order data through the enterprise resource planning system, parsing the fields, and then extracting the required processing steps and their execution order. The S2 dynamic task model is generated based on the required processing steps and their execution order. Then, the task model generation module pushes the model to the execution terminal of the corresponding workstation in the order of the processing steps. The S3 anomaly identification and judgment system collects data on the processing status of each workstation in real time through information acquisition devices set up at each workstation, and uploads the data to the central control module. The central control module then performs anomaly identification and judgment based on the preset task model and judgment rules. When the S4 early warning module is triggered, if the central control module detects that the collected data meets any abnormal condition, the early warning module will be triggered immediately to generate an abnormal information record and feed the abnormal information back to the operator and the dynamic scheduling module through the human-machine interface or communication module. S5 Dynamic Scheduling and Optimization: The dynamic scheduling module dynamically schedules the current task model based on anomaly information records, enabling flexible parallel scheduling and continuous optimization of multiple order data.
2. The method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1, characterized in that, S1 includes the following steps: S11 performs field parsing on order data received from the Enterprise Resource Planning system, including extracting and parsing product model, structural configuration parameters, panel size, material type, and customization options; S12 will match the parsed order data parameters with the preset process template library, and dynamically adjust the order or insert additional processes according to the rules based on the customization items, and select the corresponding processing process sequence. S13 Extracts the processing steps and their execution order based on the matching results, which are then used to construct the subsequent task model.
3. The method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1, characterized in that, The task model generation module in S2 includes a rule engine unit. The rule engine unit is used to customize parameters for user-customized order data and dynamically adjust standard process templates. Dynamic adjustments include, but are not limited to, inserting additional processes, deleting unnecessary processes, or adjusting the execution order of processes to adapt to specific structural configuration requirements and processing requirements.
4. The method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1, characterized in that, The preset task model in the central control module of S3 includes the execution sequence, status parameters, allowable time threshold, quality qualification standard and equipment correspondence of each processing step; The judgment rules are used to compare the collected data with the preset task model. The judgment rules include abnormal types such as process timeout, data missing, data abnormality, process skipping, equipment offline and continuous non-conformity.
5. The method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1, characterized in that, The abnormal conditions include at least one of the following: the process execution time exceeds the preset time threshold, no data is received within the set time, the data collected exceeds the allowable range, the process execution sequence is abnormal, the equipment is offline, or the continuous processing result is unqualified; When the central control module detects any abnormal condition, it triggers the early warning module to generate an abnormal information record, and then feeds it back to the operator and the dynamic scheduling module through the human-machine interface or the communication module.
6. A method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1 or 5, characterized in that, The early warning module provides visual alarms through a graphical interface and pushes information about abnormal conditions to the dynamic scheduling module via a communication bus, enabling real-time response and dynamic optimization to abnormal conditions.
7. The method for information collection and management in a mass-produced customized furniture manufacturing workshop according to claim 1, characterized in that, The dynamic scheduling module in S5 employs a scheduling optimization algorithm when performing dynamic scheduling. The scheduling optimization algorithm includes at least one of heuristic algorithms, genetic algorithms, particle swarm algorithms, or reinforcement learning algorithms.
8. The method for information collection and management in a mass-produced customized furniture manufacturing workshop according to claim 7, characterized in that, The scheduling optimization algorithm is used to optimize the production scheduling of task models under conditions such as multiple orders, resource constraints, and priority conflicts, so as to minimize order delay rate and improve equipment utilization.
9. The method for information collection and management in a mass-produced customized furniture manufacturing workshop according to claim 1, characterized in that, Upon receiving abnormal information, the dynamic scheduling module optimizes the current task model through priority adjustment and resource rescheduling strategies. The priority adjustment includes reordering task priorities based on order urgency, task lag, and equipment idle time; The resource rescheduling includes reallocating tasks to be executed to idle or low-load workstations or devices, thereby achieving dynamic reconstruction of local task paths.
10. The method for information collection and management in a large-scale customized furniture manufacturing workshop according to claim 1, characterized in that, The dynamic scheduling module constructs a multi-order task pool and combines it with resource status information to achieve flexible parallel scheduling and continuous optimization of multiple order tasks. The flexible parallel scheduling and continuous optimization includes the following steps: S51, dynamically sorting tasks according to order priority, delivery time, and historical execution status; S52. Under the condition of ensuring that resources do not conflict, allocate executable tasks to multiple workstations in parallel. S53. Based on indicators such as task completion rate and equipment load rate, the scheduling results are evaluated in real time, and scheduling optimization strategies are triggered to continuously optimize the system's execution efficiency and response capability.