CN-122018383-A - Information acquisition management method for large-scale customized furniture manufacturing workshop

CN122018383ACN 122018383 ACN122018383 ACN 122018383ACN-122018383-A

Abstract

The invention discloses a large-scale customized furniture manufacturing workshop information acquisition management method, which relates to the technical field of furniture production and comprises the following steps of S1 order acquisition, field analysis by receiving user customized order data through an enterprise resource planning system, extracting required processing procedures and execution sequences thereof, S2 dynamic task model generation, step-by-step pushing to execution terminals of corresponding stations according to the sequence of the processing procedures through a task model generation module based on the required processing procedures and the execution sequences thereof, automatic construction of personalized task models by analyzing customized order fields, combining preset process templates and rule engines, adapting to various small-batch customized scenes, and abnormal recognition by multi-source real-time data acquisition and model comparison, wherein abnormal recognition is carried out by acquiring processing state data of each station and combining task models, and closed loop quality and flow monitoring are realized.

Inventors

YE YONGZHEN
LI NING

Assignees

海太欧林集团股份有限公司

Dates

Publication Date: 20260512
Application Date: 20251203

Claims (10)

1. The information acquisition and management method for the large-scale customized furniture manufacturing workshop is characterized by comprising the following steps of: s1, acquiring an order, receiving user-customized order data through an enterprise resource planning system to perform field analysis, and extracting required processing procedures and execution sequences thereof; S2, generating a dynamic task model, and sequentially pushing the dynamic task model to an execution terminal of a corresponding station according to the sequence of the processing procedure by a task model generation module based on the required processing procedure and the execution sequence thereof; S3, performing abnormality identification and judgment by the aid of information acquisition equipment arranged at each station, acquiring data of processing states of each station in real time, uploading the data of each processing state to a central control module, and executing the abnormality identification and judgment by the central control module according to a preset task model and judgment rules; S4, triggering the early warning module, immediately triggering the early warning module when the central control module detects that the acquired data meets any abnormal condition, generating abnormal information record, and feeding the abnormal information back to an operator and the dynamic scheduling module through a man-machine interaction interface or a communication module; s5, dynamic scheduling and optimization are carried out, and the dynamic scheduling module carries out dynamic scheduling on the current task model according to the abnormal information record, so that flexible parallel scheduling and continuous optimization of a plurality of order data are realized.
2. The method for information collection and management in a large-scale customized furniture manufacturing shop according to claim 1, wherein the step S1 comprises the following sub-steps: S11, performing field analysis on order data received from the enterprise resource planning system, wherein the field analysis comprises extracting product model, structure configuration parameters, plate size, material type and customization options; s12, matching the analyzed order data parameters with a preset process template library, dynamically adjusting the sequence according to the custom items through judging rules or inserting additional procedures to carry out rules, and selecting corresponding processing procedure sequences; s13, extracting a processing procedure and an execution sequence thereof based on the matching result, and constructing a subsequent task model.
3. The method for collecting and managing information in a large-scale custom furniture manufacturing shop according to claim 1, wherein a rule engine unit is built in the task model generating module in S2, and the rule engine unit is used for performing custom parameters on custom order data and dynamically adjusting standard process templates, wherein the dynamic adjustment includes but is not limited to inserting additional processes, deleting unnecessary processes, or adjusting the process execution sequence to adapt to specific structural configuration requirements and processing requirements.
4. The method for collecting and managing information of a large-scale customized furniture manufacturing shop according to claim 1, wherein the preset task model in the central control module in S3 includes an execution sequence of each processing procedure, a state parameter, an allowable time threshold, a quality qualification standard and a corresponding relation of equipment; The judging rule is used for comparing the acquired data with a preset task model, and comprises abnormal types including procedure overtime, data missing, data abnormality, procedure jump sequence, off-line equipment and continuous disqualification.
5. The method for information collection and management of a large-scale customized furniture manufacturing shop according to claim 1, wherein the abnormal condition at least comprises any one of a process execution time exceeding a preset duration threshold, no collected data being received within a set time, the collected data exceeding an allowable range, a process execution sequence abnormality, and an off-line or continuous processing result failure; When the central control module detects that any abnormal condition is met, the early warning module is triggered to generate abnormal information records, and the abnormal information records are fed back to an operator and the dynamic scheduling module through the human-computer interaction interface or the communication module.
6. The method for collecting and managing information of the large-scale customized furniture manufacturing shop according to claim 1 or 5, wherein the early warning module performs visual warning through a graphical interface and pushes information of abnormal conditions to the dynamic scheduling module through a communication bus, so that real-time response and dynamic optimization of the abnormal conditions are realized.
7. The method for information collection and management in a large-scale customized furniture manufacturing shop according to claim 1, wherein the dynamic scheduling module in S5 adopts a scheduling optimization algorithm when performing dynamic scheduling, and the scheduling optimization algorithm includes at least one of a heuristic algorithm, a genetic algorithm, a particle swarm algorithm, or a reinforcement learning algorithm.
8. The method for information collection and management in a large-scale custom furniture manufacturing shop according to claim 7, wherein the scheduling optimization algorithm is used for optimizing the scheduling of task models under the conditions of multiple orders, resource constraint, priority conflict and the like, so as to minimize the order delay rate and improve the equipment utilization rate.
9. The method for collecting and managing information of a large-scale customized furniture manufacturing shop according to claim 1, wherein the dynamic scheduling module optimizes the current task model through priority adjustment and resource rescheduling strategy after receiving the abnormal information; the priority adjustment comprises reordering task priorities according to order urgency, task hysteresis and equipment idle time; The resource rescheduling comprises the step of reallocating the task to be executed to a station or equipment with low idle or load, so as to realize the dynamic reconstruction of the local task path.
10. The method for collecting and managing information of a large-scale customized furniture manufacturing shop according to claim 1, wherein the dynamic scheduling module is used for realizing flexible parallel scheduling and continuous optimization of a plurality of order tasks by constructing a multi-order task pool and combining resource state information; s51, dynamically sequencing tasks according to order priority, delivery time and historical execution state; s52, under the condition that the resource conflict is avoided, executable tasks are allocated to the stations in parallel; And S53, carrying out real-time evaluation on the scheduling result based on indexes such as the task completion rate, the equipment load rate and the like, and triggering a scheduling optimization strategy to continuously optimize the system execution efficiency and response capacity.

Description

Information acquisition management method for large-scale customized furniture manufacturing workshop Technical Field The invention relates to the technical field of furniture production, in particular to an information acquisition and management method for a large-scale customized furniture manufacturing workshop. Background In the patent application of the invention, publication No. CN120069838A, publication No. 2025-05-30, named intelligent furniture production workshop dust concentration monitoring and controlling method and system, relates to the technical field of intelligent furniture, comprising the steps of collecting workshop dust concentration data by laying sensors, constructing a workshop dust concentration distribution three-dimensional model by utilizing a convolutional neural network, predicting dust concentrations in different areas and different time periods, and dividing dust hazard level areas according to prediction results. When the dust concentration exceeds the standard, generating early warning information and sending the early warning information to a manager terminal. The control center determines treatment priority according to the dust concentration distribution three-dimensional model, controls the ventilation system and the mobile dust removal device to carry out classified filtration, optimizes the three-dimensional model on line according to real-time dust concentration data, and adjusts treatment strategies. The invention realizes accurate monitoring and high-efficiency treatment of the dust concentration in the workshop, reduces dust hazard and ensures the health of workers. In the prior art including the above patent, an environmental monitoring and treatment layer is provided in an intelligent furniture production workshop, but the problems of information acquisition, task generation, anomaly identification and scheduling closed-loop management closely related to a large-scale custom furniture manufacturing process are not deeply solved. Especially in the manufacturing scene of parallel and individual product frequent switching of multiple orders, the traditional intelligent furniture production workshop lacks a task model dynamic construction mechanism and intelligent recognition capability for processing abnormality, and cannot carry out self-adaptive optimization and scheduling strategy iteration on a task execution path, so that the production flow is stiff and response is lagged, and the overall manufacturing efficiency and quality controllability are affected. Disclosure of Invention The invention aims to provide a large-scale customized furniture manufacturing workshop information acquisition management method, which aims to solve the defects in the prior art. In order to achieve the above object, the present invention provides the following technical solutions: a method for collecting and managing information of a large-scale custom furniture manufacturing workshop comprises the following steps: s1, acquiring an order, receiving user-customized order data through an enterprise resource planning system to perform field analysis, and extracting required processing procedures and execution sequences thereof; S2, generating a dynamic task model, and sequentially pushing the dynamic task model to an execution terminal of a corresponding station according to the sequence of the processing procedure by a task model generation module based on the required processing procedure and the execution sequence thereof; S3, performing abnormality identification and judgment by the aid of information acquisition equipment arranged at each station, acquiring data of processing states of each station in real time, uploading the data of each processing state to a central control module, and executing the abnormality identification and judgment by the central control module according to a preset task model and judgment rules; S4, triggering the early warning module, immediately triggering the early warning module when the central control module detects that the acquired data meets any abnormal condition, generating abnormal information record, and feeding the abnormal information back to an operator and the dynamic scheduling module through a man-machine interaction interface or a communication module; s5, dynamic scheduling and optimization are carried out, and the dynamic scheduling module carries out dynamic scheduling on the current task model according to the abnormal information record, so that flexible parallel scheduling and continuous optimization of a plurality of order data are realized. Preferably, the step S1 includes the following substeps: S11, performing field analysis on order data received from the enterprise resource planning system, wherein the field analysis comprises extracting product model, structure configuration parameters, plate size, material type and customization options; s12, matching the analyzed order data parameters with a preset process template libr