Wooden furniture production collaborative data synthesis training algorithm

Abstract

The invention relates to the field of data synthesis algorithms, in particular to a wooden furniture production collaborative data synthesis training algorithm. The method comprises the following steps: S1, collecting all data generated by a wooden furniture production collaboration platform; s2, generating synthetic data through a data generation model, and generating a data type required by an operator; s3, establishing a collaborative training framework, integrating a plurality of models or algorithms, jointly learning features and rules in collaborative production data, and optimizing and adjusting model parameters according to a data evaluation result; and S4, integrating the trained model into a wooden furniture production management system to realize real-time analysis and prediction of production data. According to the method, the production cooperation efficiency is improved by generating the simulation data or the synthetic data, the training set is expanded through the synthetic data, the synthetic data without sensitive information is generated, real data is replaced for training, and the data privacy compliance requirement is met.

Description

Technical Field

[0001] This invention relates to the field of data synthesis algorithm technology, and in particular to a collaborative data synthesis training algorithm for wooden furniture production. Background Technology

[0002] The collaborative platform for wooden furniture production and manufacturing is used to collect relevant production quantities, production types, process data, quality data, etc., to provide intelligent and digital support for furniture production.

[0003] However, current data analysis relies on existing real-world data, resulting in insufficient market analysis, weak predictive capabilities, and a limited range of forecasting directions. Furthermore, the analysis and forecasting process utilizes real-world production data, compromising the security and privacy of some data. Summary of the Invention

[0004] The purpose of this invention is to address the problems of weak data prediction capabilities and the inability to guarantee data security and privacy in the background technology by proposing a collaborative data synthesis and training algorithm for wooden furniture production.

[0005] The technical solution of this invention: a collaborative data synthesis and training algorithm for wooden furniture production, comprising the following steps: S1. Collect all data generated by the wooden furniture production collaboration platform; S2. Generate synthetic data through a data generation model to generate the data types required by operators; S3. Establish a collaborative training framework to integrate multiple models or algorithms, jointly learn the features and patterns in collaboratively produced data, and optimize and adjust model parameters based on data evaluation results; S4. Integrate the trained model into the wooden furniture production management system to achieve real-time analysis and prediction of production data.

[0006] Preferably, in S1, the collected data is cleaned, and after cleaning the data from different data sources, the data is merged to form a collaborative production data element set.

[0007] Preferably, all collected production factor data are cleaned by calling the corresponding data cleaning model based on the data characteristics.

[0008] Preferably, in S2, a furniture production collaborative data generation model is constructed, which summarizes production element data with different data characteristics such as production equipment, production process, orders, materials, and production execution, and generates synthetic data based on the corresponding data characteristics.

[0009] Preferably, in S3, the synthesized data is brought into the furniture production collaborative manufacturing process for training. The synthesized data is incorporated into the collaborative production. Based on multi-dimensional conditions such as product quantity, product material requirements, delivery time, production process, and quality requirements, corresponding enterprises that can produce are matched, and the enterprise production execution is simulated. The execution results are then optimized.

[0010] Preferably, in S3, the difference between the model's predicted results and the true values ​​is measured by a data comparison loss function.

[0011] Preferably, in S4, managers operate and monitor the model through a visual interface, continuously collect and update production data and features based on actual production conditions, and update and optimize the model to ensure its accuracy and reliability.

[0012] Compared with existing technologies, this invention has the following beneficial technical effects: By generating simulated or synthetic data, it provides a new solution for model training, thereby improving production collaboration efficiency, increasing the accuracy of data prediction, promoting innovation in collaborative production business scenarios, optimizing production processes, and improving the efficiency of defect detection and the accuracy of anomaly detection in product quality inspection and material inspection. By establishing a furniture production collaboration data generation model and combining it with the data characteristics of wooden furniture production collaboration, synthetic data conforming to the target distribution is generated, and the training set is expanded using this synthetic data. The generated synthetic data, which does not contain sensitive information, replaces real data for training, meeting data privacy compliance requirements. Attached Figure Description

[0013] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0014] like Figure 1 As shown, the present invention proposes a collaborative data synthesis and training algorithm for wooden furniture production, which includes the following steps: S1. Collect all data generated by the wooden furniture production collaboration platform, including actual data information such as production equipment data, material data, order data, and process data; clean the collected data to remove duplicate, erroneous, and invalid information, effectively integrate fragmented data to improve data quality, and merge data from different data sources after cleaning to form a comprehensive and accurate set of collaborative production data elements. It should be noted that all collected production element data should be cleaned according to the data characteristics by calling the corresponding data cleaning model. S2. Generate synthetic data through a data generation model, and optimize the model according to the needs of operators to generate the data types required by operators, thereby improving the data accuracy and generalization ability of the model. By specifying data features, reduce the occurrence of data generation that does not meet the requirements; construct a furniture production collaborative data generation model to summarize production element data with different data characteristics such as production equipment, production process, orders, materials, and production execution, and generate synthetic data based on the corresponding data characteristics. S3. Establish a collaborative training framework to integrate multiple models or algorithms, enabling them to jointly learn the features and patterns in collaborative production data, thereby improving the training effect of the models. The data comparison loss function is used to measure the difference between the model's predicted results and the actual values, and the model parameters are optimized and adjusted based on the data evaluation results. Specifically, synthetic data is incorporated into the collaborative manufacturing process of furniture production for training. Synthetic data is integrated into collaborative production, and corresponding enterprises capable of production are matched based on multi-dimensional conditions such as product quantity, product material requirements, delivery time, production process, and quality requirements. The production execution of these enterprises is simulated, and the execution results are optimized. S4. Integrate the trained model into the wooden furniture production management system to achieve real-time analysis and prediction of production data. Managers can operate and monitor the model through a visual interface. Based on the actual production situation, continuously collect and update production data and features to update and optimize the model and ensure its accuracy and reliability.

[0015] Examples are given below: In collaborative manufacturing, panel furniture production involves 25 processes. The furniture production collaboration platform currently has orders for panel furniture, but only 13 of these orders have corresponding production processes. The platform collects data from these orders and, based on the current orders and the data characteristics of the remaining 12 processes, generates corresponding composite data. This data is then incorporated into the platform for training. Simultaneously, existing product quality data is used as a basis to generate corresponding composite data, combining quality data, process data, and order data. Products with different processes in different orders are distributed to different companies for simulated execution. Quality inspections are then conducted using the quality data to determine the quantity of products, delivery dates, and manufacturing processes for each order. The platform then identifies the most efficient and highest-quality collaborative manufacturing company to produce for, while ensuring on-time delivery. When such orders arrive on the platform, it can quickly provide recommended collaborative manufacturing solutions. During production, the platform provides corresponding process combinations, potential quality issues, key quality control points, and bottlenecks in production. Enterprises can also adjust their production processes and equipment according to the prompts to improve production quality, optimize production processes, solve production bottlenecks, improve production efficiency, and enhance the flexible production capabilities of furniture manufacturing.

[0016] The collaborative data synthesis training algorithm for wooden furniture production can also combine existing orders, products, delivery dates, and order placement dates from the furniture production collaboration platform as synthetic data elements to analyze future order trends and product distribution. Through data-driven adjustments to product development, the algorithm can reduce material procurement for products with poor future sales and increase advance inquiries and procurement of materials with long procurement cycles and strict quality requirements for products with good future sales. This improves the efficiency of the enterprise's supply chain, shortens the production cycle, reduces capital occupation, and enhances the enterprise's market competitiveness.

[0017] In summary, this invention provides a novel solution for model training by generating simulated or synthetic data, thereby improving production collaboration efficiency, increasing the accuracy of data prediction, promoting innovation in collaborative production business scenarios, optimizing production processes, and improving the efficiency of defect detection and the accuracy of anomaly detection in product quality inspection and material inspection. By establishing a furniture production collaboration data generation model and combining it with the data characteristics of wooden furniture production collaboration, synthetic data conforming to the target distribution is generated, and the training set is expanded using this synthetic data. The generated synthetic data, which does not contain sensitive information, is used to replace real data for training, meeting data privacy compliance requirements.

[0018] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A collaborative data synthesis and training algorithm for wooden furniture production, characterized in that, Includes the following steps: S1. Collect all data generated by the wooden furniture production collaboration platform; S2. Generate synthetic data through a data generation model to generate the data types required by operators; S3. Establish a collaborative training framework to integrate multiple models or algorithms, jointly learn the features and patterns in collaboratively produced data, and optimize and adjust model parameters based on data evaluation results; S4. Integrate the trained model into the wooden furniture production management system to achieve real-time analysis and prediction of production data.

2. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 1, characterized in that, In S1, the collected data is cleaned, and after cleaning the data from different data sources, the data is merged to form a collaborative production data element set.

3. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 2, characterized in that, All collected production factor data are cleaned by calling the corresponding data cleaning model based on the data characteristics.

4. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 1, characterized in that, In S2, a collaborative data generation model for furniture production is constructed, which summarizes production element data with different data characteristics such as production equipment, production process, orders, materials, and production execution, and generates synthetic data based on the corresponding data characteristics.

5. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 1, characterized in that, In S3, the synthesized data is brought into the furniture production collaborative manufacturing process for training. The synthesized data is incorporated into collaborative production, and corresponding enterprises capable of production are matched based on multi-dimensional conditions such as product quantity, product material requirements, delivery time, production process, and quality requirements. The enterprise production execution is simulated, and the execution results are optimized.

6. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 1, characterized in that, In S3, the data comparison loss function measures the difference between the model's predictions and the actual values.

7. The collaborative data synthesis and training algorithm for wooden furniture production according to claim 1, characterized in that, In S4, managers operate and monitor the model through a visual interface. Based on actual production conditions, they continuously collect and update production data and features to update and optimize the model, ensuring its accuracy and reliability.

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