Written by the R&D Team of Dalian Zhonghui Group
In the wave of industrial digital transformation, artificial intelligence technology is reshaping traditional production models. Large models, with their powerful feature extraction capabilities and generalization performance, provide innovative solutions to complex problems in industrial scenarios. From production process optimization to equipment health management, from supply chain collaboration to quality defect detection, large model technology is breaking through the limitations of traditional algorithms. This transformation is not only reflected in efficiency improvements but also in the establishment of a data-driven new decision-making system, enabling industrial systems to possess higher levels of autonomous perception and cognitive abilities. Currently, the application of industrial large models has moved from single-point trials to large-scale implementation, with its value realization path involving multi-dimensional systematic solutions such as technology adaptation, architecture design, and scenario integration.
Zhonghui Group
Technical Expert of the R&D Center, Zhao Junlong
Overview of Large Model Applications in the Industrial Field
In the wave of industrial digital transformation, large model technology plays a crucial role, becoming the core driving force and key technical means for the intelligent upgrade of the industrial sector. Compared to traditional artificial intelligence (AI) models, industrial large models have significant advantages and characteristics in handling complex data, recognizing patterns, and cross-scenario applications.
Industrial large models possess powerful multimodal data processing capabilities. This means they can simultaneously process various types of data, including text, images, audio, and video, effectively integrating this information to comprehensively understand the various features and needs of the industrial environment. For unstructured data, such as large amounts of image and video data, industrial large models can also efficiently analyze and mine valuable information through techniques like deep learning.
Industrial large models excel in complex pattern recognition. They can identify and analyze the complex and variable scenarios and problems in industrial environments, such as accurate prediction and diagnosis of equipment failures, as well as optimization control of production processes. This capability enables enterprises to timely discover and resolve potential issues, improving production efficiency and product quality.
Industrial large models also have excellent cross-scenario transfer capabilities. This means that once a model is trained in a specific scenario, it can be relatively easily transferred to other similar or different industrial scenarios, greatly reducing the need for enterprises to repeatedly model in different scenarios, lowering costs and improving efficiency.
Data Preparation and Model Construction
First, we need to collect and preprocess data. Industrial data is characterized by being massive, complex, and diverse, covering various aspects such as production equipment operation data, quality inspection data, and supply chain data. Before applying large models, we must comprehensively collect and organize this data, eliminating erroneous data and redundant information to ensure data accuracy and completeness. Additionally, we need to label the data so that the large model can recognize and learn from it.
Next, we need to select and customize the model. Depending on the specific needs of the industrial scenario, we choose the appropriate large model for customized development. Different industrial scenarios have varying requirements for large models; for example, the automotive manufacturing industry focuses on production process optimization and quality control, while the energy industry is more concerned with energy demand forecasting and production scheduling. Therefore, we need to combine industry characteristics and expert experience to fine-tune the base large model, forming a vertical, scenario-specific, and specialized industrial large model.
In the model training and optimization phase, we need to select appropriate algorithms and parameters to ensure optimal model performance. At the same time, through continuous testing and optimization, we enhance the model’s generalization ability and robustness, enabling it to adapt to the complex changes in industrial scenarios.
Application Scenarios and Advantages
The application scenarios of large models in the industrial field are rich and diverse, including quality control, supply chain management, equipment maintenance, R&D design, and more.
In terms of quality control, large models can quickly analyze inspection data and accurately identify the causes of non-conforming products. For example, in the automotive manufacturing industry, large models can predict which components may have quality issues by analyzing quality data, allowing for proactive measures to reduce defect rates.
In supply chain management, large models can optimize inventory and logistics, reasonably allocate resources. By integrating multi-source data from production, sales, and marketing, large models can predict changes in market demand, optimize production plans and inventory management, and reduce operational costs.
In equipment maintenance, large models can predict equipment failures and proactively develop maintenance plans. By analyzing equipment operation data, large models can identify potential failure risks, timely arrange maintenance, and avoid production interruptions caused by sudden equipment failures.
In R&D design, large models can assist in product development and design. In the field of drug development, large models can generate new structural and characteristic drug molecule design schemes by analyzing a large amount of known drug molecule data, shortening the R&D cycle and increasing success rates. In industrial design, large models can generate innovative product design schemes, assisting technicians in quickly transforming design ideas into concrete implementation plans.
Security and Stability Assurance
Data security is an important guarantee for the application of industrial large models.
Industrial data often involves core secrets of enterprises, such as production processes and formulas. Therefore, large models must have strong encryption mechanisms to prevent data leakage and tampering. At the same time, strict access controls must be implemented to ensure that only authorized personnel can access critical data.
Model stability is also a key factor in the application of industrial large models.
Industrial production is continuous and cannot afford interruptions. Therefore, large models need to have high reliability, ensuring that the model can quickly recover normal operation in the event of a failure through redundant design, backup recovery, and other means.
The application of large models in the industrial field has broad prospects and great potential. Through reasonable data preparation, model construction, application scenario selection, and security and stability assurance, large models can bring revolutionary changes to industrial production, promoting the industrial system towards high-end, intelligent, and green development.
Company Introduction and Cooperation
Zhonghui Introduction
Dalian Zhonghui Technology Development Co., Ltd. (referred to as “Zhonghui Technology”) was established in 2013 and has achieved significant results in various fields such as finance, energy, industrial manufacturing, healthcare, higher education, media, and food. It is a high-tech enterprise focused on technology product design, R&D, consulting, production, and sales, headquartered in Dalian, Liaoning Province, with branches across the country. The company has strong technical and R&D capabilities and is recognized by the government as a “high-tech enterprise.” The company currently has 87 employees, including 11 with senior professional titles, and has a presence and business in 9 key cities across 5 provinces, holding dozens of invention patents and nearly a hundred software copyrights.
With years of technical R&D accumulation and social resource integration, the company focuses on “system construction, strategic consulting, and information technology solutions,” aiming to serve customers through technologies such as the Internet of Things, big data, and artificial intelligence, achieving digital transformation, resource interconnection, and industrial upgrading. The best products, the best services, the best reputation, and a platform construction combined with comprehensive consulting and operational solutions, we also aspire to be your most trusted long-term partner.
R&D Capabilities
Establishing an integrated devops development and operation platform, an agile low-code development platform, and standardized R&D testing delivery processes in Dalian, Ningbo, and Shenzhen, utilizing technologies such as artificial intelligence, the Internet of Things, cloud computing, and big data, employing industry-leading production support and quality control facilities, forming a deeply integrated resource and security application system that combines production, learning, and research, driven by innovative scientific management for high-quality delivery at scale.
Market Layout
Based on the “1+5+N” operational model, there are currently 8 wholly-owned and joint venture subsidiaries established in Dalian, Beijing, Shanghai, Ningbo, Shenzhen, Yinchuan, Shenyang, and Chaoyang.
Upholding the core concept of “digital empowerment, interconnectivity, industrial upgrading, financial security, and rural revitalization.”
Establishing 5 centers:
Dalian—Software Delivery Center
Ningbo—R&D Center for Industry-Academia-Research Integration
Beijing—Policy Interpretation and Resource Center
Shanghai—Operation Center
Shenzhen—Hardware Factory and Capital Integration Center
Clients span various provinces and cities across the country, including government agencies, finance, digital rural development, energy, industrial manufacturing, healthcare, higher education, media, food, and many other industries.
(This article was published in the “Zhonghui Products” August 2025 issue)
Author: Liu Yong
Editor: Xiao Hui
Proofreader: Xiao Zhong