Artificial intelligence is a strategic technology leading this round of technological revolution and industrial transformation, with a strong “leading goose” effect and significant spillover effects. General artificial intelligence large models (hereinafter referred to as “large models”) represent a new stage in the development of artificial intelligence from specialization to generalization. They integrate functions such as intelligent perception, intelligent analysis, intelligent decision-making, and intelligent execution, achieving an optimized allocation of production factors through the deep integration of data, computing power, and algorithms.
From the perspective of production structure, artificial intelligence technology has become a core component of modern industrial production, significantly improving production efficiency, optimizing resource allocation, and reducing production costs, thus providing strong technical support and driving force for new industrialization development. The deep integration of general artificial intelligence and manufacturing can further accelerate the industrial system’s transition towards high-end, intelligent, and green development.
1. Overview of Large Models and Their Industrial Application Development
On one hand, there is a global surge in the development of large models. The emergence of large models has pushed artificial intelligence into a new stage of development, with leading domestic and international companies becoming the technological high ground in the competition for large models. AI large models are artificial intelligence models with large-scale parameters and complex structures. With the increase in model size, the amount of training data, and improvements in computing power, AI large models have made significant breakthroughs in the fields of natural language processing, image recognition, speech recognition, and multimodal recognition. Since 2020, large models have rapidly grown in the global market, entering a period of explosive growth.
Internationally, OpenAI, as a leading organization in the industry, has released large language models such as ChatGPT (2022) and GPT-4 (2023), speech large models like Whisper (2022), and visual large models such as DALL-E (2021). Microsoft has integrated GPT-4 capabilities into key products like Windows 11, Office365, and Bing, forming a series of Copilot applications. Meanwhile, Google launched the multimodal large model Gemini (2023), and Meta released the language large model LLaMA (2023), with foreign tech giants joining the competition for large models.
Domestically, various technology companies are actively following global trends in large model development. Baidu has released the language large model “Wenxin Yiyan,” Alibaba has released the language large model “Tongyi Qianwen,” iFLYTEK has released the language large model “Xinghuo Cognition,” Baichuan Intelligence has launched the “Baichuan Large Model,” Zhipu AI has released the ChatGLM series of language large models, and the Chinese Academy of Sciences has released the cross-modal large model “Zidong Taichu.”
On the other hand, the application of large models to the B-end, especially in the industrial sector, has become an industry consensus. Large models have shown a development trend of using foundational large models as a technical base, with industrial applications as the entry point, giving rise to the concept of industrial large models. Foundational large models enhance the parameter volume and structural generality of the models, integrating and expressing more domain knowledge and modal knowledge, forming an omniscient and omnipotent general model.
Industrial large models rely on the structure and knowledge of foundational large models, integrating data and expert experience from various industrial sub-sectors to form vertical, scenario-based, and specialized application models. Compared to foundational large models, industrial large models have advantages such as fewer parameters, higher specialization, and stronger applicability, providing low-cost solutions for technological breakthroughs, product innovation, and production transformation in industrial vertical fields.
2. Seven Major Application Patterns of Large Models in Industry
From the perspective of the entire lifecycle of industrial products, they can be divided into research and development, design, simulation, production, testing, operation and maintenance, and after-sales stages. This report analyzes the characteristics and elements of each stage and, in conjunction with the capabilities of large models, preliminarily proposes the following potential application patterns of large models in the industrial field.
(1) Principle-based R&D Large Model
This model can analyze product structural data, exploring the configuration and mechanisms of products at a microscopic level, and generate products with new structures and characteristics through the model’s emergent capabilities. For example, in drug development, AI large models can analyze a large amount of known drug molecule data to identify optimal drug candidates and generate a new drug molecule design scheme, significantly shortening the time and cost of drug development and improving the success rate of drug development.
Target discovery and drug structure design. Target discovery is a core step in the drug development process, where the target is the binding site of the drug’s action in the body. The relationship between the drug and the target can be likened to that of a key and a corresponding lock. Traditional target discovery requires extensive research work, making numerous biological hypotheses about the target, and designing a series of experiments for verification, which takes a long time. In contrast, large models can analyze known drug molecule structural data and knowledge graphs related to the relationship between drug molecules and diseases to identify molecular features that interact with disease targets, and then automatically generate new drug molecule design schemes using these molecular features. Drug scheme evaluation and optimization. After generating several drug molecule design schemes, the model can evaluate the generated schemes under human prompts, screening out the most promising drug candidates. At the same time, it can optimize the efficacy and toxicity of the drug by predicting the drug’s metabolic pathways and concentrations, thus optimizing the dosage and administration scheme.
Assisting in clinical trial design. Experimental validation is an indispensable part of drug development, but experiments typically consume a lot of time and resources. AI large models can help researchers design more effective clinical trial schemes, such as predicting the safety and efficacy of drugs, optimizing the sample size and duration of clinical trials, etc. By predicting and screening, the number and complexity of experiments can be reduced, thus improving the efficiency and quality of drug development.
(2) Forward-looking Design Large Model
This model can generate innovative product design schemes, better assisting technicians in quickly converting design ideas and intentions into concrete implementation plans. For example, in traditional industrial design, large models can achieve rapid generation of engineering drawings and design schemes while assisting in layout optimization and parameter verification, greatly reducing the time spent on industrial design and enhancing product R&D efficiency.
Mathematical modeling and code writing. In the industrial design process, many problems require the establishment of specific mathematical models for analysis and solving, such as mechanical product design often encountering issues like strength verification, size optimization, and standard part selection, which can be costly if experts need to analyze and model them. Large models can provide reference solutions for these problems in a short time based on specific design requirements and automatically generate corresponding program code to guide specific industrial design practices. Diversified design scheme generation. For example, in CAD, existing massive standardized material libraries provide a wealth of engineering drawing, layout planning, and other data. Large models can utilize this data, combined with the designer’s creative ideas and specific needs, to generate diverse design schemes for designers to reference. They can also quickly optimize and adjust design schemes, helping engineers create layouts faster and with fewer errors.
(3) Efficient Simulation Large Model
This model can leverage its generative capabilities to provide virtual simulation testing scenarios or environments that meet design requirements, addressing issues such as insufficient testing data and singular testing environments in industrial product design, enhancing product reliability. For example, in automotive manufacturing, AI large models can generate simulation testing environments for car crash simulations and safety assessments, significantly improving the accuracy and efficiency of simulations, driving advancements in vehicle design and safety performance.
Diverse simulation scenario generation. Automotive companies accumulate a wealth of real data in actual vehicle testing and simulation experiments, including vehicle structure, crash data, and material properties. Large models can learn the nonlinear relationships between vehicle structure, material properties, and crash responses from this data, understanding how different parameters (such as crash speed, angle, vehicle construction, etc.) affect crash responses, and generate new, diverse crash scenarios through their emergent capabilities. This allows vehicle models to perform simulation predictions under various conditions, including previously unencountered situations, and compensates for data deficiencies in special scenarios, enhancing the comprehensiveness and accuracy of automotive simulation testing.
Parameter optimization and rapid prediction. In traditional vehicle crash simulations, different parameter combinations must be tested multiple times to obtain the best results, with each test consuming a significant amount of time for manual parameter tuning. In contrast, large models can quickly predict crash responses for different parameter combinations through zero-shot knowledge analysis, helping to find the optimal parameter combination in a short time, thereby reducing the design and testing cycle for vehicles. Additionally, they can provide novel design suggestions based on historical crash cases, which may involve innovations in material selection, structural adjustments, etc.
(4) Refined Inspection
This model can utilize the zero-shot learning capabilities of large models, combined with AR/VR and other virtual reality technologies, to achieve rapid and efficient visual inspection of various industrial scenarios, such as product quality defects, personnel violations, and assembly errors. For example, in industrial production, in quality inspection and safety monitoring scenarios, large models can detect specified areas and personnel based on external visual sensing devices and simple instructions, quickly identifying abnormal information and significantly reducing costs associated with manual inspections, sample collection, and model training.
High-efficiency industrial quality inspection. Taking PCB defect detection as an example, general visual large models can leverage their strong generalization capabilities to perform semantic segmentation directly on raw PCB images without relying on factory sample data and localized fine-tuning training, and implement the identification of various defects such as solder voids, solder bridges, and pinholes through simple rule settings. This addresses practical issues in obtaining and labeling PCB sample data, avoiding high costs associated with training and tuning, and enhancing the efficiency of industrial product defect detection.
Intelligent safety production supervision. In the coal mining industry, leveraging the machine vision recognition technology of large models, combined with equipment operational status data, can identify scenarios such as personnel entering hazardous areas, personnel falling, cutting operations, drilling depth, quantity of anchoring agents used, stirring time, secondary fastening, and anchoring cable tensioning, transforming manual supervision of excavation operations into automated monitoring, improving the standardization of excavation processes and enhancing the safety factor of coal mining production.
Personalized inspection scenario expansion. By integrating language large models and visual large models for multimodal perception and interaction, the application scope of large models in industrial visual inspection can be expanded, enhancing the flexibility of applications. For instance, in industrial quality inspection, voice commands can control large models to perform defect inspections across different types, areas, and levels, meeting the inspection needs of various products.
(5) Intelligent Control
In large modern production lines, intelligent scheduling and control of multiple key nodes are necessary to enhance the operational efficiency of the production line. AI large models can analyze diverse historical data to better understand complex relationships in industrial scheduling tasks, such as production demand, resource availability, and task priorities, thereby optimizing task allocation and scheduling at each node, improving production efficiency and flexibility. For example, in industrial robotics, large models can automatically integrate and analyze various production data, enabling quick task allocation and dynamic task adjustments for robots, acting as the “nerve center” of large industrial production lines.
Complex pattern learning and rapid task allocation. In large-scale production lines, such as automotive manufacturing, multiple industrial robots are typically configured to handle sub-tasks like assembly, welding, and painting. AI large models can collect data on robot performance, workstation status, and production plans, learning complex information such as robot skills, task complexity, and transfer times between workstations, and predicting the efficiency of different robots performing different tasks. When new tasks arrive, the model can quickly decide which robot to allocate to minimize task waiting times and production cycles.
Dynamic task allocation adjustments. Large models can analyze robot performance and efficiency from historical data and dynamically adjust task allocation strategies based on different task requirements. This helps optimize robot usage, ensuring they perform optimally across different tasks. If robot failures, workstation malfunctions, or changes in production plans occur, large models can quickly respond and readjust task allocations to address unforeseen situations.
Motion control code generation. From the perspective of individual robot movements, production personnel can generate customized motion control code quickly through text or voice interactions based on different task requirements, using large models to control robots to perform various tasks. For example, inputting the instruction “Please write a PLC program to control the robot to move parts from point A to point B” into the large model. This large model-based motion control command generation pattern can significantly enhance the flexibility of industrial robots, enabling flexible production line control.
(6) Scientific Operation and Maintenance Large Model
This model can leverage its powerful reasoning abilities to analyze and predict various data in the production process, thereby enhancing intelligent operation and maintenance levels and improving production management mechanisms. Taking warehouse management as an example, large models can manage and integrate data across categories and modalities in the supply chain, enhancing the operational efficiency of product information flow and creating a more convenient and faster industrial product supply chain system.
Intelligent warehouse management. Using robots embedded with multimodal large models for shelf management, inventory management, and order picking operations can leverage the strong visual generalization capabilities of large models for autonomous shelf positioning, inventory management, and item transportation, enhancing warehouse operational efficiency. Additionally, using large models to predict inventory and formulate replenishment strategies based on sales speed and inventory turnover can help timely restock inventory, avoiding shortages that affect sales while also preventing overstocking that could lead to inventory buildup and capital occupation issues.
Efficient data management. Industrial production supply chains involve vast amounts of data, such as raw material data, processing technology data, equipment status information, environmental information, personnel information, and logistics information. Large models can quickly organize, classify, and analyze data from different formats and sources, reducing data organization costs. Furthermore, this classified and organized data can be used for further fine-tuning of large models, achieving a positive interaction between data and models.
(7) Customized After-sales Large Model
This model can leverage its significant advantages in natural language dialogue, making after-sales services no longer confined to fixed Q&A libraries, but rather facilitating more natural, smooth, and effective dialogues with customers, helping industrial enterprises achieve customized after-sales services that meet diverse user needs, further enhancing customer loyalty and user growth, and expanding business scope. Taking mechanical equipment after-sales as an example, large models can combine multimodal and digital human technologies, allowing customers to describe equipment faults or issues in natural language, with the system accurately understanding and providing detailed, personalized solutions.
Multidimensional interactive after-sales service. The operation and maintenance of mechanical equipment require complex operations, and traditional knowledge-based customer service systems cannot provide precise guidance to users. By utilizing large language models as backend logical reasoning support and virtual digital humans as frontend interactive avatars, the system can accurately understand customer needs, quickly provide detailed and targeted solutions based on its knowledge reserves and specific issues. Additionally, it can assist customers in equipment operation through gestures and voice interactions from more dimensions, improving the efficiency of after-sales services while also offering a more intuitive and personalized service experience.
3. Outlook on Industrial Large Models
Currently, due to issues such as fragmented industrial scenarios, insufficient computing resources, difficulties in collecting and organizing training data in the industrial field, and the safety and reliability of large models, the integration of large models and industry in China is still in the initial exploratory stage, facing certain challenges.
First, foundational large models still occupy the main position in the application market and have not yet penetrated into specialized industrial large models in vertical fields. Secondly, the current application of large models in industrial production is relatively scattered, lacking a standardized, systematic paradigm for large model industrial applications. Finally, building foundational pre-trained industrial large models has a high threshold, with only a few leading enterprises capable of conducting R&D on industrial large models.
In response to the above issues, the following recommendations are made.
Advance large model technology research targeting industrial scenarios. Identify common technological issues of large models aimed at industrial applications, focusing on safety, reliability, and real-time aspects, encouraging collaboration among professional universities, enterprises, and research institutions to tackle these issues through expanding large model industrial datasets, constructing typical industrial scenario rule sets, and optimizing model training algorithms, thereby strengthening the technological R&D of domestic large models and enhancing the industrial application capabilities of large models.
Build a scaled industrial large model data resource pool. Organize enterprises and research institutions from both the supply and demand sides of large models to develop training data specifications and testing standards for industrial large models. Relying on national industrial internet big data centers and other standardized platforms, establish an industrial large model corpus and form a management mechanism for industrial data. By providing financial subsidies, tax reductions, and policy support, guide domestic large and medium-sized manufacturing enterprises to share operational data from industrial production openly, creating a data resource pool covering key domestic industrial sectors to provide data support for the training and testing of industrial large models.
Improve the performance evaluation mechanism for large models in the industrial field. Based on national authoritative institutions, collaborate with demand-side industries to establish standardized testing datasets for large model industrial knowledge Q&A, ensuring evaluation efficiency and result credibility. At the same time, establish a long-term performance evaluation mechanism for large models in the industrial field, periodically assessing key performance indicators such as knowledge capability, stability, and safety of large models, and dynamically adjusting evaluation criteria based on changes in industrial structure and data element distribution to promote the sustained empowerment of large models for new industrialization.
Promote pilot demonstrations of typical applications of large models in the industrial field. Integrate fragmented industrial scenarios, distilling typical business scenarios of large model industrial applications represented by product-assisted design, refined quality inspection, and intelligent supply chain management, clarifying quantitative demand indicators for large models in each scenario, and promoting the establishment of relevant industry standards. Establish a bidirectional interaction mechanism between large model supply sides and enterprise application sides, facilitating the formation of several characteristic industrial clusters that promote the collaborative development of large model R&D and manufacturing industries, and advancing the establishment of benchmark and demonstrative applications of industrial large models.
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This article is reproduced from: Industrial Headlines
Editor: Zhao Min
Reviewer: Liu Shibo
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