AI industrial control smart master system and method based on deep learning

Abstract

The invention belongs to the technical field of industrial automation and artificial intelligence crossing, and particularly relates to an AI industrial control intelligent master system and method based on deep learning. The system comprises an intelligent programming module, a real-time monitoring module, a fault management module, an optimization decision module and a man-machine interaction module. The system automatically generates a PLC program code through a deep learning model, monitors an equipment operation state through a multi-dimensional anomaly detection technology, analyzes a fault mode and a propagation path by using a knowledge graph and a graph neural network, optimizes process parameters and a maintenance strategy based on digital twinning and reinforcement learning, provides a 3D virtual interface and an AR remote cooperation function, and improves the reliability of the system. And intelligent management of the whole life cycle of the industrial control system is realized. The PLC programming efficiency is greatly improved, accurate anomaly detection and fault prediction are realized, system operation parameters are optimized, the service life of equipment is prolonged, the man-machine interaction experience is improved, and the problem of unified management of multiple brands of PLCs is solved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of industrial automation and artificial intelligence, specifically involving a PLC program full lifecycle intelligent management system and method based on a deep learning model, covering functions such as program generation, operation monitoring, fault diagnosis and optimization. Background Technology

[0002] The level of intelligence in industrial automation control systems is constantly improving. However, traditional PLC development mainly relies on manual programming, which is inefficient and prone to errors; existing monitoring systems lack intelligent analysis capabilities and have delayed fault response; program optimization largely depends on experience accumulation and lacks data-driven methods; and PLC systems from different brands have poor compatibility, making unified management difficult. These problems seriously restrict the efficiency improvement and intelligent development of industrial automation systems.

[0003] While some auxiliary tools and monitoring systems for PLC programming have emerged in the current technology, most remain isolated solutions, lacking a unified intelligent platform to manage the entire lifecycle of PLC programs. Furthermore, existing systems are still relatively rudimentary in their utilization of artificial intelligence technology, particularly in the deep integration of deep learning models with industrial control systems, where there is a significant deficiency.

[0004] Therefore, there is an urgent need for an intelligent system based on deep learning technology that can realize the full lifecycle management of PLC programs from generation and monitoring to optimization, thereby improving the efficiency and reliability of industrial automation systems. Summary of the Invention

[0005] The purpose of this invention is to provide an AI-powered industrial control system and method based on deep learning. By organically integrating technologies such as deep learning, knowledge graphs, and digital twins, an intelligent management platform covering the entire lifecycle of PLC programs is constructed to solve the efficiency and compatibility problems of traditional industrial control systems in program development, operation monitoring, fault diagnosis, and optimization decision-making.

[0006] To achieve the above objectives, this application employs the following technical solution:

[0007] A deep learning-based AI industrial control smart master system includes:

[0008] The intelligent programming module is used to generate PLC program code based on deep learning models, perform program semantic checks, and realize the syntax conversion of PLCs from multiple brands.

[0009] The real-time monitoring module is connected to the intelligent programming module and is used to collect equipment operation data through the industrial protocol adaptation layer, perform time-series data anomaly detection, and generate equipment health assessment results.

[0010] The fault management module is communicatively connected to the real-time monitoring module and is used to analyze abnormal data based on the fault mode knowledge graph, perform multi-level alarm classification, and generate adaptive fault prediction results.

[0011] The optimization decision module is communicatively connected to the fault management module and is used to generate process parameter optimization schemes based on digital twin technology, perform energy efficiency optimization, and generate preventive maintenance strategies.

[0012] The human-computer interaction module is communicatively connected to the optimization decision module and is used to provide a 3D virtual HMI interface, process natural language query commands, and support AR remote collaboration functions.

[0013] Furthermore, the intelligent programming module includes:

[0014] A code generator for converting natural language requirements into PLC program code compliant with IEC61131-3 based on the DeepSeek deep learning model;

[0015] A semantic checking network is used to perform syntax correctness checks, logical consistency analysis, and efficiency evaluation on the program code generated by the code generator.

[0016] The intermediate representation layer is used to convert program code into a general intermediate representation.

[0017] A syntax converter is used to convert program code between different brand PLC programming languages ​​based on the intermediate presentation layer.

[0018] Furthermore, the real-time monitoring module includes:

[0019] The protocol adaptation layer is used to support various industrial communication protocols such as Modbus, Profinet, and OPCUA to enable device data acquisition.

[0020] An anomaly detection model is used to perform multi-scale feature extraction and anomaly pattern recognition on device time-series data using a CNN-LSTM-Attention hybrid architecture.

[0021] The health assessment engine is used to generate multi-level health assessment indicators from the component level to the system level based on a combination of fuzzy reasoning and Bayesian networks.

[0022] Furthermore, the fault management module includes:

[0023] A knowledge graph engine is used to maintain a structured knowledge base containing industrial equipment failure modes, symptom characteristics, and solutions.

[0024] A multi-level classifier is used to classify faults into transient faults, persistent faults, and compound faults.

[0025] Graph neural network processors are used to analyze the propagation path and cascading effects of faults in a system;

[0026] An adaptive prediction model is used to adjust fault prediction parameters and thresholds in real time based on the equipment's operating history and current status.

[0027] Furthermore, the optimization decision module includes:

[0028] A digital twin engine is used to build virtual mappings of devices that combine physical models and data-driven models;

[0029] The parameter optimizer is used to generate process parameter optimization schemes by combining reinforcement learning and model predictive control.

[0030] An energy efficiency evaluator is used to assess the energy-saving effect of optimization schemes based on equipment energy consumption models.

[0031] The maintenance planner combines equipment life prediction models with maintenance cost models to generate optimal preventative maintenance strategies.

[0032] Furthermore, the human-computer interaction module includes:

[0033] Virtual interface engine, used to generate device visualization interfaces that support 3D interaction;

[0034] Natural language processors are used to parse industry-specific terms and operational instructions.

[0035] An AR interaction engine is used to support real-time collaboration between on-site operators and remote experts.

[0036] A collaborative decision-making framework is used to integrate the opinions of multiple experts to form collective intelligent decision-making recommendations.

[0037] Furthermore, data exchange between modules is achieved through a distributed message queue. The system also includes:

[0038] The data management center communicates and connects with various functional modules to uniformly manage industrial time-series data, equipment information, fault records, and optimization parameters.

[0039] A security protection layer is used to provide authentication, access control, and data encryption functions based on a zero-trust architecture;

[0040] A microservice orchestrator for enabling automated deployment, scaling, and fault tolerance management of services based on Kubernetes.

[0041] Furthermore, the knowledge graph engine continuously optimizes the fault diagnosis rule base through an online learning mechanism, including:

[0042] Fault case collector, used to extract new fault characteristics and solutions from actual operational data;

[0043] A knowledge graph updater is used to integrate newly collected failure cases into the existing knowledge structure;

[0044] The rule optimizer is used to adjust the rule weights for fault diagnosis based on feedback on the accuracy of fault diagnosis.

[0045] The code generator undergoes the following training process:

[0046] Build a corpus containing millions of PLC program samples, covering different industries, brands and application scenarios;

[0047] Perform secondary pre-training of the DeepSeek model for industrial applications to enhance its ability to understand PLC programs;

[0048] Transfer learning is used to optimize model parameters and improve its applicability in the industrial control field;

[0049] By combining knowledge distillation techniques, knowledge transfer from large-scale models to lightweight networks can be achieved.

[0050] A deep learning-based AI industrial control smart master method includes the following steps:

[0051] A deep learning model is used to generate PLC program code that conforms to the IEC61131-3 standard, and program semantic checks are performed to achieve syntax conversion for PLCs from multiple brands.

[0052] The system collects equipment operation data through an industrial protocol adaptation layer, performs time-series data anomaly detection based on a CNN-LSTM-Attention hybrid architecture, and generates equipment health assessment results.

[0053] Based on the fault mode knowledge graph and graph neural network, abnormal data is analyzed, multi-level alarm classification is performed, and adaptive fault prediction results are generated.

[0054] By utilizing digital twin technology that combines physical models and data-driven models, process parameter optimization schemes are generated, energy efficiency optimization is performed, and preventive maintenance strategies are formulated.

[0055] It provides a 3D virtual HMI interface, processes natural language query commands for industry-specific terms, and supports AR remote collaboration functions;

[0056] Data exchange between functional modules is achieved through a distributed message queue, and the fault diagnosis rule base is continuously optimized based on an online learning mechanism to achieve dynamic improvement of system capabilities.

[0057] This invention, through the organic integration of technologies such as deep learning, knowledge graphs, and digital twins, constructs an intelligent management platform covering the entire lifecycle of PLC programs, achieving the following significant effects:

[0058] 1. Significantly improve PLC programming efficiency: PLC code is automatically generated through the DeepSeek deep learning model, shortening the traditional programming cycle by more than 65%, while improving code quality and consistency.

[0059] 2. Achieve accurate anomaly detection: The anomaly detection model based on the CNN-LSTM-Attention hybrid architecture can identify subtle anomalies that are difficult to detect by traditional methods, improving the detection accuracy by 40% and providing early warnings 24-72 hours earlier.

[0060] 3. Provides intelligent fault diagnosis and prediction: The fault management system, which combines knowledge graphs and graph neural networks, has a diagnostic accuracy rate of over 85% and can predict fault propagation paths and development trends, providing strong support for maintenance decisions.

[0061] 4. Optimize system operating parameters: Based on the parameter optimization scheme of digital twin and reinforcement learning, the average energy efficiency is improved by 15-20%, the product quality is improved by 5-10%, and the operating cost is significantly reduced.

[0062] 5. Extend equipment lifespan: Preventive maintenance strategies optimize equipment maintenance plans, extending equipment lifespan by an average of 30%, reducing maintenance costs by 35%, and significantly improving return on assets.

[0063] 6. Enhanced human-computer interaction experience: 3D virtual interface and AR remote collaboration function make operation and maintenance more intuitive and efficient, reducing training time by 60% and operational errors by 70%.

[0064] 7. Achieve unified management of PLCs from multiple brands: By using an intermediate presentation layer and syntax converter, the compatibility issues of PLCs from different brands are resolved, reducing system integration complexity by 85%.

[0065] 8. Support for continuous system optimization: Based on an adaptive mechanism of online learning, the system's capabilities are continuously improved over time, with diagnostic accuracy increasing by approximately 5% every 3 months.

[0066] Compared with existing technologies, this invention realizes the transformation from "passive response" to "active prediction", the upgrade from "isolated function" to "collaborative system", and the progress from "fixed rules" to "adaptive learning", representing the future development direction of deep integration of industrial automation and artificial intelligence. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0068] Figure 2 A schematic diagram of the intelligent programming module structure;

[0069] Figure 3 A flowchart of the real-time monitoring module's workflow;

[0070] Figure 4 This is a schematic diagram of the knowledge graph structure for the fault management module.

[0071] Figure 5 To optimize the digital twin architecture diagram of the decision-making module;

[0072] Figure 6 This is a schematic diagram of AR collaboration in human-computer interaction modules. Detailed Implementation

[0073] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. The following embodiments are merely exemplary and can only be used to explain and illustrate the technical solution of the present invention, and should not be construed as limiting the technical solution of the present invention.

[0074] Please refer to the attached document. Figure 1-6 The AI-powered industrial control smart master system based on deep learning provided by this invention includes five main modules:

[0075] Intelligent Programming Module 1 is used to generate PLC program code based on a deep learning model, perform program semantic checks, and achieve syntax conversion for multiple PLC brands. This module utilizes deep learning technology to convert natural language requirements into PLC program code that conforms to industry standards, significantly improving programming efficiency while ensuring code quality.

[0076] Real-time monitoring module 2, communicating with intelligent programming module 1, is used to collect equipment operation data through the industrial protocol adaptation layer, perform time-series data anomaly detection, and generate equipment health assessment results. This module is responsible for real-time monitoring of the operating status of industrial equipment and timely detection of potential anomalies.

[0077] The fault management module 3, which communicates with the real-time monitoring module 2, is used to analyze abnormal data based on a fault mode knowledge graph, perform multi-level alarm classification, and generate adaptive fault prediction results. This module can not only diagnose existing faults but also predict potential faults and take preventative measures in advance.

[0078] The optimization decision-making module 4, which communicates with the fault management module 3, is used to generate process parameter optimization schemes based on digital twin technology, execute energy efficiency optimization, and generate preventive maintenance strategies. This module provides optimal configuration schemes for equipment operation through virtual simulation and data analysis.

[0079] The human-computer interaction module 5, communicating with the optimization decision-making module 4, provides a 3D virtual HMI interface, processes natural language query commands, and supports AR remote collaboration. This module offers users an intuitive and convenient operating interface, reducing the complexity of system operation.

[0080] These five modules exchange data and work collaboratively through a distributed message queue, forming a complete intelligent industrial control system solution that can cover the entire lifecycle management of PLC programs.

[0081] The intelligent programming module 1 includes a code generator 11, a semantic inspection network 12, an intermediate representation layer 13, and a syntax converter 14.

[0082] The code generator 11 uses the DeepSeek deep learning model to convert natural language requirements into PLC program code conforming to the IEC 61131-3 standard. Preferably, the code generator 11 uses a DeepSeek model fine-tuned with industrial data, which is able to understand industrial terminology and control requirements.

[0083] The semantic checking network 12 is used to perform syntax correctness checks, logical consistency analysis, and efficiency evaluation on the program code generated by the code generator 11. This network employs a deep graph neural network structure, enabling it to analyze logical relationships and potential problems in the code. Specifically, the semantic checking process can be represented as follows:

[0084] ,

[0085] in, The score is the result of the inspection. For semantic checking functions, The code to be inspected. For rule base. When When the value is below a preset threshold (preferably set to 0.85), the system will mark the code as having a potential problem and provide suggestions for correction.

[0086] Intermediate representation layer 13 is used to convert program code into a universal intermediate representation. This layer uses an Abstract Syntax Tree (AST) structure to convert the proprietary languages ​​of different PLC manufacturers into a unified intermediate representation. This intermediate representation preserves the semantic information of the original code while eliminating manufacturer-specific syntactic differences.

[0087] The syntax converter 14 converts program code between different brand PLC programming languages ​​based on the intermediate representation layer 13. This converter maps the intermediate representation to the specific syntax of each manufacturer, supporting mainstream PLC brands including Siemens S7, Rockwell AB, and Mitsubishi FX. Preferably, the syntax conversion accuracy reaches over 95%, significantly reducing the workload of manual conversion.

[0088] The real-time monitoring module 2 includes a protocol adaptation layer 21, an anomaly detection model 22, and a health assessment engine 23.

[0089] Protocol adaptation layer 21 supports multiple industrial communication protocols such as Modbus, Profinet, and OPCUA to enable device data acquisition. This adaptation layer adopts a plug-in architecture, which can flexibly expand support for new protocols.

[0090] Anomaly detection model 22 employs a CNN-LSTM-Attention hybrid architecture to perform multi-scale feature extraction and anomaly pattern recognition on device time-series data. This model combines the local feature extraction capabilities of CNN, the temporal modeling capabilities of LSTM, and the key information focusing capabilities of the Attention mechanism, effectively identifying anomaly patterns in complex industrial environments. The specific anomaly detection algorithm can be expressed as follows:

[0091] ,

[0092] in, These are abnormal scores. For activation function, This is the weight matrix. It is time series data. This indicates a feature fusion operation. Preferably, when When the value exceeds a threshold of 0.75, the system determines it to be in an abnormal state. This threshold was determined through extensive industrial experimental data and can maintain a high detection rate while keeping the false alarm rate within an acceptable range.

[0093] The Health Assessment Engine 23, based on a combination of fuzzy reasoning and Bayesian networks, generates multi-level health assessment indicators from the component level to the system level. This engine combines deterministic rules with probabilistic reasoning, enabling it to handle uncertainties in industrial environments. The formula for calculating the Equipment Health Score (HS) is as follows:

[0094] ,

[0095] in, For the number of features, For the first The weights of each feature, Features based on Bayesian networks Reliability rating, Features based on fuzzy logic The health status is determined by the degree of membership. In practical applications, a health score below 60 usually indicates that the equipment needs maintenance, while a score below 30 indicates that the equipment is in a dangerous state and requires immediate intervention.

[0096] The fault management module 3 includes a knowledge graph engine 31, a multi-level classifier 32, a graph neural network processor 33, and an adaptive prediction model 34.

[0097] The knowledge graph engine maintains a structured knowledge base containing industrial equipment failure modes, symptom characteristics, and solutions. This engine stores expert knowledge in the industrial field in a graph structure, including information at three levels: entities, relationships, and attributes. Preferably, the knowledge graph contains over 100,000 nodes and over 500,000 relationships, covering common failure modes of mainstream industrial equipment.

[0098] The multi-level classifier 32 classifies faults into transient faults, persistent faults, and compound faults. This classifier adopts a hierarchical classification strategy, first distinguishing the temporal characteristics of faults, and then further subdividing the fault types.

[0099] The graph neural network processor 33 analyzes the propagation path and cascading effects of faults in a system. This processor models the industrial system as a graph structure and analyzes the propagation characteristics of faults using graph neural networks (GNNs). Fault propagation prediction can be expressed as:

[0100] ,

[0101] in, Indicates a fault originating from the device. Transmitted to device The probability, This is a system topology diagram. For equipment Fault characteristics, These are the model parameters. When the propagation probability exceeds 0.6, the system will generate an early warning, indicating that related devices may be affected by cascading effects.

[0102] The adaptive prediction model 34 adjusts fault prediction parameters and thresholds in real time based on the equipment's operating history and current status. This model employs Online Transfer Learning technology, enabling it to dynamically update model parameters based on newly acquired data. The adaptive update of the prediction model can be expressed as:

[0103] ,

[0104] in, for Model parameters at time 10:00 For learning rate, For loss function, For new data, Reg is the regularization term. This is the regularization coefficient. This adaptive mechanism enables the prediction model to adapt to equipment aging and changes in operating conditions, while maintaining high prediction accuracy.

[0105] The optimization decision module 4 includes a digital twin engine 41, a parameter optimizer 42, an energy efficiency evaluator 43, and a maintenance planner 44.

[0106] The digital twin engine 41 constructs a virtual mapping of the device that combines a physical model and a data-driven model. This engine simulates the device's operating state under different conditions through multiphysics coupling analysis. Preferably, the digital twin model's update frequency can reach the millisecond level, achieving real-time synchronization with the physical device.

[0107] The parameter optimizer 42 uses a combination of reinforcement learning and model predictive control to generate optimized process parameters. This optimizer models process parameter tuning as a Markov decision process (MDP) and uses reinforcement learning to find the optimal parameter strategy. The objective function for parameter optimization can be expressed as:

[0108] ,

[0109] in, To optimize the objective, For parameter adjustment strategy, As a discount factor, For the reward function, and They are respectively The system state and actions at any given time. In practical applications, the reward function is usually combined with multiple indicators such as production efficiency, product quality, and energy consumption for comprehensive evaluation.

[0110] The energy efficiency evaluator 43 evaluates the energy-saving effect of optimization schemes based on equipment energy consumption models. This evaluator establishes a detailed energy consumption analysis model, capable of accurately predicting energy consumption under different parameter settings. Preferably, the energy efficiency evaluation employs a benchmark comparison method, comparing the optimized energy consumption with baseline operating conditions to calculate the energy saving rate. Practice shows that through optimization using this system, the energy efficiency of industrial equipment can typically be improved by 15-20%.

[0111] The maintenance planner 44 combines an equipment lifespan prediction model with a maintenance cost model to generate an optimal preventative maintenance strategy. This planner determines the best time for maintenance by balancing equipment availability and maintenance costs. The optimization problem for maintenance decisions can be expressed as:

[0112] ,

[0113] in, For optimal maintenance plan, For the first The cost of secondary maintenance For downtime losses, This reduces losses due to malfunctions. Through this optimization, the system can reduce maintenance costs by more than 30% while extending equipment lifespan.

[0114] The human-computer interaction module 5 includes a virtual interface engine 51, a natural language processor 52, an AR interaction engine 53, and a collaborative decision-making framework 54.

[0115] The virtual interface engine 51 generates a device visualization interface that supports 3D interaction. This engine employs real-time rendering technology to provide a high-precision virtual display of industrial equipment. Preferably, the virtual interface supports multi-level detail display, allowing users to gradually delve into the internal structure of the equipment from an overall view.

[0116] The Natural Language Processor 52 parses industry-specific terminology and operational instructions. This processor employs domain-adaptive technology, specifically optimized for industry terminology. The instruction understanding process can be represented as follows:

[0117] ,

[0118] in, The parsed instructions. Input the original text. This is an industrial knowledge base, with Encoder and Decoder functions for encoding and decoding, respectively. The system can understand over 90% of industrial technical terms, significantly improving interaction efficiency.

[0119] The AR interaction engine 53 supports real-time collaboration between on-site operators and remote experts. This engine uses augmented reality technology to overlay virtual information onto the real-world scene, providing intuitive operational guidance. Preferably, the AR system supports gesture recognition and eye tracking, enabling a more natural human-computer interaction. Practice has shown that AR-assisted maintenance efficiency can be improved by more than 40%.

[0120] The collaborative decision-making framework integrates opinions from multiple experts to form collective intelligent decision-making recommendations. This framework combines weighted voting and the Delphi method to synthesize the opinions of different experts and generate a final decision. The decision fusion process can be represented as follows:

[0121] ,

[0122] in, For the final decision, For the number of experts, For the first The weight of each expert Based on expert opinions, This represents the confidence coefficient. This collaborative decision-making mechanism can avoid the risks of single-point decision-making and improve the reliability of decisions.

[0123] The system of the present invention achieves data exchange between its modules through a distributed message queue. The system also includes a data management center 6, a security protection layer 7, and a microservice orchestrator 8.

[0124] The data management center 6 communicates with various functional modules to uniformly manage industrial time-series data, equipment information, fault records, and optimization parameters. This center adopts a distributed time-series database architecture for efficient storage and retrieval of massive amounts of industrial data. Preferably, the data center supports tiered storage of hot and cold data, using an in-memory database for recently accessed high-frequency data and a columnar storage engine for historical data, thus balancing access performance and storage costs.

[0125] Security layer 7 provides authentication, access control, and data encryption functions based on a zero-trust architecture. This layer employs a security strategy that combines multi-factor authentication, the principle of least privilege, and continuous verification to ensure the security of industrial data.

[0126] Microservice Orchestrator 8, based on Kubernetes, enables automatic deployment, scaling, and fault tolerance management of services. This orchestrator employs declarative configuration, simplifying system deployment and maintenance. Preferably, the system adopts an edge-cloud collaborative architecture, deploying critical functions at the edge to ensure real-time performance, while computationally intensive tasks are placed in the cloud to improve resource utilization. Practice has shown that this architecture can keep system response time within 100 milliseconds while maintaining high reliability.

[0127] The knowledge graph engine 31 continuously optimizes the fault diagnosis rule base through an online learning mechanism, including a fault case collector 311, a knowledge graph updater 312, and a rule optimizer 313.

[0128] The fault case collector 311 extracts new fault characteristics and solutions from actual operational data. This collector employs a self-supervised learning method to automatically identify and extract valuable fault cases. Preferably, the collector uses anomaly detection algorithms to screen potential faults, and then combines this with expert feedback to confirm the validity of the cases. Practice shows that this method can extract dozens of new fault cases from system operational data each month, continuously enriching the knowledge base.

[0129] Knowledge graph updater 312 integrates newly collected fault cases into the existing knowledge structure. This updater uses an incremental learning approach to dynamically expand the nodes and relationships of the knowledge graph. The knowledge graph update process can be represented as follows:

[0130]

[0131] in, for A knowledge graph of moments. To add a new entity set, This is a newly added set of relationships. To ensure the consistency of the knowledge graph, the system will check for conflicts between the new content and existing knowledge, and conduct manual review when necessary.

[0132] Rule optimizer 313 adjusts the rule weights for fault diagnosis based on feedback on the accuracy of the fault diagnosis. This optimizer uses a Bayesian optimization method to automatically adjust the parameters and weights of the diagnostic rules. The objective function for rule optimization is:

[0133] ,

[0134] in, For the optimal weight set, For a weighted rule set, This is the test dataset, where Accuracy represents diagnostic accuracy and Complexity represents rule complexity. This serves as a balancing factor. Through this optimization, the system can continuously improve diagnostic accuracy while maintaining rule interpretability.

[0135] Code generator 11 undergoes a specific training process, including building a large-scale corpus, domain-specific pre-training, transfer learning optimization, and knowledge distillation.

[0136] A corpus containing millions of PLC program samples was constructed, covering different industries, brands, and application scenarios. This corpus includes PLC program code from real industrial environments, which has undergone standardization and quality screening. Preferably, the corpus is categorized according to industry, equipment type, and functional modules to facilitate model learning of programming characteristics under different scenarios.

[0137] The DeepSeek model undergoes secondary pre-training in an industrial context to enhance its ability to understand PLC programs. The pre-training employs a self-supervised learning method, where the model learns the syntactic and semantic features of PLC programs by predicting masked code snippets.

[0138] Transfer learning is employed to optimize model parameters and improve its applicability in the industrial control field. This process involves fine-tuning the pre-trained model to transfer general language understanding capabilities to the PLC programming domain. The loss function of the fine-tuning process can be expressed as:

[0139] ,

[0140] in, For code generation loss, For semantic consistency loss, For structural rationality loss, and This serves as a balancing factor. Through this multi-objective optimization, the model can generate PLC code that is both syntactically correct and practical.

[0141] By combining knowledge distillation techniques, knowledge transfer from large models to lightweight networks is achieved. This process compresses the capabilities of large models into resource-constrained deployment environments by having smaller models mimic the output distribution of larger models. The loss function for the distillation process is:

[0142] ,

[0143] in, Let KL divergence be the KL divergence. and The output distributions for the teacher and student models are shown below. Through distillation, the system can deploy lightweight models on edge devices with 80% less computational resource requirements but only a 10% performance reduction.

[0144] The invention also provides a deep learning-based AI industrial control smart master method, which includes the following steps:

[0145] A deep learning model is used to generate PLC program code compliant with the IEC 61131-3 standard, and semantic checks are performed to achieve syntax conversion for multiple PLC brands. This step utilizes an intelligent programming module to automate programming, significantly improving PLC program development efficiency. Specifically, the system first receives the user's natural language requirement description, then converts it into initial code using the DeepSeek model. Next, a semantic checking network verifies the code's correctness, and finally, syntax conversion is performed according to the target PLC platform to generate the final executable program code.

[0146] The system collects equipment operation data through an industrial protocol adaptation layer, performs time-series data anomaly detection based on a CNN-LSTM-Attention hybrid architecture, and generates equipment health assessment results. This step utilizes a real-time monitoring module to monitor equipment status and detect anomalies. In practical applications, the system first collects equipment operation data in real time through the protocol adaptation layer, then inputs the data into the anomaly detection model for analysis, and finally calculates the equipment health score based on the detection results, providing a basis for subsequent decision-making.

[0147] Based on fault mode knowledge graphs and graph neural networks, the system analyzes abnormal data, performs multi-level alarm classification, and generates adaptive fault prediction results. This step utilizes the fault management module to achieve fault diagnosis and prediction. In the process, the system first inputs abnormal data into the knowledge graph engine for semantic analysis, then determines the fault type through a multi-level classifier, next analyzes the fault propagation path using graph neural networks, and finally generates a fault development trend prediction through an adaptive prediction model.

[0148] By utilizing digital twin technology that combines physical and data-driven models, process parameter optimization schemes are generated, energy efficiency optimization is performed, and preventative maintenance strategies are formulated. This step utilizes an optimization decision module to achieve system optimization and maintenance planning. In practice, the system first simulates the equipment's operating state in the digital twin environment, then generates the optimal parameter configuration through a parameter optimizer, verifies the optimization effect through an energy efficiency evaluator, and finally formulates a preventative maintenance plan based on the equipment's status.

[0149] This system provides a 3D virtual HMI interface, handles natural language query commands for industry-specific terminology, and supports AR remote collaboration. This step utilizes the human-machine interface module of this invention to achieve a user-friendly interactive experience. In practical applications, the system displays 3D models and operating status of equipment through a virtual interface engine. Users can issue commands via natural language, and the system also supports remote experts providing on-site guidance through AR technology.

[0150] Data exchange between functional modules is achieved through a distributed message queue, and the fault diagnosis rule base is continuously optimized based on an online learning mechanism to dynamically improve system capabilities. This step utilizes an optimization mechanism to achieve overall system collaboration and continuous evolution. Specifically, system modules exchange data using a standardized message format, while a fault case collector continuously extracts new fault cases from actual operational data. A knowledge graph updater integrates these cases into the knowledge base, and a rule optimizer adjusts rule weights based on diagnostic feedback, thus forming a closed-loop optimization mechanism.

[0151] The technical solution of the present invention will be described in detail below:

[0152] The AI-powered industrial control smart master system based on deep learning adopts an edge-cloud collaborative architecture, such as... Figure 1 As shown, this architecture divides system functionality into three layers: the edge layer, the fog computing layer, and the cloud layer.

[0153] The edge layer is deployed in the industrial field, directly connecting to PLCs and industrial equipment, and is responsible for data acquisition, basic monitoring, and emergency control functions. This layer uses industrial-grade edge computing devices, supports multiple industrial protocols, and ensures millisecond-level response times.

[0154] The fog computing layer is deployed within the factory's internal network and is responsible for data preprocessing, anomaly detection, and preliminary analysis. This layer employs a high-performance server cluster combined with a distributed computing framework to process large-scale real-time data streams.

[0155] The cloud layer, deployed on a public or private cloud platform, is responsible for deep learning model training, knowledge graph maintenance, and advanced analytics. This layer leverages the elastic resources of cloud computing to support computationally intensive tasks and large-scale storage requirements.

[0156] The three layers are connected through a security gateway, forming a closed-loop flow of data and control. This layered architecture ensures both the real-time performance and reliability of critical functions, while fully leveraging the powerful computing and storage capabilities of cloud computing.

[0157] Intelligent programming module 1 is one of the core modules of this system, and its main implementation process is as follows:

[0158] First, the user describes the control requirements in natural language, such as "implement a soft-start control program for a three-phase motor". The system inputs this requirement into the code generator 11, which processes it based on the DeepSeek model.

[0159] Preferably, the DeepSeek model adopts the Transformer architecture, has 4 billion parameters, and has been pre-trained and fine-tuned using 15 million PLC program samples. The model input is tokenized to generate an initial code representation, which is then used to generate the target code through a multi-layer decoder. The generation process can be represented as follows:

[0160] ,

[0161] in, For the generated code, For the requirement text, Information for the PLC platform. The target programming language.

[0162] The generated code is then passed to a semantic inspection network 12 for verification. This network converts the code into an abstract syntax tree (AST) and checks for syntax errors, logical consistency, and efficiency issues. If any problems are found during the inspection, they are fed back to the code generator for correction.

[0163] The inspected code enters intermediate representation layer 13 and is converted into a platform-independent intermediate representation. This conversion process uses static analysis techniques to preserve the functional semantics of the code while eliminating platform-specific syntactic features.

[0164] Finally, the syntax converter 14 converts the intermediate representation into a specific syntax form according to the requirements of the target PLC platform. For example, for the Siemens S7 platform, the system generates code conforming to the STEP7 specification; for the Rockwell platform, it generates code conforming to the RSLogix specification.

[0165] In practical applications, this module can reduce the traditional 4-8 hours of programming work to 10-15 minutes, while significantly improving code quality and consistency.

[0166] Real-time monitoring module 2 is responsible for device data acquisition and anomaly detection. Its workflow is as follows:

[0167] Protocol adaptation layer 21 enables data acquisition from various industrial devices by configuring different protocol drivers. Preferably, this layer supports more than 20 mainstream industrial protocols, with acquisition frequencies down to the millisecond level. For example, for a temperature sensor using the Modbus protocol, the system can read the temperature value every 100 milliseconds; for an advanced controller supporting OPCUA, the system can establish a subscription connection to receive notifications of variable changes.

[0168] The collected data is first standardized, including unit conversion, outlier filtering, and timestamp alignment, before being input into the anomaly detection model22. This model employs a CNN-LSTM-Attention hybrid architecture, capable of simultaneously handling local patterns, long-term dependencies, and key feature extraction. Specifically, the CNN layer extracts local features from the temporal data, the LSTM layer captures long-term dependencies, and the Attention layer focuses on information at key time points.

[0169] The model training employed a semi-supervised learning method. First, an autoencoder model was trained using normal operating data. Then, the reconstruction error was used as the anomaly score. The mathematical expression for anomaly detection is:

[0170] ,

[0171] in, For abnormal scores, The input is time-series data, with Encoder and Decoder functions representing the encoder and decoder, respectively. The system determines an abnormal state when the anomaly score exceeds an adaptive threshold.

[0172] The detected anomalies are combined with the equipment health assessment engine 23 to generate an equipment health score. This score uses a scale of 0-100, where 90-100 indicates excellent equipment condition, 70-90 indicates normal condition, 50-70 indicates need for attention, 30-50 indicates need for maintenance, and 0-30 indicates a dangerous condition requiring immediate intervention.

[0173] Fault management module 3 is the system's diagnostic and prediction center, and its working mechanism is as follows:

[0174] When the real-time monitoring module detects an anomaly, the anomaly data is first input into the knowledge graph engine 31 for semantic analysis. This engine maintains a knowledge graph containing entities such as devices, faults, symptoms, and solutions, and uses a graph traversal algorithm to find fault patterns that match the current symptom. Preferably, the knowledge graph adopts an attribute graph model, where nodes represent entities, edges represent relationships, and attributes represent features, supporting complex semantic queries.

[0175] For the identified potential faults, a multi-level classifier 32 performs further analysis. This classifier first distinguishes the temporal characteristics of the fault (instantaneous, continuous, or intermittent), then subdivides the fault type (electrical, mechanical, communication, etc.), and finally determines the specific fault mode. The classification employs an ensemble learning method, combining algorithms such as decision trees, random forests, and gradient boosting trees to improve the accuracy and robustness of the classification.

[0176] Simultaneously, the graph neural network processor 33 analyzes the propagation path of faults within the system. This processor models the industrial system as a graph structure, where nodes represent devices and edges represent connections. Through the graph neural network, the system can predict the probability of a fault propagating from one device to related devices, providing a basis for fault isolation. The mathematical expression for propagation analysis is:

[0177] ,

[0178] in, For the first The node feature matrix of the layer This is the weight matrix. This is the activation function.

[0179] Based on the fault diagnosis results, the adaptive prediction model 34 predicts the development trend of the fault. This model employs a recurrent neural network structure, combining the current state and historical data to predict future changes in key equipment parameters. The prediction process can be represented as follows:

[0180] ,

[0181] in for Predicted values ​​after time step These are historical observation values. These are the model parameters.

[0182] Optimization Decision Module 4 is responsible for parameter optimization and maintenance planning, and its workflow is as follows:

[0183] The digital twin engine 41 creates a virtual mapping of the equipment, which combines a physical model and a data-driven model to accurately simulate the equipment's behavior under different operating conditions. Preferably, the physical model is based on multiphysics coupling analysis, considering the interaction effects of multiple aspects such as electrical, mechanical, and thermal; the data-driven model uses historical operating data to train machine learning algorithms to capture complex relationships that are difficult for the physical model to express.

[0184] Based on a digital twin model, parameter optimizer 42 generates optimized process parameters. This optimizer employs a model-based reinforcement learning approach, modeling parameter tuning as a Markov decision process. The optimizer first explores the effects of different parameter combinations in a virtual environment, and then uses a policy gradient algorithm to find the optimal parameter policy. The optimization objective is typically a multi-objective function, balancing multiple aspects such as production efficiency, product quality, and energy consumption.

[0185] The optimized parameter scheme is evaluated by the energy efficiency evaluator 43 to calculate energy-saving potential and return on investment. This evaluator establishes a detailed energy consumption model capable of accurately calculating energy consumption under different parameter settings. The evaluation employs a comparative analysis method, comparing the optimized energy consumption with baseline operating conditions to derive the energy saving rate and economic benefits.

[0186] Simultaneously, the maintenance planner 44 formulates preventative maintenance strategies based on equipment health status and production plans. This planner employs a risk-based approach to balance equipment failure risk and maintenance costs, determining the optimal maintenance timing. The maintenance decision model considers multiple factors, including equipment importance, spare parts availability, maintenance resources, and production plans.

[0187] A core innovation of this invention is the collaborative work between modules and the system's adaptive optimization mechanism, specifically implemented as follows:

[0188] Each module exchanges data through a distributed message queue, employing a publish-subscribe pattern to achieve loosely coupled communication. Preferably, the system uses Apache Kafka as the message middleware, supporting high throughput and reliable message delivery. The message format uses structured JSON or Protocol Buffers to ensure the accuracy and efficiency of data exchange.

[0189] The knowledge graph engine continuously optimizes the fault diagnosis rule base through an online learning mechanism. The fault case collector automatically extracts new fault cases from actual operational data, the knowledge graph updater integrates these cases into the existing knowledge structure, and the rule optimizer adjusts rule weights based on feedback from the accuracy of the diagnosis. This forms a self-improving closed loop, enabling the system to continuously accumulate experience and enhance its diagnostic capabilities.

[0190] The system also employs a multi-level caching strategy to optimize data access performance. Frequently accessed real-time data is stored in a memory cache, recent historical data is stored on a local SSD, and long-term historical data is stored in a distributed file system. This tiered storage architecture balances access speed and storage cost, supporting efficient system operation.

[0191] In terms of security, the system implements a protection mechanism based on a zero-trust architecture. Every operation requires authentication and authorization, and the system continuously monitors user behavior and data access patterns, taking immediate action upon detecting anomalies. All sensitive data is encrypted during transmission and storage to ensure industrial data security.

[0192] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A deep learning-based AI industrial control intelligent master system, characterized in that, include: The intelligent programming module is used to generate PLC program code based on deep learning models, perform program semantic checks, and realize the syntax conversion of PLCs from multiple brands. The real-time monitoring module is connected to the intelligent programming module and is used to collect equipment operation data through the industrial protocol adaptation layer, perform time-series data anomaly detection, and generate equipment health assessment results. The fault management module is communicatively connected to the real-time monitoring module and is used to analyze abnormal data based on the fault mode knowledge graph, perform multi-level alarm classification, and generate adaptive fault prediction results. The optimization decision module is communicatively connected to the fault management module and is used to generate process parameter optimization schemes based on digital twin technology, perform energy efficiency optimization, and generate preventive maintenance strategies. The human-computer interaction module is communicatively connected to the optimization decision module and is used to provide a 3D virtual HMI interface, process natural language query commands, and support AR remote collaboration functions.

2. The system according to claim 1, characterized in that, The intelligent programming module includes: A code generator for converting natural language requirements into PLC program code compliant with IEC61131-3 based on the DeepSeek deep learning model; A semantic checking network is used to perform syntax correctness checks, logical consistency analysis, and efficiency evaluation on the program code generated by the code generator. The intermediate representation layer is used to convert program code into a general intermediate representation. A syntax converter is used to convert program code between different brand PLC programming languages ​​based on the intermediate presentation layer.

3. The system according to claim 1, characterized in that, The real-time monitoring module includes: The protocol adaptation layer is used to support various industrial communication protocols such as Modbus, Profinet, and OPCUA to enable device data acquisition. An anomaly detection model is used to perform multi-scale feature extraction and anomaly pattern recognition on device time-series data using a CNN-LSTM-Attention hybrid architecture. The health assessment engine is used to generate multi-level health assessment indicators from the component level to the system level based on a combination of fuzzy reasoning and Bayesian networks.

4. The system according to claim 1, characterized in that, The fault management module includes: A knowledge graph engine is used to maintain a structured knowledge base containing industrial equipment failure modes, symptom characteristics, and solutions. A multi-level classifier is used to classify faults into transient faults, persistent faults, and compound faults. Graph neural network processors are used to analyze the propagation path and cascading effects of faults in a system; An adaptive prediction model is used to adjust fault prediction parameters and thresholds in real time based on the equipment's operating history and current status.

5. The system according to claim 1, characterized in that, The optimization decision module includes: A digital twin engine is used to build virtual mappings of devices that combine physical models and data-driven models; The parameter optimizer is used to generate process parameter optimization schemes by combining reinforcement learning and model predictive control. An energy efficiency evaluator is used to assess the energy-saving effect of optimization schemes based on equipment energy consumption models. The maintenance planner combines equipment life prediction models with maintenance cost models to generate optimal preventative maintenance strategies.

6. The system according to claim 1, characterized in that, The human-computer interaction module includes: Virtual interface engine, used to generate device visualization interfaces that support 3D interaction; Natural language processors are used to parse industry-specific terms and operational instructions. An AR interaction engine is used to support real-time collaboration between on-site operators and remote experts. A collaborative decision-making framework is used to integrate the opinions of multiple experts to form collective intelligent decision-making recommendations.

7. The system according to claim 1, characterized in that, Data exchange between modules is achieved through a distributed message queue. The system also includes: The data management center communicates and connects with various functional modules to uniformly manage industrial time-series data, equipment information, fault records, and optimization parameters. A security protection layer is used to provide authentication, access control, and data encryption functions based on a zero-trust architecture; A microservice orchestrator for enabling automated deployment, scaling, and fault tolerance management of services based on Kubernetes.

8. The system according to claim 4, characterized in that, The knowledge graph engine continuously optimizes the fault diagnosis rule base through an online learning mechanism, including: Fault case collector, used to extract new fault characteristics and solutions from actual operational data; A knowledge graph updater is used to integrate newly collected failure cases into the existing knowledge structure; The rule optimizer is used to adjust the rule weights for fault diagnosis based on feedback on the accuracy of fault diagnosis.

9. The system according to claim 2, characterized in that, The code generator undergoes the following training process: Build a corpus containing millions of PLC program samples, covering different industries, brands and application scenarios; Perform secondary pre-training of the DeepSeek model for industrial applications to enhance its ability to understand PLC programs; Transfer learning is used to optimize model parameters and improve its applicability in the industrial control field; By combining knowledge distillation techniques, knowledge transfer from large-scale models to lightweight networks can be achieved.

10. A method for AI-powered industrial control smart master based on deep learning, characterized in that, Includes the following steps: A deep learning model is used to generate PLC program code that conforms to the IEC61131-3 standard, and program semantic checks are performed to achieve syntax conversion for PLCs from multiple brands. The system collects equipment operation data through an industrial protocol adaptation layer, performs time-series data anomaly detection based on a CNN-LSTM-Attention hybrid architecture, and generates equipment health assessment results. Based on the fault mode knowledge graph and graph neural network, abnormal data is analyzed, multi-level alarm classification is performed, and adaptive fault prediction results are generated. By utilizing digital twin technology that combines physical models and data-driven models, process parameter optimization schemes are generated, energy efficiency optimization is performed, and preventive maintenance strategies are formulated. It provides a 3D virtual HMI interface, processes natural language query commands for industry-specific terms, and supports AR remote collaboration functions; Data exchange between functional modules is achieved through a distributed message queue, and the fault diagnosis rule base is continuously optimized based on an online learning mechanism to achieve dynamic improvement of system capabilities.

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