Industrial decision support system and method based on big data and artificial intelligence

Abstract

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation and intelligent manufacturing, and in particular to an industrial decision support system that combines big data analysis and artificial intelligence technology. The system aims to provide intelligent decision support for industrial production processes, and realizes comprehensive processing and in-depth analysis of industrial big data by integrating advanced data acquisition technology, efficient data storage and management systems, in-depth data analysis and modeling algorithms, precise operations optimization engines, and intuitive user interaction interfaces. The system can provide enterprises with key business decision support such as real-time monitoring, risk warning, production optimization, and resource allocation under the premise of ensuring data security and compliance, promote enterprises to transform to intelligence and digitalization, and improve production efficiency and market competitiveness. Background Art

[0002] With the rapid development of information technology and the continuous improvement of the global industrialization level, industrial production is undergoing unprecedented changes. As the core concept of this change, Industry 4.0 emphasizes the importance of intelligent manufacturing, networked production and data-driven decision-making. In this context, industrial decision support system (DSS) has become a key technology to connect data and decision-making. It can help enterprises extract valuable information from massive industrial data, conduct in-depth analysis and intelligent prediction, and thus make more scientific and efficient decisions. However, although the existing decision support systems have improved the efficiency and quality of decision-making to a certain extent, they still face problems such as insufficient data processing capabilities, limited analysis accuracy and poor user interaction experience. Especially in the context of the big data era, how to effectively process and analyze the huge amount of data from production lines, supply chains and markets has become the main challenge in the development of industrial decision support systems. In addition, with the continuous advancement of artificial intelligence technology, especially the application of machine learning and deep learning in image recognition, natural language processing and complex pattern recognition, it provides new possibilities for the development of industrial decision support systems. These technologies can not only improve the accuracy and efficiency of data analysis, but also achieve in-depth understanding and intelligent processing of unstructured data, greatly expanding the application scope and functions of industrial decision support systems. Therefore, developing an industrial decision support system that integrates big data analysis and artificial intelligence technology can not only cope with the data processing challenges currently faced by the industrial field, but also make full use of the advantages of artificial intelligence in data analysis and prediction to provide enterprises with more comprehensive, accurate and intelligent decision support. It has become an important research direction in the field of industrial automation and intelligent manufacturing. Summary of the invention

[0003] The present invention provides an industrial decision support system based on big data and artificial intelligence technology, which aims to provide accurate decision support for enterprises by efficiently processing and deeply analyzing industrial production data. The system realizes its functions through the following key components: Preferably, the data acquisition and preprocessing module is designed to monitor the sensor network of industrial equipment in real time, automatically collect data such as production parameters, operation logs, quality inspection results and supply chain dynamics, etc. It realizes real-time data cleaning, including removing noise, correcting errors and processing missing values, to ensure the accuracy and consistency of data. It uses data standardization algorithms to convert data of different formats and sources into a unified format for subsequent processing and analysis.

[0004] Optimally, data storage and management system: Build a scalable data lake architecture to support the storage of structured and unstructured data, including relational databases, NoSQL databases, and data warehouses. Implement data version control and history tracking to ensure data integrity and traceability. Ensure data security and privacy by implementing strict data security policies and access control.

[0005] Optimized, Advanced Data Analysis and Modeling Module: Use machine learning algorithms, such as random forests, neural networks, and support vector machines, to extract features and identify patterns in data. Apply deep learning techniques, such as convolutional neural networks and recurrent neural networks, to process image and time series data and improve the ability to analyze complex data. Build dynamically updated forecasting models to predict production trends, market demand, and potential risks in real time.

[0006] Preferably, the operations optimization engine: uses operations research methods such as linear programming, network flow and genetic algorithms to optimize production processes, inventory management and logistics arrangements. It achieves multi-objective optimization and balances multiple goals such as production efficiency, cost control, energy consumption and environmental impact. Through simulation and testing modules, it simulates the effects of different decision-making plans and provides feasibility analysis and risk assessment of decisions.

[0007] Optimized, decision support and intelligent recommendation module: Provide decision support reports based on data analysis results and optimization solutions, including key performance indicators, risk assessment and improvement suggestions. Implement intelligent recommendation functions, automatically generate optimized production plans and resource allocation strategies based on historical decision effects and real-time data. Support users to evaluate and select solutions through simulated decision making and effect prediction.

[0008] Optimized user interface: Design intuitive data visualization dashboards to display key business indicators, production status, and warning information. Provide personalized configuration tools to allow users to customize dashboard layout and report content according to their needs. Implement interactive query and exploration functions to support users to deeply analyze data through natural language query and multi-dimensional data drilling.

[0009] Optimally, the Security and Compliance module ensures that the system complies with international and domestic data protection regulations, such as GDPR and ISO 27001. Implement measures such as data encryption, access auditing, and security incident management to ensure data confidentiality, integrity, and availability.

[0010] The industrial decision support system of the present invention can provide enterprises with full-process support from data collection to decision execution through the collaborative work of the above modules, significantly improve the quality and efficiency of decision-making, and promote enterprises to maintain a leading position in the fierce market competition.

[0011] The present invention aims to solve a number of technical problems existing in industrial decision support systems, including but not limited to the following: 1. Efficient collection and processing of large-scale data: The amount of data generated in industrial environments is huge and complex, and traditional data collection and processing methods are difficult to meet the requirements of real-time and accuracy. The present invention realizes the rapid capture and cleaning of large-scale industrial data by designing an efficient data collection network and preprocessing algorithm, ensuring the quality and availability of data. 2. Integration and management of multi-source heterogeneous data: Industrial data comes from different data sources, including sensors, logs, databases, etc., with diverse data formats and structures. The present invention solves the integration and management problems of multi-source heterogeneous data by building a unified data lake architecture and implementing advanced data governance strategies, and achieves data consistency and traceability. 3. In-depth analysis and understanding of complex data: Industrial data contains rich information, but it often requires in-depth analysis to extract it. The present invention uses machine learning and deep learning technologies to improve the ability to analyze complex data, identify patterns, trends and anomalies in data, and provide deeper insights for decision-making. 4. Optimization of production and resource allocation: Traditional production planning and resource allocation methods often rely on experience and intuition, lacking precision and flexibility. The present invention uses an operations optimization engine and advanced optimization algorithms to achieve automation and intelligence of production processes and resource allocation, thereby improving resource utilization efficiency and the adaptability of production plans. 5. Real-time and accuracy of decision support: Industrial decisions need to respond quickly to market changes and production status, and traditional decision support systems are difficult to provide real-time decision suggestions. The present invention can provide decision support based on the latest data in a timely manner through real-time data analysis and intelligent recommendation modules, thereby improving the real-time and accuracy of decision making. 6. Improvement of user interaction experience: Industrial decision support systems usually require professional operating skills, and the threshold for ordinary users is relatively high. The present invention reduces the difficulty of using the system and improves the user experience by designing an intuitive user interaction interface and personalized configuration tools. 7. Guarantee of data security and compliance: Industrial data often involves sensitive information, and how to ensure the security and compliance of data is a major challenge. The present invention ensures the security and compliance of data throughout the entire processing process by implementing strict data security measures and compliance checks.

[0012] By solving the above technical problems, the industrial decision support system of the present invention can provide enterprises with a comprehensive, efficient and secure decision support platform, helping enterprises to make more accurate and timely decisions in complex and changing industrial environments.

[0013] The key points that need to be protected include: 1. Data security and privacy protection mechanism: Given that industrial data may contain sensitive information, such as production formulas, process parameters, employee information, etc., one of the key points that need to be protected in this invention is to implement strong data security measures. This includes encrypting data transmission and storage to ensure that only authorized users can access relevant data, and implementing strict access control and identity authentication mechanisms. At the same time, the system should comply with relevant data protection regulations, such as the EU's General Data Protection Regulation (GDPR) and China's Personal Information Protection Law, to ensure that personal privacy is not violated. 2. Accuracy and reliability of intelligent decision-making algorithms: The core competitiveness of decision support systems lies in the accuracy and reliability of their intelligent decision-making algorithms. One of the key points that need to be protected in this invention is to ensure that the algorithm can accurately predict future trends based on historical data and real-time data, and provide feasible decision-making recommendations. This needs to be achieved through continuous algorithm optimization, model training and verification testing. At the same time, the system should have a fault-tolerant mechanism that can still provide reasonable decision support when the data is incomplete or noisy.

[0014] 3. System scalability and compatibility: With the development of enterprises and the advancement of technology, industrial decision support systems need to be able to adapt to changing needs. One of the key points that the present invention needs to protect is the scalability of the system, that is, the system should be able to seamlessly integrate new data sources, analysis tools and user interfaces to adapt to business growth and technological updates. In addition, the compatibility of the system is also key. It is necessary to ensure that the system can be integrated with existing enterprise resource planning (ERP) systems, manufacturing execution systems (MES) and other industrial automation systems to achieve data sharing and business collaboration.

[0015] The industrial decision support system of the present invention brings significant beneficial effects by integrating big data analysis and artificial intelligence technology. First, the system significantly improves the efficiency and accuracy of data processing, can capture and process large-scale industrial data in real time, and ensure that decisions are based on the latest and most accurate data. Secondly, the advanced analysis and prediction capabilities of the system enable enterprises to foresee market changes and potential production problems, so as to formulate response strategies in advance and reduce risks and losses. In addition, through the operations optimization engine, the system can provide optimization solutions for resource allocation and production planning, helping enterprises to reduce costs, improve production efficiency and resource utilization. The user-friendly interactive interface and personalized configuration tools further enhance the user experience, allowing decision makers to easily understand and operate the system and quickly make data-based decisions. Finally, the system's data security and privacy protection mechanism ensures the security and compliance of enterprise data, providing a solid foundation for the long-term development of enterprises. In general, the present invention not only improves the decision-making quality and response speed of enterprises, but also provides strong support for enterprises to maintain their leading position in the fierce market competition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The industrial decision support system flow chart of the present invention Figure 2 This is the algorithm flow chart of the industrial decision support system of the present invention.

Claims

1. An industrial decision support system based on big data and artificial intelligence technology, characterized by include: a) Data acquisition and preprocessing module, which is used to capture sensor data, operation logs, quality inspection records and supply chain transaction information of industrial equipment in real time, and perform data cleaning, format conversion, missing value filling and outlier detection; b) Data storage and management system, used to build and maintain data lakes, supports the storage of multiple data types including structured data, semi-structured data, and unstructured data, and implements data version control, access rights management, and secure backup; c) Advanced data analysis and modeling module, which uses machine learning algorithms such as random forest, neural network and support vector machine, and deep learning techniques such as convolutional neural network and recurrent neural network to perform data feature extraction, pattern recognition, anomaly detection and trend prediction, and establish dynamically updated prediction models; d) Operational optimization engine, which uses mixed integer linear programming, dynamic programming and genetic algorithm to optimize production processes, inventory levels, equipment maintenance plans and logistics arrangements to improve resource utilization and reduce costs; e) Decision support and intelligent recommendation module, combining real-time data analysis results and historical decision-making effects, provides scenario-based decision suggestions, risk assessment and strategy optimization solutions, and supports users in making simulated decisions and effect predictions; f) User interaction interface, providing data visualization tools such as charts, dashboards and virtual reality simulations, as well as interactive query and report generation tools, enabling users to customize personalized information display and analysis reports based on their roles and permissions; g) Security and compliance module, which ensures that the system complies with data protection regulations such as GDPR and ISO 27001, implements data encryption, access auditing, security incident management and other measures to ensure the confidentiality, integrity and availability of data.

2. The industrial decision support system according to claim 1, characterized in that The data analysis and modeling module further includes: a) Natural language processing submodule, which is used to analyze and understand text data such as technical documents, user feedback, and market reports, and extract key information and sentiment tendencies; b) Image recognition submodule, which is used to process and analyze monitoring images, product quality photos and equipment status videos of the production site to identify defects, abnormal operations and potential safety hazards; c) The predictive maintenance submodule uses time series analysis and survival analysis techniques to predict the time point and probability of equipment failure and formulate preventive maintenance plans and spare parts inventory strategies.

3. The industrial decision support system according to claim 1 or 2, characterized in that The operations optimization engine further comprises: a) Multi-objective optimization submodule, which is used to deal with the trade-offs and decisions among multiple objectives such as production efficiency, cost control, energy consumption and environmental impact, and achieve multi-dimensional comprehensive optimization; b) The simulation and testing submodule simulates the behavior and performance of the production system by establishing mathematical models and computer simulation technology, evaluates the impact and effect of different decision-making schemes, and supports the verification and iterative optimization of decisions.

4. An industrial decision support system according to any preceding claim, characterised in that The user interaction interface further comprises: a) Personalized configuration tools allow users to customize the layout, color, data indicators and interactive controls of the dashboard according to personal needs and business scenarios, improving user experience and work efficiency; b) Interactive query and exploration tools support users to deeply explore the information and knowledge behind the data through natural language query, data filtering, multi-dimensional data drilling and association analysis, support the solution of complex problems and stimulate innovative thinking.

5. A decision-making method using the industrial decision support system, characterized in that The following steps are involved: a) Use data acquisition and preprocessing modules to monitor and record various data in the industrial production process in real time to ensure the integrity and accuracy of the data; b) Store the collected data in the data storage and management system, clean, classify and label the data, and establish a standardized data warehouse; c) Through the data analysis and modeling module, statistical analysis, association rule mining and prediction model construction are carried out on the data to discover potential patterns and business insights in the data; d) Use the operations optimization engine to develop optimal production plans, resource allocation plans and risk response strategies based on analysis results and business objectives; e) Conduct decision-making discussions, plan evaluation and effect prediction based on the suggestions and plans provided by the decision support and intelligent recommendation module to form the final decision; f) Through the user interaction interface, track the execution of decisions and business results, collect feedback information and performance data, and continuously optimize and adjust decision models and strategies.

6. An embodiment of an industrial decision support system, characterized in that The system can be deployed in a public cloud, private cloud or hybrid cloud environment, supports cross-regional and cross-platform data integration and analysis, and can be seamlessly integrated with the company's existing production management system, resource planning system and customer relationship management system to achieve data sharing and business collaboration.

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