Industrial park water vapor pipe network real-time digital twinning intelligent calculation system based on AI

Abstract

The invention discloses an industrial park water vapor pipe network real-time digital twinborn intelligent calculation system based on AI, and belongs to the technical field of water vapor pipe network management.The system comprises a data acquisition layer, a data transmission layer, a digital twinborn model layer, an AI algorithm layer and an application layer, and the data acquisition layer is used for acquiring running parameters of water vapor and running state data of a pipe network; the data transmission layer is used for transmitting digital display data; the digital twinborn model layer is used for constructing a virtual digital twinborn model consistent with an actual steam pipe network; the AI algorithm layer is used for analyzing, processing and predicting data received by the digital twin model; the application layer provides the functions of pipe network operation state monitoring, fault early warning and optimal scheduling for users according to the analysis result of the AI algorithm layer, and the system realizes comprehensive perception, accurate prediction and optimal scheduling of the pipe network state by integrating a plurality of levels of data acquisition, transmission, model construction, algorithm analysis, application service and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of water vapor pipeline network management, and in particular to an AI-based real-time digital twin intelligent computing system for water vapor pipeline networks in industrial parks. Background Art

[0002] In the process of rapid industrialization, industrial parks are important carriers of economic development. Their internal energy management systems, especially steam pipe networks, play a vital role. As a key energy medium in industrial production, the stable and efficient transmission and distribution of steam are directly related to the production efficiency and energy cost of the park. However, traditional steam pipe network management methods face many challenges.

[0003] First, traditional pipe network management relies on manual inspections, which is not only time-consuming and laborious, but also difficult to achieve comprehensive coverage. Inspectors may miss potential problems due to lack of experience or misjudgment, resulting in frequent pipe network failures and affecting production processes. In addition, manual inspections often fail to obtain pipe network data in real time, making it difficult to respond to sudden abnormal situations in a timely manner, increasing safety risks.

[0004] Secondly, although the existing automated monitoring system has improved the efficiency and accuracy of data collection to a certain extent, it still has problems such as data silos and transmission delays. Data between different devices and systems are difficult to interconnect, resulting in information fragmentation and difficulty in forming a comprehensive perception of the status of the pipeline network. At the same time, delays and losses in the data transmission process will also affect the real-time and reliability of the monitoring system.

[0005] Furthermore, most traditional pipeline network models are based on simplified physical and empirical formulas, which are difficult to accurately reflect the actual operating status of the pipeline network. As the scale of the pipeline network expands and the operating conditions become more complicated, the accuracy and applicability of this simplified model gradually decreases, and it cannot meet the needs of accurate prediction and optimized scheduling of the pipeline network status.

[0006] In order to solve the above problems, advanced technologies such as Industrial Internet of Things (IIoT), big data and artificial intelligence (AI) have been introduced into the management of water vapor pipeline network. Industrial Internet of Things technology can realize the interconnection between devices and the real-time collection and transmission of data; big data technology can store, process and analyze massive data and mine the value behind the data; artificial intelligence technology can use advanced algorithms to conduct in-depth learning, prediction and optimization of pipeline network data, thus improving the management level and operation efficiency of the pipeline network.

[0007] However, it is not easy to apply these advanced technologies to the management of steam pipe networks. On the one hand, the steam pipe networks in industrial parks often have complex structures and numerous devices, making it difficult to collect and transmit data. On the other hand, how to build a high-precision digital twin model and use AI algorithms to accurately analyze and predict the pipe network data is also a technical problem currently faced. Summary of the Invention

[0008] To solve the above problems, the present invention provides a real-time digital twin intelligent computing system for steam pipe networks in industrial parks based on AI, and the present invention is realized through the following technical solutions.

[0009] A real-time digital twin intelligent computing system for steam pipe networks in industrial parks based on AI, comprising:

[0010] A data acquisition layer, which is connected to each node of the steam pipe network in the industrial park and is used to collect the operating parameters of steam and the operating state data of the pipe network;

[0011] A data transmission layer, which on the one hand transmits the data collected by the data acquisition layer to the local data center, and on the other hand realizes the data transmission between the local data center and the user;

[0012] A digital twin model layer, which constructs a virtual digital twin model consistent with the actual steam pipe network based on the data collected by the local data center;

[0013] An AI algorithm layer, which is used to analyze, process and predict the data received by the digital twin model;

[0014] An application layer, which provides functions such as pipe network operating state monitoring, fault warning and optimization scheduling for users according to the analysis results of the AI algorithm layer.

[0015] Further, the data acquisition layer collects data through sensors distributed on the nodes and devices of the steam pipe network, and the sensors include but are not limited to temperature sensors, pressure sensors, flow sensors, humidity sensors and device state monitoring sensors.

[0016] Further, the data acquisition layer is equipped with a data acquisition terminal for processing and storing the data collected by the sensors and transmitting the processed data through the data transmission layer.

[0017] Further, the data transmission layer adopts a combined transmission method of wired and wireless. The data acquisition terminal and the local data center perform data transmission through a wired transmission method. The users include fixed users and mobile users. The fixed users perform data transmission with the local data center through a wired transmission method, and the mobile users perform data transmission with the local data center through a wireless transmission method.

[0018] Further, the wired transmission method is Ethernet or optical fiber; the wireless transmission method is 5G, ZigBee or LoRa.

[0019] Further, the digital twin model layer constructs a digital twin model of the steam pipeline network using three-dimensional modeling technology. The model presents geometric information such as the pipeline layout, pipe diameter size, valve position, and pump station distribution of the pipeline network. At the same time, combined with the material characteristics of the pipeline network, the principles of fluid mechanics, and the technical parameters of the equipment, corresponding physical properties and behavior rules are given to the digital twin model.

[0020] Further, the AI algorithm layer includes:

[0021] A data preprocessing module, which is used to clean, normalize, and extract features from the received data;

[0022] A fault diagnosis module, which uses a fault diagnosis algorithm based on deep learning to analyze the preprocessed data;

[0023] A prediction analysis module, which uses machine learning algorithms to establish a prediction model for the operating parameters of the pipeline network;

[0024] An intelligent scheduling module, which formulates a reasonable pipeline network scheduling plan according to the prediction analysis results and the actual operating conditions, using intelligent optimization algorithms.

[0025] Further, the functions of the application layer are as follows:

[0026] The pipeline network operation status monitoring function, which real-time displays the operation parameters of each node of the pipeline network and the operation status of the equipment through a visualization interface;

[0027] The fault warning function, which timely pushes fault warning information to users according to the fault diagnosis results of the AI algorithm layer;

[0028] The optimization scheduling function, which allows users to manually or automatically adjust the scheduling plan of the pipeline network according to the actual production requirements and the operation status of the pipeline network.

[0029] The present invention proposes a real-time digital twin intelligent computing system for the steam pipeline network in industrial parks based on AI. The system realizes the comprehensive perception, accurate prediction, and optimization scheduling of the pipeline network status by integrating multiple levels such as data collection, transmission, model construction, algorithm analysis, and application services. Specifically, the beneficial effects of the present invention include:

[0030] 1. Real-time data collection and transmission: Through a variety of sensors distributed at the pipe network nodes and equipment, the operating parameters of water vapor and the pipe network status data are collected in real time, and a combined wired and wireless transmission method is adopted to ensure the rapid and accurate transmission of data to the local data center and the user side. This provides users with real-time pipe network status information, helping to detect and handle potential problems in a timely manner.

[0031] 2. High-precision digital twin model: Using 3D modeling technology and the actual data of the pipe network, a virtual digital twin model highly consistent with the actual pipe network is constructed. This model not only presents the geometric information of the pipe network but also combines physical properties and behavior rules, capable of accurately simulating the actual operating state of the pipe network. This provides an accurate data basis for subsequent AI algorithm analysis and prediction.

[0032] 3. Advanced AI algorithm analysis: The AI algorithm layer includes multiple modules such as data preprocessing, fault diagnosis, prediction analysis, and intelligent scheduling. These modules can clean, normalize, and extract features from the received data, use deep learning algorithms for fault diagnosis, establish a prediction model for the pipe network operating parameters using machine learning algorithms, and formulate a reasonable pipe network scheduling plan based on the prediction results and the actual operating conditions. This helps to improve the operating efficiency and energy utilization rate of the pipe network, reduce the failure rate and maintenance costs.

[0033] 3. Rich application service functions: The application layer provides functions such as pipe network operating status monitoring, fault warning, and optimization scheduling. Users can view the operating parameters of each node of the pipe network and the equipment status in real time through the visualization interface, receive fault warning information, and manually or automatically adjust the pipe network scheduling plan according to actual needs. This provides users with convenient and efficient management means, helping to improve the overall operation level of the industrial park. Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the following description of the specific implementation manners will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0035] Figure 1 : Structural schematic diagram of a real-time digital twin intelligent computing system for water vapor pipe network in an industrial park based on AI of the present invention. Detailed Embodiments

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] As Figure 1 shown, a real-time digital twin intelligent computing system for steam pipe networks in industrial parks based on AI includes:

[0038] A data acquisition layer, which is connected to each node of the steam pipe network in the industrial park and is used to collect the operating parameters of steam and the operating status data of the pipe network;

[0039] A data transmission layer, which on the one hand transmits the data collected by the data acquisition layer to the local data center, and on the other hand realizes the data transmission between the local data center and the user;

[0040] A digital twin model layer, which constructs a virtual digital twin model consistent with the actual steam pipe network based on the data collected by the local data center;

[0041] An AI algorithm layer, which is used to analyze, process and predict the data received by the digital twin model;

[0042] An application layer, which provides functions such as pipe network operating status monitoring, fault warning and optimized scheduling for users according to the analysis results of the AI algorithm layer.

[0043] Preferably, the data acquisition layer collects data through sensors distributed on the nodes and equipment of the steam pipe network. The sensors include but are not limited to temperature sensors, pressure sensors, flow sensors, humidity sensors and equipment status monitoring sensors.

[0044] The data acquisition layer is the cornerstone of the system. It is responsible for collecting key data from each node and equipment of the steam pipe network in the industrial park. In addition to the mentioned temperature sensors, pressure sensors, flow sensors, humidity sensors and equipment status monitoring sensors, the data acquisition layer may also integrate other types of sensors, such as vibration sensors (used to monitor the vibration of equipment and prevent mechanical failures), chemical sensors (used to detect possible impurities or pollutants in steam), etc. These sensors capture the state changes of steam and the pipe network in real time through high-precision measurements, providing a basis for subsequent data analysis and processing.

[0045] Preferably, the data acquisition layer is equipped with a data acquisition terminal for processing and storing the data collected by the sensors and transmitting the processed data through the data transmission layer.

[0046] The data acquisition terminal is a bridge connecting sensors and the data transmission layer. It is not only responsible for receiving and processing the raw data collected by sensors, but also has a data storage function, which can temporarily store data when the data transmission is interrupted to ensure the integrity and continuity of the data. The data acquisition terminal is usually equipped with a powerful data processing chip and sufficient storage space to handle the real-time processing requirements of a large amount of data. In addition, the data acquisition terminal may also have preliminary data verification and anomaly detection capabilities to filter out invalid or abnormal data and reduce the burden of subsequent data processing.

[0047] Preferably, the data transmission layer adopts a combined wired and wireless transmission method. The data acquisition terminal and the local data center conduct data transmission through a wired transmission method. Users include fixed users and mobile users. Fixed users conduct data transmission with the local data center through a wired transmission method, and mobile users conduct data transmission with the local data center through a wireless transmission method.

[0048] The data transmission layer adopts a combined wired and wireless transmission method, ensuring the efficient and reliable transmission of data. Wired transmission methods (such as Ethernet or fiber optic) provide high-speed and stable data channels, which are suitable for data transmission between the data acquisition terminal and the local data center. Wireless transmission methods (such as 5G, ZigBee, or LoRa) provide greater flexibility, enabling mobile users or users in remote locations to access data in real time. The data transmission layer also has data encryption and transmission protocol conversion functions to ensure the security and compatibility of data during transmission.

[0049] Preferably, the wired transmission method is Ethernet or fiber optic; the wireless transmission method is 5G, ZigBee, or LoRa.

[0050] Wired transmission method: Ethernet is the first choice due to its wide compatibility and high-speed transmission ability. Fiber optic, with its extremely high transmission speed and anti-interference ability, is suitable for data transmission scenarios with long distances or high bandwidth requirements.

[0051] Wireless transmission method: 5G, with its high speed and low latency characteristics, is suitable for scenarios that require real-time data updates. ZigBee and LoRa, with their low power consumption and long-distance transmission characteristics, are suitable for data transmission in sensor networks or remote areas.

[0052] Preferably, the digital twin model layer uses three-dimensional modeling technology to construct a digital twin model of the steam pipeline network. The model presents geometric information such as the pipeline layout, pipe diameter size, valve position, and pump station distribution of the pipeline network; at the same time, combining the material characteristics of the pipeline network, the principles of fluid mechanics, and the technical parameters of the equipment, corresponding physical properties and behavior rules are given to the digital twin model.

[0053] The digital twin model layer uses advanced 3D modeling technology to construct a virtual model that is highly consistent with the actual steam pipeline network. This model not only presents the geometric information of the pipeline network (such as pipeline layout, pipe diameter size, valve position, pump station distribution, etc.), but also combines the physical properties and behavior rules of the pipeline network (such as material characteristics, fluid mechanics principles, equipment technical parameters, etc.). This enables the digital twin model to accurately simulate the actual operating state of the pipeline network and provide precise data support for the subsequent AI algorithm layer. In addition, the digital twin model layer also supports the dynamic update function, which can adjust the model parameters according to real-time data to ensure the accuracy and timeliness of the model.

[0054] Preferably, the AI algorithm layer includes:

[0055] A data preprocessing module, which is used to clean, normalize, and extract features from the received data;

[0056] A fault diagnosis module, which uses a deep learning-based fault diagnosis algorithm to analyze the preprocessed data;

[0057] A prediction analysis module, which uses machine learning algorithms to establish a prediction model for the operating parameters of the pipeline network;

[0058] An intelligent scheduling module, which formulates a reasonable pipeline network scheduling plan according to the prediction analysis results and the actual operating conditions, using intelligent optimization algorithms.

[0059] The AI algorithm layer is the core of the system. It uses advanced machine learning and deep learning algorithms to analyze, process, and predict the data received by the digital twin model. The data preprocessing module is responsible for cleaning, normalizing, and feature extraction processing to ensure the quality and consistency of the input data. The fault diagnosis module uses deep learning algorithms to automatically identify and analyze abnormal patterns in the data and accurately predict potential fault points. The prediction analysis module uses machine learning algorithms to establish a prediction model for the operating parameters of the pipeline network to predict the future state of the pipeline network. The intelligent scheduling module formulates a reasonable pipeline network scheduling plan according to the prediction analysis results and the actual operating conditions, using intelligent optimization algorithms to achieve efficient utilization of energy and stable operation of the pipeline network.

[0060] Preferably, the functions of the application layer are as follows:

[0061] The pipeline network operating status monitoring function, which real-time displays the operating parameters of each node of the pipeline network and the operating status of equipment through a visual interface;

[0062] The fault warning function, which timely pushes fault warning information to users according to the fault diagnosis results of the AI algorithm layer;

[0063] The optimized scheduling function, which allows users to manually or automatically adjust the scheduling plan of the pipeline network according to actual production needs and the operating conditions of the pipeline network.

[0064] The application layer is the interface for the system to interact with users. It provides functions such as monitoring the operation status of the pipe network, fault warning, and optimal scheduling. The function of monitoring the operation status of the pipe network displays the operation parameters and equipment status of each node of the pipe network in real time through a visual interface, enabling users to intuitively understand the overall operation status of the pipe network. The fault warning function pushes warning information to users in a timely manner according to the fault diagnosis results of the AI algorithm layer, reminding users to take necessary maintenance measures. The optimal scheduling function allows users to manually or automatically adjust the scheduling plan of the pipe network according to actual production needs and the operation status of the pipe network, achieving optimal allocation of energy and stable operation of the pipe network. In addition, the application layer also supports data export and report generation functions, facilitating data analysis and decision-making support for users.

[0065] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all details in detail, nor limit the present invention to the specific implementation manners described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A real-time digital twin intelligent computing system for water vapor pipeline network in industrial parks based on AI, characterized in that: include: The data collection layer is connected to each node of the water vapor pipeline network in the industrial park and is used to collect the operating parameters of the water vapor and the operating status data of the pipeline network; The data transmission layer transmits the data collected by the data collection layer to the local data center on the one hand, and realizes the data transmission between the local data center and the user on the other hand; The digital twin model layer builds a virtual digital twin model consistent with the actual water vapor pipeline network based on the data collected by the local data center; The AI ​​algorithm layer is used to analyze, process and predict the data received by the digital twin model; The application layer provides users with pipeline operation status monitoring, fault warning and optimized scheduling functions based on the analysis results of the AI ​​algorithm layer.

2. According to claim 1, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The data collection layer collects data through sensors distributed on the water vapor network nodes and equipment, and the sensors include but are not limited to temperature sensors, pressure sensors, flow sensors, humidity sensors and equipment status monitoring sensors.

3. According to claim 2, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The data collection layer is equipped with a data collection terminal for processing and storing the data collected by the sensor, and transmitting the processed data through the data transmission layer.

4. According to claim 3, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The data transmission layer adopts a transmission method that combines wired and wireless transmission. The data acquisition terminal and the local data center transmit data through wired transmission. The users include fixed users and mobile users. Fixed users and the local data center transmit data through wired transmission, and mobile users and the local data center transmit data through wireless transmission.

5. According to claim 4, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The wired transmission method is Ethernet or optical fiber; the wireless transmission method is 5G, ZigBee or LoRa.

6. According to claim 1, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The digital twin model layer uses three-dimensional modeling technology to construct a digital twin model of the water steam pipeline network. The model presents geometric information such as the pipeline layout, pipe diameter, valve location, and pump station distribution of the pipeline network. At the same time, combined with the material characteristics of the pipeline network, fluid mechanics principles, and technical parameters of the equipment, the digital twin model is given corresponding physical properties and behavioral rules.

7. According to claim 1, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The AI ​​algorithm layer includes: A data preprocessing module, which is used to clean, normalize and extract features from the received data; Fault diagnosis module, which uses a deep learning-based fault diagnosis algorithm to analyze the preprocessed data; A predictive analysis module, which uses machine learning algorithms to build a predictive model for pipeline network operating parameters; The intelligent scheduling module uses intelligent optimization algorithms to formulate reasonable pipeline network scheduling plans based on the prediction analysis results and actual operation conditions.

8. According to claim 1, the AI-based real-time digital twin intelligent computing system for industrial park water vapor pipeline network is characterized in that: The functions of the application layer are as follows: The pipeline network operation status monitoring function displays the operation parameters of each node in the pipeline network and the operation status of the equipment in real time through a visual interface; Fault warning function: according to the fault diagnosis results of the AI ​​algorithm layer, fault warning information is pushed to users in a timely manner; The optimized scheduling function allows users to manually or automatically adjust the scheduling plan of the pipeline network according to actual production needs and pipeline network operation status.

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