Large-scale terminal-oriented real-time intelligent operation and maintenance system framework

Abstract

The invention relates to a real-time intelligent operation and maintenance system framework for a large-scale terminal, and belongs to the field of operation and maintenance systems. A terminal equipment layer; the data acquisition layer is responsible for real-time acquisition and transmission of underlying data and is used for data input of a large-scale terminal system; the edge computing layer is an adjusting and deploying layer of the system, provides a bridge function between a terminal and a cloud end, and is used for enhancing real-time performance and expandability; the cloud platform layer is responsible for calculation processing of the system and is responsible for global analysis, optimization and coordination, the cloud platform layer is a core for realizing intelligent operation and maintenance, elastic expansion is realized through a micro-service architecture, dynamic access of terminal equipment and rapid allocation of tasks are supported, the risk of a single point of failure is avoided through a cooperative processing mode of edge calculation and a cloud platform, and the operation and maintenance efficiency is improved. And the overall reliability and stability of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of operation and maintenance systems, and in particular relates to a real-time intelligent operation and maintenance system framework for large-scale terminals. Background Art

[0002] With the rapid development of the industrial Internet, large-scale terminal network systems have gradually become an important infrastructure for realizing industrial intelligence. Such systems need to cope with the access of massive terminal devices and the real-time processing and response of massive data. However, the traditional centralized architecture has gradually exposed performance bottlenecks and insufficient scalability in the face of a sharp increase in the number of terminals, a surge in data transmission needs, and increasing requirements for system reliability and flexibility.

[0003] Centralized architecture mainly relies on a single or a small number of data centers for data storage, processing, and scheduling. When the number of terminals and the amount of data continue to increase, this architecture will lead to problems such as communication delays, exhaustion of computing resources, and single point failures in the system. In addition, traditional operation and maintenance methods are difficult to meet the needs of rapid response and flexible deployment in complex industrial scenarios, thus limiting the potential for further development of the Industrial Internet.

[0004] Based on the above problems, a real-time intelligent operation and maintenance system framework for large-scale terminals is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time intelligent operation and maintenance system framework for large-scale terminals, which is used to improve the processing speed of the real-time intelligent operation and maintenance system for large-scale terminals.

[0006] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0007] A real-time intelligent operation and maintenance system framework for large-scale terminals, the system framework adopts a layered design, including:

[0008] Terminal device layer: includes terminal devices for monitoring key parameters in industrial production processes, data acquisition modules for preprocessing and uploading data, network connection interfaces, support for multiple communication protocols, and interaction with edge computing layers or cloud platform layers;

[0009] Edge computing layer: includes edge gateway devices, which are responsible for local data aggregation, protocol conversion, and unifying the data formats of different terminals; edge computing nodes, which deploy lightweight containerized microservices for real-time data analysis and anomaly detection; task distribution management modules, which dynamically allocate computing tasks to ensure efficient use of edge computing resources;

[0010] Cloud platform layer: includes microservice management module to realize centralized management of all devices, data storage and management module to support distributed storage, fast access and in-depth analysis, analysis engine to analyze equipment operation trends and provide intelligent optimization suggestions based on artificial intelligence algorithms, user interface layer to provide real-time dashboard, report generation and alarm push functions.

[0011] Beneficial effects:

[0012] The present invention uses the edge computing layer to locally process high-priority and real-time data, reducing data transmission delays and cloud pressure, thereby significantly improving the real-time response capability of the system and meeting the needs of rapid decision-making and emergency control in industrial production.

[0013] The edge computing layer of the system of the present invention adopts lightweight containerized microservices, combined with a task distribution management module, to achieve dynamic resource allocation and effectively avoid waste of computing resources.

[0014] The present invention achieves elastic expansion through a microservice architecture, supports dynamic access to terminal devices and rapid deployment of tasks, and the collaborative processing mode of edge computing and cloud platform avoids the risk of single point failure and improves the overall reliability and stability of the system.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a system architecture diagram of an embodiment of the present disclosure. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] A real-time intelligent operation and maintenance system framework for large-scale terminals, the system framework adopts a layered design and includes the following three core layers:

[0020] Terminal device layer: responsible for the real-time collection and transmission of underlying data, and is the data input of large-scale terminal systems. The terminal device layer includes: Terminal devices: including sensors, actuators, etc., used to monitor key parameters in industrial production processes, such as temperature, pressure, flow, etc. Data acquisition module: a lightweight software / hardware module embedded in the device to complete data preprocessing and upload. Network connection interface: supports multiple communication protocols to interact with edge computing nodes or the cloud.

[0021] The specific operation steps of the terminal device layer are as follows: Terminal device is responsible for obtaining the operation data of various equipment in the industrial system, such as real-time parameters such as temperature, pressure, and position.

[0022] The data acquisition module pre-processes the device and performs preliminary processing capabilities to reduce the amount of transmitted data. The preliminary processing includes denoising and compression.

[0023] The network connection interface realizes device control and performs specific operations under edge computing or cloud instructions, including adjusting device status, shutting down, etc.

[0024] Edge computing layer: The edge computing layer is the system's regulation and deployment layer, providing a bridge between the terminal and the cloud to enhance real-time performance and scalability. The edge computing layer includes: edge gateway devices, which are responsible for local data aggregation and transmission, provide protocol conversion functions, and unify the data formats of different terminals. Edge computing nodes deploy lightweight containerized microservices to achieve real-time data analysis, anomaly detection, and some control logic. Support equipment health diagnosis and early warning functions. Task distribution management module dynamically allocates computing tasks to ensure efficient use of edge resources.

[0025] The specific operation steps of the edge computing layer are: process and analyze local data through edge computing nodes to reduce the frequency of data upload to the cloud platform. Through the task distribution management module: tasks with high real-time requirements are completed locally to avoid response delays caused by network delays. The tasks with real-time requirements include anomaly detection and device protection. The edge gateway unifies the data protocol of terminal devices to prepare for subsequent analysis.

[0026] Cloud platform layer: The cloud platform layer is responsible for the system's computational processing, global analysis, optimization and coordination, and is the core of intelligent operation and maintenance. The cloud platform layer includes a microservice management module, which manages various services in a containerized manner based on a cloud-native architecture to achieve elastic expansion and high availability. Data storage and management, including a distributed storage system for efficient storage and rapid access to massive amounts of data, supports the storage of structured and unstructured data. The analysis engine uses artificial intelligence and machine learning algorithms to analyze historical and real-time data, identify equipment operation trends and potential risks, and provide intelligent optimization recommendations. User interface layer: supports multiple access terminals for data monitoring and operation. Furthermore, the display terminal can provide real-time dashboards, report generation and alarm push functions.

[0027] The operation steps of the cloud platform layer are as follows: Data storage and management module, which stores and deeply analyzes massive data to identify equipment operation modes and trends. Analysis engine, which provides decision support through artificial intelligence and big data analysis. Microservice management module, which realizes centralized management of all equipment, including fault prediction, operation and maintenance planning, and resource allocation.

[0028] The specific data transmission relationship between the layers is as follows: the terminal device layer is responsible for data collection and uploading to the edge computing layer. After the edge computing completes the initial processing, some key data is sent to the cloud platform layer. The cloud platform layer transmits the optimization decision back to the terminal device through the edge computing layer to achieve precise control of the device.

[0029] The terminal device layer focuses on data collection and execution, the edge computing layer focuses on real-time requirements, and the cloud platform layer handles large-scale data analysis and global optimization.

[0030] At the same time, the edge layer and the cloud platform dynamically allocate computing tasks according to real-time needs to improve overall efficiency. The decision results of the cloud platform are issued through the edge layer to ensure that the instructions are implemented accurately and efficiently. Improve the management efficiency of large-scale terminal devices.

[0031] Furthermore, in the present disclosure, a cloud-edge collaborative architecture is adopted, and edge computing undertakes some real-time and computing-intensive tasks to relieve the pressure on the cloud. The cloud platform centrally handles highly complex and global tasks. Data classification is adopted, and edge computing processes high-priority and real-time data to reduce unnecessary transmission. The cloud platform is responsible for big data analysis and global optimization.

[0032] The specific steps of data classification and cloud-edge collaboration are as follows;

[0033] Establish a priority factor for each piece of data. Some of the priority factors disclosed are as follows:

[0034] Real-time requirement: how quickly the data needs to be processed. The higher the real-time requirement, the higher the priority.

[0035] Computational complexity: how much resources are needed to complete the calculation of this data. The more complex the calculation, the lower the priority when it is processed on the edge node.

[0036] Data size: The larger the data volume, the higher the bandwidth required for transmission, so the priority will be lower.

[0037] By assigning weights to these three factors, the comprehensive priority of each piece of data is calculated. A rule can be used to describe the priority. In some public publications, real-time accounts for 50% of the importance, computational complexity accounts for 30%, and data size accounts for 20%.

[0038] Data classification rules set a "priority threshold". If the priority score of a piece of data exceeds the set "priority threshold", it is classified as high priority data. This data is usually processed directly on the edge node. If it is lower than this threshold, it is classified as low priority data and sent to the cloud for processing.

[0039] When determining the task processing of edge nodes, the edge nodes will first check their remaining computing resources and calculate how many tasks they can currently carry. If the total computing demand of high-priority tasks is lower than the remaining resources of the node, all of them will be processed at the edge node. If they exceed the processing capacity of the node, they will be sorted from high to low priority, processing the most important tasks first, and transferring the remaining tasks to the cloud.

[0040] Transmission bandwidth judgment: When low-priority data needs to be transmitted to the cloud, the system will check whether the current network bandwidth is sufficient. If the bandwidth allows, all data will be transmitted. If the bandwidth is insufficient, the system will select data transmission in order from high to low priority to ensure that important data is sent first.

[0041] The global optimization processing steps of the cloud platform include: After receiving the data, the cloud platform is responsible for more complex computing tasks, such as big data analysis or global optimization. The cloud will generate an overall optimization strategy based on the received data, such as adjusting the edge node resource allocation or providing equipment maintenance suggestions. After completing these tasks, the cloud will send the optimization results back to the edge node or terminal device to guide the next step.

[0042] Through precise collection at the terminal device layer, efficient processing at the edge computing layer, and global optimization at the cloud platform layer, the system forms a flexible, efficient, stable and reliable real-time intelligent operation and maintenance framework to meet the intelligent management needs of large-scale terminal devices.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time intelligent operation and maintenance system framework for large-scale terminals, characterized by: The system framework adopts a layered design, including: Terminal device layer: includes terminal devices for monitoring key parameters in industrial production processes, data acquisition modules for preprocessing and uploading data, network connection interfaces, support for multiple communication protocols, and interaction with edge computing layers or cloud platform layers; Edge computing layer: It includes edge gateway devices, which are responsible for local data aggregation, protocol conversion, and unifying the data formats of different terminals; edge computing nodes, which deploy lightweight containerized microservices to perform real-time data analysis and anomaly detection; and task distribution management modules, which dynamically allocate computing tasks to ensure efficient use of edge computing resources; Cloud platform layer: includes microservice management module to realize centralized management of all devices, data storage and management module to support distributed storage, fast access and in-depth analysis, analysis engine to analyze equipment operation trends and provide intelligent optimization suggestions based on artificial intelligence algorithms, user interface layer to provide real-time dashboard, report generation and alarm push functions.

2. According to claim 1, a large-scale terminal real-time intelligent operation and maintenance system framework is characterized in that: The data acquisition module of the terminal device layer performs data preprocessing including denoising and compression.

3. According to claim 1, a large-scale terminal real-time intelligent operation and maintenance system framework is characterized in that: The edge computing nodes of the edge computing layer perform real-time processing according to task priorities, giving priority to completing tasks with high real-time requirements, including anomaly detection and equipment protection.

4. According to claim 1, a large-scale terminal real-time intelligent operation and maintenance system framework is characterized in that: The edge computing layer and the cloud platform layer dynamically allocate data and tasks through a cloud-edge collaborative architecture. The edge computing layer processes high-priority and real-time data, and the cloud platform layer processes low-priority and more complex data.

5. According to claim 1, a large-scale terminal real-time intelligent operation and maintenance system framework is characterized in that: The system framework prioritizes the collected data based on data classification rules. The priority is determined according to real-time requirements, computational complexity and data size, and a comprehensive priority is assigned to each piece of data.

6. A real-time intelligent operation and maintenance system framework for large-scale terminals according to claim 1, characterized in that: When the remaining computing resources of the edge computing node are insufficient, the tasks are sorted according to priority, high-priority tasks are processed first, and the remaining tasks are transferred to the cloud platform layer for processing.

7. A real-time intelligent operation and maintenance system framework for large-scale terminals according to claim 1, characterized in that: The terminal device layer receives optimization instructions from the cloud platform layer through the network connection interface and adjusts the operating status of the terminal device.

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