High-availability industrial Internet of Things data acquisition method

Abstract

According to the high-availability industrial Internet of Things data acquisition method provided by the invention, a cloud platform and an edge computing mode are combined to adapt to special requirements of the Internet of Things in the industry, such as challenges of various equipment types, complex communication protocols, short project periods, high personnel mobility and the like; according to the invention, the method also supports the rapid access of multiple types of devices or devices using different protocols to the network, and can ensure the high efficiency and safety of data transmission.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet of Things data control, and in particular to a high-availability industrial Internet of Things data acquisition method. Background Art

[0002] As the foundation of smart manufacturing and Industry 4.0, industrial IoT data collection ensures the effectiveness of front-end perception and provides necessary input for subsequent data processing and analysis. However, in actual operation, industrial IoT data collection faces a series of challenges: huge data volume. As the number of sensors increases, the amount of data generated increases exponentially; data quality issues. Data collected in industrial environments are often "dirty" data and need to be normalized and cleaned before storage so that they can be used for effective analysis.

[0003] In the industrial production process, it is necessary to collect all data in the production process in order to understand the implementation and operation of the production process, and to process various production situations as the basis for production scheduling and management. However, due to the complexity, dynamics and instability of production data, the accuracy and real-time performance of the existing collection methods still have many deficiencies, resulting in reduced work efficiency in the production workshop. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a high-availability industrial Internet of Things data acquisition method.

[0005] In order to achieve the above object, the present invention provides a high-availability industrial Internet of Things data collection method, which includes:

[0006] Build a cloud platform network environment and use wireless communication technology to obtain relevant production information;

[0007] Build a cloud platform cluster, which includes management and control nodes and collection nodes; the management and control nodes generate and schedule tasks through a multi-machine load sharing mechanism; the collection nodes upload the data of the corresponding nodes to the management and control nodes;

[0008] The cloud platform cluster performs data analysis based on the received collected data and stores the analyzed data in the cloud platform database.

[0009] Process and analyze the stored data.

[0010] Preferably, the wireless communication method includes Wi-Fi, Bluetooth, 4G, and LoRa communication.

[0011] Preferably, the collection points use a load balancing strategy to complete the multi-collection point allocation of the task.

[0012] Preferably, the server of the cloud platform cluster adopts intermediate cluster control.

[0013] Preferably, the cloud platform cluster uses a machine learning algorithm to parse data, and the steps include: data cleaning and data extraction.

[0014] Preferably, when extracting data, a principal component analysis algorithm is used to extract data.

[0015] Beneficial effects:

[0016] The present invention adopts a combination of cloud platform and edge computing to meet the special needs of the Internet of Things in industry, such as various types of equipment, complex communication protocols, short project cycles, high personnel mobility, etc. The present invention also supports the rapid access of multiple types of equipment or equipment using different protocols to the network, ensuring the efficiency and security of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 An architectural diagram of a collection system corresponding to a high-availability industrial Internet of Things data collection method provided by the present invention. DETAILED DESCRIPTION

[0018] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0019] like Figure 1 As shown, the present invention provides a high-availability industrial Internet of Things data collection method, which includes:

[0020] Build a cloud platform network environment and use wireless communication technology to obtain relevant production information;

[0021] Build a cloud platform cluster, which includes management and control nodes and collection nodes; the management and control nodes generate and schedule tasks through a multi-machine load sharing mechanism; the collection nodes upload the data of the corresponding nodes to the management and control nodes, so that the collection tasks are distributed among multiple collection nodes;

[0022] The cloud platform cluster performs data analysis based on the received collected data and stores the analyzed data in the cloud platform database.

[0023] Process and analyze the stored data.

[0024] The machine load sharing mechanism refers to a mechanism that uses multiple control points to work together to reduce the burden of a single control point.

[0025] Specifically, in order to provide a secure and controllable data storage and processing space, the cloud platform environment built by the present invention is a private network environment. In order to ensure that various types of devices can communicate smoothly with the private cloud platform, the platform supports multiple access methods. A data transmission device is placed on the device side, and a stable data connection is provided through 4G or Wi-Fi. The electrical signal is converted into a digital signal and then forwarded to the cloud platform through a protocol. After receiving the digital signal on the device side, the cloud platform converts it into business data through a protocol and stores it in the database.

[0026] In this embodiment, the wireless communication between the device and the cloud platform includes but is not limited to: Wi-Fi, Bluetooth, 4G, LoRa, etc. The device realizes real-time data upload and remote control by wirelessly communicating with the cloud platform.

[0027] The cloud platform includes management and control nodes and collection nodes. The collection processing capacity can be flexibly expanded by flexibly expanding the number of management and control machines and collection nodes. The directory supports clustered deployment to avoid the problem of failure of a single collection point. For the management and control nodes, the cloud platform uses a multi-machine load sharing mechanism to ensure efficient generation and scheduling of tasks. Under this architecture, different management and control nodes share the workload and collaborate to complete key tasks such as job generation and scheduling. The collection nodes build a collection resource pool in the cloud platform, allowing the system to dynamically allocate and manage resources as needed. The collection points use advanced load balancing strategies to enable intelligent distribution of collection tasks among multiple collection nodes, ensuring efficient execution of tasks and optimal performance of the system. The server uses a middleware cluster, including Zookeeper, MySQL, Redis, Kafka, etc.

[0028] For the data collection process, each collection node actively provides the data source. When it is reported to the cloud platform, the steps include:

[0029] Data reporting: The device reports the collected data (such as sensor readings, status information, etc.) to the cloud platform at predetermined intervals or under specific conditions.

[0030] Data transmission: Data is transmitted securely to the cloud platform via the network. Encryption technology is used to protect the data during this process.

[0031] Data reception: The services on the cloud platform receive the data reported by the device and perform preliminary processing.

[0032] Data parsing: The cloud platform parses the received data and converts the raw data into structured information.

[0033] In this embodiment, a machine learning algorithm is used to analyze data, and its steps include:

[0034] Data cleaning: 1. Missing value processing: According to the missing rate and importance, it is divided into removing fields, filling in missing values, and re-acquiring data; 2. Format and content processing, obtaining data according to the set format.

[0035] Extracting data features: Principal Component Analysis (PCA) maps n-dimensional features to k dimensions (k < n), and these k dimensions are brand-new orthogonal features. These k-dimensional features are called principal components and are re-constructed k-dimensional features.

[0036] Data storage: The analyzed data is stored in the database of the cloud platform for further processing and analysis.

[0037] Data processing and analysis: Process and analyze the stored data, extract valuable information, support decision-making, or trigger corresponding business logics.

[0038] Among them, after the data is collected through the cloud platform in the present invention, when data distribution is required, the cloud platform first detects the working status of each collection point and scores each collection point, and the collection point with the highest score is given priority for resource allocation. Among them, the communication interface between the device and the cloud platform is connected using a standard interface, and this standard interface can support various types of device interfaces.

[0039] Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all 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.

Claims

1. A high-availability industrial Internet of Things data collection method, characterized in that: The specific steps include: Build a cloud platform network environment and use wireless communication technology to obtain relevant production information; Build a cloud platform cluster, which includes management and control nodes and collection nodes; the management and control nodes generate and schedule tasks through a multi-machine load sharing mechanism; the collection nodes upload the data of the corresponding nodes to the management and control nodes; The cloud platform cluster performs data analysis based on the received collected data and stores the analyzed data in the cloud platform database. Process and analyze the stored data.

2. The high-availability industrial Internet of Things data collection method according to claim 1 is characterized in that: The wireless communication methods include Wi-Fi, Bluetooth, 4G, and LoRa communications.

3. The high-availability industrial Internet of Things data collection method according to claim 1 is characterized in that: The collection point completes the multi-collection point allocation of the task through a load balancing strategy.

4. The high-availability industrial Internet of Things data collection method according to claim 1, characterized in that: The cloud platform cluster server adopts intermediate cluster control internally.

5. The high-availability industrial Internet of Things data collection method according to claim 1, characterized in that: The cloud platform cluster uses a machine learning algorithm to parse data, and the steps include: Data cleaning and data extraction.

6. The high-availability industrial Internet of Things data collection method according to claim 5, characterized in that: When extracting the data, a principal component analysis algorithm is used to extract the data.

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