1024programmer Asp.Net iNeuOS industrial Internet operating system, efficient data collection configuration and application

iNeuOS industrial Internet operating system, efficient data collection configuration and application

iNeuOS industrial Internet operating system, efficient data collection configuration and application

The construction of a group energy management and control platform project for a Fortune 500 manufacturing company realizes data integration in remote factories through a dedicated line network. Each terminal energy meter has an IP address. There are more than 1,000 energy meters in total, with a total of more than 10,000 data points. . The iNeuOS industrial Internet operating system is deployed on the group side. The terminal energy meters adopt the Modbus (Slave) protocol. The group platform actively connects the terminal energy meters to the network for real-time data collection.

1. Overview

2. Communication principles

3. Parameter configuration


1. Overview

The construction of a group energy management and control platform project for a Fortune 500 manufacturing company realizes data integration in remote factories through a dedicated line network. Each terminal energy meter has strong>IPaddresses, a total of more than 1,000energy meters, and a total of nearly 10,000data points. The iNeuOS industrial Internet operating system is deployed on the group side. The terminal energy meters adopt the Modbus (Slave) protocol. The group platform actively connects the terminal energy meters to the network for real-time data collection. Structural diagram, as shown below:

However, as more and more energy meters are connected to the factory area, some phenomena also appear: ( 1) The order of data collection by equipment is inconsistent; (2) Data collection is not timely.

2. Communication Principles

 The platform in the industrial field is definitely a real-time system. Using iNeuOS for data collection is different from other systems. Other system data collection drivers generally include IO operations, which is difficult to cope with complex application scenarios. The data collection driver of the iNeuOS system is only responsible for protocol driver parsing, data processing, and triggering reverse control. As for the communication mechanism and IO operations, a dedicated service instance scheduler and IO manager are responsible for it. The benefits of this design are: (1) Suitable for various communication application scenarios, such as: 4G, DTU, fixed or unfixed IP, etc.; (2) Supports high concurrent data interaction, such as cloud platform construction; (3) The system is more stable and robust, and will not be affected by certain factors. The item abnormality affects the communication of other instruments. The overall framework is as shown below:

The communication principle abstracted based on the iNeuOS core framework and the number of devices on the group’s energy management and control platform is as follows:

 iNeuOS includes multiple service scheduling instances. A service scheduling instance includes multiple IO controllers. The IO controller includes multiple instruments. Then the service scheduling instance and IO control The number of instruments is set according to the actual site conditions, so to improve the data collection efficiency of the instruments, it is set in the service scheduling instance and IO controller. At the same time, considering that the Modbus communication protocol mechanism itself is in response mode, the request data command and return There is an interruption time between data.

The book “Internet of Things Software Architecture Design and Implementation” mainly introduces these contents.

3. Parameter configuration

 This project case improves data collection efficiency by properly configuring service instances, control models, intervals and control groupings to fulfill.

(1) Service instance configuration

Service instance configuration principles: configure one service instance for one factory, one service instance for one type of instrument, one service instance for the same data collection cycle, etc., based on on-site data collection The actual demand balance load configures the corresponding service instance. The configuration is as shown below:

(2) Control model configuration

According to the actual situation of this case, each terminal energy meter has an independent IP address, and there are more than 1,000 energy meters in total. You can consider setting the control model to a concurrent model (Parallel). As shown below:

Because network communication is full-duplex, the concurrency control model is used to express: under the current service instance, a one-time concurrent request command is sent to the instrument and the returned data is received asynchronously information. This is more efficient than the synchronization model of each device polling to send request commands -> waiting for data -> receiving data. Schematic diagram of the concurrency control model, as shown below:

(3) Interval time configuration

There is at least one device driver in an IO controller. Under the concurrency control model, you can set the concurrency interval (the interval between each centralized sending of request commands) and the concurrent device interval. (The interval between sending request commands between each set driver) to improve instrument data collection efficiency. The configuration is as shown below:

(4) Control grouping configuration

Under the service instance, you can set up logical groupings of collection devices, and each group can independently schedule data collection tasks. If each device has a different control group name, it means that one IO controller is only responsible for data collection on one device, which is suitable for high-frequency data collection. The configuration is as shown below:

Through the above configuration, the cycle calculation formula for each instrument to collect data is: Data point collection cycle time=Concurrency interval +Concurrent device interval*Control the number of devices in the group,If there are commands to read multiple data points in one device , then there is a certain error, but it will not be too large.


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