Abstract: Fieldbus integrates digital communication technology, computer technology, automatic control technology, network technology, and intelligent instruments, offering high reliability, real-time performance, and flexibility in data communication. In a fieldbus control network, various devices in the system are connected to a common bus through multiple nodes, enabling point-to-point peer-to-peer communication and information resource sharing among nodes, while significantly reducing the number of connection lines. Therefore, to achieve a low-cost, long-distance, large-scale, distributed target measurement and control system, this paper adopts fieldbus technology (Lonworks) and a single bus to construct this hybrid data acquisition and processing system. Keywords: Fieldbus; Lonworks; Data acquisition 1 Overview Distributed control systems (DCS) and fieldbus control systems (FCS) have reached a considerable level of maturity, but they still have shortcomings and deficiencies. Therefore, it is necessary to establish a networked measurement and control system with good flexibility, system reconfiguration capability, fault tolerance, and rapid response. Distributed artificial intelligence technology (DAI), Agent theory, and the integration technology of fieldbus and single bus provide a practical and effective approach for designing and implementing such a system. Based on this, this chapter proposes an intelligent data acquisition system based on bus technology. In this system, each measurement and control unit is constructed as an agent with autonomy and adaptability. The reliability, stability, and efficiency of the entire system are improved through the collaborative work of multiple agents. Thus, this fieldbus measurement and control system possesses the characteristics of information perception, distribution, concurrency, initiative, and adaptability. Recently, agent technology has been considered an important method for modeling distributed industrial systems, the most natural means of designing and implementing distributed intelligent measurement and control environments, and one of the important technologies for building next-generation measurement and control systems. 2. Multi-Agent Model of the System This system mainly consists of three parts: a system management agent, a control agent, and a perception and execution agent. These are all autonomous or semi-autonomous entities with independent working capabilities. They collaborate to complete the tasks assigned to them by the system, forming a concurrent and distributed MAS (Multi-Agent System). In fact, a detection and monitoring system in a MAS environment should be an agile multi-agent system. Due to the different decomposition and allocation of tasks and the occurrence of other unpredictable factors, this multi-agent system can change its organizational configuration at any time to achieve system reconstruction. Once the task is completed, the temporarily assembled system is immediately disbanded. Based on the above ideas, Figure 1 defines the distributed system model in terms of system composition and communication methods. In this system, the management agent is a comprehensive state recognition system that monitors the overall operating status of the system and provides real-time monitoring, evaluation, and decision-making functions for each monitored object. [align=center] Figure 1 System Structure Diagram[/align] The control agent and the perception and execution agent are agents with autonomy and adaptability, but there is no strict one-to-one correspondence between them. Instead, they are dynamically assembled by the management agent according to the needs of the task. Among them, the perception and execution agents are intelligent devices running in the control field. They are the main source of information for the management agent when performing tasks and are also the basic manifestation of the agent's perception capabilities. They are responsible for the acquisition and preprocessing of field signals, extracting the features of sensor signals to form monitoring variables, and determining where the signals are sent. Simultaneously, the Agent receives instructions from the Control Agent and converts them into switch and analog outputs that match the field equipment. The Control Agent is the core component of the system and has the ability to make autonomous decisions. Furthermore, in this multi-Agent system, the sources of tasks are diverse. They can be commands from one control level or collaborative requests from the management Agent in another multi-Agent system. 3 System Working Process Under normal circumstances, the system's working process is shown in Figure 2. [align=center] Figure 2 System Working Principle Diagram[/align] When a task from the task source is transmitted to the management Agent, the task processing module first decomposes the task and then queries the knowledge base to see if the control Agent it manages can complete all the decomposed sub-tasks. If it can complete them, the task is assigned and the system is started; if it cannot complete them, the task execution is abandoned and the task source is notified. After the system starts, the control Agent summons relevant sensing and execution agents to process the tasks assigned to it, and achieves collaboration with other control agents and information and resource sharing through its own interaction mechanism. When a control agent malfunctions, the monitoring module managing the agent first makes a corresponding diagnostic decision based on the detected fault information, and then notifies the task processing module to transfer the control authority of the control agent to other control agents or to reassign tasks, thus ensuring the continued normal operation of the entire system. This achieves improved system reliability through collaboration among agents, rather than relying on the reliability of individual devices and the redundancy of critical components. 4. System Hardware Structure This paper uses a fieldbus as the system's communication platform to construct an open, interoperable real-time fieldbus data acquisition system. The specific implementation scheme for this measurement and control system is as follows: Considering the requirement to reduce system costs and combining the characteristics of existing bus control systems, the authors utilize field measurement and control equipment, Lonworks nodes, and their network devices to form a field measurement and control network. Here, we only need to add a Neuron chip to each module when designing each agent; the communication line only requires ordinary twisted-pair cables, thus enabling arbitrary communication between agents. Simultaneously, a microcontroller system is used as the hardware support, and MCS51 language is used as the software development tool, enabling it to form corresponding intelligent agents with new sensors and actuators. The main function is to perform basic control of the monitored and controlled objects. This involves collecting necessary monitoring information through temporary field nodes, processing it, and transmitting it via the bus to the management agent for overall data analysis, processing, and fault diagnosis. Dynamic node agents follow the Lontalk protocol, using network variables to connect them. Data communication between nodes uses a window protocol to display messages for data transmission, managed through network variables, thus enabling interoperability between node agents. A KQML-like communication mode is used to achieve information and knowledge sharing between agents. 4.1 Control Agent The main function of the control agent is to complete its own control algorithm and, according to task requirements, form a dynamic multi-agent cooperative system with other agents. Control commands and data required by the control agent are transmitted via the Lon bus. The control agent only has a Lonworks interface chip and an external EZPROM, without any other peripherals. This paper uses a TMP3150 neuron chip connected to an AT89c51 microcontroller to form a Lon bus interface circuit, with parallel communication between the two. The P0 port of the AT89C51 microcontroller is connected to IO0-IO7 of the 3150 as an 8-bit data bus; P3.2 of the AT89C51 is connected to IO8 of the 3150, serving as the signal line for the microcontroller to request data transmission and the response line for receiving control commands from the 3150; P3.3 is connected to IO9, serving as the response signal line for the neuron chip to receive data; P3.4 is connected to IO10, used as the signal line for the 3150 to send control commands. This selection of P3.2 and P3.3 as handshake signals ensures strict synchronization between the microcontroller and the 3150. Simultaneously, to prevent system crashes due to interference, a delay is added each time the microcontroller waits for a response signal. If no response signal is received by the end of the delay, the microcontroller jumps to the initial state. The circuit principle of the Agent is shown in Figure 3: [align=center] Figure 3 Control Principle Diagram[/align] 4.2 Sensing and Execution Agent This Agent can not only complete signal acquisition, but also preprocess sensor signals, extract sensor signal features to form monitoring variables, and transmit them to the control Agent through the Lonworks interface. It is also the signal output interface of the controller node, responsible for receiving control commands from the control Agent and converting them into control or switching outputs that match the field equipment. To achieve direct acquisition of field data, the authors used a novel single-bus digital temperature sensor as the field measurement device. The single-bus digital sensor eliminates the need for channel switching, A/D conversion, and result correction during measurement, and can directly output digital signals, thus simplifying the system structure and increasing reliability. Simultaneously, the TMP3150 was used to construct the Lon bus interface circuit. The circuit principle is shown in Figure 4: [align=center] Figure 4 Schematic diagram of perception and execution agent[/align] 5 Summary The intelligent data acquisition system based on Lonworks bus technology designed in this paper has the following advantages: (1) Unlike the existing control system, which improves the reliability of the whole system by the reliability of a single device and the redundancy of key components, it relies on the cooperation between various intelligent agents to improve reliability. (2) The whole control system is intelligent in handling abnormal sudden events such as system failure. (3) The system performance, such as reliability and speed, can have good scalability. The innovation of the author: This paper combines fieldbus technology and distributed artificial intelligence technology to give the multi-Agent structure of the data acquisition system, and builds the system based on Lonworks bus and single bus technology. The system has the characteristics of low cost, good reliability and stability, and high intelligence. References: [1] Wang Jinbiao, Liu Yu. Integration technology of fieldbus control system[J]. Metallurgical Automation, 2003 (3): 21-230 [2] Yang Xianhui. Fieldbus technology and its application[M]. Beijing: Tsinghua University Press, 1999: 1-2, 6-8. [3] Yin Bo, Meng Qingchun, Zhuang Xiaodong, et al. Design of multi-agent intelligent field data acquisition system [J]. Journal of Ocean University of China, 2003. Vol. 33 (3): 443-448 [4] Yang Shizhong, Xing Lijuan. Configuration of DDC module based on Lonworks fieldbus technology [J]. Microcomputer Information, 2007, 1-1: P66-67 [5] Fu Xiaofeng et al. Research on the application of Lonworks technology and fuzzy PID control in central air conditioning system [J]. Electrical Drive Automation, 2005, 27(2): 23-26 [6] Zhong Liyuan, Pang Xiaohong. Implementation method of fuzzy controller based on Lonworks fieldbus [J]. Computer Simulation, 2005, 22(10): 155-158 Author's profile: Zhu Lianjun (1968.12-), male (Han), from Queshan County, Henan Province, lecturer of Information Technology Department of Henan Education Institute, mainly engaged in database, computer network and detection technology and automation device.