Abstract: With the development of automated measurement and control systems, the research and development of sensor measurement and control systems are receiving increasing attention. This paper, based on a detailed analysis of the definition and functional requirements of sensor measurement and control systems, establishes a general-purpose sensor measurement and control system requirement model using the UML unified modeling language, and describes various user requirements of the intelligent sensor system using use case diagrams. The system structure of the sensor measurement and control system is analyzed, and collaboration diagrams are used to analyze and describe the information transmission and collaboration relationships between various functional modules of the system. Furthermore, design ideas are proposed for the hardware modules in the system. Keywords: UML, modeling, measurement and control system[b][align=center]Multi-purpose Senor Measuring and Control System based on the UML Model ZHANG Hui, ZHAI Hongsheng[/align][/b] Abstract: With the development of automation control system, the control systems based on sensors have been paid more attentions. On the basis of the analysis of definition and function requirement of sensor control systems, we used the UML to establish a model for the multi-purposes sensor control system, and describe the requirements of users in the system. We also analyzed the system structure of the control system, and proposed a design scheme for the circuit. Keywords: UML; Modeling; Control and Measuring System. 1 Introduction Intelligent sensors are sensors with embedded microprocessors. They are sensor measurement and control systems that combine embedded microprocessors, intelligent theory, and sensors, possessing functions such as detection, judgment, networking, communication, and information processing. Compared with traditional sensors, they have many characteristics: they have thinking, judgment, and information processing functions, enabling them to correct measured values ​​and compensate for errors, thus improving measurement accuracy; they possess knowledge, allowing for comprehensive processing of multiple sensor parameters; they can perform self-diagnosis and self-calibration as needed, improving data reliability; they offer convenient storage and retrieval of measurement data; they have data communication interfaces, enabling direct communication with microcomputers for remote control; they can transmit data over networks for global monitoring and control; and they can achieve wireless transmission. 2. Sensor Function Analysis The characteristics of sensor measurement and control systems can be defined by their basic functions as follows: 1. Composite Sensing Function—Intelligent sensor measurement and control systems have composite functions, capable of simultaneously measuring multiple physical and chemical quantities, providing information that comprehensively reflects the laws of material motion. 2. Self-compensation and calculation function — As long as the repeatability of the sensor can be guaranteed, the microprocessor can calculate the test signal through software, and use multiple fitting and difference calculation methods to compensate for drift and nonlinearity, thereby obtaining more accurate measurement results. 3. Self-testing, self-calibration, and self-diagnostic functions — Using an intelligent sensor measurement and control system, the self-diagnostic function first performs a self-test when the power is turned on to determine if there are any component faults. Secondly, it can be calibrated online according to the usage time, and the microprocessor uses the measurement characteristic data stored in the EPROM for comparison and calibration. 4. Information storage and transmission — The sensor measurement and control system realizes various functions by transmitting test data or receiving instructions. Examples include gain setting, compensation parameter setting, internal test parameter setting, and test data output. 3 Sensor System Use Case Analysis Use case modeling is a part of UML modeling and is also the most basic part of UML. The main function of use case modeling is to express the functional requirements or behaviors of the system. The position and interrelationship of the sensor in the entire control system can be determined as shown in Figure 1. [align=center] Figure 1 Sensor and external environment relationship diagram[/align] Therefore, the users of the sensor measurement and control system can be defined. Operator: The operator establishes a connection between the sensor and the system, powers on the sensor and system, and the sensor locates the controller via the communication network. The controller then identifies itself and sets its parameters. Users can also set parameters for specific sensors through the controller. Detection Parameters: These are the parameters of the physical quantity detected by the sensor. Changes in these parameters will cause changes in the sensor's detection data. The sensor control system will convert, process, and store the detection data, and transmit data based on communication with the controller. Furthermore, the sensor control system needs to complete low-level control tasks, receiving instructions and data from the controller; that is, receiving, storing, and executing data and instructions sent by the controller. Associated Sensors: Any intelligent sensor may be connected to a sensor network (currently most commonly through various fieldbuses). Communication may exist between sensors. The intelligent sensor can respond to data sent by associated sensors, store the data, send control commands, or modify its own set parameters. Controller: During system initialization, based on sensor requests, the controller assigns an identification ID and sets relevant parameters; based on the sensor's detection data, it determines control parameters and control commands, and sends them to the designated sensor. Driver: The driver receives control commands from the sensor control system and implements the driving action. Therefore, the use case diagram of the sensor measurement and control system is shown in Figure 2. [align=center] Figure 2 Use Case Diagram of Sensor Measurement and Control System[/align] Each use case involves information interaction between the user and the sensor system, and between the sensor system and peripheral devices. Interaction is a set of messages in a collaboration, exchanged by class roles through associated roles. When the collaboration is running, objects constrained by class roles exchange message instances through connections constrained by associated roles. Interactions can model the execution of operations, use cases, or other behavioral entities. A message is a one-way communication between two objects, a flow of control information from sender to receiver. Messages have parameters used to pass values ​​between objects. Messages can be signals (an explicit, named, asynchronous communication between objects) or calls (synchronous calls to operations with a return control mechanism). Creating a new object is expressed in the model as an event caused by the object's creation and accepted by the class itself. The creation event serves as the current event of a transition from the top-level initial state. This is feasible for new instances. Messages can be organized into sequential control threads. Separate threads represent several concurrent message sets. Thread synchronization is modeled through constraints on messages between different threads. Synchronization structures can model forking control, associative control, and branching control. Message sequences can be represented using two types of diagrams: sequence diagrams (emphasizing the temporal order of messages) and collaboration diagrams (emphasizing the relationships between objects exchanging messages). Sequence diagrams are typically used to describe system information interactions to represent system requirements. Sequence diagrams represent interactions as a two-dimensional graph. The vertical axis is the time axis, extending downwards. The horizontal axis represents the class roles of independent objects in the collaboration. Class roles are represented by lifelines. When an object exists, the role is represented by a dashed line; when an object's process is active, the lifeline is a double line. 4. Sensor Measurement and Control System Hardware Structure Design 4.1 Sensor System Composition Based on the functional and use case analysis of the sensor system in the previous chapter, the modular structure of the sensor measurement and control system can be determined. The component diagram of the sensor system defined in Rational Rose is shown in Figure 3. [align=center] Figure 3 Component Diagram of Sensor Measurement and Control System[/align] The main modules of the sensor measurement and control system include: signal conditioning module, multi-channel data acquisition module, A/D conversion module, data storage module, data encoding module, data transmission module, control decision module, data processing module, drive module, and status display module. All control logic and data calculation are implemented by the main controller software. In the modular structure of the sensor measurement and control system, the signal conditioning module, A/D conversion module, data storage module, data encoding module, data transmission module, status display module, and drive module all require hardware support. Therefore, the hardware architecture of the sensor system can be determined as shown in Figure 4. [align=center]Figure 4 Sensor System Module Structure Diagram[/align] 4.2 Hardware Design Because the sensor system needs to serve as the basic unit for field data acquisition, it requires small size, low power consumption, low cost, and high performance; it should also be able to achieve online control. Therefore, the hardware circuit selection should differ from that of ordinary embedded systems. 4.2.1 Basic Microcontroller System Design The microprocessor unit is the core of the sensor measurement and control system, mainly responsible for signal data acquisition, processing (such as digital filtering, nonlinear compensation, and self-diagnosis), and data output scheduling (including data communication and local output of control quantities). Considering the high reliability, low power consumption, low cost, and small size characteristics of intelligent sensors, an embedded microprocessor system is the best choice. 4.2.2 Signal Conditioning Circuit Design During the design of the data acquisition system, the electrical signal input to the data acquisition system may not necessarily match the input range of the ADC. Therefore, it is generally not directly fed into the ADC for conversion; signal conditioning is necessary. The analog signal after signal conditioning meets the requirements of the ADC. 4.2.3 A/D Conversion Circuit Selection The analog-to-digital converter is an important interface connecting the analog and digital worlds. An A/D converter transforms analog signals from the real world into a digital bit stream for processing, transmission, and other operations. The selection of the A/D converter is crucial. The chosen A/D converter should ensure that the analog signal is accurately represented in the digital bit stream and provide a smooth interface with any necessary digital signal processing capabilities. 4.2.4 Selection of D/A Conversion Circuit The selection of the D/A conversion circuit mainly considers the resolution, accuracy, and linearity of the conversion circuit. First, the relationship between resolution and linearity error is analyzed. According to the definition of resolution, the more bits, the higher the resolution. 4.2.5 Design of Sensor Communication Circuit Most intelligent sensors have bidirectional communication capabilities, meaning data transmission exists between intelligent sensors and between intelligent sensors and the controller. The controller not only receives and processes sensor data but can also feed information back to the sensors to adjust and control the measurement process. 5. Summary Although the research and development of intelligent sensor measurement and control systems has achieved certain results, it is still far from meeting the urgent needs of production practice. This project designed an intelligent sensor system. Based on a detailed analysis of the definition and functional requirements of the sensor measurement and control system, a requirement model of the sensor system was established using the UML unified modeling language. Use case diagrams were used to describe various user requirements of the sensor system, and sequence diagrams were used to describe the participants and information transmission process of each use case. Based on this, the structure of the sensor system was described, and collaboration diagrams were used to analyze the information transmission and collaboration relationships between various functional modules of the system. Furthermore, design ideas were proposed for the hardware modules of the sensor system. The innovation of this paper: This paper discusses the hardware framework of the sensor measurement and control system and establishes the physical view of the system based on the analysis of the sensor system's functions and use cases. Based on the hardware structure described in the system component diagram, the design requirements of each major component of the sensor measurement and control system are discussed. References: [1] Kang Lee, Gao RX, Schneeman R. 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