电阻式传感器可用于温度、压力、位移、应变、磁场等多种物理量的测量。随着物联网等技术的快速发展，各行业中电阻式传感器的感知和物联需求激增，传统的为每种传感器定制测量节点的方法难以适应智能物联系统的快速构建需求。为此，本论文对电阻式传感器的自适应测量技术及其智能物联节点的设计方法展开研究，主要研究内容包括：针对不同结构的电阻式传感器的统一接入和自动测量需求，提出了一种电阻式传感器自适应测量接口模型。设计了传感器接入状态及线制的自动识别方法。研究了2/3/4线制单体式电阻传感器以及6种主流结构电桥式电阻传感器接入后的接口自动配置及测量方法。实现了以一个四端子接口兼容接入上述所有结构的电阻式传感器，消除了传感器引线电阻以及激励源的误差和波动对测量精度的影响，解决了1/4桥等电桥式电阻传感器测量时的非线性问题。基于该模型构建了实验电路对模型的上述特性进行了验证与评估。为了满足不同种类和型号电阻式传感器的阻值测量需求，基于提出的接口模型对阻值自适应测量技术与接口自动校准方法展开了研究。设计了一种与切换开关导通电阻无关的开尔文型多参考切换阵列。提出了一种基于参考电阻、激励电流、增益多参数协同调节的阻值自适应测量方法，解决了测量范围与测量分辨力难以兼顾的问题。构建了接口测量链路误差模型，提出了一种接口自动校准方法，有效提升了接口的阻值测量精度并降低了校准难度。研究了电阻式传感器自适应测量智能物联节点的关键技术并研制了节点实物。构建了节点的软硬件结构，实现了数据和功能接口的标准化以及软硬件结构的模块化。设计了一种基于DMA的节点MCU与网络接口器件间的信息映射方法，有效降低了CPU的占用率。提出了一种基于参数化模板的多类型传感器通用信息化方法，解决了不同传感器的驱动软件重复开发问题。最后，基于本论文节点构建了一个多类型传感器智能物联实验系统，给出了节点的配置方法及系统的具体设计流程。通过实验评估了节点对不同结构、不同种类、不同型号的传感器的自适应测量能力和智能物联能力，验证了系统的鲁棒性，证明了节点的应用价值。

Resistive sensors can be used to measure various physical quantities such as temperature, pressure, displacement, strain, and magnetic field.With the rapid development of technologies such as the Internet of Things, the sensing and networking needs of resistive sensors in various industries have surged. The traditional method of customizing measurement nodes for each sensor is difficult to adapt to the rapid construction requirements of intelligent IoT systems. To this end, this paper studies the adaptive measurement technology of resistive sensors and the design method of intelligent IoT nodes. The main research contents include:Aiming at the unified access and automatic measurement requirements of resistive sensors of different structures, an adaptive measurement interface model of resistive sensors is proposed. An automatic identification method of sensor access state and lead form is designed. Based on this model, the interface automatic configuration and measurement methods of 2/3/4-wire single-element resistive sensors and 6 kinds of bridge-type resistive sensors with common structures are studied. The resistive sensors of all the above structures realize compatible access through the four-terminal interface of the model. The influence of sensor lead resistance and excitation source errors and fluctuations on measurement accuracy is eliminated. The model solves the nonlinear problem when measuring bridge-type resistive sensors such as 1/4 bridge. The experimental circuit of the model is constructed, and the above characteristics of the model are verified and evaluated.In order to meet the resistance measurement requirements of different types of resistive sensors, based on the proposed interface model, the resistance adaptive measurement technology and the interface automatic calibration method are studied. A Kelvin-type multi-reference switching array is designed which is independent of the on-resistance of the switch. An adaptive resistance measurement method based on multi-parameter coordinated adjustment of reference resistance, excitation current and gain is proposed. It solves the problem that the measurement range and measurement resolution are difficult to balance. The error model of the interface measurement link is constructed, and an automatic interface calibration method is proposed, which effectively improves the resistance measurement accuracy of the interface and reduces the difficulty of calibration.The key technology of the "resistive sensor adaptive measurement intelligent IoT node" is studied and the actual node is developed. The software and hardware structure of the node is constructed, the standardization of data and function interfaces and the modularization of the software and hardware structure are realized.A DMA-based information mapping method between node MCU and network interface device is designed, which effectively reduces CPU occupancy. A general informatization method for multi-type sensors based on parameterized templates is proposed, which solves the problem of repeated development of driver software for different sensors.Finally, a multi-type sensor intelligent IoT experimental system is constructed based on the nodes in this paper. The configuration method of nodes and the specific design process of the system are given. Through experiments, the adaptive measurement capability and intelligent IoT capability of the node to sensors of different structures, types and models are evaluated. The robustness of the system is verified and the application value of the node is proved.