Array type temperature magnetic field cooperative sensing device and method
By using an array-type temperature and magnetic field co-sensing device, and utilizing a multi-material component array of sensitive resistors and a machine learning model, the problem of narrow detection range and small temperature range of magnetic field sensors in deep space exploration has been solved, realizing the miniaturization of the sensor and efficient co-sensing of magnetic field and temperature.
CN120847689APending Publication Date: 2025-10-28UNIV OF SCI & TECH BEIJING
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Patent Information
- Application Number
- CN202510701920.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-28
- Publication Date
- 2025-10-28
AI Technical Summary
Technical Problem
Existing magnetic field sensors have a narrow detection range, a small operating temperature range, a large size, and a slow response speed, which cannot meet the needs of deep space exploration.
Method used
An array-type temperature and magnetic field co-sensing device is adopted, which utilizes an array of sensitive resistors with different material compositions and combines them with a machine learning model to achieve co-sensing of magnetic field scalar and temperature.
Benefits of technology
It enables the coordinated measurement of magnetic field and temperature over a wide temperature and magnetic field range. The sensor is small in size and requires no additional calibration, making it suitable for scenarios such as space exploration and unmanned spacecraft.
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Abstract
The invention discloses an array-type temperature magnetic field cooperative sensing device and method, and relates to the technical field of temperature magnetic field sensors, and the device comprises an array sensing unit which is composed of a plurality of sensitive resistor components of different material components in an array arrangement manner, and an upper computer. The sensitive resistor component comprises a component with a colossal magnetoresistance effect; and the upper computer is used for acquiring a resistance value data set of the array sensing unit in the scene to be detected, and inputting the resistance value data set as input data into the trained monitoring learning model to obtain a magnetic field scalar and a temperature value of the scene to be detected. The technical problems of narrow detection range and low detection efficiency in the prior art are solved.
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