## 5 Conclusions  
This paper presents a multi-parameter sensor based on SAWS. Finite element analysis models were established for both the SAWS component and the magnetostrictive material component. Specific dimensions and structures were designed for the MP-SAWS model. The resonant modes of the MP-SAWS model were studied. Frequency-domain analysis was conducted on the SAWS component. The characteristic curves, including admittance curves, S11 parameter curves, and mechanical quality factor curves, were analyzed. The resonant frequency was found to be 1.8852 GHz, with an anti-resonant frequency of 1.9610 GHz. The peak values of the admittance curve and S11 parameter curve occurred at the resonant and anti-resonant frequencies, and the mechanical quality factor reached 10\({}^{21}\). Unidirectional magnetic field analysis was performed on the magnetostrictive component, revealing various relationships between the magnetic field, magnetostrictive length, inter-material stress, and SAWS resonant frequency. To ensure the accuracy and noise resistance of finite element simulations, electrode-voltage boosting was performed through noise testing. Thermal and stress analyses were also conducted, demonstrating the sensing capabilities of MP-SAWS to temperature and stress. Under stress, the offset of the MP-SAWS resonant frequency showed a discernible inverse correlation with stress changes. Similarly, the offset of the MP-SAWS resonant frequency showed an inverse correlation with temperature changes. The obtained sensing relationship between stress and temperature provides support for medical diagnosis and treatment while also allowing for the calibration of magnetic field sensing results in various magnetic field-testing environments. Multi-parameter measurements were realized via the control variable method. The device's sensitivities to stress, temperature, and magnetic field were 10.426 kHz/MPa, 68.211 kHz/K, and 0.171 Hz/uT, respectively. By coupling the magnetostrictive material with the SAWS, the sensor can be used for magnetic field measurements. During magnetic field scanning,  
Figure 12: Detailed design of the MP-SAWS array. (**a**) Distribution diagram of the MP-SAWS sensing units. (**b**) Design of MP-SAWS dimensions and number of units. the resonant frequency of the MP-SAWS decreased as the magnetic flux increased, and the sensing curve exhibited a non-linear relationship. When mapping the MCG, a constant static magnetic field was set, and the sensing curve was simplified to a linear relationship. The simulation model was validated by comparing the simulated MCG data of healthy individuals and patients with existing database data. The results showed that the designed magnetic field sensor and the obtained sensing relationship could accurately sense the MCG with good performance. However, current MP-SAWS models exhibit certain limitations. In practical applications, an array of the MP-SAWS is planned to be used in a signal detection system, but due to computational constraints, the actual array simulation model of MP-SAWS has not been constructed. Furthermore, specific design and data processing algorithms are required for the construction of a two-dimensional MCG. In two-dimensional simulations, the impact of multi-directional magnetic fields on the device should be considered to better replicate real-world scenarios. The differential algorithm proposed in this paper should also be tested in practice. AI tools may be needed to assist in adjusting numerical weights for generating two-dimensional MCG. In future research and work, the device fabrication processes and manufacturing of MP-SAWS will be specifically studied based on the simulation results presented herein. Furthermore, to obtain better data in practical testing, future designs will focus on enhancing the sensitivity of cardiac magnetic field measurements.