Multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method

Abstract

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

Description

Technical Field

[0001] The present invention belongs to the technical field of sensor performance evaluation, and in particular relates to a multi-parameter ultra-high frequency sensor performance comprehensive evaluation method. Background Art

[0002] High-frequency sensor performance evaluation refers to the process of systematically and comprehensively evaluating and testing the various technical indicators of high-frequency sensors under specific application conditions to determine whether they meet design and application requirements. Evaluation typically includes key performance parameters such as sensitivity, linearity, response speed, frequency response range, resolution, stability, anti-interference capability, and reliability. Through precise instrumentation and specialized testing methods, it is possible to obtain performance data from sensors in actual operating environments and conduct in-depth analysis. High-frequency sensor performance evaluation is crucial for optimizing sensor design, improving product quality, and ensuring stable and reliable operation in fields such as communications, radar, and aerospace. It is an indispensable step in sensor research and development and application.

[0003] However, existing technologies have core defects in sensor performance evaluation, such as insufficient analysis of multi-dimensional coupling failure mechanisms and the lack of dynamic anti-interference testing: traditional methods rely on static indicator testing under a single environmental parameter, which makes it difficult to capture the synergistic effect of performance degradation caused by rapid electromagnetic interference and temperature and humidity changes; anti-interference evaluation mostly uses a fixed field strength threshold method, which cannot quantify the nonlinear impact of the dynamic increase in noise power on the false trigger probability; at the same time, there is a lack of joint analysis methods for time-frequency domain fidelity, resulting in unclear positioning of the causes of high-frequency signal distortion, and there is a lack of a dynamic weight association model between qualitative environmental adaptability indicators and quantitative parameters, making it difficult for the evaluation results to accurately guide iterative optimization. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-parameter UHF sensor performance comprehensive evaluation method in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a multi-parameter ultra-high frequency sensor performance comprehensive evaluation method, the method comprising the following steps:

[0006] S1: Determine the set of core performance parameters of the sensor, including sensitivity, linearity, dynamic range, response time, noise figure, frequency stability, anti-interference ability and environmental adaptability.

[0007] S2: Build a multi-dimensional test platform that integrates a standard signal generator, a tunable load module, a high-precision data acquisition system, and an environmental simulation chamber.

[0008] S3: Perform baseline performance testing by injecting a stepped UHF signal and recording the sensor output to generate a static characteristic curve.

[0009] S4: Introducing dynamic interference field simulation testing, using a multi-source controllable electromagnetic interference array to create a non-uniform interference environment, and monitoring sensor signal fidelity and false trigger rate in real time;

[0010] S5: Conduct time-varying temperature and humidity coupling experiments to evaluate the sensor parameter drift characteristics under conditions of rapid temperature cycling and humidity gradient changes.

[0011] S6: Collect multi-condition test data and establish a three-dimensional performance mapping matrix, and extract key performance weight factors through principal component analysis.

[0012] S7: Construct a fuzzy comprehensive evaluation model and convert the quantitative test data and qualitative environmental adaptability indicators into membership functions.

[0013] S8: Design a double-blind cross-validation experiment, and have an independent test group replicate the key test processes to verify the reliability of the method.

[0014] S9: Generate radar charts and normalized performance index reports, and provide targeted sensor optimization and iteration suggestions.

[0015] In a preferred embodiment, step S1 establishes a sensor performance evaluation benchmark system through multi-source feature fusion, focusing on balancing parameter redundancy and completeness. Using the physical model of ultra-high frequency sensors and industry standards as a benchmark, eight core parameters, including sensitivity and linearity, are extracted through failure mode analysis. Interference resistance is pre-screened through frequency-domain pulse group tolerance testing, and environmental adaptability is quantified based on statistical data from extreme operating conditions. Gray correlation analysis is used to eliminate duplicate parameters with correlations exceeding 0.85, ultimately forming a set of evaluation indicators that comprehensively characterize sensor characteristics while avoiding the curse of dimensionality, providing a theoretical framework for subsequent testing.

[0016] In a preferred embodiment, in step S2, the environmental simulation chamber uses layered temperature control technology to achieve rapid temperature changes from -40°C to 150°C, and the humidity control module achieves precise regulation of 10% to 98% RH through a combined system of ultrasonic atomization and condensation dehumidification. The electrical test unit has an embedded impedance matching network, which makes the output impedance of the standard signal generator continuously adjustable within the range of 50Ω to 1kΩ, ensuring realistic simulation of the sensor's operating conditions under different load conditions. The platform achieves microsecond-level synchronization of each subsystem through a fiber-optic timing system, laying the hardware foundation for multi-parameter collaborative testing.

[0017] In a preferred embodiment, in step S3, a signal generator generates a constant-amplitude signal in 0.1 GHz increments across the 1-6 GHz frequency range, while simultaneously performing an exponentially increasing amplitude sweep from 10 mVpp to 5 Vpp at each fixed frequency. During the test, the data acquisition system simultaneously records the sensor's output voltage, phase offset, and harmonic components. The amplitude-frequency response surface is fitted using the least-squares method, and the sensitivity frequency response curve and linearity envelope are then separated to establish a baseline performance database for the sensor in static operating mode.

[0018] In a preferred embodiment, in step S4, a controllable non-uniform electromagnetic interference environment is constructed and the performance degradation law of the sensor under extreme interference is quantified. The test is first based on a multi-source controllable electromagnetic interference array. By independently adjusting the frequency, amplitude and phase parameters of each interference source, three typical interference signals of sweep mode, pulse group mode and random modulation mode are generated within the sensor operating frequency band. The test platform adopts a spatial field intensity distribution optimization algorithm to ensure that the interference field forms a dynamically changing non-uniform field intensity gradient on the sensor surface. At the same time, the interference signal is superimposed with the standard ultra-high frequency test signal and injected into the sensor input through a synchronous trigger mechanism. During the test, the high-precision data acquisition system synchronously records the sensor output signal waveform, amplitude-frequency characteristics and abnormal pulse count with microsecond time resolution, and extracts the signal fidelity index through the real-time spectrum analysis module. The anti-interference robustness of the sensor is evaluated by combining the correlation curve between the interference field intensity and the number of false triggers.

[0019] In a preferred embodiment, in order to simulate a real complex electromagnetic environment, the test process introduces a dynamic interference field adaptive adjustment mechanism. The system uses a closed-loop feedback strategy to dynamically adjust the modulation depth and spatial distribution pattern of the interference source based on the real-time output characteristics of the sensor. For example, when the sensor has signal distortion in a certain frequency band, the interference array automatically enhances the narrowband interference power of the frequency band and expands the interference bandwidth, while reducing the field strength in other areas to focus the interference energy. The test data is mapped to a unified time-frequency-space coordinate system through a multi-dimensional parameter association model, and finally generates the sensor's anti-interference degradation surface and critical failure threshold under dynamic interference conditions. The dynamic evaluation formula of the signal fidelity is:

[0020]

[0021] in

[0022] T: total test duration, in seconds, covering the complete cycle of dynamic changes in the interference field;

[0023] S_out(t): The time domain waveform function of the actual output signal of the sensor, in volts;

[0024] S_ref(t): time domain waveform function of the ideal reference signal, generated by a standard signal generator;

[0025] ‖·‖2: L2 norm operator, used to calculate the time domain difference between the output signal and the reference signal;

[0026] max(S_ref(t)): the maximum amplitude of the reference signal, used to normalize the difference signal;

[0027] Δf(t): Real-time frequency deviation, defined as the absolute deviation between the instantaneous frequency of the sensor output signal and the reference frequency, in Hertz;

[0028] α: Frequency offset attenuation coefficient, a dimensionless constant calibrated through experiments, which controls the penalty intensity of high-frequency distortion.

[0029] The formula for the false trigger rate dynamic threshold model parameter is:

[0030]

[0031] in:

[0032] N_false: The number of false triggers of the sensor in an interference environment, counted by the abnormal pulse counter;

[0033] N_total: The total number of times the sensor should theoretically trigger during the test, determined by the number of injected standard signals;

[0034] P_noise: The noise power measured at the sensor input, in watts, measured in real time by a spectrum analyzer; σ_th 2 : The preset false trigger judgment threshold power, the critical noise tolerance set according to the sensor specifications;

[0035] β: nonlinear correction coefficient, obtained by fitting the interference-false trigger correlation experiment, which represents the accelerating effect of noise rise on false triggering.

[0036] In a preferred embodiment, in step S5, the temperature control system cycles through a triangular wave pattern at a rate of 15°C / min between -20°C and 85°C, while the humidity module simultaneously completes a gradient change from 30% to 90% to 30% RH over 30 minutes. The sensor operates continuously in the alternating environment, and the data acquisition system records zero drift, full-scale output fluctuation, and Q-value decay data every 10 seconds. By establishing a partial differential equation model for temperature, humidity, and performance parameters, the sensor's parameter degradation due to thermal hysteresis and moisture penetration is quantified.

[0037] In a preferred embodiment, step S6 utilizes tensor decomposition technology to process multi-condition test data. A hypercube data matrix is ​​constructed, with frequency, interference intensity, and environmental parameters serving as three-dimensional coordinate axes and sensor performance indicators serving as fourth-dimensional eigenvectors. High-order singular value decomposition is used to extract principal components, and the contribution of eigenvalues ​​in each dimension is calculated. It is determined that frequency stability contributes 37.2% to overall performance, while the weight of anti-interference capability increases exponentially with increasing temperature, revealing a multi-dimensional coupling mechanism underlying sensor performance degradation.

[0038] In a preferred embodiment, step S7 establishes a dynamic membership function and an adaptive weight allocation mechanism. For qualitative indicators such as environmental adaptability, a trapezoidal membership function is designed to convert expert scores into quantitative values ​​in the [0, 1] range. For quantitative parameters such as response time, a Gaussian membership function is used to eliminate dimensional differences. The model incorporates variable weight theory. When the measured value of a parameter approaches the failure threshold, its weight is automatically increased by 20% to 50%, ensuring that the evaluation results are highly targeted at sensor weaknesses. A fuzzy inference engine is used to output a comprehensive score and risk level warning.

[0039] In a preferred embodiment, in step S8, the original test group encrypted the sensor sample numbers and handed them over to the validation group. The validation group, however, replicated only the key steps S3-S7 according to the standard operating procedure, but without knowing the specific parameter thresholds. The two groups collected data using different brands of instruments, and the Kolmogorov-Smirnov test was used to compare the distribution consistency of the two data sets. A P-value greater than 0.05 for the false trigger rate test indicates that the method meets the reliability standard, effectively eliminating the impact of human bias on the evaluation system.

[0040] In a preferred embodiment, in step S9, the radar chart utilizes an asymmetric polar coordinate design, with the eight performance axes dynamically adjusted according to weighting factors, visually displaying the sensor's shortcomings in anti-interference capability and frequency stability. The normalized performance index combines the entropy weighting method with the TOPSIS algorithm to calculate the sensor's proximity to the ideal target. An optimization suggestion engine, based on a decision tree rule base, automatically triggers the push of improvement suggestions when a parameter score falls below 0.7. For example, it recommends optimizing the spiral resonator structure when sensitivity is insufficient.

[0041] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0042] 1. In this invention, through dynamic interference field simulation testing and an original fidelity-false trigger joint evaluation model, the method effectively breaks through the limitations of traditional static testing for transient interference response blind spots. The adaptive adjustment mechanism of the multi-source interference field can realistically simulate the spatiotemporal non-uniform characteristics of electromagnetic noise in industrial sites. Combined with the time-frequency domain composite evaluation algorithm, it not only quantifies the degree of signal distortion of the sensor under typical interference modes such as sweep frequency and pulse group, but also reveals the acceleration law of false operation caused by high-frequency noise coupling. This dynamic anti-interference evaluation capability enables designers to accurately locate the weaknesses of the sensor's electromagnetic shielding, optimize the front-end filtering circuit or signal processing algorithm in a targeted manner, and significantly improve the reliability of the product in strong interference scenarios such as radar detection and wireless communication.

[0043] 2. In this invention, by coupling environmental stress testing with principal component analysis, the interaction mechanism between environmental factors such as temperature shock and humidity penetration and electrical parameters is systematically analyzed, especially the frequency deviation degradation caused by thermal hysteresis, which provides guidance for packaging material selection and thermal design optimization. The fuzzy comprehensive evaluation model integrates quantitative test data with engineering experience thresholds, and the dynamic weight allocation mechanism ensures sensitive warning of early performance degradation. The resulting normalized performance index and three-dimensional radar map transform the complex multi-parameter test results into an intuitive improvement priority map, guiding the R&D team to quickly focus on key technology iterations to improve anti-interference capabilities and frequency stability, significantly shortening the development cycle of high-reliability sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the process principle of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] Example:

[0047] Reference Figure 1 ,

[0048] A multi-parameter UHF sensor performance comprehensive evaluation method comprises the following steps:

[0049] S1: Determine the set of core performance parameters of the sensor, including sensitivity, linearity, dynamic range, response time, noise figure, frequency stability, anti-interference ability and environmental adaptability.

[0050] S2: Build a multi-dimensional test platform that integrates a standard signal generator, a tunable load module, a high-precision data acquisition system, and an environmental simulation chamber.

[0051] S3: Perform baseline performance testing by injecting a stepped UHF signal and recording the sensor output to generate a static characteristic curve.

[0052] S4: Introducing dynamic interference field simulation testing, using a multi-source controllable electromagnetic interference array to create a non-uniform interference environment, and monitoring sensor signal fidelity and false trigger rate in real time;

[0053] S5: Conduct time-varying temperature and humidity coupling experiments to evaluate the sensor parameter drift characteristics under conditions of rapid temperature cycling and humidity gradient changes.

[0054] S6: Collect multi-condition test data and establish a three-dimensional performance mapping matrix, and extract key performance weight factors through principal component analysis.

[0055] S7: Construct a fuzzy comprehensive evaluation model and convert the quantitative test data and qualitative environmental adaptability indicators into membership functions.

[0056] S8: Design a double-blind cross-validation experiment, and have an independent test group replicate the key test processes to verify the reliability of the method.

[0057] S9: Generate radar charts and normalized performance index reports, and provide targeted sensor optimization and iteration suggestions.

[0058] In step S1, a sensor performance evaluation benchmark system was established through multi-source feature fusion, focusing on balancing parameter redundancy and completeness. Using the physical model of UHF sensors and industry standards as a benchmark, eight core parameters, including sensitivity and linearity, were extracted through failure mode analysis. Interference resistance was pre-screened through frequency-domain pulse group tolerance testing, and environmental adaptability was quantified based on statistical data from extreme operating conditions. Duplicate parameters with correlations exceeding 0.85 were eliminated through gray correlation analysis. Ultimately, a set of evaluation indicators was developed that comprehensively characterized sensor characteristics while avoiding the curse of dimensionality, providing a theoretical framework for subsequent testing.

[0059] In step S2, the environmental simulation chamber uses layered temperature control technology to achieve rapid temperature changes from -40°C to 150°C. The humidity control module achieves precise regulation of 10% to 98% RH through a combined ultrasonic atomization and condensation dehumidification system. The electrical test unit features an embedded impedance matching network, enabling continuous adjustment of the standard signal generator's output impedance from 50Ω to 1kΩ, ensuring realistic simulation of the sensor's operating conditions under varying load conditions. The platform achieves microsecond-level synchronization of its subsystems through a fiber-optic timing system, laying the hardware foundation for multi-parameter collaborative testing.

[0060] In step S3, the signal generator generates a constant-amplitude signal in 0.1GHz increments across the 1-6GHz frequency range, while simultaneously sweeping the amplitude exponentially from 10mVpp to 5Vpp at each fixed frequency. During the test, the data acquisition system simultaneously records the sensor's output voltage, phase offset, and harmonic components. Using the least-squares method, the amplitude-frequency response surface is fitted. The sensitivity frequency response curve and linearity envelope are then separated to establish a baseline performance database for the sensor in static operating mode.

[0061] In step S4, a controllable non-uniform electromagnetic interference environment is constructed and the performance degradation of the sensor under extreme interference is quantified. The test first uses a multi-source controllable electromagnetic interference array to independently adjust the frequency, amplitude, and phase parameters of each interference source to generate three typical interference signals within the sensor's operating frequency band: a sweep mode, a pulse group mode, and a random modulation mode. The test platform uses a spatial field intensity distribution optimization algorithm to ensure that the interference field forms a dynamically changing non-uniform field intensity gradient on the sensor surface. At the same time, a synchronous trigger mechanism is used to superimpose the interference signal and a standard ultra-high frequency test signal and inject it into the sensor input. During the test, a high-precision data acquisition system synchronously records the sensor output signal waveform, amplitude-frequency characteristics, and abnormal pulse counts with microsecond time resolution. A real-time spectrum analysis module extracts signal fidelity indicators, and the sensor's anti-interference robustness is evaluated based on the correlation curve between the interference field intensity and the number of false triggers.

[0062] In order to simulate a real and complex electromagnetic environment, the test process introduces a dynamic interference field adaptive adjustment mechanism. The system uses a closed-loop feedback strategy to dynamically adjust the modulation depth and spatial distribution pattern of the interference source based on the real-time output characteristics of the sensor. For example, when the sensor has signal distortion in a certain frequency band, the interference array automatically enhances the narrowband interference power of the frequency band and expands the interference bandwidth, while reducing the field strength in other areas to focus the interference energy. The test data is mapped to a unified time-frequency-space coordinate system through a multi-dimensional parameter association model, and finally generates the sensor's anti-interference degradation surface and critical failure threshold under dynamic interference conditions.

[0063] The dynamic evaluation formula of signal fidelity is:

[0064]

[0065] in

[0066] T: total test duration, in seconds, covering the complete cycle of dynamic changes in the interference field;

[0067] S_out(t): The time domain waveform function of the actual output signal of the sensor, in volts;

[0068] S_ref(t): time domain waveform function of the ideal reference signal, generated by a standard signal generator;

[0069] ‖·‖2: L2 norm operator, used to calculate the time domain difference between the output signal and the reference signal;

[0070] max(S_ref(t)): the maximum amplitude of the reference signal, used to normalize the difference signal;

[0071] Δf(t): Real-time frequency deviation, defined as the absolute deviation between the instantaneous frequency of the sensor output signal and the reference frequency, in Hertz;

[0072] α: Frequency offset attenuation coefficient, a dimensionless constant calibrated through experiments, which controls the penalty intensity of high-frequency distortion.

[0073] The formula for the false trigger rate dynamic threshold model parameter is:

[0074]

[0075] in:

[0076] N_false: The number of false triggers of the sensor in an interference environment, counted by the abnormal pulse counter;

[0077] N_total: The total number of times the sensor should theoretically trigger during the test, determined by the number of injected standard signals;

[0078] P_noise: The noise power measured at the sensor input, in watts, measured in real time by a spectrum analyzer.

[0079] σ_th 2 : The preset false trigger judgment threshold power, the critical noise tolerance set according to the sensor specifications;

[0080] β: nonlinear correction coefficient, obtained by fitting the interference-false trigger correlation experiment, which represents the accelerating effect of noise rise on false triggering.

[0081] In step S5, the temperature control system cycles through a triangular wave pattern at a rate of 15°C / min between -20°C and 85°C. Simultaneously, the humidity module completes a gradient change from 30% to 90% to 30% RH over 30 minutes. The sensor operates continuously in this alternating environment, with the data acquisition system recording zero drift, full-scale output fluctuation, and Q-value decay data every 10 seconds. By establishing a partial differential equation model for temperature, humidity, and performance parameters, the sensor's parameter degradation due to thermal hysteresis and moisture penetration is quantified.

[0082] In step S6, tensor decomposition technology is used to process the multi-condition test data. A hypercube data matrix is ​​constructed, with frequency, interference intensity, and environmental parameters as the three-dimensional coordinate axes and the sensor's performance indicators as the fourth-dimensional eigenvectors. High-order singular value decomposition is used to extract the principal components and calculate the contribution of the eigenvalues ​​of each dimension. It is determined that frequency stability contributes 37.2% to the overall performance, while the weight of anti-interference ability increases exponentially with increasing temperature, revealing a multi-dimensional coupling mechanism underlying sensor performance degradation.

[0083] In step S7, a dynamic membership function and adaptive weight allocation mechanism are established. For qualitative indicators such as environmental adaptability, a trapezoidal membership function is designed to convert expert scores into quantitative values ​​in the [0, 1] range. For quantitative parameters such as response time, a Gaussian membership function is used to eliminate dimensional differences. The model incorporates variable weight theory. When the measured value of a parameter approaches the failure threshold, its weight is automatically increased by 20% to 50%, ensuring that the evaluation results are highly targeted at sensor weaknesses. A fuzzy inference engine is used to output a comprehensive score and risk level warning.

[0084] In step S8, the original test group encrypted the sensor sample numbers and handed them over to the validation group. The validation group, however, replicated key steps S3-S7 according to the standard operating procedures, but without knowing the specific parameter thresholds. The two groups collected data using different brands of instruments, and the Kolmogorov-Smirnov test was used to compare the distribution consistency of the two data sets. When the P value of the false trigger rate test result was greater than 0.05, the method was considered reliable, effectively eliminating the impact of human bias on the evaluation system.

[0085] In step S9, the radar chart uses an asymmetric polar coordinate design. The scale range of the eight performance axes is dynamically adjusted according to weighting factors, visually displaying the shortcomings of the sensor's anti-interference ability and frequency stability. The normalized performance index combines the entropy weight method with the TOPSIS algorithm to calculate the sensor's proximity to the ideal target. The optimization suggestion engine, based on a decision tree rule base, automatically triggers the push of improvement suggestions when a parameter score falls below 0.7. For example, it recommends optimizing the spiral resonator structure when sensitivity is insufficient.

[0086] From the above we can know:

[0087] In this invention, through dynamic interference field simulation testing and an original fidelity-false trigger joint evaluation model, the method effectively breaks through the limitations of traditional static testing for transient interference response blind spots. The adaptive adjustment mechanism of the multi-source interference field can realistically simulate the spatiotemporal non-uniform characteristics of electromagnetic noise in industrial sites. Combined with the time-frequency domain composite evaluation algorithm, it not only quantifies the degree of signal distortion of the sensor under typical interference modes such as sweep frequency and pulse group, but also reveals the acceleration law of false operation caused by high-frequency noise coupling. This dynamic anti-interference evaluation capability enables designers to accurately locate the weaknesses of the sensor's electromagnetic shielding, optimize the front-end filtering circuit or signal processing algorithm in a targeted manner, and significantly improve the reliability of the product in strong interference scenarios such as radar detection and wireless communication.

[0088] In this invention, by coupling environmental stress testing with principal component analysis, the interaction mechanism between environmental factors such as temperature shock and humidity penetration and electrical parameters is systematically analyzed, especially the frequency deviation degradation caused by thermal hysteresis, which provides guidance for packaging material selection and thermal design optimization. The fuzzy comprehensive evaluation model integrates quantitative test data with engineering experience thresholds, and the dynamic weight allocation mechanism ensures sensitive warning of early performance degradation. The resulting normalized performance index and three-dimensional radar map transform the complex multi-parameter test results into an intuitive improvement priority map, guiding the R&D team to quickly focus on key technology iterations to improve anti-interference capabilities and frequency stability, significantly shortening the development cycle of high-reliability sensors.

[0089] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0090] The above description is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-parameter UHF sensor performance comprehensive evaluation method, characterized by: The method comprises the following steps: S1: Determine the set of core performance parameters of the sensor, including sensitivity, linearity, dynamic range, response time, noise figure, frequency stability, anti-interference ability and environmental adaptability; S2: Build a multi-dimensional test platform that integrates a standard signal generator, a tunable load module, a high-precision data acquisition system, and an environmental simulation chamber; S3: Perform baseline performance testing by injecting a step-by-step increasing UHF signal and recording the sensor output to generate a static characteristic curve; S4: Introducing dynamic interference field simulation testing, using a multi-source controllable electromagnetic interference array to create a non-uniform interference environment, and monitoring sensor signal fidelity and false trigger rate in real time; S5: Conduct time-varying temperature and humidity coupling experiments to evaluate the sensor parameter drift characteristics under conditions of rapid temperature cycling and humidity gradient changes; S6: Collect multi-condition test data and establish a three-dimensional performance mapping matrix, and extract key performance weight factors through principal component analysis; S7: Construct a fuzzy comprehensive evaluation model and convert the quantitative test data and qualitative environmental adaptability indicators into membership functions; S8: Design a double-blind cross-validation experiment, and have an independent test group replicate the key test processes to verify the reliability of the method; S9: Generate radar charts and normalized performance index reports, and provide targeted sensor optimization and iteration suggestions.

2. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S1, a sensor performance evaluation benchmark system is established through a multi-source feature fusion method, focusing on solving the balance problem between parameter redundancy and completeness; based on the physical model of the ultra-high frequency sensor and industry standards, eight core parameters such as sensitivity and linearity are extracted in combination with failure mode analysis. Among them, the anti-interference ability is pre-screened through a frequency domain pulse group tolerance test, and the environmental adaptability is quantified based on extreme working condition statistical data; repeated parameters with correlations higher than 0.85 are eliminated through grey correlation analysis, and finally a set of evaluation indicators is formed that can comprehensively characterize the sensor characteristics and avoid the dimensionality disaster, providing a theoretical framework for subsequent tests.

3. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S2, the environmental simulation chamber adopts layered temperature control technology to achieve rapid temperature changes from -40°C to 150°C, and the humidity control module achieves precise adjustment of 10% to 98% RH through an ultrasonic atomization and condensation dehumidification composite system; the electrical test unit has an embedded impedance matching network, so that the output impedance of the standard signal generator is continuously adjustable in the range of 50Ω to 1kΩ, ensuring the simulation of the real working conditions of the sensor under different load conditions.

4. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S3, the signal generator generates a signal of constant amplitude in the frequency range of 1-6 GHz in 0.1 GHz steps, and performs an exponentially increasing sweep of the amplitude from 10 mVpp to 5 Vpp at each fixed frequency point; During the test, the data acquisition system synchronously records the sensor output voltage, phase offset and harmonic components, fits the frequency-amplitude response surface through the least squares method, and then separates the sensitivity frequency response curve and linearity envelope to establish a benchmark performance database for the sensor in static working mode.

5. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S4, the test is first based on a multi-source controllable electromagnetic interference array, and by independently adjusting the frequency, amplitude and phase parameters of each interference source, three typical interference signals of sweep mode, pulse group mode and random modulation mode are generated within the operating frequency band of the sensor; the test platform adopts a spatial field intensity distribution optimization algorithm to ensure that the interference field forms a dynamically changing non-uniform field intensity gradient on the surface of the sensor, and at the same time, the interference signal is superimposed on the standard ultra-high frequency test signal and injected into the sensor input end through a synchronous trigger mechanism; during the test, the high-precision data acquisition system synchronously records the sensor output signal waveform, amplitude-frequency characteristics and abnormal pulse count with microsecond time resolution, and extracts signal fidelity indicators through a real-time spectrum analysis module, and evaluates the anti-interference robustness of the sensor in combination with the correlation curve between the interference field intensity and the number of false triggers; The dynamic evaluation formula of signal fidelity is: in T: total test duration, in seconds, covering the complete cycle of dynamic changes in the interference field; S_out(t): The time domain waveform function of the actual output signal of the sensor, in volts; S_ref(t): time domain waveform function of the ideal reference signal, generated by a standard signal generator; ‖·‖2: L2 norm operator, used to calculate the time domain difference between the output signal and the reference signal; max(S_ref(t)): the maximum amplitude of the reference signal, used to normalize the difference signal; Δf(t): Real-time frequency deviation, defined as the absolute deviation between the instantaneous frequency of the sensor output signal and the reference frequency, in Hertz; α: frequency offset attenuation coefficient, a dimensionless constant calibrated experimentally, which controls the penalty intensity of high-frequency distortion; The formula for the false trigger rate dynamic threshold model parameter is: in: N_false: The number of false triggers of the sensor in an interference environment, counted by the abnormal pulse counter; N_total: The total number of times the sensor should theoretically trigger during the test, determined by the number of injected standard signals; P_noise: The noise power measured at the sensor input, in watts, measured in real time by a spectrum analyzer. σ_th 2 : The preset false trigger judgment threshold power, the critical noise tolerance set according to the sensor specifications; β: nonlinear correction coefficient, obtained by fitting the interference-false trigger correlation experiment, which represents the accelerating effect of noise rise on false triggering.

6. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S5, the temperature control system performs a triangular wave cycle in the range of -20°C to 85°C at a rate of 15°C / min, and the humidity module completes a gradient change from 30% to 90% to 30% RH within 30 minutes; the sensor is placed in an alternating environment and operates continuously, and the data acquisition system records zero point drift, full-scale output fluctuation, and Q value attenuation data every 10 seconds; by establishing a partial differential equation model of temperature-humidity-performance parameters, the parameter degradation law caused by the thermal hysteresis effect and moisture penetration of the sensor is quantified.

7. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S6, tensor decomposition technology is used to process multi-condition test data; frequency, interference intensity, and environmental parameters are used as three-dimensional coordinate axes, and various sensor performance indicators are used as fourth-dimensional eigenvectors to construct a hypercube data matrix; principal components are extracted through high-order singular value decomposition, and the contribution rate of the eigenvalues ​​of each dimension is calculated. It is determined that the weight of frequency stability to the overall performance accounts for 37.2%, while the weight of anti-interference ability increases exponentially with increasing temperature, revealing the multi-dimensional coupling mechanism of sensor performance degradation.

8. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S7, a dynamic membership function and an adaptive weight distribution mechanism are established; for qualitative indicators such as environmental adaptability, a trapezoidal membership function is designed to convert expert scores into quantitative values ​​in the interval [0,1]; for quantitative parameters such as response time, a Gaussian membership function is used to eliminate dimensional differences; The model introduces variable weight theory. When the measured value of a parameter approaches the failure threshold, its weight automatically increases by 20% to 50%, ensuring that the evaluation results are highly targeted at the weak links of the sensor.

9. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S8, the original test group encrypted the sensor sample number and handed it over to the verification group. The latter reproduced the key steps S3-S7 according to the standard operating procedures under the condition of unknown specific parameter thresholds; the two groups of personnel used instruments of different brands to collect data respectively, and compared the distribution consistency of the two groups of data through the Kolmogorov-Smirnov test; when the P value of the false trigger rate test result was greater than 0.05, it was determined that the reliability of the method met the standard, effectively eliminating the influence of human preset bias on the evaluation system.

10. The method for comprehensive performance evaluation of a multi-parameter ultra-high frequency sensor according to claim 1, wherein: In step S9, the radar chart adopts an asymmetric polar coordinate design, and the scale range of the eight performance axes is dynamically adjusted according to the weight factors, intuitively showing the shortcomings of the sensor's anti-interference ability and frequency stability; The normalized performance index combines the entropy weight method and the TOPSIS algorithm to calculate the proximity of the sensor to the ideal target; the optimization suggestion engine is based on a decision tree rule library. When a parameter score is lower than 0.7, it automatically triggers the push of improvement plans. For example, when the sensitivity is insufficient, it recommends a spiral resonator structure optimization strategy.

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