Moisture detection system based on multi-cavity compensation algorithm

Abstract

The invention discloses a moisture detection system based on a multi-cavity compensation algorithm, a main detection cavity, a reference compensation cavity and a temperature compensation cavity are arranged, a three-dimensional compensation algorithm is operated in combination with an operation control unit, and the technical problem that an existing moisture detection system is low in measurement precision in a temperature fluctuation environment is effectively solved. The temperature compensation cavity is matched with the distributed temperature sensor and the thermal drift compensation module to correct frequency deviation caused by temperature in real time; the main detection cavity and the reference compensation cavity eliminate environmental noise interference through comparison of frequency difference and quality factor difference; the computing control unit adopts a dynamic frequency shift compensation algorithm and combines with the environment monitoring module to dynamically adjust emission parameters and suppress external interference. According to the overall scheme, the stability and accuracy of the system in a complex industrial environment are improved, high-precision online moisture detection is realized, and the method has a good application prospect.

Description

Technical Field

[0001] This invention belongs to the field of moisture detection technology, specifically relating to a moisture detection system based on a multi-cavity compensation algorithm. Background Technology

[0002] In industrial production, the moisture content of process materials is a crucial indicator for controlling production processes and ensuring product quality. Traditional moisture detection methods typically rely on offline sampling and analysis. This approach is not only time-consuming but also fails to reflect real-time changes in moisture content on the production line, making it difficult to meet the demands of modern factories for rapid and accurate detection. In recent years, various online moisture analysis technologies and equipment have emerged, but they generally suffer from the following problems:

[0003] For example, the method described in patent CN115096915A measures the moisture content in a sample using a single-frequency microwave signal. However, this method is susceptible to the conductivity of the material, leading to significant measurement errors. Many existing online moisture detection systems lack effective temperature compensation mechanisms. For instance, the microwave moisture meter system disclosed in patent CN203216868U suffers from significant deviations in measurement data due to resonant frequency shift caused by thermal drift in environments with large temperature variations. Similarly, the improved infrared moisture measuring device mentioned in patent CN221007327U, while capable of detecting surface moisture, cannot effectively detect internal moisture due to the limited penetration capability of infrared spectra and is easily affected by environmental factors (such as sunlight and dust).

[0004] The main problem faced by existing moisture detection systems in complex industrial environments is measurement error caused by temperature fluctuations. Especially in scenarios with high temperature, high humidity, or large temperature differences, the lack of an effective temperature compensation mechanism leads to inaccurate moisture detection results, affecting production process control and product quality. Summary of the Invention

[0005] The purpose of this invention is to provide a moisture detection system based on a multi-cavity compensation algorithm. By using the multi-cavity compensation algorithm and temperature compensation mechanism, the measurement error problem of existing moisture detection systems under temperature fluctuations is solved, and high-precision and high-stability moisture detection is achieved.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a moisture detection system based on a multi-cavity compensation algorithm, comprising: a main detection cavity, a reference compensation cavity, a temperature compensation cavity, and a computational control unit; wherein, the main detection cavity is a sensing unit for the interaction between sample moisture and microwave field, its cavity shape is a cylindrical resonant cavity, and its inner wall is coated with a conductive layer, reflecting changes in moisture content through resonant frequency shift and quality factor decrease; the reference compensation cavity is a fully enclosed structure, filled with inert gas, and is processed in pairs with the main detection cavity, its frequency being similar to that of the main detection cavity to eliminate inherent drift; the temperature sensors of the temperature compensation cavity are distributed throughout the cavity. The system monitors the temperature within the cell and corrects for temperature-induced resonant frequency shifts using a thermal drift compensation module. The computational control unit combines measurement data from the main detection cavity, reference compensation cavity, and temperature compensation cavity to run a three-dimensional compensation algorithm and output accurate moisture values. The main detection cavity and reference compensation cavity work together to eliminate environmental noise interference through frequency difference and quality factor difference comparisons. The temperature compensation cavity, in conjunction with the computational control unit, corrects thermal drift errors in real time, ensuring measurement accuracy under temperature fluctuations. The computational control unit optimizes the signal transmission frequency using a dynamic frequency shift compensation algorithm and dynamically adjusts transmission parameters in conjunction with a real-time environmental monitoring module to suppress external interference.

[0007] Preferably, the temperature coefficient of the main detection cavity is negative in the uncompensated state, and dynamic frequency compensation is achieved through the following steps: real-time acquisition of the resonant frequency shift and temperature of the main detection cavity; calculation of the theoretical frequency shift value according to the preset temperature-frequency relationship model; comparison of the actual measured frequency shift with the theoretical value to generate a compensation signal; and adjustment of the signal transmission frequency through a dynamic frequency shift algorithm to offset the temperature drift and reduce temperature sensitivity.

[0008] Preferably, the computation control unit performs multi-parameter fusion calculations in conjunction with the three-dimensional calibration matrix, specifically including the following steps: constructing a three-dimensional calibration matrix, wherein the frequency difference is the frequency difference between the main detection cavity and the reference cavity, the quality factor difference is the quality factor difference between the main detection cavity and the reference cavity, and the temperature is the temperature difference of the temperature compensation cavity; establishing a mapping relationship between the moisture value and the calibration matrix using the partial least squares regression method; calibrating the calibration coefficients using standard sample data, updating the calibration coefficients in real time, and optimizing the calibration parameters through a machine learning model.

[0009] Preferably, the electromagnetic field disturbance characteristics of the main detection cavity are related to the moisture content. Cross-validation is performed through multi-band resonance analysis, specifically including the following steps: selecting at least two independent frequency bands, transmitting radio frequency signals of different frequencies to the main detection cavity respectively, measuring the resonant frequency shift and quality factor decrease of the main detection cavity in each frequency band; integrating the multi-band measurement results through a cross-validation algorithm to eliminate single-band measurement errors and output a comprehensive moisture value.

[0010] Preferably, the operation and control unit optimizes the signal transmission frequency through a dynamic frequency shift compensation algorithm, specifically including the following steps: real-time monitoring of environmental parameters and the resonant frequency shift of the main detection cavity; calculating the optimal transmission frequency under the current interference based on a preset interference-frequency response model; dynamically adjusting the signal transmission power through a numerically controlled attenuator to ensure that the signal-to-noise ratio at the transmission frequency is maximized; and feeding back the optimized transmission parameters to the signal transmission unit to suppress the influence of external interference on measurement accuracy.

[0011] Preferably, the temperature compensation cavity is combined with the computing control unit, and the resonant frequency shift caused by temperature is corrected by the thermal drift compensation module. Specifically, the following steps are included: real-time acquisition of temperature sensor data at four points in the temperature compensation cavity, calculation of the average temperature of the cavity; prediction of the theoretical resonant frequency shift according to the preset temperature-resonant frequency relationship model; comparison of the actual measured resonant frequency shift with the theoretical value, generation of residuals, correction of the residuals by a polynomial model, output of the corrected resonant frequency shift, and input to the computing control unit for moisture calculation.

[0012] Preferably, the operation and control unit integrates data from the main detection cavity, the reference compensation cavity, and the temperature compensation cavity through a multi-cavity compensation algorithm module, specifically including the following steps: collecting the resonant frequency shift and quality factor decrease of the main detection cavity, the resonant frequency shift and quality factor decrease of the reference compensation cavity, and the resonant frequency shift and temperature of the temperature compensation cavity; calculating the final moisture value through the multi-cavity compensation algorithm, wherein the calibration coefficient is obtained by standard sample calibration.

[0013] Preferably, it also includes a data encryption module to perform end-to-end encrypted transmission of measurement data, specifically including the following steps: generating an encryption key in the operation and control unit, packaging the measurement data into a data packet, encrypting the data packet using an encryption algorithm to generate an encrypted data packet, and transmitting the encrypted data packet to a remote server through a wireless communication protocol to ensure data security and privacy protection.

[0014] Preferably, it also includes a remote monitoring module, which realizes remote device status monitoring and parameter adjustment through a wireless communication protocol. Specifically, it includes the following steps: configuring a remote monitoring interface in the computing control unit, sending device status information to a remote server through a wireless communication protocol, receiving control commands issued by the remote server, parsing the control commands, and updating the operating parameters of the signal transmitting unit or the computing control unit.

[0015] Preferably, it also includes a user interaction module that provides a visual interface via a multi-touch display.

[0016] Technical effects and advantages of the present invention: The moisture detection system based on a multi-cavity compensation algorithm proposed in this invention has the following advantages compared with the prior art:

[0017] This invention effectively solves the technical problem of low measurement accuracy in existing moisture detection systems under fluctuating temperature environments by setting up a main detection cavity, a reference compensation cavity, and a temperature compensation cavity, and combining this with a three-dimensional compensation algorithm run by a computational control unit. The temperature compensation cavity, in conjunction with a distributed temperature sensor and a thermal drift compensation module, corrects frequency shifts caused by temperature in real time. The main detection cavity and the reference compensation cavity eliminate environmental noise interference by comparing frequency differences and quality factor differences. The computational control unit employs a dynamic frequency shift compensation algorithm, combined with an environmental monitoring module, to dynamically adjust transmission parameters and suppress external interference. The overall solution improves the stability and accuracy of the system in complex industrial environments, achieving high-precision online moisture detection and showing promising application prospects. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system structure principle of the present invention;

[0019] Figure 2 This is a schematic diagram illustrating the analytical calculation principle of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1

[0022] This embodiment provides a moisture detection system based on a multi-cavity compensation algorithm. Its working principle is to detect moisture content based on the electromagnetic field disturbance characteristics of the TM010 cylindrical resonant cavity.

[0023] When an electromagnetic wave signal of a certain frequency forms a standing wave within the cavity, water molecules in the analyte (water's dielectric constant ε≈78) exhibit a significant dielectric polarization effect compared to dry materials (ε≈2-5). This polarization leads to changes in two measurable parameters:

[0024] First, the resonant frequency shift (Δf): has a non-linear relationship with the moisture content, following the formula Δf / f0=(ε'-1) / 2ε'·V s / V0, where V s / V0 represents the percentage of the cavity volume occupied by the sample.

[0025] Second, the quality factor (ΔQ) -1 The decrease primarily reflects dielectric loss, satisfying Q.-1 =tanhδ=(ε”+σ / ωε0) / ε', where σ is the conductivity. The frequency signal generated by the radio frequency signal transmitting unit excites the TM010 mode resonance in the main detection cavity through the antenna. The polar rotation of water molecules in the test material causes the resonant frequency to shift and the Q value to decay. The reference cavity provides a background reference in the sample-free environment, eliminating the inherent drift of the system.

[0026] The temperature compensation cavity monitors thermal deformation in real time and corrects thermal drift errors using a third-order polynomial model. After the RF signal receiving unit detects changes in resonant parameters, the signal processing unit uses I / Q demodulation to extract phase and amplitude information. The computation and control unit performs multi-parameter fusion calculations using a three-dimensional calibration matrix (including frequency difference Δf, Q-value difference ΔQ, and temperature ΔT), and finally outputs the moisture value using a Beer-Lambert correction model. Through triple cavity data cross-verification and an adaptive Kalman filter algorithm, the temperature sensitivity of the traditional single-cavity system is reduced by 50 times, achieving a stability of ±5ppm / ℃ and a detection limit of 50ppm (3σ).

[0027] The moisture detection system based on the multi-cavity compensation algorithm includes: a main detection cavity, a reference compensation cavity, a temperature compensation cavity, and a calculation and control unit;

[0028] The main detection cavity is a sensing unit for the interaction between sample moisture and microwave field. It is a cylindrical resonant cavity with a conductive layer coated on its inner wall. Changes in moisture content are reflected by resonant frequency shift and quality factor decrease. Furthermore, the temperature coefficient of the main detection cavity is negative in its uncompensated state. Dynamic frequency compensation is achieved through the following steps: real-time acquisition of the resonant frequency shift and temperature of the main detection cavity; calculation of the theoretical frequency shift value based on a preset temperature-frequency relationship model; comparison of the actual measured frequency shift with the theoretical value to generate a compensation signal; and adjustment of the signal transmission frequency using a dynamic frequency shift algorithm to offset temperature drift and reduce temperature sensitivity.

[0029] The reference compensation cavity is a fully enclosed structure filled with inert gas. It is machined in pairs with the main detection cavity, and its frequency is similar to that of the main detection cavity to eliminate inherent drift.

[0030] Temperature sensors are distributed throughout the temperature compensation cavity to monitor the temperature, and the resonant frequency shift caused by temperature is corrected by a thermal drift compensation module.

[0031] The computational control unit combines measurement data from the main detection chamber, the reference compensation chamber, and the temperature compensation chamber to run a three-dimensional compensation algorithm and output accurate moisture values. Furthermore, the computational control unit performs multi-parameter fusion calculations using a three-dimensional calibration matrix, specifically including the following steps: constructing a three-dimensional calibration matrix, where the frequency difference is the frequency difference between the main detection chamber and the reference chamber, the quality factor difference is the quality factor difference between the main detection chamber and the reference chamber, and the temperature is the temperature difference in the temperature compensation chamber; establishing a mapping relationship between moisture values ​​and the calibration matrix using partial least squares regression; calibrating calibration coefficients using standard sample data, updating the calibration coefficients in real time, and optimizing calibration parameters using a machine learning model.

[0032] The main detection cavity and the reference compensation cavity work together to eliminate environmental noise interference by comparing frequency difference and quality factor difference. Furthermore, the electromagnetic field disturbance characteristics of the main detection cavity are related to the moisture content. This is cross-validated through multi-band resonance analysis, specifically including the following steps: selecting at least two independent frequency bands and transmitting radio frequency signals of different frequencies to the main detection cavity respectively, measuring the resonant frequency shift and quality factor decrease of the main detection cavity in each frequency band; integrating the multi-band measurement results through a cross-validation algorithm to eliminate single-band measurement errors and output a comprehensive moisture value.

[0033] The temperature compensation cavity is combined with the computational control unit to correct thermal drift errors in real time, ensuring the measurement accuracy of the system under temperature fluctuations. Furthermore, the temperature compensation cavity, combined with the computational control unit, corrects the resonant frequency shift caused by temperature through a thermal drift compensation module. Specifically, this includes the following steps: real-time acquisition of temperature sensor data from four points in the temperature compensation cavity to calculate the average temperature of the cavity; prediction of the theoretical resonant frequency shift based on a preset temperature-resonant frequency relationship model; comparison of the actual measured resonant frequency shift with the theoretical value to generate a residual; correction of the residual using a polynomial model; output of the corrected resonant frequency shift; and input to the computational control unit for moisture calculation.

[0034] The operational control unit optimizes the signal transmission frequency through a dynamic frequency shift compensation algorithm and dynamically adjusts the transmission parameters in conjunction with a real-time environmental monitoring module to suppress external interference. Furthermore, the operational control unit optimizes the signal transmission frequency through the dynamic frequency shift compensation algorithm, specifically including the following steps: real-time monitoring of environmental parameters and the resonant frequency shift of the main detection cavity; calculating the optimal transmission frequency under the current interference based on a preset interference-frequency response model; dynamically adjusting the signal transmission power through a digitally controlled attenuator to ensure maximum signal-to-noise ratio at the transmission frequency; and feeding back the optimized transmission parameters to the signal transmission unit to suppress the impact of external interference on measurement accuracy.

[0035] In addition, the operation and control unit integrates the data from the main detection cavity, the reference compensation cavity, and the temperature compensation cavity through the multi-cavity compensation algorithm module. Specifically, it includes the following steps: collecting the resonant frequency shift and quality factor decrease of the main detection cavity, the resonant frequency shift and quality factor decrease of the reference compensation cavity, and the resonant frequency shift and temperature of the temperature compensation cavity; and calculating the final moisture value through the multi-cavity compensation algorithm, wherein the calibration coefficient is obtained by standard sample calibration.

[0036] Example 2

[0037] In another embodiment, the moisture detection system based on the multi-cavity compensation algorithm further includes a data encryption module to perform end-to-end encrypted transmission of measurement data. Specifically, it includes the following steps: generating an encryption key in the operation and control unit, packaging the measurement data into a data packet, encrypting the data packet using an encryption algorithm, generating an encrypted data packet, and transmitting the encrypted data packet to a remote server through a wireless communication protocol to ensure data security and privacy protection.

[0038] Example 3

[0039] In another embodiment, the moisture detection system based on the multi-cavity compensation algorithm also includes a remote monitoring module, which realizes remote equipment status monitoring and parameter adjustment through a wireless communication protocol. Specifically, it includes the following steps: configuring a remote monitoring interface in the computing control unit, sending equipment status information to a remote server through a wireless communication protocol, receiving control commands issued by the remote server, parsing the control commands, and updating the operating parameters of the signal transmitting unit or the computing control unit.

[0040] Example 4

[0041] In another embodiment, the moisture detection system based on the multi-cavity compensation algorithm further includes a user interaction module, which provides a visual interface through a multi-touch display screen and supports the following functions:

[0042] Custom calibration curve: Users input standard sample data through the interface, and the system automatically calculates and updates the calibration coefficients;

[0043] Historical data analysis: Users can retrieve historical measurement data from the main memory for trend analysis;

[0044] Abnormal alarm threshold setting: Users can set alarm thresholds through the interface, and an alarm signal will be triggered when the measured value exceeds the threshold;

[0045] Real-time parameter adjustment: Users can modify the transmission frequency or power of the signal transmitting unit through the interface to optimize measurement accuracy.

[0046] Example 5

[0047] like Figure 1As shown, this embodiment discloses a moisture detection system based on a multi-cavity compensation algorithm, including a power supply unit 101, a multi-touch display screen 102, a main memory 103, an arithmetic control unit 104, a signal processing unit 105, a signal transmitting unit 106, a buffer 107, a signal receiving unit 108, a main detection cavity 109, a reference compensation cavity 110, and a temperature compensation cavity 111.

[0048] like Figure 2 As shown, there is a real-time measurement module 201; a background calibration module 202; a thermal drift compensation module 203; and a multi-cavity compensation algorithm module 204.

[0049] Specifically, the system includes: a power supply unit 101 that supplies power to the circuit boards containing the multi-touch display 102, the arithmetic control unit 104, the signal processing unit 105, and other units via cables. The arithmetic control unit 104 begins measurement by reading preset parameter thresholds or real-time commands from the multi-touch display 102. Its operating commands are sent to the signal transmitting unit 106 via the buffer 107. The signal transmitting unit 106 transmits radio frequency signals of a certain frequency to the main detection cavity 109 and the reference compensation cavity 110 according to a preset program. The signal receiving unit 108 reads the data from the main detection cavity 109, the reference compensation cavity 110, and the temperature compensation cavity 111 in real time, and sends it to the signal processing unit 105 via the buffer 107 for processing. The processed signal data is then sent to the arithmetic control unit 104 for relevant calculations. The final data after the calculations are completed is stored in the main memory 103 and simultaneously displayed on the multi-touch display 102. The multi-touch display 102 can also access historical data in the main memory 103 via an interface for viewing.

[0050] After the system acquires the signal through the main detection cavity 109, the real-time measurement module 201 calculates f1 and Q1. At the same time, after the reference compensation cavity 110 and the temperature compensation cavity 111 acquire the signal, the background calibration module 202 calculates f2 and Q2, the thermal drift compensation module 203 calculates f3 and ΔT, and finally the multi-cavity compensation algorithm module 204 calculates the final moisture value W using formula 1.

[0051] Formula 1:

[0052] W: Moisture content of the material to be tested (unit: ppm or %)

[0053] Δf i Volume frequency offset of the main detection cavity and the reference compensation cavity (unit: kHz)

[0054] ΔQ -1 : Difference between the reciprocal of the quality factors of the main cavity and the reference cavity (unit: ×10) -6 )

[0055] ΔT: Temperature difference monitored by the temperature compensation chamber (unit: °C)

[0056] K0~K5: Calibration coefficients (obtained through calibration using standard samples)

[0057]

[0058] Method for obtaining the standard coefficient:

[0059] Step 1: Calibration of multiple standard samples;

[0060] Using more than 5 sets of standard samples with known moisture content (such as 50ppm, 200ppm, 500ppm, 1000ppm, 2000ppm), the frequency, Q value, temperature data matrix X and the true moisture value matrix Y are measured.

[0061] Step 2: Partial Least Squares (PLS) Regression:

[0062] Y = X × K + E;

[0063] K = [K1, K2, K3, K4, K5] T The coefficient matrix to be determined;

[0064] E: Residual matrix, obtained by minimizing ||E|| 2 Solve for K;

[0065] Step 3: Verify calibration results

[0066] Serial Number Standard sample (ppm) Forecast value (ppm) relative error 1 50 52.4 +4.8％ 2 200 195.1 -2.45％ 3 500 486.2 -2.76％ 4 1000 1031.6 +3.16％ 5 2000 1968.5 -1.58％

[0067] Code example of dynamic compensation algorithm:

[0068] 1. Collect raw data:

[0069] f_main = 2.450123 GHz / / (Main detection cavity);

[0070] f_ref = 2.450001 GHz / / (reference compensation cavity);

[0071] f_temp = 2.450056 GHz / / (temperature compensation cavity);

[0072] Q_main = 5200, Q_ref = 8100;

[0073] ΔT = 0.3℃.

[0074] 2. Calculate intermediate variables:

[0075] Δf1=f_main-f_initial=+123kHz;

[0076] Δf2=f_ref-f_initial=+1kHz;

[0077] Δf3=f_temp-f_initial=+56kHz;

[0078] ΔQ-1=(1 / 5200-1 / 8100)×10 6 =68.5.

[0079] 3. Substitute into the formula:

[0080] W=-2.3+0.15×123-0.12×1+0.08×56+210×68.5-0.5×0.3=152.7ppm.

[0081] Example 1: The main detection cavity 109 is made of 316L stainless steel and is a TM010 cylindrical resonant cavity with dimensions of 80±0.01mm in diameter and 25±0.05mm in height. The inner wall is gold-plated (gold layer thickness >3μm, surface roughness Ra<0.1μm). Q value: no load >5000, load >3000 (water-containing sample). Sensitivity is 0.3Hz / ppm (water molecules), and the temperature coefficient is -12kHz / ℃ (uncompensated).

[0082] The reference compensation cavity 110 and the main detection cavity 109 are manufactured in pairs and have a fully enclosed structure. The interior is filled with 99.999% high-purity nitrogen, and its frequency deviates from that of the main detection cavity by <±10kHz.

[0083] The temperature compensation cavity is made of Invar steel (coefficient of expansion 0.5×10). -6 / ℃). Its temperature detection probe uses a PT1000 temperature sensor (±0.02℃), with four points evenly distributed inside the cavity for temperature monitoring.

[0084] The signal transmitting unit 106 generates radio frequency excitation signals with a frequency range of 0.5-10GHz (0.1MHz step), phase noise of <-115dBc / Hz@1kHz, and output power of 20dBm±0.5dB.

[0085] The signal receiving unit 108 acquires the response signals in the main detection cavity 109 and the reference compensation cavity 110. The dynamic range is 90dB, the ADC resolution is 24bit@1MSPS, and the noise figure is <2dB.

[0086] The signal processing unit 105 extracts phase difference and amplitude attenuation features, adopts a superheterodyne architecture (local oscillator frequency 2.5GHz), digital phase-locked amplification (bandwidth 1Hz), and phase resolution: 0.005°.

[0087] The arithmetic control unit 104 uses a dual-core ARM Cortex-M7 (400MHz) processor and the real-time operating system is RT-Thread.

[0088] The main memory 103 uses 256MB Flash (100,000 erase / write cycles) and 8MB FRAM, and supports SD card expansion (up to 1TB). The cache 107 uses 128MB Flash (100,000 erase / write cycles) and 4MB FRAM.

[0089] The power supply unit 101 uses a MEANWELL RS-100-24 switching power supply with input of 24VDC±20%, multiple outputs of ±5V / ±12V (ripple <0.5mV), and overcurrent protection response time of <1μs.

[0090] The multi-touch display 102 uses a Weintek MT8103 IE touchscreen with a resolution of 1024×600, enabling human-computer interaction and data visualization.

[0091] System performance test - sensitivity:

[0092] Serial Number Sample moisture content (ppm) Frequency offset Δf (kHz) Signal-to-noise ratio (SNR) 1 50 15.2±0.8 19:1 2 100 30.7±1.1 28:1 3 200 61.9±1.5 41:1 4 500 149.6±0.7 63:1 5 1000 301.2±0.6 102:1

[0093] System performance testing - long-term stability (72-hour continuous test):

[0094] Serial Number Time (h) Moisture content (ppm) Drift value (ppm) 1 0 100.1 +0.1 2 24 100.2 +0.2 3 48 99.8 -0.2 4 72 100.1 +0.1

[0095] System performance testing - cross-interference testing:

[0096] Serial Number Interference conditions Measurement error (ppm) 1 Vibration (5-500Hz, 1g) ±0.5 2 Electromagnetic interference (3V / m) ±0.2 3 Air pressure change (±5 kPa) ±0.3

[0097] System performance testing - sample testing:

[0098] Serial Number Standard sample (ppm) Forecast value (ppm) relative error 1 50 51.3 +2.60％ 2 100 98.2 -1.80％ 3 200 196.5 -1.75％ 4 500 512.7 +2.54％ 5 1000 1016.4 -1.64％

[0099] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A moisture detection system based on a multi-cavity compensation algorithm, characterized in that, include: Main detection chamber, reference compensation chamber, temperature compensation chamber, and arithmetic control unit; The main detection cavity is a sensing unit for the interaction between sample moisture and microwave field. Its cavity shape is a cylindrical resonant cavity with a conductive layer plated on the inner wall. It reflects the change in moisture content through resonant frequency shift and quality factor decrease. The reference compensation cavity is a fully enclosed structure filled with inert gas. It is machined in pairs with the main detection cavity, and its frequency is similar to that of the main detection cavity to eliminate inherent drift. The temperature sensors in the temperature compensation cavity are distributed within the cavity to monitor the temperature, and the resonant frequency shift caused by temperature is corrected by the thermal drift compensation module. The computation and control unit combines the measurement data from the main detection chamber, the reference compensation chamber, and the temperature compensation chamber to run a three-dimensional compensation algorithm and output an accurate moisture value. The main detection cavity and the reference compensation cavity work together to eliminate environmental noise interference by comparing frequency difference and quality factor difference; The temperature compensation cavity is combined with the computing and control unit to correct thermal drift errors in real time, ensuring the measurement accuracy of the system under temperature fluctuations. The computation and control unit optimizes the signal transmission frequency through a dynamic frequency shift compensation algorithm and dynamically adjusts the transmission parameters in conjunction with a real-time environmental monitoring module to suppress external interference.

2. The moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The temperature coefficient of the main detection cavity is negative in the uncompensated state, and dynamic frequency compensation is achieved through the following steps: The resonant frequency shift and temperature of the main detection cavity are collected in real time. The theoretical frequency offset value is calculated based on the preset temperature-frequency relationship model; The actual measured frequency offset is compared with the theoretical value to generate a compensation signal; By adjusting the signal transmission frequency using a dynamic frequency shift algorithm, temperature drift can be offset, thereby reducing temperature sensitivity.

3. The moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The computation and control unit performs multi-parameter fusion calculations in conjunction with the three-dimensional calibration matrix, specifically including the following steps: A three-dimensional calibration matrix is ​​constructed, in which the frequency difference is the frequency difference between the main detection cavity and the reference cavity, the quality factor difference is the quality factor difference between the main detection cavity and the reference cavity, and the temperature is the temperature difference of the temperature compensation cavity. A mapping relationship between moisture values ​​and calibration matrices is established using partial least squares regression. The calibration coefficients are calibrated using standard sample data, updated in real time, and the calibration parameters are optimized using a machine learning model.

4. The moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The electromagnetic field disturbance characteristics of the main detection cavity are related to the moisture content, which are cross-verified through multi-band resonance analysis, specifically including the following steps: Select at least two independent frequency bands and transmit radio frequency signals of different frequencies to the main detection cavity respectively. Measure the resonant frequency shift and quality factor decrease of the main detection cavity in each frequency band. By integrating multi-band measurement results through cross-validation algorithm, the measurement error of a single frequency band is eliminated, and a comprehensive moisture value is output.

5. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The computation and control unit optimizes the signal transmission frequency through a dynamic frequency shift compensation algorithm, specifically including the following steps: Real-time monitoring of environmental parameters and the resonant frequency shift of the main detection cavity; and calculation of the optimal transmission frequency under the current interference based on the preset interference-frequency response model. The signal transmission power is dynamically adjusted by a digitally controlled attenuator to ensure the signal-to-noise ratio is maximized at the transmission frequency; The optimized transmission parameters are fed back to the signal transmission unit to suppress the impact of external interference on measurement accuracy.

6. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The temperature compensation cavity is combined with the computational control unit, and the resonant frequency shift caused by temperature is corrected through the thermal drift compensation module. Specifically, the following steps are included: The average temperature of the cavity is calculated by collecting data from four temperature sensors in real time. Predict the theoretical resonant frequency shift based on a pre-defined temperature-resonant frequency relationship model; The actual measured resonant frequency offset is compared with the theoretical value to generate a residual. The residual is then corrected using a polynomial model, and the corrected resonant frequency offset is output and input to the calculation control unit for moisture calculation.

7. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: The computation and control unit integrates data from the main detection cavity, the reference compensation cavity, and the temperature compensation cavity through a multi-cavity compensation algorithm module, specifically including the following steps: The resonant frequency shift and quality factor decrease of the main detection cavity, the resonant frequency shift and quality factor decrease of the reference compensation cavity, and the resonant frequency shift and temperature of the temperature compensation cavity are collected. The final moisture value is calculated using a multi-cavity compensation algorithm, where the calibration coefficient is obtained through standard sample calibration.

8. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: It also includes a data encryption module to perform end-to-end encrypted transmission of measurement data, specifically including the following steps: An encryption key is generated in the operation and control unit, the measurement data is packaged into a data packet, the data packet is encrypted using an encryption algorithm, an encrypted data packet is generated, and the encrypted data packet is transmitted to a remote server through a wireless communication protocol to ensure data security and privacy protection.

9. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: It also includes a remote monitoring module, which uses a wireless communication protocol to remotely monitor device status and adjust parameters, specifically including the following steps: A remote monitoring interface is configured in the computing control unit to send device status information to a remote server via a wireless communication protocol, receive control commands issued by the remote server, parse the control commands, and update the operating parameters of the signal transmitting unit or the computing control unit.

10. A moisture detection system based on a multi-cavity compensation algorithm according to claim 1, characterized in that: It also includes a user interaction module that provides a visual interface via a multi-touch display.

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