Microwave sensor performance detection system based on machine learning

Abstract

The present invention discloses a microwave sensor performance detection system based on machine learning, which belongs to the field of microwave sensor technology. The system can timely identify moments of large operating deviations by real-time monitoring of the temperature, ambient temperature and humidity, and vibration amplitude of the microwave sensor, and judge the stability of the operating state by calculating abnormal values, thereby obtaining operating values. The system also identifies baseline drift and calculates the average drift rate by comparing the baseline value with the initial base value. At the same time, the target peak and miscellaneous peaks in the spectrum are analyzed, and the accuracy and interference degree of the spectrum are judged by the symmetry value and miscellaneous peak interference value, and finally the symmetry value, kurtosis, and miscellaneous peak interference value are obtained. The method can effectively distinguish the interference between the object to be tested and the miscellaneous peaks. The system can calculate the confidence value of each detection result, thereby quantifying the reliability of the detection result, and automatically judge the credibility of the detection result based on the confidence value.

Description

Technical Field

[0001] The present invention relates to the field of microwave sensor technology, and in particular to a microwave sensor data processing system and method based on machine learning, belonging to the field of combined application of microwave sensor technology and machine learning. Background Art

[0002] Microwave sensors are high-performance sensing devices based on microwave resonance technology. They detect the interaction between the substance and the microwave signal to identify and quantitatively analyze the target substance. These sensors offer advantages such as high sensitivity, rapid response, and non-contact detection, and are widely used in environmental monitoring, medical diagnosis, industrial process control, food safety, and material characterization. In particular, in the medical diagnosis field, the use of microwave sensors for biomolecule detection requires extremely high sensitivity and selectivity, placing high demands on sensor performance.

[0003] Currently, microwave sensor performance evaluation typically uses calibration and verification with standard samples. However, this method has the following limitations: First, microwave sensors are significantly affected by ambient temperature and humidity. This is because the sensitive area of ​​a microwave sensor is exposed to the surrounding medium, which can change its dielectric constant and, consequently, its resonant frequency. This makes real-time monitoring and evaluation of microwave sensor performance difficult. Second, there has long been a lack of quantitative analysis of long-term performance changes in microwave sensors, making it impossible to effectively predict and evaluate performance degradation trends. These issues make it difficult to ensure the reliability of microwave sensor detection results in practical applications, necessitating the establishment of an intelligent data processing and performance evaluation system. Summary of the Invention

[0004] Based on this, it is necessary to provide a microwave sensor data processing system and method based on machine learning to address the problems mentioned in the above background technology, and to realize intelligent data processing and adaptive performance evaluation by introducing machine learning algorithm technology.

[0005] The object of the present invention can be achieved by the following technical solutions: A microwave sensor performance detection system based on machine learning includes: a sensor hardware data acquisition module, a data fusion module and a performance detection module;

[0006] First, the sensor hardware data acquisition module communicates with the microwave sensor to collect and store operating information and spectrum information. Specific operating information includes sensor temperature T, vibration amplitude F, ambient temperature H, and ambient humidity S; spectrum information includes baseline and spectrum.

[0007] Secondly, the data fusion module monitors and analyzes the operating status and spectrum status of the microwave sensor based on its operating information during operation to obtain the performance parameters of the microwave sensor, where the performance parameters include the operating value PC and the spectrum value RG;

[0008] Finally, the performance detection module determines the confidence level of the output spectrogram based on the received performance parameters to guide the output of the detection results, specifically:

[0009] Step 1: A person skilled in the art first preliminarily sets a confidence coefficient corresponding to each microwave sensor and denotes it as δ. Then, the microwave sensor is started and data collection begins. The calculated operating value PC, spectrum value RG, and confidence coefficient δ are formally calculated and analyzed to obtain the confidence value U. The specific calculation formula is:

[0010]

[0011] Wherein α1 and α2 are respectively set weight constants, and their values ​​are set by those skilled in the art according to actual needs; for example, α1 is set to 0.37 and α2 is set to 0.63;

[0012] Step 2: When the confidence value is greater than or equal to the set confidence threshold, the spectrum is output as an accurate result. When the confidence value is less than the set confidence threshold, the spectrum is recorded as a risk result and retested until the confidence value is greater than the set confidence threshold, and the spectrum is output as an accurate result. The number of repeated tests is accumulated. If the number of repeated tests is greater than the set threshold, the performance of the microwave sensor is considered poor, and a sensor maintenance instruction is generated to the corresponding engineer.

[0013] Each time a test is performed, a historical test record is generated. Each specific test record includes the response time, recovery time, and test results. The test results can be accurate or risky.

[0014] Step 3: Retrieve historical detection records, including the number of historical detections, the response time, the recovery time, and the detection results for each detection; perform cumulative analysis on the historical detection records to obtain the updated confidence coefficient δ, and update it to step 1.

[0015] In some embodiments, the specific steps for cumulatively analyzing historical detection records are:

[0016] The response time and recovery time of each detection are weighted to calculate the delay value;

[0017] Compare and analyze the delay value of each test with the set delay interval. When the delay value is greater than the maximum value in the set delay interval, a high delay is accumulated; when the delay value is within the set delay interval, a moderate delay is accumulated; when the delay value is less than the minimum value in the set delay interval, a mild delay is accumulated; the cumulative number of high delays, moderate delays, and mild delays are accumulated and recorded as L1, L2, and L3 respectively; the average delay value of each test is calculated to obtain the average delay value and recorded as L4;

[0018] The response value Lg is obtained by formulating and analyzing the cumulative number of high delays L1, the cumulative number of medium delays L2, the cumulative number of mild delays L3 and the delay mean L4. The specific calculation formula is:

[0019]

[0020] Where g1, g2, and g3 are respectively set weight constants, and g1>g2>g3>1; the delay value of each detection is averaged to obtain the delay mean;

[0021] The number of accurate and risky test results in the test records is counted and recorded as accurate number Q1 and risky number Q2 respectively; the accurate number Q1, risky number Q2 and response value Lg are normalized and their values ​​are taken, and the confidence coefficient δ is obtained by formulating and analyzing the values. The specific calculation formula is:

[0022]

[0023] Among them, γ1 and γ2 are the set weight constants respectively.

[0024] In some embodiments, the specific steps for performing data monitoring and analysis on the operating status are:

[0025] 3-1: Retrieve the operating information of the microwave sensor at each collection moment. Specific operating information includes sensor temperature, vibration amplitude, ambient temperature, and ambient humidity. These are recorded as Ti, Fi, Hi, and Si, respectively, where i = 1, 2, 3, ..., I, where I is a positive integer, I represents the total number of collection moments of the microwave sensor within the detection period, and i represents the number of any collection moment. Set an operating parameter for each type of microwave sensor. Specific standard operating parameters include the standard operating temperature range, standard ambient temperature range, and standard ambient humidity range. These are recorded as [BT1, BT2], [BH1, BH2], and [BS1, BS2], respectively.

[0026] 3-2: Calculate the distance between the sensor temperature, ambient temperature, and ambient humidity at each acquisition moment and the standard operating temperature range, standard ambient temperature range, and standard ambient humidity range, respectively, to obtain the operating temperature deviation value, ambient temperature deviation value, and ambient humidity deviation value at each acquisition moment, and record them as PTi, PHi, and PSi, respectively;

[0027] 3-3: Normalize the working temperature deviation value PTi, vibration amplitude Fi, ambient temperature deviation value PHi, and ambient humidity deviation value PSi at each collection moment and take their values. Perform formulaic calculation and analysis on the values ​​to obtain the deviation value PZi at each collection moment. The specific calculation formula is:

[0028] PZi=log2(β1×e PTi +β2×e PHi +β3×e PSi +1)+β4×Fi

[0029] Where β1, β2, β3, and β4 are respectively set weight constants, and their values ​​are set by those skilled in the art according to the actual detection situation. When the deviation value is greater than the set deviation threshold, the acquisition moment is recorded as an abnormal deviation moment. Thus, several abnormal deviation moments can be obtained and sorted in chronological order.

[0030] 3-4: Calculate the interval between two adjacent abnormal deviation moments and record it as C1, and calculate the average of the deviation values ​​of the two adjacent abnormal deviation moments to obtain the average deviation value and record it as C2; normalize the interval between the two adjacent abnormal deviation moments C1 and the average deviation value C2 and take their values, and perform formula calculation and analysis on the values ​​to obtain the abnormal value PC of the two adjacent abnormal deviation moments; the specific calculation formula is:

[0031]

[0032] β5 and β6 are respectively set weight constants, and their values ​​are set by those skilled in the art according to the actual detection situation. For example, the value of β5 is 1.355, and the value of β6 is 1.109.

[0033] In some embodiments, the specific process of distance calculation and analysis is:

[0034] The sensor temperature at each acquisition moment is compared with the standard operating temperature range to obtain the operating temperature deviation value at each acquisition moment, which is recorded as PTi. The specific comparison and analysis method is as follows: when the sensor temperature is within the standard operating temperature range [BT1, BT2], the operating temperature deviation is assigned to zero; when the sensor temperature is not within the standard operating temperature range, PTi = min[BT1-Ti, Ti-BT2], where Ti is the sensor temperature, i = 1, 2, 3, ..., I, where I is a positive integer, I represents the total number of acquisition moments, and i represents the number of any acquisition moment.

[0035] Similarly, the ambient temperature and ambient humidity at each collection moment are respectively calculated and analyzed for distance from the standard ambient temperature range and the standard ambient humidity range to obtain the ambient temperature deviation value and ambient humidity deviation value at each collection moment.

[0036] In some embodiments, the specific steps for performing data monitoring and analysis on the operating status are:

[0037] 5-1: Retrieve the spectrum information output by the microwave sensor, including the baseline and spectrum; perform baseline drift analysis based on the baseline to obtain the drift average speed;

[0038] 5-2: Identify the absorption peak corresponding to the target analyte in the spectrum and mark it as the target peak. Other absorption peaks are marked as miscellaneous peaks. Draw the symmetry axis of the target peak and perform symmetry analysis based on the symmetry axis of the target peak to obtain the symmetry value.

[0039] 5-3: Use data fitting to fit the target peak, extract characteristic data from the fitting curve, obtain the function expression f(x) after the target peak is fitted, and calculate the kurtosis based on the fitted target peak;

[0040] 5-4: Identify the miscellaneous peaks in the spectrum and analyze the interference of the miscellaneous peaks on the target peak to obtain the miscellaneous peak interference value;

[0041] 5-5: Normalize the drift average speed R, symmetry value AM, kurtosis K, and miscellaneous peak interference value G and take their values. Then, perform formula calculation and analysis on the values ​​to obtain the spectrum value RG. The specific calculation formula is:

[0042]

[0043] Among them, λ1, λ2, λ3, and λ4 are set weight constants respectively, and their values ​​are set by those skilled in the art according to actual needs.

[0044] In some embodiments, the specific process of baseline drift analysis is:

[0045] Assume that there is an initial base value; compare the baseline value at each time point in the baseline with the initial base value. When the baseline value is greater than the initial base value, record the baseline value as the drift value; thus, several drift values ​​in the baseline can be obtained, calculate the interval length between two adjacent drift values, sum the two adjacent drift values, and then divide the sum by the interval length to obtain the drift speed of the two adjacent drift values. Average all drift speeds to calculate the average drift speed.

[0046] In some embodiments, the specific method for determining the symmetry axis of the target peak is as follows: take the vertex of the target peak, draw a vertical line along the vertex and intersect the horizontal axis, and the vertical line is the symmetry axis of the target peak.

[0047] In some embodiments, the specific process of performing symmetry analysis based on the symmetry axis of the target peak is:

[0048] Divide the target peak into a left half and a right half along the symmetry axis, overlap the left half and the right half along the symmetry axis, and calculate the intersection area and union area of ​​the left half and the right half in the overlapping state;

[0049] Substitute the intersection area M∩ and the union area M∪ into the set formula

[0050]

[0051] The symmetry value AM is obtained by calculation.

[0052] In some embodiments, the kurtosis is calculated based on the fitted target peak. The specific calculation process is:

[0053] Retrieve the function expression f(x) after fitting the target peak, and mark the coordinate of each data point in the target peak as xj, where j = 1, 2, 3...J, J is a positive integer, J represents the total number of points that make up the target peak, and j represents the number of any point in it; substitute all points xj in the target peak into the set formula to calculate the kurtosis K, which is as follows;

[0054]

[0055] In some embodiments, the interference of miscellaneous peaks on the target peak is analyzed, and the specific process is as follows:

[0056] Calculate the distance between the clutter peak and the target peak. When the distance is greater than the maximum value in the set distance interval, a highly influential clutter peak is accumulated; when the distance is within the set distance interval, a moderately influential clutter peak is accumulated; when the distance is less than the minimum value in the set distance interval, a slightly influential clutter peak is accumulated; count the cumulative number of highly influential clutter peaks, moderately influential clutter peaks, and slightly influential clutter peaks respectively, and record them as Z1, Z2, and Z3 respectively; calculate the mean of the distances corresponding to highly influential clutter peaks, moderately influential clutter peaks, and slightly influential clutter peaks to obtain highly influential distances, moderately influential distances, and slightly influential distances, and record them as Z4, Z5, and Z6 respectively;

[0057] Normalize the cumulative number Z1 of highly influential miscellaneous peaks, the cumulative number Z2 of moderately influential miscellaneous peaks, the cumulative number Z3 of slightly influential miscellaneous peaks, the highly influential distance Z4, the moderately influential distance Z5, and the slightly influential distance Z6, and take their values. Formulate and analyze the values ​​to obtain the miscellaneous peak interference value G. The specific calculation formula is:

[0058]

[0059] Wherein η1, η2, and η3 are set weight constants respectively, and η1>η2>η3>1.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. Through real-time monitoring of sensor temperature, ambient temperature and humidity, and vibration amplitude, it is possible to promptly identify moments of large operating deviations, and to determine the stability of the operating status by calculating abnormal values ​​to obtain the operating value; thus, it is possible to accurately evaluate the operating status of the sensor and provide a reference basis for the accuracy of the test results;

[0062] 2. By comparing the baseline value with the initial base value, identifying the baseline drift and calculating the drift average speed, the stability of the spectrum is evaluated. Then, the target peak and the miscellaneous peaks in the spectrum are analyzed, and the symmetry value and the miscellaneous peak interference value are used to judge the accuracy and interference degree of the spectrum, and the symmetry value, kurtosis and miscellaneous peak interference value are obtained. It can effectively distinguish the interference of the target analyte and the miscellaneous peaks; the drift average speed, symmetry value, kurtosis and miscellaneous peak interference value are comprehensively analyzed to obtain the spectrum value, which can achieve accurate judgment of the spectrum status and provide a reference basis for the accuracy of the test results;

[0063] 3. By sending the operating values ​​and spectrum values ​​as performance parameters to the performance detection module, a comprehensive evaluation of the microwave sensor's operating status and spectrum status is achieved. The multi-dimensional data fusion method can more comprehensively reflect the actual performance of the equipment and provide data support for subsequent performance testing and maintenance;

[0064] 4. By combining the operating value, spectrum value and confidence coefficient, the confidence value of each test result can be calculated, thereby quantifying the reliability of the test result. Based on this, the confidence level of the test result is automatically determined, and results with low confidence levels are retested to ensure the accuracy of the final output result. When the test result is at a low confidence level multiple times, the system will accumulate the number of repeated tests and determine the sensor performance status based on the set number threshold. If the threshold is exceeded, the system will automatically generate a maintenance instruction and notify the engineer to perform maintenance to ensure the continued stable operation of the sensor.

[0065] In summary, the data fusion module and performance detection module provide a comprehensive assessment of the microwave sensor's operating status and spectrum status through real-time monitoring and historical data analysis. Confidence assessment and automatic maintenance strategies ensure the accuracy of detection results and the long-term stable operation of the sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the following briefly introduces the drawings required for use in the embodiments or the conventional technology descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0067] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0068] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0069] like Figure 1 As shown in FIG, a microwave sensor performance detection system based on machine learning includes a sensor hardware data acquisition module, a data fusion module, and a performance detection module;

[0070] Microwave sensors can support multi-mode resonance (e.g., dual-band or broadband response) based on their design. This design allows the microwave sensor to respond to a variety of analytes (including gases and liquids). It should be noted that the microwave sensor specifically described in this invention is suitable for single-target detection. That is, the microwave sensor described in this invention only detects a single target analyte.

[0071] The sensor hardware data acquisition module communicates with the microwave sensor to collect and store operating information and spectrum information; specific operating information includes sensor temperature, vibration amplitude, ambient temperature and ambient humidity; spectrum information includes baseline and spectrum;

[0072] The data fusion module monitors and analyzes the operating status and spectrum status of the microwave sensor based on its operating information during operation to obtain the performance parameters of the microwave sensor, where the performance parameters include operating values ​​and spectrum values. The specific steps of the monitoring and analysis are as follows:

[0073] Run the analysis:

[0074] Retrieve the operating information of the microwave sensor at each collection moment. Specific operating information includes sensor temperature, vibration amplitude, ambient temperature, and ambient humidity, and record them as Ti, Fi, Hi, and Si, respectively, where i = 1, 2, 3, ..., I, where I is a positive integer, I represents the total number of collection moments of the microwave sensor within the detection period, and i represents the number of any collection moment. It should be noted that the material of the microwave sensor is sensitive to the temperature and humidity of the environment, which can easily be affected, resulting in deviations in its detection results. Similarly, the microwave sensor is affected by mechanical stress and vibrates, resulting in an unstable baseline during detection, which in turn causes deviations in the detection results.

[0075] Each type of microwave sensor is assigned a corresponding operating parameter. Specific standard operating parameters include the standard operating temperature range, standard ambient temperature range, and standard ambient humidity range, and are denoted as [BT1, BT2], [BH1, BH2], and [BS1, BS2], respectively. It should be noted that the standard operating parameters are determined by the production materials and production processes of the microwave sensor and are usually noted in the microwave sensor's instructions to provide clear specifications for the sensor's normal operation and stability.

[0076] The sensor temperature at each acquisition moment is compared with the standard operating temperature range to obtain the operating temperature deviation value at each acquisition moment, which is recorded as PTi. The specific comparison and analysis method is as follows: when the sensor temperature is within the standard operating temperature range [BT1, BT2], the operating temperature deviation is assigned to zero; when the sensor temperature is not within the standard operating temperature range, PTi = min[BT1-Ti, Ti-BT2];

[0077] The ambient temperature at each acquisition moment is compared with the standard ambient temperature interval to obtain the ambient temperature deviation value at each acquisition moment, which is recorded as PHi. The specific comparison and analysis method is as follows: when the ambient temperature is within the standard ambient temperature interval [BH1, BH2], the ambient temperature deviation is assigned to zero; when the ambient temperature is not within the standard ambient temperature interval, PHi = min[BH1-Hi, Hi-BH2];

[0078] The ambient humidity at each acquisition moment is compared with the standard ambient humidity interval to obtain the ambient humidity deviation value at each acquisition moment, recorded as PSi. The specific comparison and analysis method is: when the ambient humidity is within the standard ambient humidity interval [BS1, BS2], the ambient humidity deviation is assigned to zero; when the ambient humidity is not within the standard ambient humidity interval, PSi = min[BS1-Si, Si-BS2];

[0079] The working temperature deviation value PTi, vibration amplitude Fi, ambient temperature deviation value PHi and ambient humidity deviation value PSi at each collection moment are normalized and their values ​​are taken. The values ​​are then analyzed and calculated using a formula to obtain the deviation value PZi at each collection moment. The specific calculation formula is:

[0080] PZi=log2(β1×e PTi +β2×e PHi +β3×e PSi +1)+β4×Fi

[0081] Wherein β1, β2, β3, and β4 are respectively set weight constants, and their values ​​are set by those skilled in the art according to the actual detection situation; the deviation value is compared with the set deviation threshold. When the deviation value is greater than the set deviation threshold, it means that the operation deviation of the microwave sensor at the acquisition moment is relatively large, and the acquisition moment is recorded as the abnormal deviation moment; thus, several abnormal deviation moments can be obtained, and they are sorted in chronological order; the interval length between two adjacent abnormal deviation moments is calculated and recorded as C1, and the deviation values ​​of the two adjacent abnormal deviation moments are averaged to obtain the deviation mean value recorded as C2; the interval length C1 and the deviation mean value C2 of the two adjacent abnormal deviation moments are normalized and their values ​​are taken, and the values ​​are formulaically calculated and analyzed to obtain the abnormal value PC of the two adjacent abnormal deviation moments; the specific calculation formula is:

[0082]

[0083] Wherein β5 and β6 are respectively set weight constants, and their values ​​are set by those skilled in the art according to the actual detection situation. Specifically, the value of β5 is 1.355 and the value of β6 is 1.109. It can be seen from the formula that when the interval length is shorter, the abnormal frequency is higher, and the abnormal value is larger; when the deviation mean is larger, the deviation degree is higher, and the abnormal value is larger. Thus, the abnormal values ​​of all two adjacent abnormal deviation moments can be obtained, and the average is calculated to obtain the operating value PC. It is not difficult to obtain from the calculation and analysis process of the operating value that when the operating value is larger, the abnormality during the operation of the microwave sensor is more serious.

[0084] By real-time monitoring of sensor temperature, ambient temperature and humidity, and vibration amplitude, it is possible to promptly identify moments of large operating deviations, and to determine the stability of the operating status by calculating abnormal values ​​to obtain operating values. This allows for accurate assessment of the sensor's operating status and provides a reference for the accuracy of the test results.

[0085] Spectral analysis:

[0086] Retrieve the baseline and spectrum output by the microwave sensor. It should be noted that the microwave sensor needs to preheat and stabilize the baseline before detecting the target object to be tested, and then the target object to be tested will be detected. After the detection is completed, the spectrum will be output, so each detection will have a corresponding baseline and spectrum; set an initial base value (usually the baseline value when the sensor starts working); compare the baseline value at each time point in the baseline with the initial base value. When the baseline value is greater than the initial base value, it means that there is baseline drift, and the baseline value is recorded as the drift value; thus, several drift values ​​in the baseline can be obtained, and the interval between two adjacent drift values ​​is calculated. The two adjacent drift values ​​are summed and the sum is divided by the interval to obtain the drift speed of the two adjacent drift values. The average of all drift speeds is calculated and the drift average speed is recorded as R; it should be noted that the larger the drift value and the shorter the interval between the drift values, the more serious the baseline drift, the larger the drift average speed, and the greater the impact on the accuracy of the spectrum;

[0087] The absorption peak corresponding to the target analyte in the identification spectrum is marked as the target peak, and the other absorption peaks are recorded as miscellaneous peaks. It should be noted that each analyte molecule will absorb or reflect electromagnetic waves (such as microwaves, infrared rays, etc.) at a specific frequency or wavelength after binding to the sensitive material on the microwave sensor. These absorption peaks are caused by the interaction between the analyte molecules and the electromagnetic waves and belong to the characteristic absorption spectrum of the gas. By analyzing the peak position in the spectrum, the characteristic absorption peak of the gas can be identified, thereby identifying the target peak in the spectrum;

[0088] Draw the symmetry axis of the target peak in the spectrum. The specific method is as follows: take the vertex of the target peak (the vertex refers to the highest point of the absorption peak on the spectrum, that is, the point with the largest signal intensity of the target peak in the spectrum), draw a vertical line along the vertex and intersect the horizontal axis, and the vertical line is the symmetry axis of the target peak; it should be noted that, ideally, the target peak should be symmetrical along the symmetry axis; divide the target peak into a left half and a right half along the symmetry axis, overlap the left half and the right half along the symmetry axis, and calculate the intersection area of ​​the left half and the right half in the overlapping state as M∩ and the union area as M ∪ ; The intersection area M ∩ Substitute the area of ​​the union M∪ into the set formula

[0089]

[0090] The symmetry value AM is calculated. According to the calculation formula, the greater the difference between the intersection area and the union area, the greater the asymmetry of the target peak, the greater the impact on the accuracy of the spectrum, and the smaller the symmetry value.

[0091] The target peak is fitted using data fitting, and characteristic data (specific characteristic parameters include peak position, peak width and amplitude, etc.) are extracted from the fitting curve. The model usually used is Gaussian distribution. The specific Gaussian distribution model is:

[0092]

[0093] Where A is the amplitude (indicating the peak height), μ is the peak position (i.e., the location of the symmetry axis), and σ is the standard deviation (indicating the width of the peak; a larger value indicates a wider peak, and a smaller value indicates a sharper peak).

[0094] Thus, the function expression f(x) after fitting the target peak can be obtained. The horizontal coordinate of each data point in the target peak is marked as xj (the horizontal coordinate in common terms usually refers to the frequency or wavelength value), where j = 1, 2, 3...J, J is a positive integer, J represents the total number of points that make up the target peak, and j represents the number of any one of them; all points xj in the target peak are substituted into the set formula to calculate the kurtosis K, the specific calculation formula is:

[0095]

[0096] It should be noted that for an ideal Gaussian peak, its kurtosis is 3;

[0097] Calculate the distance between the miscellaneous peak and the target peak. It should be noted that the smaller the distance between the miscellaneous peak and the target peak, the less clear the separation between the miscellaneous peak and the target peak, and the greater the impact of the miscellaneous peak on the target peak; compare and analyze the distance with the set distance interval. When the distance is greater than the maximum value in the set distance interval, a highly influential miscellaneous peak is accumulated; when the distance is within the set distance interval, a moderately influential miscellaneous peak is accumulated; when the distance is less than the minimum value in the set distance interval, a slightly influential miscellaneous peak is accumulated; the cumulative number of highly influential miscellaneous peaks, moderately influential miscellaneous peaks and slightly influential miscellaneous peaks are counted respectively. The number of highly influential miscellaneous peaks is calculated and recorded as Z1, Z2 and Z3 respectively; the distances corresponding to the highly influential miscellaneous peaks, the moderately influential miscellaneous peaks and the slightly influential miscellaneous peaks are respectively averaged to obtain the highly influential distance, the moderately influential distance and the slightly influential distance, and recorded as Z4, Z5 and Z6 respectively; the cumulative number of highly influential miscellaneous peaks Z1, the cumulative number of moderately influential miscellaneous peaks Z2, the cumulative number of slightly influential miscellaneous peaks Z3, the highly influential distance Z4, the moderately influential distance Z5 and the slightly influential distance Z6 are normalized and their values ​​are taken, and the values ​​are analyzed and calculated by formula to obtain the miscellaneous peak interference value G; the specific calculation formula is:

[0098]

[0099] Where η1, η2, and η3 are the set weight constants, and η1>η2>η3>1. From the formula, we can see that when the distance between the impurity peak and the target peak is smaller, it means that the interference to the target peak is greater, and the greater the impurity peak interference value, the greater the possibility of inaccurate target peak;

[0100] The drift average speed R, symmetry value AM, kurtosis K and miscellaneous peak interference value G are normalized and their values ​​are taken. The values ​​are then analyzed and calculated using a formula to obtain the spectrum value RG. The specific calculation formula is:

[0101]

[0102] Among them, λ1, λ2, λ3, and λ4 are respectively set weight constants, and their values ​​are set by those skilled in the art according to actual needs. It can be seen from the formula that when the drift average speed is larger, the spectrum value is smaller; when the symmetry value is larger, the spectrum value is larger; when the kurtosis K>3, it means that the peak shape is sharp and conforms to the ideal Gaussian distribution, and the spectrum value is larger; otherwise, it means that the peak shape is flat or blunt, and the spectrum value is smaller; when the miscellaneous peak interference value is larger, the spectrum value is smaller;

[0103] Record the operating values ​​and the spectrum values ​​as performance parameters and send them to the performance detection module;

[0104] By comparing the baseline value with the initial base value, identifying baseline drift and calculating the average drift rate, the stability of the spectrum is evaluated. The target peak and miscellaneous peaks in the spectrum are then analyzed, and the symmetry value and miscellaneous peak interference value are used to determine the accuracy and degree of interference of the spectrum. The symmetry value, kurtosis, and miscellaneous peak interference value are obtained. This can effectively distinguish the interference of the target analyte and miscellaneous peaks. The drift average rate, symmetry value, kurtosis, and miscellaneous peak interference value are comprehensively analyzed to obtain the spectrum value, achieving an accurate judgment of the spectrum status and providing a reference for the accuracy of the test results. By sending the operating value and spectrum value as performance parameters to the performance detection module, a comprehensive evaluation of the microwave sensor's operating status and spectrum status is achieved. The multi-dimensional data fusion method can more comprehensively reflect the actual performance of the equipment and provide data support for subsequent performance testing and maintenance.

[0105] The performance detection module determines the confidence level of the microwave sensor's detection results based on the received performance parameters and outputs the confidence level of the detection results accordingly. Based on the confidence level of the microwave sensor's output detection results, the module determines its performance and implements corresponding strategies to ensure the stable detection capability of the microwave sensor. Specifically:

[0106] Step 1: Set a confidence coefficient corresponding to each microwave sensor and record it as δ; perform formal calculation and analysis on the operating value PC, the spectrum value RG and the confidence coefficient δ to obtain the confidence value U. The specific calculation formula is:

[0107]

[0108] Wherein α1 and α2 are respectively set weight constants, and their values ​​are set by those skilled in the art according to actual needs; specifically, the value of α1 is 0.37 and the value of α2 is 0.63;

[0109] Step 2: Compare and analyze the confidence value with the set confidence threshold. When the confidence value is greater than or equal to the set confidence threshold, it means that the confidence level of the spectrum output by this detection is high, and the spectrum is output as an accurate result; when the confidence value is less than the set confidence threshold, it means that the confidence level of the spectrum output by this detection is relatively low, and there is a risk of comparison error, so the spectrum is recorded as a risk result and re-tested until the confidence value is greater than the set confidence threshold, and the spectrum is output as an accurate result; accumulate the number of repeated tests. If the number of repeated tests is greater than the set number threshold (the specific number is set by personnel in this field according to actual needs, for example, engineers will set the number threshold to 5 times), the performance of the microwave sensor is relatively poor, and a sensor maintenance instruction is generated to the corresponding engineer;

[0110] Each time a test is performed, a historical test record is generated. Each specific test record includes the response time (the time it takes to start detecting the output spectrum. A longer response time indicates that the material is aged or the diffusion efficiency of the analyte is reduced, and the performance of the microwave sensor is degraded), the recovery time (the time from the removal of the target analyte to the signal returning to the baseline, indicating that the adsorption material has hysteresis or the removal efficiency is reduced, and the performance of the microwave sensor is degraded) and the test result (accurate result or risk result).

[0111] Step 3: Retrieve historical detection records, which include the number of historical detections, the response time, the recovery time, and the detection results of each detection; perform weighted calculation on the response time and the recovery time of each detection to obtain the delay value; compare and analyze the delay value of each detection with the set delay interval. When the delay value is greater than the maximum value in the set delay interval, a high delay is accumulated; when the delay value is in the set delay interval, a moderate delay is accumulated; when the delay value is less than the minimum value in the set delay interval, a mild delay is accumulated; accumulate the cumulative number of high delays, moderate delays, and mild delays respectively, and record them as L1, L2, and L3 respectively; calculate the average value of the delay value of each detection to obtain the delay average value, which is recorded as L4; formulate and analyze the cumulative number of high delays L1, the cumulative number of moderate delays L2, the cumulative number of mild delays L3, and the delay average L4 to obtain the response value Lg; the specific calculation formula is:

[0112]

[0113] Where g1, g2, and g3 are the set weight constants, and g1>g2>g3>1; the larger the delay value of each detection, the smaller the corresponding response value;

[0114] The number of accurate and risky test results in the test records is counted and recorded as accurate number Q1 and risky number Q2 respectively; the accurate number Q1, risky number Q2 and response value Lg are normalized and their values ​​are taken, and the confidence coefficient δ is obtained by formulating and analyzing the values. The specific calculation formula is:

[0115]

[0116] Where γ1 and γ2 are the set weight constants respectively, and the confidence coefficient is updated to step 1. By dynamically updating the confidence coefficient, the confidence evaluation strategy of the detection result is continuously optimized;

[0117] By combining the operating value PC, the spectrum value RG and the confidence coefficient δ, the confidence value U of each test result can be calculated, thereby quantifying and evaluating the reliability of the test results, and automatically judging the credibility of the test results based on this. For test results with low confidence, the system will automatically trigger retesting to ensure the accuracy of the final output result; when the test results are in a low confidence state for many times, the system will accumulate the number of repeated tests and evaluate the performance of the sensor based on the set number threshold; if the number of repeated tests exceeds the threshold, the system will automatically generate a maintenance instruction and notify the engineer to perform maintenance to ensure the continued stable operation of the sensor.

[0118] It should be noted that the above formulas are based on a large amount of collected data, obtained through software numerical simulation, and select formula forms that are highly close to the actual values. The coefficients in the formulas are set by technical personnel in this field according to specific application scenarios and actual conditions.

[0119] The various technical features of the above-mentioned embodiments can be freely combined as needed. To simplify the description, not all possible combinations of technical features are described in detail; however, as long as there is no contradiction in the combination of these technical features, they should be regarded as covered by the scope of this specification.

[0120] The above embodiments only illustrate several implementations of the present invention, and their descriptions are relatively specific and detailed, but do not limit the scope of patent protection of the present invention. It should be pointed out that, for those skilled in the art, various modifications and improvements can be made to the present invention without departing from the core concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of patent protection of the present invention shall be based on the appended claims.

Claims

1. A microwave sensor performance detection system based on machine learning, including a sensor hardware data acquisition module, a data fusion module, and a performance detection module; its characteristics are: The sensor hardware data acquisition module connects to the microwave sensor to collect the operating information and spectrum data of the microwave sensor in real time. It is responsible for obtaining the sensor's temperature, vibration amplitude, ambient temperature and humidity operating parameters, and recording related spectrum information, including baseline and spectrum data. The data fusion module monitors and analyzes the operating status and spectrum status of the microwave sensor based on its operating information during operation, and obtains the performance parameters of the microwave sensor, where the performance parameters include operating values ​​and spectrum values; The performance detection module determines the confidence level of the output spectrum based on the received performance parameters to guide the output of the detection results, specifically: Step 1: Set a confidence coefficient for each microwave sensor , the running value PC, spectrum value RG and confidence coefficient The confidence value U is obtained by formulating calculations and analysis. The confidence value is a quantitative indicator of the reliability of the detection result, the operation value is a comprehensive parameter representing the operating status of the sensor, and the spectrum value is a comprehensive evaluation quality of the spectrum quality. Step 2: When the confidence value is greater than or equal to the set confidence threshold, the spectrum is output as an accurate result. When the confidence value is less than the set confidence threshold, the spectrum is recorded as a risk result and retested until the confidence value is greater than the set confidence threshold, and the spectrum is output as an accurate result. The number of repeated tests is accumulated. If the number of repeated tests is greater than the set threshold, the performance of the microwave sensor is relatively poor, and a sensor maintenance instruction is generated to the corresponding engineer. After each detection, the data fusion module will generate a historical detection record. Each specific detection record includes the response time, recovery time and detection result, where the detection result is an accurate result or a risk result; Step 3: Retrieve historical detection records, including the number of historical detections, the response time, recovery time, and detection results of each detection; perform cumulative analysis on the historical detection records to obtain the confidence coefficient. , and update it to step 1; The specific steps for cumulative analysis of historical detection records are as follows: The response time and recovery time of each detection are weighted to calculate the delay value; Compare and analyze the delay value of each test with the set delay interval. When the delay value is greater than the maximum value in the set delay interval, a high delay is accumulated; when the delay value is within the set delay interval, a moderate delay is accumulated; when the delay value is less than the minimum value in the set delay interval, a mild delay is accumulated. The cumulative number of high delays, moderate delays, and mild delays is accumulated respectively, and the average of the delay values ​​of each test is calculated to obtain the delay average. The cumulative number of high delays L1, the cumulative number of medium delays L2, the cumulative number of mild delays L3 and the delay mean L4 are calculated and analyzed to obtain the response value Lg; The number of accurate and risky test results in the test records is counted and recorded as accurate number Q1 and risky number Q2 respectively; the accurate number Q1, risky number Q2 and response value Lg are normalized and their values ​​are taken, and the values ​​are calculated and analyzed by formula to obtain the confidence coefficient .

2. The microwave sensor performance detection system based on machine learning according to claim 1 is characterized in that: The specific steps for data monitoring and analysis of operating status are as follows: 3-1: Retrieve the operating information of the microwave sensor at each acquisition time. Specific operating information includes sensor temperature, vibration amplitude, ambient temperature, and ambient humidity. Set an operating parameter for each type of microwave sensor. Specific standard operating parameters include the standard operating temperature range [BT1, BT2], the standard ambient temperature range [BH1, BH2], and the standard ambient humidity range [BS1, BS2]. 3-2: Calculate the distance between the sensor temperature, ambient temperature, and ambient humidity at each acquisition moment and the standard operating temperature range, standard ambient temperature range, and standard ambient humidity range, respectively, to obtain the operating temperature deviation value PTi, ambient temperature deviation value PHi, and ambient humidity deviation value PSi at each acquisition moment; 3-3: Normalize the working temperature deviation value PTi, vibration amplitude Fi, ambient temperature deviation value PHi, and ambient humidity deviation value PSi at each collection moment and take their numerical values. Formulate and analyze these numerical values ​​to obtain the deviation value PZi at each collection moment. When the deviation value PZi is greater than the set deviation threshold PZ0, the collection moment is recorded as an abnormal deviation moment. In this way, several abnormal deviation moments can be obtained and sorted in chronological order. 3-4: Calculate the interval duration C1 between two adjacent abnormal deviation moments, and average the deviation values ​​of the two adjacent abnormal deviation moments to obtain the deviation mean C2; normalize the interval duration and deviation mean between two adjacent abnormal deviation moments and take their numerical values, perform formulaic calculation and analysis on the numerical values ​​to obtain the abnormal value PCi of the two adjacent abnormal deviation moments; thus, the abnormal values ​​PC of all two adjacent abnormal deviation moments can be obtained, and the average value is calculated to obtain the operating value PC.

3. The microwave sensor performance detection system based on machine learning according to claim 2 is characterized in that: The specific process of distance calculation analysis is as follows: The sensor temperature at each acquisition moment is compared with the standard operating temperature range to obtain the operating temperature deviation value at each acquisition moment, which is recorded as PT i The specific comparative analysis method is: when the sensor temperature is in the standard operating temperature range [BT1, BT2], the operating temperature deviation is assigned to zero; when the sensor temperature is not in the standard operating temperature range, PT i =min[BT1-T i , T i -BT2], T i is the sensor temperature, i=1,2,3…I, I is a positive integer, I represents the total number of acquisition moments, and i represents the number of any acquisition moment; Similarly, the ambient temperature and ambient humidity at each collection moment are respectively calculated and analyzed for distance from the standard ambient temperature range and the standard ambient humidity range to obtain the ambient temperature deviation value and ambient humidity deviation value at each collection moment.

4. The microwave sensor performance detection system based on machine learning according to claim 1, wherein the specific steps of performing data monitoring and analysis on the sensor operating status are: 5-1: Retrieve the spectrum information output by the microwave sensor, including the baseline and spectrum; perform baseline drift analysis based on the baseline to obtain the drift average rate R; 5-2: Identify the absorption peak corresponding to the target substance in the spectrum and mark it as the target peak. Other absorption peaks are marked as miscellaneous peaks. Draw the symmetry axis of the target peak and perform symmetry analysis based on the symmetry axis of the target peak to obtain the symmetry value. 5-3: Use the computer's data fitting function to fit the target peak, extract characteristic data from the fitting curve, obtain the function expression f(x) after the target peak is fitted, and calculate the kurtosis based on the fitted target peak; 5-4: Identify the miscellaneous peaks in the spectrum and analyze the interference of the miscellaneous peaks on the target peak to obtain the miscellaneous peak interference value; 5-5: Normalize the drift average speed, symmetry value, kurtosis and miscellaneous peak interference value and take their numerical values, and perform formula calculation and analysis on the numerical values ​​to obtain the spectrum value.

5. The microwave sensor performance detection system based on machine learning according to claim 4 is characterized in that: The specific process of baseline drift analysis is as follows: Assume that there is an initial base value; compare the baseline value at each time point in the baseline with the initial base value. When the baseline value is greater than the initial base value, record the baseline value as the drift value; thus, several drift values ​​in the baseline can be obtained, calculate the interval length between two adjacent drift values, sum the two adjacent drift values, and then divide the sum by the interval length to obtain the drift speed of the two adjacent drift values. Average all drift speeds to calculate the average drift speed.

6. The microwave sensor performance detection system based on machine learning according to claim 4 is characterized in that: The specific method of determining the symmetry axis of the target peak is: take the vertex of the target peak, draw a vertical line along the vertex and intersect it with the horizontal axis. The vertical line is the symmetry axis of the target peak.

7. The microwave sensor performance detection system based on machine learning according to claim 6, characterized in that: The specific process of symmetric analysis based on the symmetry axis of the target peak is as follows: Divide the target peak into a left half and a right half along the symmetry axis, overlap the left half and the right half along the symmetry axis, and calculate the intersection area and union area of ​​the left half and the right half in the overlapping state; The intersection area and the union area are numerically calculated and analyzed to obtain the symmetry value.

8. The microwave sensor performance detection system based on machine learning according to claim 4 is characterized in that: Calculate the kurtosis based on the fitted target peak. The specific calculation process is as follows: Retrieve the function expression f(x) after fitting the target peak, and mark the coordinate of each data point in the target peak as xj, where j = 1, 2, 3...J, J is a positive integer, J represents the total number of points that make up the target peak, and j represents the number of any one of them; substitute all points xj in the target peak into the set formula, which is as follows: Calculate and obtain the kurtosis K.

9. The microwave sensor performance detection system based on machine learning according to claim 4, characterized in that: Analyze the interference of miscellaneous peaks on the target peak. The specific process is as follows: Calculate the distance between the clutter peak and the target peak. When the distance is greater than the maximum value in the set distance interval, a highly influential clutter peak is accumulated; when the distance is within the set distance interval, a moderately influential clutter peak is accumulated; when the distance is less than the minimum value in the set distance interval, a slightly influential clutter peak is accumulated; the cumulative number of highly influential clutter peaks, moderately influential clutter peaks, and slightly influential clutter peaks is counted respectively, and the distances corresponding to the highly influential clutter peaks, moderately influential clutter peaks, and slightly influential clutter peaks are averaged to obtain the highly influential distance, moderately influential distance, and slightly influential distance, respectively. The cumulative number of highly influential miscellaneous peaks, the cumulative number of moderately influential miscellaneous peaks, the cumulative number of slightly influential miscellaneous peaks, the highly influential distance, the moderately influential distance and the slightly influential distance are normalized and their values ​​are taken, and the values ​​are calculated and analyzed using a formula to obtain the miscellaneous peak interference value.

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