Grain Moisture Monitoring Method, Device, Equipment and Medium Based on Probe Wave Analysis

Abstract

This invention discloses a method, apparatus, equipment, and medium for monitoring grain moisture based on sounding wave analysis. The method includes: controlling a driver via a scanning command to drive a sounding radar to scan stored grain; receiving and preprocessing the echo signals obtained from the scan to obtain corresponding processed signals; extracting features from the processed signals by combining distance and air pressure values ​​to obtain corresponding signal features; calibrating the signal features based on temperature values ​​to obtain calibration features; and then performing feature analysis to obtain the corresponding moisture monitoring value. This grain moisture monitoring method based on sounding wave analysis can scan stored grain using a sounding radar and analyze the echo signals to accurately obtain the internal moisture content of the grain, significantly improving the efficiency and reliability of moisture monitoring, while also increasing the accuracy of detecting the internal moisture of the grain.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology, and in particular to a method, device, equipment and medium for monitoring grain moisture based on sounding wave analysis. Background Technology

[0002] To facilitate grain storage, grain can be piled up in large grain silos. Changes in grain moisture content affect the quality of stored grain, necessitating moisture monitoring for reliable storage. Traditional methods typically involve inserting a probe into the grain to detect localized moisture levels. However, this method requires frequent probe movement, resulting in low efficiency. Furthermore, it only detects the surface area of ​​the grain; when the grain is piled high, accurate measurement of internal moisture content becomes impossible. Therefore, existing methods suffer from low efficiency in grain moisture detection. Summary of the Invention

[0003] This invention provides a method, apparatus, equipment, and medium for monitoring grain moisture based on probe wave analysis, aiming to solve the problem of low efficiency in grain moisture detection in existing technologies.

[0004] In a first aspect, embodiments of the present invention provide a method for monitoring grain moisture based on sounding wave analysis, wherein the method is applied to a monitoring terminal, the monitoring terminal establishing a communication connection with a sounding radar, a temperature sensor, a barometric pressure sensor, a distance sensor, and a driver, the driver being mounted on a slide rail, and the distance sensor and the sounding radar being fixedly mounted on the driver, the method comprising:

[0005] A scan command is sent to the driver to control the driver to drive the detection radar to scan the stored grain;

[0006] The system receives the echo signal obtained by the detection radar scan, and preprocesses the echo signal according to the signal processing rules to obtain the corresponding processed signal.

[0007] Based on the preset signal feature extraction rules, the distance value detected by the distance sensor, and the air pressure value detected by the air pressure sensor, the processed signal is subjected to feature extraction to obtain the corresponding signal features;

[0008] The signal features are calibrated according to the preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration features;

[0009] The calibration features are analyzed according to a pre-set standard moisture monitoring model to obtain the corresponding moisture monitoring values.

[0010] Secondly, embodiments of the present invention also provide a grain moisture monitoring device based on probe wave analysis, wherein the device is configured in a monitoring terminal, the monitoring terminal establishes a communication connection with a probe radar, a temperature sensor, a pressure sensor, a distance sensor, and a driver, the driver is disposed on a slide rail, and the distance sensor and the probe radar are both fixedly disposed on the driver, the device is used to execute the grain moisture monitoring method based on probe wave analysis as described in the first aspect above, the device comprising:

[0011] A scanning unit is used to send scanning commands to the driver to control the driver to drive the detection radar to scan the stored grain;

[0012] The signal preprocessing unit is used to receive the echo signal obtained by the detection radar scanning, preprocess the echo signal according to the signal processing rules, and obtain the corresponding processed signal.

[0013] The feature extraction unit is used to extract features from the processed signal according to preset signal feature extraction rules, the distance value detected by the distance sensor, and the air pressure value detected by the air pressure sensor, so as to obtain the corresponding signal features;

[0014] The calibration unit is used to calibrate the signal features according to a preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration features;

[0015] The feature analysis unit is used to perform feature analysis on the calibration features according to the preset standard moisture monitoring model to obtain the corresponding moisture monitoring value.

[0016] Thirdly, embodiments of the present invention also provide a computer device, wherein the device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0017] Memory, used to store computer programs;

[0018] When the processor executes the program stored in the memory, it implements the steps of the grain moisture monitoring method based on probe wave analysis described in the first aspect above.

[0019] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the grain moisture monitoring method based on probe wave analysis as described in the first aspect above.

[0020] This invention provides a method, apparatus, device, and medium for monitoring grain moisture based on sounding wave analysis. The method includes: controlling a driver via a scanning command to drive a sounding radar to scan stored grain; receiving and preprocessing the echo signals obtained from the scan to obtain corresponding processed signals; extracting features from the processed signals by combining distance and air pressure values ​​to obtain corresponding signal features; calibrating the signal features based on temperature values ​​to obtain calibration features; and finally performing feature analysis to obtain the corresponding moisture monitoring value. This grain moisture monitoring method based on sounding wave analysis can scan stored grain using a sounding radar and analyze the echo signals to accurately obtain the internal moisture content of the grain, significantly improving the efficiency and reliability of moisture monitoring, while also increasing the accuracy of detecting the internal moisture of the grain. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating the grain moisture monitoring method based on probe wave analysis provided in this embodiment of the invention;

[0023] Figure 2 A schematic diagram illustrating an application scenario of the grain moisture monitoring method based on probe wave analysis provided in this embodiment of the invention;

[0024] Figure 3 The image shows the application effect of the grain moisture monitoring method based on probe wave analysis provided in the embodiment of the present invention.

[0025] Figure 4 Another application effect diagram of the grain moisture monitoring method based on probe wave analysis provided in this embodiment of the invention;

[0026] Figure 5 A schematic block diagram of a grain moisture monitoring device based on probe wave analysis provided in an embodiment of the present invention;

[0027] Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0028] 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, not all, of the embodiments of 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.

[0029] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0030] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0031] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0032] This invention application provides a method for monitoring grain moisture based on sounding wave analysis. This method is applied in a monitoring terminal, which executes a stored software program to implement the aforementioned method for monitoring grain moisture based on sounding wave analysis. Figure 2 and Figure 3As shown, the monitoring terminal 10 can be a monitor or server configured within a grain storage enterprise or institution. The monitoring terminal 10 establishes a communication connection with the detection radar 20, temperature sensor 30, air pressure sensor 40, distance sensor 50, and driver 60. The driver 60 is mounted on a slide rail 61, allowing it to slide along the rail. The slide rail 61 can be positioned on the top of the grain silo. Simultaneously, the distance sensor 50 and the detection radar 20 are fixedly mounted on the driver 60. When the driver 60 moves horizontally on a plane, it simultaneously moves the distance sensor 50 and the detection radar 20, enabling a vertical scan of the grain below the detection radar 20. The detection radar 20 acquires echo signals, the temperature sensor 30 acquires the temperature value inside the grain silo, the air pressure sensor 40 acquires the air pressure value inside the grain silo, and the distance sensor 50 detects the distance between the distance sensor 50 and the top surface of the grain during vertical scanning. The echo signals, temperature values, air pressure values, and distance values ​​are then sent to the monitoring terminal 10. The monitoring terminal 10 analyzes and processes the detection information to obtain the corresponding moisture monitoring value, which is the overall moisture content of the grain stored in the current grain silo. The monitoring terminal 10 can be configured with a display screen to display the acquired moisture monitoring information for management personnel to view. Simultaneously, the monitoring terminal 10 can establish network connections with detectors configured in multiple grain silos, thereby enabling simultaneous moisture monitoring of multiple grain silos. The grain moisture monitoring method in this specific embodiment can be applied to grain silos with circular cross-sections, rectangular cross-sections, or other cross-sectional shapes.

[0033] like Figure 1 As shown, the method includes steps S110 to S150.

[0034] S110. Send a scanning command to the driver to control the driver to drive the detection radar to scan the stored grain.

[0035] A scan command is sent to the driver, which in turn controls the driver to drive the detection radar to scan the stored grain. The radar frequency is preferably selected between 300MHz and 1GHz to achieve deep penetration into the grain. This frequency band is also sensitive to the moisture content of the grain, making it suitable for detecting this moisture content. Preferably, the center frequency of the detection radar can be set to 500MHz, with a bandwidth of 200MHz, and a detection range resolution of approximately 0.3m. Since a single detection radar is used, there is no need to consider interference between radars. To achieve accurate detection, the power of the detection wave emitted by the radar can be set to 0.2-10W.

[0036] Specifically, the scanning command can be configured with the scanning points and the number of scans. The driver then moves the detection radar to each scanning point and performs a single scan. If the number of scans is configured to be 1, only one scan is performed. If the number of scans is configured to be multiple, the same scanning point is scanned multiple times. Data is acquired from multiple scans and averaged to reduce detection errors. For example, if the distance between adjacent scanning points along the x-axis or y-axis is set to 0.3m, a stepping motor is configured on the driver to provide power for movement. The translational speed of the driver is controlled by the stepping motor to be 0.5-1m / s.

[0037] S120. Receive the echo signal obtained by the detection radar scan, and preprocess the echo signal according to the signal processing rules to obtain the corresponding processed signal.

[0038] The probe wave penetrates the grain and is reflected, forming an echo signal. The detection radar detects the reflected echo signal. To improve detection accuracy, the sampling frequency of the echo signal can be set to 5-10 times the center frequency of the detection radar. The acquired echo signal can be preprocessed to obtain a processed signal that accurately reflects the moisture detection characteristics.

[0039] In a specific embodiment, step S120 includes the following sub-steps: filtering the echo signal according to the filtering algorithm in the signal processing rules to obtain a corresponding filtered signal; and enhancing the filtered signal according to the signal enhancement function in the signal processing rules to obtain a corresponding processed signal.

[0040] Specifically, the echo signal can be filtered using the filtering algorithm defined in the signal processing rules. This filtering algorithm can be based on a Butterworth high-pass filter. The core of this algorithm is to remove DC drift by attenuating low-frequency components and retaining the useful high-frequency signal. First, a Fast Fourier Transform (FFT) can be performed on the signal at each scan point in the echo signal to convert the time-domain signal into a frequency-domain signal for filtering. Then, the sampling frequency, cutoff frequency, order, and attenuation requirements are determined. The sampling frequency is the frequency used to detect the echo signal. The cutoff frequency of the high-pass filter is the "3dB attenuation point," such as the cutoff frequency f. c The frequency can be set to 0.15-0.5 times the center frequency f of the detection radar. The attenuation requirement can be set to no more than 3dB (retaining over 90% energy). The order of the Butterworth filter determines the steepness of the transition band (a higher order results in a steeper transition band, but also higher computational complexity), and needs to be calculated using the amplitude-frequency response formula.

[0041] The amplitude-frequency response of the Butterworth filter is shown in equation (1):

[0042]

[0043] Where, ω = 2πf (angular frequency), ω c =2πf c (3dB cutoff angular frequency), where n is the order. For the stopband frequency f stop The decay must satisfy Rs = 10log 10 [1+(ω c / ω stop ) 2n ≥40dB (In a high-pass filter, the stopband frequency is lower than the cutoff frequency, so ω is used) c / ω stop ), ω stop That is, with f stop The corresponding stopband angular frequency, such as setting the stopband frequency f stop The order n can be calculated by taking the center frequency of the detection radar as 0.1 times. The higher the order n, the better the filtering effect; however, the larger n is, the higher the computing power required for filtering.

[0044] Calculation with ω c The corresponding normalized cutoff frequency: f c / (f s / 2), where f s Let be the sampling frequency; and calculate ω. stop The corresponding normalized stopband frequency: f stop / (f s / 2). The core parameters in the filtering algorithm are configured by calculating the normalized cutoff frequency, normalized stopband frequency, and order n, and the frequency domain signal is filtered by the filtering algorithm with the parameters configured to obtain the corresponding filtered signal.

[0045] Furthermore, the filtered signal can be enhanced using the signal enhancement function configured in the signal processing rules to obtain the corresponding processed signal. The signal enhancement function can be a wavelet transform-based function, which performs a wavelet transform on the filtered signal. Depending on the type of grain in the granary, a suitable wavelet basis function (such as Daubechies wavelet, 3-level decomposition) can be selected to perform multi-level wavelet decomposition on the filtered signal, obtaining coefficients for different frequency sub-bands. Feature coefficients are then selected to amplify those that reflect the characteristics of the effective signal. Noise-related coefficients are thresholded by setting soft or hard thresholds. Finally, the signal is reconstructed using inverse wavelet transform, and the frequency domain signal is converted back to the corresponding time domain signal to achieve signal enhancement and obtain the processed signal corresponding to the filtered signal. The resulting processed signal highlights the reflection signal characteristics related to moisture detection of specific types of grain.

[0046] S130. Based on the preset signal feature extraction rules, the distance value detected by the distance sensor, and the air pressure value detected by the air pressure sensor, the processed signal is subjected to feature extraction to obtain the corresponding signal features.

[0047] The monitoring terminal can acquire distance values ​​detected by the distance sensor and simultaneously acquire air pressure values ​​detected by the air pressure sensor. It then uses signal feature extraction rules, distance values, and air pressure values ​​to extract features from the processed signal obtained in the previous steps, thereby obtaining signal features. These signal features include the reflection coefficient and dielectric constant corresponding to each scanning point. Specifically, based on the propagation law of electromagnetic waves in a medium, when the moisture content of a certain area in the medium changes, its ability to absorb electromagnetic waves also changes. The higher the moisture content in grain, the greater the dielectric constant εr and the greater the reflection coefficient Γ. Therefore, by analyzing and processing the signal to obtain the dielectric constant and reflection coefficient of the grain, the moisture content of the grain can be analyzed.

[0048] In a specific embodiment, step S130 includes the following sub-steps: extracting corresponding key information from a set of signals corresponding to each distance value in the processed signal according to the extraction parameters set in the signal feature extraction rules and the distance value; analyzing each key information and its corresponding distance value according to the analytical expression of the reflection coefficient in the signal feature extraction rules and the air pressure value to obtain the reflection coefficient corresponding to each distance value; analyzing each key information and its corresponding distance value according to the analytical expression of the dielectric constant in the signal feature extraction rules and the air pressure value to obtain the dielectric constant corresponding to each distance value; and combining the reflection coefficient and dielectric constant of each distance value to obtain the corresponding signal feature.

[0049] Each scanning point corresponds to a distance value detected by the distance sensor. Each distance value then corresponds to a set of signals for that scanning point. Key information can be extracted from each set of signals in the processed signal. Specifically, a set of time-domain signals in the processed signal includes... Figure 4 As shown, the incident interval time can be calculated based on the distance value. The incident interval time is the time between the emission of the probe wave and the incident time of the probe wave (the time when the probe wave enters the grain surface). The incident interval time t1 = h / c, where c is the speed of electromagnetic wave signal propagation in vacuum (c = 3 × 10⁻⁶). 8 m / s), where h is the distance value; here, only a rough time interval of incidence is needed, so the propagation speed of electromagnetic wave signals in air can be directly taken as a rough value of 3×10. 8m / s. The corresponding incident time interval can be determined based on the incident interval, which is also the interval in the echo signal corresponding to the received electromagnetic wave reflected from the grain surface. For example, if the incident time interval can be determined to be 1.8t1-2.2t1, this incident time interval can be... Figure 4 The corresponding time-domain signal is labeled, and the signal value with the highest amplitude within the incident time interval from the corresponding set of signals is taken as the incident signal amplitude. Further, the corresponding rebound time point is determined based on the extracted parameters. When the electromagnetic wave signal passes through the grain and contacts the bottom of the warehouse, it will also generate a strong reflected signal, and the intensity of the reflected signal will increase sharply at this time. The extracted parameters include a bottoming amplitude threshold and a bottoming slope threshold, which can be used to judge the signal after the incident signal amplitude. It is determined whether the slope of the signal curve between the previous moment and the current moment is greater than the bottoming slope threshold. If the slope of the signal curve between the previous moment and the current moment is greater than the bottoming slope threshold, and the signal intensity at the current moment is greater than the bottoming amplitude threshold, then the current moment can be taken as the rebound time point (e.g., ...). Figure 4 (Time t2 in the equation is the rebound time point). The highest amplitude signal value among the signals at and after the rebound time point is taken as the bottoming signal amplitude. By combining the incident signal amplitude, the rebound time point, and the bottoming signal amplitude, a corresponding set of key information can be obtained for each group of signals in the processed signal.

[0050] In a specific embodiment, before extracting the corresponding key information from a group of signals corresponding to each distance value in the processed signal according to the extraction parameters set in the signal feature extraction rules and the distance value, the method further includes: organizing the signals located at the same scanning point in the processed signal according to the scanning point set in the scanning instruction; and associating and combining a group of signals at the same scanning point with a corresponding distance value according to the correspondence between the scanning point and the distance value.

[0051] Before obtaining key information, signals at the same scanning point can be organized according to the scanning point set in the scanning command. If the same scanning point contains multiple sets of signals, the average of the multiple sets of signals can be calculated to obtain the signal curves corresponding to the multiple sets of signals, which can then be used as... Figure 4Key information is extracted from the time-domain signal shown. Each scan point corresponds to one or more distance values. If the same scan point is scanned multiple times, multiple distance values ​​can be obtained. Based on the correspondence between the scan point and the distance value, the average of the multiple distance values ​​for the same scan point is calculated, and the calculated average distance value is associated with a set of signals for the same scan point. Through association and combination, it can be ensured that the same scan point corresponds to a distance value (here, the distance value can also be the average of multiple distance values) and a set of signals (here, the signal can be the signal curve obtained by averaging multiple sets of signals for the same scan point).

[0052] In a specific embodiment, the step of parsing each key piece of information and its corresponding distance value according to the reflection coefficient analytical expression in the signal feature extraction rules and the air pressure value to obtain the reflection coefficient corresponding to each distance value includes: calculating the ratio between the air pressure value and the standard atmospheric pressure to obtain the air pressure ratio; and inputting the air pressure ratio, the signal amplitude in the key information and the corresponding distance value into the reflection coefficient analytical expression for parsing to obtain the corresponding reflection coefficient.

[0053] Further, based on the analytical formula of reflection coefficient in the signal feature extraction rules and the air pressure value, the key information and the distance value corresponding to the key information are analyzed to obtain the reflection coefficient corresponding to each distance value. Specifically, the higher the air pressure value, the greater the air density. The greater the air density, the slower the propagation speed of electromagnetic waves and the stronger the scattering of electromagnetic waves. The lower the air pressure value, the faster the propagation speed of electromagnetic waves and the weaker the scattering of electromagnetic waves. The ratio P / P0 between the air pressure value and the standard atmospheric pressure can be calculated, where P is the air pressure value and P0 is the standard atmospheric pressure. The air pressure ratio, the signal amplitude in the key information and the corresponding distance value are further input into the analytical formula of reflection coefficient to obtain the corresponding reflection coefficient. Then, a distance value and the signal amplitude of a set of signals corresponding to that distance value can be analyzed to obtain a reflection coefficient. The analytical formula of reflection coefficient is shown in formula (2):

[0054]

[0055] Where k is the basic scattering coefficient (the scattering coefficient of air to electromagnetic waves under standard atmospheric pressure), P is the air pressure value, P0 is the standard atmospheric pressure, A1 is the amplitude of the incident signal in the signal amplitude, A2 is the amplitude of the transmitted signal of the probe wave (signal transmission intensity), and Γ is the calculated reflection coefficient. The higher the moisture content inside the grain, the greater the dielectric constant εr of the grain pile, and the greater the corresponding reflection coefficient Γ. The reflection coefficient Γ and the dielectric constant εr do not follow a linear relationship.

[0056] In a specific embodiment, the step of analyzing each key piece of information and its corresponding distance value according to the dielectric constant analytical expression in the signal feature extraction rules and the air pressure value to obtain the dielectric constant corresponding to each distance value includes: calculating the ratio between the air pressure value and the standard atmospheric pressure to obtain the air pressure ratio; calculating the interval between the transmission time of the probe wave and the echo reception time in the key information; and inputting the air pressure ratio, the interval time, and the corresponding distance value into the dielectric constant analytical expression for analysis to obtain the corresponding dielectric constant.

[0057] Furthermore, the time interval between the transmission time of the probe wave and the reception time of the echo is calculated, which is to obtain... Figure 4 The interval between time t2 and the transmission time of the probe wave; if the transmission time of the probe wave is taken as zero, then the time at time t2 can be directly obtained as the corresponding interval. Input the pressure ratio, interval, and distance into the dielectric constant analytical formula, and the corresponding dielectric constant can be obtained analytically. Specifically, the change in the dielectric constant inside the grain directly affects the propagation speed of electromagnetic waves inside the grain, and the increase in the moisture content of the grain will correspondingly increase the dielectric constant inside the grain. The dielectric constant analytical formula is shown in formula (3):

[0058]

[0059] Where c is the propagation speed of electromagnetic waves in a vacuum, εr is the calculated dielectric constant of the grain, L is the vertical distance between the installation position of the distance sensor and the bottom of the grain silo (since the distance sensor scans on a plane, the vertical distance between the distance sensor and the bottom of the grain silo is theoretically constant), t2 is the interval time, and s is the distance between the sensor and the bottom of the grain silo. p Let be the dielectric constant of air at pressure p. According to the principles of physics, s p It can be calculated by corresponding air pressure ratio, and the specific calculation formula is shown in formula (4):

[0060]

[0061] Where s0 is the dielectric constant of air at standard atmospheric pressure P0, and p is the measured air pressure value.

[0062] Each distance value corresponds to a reflection coefficient and a dielectric constant. By combining the reflection coefficient and dielectric constant of each distance value, the corresponding signal characteristics can be obtained.

[0063] S140. The signal features are calibrated according to the preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration features.

[0064] Furthermore, temperature changes have a certain impact on the dielectric constant and reflection coefficient of grains. To eliminate the influence of temperature changes on the measurement results, the aforementioned signal characteristics can be calibrated using a feature calibration strategy and the actual measured temperature values, thereby obtaining calibration characteristics corresponding to the signal characteristics. Specifically, the feature calibration strategy is based on a configuration corresponding to a reference temperature. By calibrating the signal characteristics, calibration characteristics corresponding to the reference temperature are obtained, enabling accurate measurement of the internal moisture content of grains within a standard model corresponding to the reference temperature.

[0065] In a specific embodiment, step S140 includes the following sub-steps: inputting the temperature value into the feature calibration strategy to obtain a corresponding correction value; calibrating the signal feature according to the correction value to obtain a calibration feature corresponding to the signal feature.

[0066] Specifically, the currently measured temperature value is input into the feature calibration strategy to obtain the correction value. Since the detection radar scan requires a certain amount of time, the temperature value can be continuously measured during the scan and the average value can be calculated for calibration, thereby further improving the accuracy of the calibration; the average value of the continuously measured temperature values ​​can be input into the aforementioned feature calibration strategy. For example, the specific process of obtaining the correction value corresponding to the dielectric constant can be represented by formula (5):

[0067] X = (T - T0) × Δε (5);

[0068] Where X is the calculated correction value, T is the currently measured temperature value (or the average of continuously measured temperature values ​​during the scanning process), T0 is the reference temperature (e.g., T0 is set to 20℃), and Δε is the correction coefficient, such as Δε = -0.02. Based on the above parameter settings, if T = 23.5℃, the corresponding correction value X for the dielectric constant can be calculated to be -0.07.

[0069] Based on the principle of similarity, the correction value corresponding to the reflection coefficient can be calculated (the correction coefficient Δε corresponding to the reflection coefficient is different from the value in the example above).

[0070] The signal characteristics are calibrated using correction values ​​to obtain the corresponding calibration characteristics. For example, the dielectric constant of each distance value in the signal characteristics can be corrected using a correction value of -0.07 corresponding to the dielectric constant. The original dielectric constant of each distance value is then added to the correction value to obtain the corrected dielectric constant. Based on the same principle, the reflection coefficient of each distance value in the signal characteristics can be added to its corresponding correction value to obtain the corrected reflection coefficient. Combining the corrected dielectric constant and the corrected reflection coefficient yields the calibrated calibration characteristics.

[0071] S150. Perform feature analysis on the calibration features according to the preset standard moisture monitoring model to obtain the corresponding moisture monitoring value.

[0072] Based on the standard moisture monitoring model corresponding to the current grain type, feature analysis is performed on the calibration features to obtain the corresponding moisture detection value. Before using the standard moisture monitoring model, a model corresponding to the current grain type must first be selected. The reflection coefficient and dielectric constant of each distance value in the calibration features can be analyzed to obtain a corresponding detection value. Each distance value corresponds to a scanning point, and this detection value reflects the moisture content of a scanning point in the grain.

[0073] In practical applications, standard moisture monitoring models corresponding to various grain types can be constructed at a reference temperature T0. For example, at the reference temperature T0 and under experimental conditions, the reflectance coefficient and dielectric constant of the same grain type under different humidity levels can be measured to construct the corresponding function model as the standard moisture monitoring model. In the constructed standard moisture monitoring model, the moisture content M is the dependent variable, and the reflectance coefficient and dielectric constant are the independent variables. The least squares method can be used to fit the moisture content obtained for each independent variable.

[0074] For example, the relationship between dielectric constant and grain moisture content can be constructed using a function model based on linear fitting, as shown in formula (6):

[0075]

[0076] Where M is the water content at a certain scanning point, εr is the dielectric constant at a certain scanning point, and v1, v2, v3, b1, and b2 are all parameter values ​​in the formula. For example, at a probe wave center frequency of 500MHz, M≥14%, and its experimentally measured function is M=0.82×εr-3.15(R 2 =0.97, R 2 For consistent function fitting, R 2 The larger the value, the higher the consistency of the function fit. Through the above steps, the water content corresponding to the dielectric constant can be obtained. Then, the water content corresponding to the reflection coefficient can be obtained through a similar function model. The water content of the two independent variables is fitted by the least squares method to obtain an accurate moisture monitoring value for a scanning site.

[0077] Furthermore, in other embodiments, a neural network model corresponding to the water content M, reflectance coefficient, and dielectric constant can be constructed as the aforementioned standard moisture monitoring model. The input layer of the neural network model is used to input the reflectance coefficient and dielectric constant; the neural network model can be configured to include one or more hidden layers, each with multiple hidden nodes; the output layer of the neural network model is configured to output the water content M. The constructed neural network model is trained and tested using test and validation datasets to ensure it can efficiently analyze and accurately obtain the water content M. Based on the tested neural network model as the standard moisture monitoring model, correlation analysis is performed on the reflectance coefficient and dielectric constant for each distance value to obtain the water content M corresponding to each distance value as the moisture monitoring value for the corresponding scanning site.

[0078] The grain moisture monitoring method based on sounding wave analysis disclosed in the above embodiments includes: controlling a driver to drive a sounding radar to scan the stored grain using a scanning command; receiving and preprocessing the echo signals obtained from the scan to obtain corresponding processed signals; extracting features from the processed signals by combining distance and air pressure values ​​to obtain corresponding signal features; calibrating the signal features according to temperature values ​​to obtain calibration features; and then performing feature analysis to obtain the corresponding moisture monitoring value. This grain moisture monitoring method based on sounding wave analysis can scan the stored grain using a sounding radar and analyze the echo signals to accurately obtain the internal moisture content of the grain, significantly improving the efficiency and reliability of moisture monitoring, while also increasing the accuracy of detecting the internal moisture of the grain.

[0079] This invention also provides a grain moisture monitoring device based on probe wave analysis. This device can be configured in a monitoring terminal and is used to execute any of the aforementioned embodiments of the grain moisture monitoring method based on probe wave analysis. Specifically, please refer to... Figure 5 , Figure 5 This is a schematic block diagram of a grain moisture monitoring device based on probe wave analysis provided in an embodiment of the present invention.

[0080] like Figure 5 As shown, the grain moisture monitoring device 100 based on probe wave analysis includes a scanning unit 110, a signal preprocessing unit 120, a feature extraction unit 130, a calibration unit 140, and a feature analysis unit 150.

[0081] The scanning unit 110 is used to send scanning commands to the driver to control the driver to drive the detection radar to scan the stored grain.

[0082] The signal preprocessing unit 120 is used to receive the echo signal obtained by the detection radar scanning, and preprocess the echo signal according to the signal processing rules to obtain the corresponding processed signal.

[0083] The feature extraction unit 130 is used to extract features from the processed signal according to the preset signal feature extraction rules, the distance value detected by the distance sensor, and the air pressure value detected by the air pressure sensor, so as to obtain the corresponding signal features.

[0084] The calibration unit 140 is used to calibrate the signal features according to a preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration features.

[0085] The feature analysis unit 150 is used to perform feature analysis on the calibration features according to a preset standard moisture monitoring model to obtain the corresponding moisture monitoring value.

[0086] The grain moisture monitoring device based on probe wave analysis provided in this embodiment of the invention applies the aforementioned grain moisture monitoring method based on probe wave analysis. A scanning command controls a driver to drive a probe radar to scan the stored grain. The received echo signals are preprocessed to obtain corresponding processed signals. Features are extracted from the processed signals by combining distance and air pressure values ​​to obtain corresponding signal features. The signal features are calibrated based on temperature values ​​to obtain calibration features. Finally, feature analysis is performed to obtain the corresponding moisture monitoring value. This grain moisture monitoring method based on probe wave analysis can scan stored grain using a probe radar and analyze the echo signals to accurately obtain the internal moisture content of the grain, significantly improving the efficiency and reliability of moisture monitoring, while also increasing the accuracy of detecting the internal moisture of the grain.

[0087] The aforementioned grain moisture monitoring device based on probe wave analysis can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0088] Please see Figure 6 , Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device can be a monitoring terminal used to execute a grain moisture monitoring method based on probe wave analysis to monitor the moisture content of grain inside a grain warehouse.

[0089] See Figure 6 The computer device 500 includes a processor 502, a memory, and a communication interface 505 connected via a communication bus 501. The memory may include a storage medium 503 and internal memory 504.

[0090] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it enables the processor 502 to execute a grain moisture monitoring method based on probe wave analysis. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0091] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0092] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a grain moisture monitoring method based on probe wave analysis.

[0093] This communication interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device 500 to which the present invention is applied. The specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0094] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned grain moisture monitoring method based on probe wave analysis.

[0095] Those skilled in the art will understand that Figure 6 The embodiments of the computer device shown do not constitute a limitation on the specific configuration of the computer device. In other embodiments, the computer device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. For example, in some embodiments, the computer device may include only memory and a processor. In such embodiments, the structure and function of the memory and processor are different from those shown. Figure 6 The embodiments shown are consistent and will not be described again here.

[0096] It should be understood that, in this embodiment of the invention, the processor 502 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0097] In another embodiment of the invention, a computer-readable storage medium is provided. This computer-readable storage medium may be volatile or non-volatile. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps included in the above-described grain moisture monitoring method based on probe wave analysis.

[0098] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0099] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Units with the same function may be grouped into one unit. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned computer-readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks.

[0103] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A grain moisture monitoring method based on analysis of a probe wave, characterized by, The method is applied to a monitoring terminal, the monitoring terminal is in communication connection with a detection radar, a temperature sensor, an air pressure sensor, a distance sensor and a driver, the driver is arranged on a slide rail, the distance sensor and the detection radar are fixedly arranged on the driver, the driver slides along the slide rail, the slide rail is arranged on the top of a granary, the driver drives the distance sensor and the detection radar to move synchronously while horizontally moving on a plane, so as to realize vertical scanning of the granary below the detection radar, the detection radar obtains a return signal, the temperature sensor obtains a temperature value in the granary, the air pressure sensor obtains an air pressure value in the granary, the distance sensor detects a distance value between the distance sensor and the top surface of the grain during vertical scanning, and the return signal, the temperature value, the air pressure value and the distance value are sent to the monitoring terminal, and the method comprises: sending a scanning instruction to the driver to control the driver to drive the detection radar to scan the stored grain; receiving a return signal scanned by the detection radar, pre-processing the return signal according to a signal processing rule to obtain a corresponding processing signal; extracting features of the processing signal according to a preset signal feature extraction rule, a distance value detected by the distance sensor and an air pressure value detected by the air pressure sensor to obtain corresponding signal features; calibrating the signal features according to a preset feature calibration strategy and a temperature value monitored by the temperature sensor to obtain corresponding calibration features; performing feature analysis on the calibration features according to a preset standard moisture monitoring model to obtain a corresponding moisture monitoring value; the extracting features of the processing signal according to the preset signal feature extraction rule, the distance value detected by the distance sensor and the air pressure value detected by the air pressure sensor to obtain the corresponding signal features, comprising: extracting corresponding key information from a group of signals corresponding to each distance value in the processing signal according to extraction parameters set in the signal feature extraction rule and the distance value; According to the reflection coefficient analytical expression in the signal feature extraction rule and the air pressure value, each key information and the corresponding distance value are analyzed to obtain the reflection coefficient corresponding to each distance value. Specifically, the ratio between the air pressure value and the standard atmospheric pressure is calculated to obtain an air pressure ratio. The air pressure ratio and the signal amplitude in the key information are input into the reflection coefficient analytical expression to obtain the corresponding reflection coefficient. The reflection coefficient analytical expression is: wherein, is the reflection coefficient, k is the basic scattering coefficient, the basic scattering coefficient is: the scattering coefficient of air to electromagnetic waves under standard atmospheric pressure, P is the air pressure value, P0 is the standard atmospheric pressure, A1 is the incident signal amplitude in the signal amplitude, and A2 is the transmission signal amplitude of the detection wave. analyzing each key information and corresponding distance value according to a dielectric constant analysis formula in the signal feature extraction rule and the air pressure value to obtain a dielectric constant corresponding to each distance value, comprising: calculating the ratio between the air pressure value and the standard atmospheric pressure to obtain an air pressure ratio; calculating the interval time between the transmission time of the detection wave and the echo reception time in the key information; The air pressure ratio, the interval time and the corresponding distance value are input into the dielectric constant analysis formula to obtain the corresponding dielectric constant, including: according to the dielectric constant of air under standard atmospheric pressure and the corresponding calculation of the dielectric constant of air under the air pressure value ; s0 is the standard atmospheric pressure P 0 The dielectric constant of air under the air pressure value, P is the measured air pressure value, s p The dielectric constant of air under the air pressure value, P is the measured air pressure value, s ; c is the propagation speed of electromagnetic wave signal in vacuum, εr is the calculated dielectric constant of grain, L The vertical distance between the installation position of the distance sensor and the bottom of the grain depot, h is the distance value between the distance sensor and the top surface of the grain, t2 is the interval time; combining the reflection coefficient of each distance value and the dielectric constant to obtain the corresponding signal features.

2. The method of claim 1, wherein the method further comprises: the pre-processing of the return signal according to the signal processing rule to obtain the corresponding processing signal, comprising: filtering the return signal according to a filtering algorithm in the signal processing rule to obtain a corresponding filtered signal; performing signal enhancement on the filtered signal according to a signal enhancement function in the signal processing rule to obtain the corresponding processing signal.

3. The method of claim 2, wherein the step of analyzing the probe wave is performed by a method comprising: Before the corresponding key information is extracted from the group of signals corresponding to each distance value in the processed signal according to the extraction parameters set in the signal feature extraction rule and the distance value, the method further comprises the following steps of: According to the scanning point set in the scanning instruction, the signals in the processed signal located at the same scanning point are arranged; According to the correspondence between the scanning point and the distance value, the group of signals at the same scanning point is associated and combined with the corresponding distance value.

4. The method of claim 1-3, wherein, The signal feature is calibrated according to the preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration feature, including: The temperature value is input into the feature calibration strategy to obtain the corresponding correction value; The signal feature is calibrated according to the correction value to obtain the calibration feature corresponding to the signal feature.

5. A grain moisture monitoring device based on analysis of a probe wave, characterized by The device is configured in a monitoring terminal, and the monitoring terminal is in communication connection with a detection radar, a temperature sensor, an air pressure sensor, a distance sensor and a driver. The driver is arranged on a slide rail. The distance sensor and the detection radar are fixedly arranged on the driver. The driver slides along the slide rail. The slide rail is arranged at the top of the grain warehouse. The driver drives the distance sensor and the detection radar to move synchronously while moving horizontally on the plane, so as to realize vertical scanning of the grain in the grain warehouse below the detection radar. The detection radar obtains the echo signal. The temperature sensor obtains the temperature value in the grain warehouse. The air pressure sensor obtains the air pressure value in the grain warehouse. The distance sensor detects the distance value between the distance sensor and the top surface of the grain during vertical scanning. The echo signal, the temperature value, the air pressure value and the distance value are sent to the monitoring terminal. The device is used to execute the grain moisture monitoring method based on detection wave analysis according to any one of claims 1-4. The device comprises: A scanning unit is configured to send a scanning instruction to the driver to control the driver to drive the detection radar to scan the stored grain; A signal preprocessing unit is configured to receive the echo signal scanned by the detection radar, and preprocess the echo signal according to the signal processing rule to obtain the corresponding processed signal; A feature extraction unit is configured to extract the signal feature from the processed signal according to the preset signal feature extraction rule, the distance value detected by the distance sensor and the air pressure value detected by the air pressure sensor; A calibration unit is configured to calibrate the signal feature according to the preset feature calibration strategy and the temperature value monitored by the temperature sensor to obtain the corresponding calibration feature; A feature analysis unit is configured to analyze the calibration feature according to the preset standard moisture monitoring model to obtain the corresponding moisture monitoring value.

6. A computer device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store a computer program. A processor for implementing the steps of the method of grain moisture monitoring based on analysis of a probe wave as claimed in any one of claims 1 to 4 when executing a program stored on a memory.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program for implementing the steps of the method of grain moisture monitoring based on analysis of a probe wave as claimed in any one of claims 1 to 4 when executed by a processor.

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