Grain moisture content measurement method and device based on pulse radar measurement signal analysis

Abstract

The application discloses a grain moisture content measurement method and device based on pulse radar measurement signal analysis, and the method comprises the following steps: receiving initial measurement signals containing grain measurement signals and metal measurement signals input by a detection radar, intercepting key measurement signals therefrom, and performing background noise suppression on the key measurement signals according to a background noise suppression strategy to obtain measurement suppression signals; extracting corresponding reflection coefficients from the measurement suppression signals according to a reflection coefficient extraction rule, and performing inversion operation on the reflection coefficients to obtain corresponding inversion dielectric constants; and performing mapping on the inversion dielectric constants according to a mapping curve to obtain a moisture content measurement value. The grain moisture content measurement method described above can obtain two groups of signals through grain detection and metal detection respectively, remove the influence of noise through background noise suppression, extract reflection coefficients and perform inversion operation to accurately obtain a moisture content measurement value corresponding to the inversion dielectric constant, and greatly improve the accuracy of grain moisture content measurement.

Description

Technical Field

[0001] This invention relates to the field of radar signal analysis technology, and in particular to a method and apparatus for measuring the moisture content of grains based on pulse radar measurement signal analysis. Background Technology

[0002] To accurately measure the moisture content of grains in granaries, existing technologies typically employ pulse radar to detect and acquire measurement signals. These signals are then analyzed to obtain the dielectric constant of the grain, which is used to map the moisture content. However, the dielectric constants obtained from analyzing radar signals of different frequencies vary significantly, and system interference and noise cannot be effectively eliminated, making it difficult to accurately obtain the dielectric constant and measure the grain moisture content. This results in a large discrepancy between the measured moisture content and the actual moisture content. Therefore, existing methods for measuring grain moisture content based on radar signal analysis suffer from poor measurement accuracy. Summary of the Invention

[0003] This invention provides a method and apparatus for measuring grain moisture content based on pulse radar measurement signal analysis, aiming to solve the problem of poor measurement accuracy in existing methods for measuring grain moisture content based on radar signal analysis.

[0004] In a first aspect, embodiments of the present invention provide a method for measuring the moisture content of grains based on pulse radar measurement signal analysis, wherein the method is applied to a measurement terminal, the measurement terminal being communicatively connected to a detection radar to achieve data transmission, and the method includes: The system receives the initial measurement signal input from the detection radar and extracts the corresponding key measurement signal from the initial measurement signal according to a preset signal extraction rule; the initial measurement signal includes grain measurement signal and metal measurement signal. The key measurement signal is subjected to background noise suppression according to a preset background noise suppression strategy to obtain the corresponding measurement suppression signal; The corresponding reflection coefficient is extracted from the measurement suppression signal according to the preset reflection coefficient extraction rules; The reflection coefficient is inverted according to the preset inversion strategy to obtain the corresponding inversion dielectric constant; The inversion dielectric constant is mapped according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

[0005] Secondly, embodiments of the present invention also provide a grain moisture content measuring device based on pulse radar measurement signal analysis, wherein the device is configured in a measuring terminal, the measuring terminal is communicatively connected to a detection radar to realize data information transmission, and the device is used to execute the grain moisture content measuring method based on pulse radar measurement signal analysis as described in the first aspect above, the device comprising: The signal interception and processing unit is used to receive the initial measurement signal input by the detection radar, and to intercept the corresponding key measurement signal from the initial measurement signal according to the preset signal interception rules; the initial measurement signal includes grain measurement signal and metal measurement signal; The noise suppression processing unit is used to suppress background noise on the key measurement signal according to a preset background noise suppression strategy to obtain a corresponding measurement suppression signal. A reflection coefficient extraction unit is used to extract the corresponding reflection coefficient from the measurement suppression signal according to a preset reflection coefficient extraction rule; The inversion operation unit is used to perform inversion operation on the reflection coefficient according to the preset inversion strategy to obtain the corresponding inversion dielectric constant; The mapping processing unit is used to map the inverted dielectric constant according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

[0006] 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; Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the steps of the grain moisture content measurement method based on pulse radar measurement signal analysis described in the first aspect above.

[0007] 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 content measurement method based on pulse radar measurement signal analysis as described in the first aspect above.

[0008] This invention provides a method and apparatus for measuring grain moisture content based on pulse radar measurement signal analysis. The method includes: receiving an initial measurement signal input from the detection radar, which includes grain measurement signals and metal measurement signals; extracting key measurement signals from the signal and performing background noise suppression according to a background noise suppression strategy to obtain a measurement suppression signal; extracting the corresponding reflection coefficient from the measurement suppression signal according to a reflection coefficient extraction rule and performing an inversion operation to obtain the corresponding inversion dielectric constant; and mapping the inversion dielectric constant according to a mapping curve to obtain the moisture content measurement value. This grain moisture content measurement method significantly improves the accuracy of grain moisture content measurement by acquiring two sets of signals through grain detection and metal detection, removing noise influence through background noise suppression, and accurately obtaining the moisture content measurement value corresponding to the inversion dielectric constant through reflection coefficient extraction and inversion operation. Attached Figure Description

[0009] 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.

[0010] Figure 1 This is a flowchart of a method for measuring grain moisture content based on pulse radar measurement signal analysis, provided in an embodiment of the present invention. Figure 2 A schematic diagram illustrating an application scenario of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in an embodiment of the present invention; Figure 3 This is an application effect diagram of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in an embodiment of the present invention; Figure 4 This is another application effect diagram of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in the embodiments of the present invention; Figure 5 This is another application effect diagram of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in the embodiments of the present invention; Figure 6 This is a diagram illustrating another application of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in this embodiment of the invention. Figure 7 The diagram shows the effect of a subsequent application of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in this embodiment of the invention. Figure 8 This is a diagram illustrating another application effect of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in an embodiment of the present invention. Figure 9 This is a subsequent application effect diagram of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in the embodiments of the present invention; Figure 10 The following is a further application effect diagram of the grain moisture content measurement method based on pulse radar measurement signal analysis provided in the embodiment of the present invention; Figure 11 A schematic block diagram of a grain moisture content measuring device based on pulse radar measurement signal analysis provided in an embodiment of the present invention; Figure 12 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0011] 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.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] This invention application provides a method for measuring grain moisture content based on pulse radar measurement signal analysis. This method is applied in a measurement terminal, which executes a stored software program to implement the aforementioned method for measuring grain moisture content based on pulse radar measurement signal analysis. Figure 2As shown, the measuring terminal 10 communicates with the detection radar 20 to transmit data. The detection radar 20 is a radar component used for pulse signal transmission and reception. The detection radar 20 detects the object 1 in the grain silo below and collects the detection data, then sends it to the measuring terminal 10. Alternatively, the measuring terminal 10 and the detection radar 20 can be integrated into a single detection device. In this case, the detection radar 20 acquires the detection data and processes it locally through the measuring terminal 10 to obtain the moisture content measurement value of the grain. The measuring terminal 10 can be a terminal module with an MCU chip built into the detection device. Alternatively, the measuring terminal 10 and the detection radar 20 can be set up independently. In this case, the detection radar 20 acquires the detection data and transmits it to the measuring terminal 10 for remote processing to obtain the moisture content measurement value of the grain. The measuring terminal 10 can be a desktop computer, laptop computer, tablet computer, or mobile phone, etc.

[0016] like Figure 1 As shown, the method includes steps S110 to S150.

[0017] S110. Receive the initial measurement signal input by the detection radar, and extract the corresponding key measurement signal from the initial measurement signal according to the preset signal interception rules.

[0018] First, the initial measurement signal obtained from the detection radar is received. This initial measurement signal includes both grain measurement signals and metal measurement signals. Specifically, the detection radar can be installed at a specific distance above the top surface of the object (the top surface of the grain), such as 10cm. Other signals inside the grain silo (such as mobile phone signals) are removed, and multiple rounds of detection are performed. Outliers are eliminated, and the signals from the multiple rounds of detection are averaged to obtain the corresponding grain measurement signal. The specific detection method is as follows: Figure 2 As shown, a metal plate is laid on the top surface of the object (the top surface of the grain). The distance between the detection radar and the metal plate is maintained at the specific distance mentioned above. Other signals inside the grain silo (such as mobile phone signals) are removed, and multiple rounds of detection (such as 10 rounds) are performed. Outliers are eliminated, and the signals from the multiple rounds of detection are averaged to obtain the corresponding metal measurement signal. The obtained grain measurement signal and metal measurement signal are combined as the initial measurement signal; the detection parameters of the metal measurement signal (pulse period, sampling frequency, etc.) are kept consistent with those of the grain measurement signal.

[0019] After receiving the initial measurement signal, the key measurement signal can be extracted from the initial measurement signal according to the pre-configured signal extraction rules. The key measurement signal focuses only on the key information in the initial measurement signal.

[0020] In a specific embodiment, step S110 includes the following sub-steps: obtaining the single-pulse maximum amplitude points corresponding to the grain measurement signal and the metal measurement signal of the initial measurement signal, respectively; using the single-pulse maximum amplitude points to extract sampling points from the grain measurement signal and the metal measurement signal to obtain the corresponding single-cycle grain signal and single-cycle metal signal; performing multi-cycle averaging calculations on the single-cycle grain signal and the single-cycle metal signal to obtain the corresponding single-cycle grain signal and single-cycle metal signal; and extracting the corresponding key grain measurement signal and key metal measurement signal from the single-cycle grain signal and the single-cycle metal signal, respectively, according to the number of extraction points set in the signal extraction rules, as the corresponding key measurement signals.

[0021] Specifically, the location of the maximum amplitude of a single pulse in the grain measurement signal can be obtained. Multiple sets of pulse signals can be sent in each round of detection. After each set of pulse signals is sent, the corresponding signal echo can be received. The location corresponding to the maximum amplitude in the signal echo of each pulse signal is the location of the maximum amplitude of the single pulse. For example... Figure 3 As shown, the original grain measurement signal obtained after the grain continuously emits multiple sets of pulse signals is as follows: Figure 3 As shown, the horizontal axis represents the acquisition time, and the vertical axis represents the signal amplitude. The point corresponding to the largest amplitude value in a single pulse signal is also the point of maximum amplitude of the single pulse. The same method is used to obtain the point of maximum amplitude of the single pulse in the metal measurement signal.

[0022] Based on the determined maximum amplitude point of the single pulse, sampling points are extracted from the grain measurement signal. A single pulse cycle can acquire amplitude signals from 4000 sampling points. Therefore, starting from the maximum amplitude point of each pulse cycle, extending forward by 3999 sampling points, the amplitude signal corresponding to 4000 sampling points for one pulse cycle can be extracted as the single-cycle grain signal. Since multiple pulses are detected, multiple sets of single-cycle grain signals can be extracted separately. The single-cycle grain signals corresponding to a single pulse cycle are as follows: Figure 4 As shown, the horizontal axis represents the sampling points, and the vertical axis represents the signal amplitude. Using the same processing method, the sampling points of the metal measurement signal can be truncated to obtain the corresponding single-cycle metal signal.

[0023] Multi-period averaging is performed on the single-period grain signal. For example, 20 periods of single-period grain signal can be acquired, superimposed, and the signal amplitude of each sampling point can be averaged to obtain the averaged detection signal over 20 periods as the single-period grain signal. The number of sampling points in the single-period grain signal is equal to the number of sampling points in the single-period grain signal; for example, the number of sampling points in the single-period grain signal can still be 4000. However, the single-period grain signal integrates the characteristic information of multiple periods of single-period grain signal. The obtained single-period grain signal is as follows: Figure 5 As shown, the horizontal axis represents the sampling points, and the vertical axis represents the signal amplitude. Using the same processing method, multi-cycle averaging of the single-cycle metal signal can be performed to obtain the corresponding single-cycle average metal signal.

[0024] Based on the number of interception points set in the signal interception rules, the corresponding key grain measurement signals and key metal measurement signals are extracted from the single-cycle average signal of grain and the single-cycle average signal of metal, respectively. For example, if the number of interception points N=800, the signal amplitude of the first 800 sampling points in the single-cycle average signal of grain is directly extracted as the corresponding key grain measurement signal. Figure 5 As shown, based on the number of intercept points N=800, the specific location of the 800th sampling point can be determined, and the signal with the sampling point number in the range of [1, 800] in the single-cycle average signal of grain can be obtained as the key measurement signal of grain. Using the same method, the key measurement signal of metal can be intercepted from the single-cycle average signal of metal; by combining the obtained key measurement signal of grain and key measurement signal of metal, the corresponding key measurement signal can be obtained.

[0025] S120. The key measurement signal is subjected to background noise suppression according to the preset background noise suppression strategy to obtain the corresponding measurement suppression signal.

[0026] After acquiring the key measurement signal, background noise suppression is applied to the key measurement signal based on background noise suppression measurement to filter out noise interference and obtain the suppressed measurement signal. The background noise suppression strategy is configured with background noise.

[0027] In a specific embodiment, step S120 includes the following sub-steps: aligning the grain key measurement signal and the metal key measurement signal in the key measurement signals with the background noise in the background noise suppression strategy in terms of timing; subtracting the signal amplitude of the sampling point at the same position in the background noise from the signal amplitude of each sampling point in the grain key measurement signal to obtain the corresponding grain suppression signal; subtracting the signal amplitude of the sampling point at the same position in the background noise from the signal amplitude of each sampling point in the metal key measurement signal to obtain the corresponding metal suppression signal; and combining the grain suppression signal and the metal suppression signal to obtain the corresponding measurement suppression signal.

[0028] Specifically, the key grain measurement signal in the key measurement signal can be time-aligned with the background noise in the background noise suppression strategy. Since the number of sampling points in the background noise is equal to the number of sampling points in the key grain measurement signal, the two sets of signals can be aligned based on the sampling point sequence number. The same method is used to time-align the key metal measurement signal with the background noise.

[0029] Furthermore, the signal amplitude of the sampling point at the same location in the background noise is subtracted from the signal amplitude of each sampling point in the critical grain measurement signal to obtain the corresponding grain suppression signal. Similarly, the signal amplitude of the corresponding sampling point in the background noise is subtracted from the signal amplitude of each sampling point in the critical metal measurement signal to obtain the metal suppression signal. The grain suppression signal and the metal suppression signal obtained in the above steps are combined to obtain the corresponding measurement suppression signal.

[0030] In a specific embodiment, before step S120, the steps include: acquiring the background measurement signal collected by the detection radar under no-load conditions; extracting the corresponding background noise from the background measurement signal and configuring it in the background noise suppression strategy.

[0031] Furthermore, before executing step S120, the background measurement signal collected by the detection radar in the empty state can also be obtained. The empty state is that there is no grain or metal plate in the grain warehouse. The measurement signal collected by the detection radar at this time is the background measurement signal. The detection radar performs multiple rounds of detection (such as 10 rounds), removes outliers, and averages the signals from multiple rounds of detection to obtain the background measurement signal. The detection parameters (pulse period, sampling frequency, etc.) for obtaining the background measurement signal are consistent with those for the grain measurement signal.

[0032] Furthermore, background noise is extracted from the background measurement signal. This step can be achieved through signal truncation rules. Specifically, the maximum amplitude point of a single pulse in the background measurement signal can be obtained, and the sampling points of the background measurement signal can be truncated using the maximum amplitude point. Then, the truncated single-cycle signal is averaged over multiple cycles to obtain a single-cycle average signal. The corresponding signal is then extracted from the single-cycle average signal as background noise according to the number of truncation points specified in the signal truncation rules. In other words, the specific process of obtaining background noise is the same as that of obtaining the key measurement signal for grain. The obtained background noise is then configured in a background noise suppression strategy, and background noise suppression is performed based on the configured strategy.

[0033] S130. Extract the corresponding reflection coefficient from the measurement suppression signal according to the preset reflection coefficient extraction rules.

[0034] The reflection coefficient is extracted from the measurement suppression signal according to the reflection coefficient extraction rule. The reflection coefficient is used to characterize the reflection difference between the grain suppression signal and the metal suppression signal in the measurement suppression signal.

[0035] In a specific embodiment, step S130 includes the following sub-steps: extracting the effective grain signal and the effective metal signal from the grain suppression signal and the metal suppression signal of the measured suppression signal according to the signal truncation parameters in the reflection coefficient extraction rule; converting the effective grain signal and the effective metal signal according to the conversion function in the reflection coefficient extraction rule to obtain the corresponding grain frequency domain signal and the metal frequency domain signal; extracting the corresponding grain frequency domain cut-out signal and the metal frequency domain cut-out signal from the grain frequency domain signal and the metal frequency domain signal according to the cut-out frequency band in the reflection coefficient extraction rule; and calculating the reflection coefficient corresponding to each frequency point in the cut-out frequency band according to the coefficient calculation formula in the reflection coefficient extraction rule.

[0036] Specifically, based on the signal truncation parameters in the reflection coefficient extraction rules, the corresponding effective grain signals and effective metal signals are extracted from the grain suppression signals and metal suppression signals, respectively. First, the maximum amplitude points in the grain suppression signals and the metal suppression signals are determined, and these two maximum amplitude points are overlapped to align the grain suppression signals and metal suppression signals. Then, based on the signal truncation parameters, the signal amplitudes of specific sampling points are extracted from the aligned signals as the corresponding effective grain signals and effective metal signals. The signal truncation parameters include a direct wave reference point and an effective truncation quantity. Based on the direct wave reference point, sampling points corresponding to the direct wave reference point are determined from the aligned signals. Based on the effective truncation quantity, the signal amplitudes near a specific distance (e.g., 10cm) after the direct wave is extracted from the aligned signals. For example, if the effective truncation quantity is set to 90, then 90 sampling points of signal amplitude can be extracted from the aligned signals. The two extracted signal amplitudes are the effective grain signals and effective metal signals, respectively, and are represented by S. g and S m The signal curves corresponding to the two sets of valid signals are as follows: Figure 6 As shown, the horizontal axis represents the sampling points, and the vertical axis represents the signal amplitude.

[0037] The effective signals of grain and metal are converted according to the conversion function in the reflection coefficient extraction rule. Specifically, the conversion function can be the Fast Fourier Transform function. The effective signals of grain and metal can be Fourier transformed by the conversion function to obtain two sets of frequency domain signals, namely the grain frequency domain signal and the metal frequency domain signal. Each set of frequency domain signals corresponds to a complex signal with 23 frequency points (covering 23 frequency points in the full frequency band).

[0038] According to the cutoff frequency band in the reflection coefficient extraction rule, the corresponding grain frequency domain cutoff signal and metal frequency domain cutoff signal are respectively extracted from the grain frequency domain signal and the metal frequency domain signal. For example, if the cutoff frequency band set in the reflection coefficient extraction rule is 3.0GHz~8.0GHz, then the signal value with the frequency point located in this cutoff frequency band can be obtained from the grain frequency domain signal as the corresponding grain frequency domain cutoff signal S. gz Similarly, the signal value whose frequency point is located in the intercepted frequency band is obtained from the metal frequency domain signal as the corresponding metal frequency domain intercepted signal S. mz The specific values ​​of the two sets of frequency domain truncation signals obtained are as follows: Figure 7 As shown, each set of frequency domain truncation signals contains complex signals corresponding to 12 frequency points, where i = (-1). 1 / 2 .

[0039] The reflection coefficients of the grain and metal frequency domain intercepted signals are calculated according to the coefficient calculation formula configured in the reflection coefficient extraction rules to obtain the reflection coefficients corresponding to each frequency point in the intercepted frequency band; that is, a reflection coefficient can be calculated for each frequency point, and 12 frequency points correspond to 12 reflection coefficients.

[0040] Specifically, the coefficient calculation formula can be expressed as R i =S gz (i) / S mz (i), i=1, 2, …12; S gz (i) is the complex signal corresponding to the i-th frequency point in the frequency domain truncation signal of the grain, S mz (i) is the complex signal corresponding to the i-th frequency point in the metal frequency domain truncation signal, R i Let be the reflection coefficient corresponding to the i-th frequency point.

[0041] S140. Perform inversion calculation on the reflection coefficient according to the preset inversion strategy to obtain the corresponding inversion dielectric constant.

[0042] Furthermore, the reflection coefficient is inverted using the inversion strategy to obtain the corresponding inversion dielectric constant. For lossy media, the reflection coefficient R is a complex number. Its amplitude |R| alone cannot uniquely determine the complex dielectric constant; therefore, it is necessary to simultaneously solve for the real part (energy storage) and the imaginary part (loss) of the reflection coefficient R, which cannot be achieved through direct mathematical inversion using formulas. Since each detection by the radar inevitably involves systematic errors and noise interference, employing an inversion strategy for global grid search is a unique method used in this scheme to calculate and obtain accurate water content measurements.

[0043] In a specific embodiment, step S140 includes the following sub-steps: obtaining the optimal matching coefficient corresponding to each reflection coefficient from the coefficient candidate library of the inversion strategy based on the reflection coefficient corresponding to each frequency point; calculating the error matrix between the reflection coefficient of each frequency point and each theoretical reflection coefficient in the coefficient candidate library; obtaining the global minimum value and the corresponding matrix coordinates in the error matrix of each frequency point; determining whether the global minimum value of the reflection coefficient is less than the historical minimum error recorded for the optimal matching coefficient; if the global minimum value is less than the historical minimum error recorded for the corresponding value, updating the corresponding historical minimum error based on the global minimum value; performing inversion operations on the optimal matching coefficient of each frequency point to obtain the corresponding optimal complex permittivity; and averaging the squared optimal complex permittivity of each frequency point to obtain the corresponding inversion permittivity.

[0044] Specifically, the optimal matching coefficients for each reflection coefficient can be obtained from the coefficient candidate library of the inversion strategy based on the reflection coefficients corresponding to each frequency point. The coefficient candidate library includes some theoretical reflection coefficients, such as... Figure 8 As shown. Specifically, the geometric distance between the reflection coefficient and each theoretical reflection coefficient is calculated, and the theoretical reflection coefficient with the smallest geometric distance is taken as the best matching coefficient. Then, the reflection coefficient at each frequency point can be assigned a corresponding nearest matching coefficient.

[0045] Calculate the error matrix between the reflection coefficient at each frequency point and each theoretical reflection coefficient in the candidate coefficient library. That is, for the reflection coefficient at a frequency point, calculate the error value between the reflection coefficient and each theoretical reflection coefficient in turn. The number of error values ​​obtained is equal to the number of theoretical reflection coefficients. The obtained error values ​​constitute the error matrix. Then, a set of error matrices can be obtained for each reflection coefficient.

[0046] To obtain the global minimum value and corresponding matrix coordinates in the error matrix of a frequency point, specifically, obtain the minimum value of each column and the row number corresponding to the minimum value of each column from the error matrix of a frequency point; obtain the minimum value of all columns corresponding to the minimum value of each column as the global minimum value, determine the row number and column number of the global minimum value, and obtain the matrix coordinates corresponding to the global minimum value.

[0047] Determine whether the global minimum value of the reflection coefficient is less than the historical minimum error corresponding to the best matching coefficient that matches the reflection coefficient; if the global minimum value is less than the historical minimum error of the corresponding record, update the recorded historical minimum error based on the currently obtained global minimum value; if the global minimum value is not less than the historical minimum error of the corresponding record, do not update the historical minimum error value.

[0048] The optimal matching coefficients at each frequency point are inverted to obtain the corresponding optimal complex permittivity. The optimal complex permittivity at each frequency point is squared, and the squared values ​​at each frequency point are averaged to obtain the corresponding inverted permittivity. At this point, there is only one inverted permittivity.

[0049] In a specific embodiment, before step S140, the method further includes the following steps: constructing a corresponding candidate library of complex dielectric constants according to preset construction parameters; solving for the theoretical reflection coefficients corresponding to each candidate dielectric constant in the candidate library of complex dielectric constants to obtain a corresponding theoretical reflection coefficient library; filtering the theoretical reflection coefficient library according to preset filtering rules and retaining theoretical reflection coefficients that satisfy the filtering rules; constructing a coefficient candidate library and configuring it in the inversion strategy according to the inversion relationship between the filtered theoretical reflection coefficients and the corresponding target complex dielectric constants in the candidate library of complex dielectric constants.

[0050] Before performing the inversion operation using the inversion strategy, a candidate library of complex permittivity corresponding to the pre-set construction parameters can be constructed. The construction parameters include the range of the real part, the range of the imaginary part, and a step value; the real part can be constructed according to the range of the real part and the step value, and the difference between the real parts of two adjacent columns of values ​​is equal to the step value; the imaginary part can be constructed according to the range of the imaginary part and the step value, and the difference between the imaginary parts of two adjacent rows of values ​​is equal to the step value.

[0051] For example, if the real part range is set to [2, 80] and the imaginary part range is set to [0, 56], with a step value of 0.05, then the constructed real parts are 2.00, 2.05, 2.10…, and the corresponding constructed imaginary parts are 0.00i, 0.05i, 0.10i…, where i = (-1). 1 / 2The obtained real parts are combined with each imaginary part to construct a candidate library of complex permittivity. For example, the local information in the candidate library of complex permittivity constructed according to the above method is as follows: Figure 9 As shown.

[0052] Using the aforementioned candidate library of complex permittivity, the theoretical reflection coefficients corresponding to each candidate permittivity are obtained, thereby further constructing the corresponding theoretical reflection coefficient library. Specifically, the calculation process can be expressed as: Γ t =(Z2-Z1) / (Z2+Z1); where Z2 is the preset complex dielectric constant of the metal, Z1 is the candidate dielectric constant, and Γ t This is the theoretical reflection coefficient obtained through calculation.

[0053] The theoretical reflectance coefficients in the theoretical reflectance coefficient library are filtered according to pre-set filtering rules to obtain theoretical reflectance coefficients that satisfy the filtering rules. For example, the filtering rule can be configured such that the imaginary part value is no greater than 0.7 times the real part value; then, the reasonable upper limit index of the imaginary part corresponding to the real part of each theoretical reflectance coefficient can be determined according to the filtering rule, and theoretical reflectance coefficients whose imaginary part value is no greater than 0.7 times the real part value can be obtained as theoretical reflectance coefficients that satisfy the filtering rule.

[0054] Since each theoretical reflection coefficient corresponds to a set of target complex permittivity in the candidate library of complex permittivity, an inversion relationship can be constructed between the selected theoretical reflection coefficients and their corresponding target complex permittivity values ​​based on this correspondence. Based on the obtained multiple sets of inversion relationships, a corresponding candidate library of coefficients is constructed and configured in the inversion strategy. During the initial construction process, the historical minimum error of each theoretical reflection coefficient is infinite.

[0055] S150. Map the inverted dielectric constant according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

[0056] Furthermore, the inverted dielectric constant is mapped according to the pre-stored mapping curve to obtain the corresponding moisture content measurement value. The mapping curve includes the mapping relationship between grains with different moisture contents and their corresponding dielectric constants. For example, rice of the same variety and batch, free from mold and insect infestation (this embodiment uses rice as the sample for measurement; in other embodiments, it can be extended to other grains such as corn and wheat), after cleaning impurities (impurity rate <0.5%), can be used as the measurement benchmark. Multiple groups of grain moisture contents are set at 10%, 12%, 13.5%, 15%, 17%, 20%, 25%, and 27%, respectively. The standard dielectric constant corresponding to each group of grains is measured, and a dielectric constant-moisture content model is established based on the moisture content and standard dielectric constant of each group of grains to construct the mapping curve. For example, the obtained mapping curve is as follows: Figure 10 As shown, the horizontal axis represents the dielectric constant (corresponding to the inverted dielectric constant), the vertical axis represents the grain moisture content (corresponding to the measured moisture content), the circles represent the standard dielectric constants corresponding to grains with different moisture contents, and the solid line represents the mapping curve.

[0057] The grain moisture content measurement method based on pulse radar measurement signal analysis disclosed in the above embodiments includes: receiving an initial measurement signal input from the detection radar, which includes grain measurement signals and metal measurement signals; extracting key measurement signals from the signal and performing background noise suppression according to a background noise suppression strategy to obtain a measurement suppression signal; extracting the corresponding reflection coefficient from the measurement suppression signal according to a reflection coefficient extraction rule and performing an inversion operation to obtain the corresponding inversion dielectric constant; and mapping the inversion dielectric constant according to a mapping curve to obtain the moisture content measurement value. This grain moisture content measurement method significantly improves the accuracy of grain moisture content measurement by acquiring two sets of signals through grain detection and metal detection, removing noise influence through background noise suppression, and accurately obtaining the moisture content measurement value corresponding to the inversion dielectric constant through reflection coefficient extraction and inversion operation.

[0058] This invention also provides a grain moisture content measuring device based on pulse radar measurement signal analysis. This device can be configured in a measuring terminal 10 and is used to execute any embodiment of the aforementioned grain moisture content measuring method based on pulse radar measurement signal analysis. Specifically, please refer to... Figure 11 , Figure 11 This is a schematic block diagram of a grain moisture content measuring device based on pulse radar measurement signal analysis, provided in an embodiment of the present invention.

[0059] like Figure 11As shown, the grain moisture content measuring device 100 based on pulse radar measurement signal analysis includes a signal interception and processing unit 110, a noise suppression processing unit 120, a reflection coefficient extraction unit 130, an inversion operation unit 140, and a mapping processing unit 150.

[0060] The signal interception and processing unit 110 is used to receive the initial measurement signal input by the detection radar and extract the corresponding key measurement signal from the initial measurement signal according to the preset signal interception rules; the initial measurement signal includes grain measurement signal and metal measurement signal.

[0061] The noise suppression processing unit 120 is used to suppress background noise on the key measurement signal according to a preset background noise suppression strategy to obtain a corresponding measurement suppression signal.

[0062] The reflection coefficient extraction unit 130 is used to extract the corresponding reflection coefficient from the measurement suppression signal according to the preset reflection coefficient extraction rules.

[0063] The inversion operation unit 140 is used to perform inversion operation on the reflection coefficient according to a preset inversion strategy to obtain the corresponding inversion dielectric constant.

[0064] The mapping processing unit 150 is used to map the inversion dielectric constant according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

[0065] The grain moisture content measuring device based on pulse radar measurement signal analysis provided in this embodiment of the invention applies the aforementioned grain moisture content measuring method based on pulse radar measurement signal analysis. It receives an initial measurement signal from the detection radar, containing both grain and metal measurement signals. Key measurement signals are extracted from this signal, and background noise is suppressed using a background noise suppression strategy to obtain a suppressed measurement signal. The corresponding reflection coefficient is extracted from the suppressed measurement signal according to a reflection coefficient extraction rule and inverted to obtain the corresponding inverted dielectric constant. The inverted dielectric constant is mapped using a mapping curve to obtain the moisture content measurement value. This grain moisture content measuring method significantly improves the accuracy of grain moisture content measurement by acquiring two sets of signals through grain detection and metal detection, removing noise through background noise suppression, and accurately obtaining the moisture content measurement value corresponding to the inverted dielectric constant through reflection coefficient extraction and inversion calculation.

[0066] The aforementioned grain moisture content measuring device based on pulse radar signal analysis can be implemented as a computer program, which can be used in various applications such as... Figure 12 It runs on the computer device shown.

[0067] Please see Figure 12 , Figure 12 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device can be a measurement terminal used to perform a grain moisture content measurement method based on pulse radar measurement signal analysis to measure grain moisture content.

[0068] See Figure 12 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.

[0069] The storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it causes the processor 502 to execute a grain moisture content measurement method based on pulse radar measurement signal analysis. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0070] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0071] 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 method for measuring the moisture content of grains based on pulse radar measurement signal analysis.

[0072] This communication interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 12 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.

[0073] 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 content measurement method based on pulse radar measurement signal analysis.

[0074] Those skilled in the art will understand that Figure 12 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 12 The embodiments shown are consistent and will not be repeated here.

[0075] 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.

[0076] 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, wherein when executed by a processor, the computer program implements the steps included in the above-described method for measuring grain moisture content based on pulse radar measurement signal analysis.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 method for measuring grain moisture content based on pulse radar measurement signal analysis, characterized in that, The method is applied to a measurement terminal, which is communicatively connected to a detection radar to transmit data information. The method includes: The system receives the initial measurement signal input from the detection radar and extracts the corresponding key measurement signal from the initial measurement signal according to a preset signal extraction rule; the initial measurement signal includes grain measurement signal and metal measurement signal. The key measurement signal is subjected to background noise suppression according to a preset background noise suppression strategy to obtain the corresponding measurement suppression signal; The corresponding reflection coefficient is extracted from the measurement suppression signal according to the preset reflection coefficient extraction rules; The reflection coefficient is inverted according to the preset inversion strategy to obtain the corresponding inversion dielectric constant; The inversion dielectric constant is mapped according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

2. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to claim 1, characterized in that, The step of extracting the corresponding key measurement signal from the initial measurement signal according to the preset signal extraction rules includes: Obtain the single pulse maximum amplitude points corresponding to the grain measurement signal and the metal measurement signal of the initial measurement signal, respectively; The grain measurement signal and the metal measurement signal are sampled and truncated using the maximum amplitude point of the single pulse to obtain the corresponding single-cycle grain signal and single-cycle metal signal. The single-cycle signals of the grain and metal are averaged over multiple cycles to obtain the corresponding single-cycle average signals of the grain and metal. Based on the number of interception points set in the signal interception rules, the corresponding key measurement signals of grain and metal are intercepted from the single-cycle average signal of grain and the single-cycle average signal of metal, respectively, as the corresponding key measurement signals.

3. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to claim 2, characterized in that, The step of suppressing background noise in the key measurement signal according to a preset background noise suppression strategy to obtain a corresponding suppressed measurement signal includes: The key measurement signals for grains and metals in the key measurement signals are time-aligned with the background noise in the background noise suppression strategy. Subtracting the signal amplitude of the sampling point at the same position in the background noise from the signal amplitude of each sampling point in the key grain measurement signal yields the corresponding grain suppression signal. Subtracting the signal amplitude of the sampling point at the same position in the background noise from the signal amplitude of each sampling point in the metal critical measurement signal yields the corresponding metal suppression signal. The grain inhibition signal is combined with the metal inhibition signal to obtain the corresponding measurement inhibition signal.

4. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to claim 3, characterized in that, Before performing background noise suppression on the key measurement signal according to a preset background noise suppression strategy to obtain the corresponding suppressed measurement signal, the method further includes: Acquire the background measurement signal collected by the detection radar under no-load conditions; The corresponding background noise is extracted from the background measurement signal and configured in the background noise suppression strategy accordingly.

5. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to any one of claims 1-4, characterized in that, The step of extracting the corresponding reflection coefficient from the measurement suppression signal according to a preset reflection coefficient extraction rule includes: According to the signal extraction parameters in the reflection coefficient extraction rule, the effective grain signal and the effective metal signal are respectively extracted from the grain suppression signal and the metal suppression signal of the measured suppression signal; The effective signals of the grain and the effective signals of the metal are converted according to the conversion function in the reflection coefficient extraction rule to obtain the corresponding grain frequency domain signal and metal frequency domain signal. According to the frequency bands extracted in the reflection coefficient extraction rules, the corresponding grain frequency domain cut-out signals and metal frequency domain cut-out signals are respectively extracted from the grain frequency domain signal and the metal frequency domain signal; The reflection coefficients of the grain frequency domain intercepted signal and the metal frequency domain intercepted signal are calculated according to the coefficient calculation formula in the reflection coefficient extraction rule to obtain the reflection coefficients corresponding to each frequency point in the intercepted frequency band.

6. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to claim 5, characterized in that, The step of performing an inversion operation on the reflection coefficient according to a preset inversion strategy to obtain the corresponding inversion dielectric constant includes: Based on the reflection coefficients corresponding to each frequency point, the optimal matching coefficients corresponding to each reflection coefficient are obtained from the coefficient candidate library of the inversion strategy. Calculate the error matrix between the reflection coefficient at each frequency point and each theoretical reflection coefficient in the candidate coefficient library; Obtain the global minimum value and the corresponding matrix coordinates in the error matrix of each frequency point; Determine whether the global minimum value of the reflection coefficient is less than the historical minimum error recorded for the best matching coefficient; If the global minimum value is less than the historical minimum error of the corresponding record, the corresponding historical minimum error is updated according to the global minimum value; The optimal matching coefficients at each frequency point are inverted to obtain the corresponding optimal complex permittivity. The optimal complex permittivity at each of the aforementioned frequency points is squared and averaged to obtain the corresponding inverse permittivity.

7. The method for measuring grain moisture content based on pulse radar measurement signal analysis according to claim 6, characterized in that, Before performing the inversion operation on the reflection coefficient according to the preset inversion strategy to obtain the corresponding inversion dielectric constant, the following steps are also included: The corresponding candidate library of complex permittivity is constructed based on the preset construction parameters; The theoretical reflection coefficients corresponding to each candidate dielectric constant in the candidate library of complex dielectric constants are obtained by solving the problem, so as to obtain the corresponding theoretical reflection coefficient library; The theoretical reflectance coefficient library is filtered according to preset filtering rules, and theoretical reflectance coefficients that meet the filtering rules are retained. Based on the inversion relationship between the theoretical reflection coefficients obtained through screening and the corresponding target complex permittivity in the candidate library of complex permittivity, a candidate library of coefficients is constructed and configured in the inversion strategy.

8. A grain moisture content measuring device based on pulse radar measurement signal analysis, characterized in that, The device is configured on a measurement terminal, which is communicatively connected to a detection radar to transmit data information. The device is used to execute the grain moisture content measurement method based on pulse radar measurement signal analysis as described in any one of claims 1-7. The device comprises: The signal interception and processing unit is used to receive the initial measurement signal input by the detection radar, and to intercept the corresponding key measurement signal from the initial measurement signal according to the preset signal interception rules; the initial measurement signal includes grain measurement signal and metal measurement signal; The noise suppression processing unit is used to suppress background noise on the key measurement signal according to a preset background noise suppression strategy to obtain a corresponding measurement suppression signal. A reflection coefficient extraction unit is used to extract the corresponding reflection coefficient from the measurement suppression signal according to a preset reflection coefficient extraction rule; The inversion operation unit is used to perform inversion operation on the reflection coefficient according to the preset inversion strategy to obtain the corresponding inversion dielectric constant; The mapping processing unit is used to map the inverted dielectric constant according to the pre-stored mapping curve to obtain the corresponding water content measurement value.

9. A computer device, characterized in that, 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; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the steps of the grain moisture content measurement method based on pulse radar measurement signal analysis as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the grain moisture content measurement method based on pulse radar measurement signal analysis as described in any one of claims 1-7.

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