Grain bin density measurement method and apparatus based on wideband continuous scanning

Abstract

The application discloses a grain bin density measurement method and device based on wideband continuous scanning, and the method comprises the following steps: receiving continuous scanning signals collected by a detection radar and obtaining a plurality of sub-band scanning signals according to preset spectrum segmentation information; analyzing each sub-band scanning signal to obtain a sub-band independent distance image; detecting the sub-band independent distance image to obtain sub-band peak value detection information; inverting the sub-band peak value detection information based on a multilayer medium model to obtain inversion coefficient information; using a weighted fusion strategy to weight and fuse the inversion coefficient information to obtain fusion coefficient information; and finally converting the fusion coefficient information into grain bin density measurement information according to a density conversion rule. The above-mentioned grain bin density measurement method can effectively solve the problem that single frequency band detection is difficult to perform high-precision measurement in a complex multilayer medium environment by segmenting to obtain sub-band scanning signals, respectively analyzing the sub-band scanning signals and inverting based on a multilayer medium model, and the accuracy of full-depth density distribution measurement of grain piles is improved.

Description

Technical Field

[0001] This invention relates to the field of radar detection and grain storage monitoring technology, and in particular to a method and device for measuring grain storage density based on broadband continuous scanning. Background Technology

[0002] In the grain storage industry, real-time and accurate monitoring of grain pile density is crucial for assessing grain storage safety and preventing mold and pest infestation. Currently, non-contact grain condition detection using electromagnetic wave technology has become the mainstream solution in the industry. Among them, stepped-frequency continuous-wave radar is widely used for detecting the internal condition of grain silos due to its high range resolution and penetration capability. Existing technical solutions typically employ a full-band synthetic processing mode, which integrates the broadband signals collected by radar into a range image through a single inverse Fourier transform, and then calculates the overall height or average dielectric properties of the grain pile based on echo intensity or phase changes. This processing method relies on the propagation model of electromagnetic waves in a homogeneous medium, and achieves condition perception by establishing a mapping relationship between signal characteristics and material physical parameters.

[0003] However, in practical applications of grain pile density measurement, due to the complex layered structure and non-uniform distribution characteristics of grain piles, single-band detection methods often struggle to simultaneously meet the requirements of deep penetration and high resolution at shallow depths. While low-frequency signals offer great penetration depth, their resolution is insufficient, while high-frequency signals, despite their high resolution, are ineffective at detecting deep information. This inherent contradiction in physical characteristics limits the current technology's ability to invert density distributions at different depths. For a long time, overcoming the shortcomings of single-band detection in complex multi-layered media environments and achieving high-precision, high-consistency measurement of density distribution across the entire depth range of grain piles has been a pressing technical challenge in this field. Therefore, existing radar-based methods for grain pile density measurement suffer from poor measurement accuracy. Summary of the Invention

[0004] This invention provides a method and apparatus for measuring grain density based on broadband continuous scanning, aiming to solve the problem of poor measurement accuracy in existing technologies that use radar signals to measure grain density.

[0005] In a first aspect, embodiments of the present invention provide a method for measuring grain density in a grain warehouse based on broadband continuous scanning, wherein the method is applied to a measuring terminal, the measuring terminal being communicatively connected to a detection radar to transmit data information, and the method includes: The system receives the continuous scanning signal collected by the detection radar through continuous bandwidth scanning, and segments the continuous scanning signal according to preset spectrum segmentation information to obtain multiple sub-band scanning signals. The sub-band scanning signals are analyzed according to the preset imaging analysis strategy to obtain the corresponding sub-band independent distance image; The independent distance images of each sub-band are detected according to the preset peak detection rules to obtain the corresponding sub-band peak detection information; The sub-band peak detection information is inverted according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information; According to the preset weighted fusion strategy, the inversion coefficient information corresponding to the peak detection information of each sub-band is weighted and fused to obtain the fusion coefficient information; The fusion coefficient information is converted according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

[0006] Secondly, embodiments of the present invention also provide a grain storage density measurement device based on broadband continuous scanning, wherein the device is configured in a measurement terminal, the measurement terminal is communicatively connected to a detection radar to realize data information transmission, and the device is used to perform the grain storage density measurement method based on broadband continuous scanning as described in the first aspect above, the device comprising: The continuous scanning signal segmentation unit is used to receive the continuous scanning signal collected by the detection radar during bandwidth continuous scanning, and to segment the continuous scanning signal according to the preset spectrum segmentation information to obtain multiple sub-band scanning signals. The analysis unit is used to analyze each sub-band scanning signal according to a preset imaging analysis strategy to obtain the corresponding sub-band independent distance image; The image detection unit is used to detect the independent distance images of each sub-band according to the preset peak detection rules, so as to obtain the corresponding sub-band peak detection information. The inversion unit is used to invert the sub-band peak detection information according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information. The weighted fusion unit is used to perform weighted fusion on the inversion coefficient information corresponding to the peak detection information of each sub-band according to a preset weighted fusion strategy to obtain fusion coefficient information; The conversion unit is used to convert the fusion coefficient information according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

[0007] 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 storage density measurement method based on broadband continuous scanning described in the first aspect above.

[0008] 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 silo density measurement method based on broadband continuous scanning as described in the first aspect above.

[0009] This invention provides a method and apparatus for measuring grain density in a grain storage facility based on broadband continuous scanning. The method includes: receiving continuous scanning signals collected by a detection radar and obtaining multiple sub-band scanning signals by segmenting the signals according to a preset spectrum; analyzing each sub-band scanning signal to obtain a sub-band independent distance image; detecting the sub-band independent distance image to obtain sub-band peak detection information; inverting the sub-band peak detection information based on a multi-layer medium model to obtain inversion coefficient information; using a weighted fusion strategy to perform weighted fusion of the inversion coefficient information to obtain fusion coefficient information; and finally converting the fusion coefficient information into grain storage facility density measurement information according to a density conversion rule. This grain storage facility density measurement method, by segmenting the sub-band scanning signals, analyzing them separately, and performing inversion based on a multi-layer medium model, effectively solves the problem of high-precision measurement under complex multi-layer medium environments using single-band detection, thus improving the accuracy of full-depth density distribution measurement of grain piles. Attached Figure Description

[0010] 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.

[0011] Figure 1 A flowchart of a grain storage density measurement method based on broadband continuous scanning provided in an embodiment of the present invention; Figure 2 A schematic diagram illustrating an application scenario of the grain storage density measurement method based on broadband continuous scanning provided in an embodiment of the present invention; Figure 3 A schematic block diagram of a grain storage density measuring device based on broadband continuous scanning provided in an embodiment of the present invention; Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0012] 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.

[0013] 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.

[0014] 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.

[0015] 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.

[0016] This invention application provides a method for measuring grain warehouse density based on broadband continuous scanning. This method is applied in a measuring terminal, which executes a stored software program to implement the aforementioned method for measuring grain warehouse density based on broadband continuous scanning. Figure 2 As 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 continuously scans the object 1 in the grain silo below and sends the scan signal to the measuring terminal 10 after acquiring the scan signal. Alternatively, the measuring terminal 10 and the detection radar 20 can be integrated into a 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 grain silo density measurement information. The measuring terminal 10 can be a terminal module equipped 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 scan signal and transmits it to the measuring terminal 10 for remote processing to obtain the grain silo density measurement information. In this case, the measuring terminal 10 can be a desktop computer, laptop computer, tablet computer, or mobile phone.

[0017] like Figure 1 As shown, the method includes steps S110 to S160.

[0018] S110. Receive the continuous scanning signal collected by the detection radar through bandwidth continuous scanning, and segment the continuous scanning signal according to the preset spectrum segmentation information to obtain multiple sub-band scanning signals.

[0019] The measurement terminal establishes a communication connection with the detection radar and receives the continuous scanning signal acquired by the radar during its bandwidth continuous scanning process in real time. This continuous scanning signal contains electromagnetic wave echo information across the entire frequency band. To resolve the conflict between penetration depth and resolution in a single frequency band, the measurement terminal utilizes preset spectrum segmentation information. This information defines the boundary parameters that divide the entire broadband spectrum into multiple specific frequency bands (e.g., low-frequency, mid-frequency, and high-frequency bands). Based on this spectrum segmentation information, the measurement terminal performs frequency domain segmentation processing on the received continuous scanning signal, splitting the full-band signal into multiple independent sub-band scanning signals. Because a full-band scan is performed, the resulting continuous scanning signal is a frequency domain signal.

[0020] Specifically, spectral segmentation information can divide the original scanning signal into low-frequency sub-band scanning signals corresponding to low-frequency penetration characteristics, mid-frequency sub-band scanning signals corresponding to mid-frequency detection characteristics, and high-frequency sub-band scanning signals corresponding to high-resolution characteristics. For example, the frequency range of the low-frequency band (low-frequency sub-band) can be determined to be 200~499MHz, which has a large penetration depth and can detect deep layers of grain piles; the frequency range of the mid-frequency band (mid-frequency sub-band) can be determined to be 500~799MHz, which offers a trade-off between penetration and resolution; and the frequency range of the high-frequency band (high-frequency sub-band) can be determined to be 800~1200MHz, which has high resolution and is sensitive to changes in shallow density. Through this segmentation process, multiple sub-band data sources optimized for different detection depths and resolutions can be obtained, providing a data foundation for subsequent multi-band fusion and inversion.

[0021] S120. The scanning signals of each sub-band are analyzed according to the preset imaging analysis strategy to obtain the corresponding sub-band independent distance image.

[0022] For each sub-band scan signal, the measurement terminal applies a preset imaging analysis strategy for independent analysis, obtaining sub-band independent range images corresponding to each sub-band scan signal. This imaging analysis strategy defines the algorithm flow for converting sub-band signals in the frequency domain into range domain images. For each sub-band scan signal, the measurement terminal performs windowing, inverse fast fourier transform (IFFT), and signal truncation processing to calculate the range-direction echo intensity distribution corresponding to that sub-band signal, thereby generating the corresponding sub-band independent range image. For example, the imaging analysis strategy can use a standard IFFT transform process to directly map the frequency domain data to the sub-band independent range image; alternatively, a specific interpolation algorithm or zero-padding operation can be introduced before performing the IFFT transform to improve the display accuracy of the sub-band independent range image. Each sub-band independent range image reflects the energy reflection of radar waves at different distances in the corresponding sub-frequency band, characterizing the longitudinal structural features of the medium inside the grain silo in that frequency band.

[0023] In a specific embodiment, step S120 includes the following sub-steps: calculating the sub-band bandwidth of each sub-band scanning signal and determining the corresponding theoretical resolution; performing windowing processing on the sub-band scanning signal according to the windowing parameters in the imaging analysis strategy and the theoretical resolution to obtain the corresponding windowed processing signal; performing conversion processing on the windowed processing signal according to the conversion function in the imaging analysis strategy to obtain the corresponding converted signal; and obtaining the corresponding signal amplitude information from the converted signal to generate a distance axis signal image as the corresponding sub-band independent distance image.

[0024] First, the subband bandwidth of each subband scan signal is calculated, and the theoretical resolution is determined. From the spectral characteristics of each subband scan signal, the upper cutoff frequency and the lower cutoff frequency are extracted, and the difference between the two is determined as the subband bandwidth. This subband bandwidth characterizes the width range of the corresponding subband scan signal in the frequency domain and directly determines the physical limit of the range resolution.

[0025] Based on the speed of light and the aforementioned subband bandwidth, the theoretical resolution is calculated, and its value can be expressed as follows: c / (2× B sub ) ,in c Represents the speed of light. B sub This represents the subband bandwidth. This theoretical resolution serves as a benchmark for windowing processing and constrains the adjustment sequence of the windowing process.

[0026] Based on the window function type and related parameters configured in the imaging analysis strategy, a corresponding window function sequence is constructed. Taking the Hamming window as an example, a sequence with a length of [missing information] is generated. N subHamming window sequence hamming ( N sub ), and combine it with the sub-band scanning signal H sub Perform element-wise multiplication, that is H wsub = H sub ×hamming ( N sub This process yields a weighted, windowed signal. The aim is to suppress high-range sidelobes that may appear after the inverse Fourier transform, preventing weak target signals from being masked by the sidelobes of strong targets. Although introducing a window function broadens the main lobe, resulting in a slightly lower actual resolvable range than the theoretical resolution, it effectively reduces sidelobe levels, increases the main-to-sidelobe ratio, and thus enhances the detection capability for weak targets. Windowing the sub-band scanning signal under the constraint of theoretical resolution achieves a balance between resolution loss and sidelobe suppression.

[0027] Using the inverse Fourier transform as the transformation function, the windowed signal in the frequency domain is mapped to the time domain, resulting in a complex sequence containing target distance and intensity information, i.e., the transformed signal. To increase the frequency domain sampling density and improve the smoothness of the signal waveform, a zero-padded inverse Fourier transform is employed to perform this transformation process. Specifically, a 1024-point inverse Fourier transform operation is performed on the windowed signal, formally represented as P. sub =IFFT( H wsub (1024). Through this transformation calculation, the originally discrete frequency domain sampling points are reconstructed into a time domain waveform with better continuity, which not only improves the data accuracy of subsequent distance axis generation, but also lays the data foundation for accurate peak detection.

[0028] First, calculate the magnitude of the converted signal, for example, by... range P = abs(P sub ) Amplitude information is acquired to form range envelope data that reflects the distribution of reflection intensity at different distances, which means obtaining signal amplitude values ​​at different distance lengths as signal amplitude information. Each detection distance in the signal amplitude information corresponds to a signal amplitude value. All the obtained signal amplitude values ​​are combined along the distance direction to obtain the corresponding range axis signal image. Each sub-band scanning signal corresponds to a range axis signal image, which can be used as the sub-band independent range image corresponding to each sub-band scanning signal.

[0029] Through the collaborative processing flow of subband bandwidth calculation, windowing, function transformation and signal amplitude acquisition, the scanning signals of each subband are transformed into high-quality, low-sidelobe interference and accurately focused subband independent distance images, providing reliable data support for subsequent peak detection and inversion calculation.

[0030] In a specific embodiment, the step of windowing the sub-band scanning signal according to the windowing parameters in the imaging analysis strategy and the theoretical resolution to obtain a corresponding windowed signal includes: obtaining a target windowing parameter from the windowing parameters that matches the theoretical resolution of each sub-band scanning signal; and windowing the sub-band scanning signal according to the target windowing parameter to obtain a corresponding windowed signal.

[0031] Target windowing parameters are a set of specific function configurations used for frequency domain weighting of sub-band scanning signals of a specific bandwidth. In radar signal processing, windowing aims to suppress spectral sidelobes generated during Fast Fourier Transform (FFT) or Inverse Fast Fourier Transform (IFT), preventing the sidelobe energy of strong reflective targets from masking nearby weak reflective targets. Given that sub-band signals of different bandwidths have different theoretical resolution characteristics, using uniform windowing parameters may lead to a loss of range resolution in narrow-band sub-bands due to excessive main lobe broadening, or insufficient sidelobe suppression in wide-band sub-bands. Therefore, target windowing parameters are not a single value, but a combination of parameters including window function type (such as Hamming window, Hanning window, Chebyshev window, etc.), window function length, and sidelobe attenuation level. These parameters are pre-established based on historical scanning data and imaging quality statistics under different resolution conditions during system initialization or offline configuration, and stored in a windowing parameter library. During the configuration phase, for different theoretical resolution ranges, the optimal window function parameter set that balances the main lobe broadening effect and the side lobe suppression effect is calibrated through simulation or experimental analysis, thus forming a mapping relationship from theoretical resolution to windowing parameters. This mapping relationship can be dynamically updated and expanded according to new grain storage medium characteristics or radar hardware upgrades to adapt to diverse detection environments.

[0032] Specifically, firstly, based on the theoretical resolution of the current sub-band scanning signal, a pre-set windowing parameter library is consulted to retrieve and extract the matching target windowing parameters. In other words, the obtained target windowing parameters are adapted to the theoretical resolution of the current sub-band scanning signal. This process utilizes the previously established mapping relationship, using the theoretical resolution of the sub-band signal as an index key to locate the corresponding optimal window function parameter set. This adaptive matching mechanism ensures that the parameters used in subsequent processing can be specifically adapted to the bandwidth and resolution requirements of the sub-band, providing fundamental data support for differentiated spectrum optimization.

[0033] The acquired target windowing parameters are retrieved, a corresponding window function sequence is generated, and this window function sequence is multiplied with the current sub-band scanning signal to complete the windowing process. Specifically, if the target windowing parameters indicate the use of a Hamming window, a Hamming window coefficient sequence of equal length to the number of data points in the sub-band scanning signal is generated, and each frequency point of the sub-band scanning signal is multiplied by the corresponding window coefficient. This process causes the signal amplitude at the frequency band edges to gradually decay to zero, thereby eliminating the Gibbs phenomenon caused by signal truncation. Compared to traditional methods that do not use windowing or use fixed-parameter windowing, this strategy of adaptively matching target windowing parameters based on theoretical resolution can apply different degrees of smoothing constraints to the inherent resolution differences of low-frequency, mid-frequency, and high-frequency sub-bands. For example, for high-frequency sub-bands with wider bandwidth and higher theoretical resolution, window function parameters with stronger sidelobe suppression but slightly wider main lobe are selected to ensure purity in shallow high-resolution imaging; while for low-frequency sub-bands with narrower bandwidth, window function parameters with smaller main lobe widening are selected to preserve their resolution capability for detecting deep targets as much as possible. Through this differentiated and refined windowing process, the scanning signals of each sub-band are converted into windowed signals with optimized spectral characteristics, laying the data foundation for the subsequent generation of high-quality sub-band independent distance images.

[0034] S130. Detect the independent distance images of each sub-band according to the preset peak detection rules to obtain the corresponding sub-band peak detection information.

[0035] The measurement terminal uses preset peak detection rules to detect each generated sub-band independent distance image one by one to identify significant energy peak points in the image. The peak detection rules set parameters such as threshold conditions for determining valid peaks, noise suppression mechanisms, and peak search windows. During the detection process, the measurement terminal first analyzes the sub-band independent distance images, extracting the signal amplitude envelope curve. Then, it searches for local maxima points on the envelope curve that satisfy preset constraints. These maxima points correspond to reflected echoes from different media layers inside the grain silo (such as the air-grain interface, the interface between grain layers of different densities, etc.). The detected peak information includes the peak's location coordinates (corresponding to depth distance) and peak amplitude (corresponding to reflection intensity), which are recorded as the corresponding sub-band peak detection information. For example, the peak detection rule can be set to a fixed amplitude threshold, retaining only peaks with amplitudes exceeding this threshold; alternatively, a dynamic background noise estimation algorithm can be used to adaptively adjust the detection threshold to filter out environmental interference. Through this step, key feature information reflecting the layered structure inside the grain silo can be accurately extracted from complex distance images.

[0036] In a specific embodiment, step S130 includes the following sub-steps: performing envelope calculation on each of the sub-band independent distance images to obtain the corresponding envelope curves; matching and obtaining the target detection distance corresponding to each of the sub-band independent distance images from the distance parameters of the peak detection rule; and performing peak identification on each of the envelope curves according to the peak constraint conditions of the peak detection rule and the target detection distance to obtain the corresponding target peak amplitude as the corresponding sub-band peak detection information.

[0037] Envelope calculation is performed on the independent range images of each sub-band to extract the overall trend of signal amplitude variation with distance, thereby smoothing local noise and highlighting the strong reflection characteristics at the interface of the medium. The resulting envelope curve is not a simple sequence of absolute signal values, but a structural contour characterizing the spatial distribution of radar echo energy, and its shape is directly related to the reflection intensity of interfaces with different densities inside the grain pile. In specific implementation, the complex data or real sequence of the independent range images of each sub-band can be input into the Hilbert transform algorithm to calculate the instantaneous amplitude of the signal; alternatively, the sliding window maximum search method can be used to extract the local amplitude of the sampling points on the range axis, and the maximum values ​​within each window can be connected sequentially to form a continuous envelope trajectory. For example, the specific process of envelope calculation can be represented as follows: envelope = abs(range) P {b}) Where b is the index of the frequency sub-band. Through the above processing, the original sub-band independent distance image, which originally contained details of high-frequency oscillations, is transformed into a smooth energy distribution curve, laying a stable signal foundation for subsequent peak identification.

[0038] The distance parameter, a key configuration item in the preset peak detection rules, is used to define the effective detection range of different frequency band sub-bands, avoiding indiscriminate blind searches across the entire distance axis. This parameter is determined based on the frequency-optimal detection distance mapping established during system calibration: combining the attenuation characteristics and penetration depth test data of electromagnetic waves in grain media at different frequencies, and comprehensively considering the grain silo's geometry and safety redundancy, a parameter table containing dedicated search intervals for each sub-band is generated. During detection, the frequency identifier of the current sub-band is read, and the corresponding distance interval is indexed or matched in the distance parameter table, determining it as the target detection distance. For example, for low-frequency sub-bands, the target detection distance is set to cover the deep layers of the grain pile, such as a target detection distance of 5-25 meters; for high-frequency sub-bands, the focus is on the shallow high-resolution region, such as a target detection distance of 0.5-1 meter. This physically based interval locking mechanism significantly reduces computation and effectively avoids misjudgments caused by deep signal attenuation or shallow near-field interference.

[0039] After determining the search range limited by the target detection distance, peak constraint conditions are introduced to refine the screening of the envelope curve, identifying effective peaks that truly reflect the medium interface from numerous extreme points. The peak constraint conditions are a pre-defined set of multiple criterion logics, constructed based on statistical differences in amplitude, width, and spacing between real interface peaks and noise spurious peaks in historical test data: low-level noise is filtered out by setting an amplitude threshold, and multiple reflections or oscillating sidelobes caused by the same interface are excluded by setting a minimum spacing threshold. In actual execution, data segments falling within the target detection distance range of the envelope curve are selected as processing objects, and all local maxima points within this range are scanned; subsequently, the peak constraint conditions are used for verification, eliminating extreme points whose amplitudes are lower than a preset proportion (e.g., 12%) of the reference main peak, and ensuring that the distance between any two selected peak points is not less than a preset proportion (e.g., 1 / 40) of the total axis length to prevent false detection of multiple reflections from the same interface. The remaining peak points after filtering by the above rules are determined as target peaks, and their corresponding amplitudes are recorded as target peak amplitudes. These amplitude data constitute sub-band peak detection information, accurately characterizing the reflection intensity at each dielectric interface, and providing crucial quantitative input for subsequent dielectric constant inversion based on a multilayer dielectric model.

[0040] Through the above steps, envelope extraction enhances signal features, and combined with frequency band adaptation distance range constraints and multi-dimensional peak selection rules, the accurate identification of effective interface peaks in complex grain storage environments is achieved collaboratively. This mechanism effectively suppresses noise interference and spurious signals, ensuring that the extracted target peak amplitude has high accuracy and physical reliability, thereby significantly improving the robustness and inversion accuracy of the entire density measurement system.

[0041] S140. The sub-band peak detection information is inverted according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information.

[0042] The measurement terminal, based on a pre-set multilayer dielectric model, performs physical inversion calculations on the peak detection information of each sub-band to derive the electromagnetic characteristic parameters of each dielectric layer inside the grain silo. The multilayer dielectric model describes the physical laws governing the propagation, reflection, and transmission of electromagnetic waves in a multilayer dielectric, establishing a mathematical relationship between the reflection coefficient and the dielectric constant of the medium. The inversion process utilizes the peak amplitudes of each sub-band extracted in step S130, combined with the analytical reflection formula in the multilayer dielectric model, to calculate the reflection coefficient or transmission coefficient of each interface layer by layer, thereby obtaining the corresponding dielectric constant or inversion coefficient information. For example, the multilayer dielectric model can be constructed based on the Fresnel reflection formula, assuming perpendicular incidence of the electromagnetic wave, and the relative dielectric constants of the media on both sides of the interface can be calculated using the known incident and reflected wave amplitudes. Alternatively, the influence of dielectric loss on amplitude attenuation can be considered, introducing a loss correction factor for iterative inversion calculations. This inversion process is performed independently for each sub-band, resulting in a set of inversion coefficient information specific to the characteristics of that sub-band. This information quantitatively characterizes the density distribution characteristics of the grain pile at different frequency bands.

[0043] In a specific embodiment, step S140 includes the following sub-steps: analyzing the shallow peak amplitude of each sub-band peak detection information according to the basic reflection analytical model in the multilayer dielectric model to obtain the corresponding shallow dielectric constant; analyzing the shallow dielectric constant and intermediate layer peak amplitude of each sub-band peak detection information according to the loss analytical function and the basic reflection analytical model in the multilayer dielectric model to obtain the corresponding intermediate layer dielectric constant; analyzing the intermediate layer dielectric constant and bottom layer peak amplitude of each sub-band peak detection information according to the loss analytical function and the basic reflection analytical model in the multilayer dielectric model to obtain the corresponding bottom layer dielectric constant; combining the shallow dielectric constant, intermediate layer dielectric constant, and bottom layer dielectric constant of each sub-band peak detection information to obtain the corresponding inversion coefficient information.

[0044] Based on the physical assumption of perpendicular incidence of electromagnetic waves, the fundamental analytical reflection model pre-establishes the mapping relationship between the impedance difference of the media on both sides of the interface and the reflection coefficient. For the shallow interface composed of air and grain, the model defines the inversion logic between the reflection coefficient and the relative permittivity of the interface. From the sub-band peak detection information obtained in the preceding steps, the shallow peak amplitude characterizing the intensity of the first reflected echo generated by the electromagnetic wave on the grain pile surface is extracted.

[0045] An analytical operation is performed on the aforementioned shallow peak amplitude using a basic reflection analytical model to establish the correspondence between the shallow peak amplitude and the shallow interface reflection coefficient. This analytical process relies on the positive correlation between the two and eliminates the influence of radar system gain and detection range through normalization. For example, this analytical logic can be formalized into the following calculation model: ; In the formula, γ1 represents the shallow interface reflection coefficient to be solved; pks sub1 Indicates the shallow peak amplitude detected in the current subband; profile interp(1:50) If the signal amplitude sequence in the first segment of the sub-band independent distance image is represented, then the denominator in the above formula is the maximum signal amplitude in the first segment of the sub-band independent distance image; the denominator part is used to obtain the maximum signal amplitude in this region as a reference benchmark, thereby realizing the dimensionless calculation of the reflection coefficient.

[0046] Based on the calculated shallow interface reflection coefficient, the relative permittivity of the shallow medium is further deduced. This process is part of the basic analytical reflection model. This inversion result provides crucial physical parameter inputs for subsequent calculations of the intermediate and bottom layers. Specifically, the relative permittivity of the shallow medium is deduced as follows: ; ε1 is the obtained shallow dielectric constant. Each sub-band peak detection information can be used to calculate a corresponding shallow dielectric constant using the above method. Through this analytical approach, the original amplitude data is converted into a reflection coefficient with a clear physical meaning, achieving accurate characterization of the surface properties of the grain pile.

[0047] The peak amplitude of the intermediate layer reflects the signal strength of the electromagnetic wave after transmission through the shallow layer and reflection at the intermediate layer interface. This amplitude depends not only on the reflectivity of the intermediate layer interface itself but also on the transmission efficiency of the shallow layer interface. To overcome the transmission loss caused by electromagnetic waves passing through the shallow medium, a loss analytical function pre-established during the model construction phase is introduced. The core function of this function is to calculate the energy attenuation of the electromagnetic wave as it passes through the previous layer based on the dielectric constant of the previous layer.

[0048] In a specific embodiment, the step of analyzing the shallow dielectric constant and intermediate peak amplitude of each sub-band peak detection information according to the loss analysis function and the basic reflection analysis model in the multilayer dielectric model to obtain the corresponding intermediate layer dielectric constant includes: analyzing the shallow dielectric constant according to the loss analysis function to obtain the corresponding projection loss coefficient; multiplying the projection loss coefficient by the shallow peak amplitude to obtain the corresponding reference amplitude; and analyzing the reference amplitude and the intermediate peak amplitude according to the basic reflection analysis model to obtain the corresponding intermediate layer dielectric constant.

[0049] Using the shallow dielectric constant obtained in the previous steps, the transmission loss coefficient can be calculated. First, the corresponding shallow characteristic coefficients can be determined. These shallow characteristic coefficients can be expressed as follows: ε est (b,1)=max(1.5,min(6, ε1 )) ; b is the index of the frequency sub-band. ε est (b,1) These are the shallow characteristic coefficients extracted from the shallow dielectric constant of the b-th frequency subband. Substituting these shallow characteristic coefficients into the loss analytical function, we calculate the transmission loss coefficient of the shallow medium. This loss analytical function characterizes the continuity condition of the wave impedance at different medium interfaces, and its calculation logic can be expressed as follows: ; In the formula, T 1 This represents the transmission loss coefficient of the shallow medium. T 1 It quantifies the proportion of energy retained when electromagnetic waves enter a shallow medium from the air and are then transmitted.

[0050] By combining the basic analytical reflection model, the peak amplitude of the intermediate layer is corrected using the output of the analytical loss function. Specifically, the detected original peak amplitude of the intermediate layer is divided by the reference amplitude determined by the shallow layer (i.e., the product of the shallow layer peak amplitude and the transmission loss coefficient), thereby removing the attenuation effect caused by the shallow layer and restoring the true reflection coefficient of the intermediate layer interface. This inversion process can be formalized as the following decision function: ; In the formula, γ2 represents the reflection coefficient of the intermediate layer interface; pks sub2 This indicates the peak amplitude of the intermediate layer detected in the current sub-band; pks sub1 Indicates the shallow peak amplitude; T 1 This represents the transmission loss coefficient calculated using the aforementioned analytical loss function. This layer-by-layer calculation method accurately eliminates interlayer coupling effects, obtains the true reflection characteristics of the intermediate layer interfaces, and provides corrected input data for subsequent bottom-layer inversion. Furthermore, γ2 is calculated by Li Tong.

[0051] By performing a reverse calculation, the dielectric constant ε2 of the intermediate layer corresponding to γ2 can be obtained; the specific solution process is the same as that for obtaining the dielectric constant of the shallow layer, and will not be described in detail here.

[0052] Following the logic of the layer-by-layer method, the dielectric constant of the intermediate layer obtained from the inversion results is used to further analyze the reflection coefficient of the bottom layer interface, so as to complete the inversion of the multi-layer dielectric characteristics of the entire grain pile profile. The signal corresponding to the peak amplitude of the bottom layer has undergone dual transmission and reflection in the shallow and intermediate layers, resulting in significant energy attenuation. Therefore, the transmission loss of each layer must be accumulated sequentially.

[0053] The intermediate layer characteristic coefficients corresponding to the intermediate layer dielectric constant ε2 can be further obtained. The intermediate characteristic coefficients can be expressed as follows: ε est (b,2)=max(1.5,min(6, ε2 )) ; b is the index of the frequency sub-band. ε est (b,2) The intermediate layer characteristic coefficients are extracted from the intermediate dielectric constant of the b-th frequency subband. These intermediate layer characteristic coefficients are then substituted into the loss analytical function to calculate the transmission loss coefficient of the intermediate layer medium. This loss analytical function characterizes the continuity condition of the wave impedance at different medium interfaces, and its calculation logic can be expressed as follows: ; Subsequently, by combining the basic reflection analytical model, the detected bottom-layer peak amplitude is divided by the total reference amplitude after corrections for the shallow and intermediate layers, thereby resolving the bottom-layer interface reflection coefficient. This inversion calculation can, for example, follow the following formal model: ; In the formula, γ3 represents the reflection coefficient of the bottom interface; pks sub3 This indicates the peak amplitude of the bottom layer detected in the current sub-band; T 1 This represents the transmission loss coefficient of shallow media; T 2 This represents the transmission loss coefficient of the interlayer medium. Through the above steps, accurate layer-by-layer inversion is achieved from the surface to the bottom, ensuring that the reflection coefficient calculation of each interface eliminates the physical interference of the upper medium.

[0054] By performing a reverse calculation, the intermediate layer dielectric constant ε3 corresponding to γ3 can be obtained; the specific solution process is the same as that for obtaining the shallow layer dielectric constant, and will not be elaborated here.

[0055] The inversion results from different sub-bands and depth levels will be structurally integrated to form complete inversion coefficient information for subsequent weighted fusion processing. Specifically, the shallow interface reflection coefficient, intermediate interface reflection coefficient, and bottom interface reflection coefficient obtained from the aforementioned steps for each sub-band will be arranged and combined according to a preset data structure. Through the above calculations, each sub-band will have three dielectric constants, and for a total of three sub-bands, a 3×3 dielectric constant matrix will be obtained as the corresponding inversion coefficient information.

[0056] S150. According to the preset weighted fusion strategy, the inversion coefficient information corresponding to the peak detection information of each sub-band is weighted and fused to obtain the fusion coefficient information.

[0057] To integrate the detection advantages of different frequency bands, the measurement terminal performs weighted fusion processing on the inversion coefficient information corresponding to each sub-band according to a pre-set weighted fusion strategy. The weighted fusion strategy defines the weight allocation principle for the inversion results of different sub-bands in the final fusion result. This principle is typically based on the detection sensitivity and signal-to-noise ratio of each frequency band for different depth media. For example, for shallow media, high-frequency sub-bands have higher resolution and are therefore assigned higher weights; while for deep media, low-frequency sub-bands have stronger penetration capabilities, so their inversion coefficients are given dominant weights. The measurement terminal calculates the weighted sum of the inversion coefficients of each sub-band according to this strategy to generate fusion coefficient information. For example, the weighted fusion strategy can use a simple linear weighted average method, with the weight coefficients fixed based on empirical values; alternatively, an adaptive weighting algorithm based on signal-to-noise ratio estimation can be used to dynamically adjust the weight ratio of each sub-band to optimize fusion accuracy. Through weighted fusion, the measurement errors and limitations of a single frequency band can be effectively suppressed, resulting in high-quality fusion coefficients that more accurately reflect the overall density distribution of the grain silo.

[0058] In a specific embodiment, step S150 includes the following sub-steps: calculating the shallow dielectric constant of each sub-band peak detection information according to the shallow weighting function corresponding to the shallow interface in the weighted fusion strategy to obtain the corresponding shallow weighting coefficient; calculating the intermediate layer dielectric constant of each sub-band peak detection information according to the intermediate layer weighting function corresponding to the intermediate layer interface in the weighted fusion strategy to obtain the corresponding intermediate layer weighting coefficient; calculating the bottom dielectric constant of each sub-band peak detection information according to the bottom layer weighting function corresponding to the bottom layer interface in the weighted fusion strategy to obtain the corresponding bottom layer weighting coefficient; and combining the shallow weighting coefficient, the intermediate layer weighting coefficient, and the bottom layer weighting coefficient as the corresponding fusion coefficient information.

[0059] After extracting the shallow interface reflection coefficients from each sub-band, a weighted calculation is performed according to a pre-configured shallow weighting function. This function establishes a mapping relationship between frequency sub-bands and detection reliability, aiming to quantify the sensitivity differences of different frequency bands to the shallow region of the grain silo. Given that high-frequency sub-bands (e.g., 800MHz~1.2GHz) have higher range resolution and are more sensitive to changes in shallow medium density, they are assigned higher weights in the function. In contrast, while low-frequency sub-bands have strong penetration, their signal amplitude changes more gradually in the shallow region, thus receiving lower weights. By linearly or nonlinearly aggregating the shallow interface reflection coefficients of each sub-band with their corresponding weight values, a shallow weighting coefficient that comprehensively reflects the advantages of each frequency band in shallow detection is calculated.

[0060] For density inversion in the middle layer region of the grain pile, a middle layer weighting function is used to process the reflection coefficient of the middle layer interface. This function is designed to balance the performance of the mid-frequency sub-bands between penetration depth and resolution. The reflection coefficient of the middle layer interface reflects the reflection characteristics of electromagnetic waves after passing through the shallow medium and reaching the middle interface; its value is affected by transmission loss caused by the dielectric properties of the shallow layer. Weights are dynamically assigned based on the signal-to-noise ratio distribution, attenuation characteristics, and accuracy of historical verification data for each sub-band signal at the middle layer depth. For example, mid-frequency sub-bands (e.g., 500MHz~800MHz) typically provide stable echo signals in the middle layer and are therefore given higher weights; while high-frequency sub-band signals, due to significant attenuation at this depth, have correspondingly lower weights. By performing weighted summation and other operations on the reflection coefficients of each sub-band's middle layer interface, the middle layer weighting coefficients characterizing the overall medium properties of the middle layer are obtained.

[0061] In the density measurement stage at the bottom of the grain silo, a bottom-level weighting function is used to weight the reflection coefficients of the bottom interface. This function aims to highlight the advantages of low-frequency sub-bands in deep detection, utilizing the characteristics of low-frequency signals—low propagation loss, ability to penetrate thick grain piles, and strong signal reflection from the bottom surface or deep impurity interfaces. The bottom interface reflection coefficients calculated for each sub-band are substituted into the bottom-level weighting function, where the coefficients of low-frequency sub-bands (e.g., 200MHz~500MHz) dominate, while the coefficients of high-frequency sub-bands serve as auxiliary references or are assigned minimal weights. This weighting mechanism, combining frequency band and depth, effectively suppresses noise interference from high-frequency signals in deep detection, thereby extracting high-confidence bottom-level weighting coefficients that accurately reflect the compaction density or interface state at the bottom of the grain silo.

[0062] For example, the weighting coefficients configured in the three sets of weighting functions are shown in Table 1: Table 1

[0063] The weighting coefficients mentioned above can be adjusted based on actual model training.

[0064] After completing the weighted calculations for each interface layer, the generated shallow, intermediate, and bottom layer weighted coefficients are combined according to a preset data structure to form fusion coefficient information. This information is essentially a vector or matrix containing the medium characteristic parameters of different depth layers in the vertical direction of the grain silo, with each dimension corresponding to the inversion results of the shallow, intermediate, and bottom layers, respectively. Through the above-mentioned layered weighted fusion process, the inherent contradiction between detection depth and resolution in single-band radar is overcome, achieving an organic combination of the high-resolution advantage of the high-frequency subband in shallow layers and the high-penetration advantage of the low-frequency subband in deep layers. This multi-dimensional fusion coefficient information can more comprehensively and accurately map the density distribution that varies along the depth direction inside the grain pile, providing reliable data support for the subsequent generation of high-precision grain silo density maps.

[0065] S160. The fusion coefficient information is converted according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

[0066] Finally, the measurement terminal uses a preset density conversion rule to convert the fused coefficient information into the final grain density measurement information. The density conversion rule establishes a quantitative mapping relationship between the dielectric constant (or other inversion coefficients) and the physical density of grain, typically constructed based on fitting extensive experimental data or empirical formulas. The measurement terminal substitutes the fused coefficient information into this rule for calculation and outputs the corresponding density value. For example, the density conversion rule can use a polynomial fitting model, with the dielectric constant as the independent variable, to directly calculate the bulk density of the grain; alternatively, it can use a lookup table method, determining the corresponding density value by searching a pre-calibrated dielectric constant-density lookup table. For instance, in a specific embodiment, an empirical formula can be set to convert the dielectric constant of each medium layer separately, obtaining the grain bulk density of each medium layer. The specific conversion process can be expressed as follows: ; Where K is the dielectric constant-density conversion coefficient, which can be obtained through numerous calibration experiments; ρ is the converted grain pile density. This conversion process transforms electromagnetic measurement parameters into intuitive physical density parameters, thereby completing the accurate measurement of grain silo density. Using the above method, the grain pile densities corresponding to the shallow, intermediate, and deep layers can be obtained sequentially, and the grain pile densities of each medium layer can be combined to obtain the grain silo density measurement information.

[0067] By dividing the broadband continuous scanning signal into multiple sub-bands and performing imaging, peak detection, and inversion separately, and then utilizing a weighted fusion strategy to combine the advantages of each frequency band, the method provided in this embodiment can effectively overcome the inherent contradiction between penetration depth and resolution in single-band detection. The low-frequency sub-band ensures the penetration of deep signals, the high-frequency sub-band provides high resolution for shallow interfaces, and the weighted fusion optimizes the inversion results based on the detection reliability at different depths, thereby significantly improving the accuracy and reliability of density measurement at different depths in grain silos.

[0068] The grain storage density measurement method based on broadband continuous scanning disclosed in the above embodiments includes: receiving continuous scanning signals collected by a detection radar and obtaining multiple sub-band scanning signals by segmenting the spectrum according to a preset standard; analyzing each sub-band scanning signal to obtain a sub-band independent distance image; detecting the sub-band independent distance image to obtain sub-band peak detection information; inverting the sub-band peak detection information based on a multi-layer medium model to obtain inversion coefficient information; using a weighted fusion strategy to perform weighted fusion of the inversion coefficient information to obtain fusion coefficient information; and finally converting the fusion coefficient information into grain storage density measurement information according to density conversion rules. This grain storage density measurement method, by segmenting the sub-band scanning signals, analyzing them separately, and inverting them based on a multi-layer medium model, can effectively solve the problem of high-precision measurement in complex multi-layer medium environments using single-band detection, thus improving the accuracy of full-depth density distribution measurement of grain piles.

[0069] This invention also provides a grain storage density measuring device based on broadband continuous scanning. This device can be configured in a measuring terminal 10 and is used to perform any embodiment of the aforementioned grain storage density measuring method based on broadband continuous scanning. Specifically, please refer to... Figure 3 , Figure 3 This is a schematic block diagram of a grain storage density measurement device based on broadband continuous scanning, provided in an embodiment of the present invention.

[0070] like Figure 3 As shown, the grain storage density measurement device 100 based on broadband continuous scanning includes a continuous scanning signal segmentation unit 110, a parsing unit 120, an image detection unit 130, an inversion unit 140, a weighted fusion unit 150, and a conversion unit 160.

[0071] The continuous scanning signal segmentation unit 110 is used to receive the continuous scanning signal collected by the detection radar during bandwidth continuous scanning, and to segment the continuous scanning signal according to the preset spectrum segmentation information to obtain multiple sub-band scanning signals.

[0072] The analysis unit 120 is used to analyze each of the sub-band scanning signals according to a preset imaging analysis strategy to obtain the corresponding sub-band independent distance image.

[0073] The image detection unit 130 is used to detect each of the sub-band independent distance images according to a preset peak detection rule, so as to obtain the corresponding sub-band peak detection information.

[0074] The inversion unit 140 is used to invert the sub-band peak detection information according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information.

[0075] The weighted fusion unit 150 is used to perform weighted fusion on the inversion coefficient information corresponding to the peak detection information of each sub-band according to a preset weighted fusion strategy to obtain fusion coefficient information.

[0076] The conversion unit 160 is used to convert the fusion coefficient information according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

[0077] The grain storage density measurement device based on broadband continuous scanning provided in this embodiment of the invention applies the aforementioned grain storage density measurement method based on broadband continuous scanning. It receives continuous scanning signals collected by a detection radar and obtains multiple sub-band scanning signals by segmenting the information according to a preset spectrum. Each sub-band scanning signal is analyzed to obtain an independent sub-band distance image. The independent sub-band distance image is detected to obtain sub-band peak detection information. The sub-band peak detection information is inverted based on a multi-layer medium model to obtain inversion coefficient information. The inversion coefficient information is weighted and fused using a weighted fusion strategy to obtain fusion coefficient information. Finally, the fusion coefficient information is converted into grain storage density measurement information according to density conversion rules. This grain storage density measurement method, by segmenting the sub-band scanning signals, analyzing them separately, and inverting them based on a multi-layer medium model, effectively solves the problem of high-precision measurement in complex multi-layer medium environments using single-band detection, thus improving the accuracy of full-depth density distribution measurement of grain piles.

[0078] The aforementioned grain silo density measurement device based on broadband continuous scanning can be implemented as a computer program, which can be used in various ways, such as... Figure 4 It runs on the computer device shown.

[0079] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device may be a measurement terminal used to perform a grain silo density measurement method based on broadband continuous scanning to measure the density inside a grain silo.

[0080] See Figure 4 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.

[0081] 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 silo density measurement method based on broadband continuous scanning. The storage medium 503 may be a volatile storage medium or a non-volatile storage medium.

[0082] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0083] 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 silo density measurement method based on broadband continuous scanning.

[0084] This communication interface 505 is used for network communication, such as providing data transmission. Those skilled in the art will understand that... Figure 4 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.

[0085] The processor 502 is used to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned method for measuring grain density based on broadband continuous scanning.

[0086] Those skilled in the art will understand that Figure 4 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 4 The embodiments shown are consistent and will not be repeated here.

[0087] 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.

[0088] 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 method for measuring grain density based on broadband continuous scanning.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 density in a grain warehouse based on broadband continuous scanning, 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 continuous scanning signal collected by the detection radar through continuous bandwidth scanning, and segments the continuous scanning signal according to preset spectrum segmentation information to obtain multiple sub-band scanning signals. The sub-band scanning signals are analyzed according to the preset imaging analysis strategy to obtain the corresponding sub-band independent distance image; The independent distance images of each sub-band are detected according to the preset peak detection rules to obtain the corresponding sub-band peak detection information; The sub-band peak detection information is inverted according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information; According to the preset weighted fusion strategy, the inversion coefficient information corresponding to the peak detection information of each sub-band is weighted and fused to obtain the fusion coefficient information; The fusion coefficient information is converted according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

2. The grain storage density measurement method based on broadband continuous scanning according to claim 1, characterized in that, The step of analyzing each sub-band scanning signal according to a preset imaging analysis strategy to obtain the corresponding sub-band independent distance image includes: Calculate the subband bandwidth of each subband scan signal and determine the corresponding theoretical resolution; The sub-band scanning signal is windowed according to the windowing parameters in the imaging analysis strategy and the theoretical resolution to obtain the corresponding windowed signal. The windowed signal is converted according to the conversion function in the imaging analysis strategy to obtain the corresponding converted signal. The corresponding signal amplitude information is obtained from the converted signal to generate a distance axis signal image as the corresponding sub-band independent distance image.

3. The grain storage density measurement method based on broadband continuous scanning according to claim 2, characterized in that, The step of windowing the sub-band scanning signal according to the windowing parameters in the imaging analysis strategy and the theoretical resolution to obtain the corresponding windowed signal includes: Obtain target windowing parameters from the windowing parameters that match the theoretical resolution of each sub-band scanning signal; The sub-band scanning signal is windowed according to the target windowing parameters to obtain the corresponding windowed signal.

4. The grain storage density measurement method based on broadband continuous scanning according to claim 3, characterized in that, The step of detecting the independent distance images of each sub-band according to a preset peak detection rule to obtain the corresponding sub-band peak detection information includes: Envelope calculations are performed on the independent distance images of each sub-band to obtain the corresponding envelope curves; The target detection distance corresponding to the independent distance image of each sub-band is obtained by matching the distance parameters of the peak detection rule; Peak identification is performed on each envelope curve according to the peak constraint conditions of the peak detection rule and the target detection distance, and the corresponding target peak amplitude is obtained as the corresponding sub-band peak detection information.

5. The grain storage density measurement method based on broadband continuous scanning according to any one of claims 1-4, characterized in that, The step of inverting the sub-band peak detection information according to a preset multi-layer medium model to obtain the corresponding inversion coefficient information includes: Based on the fundamental reflection analytical model in the multilayer dielectric model, the shallow peak amplitude of each sub-band peak detection information is analyzed to obtain the corresponding shallow dielectric constant. Based on the loss analytical function and the basic reflection analytical model in the multilayer dielectric model, the shallow dielectric constant and the intermediate layer peak amplitude of each sub-band peak detection information are analyzed to obtain the corresponding intermediate layer dielectric constant. Based on the loss analytical function and the basic reflection analytical model in the multilayer dielectric model, the intermediate layer dielectric constant and the bottom layer peak amplitude of each sub-band peak detection information are analyzed to obtain the corresponding bottom layer dielectric constant. The shallow, intermediate, and bottom dielectric constants of each sub-band peak detection information are combined to obtain the corresponding inversion coefficient information.

6. The grain storage density measurement method based on broadband continuous scanning according to claim 5, characterized in that, The step of analyzing the shallow dielectric constant and intermediate layer peak amplitude of each sub-band peak detection information based on the loss analytical function and the basic reflection analytical model in the multilayer dielectric model to obtain the corresponding intermediate layer dielectric constant includes: The shallow dielectric constant is analyzed based on the loss analysis function to obtain the corresponding projection loss coefficient; Multiply the projection loss coefficient by the shallow peak amplitude to obtain the corresponding reference amplitude; The reference amplitude and the peak amplitude of the intermediate layer are analyzed based on the basic reflection analytical model to obtain the corresponding dielectric constant of the intermediate layer.

7. The grain storage density measurement method based on broadband continuous scanning according to claim 6, characterized in that, The step of weighting and fusing the inversion coefficient information corresponding to the peak detection information of each sub-band according to a preset weighted fusion strategy to obtain fusion coefficient information includes: The shallow dielectric constant of each sub-band peak detection information is weighted according to the shallow weighting function corresponding to the shallow interface in the weighted fusion strategy to obtain the corresponding shallow weighting coefficient. According to the intermediate layer weighting function corresponding to the intermediate layer interface in the weighted fusion strategy, the intermediate layer dielectric constant of each sub-band peak detection information is weighted and calculated to obtain the corresponding intermediate layer weighting coefficient. According to the weighted fusion strategy, the underlying dielectric constant of each sub-band peak detection information is weighted and calculated based on the underlying weighting function corresponding to the underlying interface to obtain the corresponding underlying weighting coefficient. The shallow layer weighting coefficient, the intermediate layer weighting coefficient, and the bottom layer weighting coefficient are combined to form the corresponding fusion coefficient information.

8. A grain storage density measuring device based on broadband continuous scanning, characterized in that, The device is configured on a measurement terminal, which is communicatively connected to a detection radar to transmit data. The device is used to perform the grain storage density measurement method based on broadband continuous scanning as described in any one of claims 1-7. The device comprises: The continuous scanning signal segmentation unit is used to receive the continuous scanning signal collected by the detection radar during bandwidth continuous scanning, and to segment the continuous scanning signal according to the preset spectrum segmentation information to obtain multiple sub-band scanning signals. The analysis unit is used to analyze each sub-band scanning signal according to a preset imaging analysis strategy to obtain the corresponding sub-band independent distance image; The image detection unit is used to detect the independent distance images of each sub-band according to the preset peak detection rules, so as to obtain the corresponding sub-band peak detection information. The inversion unit is used to invert the sub-band peak detection information according to the preset multi-layer medium model to obtain the corresponding inversion coefficient information. The weighted fusion unit is used to perform weighted fusion on the inversion coefficient information corresponding to the peak detection information of each sub-band according to a preset weighted fusion strategy to obtain fusion coefficient information; The conversion unit is used to convert the fusion coefficient information according to the preset density conversion rules to obtain the corresponding grain storage density measurement information.

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 storage density measurement method based on broadband continuous scanning 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 storage density measurement method based on broadband continuous scanning as described in any one of claims 1-7.

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