Method and system for detecting maturity of agricultural products

Abstract

The invention relates to the technical field of maturity detection, in particular to an agricultural product maturity detection method and system. The method comprises the following steps: performing multi-angle scanning on pears through an ultrasonic detector, obtaining reflected sound wave frequency domain data, and performing bandwidth change analysis to obtain regional sound wave bandwidth change data; then, through high-low frequency energy ratio integral processing, high-frequency energy attenuation structure difference data between the regions are obtained; then, pulp viscosity increment simulation evaluation is carried out, the softness and toughness of the pulp are further evaluated, and finally, the maturity of the pears is evaluated according to the evaluation result of the softness and toughness of the pulp. According to the method, the maturity detection technology is optimized, so that the maturity detection technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of maturity detection, and in particular to a method and system for detecting the maturity of agricultural products. Background Art

[0002] Ultrasonic testing technology, as a nondestructive physical testing method, offers advantages such as high sensitivity, ease of operation, and immunity to environmental influences. Ultrasonic testing of the internal structure of agricultural products can reveal physical properties such as tissue density, hardness, and elasticity, thereby inferring their maturity. In particular, analysis of ultrasonic wave velocity, attenuation, and reflection data can accurately reveal the maturity of fruits or agricultural products. Furthermore, frequency analysis based on sound waves can provide valuable information on the internal distribution and structural changes of fruit pulp, further optimizing the accuracy and timeliness of maturity testing. However, a traditional method for detecting the maturity of agricultural products suffers from low accuracy in analyzing sound wave attenuation, resulting in large errors in detecting the softness and toughness of the flesh and low precision in determining maturity. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for detecting the maturity of agricultural products to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for detecting the maturity of agricultural products is provided, the method comprising the following steps: Step S1: Scanning the pear at multiple angles using an ultrasonic detector, then performing frequency domain analysis on the regional division to generate regional frequency domain data of reflected sound waves; performing sound wave bandwidth variation analysis on the regional frequency domain data of reflected sound waves to obtain regional sound wave bandwidth variation data; Step S2: performing high-low frequency energy ratio integration processing between different regions on the regional acoustic wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; performing high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integration data to obtain attenuation structure localization difference data; Step S3: performing a simulation evaluation of the pulp viscosity increment based on the attenuation structure localization difference data to obtain pulp viscosity increment data; performing a pulp softness and toughness evaluation based on the pulp viscosity increment data to obtain pulp softness and toughness evaluation data; Step S4: evaluating the maturity of the pears according to the pulp softness and toughness evaluation data, thereby obtaining the pear maturity evaluation data.

[0005] Preferably, step S1 includes the following steps: Step S11: Scan the pear at multiple angles using an ultrasonic detector to obtain multi-angle reflected sound waves from the pear; Step S12: performing noise filtering on the pear multi-angle reflected sound waves to obtain the pear multi-angle reflected filtered sound waves; Step S13: performing frequency domain analysis on the pear multi-angle reflected filtered sound waves to generate frequency domain data of the reflected sound waves; Step S14: performing acoustic wave bandwidth change analysis on the regional frequency domain data of the reflected acoustic wave to obtain regional acoustic wave bandwidth change data.

[0006] Preferably, step S2 includes the following steps: Step S21: calculating the sound wave frequency change rate ratios between different regions based on the regional sound wave bandwidth change data to obtain the regional sound wave frequency change rate ratios; Step S22: performing high-low frequency energy ratio integration processing on the regional sound wave bandwidth change data between different regions according to the regional sound wave frequency change rate ratio to obtain regional high-low frequency energy ratio integration data; Step S23: performing high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integral data to obtain regional high-frequency energy attenuation structure data; Step S24: performing multi-scale localized difference analysis between different regions on the regional high-frequency energy attenuation structure data to obtain attenuation structure localized difference data.

[0007] Preferably, step S22 includes the following steps: Step S221: performing high-low frequency change morphology identification of sound waves between different regions based on the regional sound wave frequency change rate ratio and the regional sound wave bandwidth change data to obtain high-low frequency change morphology data of sound waves between different regions; Step S222: performing high-low frequency discrete entropy regression analysis on the high-low frequency variation morphological data of the sound waves between different regions to obtain high-low frequency discrete entropy regression data; Step S223: performing nonlinear fluctuation polynomial interpolation on the high-low frequency variation morphological data of the sound waves between different regions based on the high-low frequency discrete entropy value regression data to obtain high-low frequency nonlinear fluctuation interpolation data; Step S224: calculating the variance of the segmented fluctuation slope growth rate on the high-low frequency nonlinear fluctuation interpolation data to obtain the variance of the high-low frequency segmented fluctuation slope growth rate; Step S225: performing high-low frequency energy ratio integration processing on the variance of the high-low frequency segmented fluctuation slope growth rate between different regions according to the Romberg integration method to obtain regional high-low frequency energy ratio integration data.

[0008] Preferably, step S23 includes the following steps: Step S231: identifying high-frequency energy attenuation fluctuation frequency bands between different regions based on regional high-low frequency energy ratio integral data, and obtaining high-frequency energy attenuation fluctuation frequency bands between different regions; Step S232: Conduct an attenuation clustering density analysis on the high-frequency energy attenuation fluctuations between different regions to obtain the high-frequency energy attenuation clustering density between different regions; Step S233: Perform a non-uniform complex wave number gradient differential processing on the high-frequency energy attenuation fluctuation frequency band based on the high-frequency energy attenuation clustering density to obtain high-frequency attenuation complex wave number gradient differential data; Step S234: Calculate the attenuation acceleration variance between different regions for the high-frequency energy attenuation fluctuation frequency band according to the high-frequency attenuation complex wave number gradient differential data to obtain the attenuation acceleration variance; Step S235: Conduct an analysis of the high-frequency energy attenuation structure between different regions based on the high-frequency attenuation complex wave number gradient differential data and the attenuation acceleration variance to obtain regional high-frequency energy attenuation structure data.

[0009] Preferably, Step S233 includes the following steps: Conduct a non-uniformity analysis of the waveform frequency decay nodes of the clustering center on the high-frequency energy attenuation fluctuation frequency band based on the high-frequency energy attenuation clustering density to obtain non-uniformity data of the frequency decay nodes; Perform a complex frequency spectrum construction process on the high-frequency energy attenuation fluctuation frequency band according to the non-uniformity data of the frequency decay nodes to obtain a non-uniformly distributed complex frequency spectrum matrix; Perform a first-order complex wave number differential process on the non-uniformly distributed complex frequency spectrum matrix to obtain complex wave number gradient change data; Calculate the complex domain phase singular spectrum entropy for the complex wave number gradient change data to obtain the complex domain phase singular spectrum entropy; Conduct a non-uniform complex wave gradient differential process according to the complex domain phase singular spectrum entropy to obtain high-frequency attenuation complex wave number gradient differential data.

[0010] Preferably, Step S3 includes the following steps: Step S31: Identify the acoustic wave scattering enhancement gradient between different regions based on the attenuation structure localization difference data to obtain the acoustic wave scattering enhancement gradient between different regions; Step S32: Conduct a simulation evaluation of the expansion of the pear cell gaps according to the acoustic wave scattering enhancement gradient between different regions to obtain cell gap expansion data; Step S33: Conduct a simulation evaluation of the increase in pulp viscosity based on the attenuation structure localization difference data to obtain pulp viscosity increase data; Step S34: Evaluate the pulp softness and toughness based on the cell gap expansion data and the pulp viscosity increase data to obtain pulp softness and toughness evaluation data.

[0011] Preferably, Step S33 includes the following steps: Step S331: Conduct a pulp-associated absorption attenuation measurement mapping based on the attenuation structure localization difference data to obtain pulp-associated absorption attenuation measurement data; Step S332: Deduce the regional pulp density difference interval based on the pulp-associated absorption attenuation measurement data to obtain the regional pulp density difference interval; Step S333: Fit the pulp shear viscosity increment according to the pulp-associated absorption attenuation measurement data and the regional pulp density difference interval to obtain the pulp shear viscosity increment fitting data; Step S334: Analyze the pulp volume viscosity ratio according to the pulp-associated absorption attenuation measurement data, the pulp shear viscosity increment fitting data, and the pulp density difference interval to obtain the pulp volume viscosity ratio; Step S335: Simulate and evaluate the pulp viscosity increment according to the pulp shear viscosity increment fitting data and the pulp volume viscosity ratio to obtain the pulp viscosity increment data.

[0012] Preferably, the present invention also provides an agricultural product maturity detection system for performing the above-mentioned agricultural product maturity detection method. The agricultural product maturity detection system includes: An acoustic wave bandwidth change analysis module, which is used to scan the pear from multiple angles through an ultrasonic detector, and then perform regional division frequency domain analysis to generate reflected acoustic wave regional frequency domain data; perform acoustic wave bandwidth change analysis on the reflected acoustic wave regional frequency domain data to obtain regional acoustic wave bandwidth change data; A high-frequency energy attenuation structure analysis module, which is used to perform high-low frequency energy ratio integration processing between different regions on the regional acoustic wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; perform high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integration data to obtain attenuation structure localization difference data; A pulp softness and toughness evaluation module, which is used to simulate and evaluate the pulp viscosity increment based on the attenuation structure localization difference data to obtain the pulp viscosity increment data; evaluate the pulp softness and toughness based on the pulp viscosity increment data to obtain the pulp softness and toughness evaluation data; A maturity evaluation module, which is used to evaluate the pear maturity according to the pulp softness and toughness evaluation data, so as to obtain the pear maturity evaluation data.

[0013] The beneficial effects of the present invention are as follows. By using an ultrasonic detector to scan pears from multiple angles, frequency-domain data of the reflected sound wave regions is generated, and the analysis of the sound wave bandwidth change is carried out. The beneficial effect of this step is that through the multi-angle data collection of ultrasonic scanning, the internal physical structure and density distribution of pears can be comprehensively understood, detailed frequency-domain data can be generated, reflecting the sound wave propagation characteristics of each region of the fruit. The analysis of the bandwidth change further reveals the changes in the internal distribution of the fruit, can accurately reflect the tissue characteristics during the ripening process of pears, and provides a high-precision non-destructive detection method. Integrate the high-low frequency energy ratio of the regional sound wave bandwidth change data to obtain the integrated high-low frequency energy ratio data of the region, and analyze the high-frequency energy attenuation structure based on these data. This processing can reveal the differences in sound wave attenuation between different regions, thereby reflecting the structural differences of the fruit. The attenuation of high-frequency energy is often closely related to the maturity, density and physical changes of the pulp. Through the analysis of the localized differences in this attenuation structure, it helps to accurately judge the changes in different regions during the fruit ripening process, further improving the accuracy and reliability of the detection. Based on the localized difference data of the attenuation structure, simulate and evaluate the increment of pulp viscosity to obtain the increment data of pulp viscosity, and further evaluate the softness and toughness of the pulp. The beneficial effect of this step is that by using the simulation of viscosity increment, the viscosity changes of the pulp tissue can be carefully captured and its softness and toughness characteristics can be deduced. The softness and toughness of the pulp directly affect the taste and edible value of the fruit. Therefore, through the accurate evaluation of the softness and toughness, a more quantitative and objective basis can be provided for the judgment of fruit maturity. According to the evaluation data of pulp softness and toughness, evaluate the maturity of pears to obtain the evaluation data of pear maturity. The beneficial effect of this step is that through the accurate evaluation of pulp softness and toughness in the early stage, combined with the comprehensive judgment of maturity, the true ripening state of pears can be obtained. This method is not only non-destructive and efficient, avoiding the errors introduced in traditional methods, making the evaluation results of maturity more accurate and reliable. Therefore, the present invention is an optimized treatment for a traditional method for detecting the maturity of agricultural products, solving the problems of low accuracy in analyzing sound wave attenuation, large errors in detecting the softness and toughness inside the pulp, and low accuracy in judging maturity in a traditional method for detecting the maturity of agricultural products, improving the accuracy of sound wave attenuation analysis, reducing the errors in detecting the softness and toughness inside the pulp, and improving the accuracy of maturity judgment. Description of the Drawings

[0014] Figure 1 It is a schematic diagram of the step flow of a method for detecting the maturity of agricultural products; Figure 2 is Figure 1 a detailed implementation step flow diagram of step S3 in Figure 3 is Figure 2 a detailed implementation step flow diagram of step S33 in Detailed implementation mode

[0015] Please refer to Figures 1 to 3 , a method for detecting the maturity of agricultural products, the method comprising the following steps: Step S1: Multiangularly scan the pears with an ultrasonic detector, and then perform regional division frequency domain analysis to generate reflected acoustic wave region frequency domain data; perform acoustic wave bandwidth change analysis on the reflected acoustic wave region frequency domain data to obtain regional acoustic wave bandwidth change data; Step S2: Perform high-low frequency energy ratio integration processing between different regions on the regional acoustic wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; perform high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integration data to obtain attenuation structure localization difference data; Step S3: Perform pulp viscosity increment simulation evaluation based on the attenuation structure localization difference data to obtain pulp viscosity increment data; perform pulp softness and toughness evaluation based on the pulp viscosity increment data to obtain pulp softness and toughness evaluation data; Step S4: Perform pear maturity evaluation according to the pulp softness and toughness evaluation data, so as to obtain pear maturity evaluation data.

[0016] In the embodiment of the present invention, referring to Figure 1 as described, it is a schematic flow chart of the steps of a method for detecting the maturity of agricultural products according to the present invention. In this example, the method for detecting the maturity of agricultural products comprises the following steps: Step S1: Multiangularly scan the pears with an ultrasonic detector, and then perform regional division frequency domain analysis to generate reflected acoustic wave region frequency domain data; perform acoustic wave bandwidth change analysis on the reflected acoustic wave region frequency domain data to obtain regional acoustic wave bandwidth change data; In the embodiments of the present invention, multiple ultrasonic detection heads are evenly fixed on the inner wall of the detection cavity. The ultrasonic emission frequency is set to 2.25 MHz, the emission power is 10 W, and the emission angle is set to scan step by step at intervals of 5° within ±45°, ensuring the acquisition of reflected sound waves at multiple spatial angles on the surface and inside of the pear. The collected original echo signals are recorded at a sampling rate of 500 MS / s. Through time-sequence synchronization control, time-window truncation processing is performed on the echo signals numbered by angle. The Butterworth band-pass filter is used to filter the original echo signals, and the passband range of the filter is set to 1.5 MHz to 3.0 MHz to eliminate environmental noise and equipment stray frequency interference. In the filtered acoustic wave data, by performing Fourier transform on the reflection waveforms of each angle data, the frequency-domain signal spectrogram of each region is obtained. The pear is divided into 20 spherical regions according to its geometric spherical coordinate system, and the reflected frequency-domain energy density of each region is processed by frequency band division. The division range is four segments: 1.5–1.8 MHz, 1.8–2.2 MHz, 2.2–2.5 MHz, and 2.5–3.0 MHz. Then, the bandwidth calculation is performed on the energy amplitudes of different frequency bands. By calculating the change in the peak energy density in each bandwidth segment, the characteristics of the regional acoustic wave bandwidth change are extracted. Finally, a matrix is formed to record the energy fluctuation data corresponding to the bandwidth and center frequency of each frequency band in each region, forming the "regional acoustic wave bandwidth change data".

[0017] In another embodiment, the pear is scanned from multiple angles by an ultrasonic detector. First, a pulsed ultrasonic transmitter with a frequency of 1 MHz is used, and the scanning angle covers 360 degrees on the surface of the pear, with a step angle of 5 degrees, ensuring that the acoustic wave signals cover all directions of the pear. The collected original reflected acoustic wave signals are processed by a band-pass filter to filter out the noise components with frequencies lower than 0.5 MHz and higher than 2 MHz, improving the signal-to-noise ratio. Subsequently, the short-time Fourier transform is used to perform frequency-domain analysis on the filtered reflected acoustic wave signals. The surface of the pear is divided into several regions of a fixed size (for example, each region covers 10 square millimeters), and the spectral calculation is performed on the acoustic wave signals within each region to obtain the frequency-domain data of the regional reflected acoustic waves. For the spectral data of each region, the bandwidth measurement algorithm is used to calculate the 3 dB bandwidth change of the acoustic wave signals. Specifically, by measuring the frequency range corresponding to a 3 dB energy drop in the spectrum, the acoustic wave bandwidth change data of each region is obtained. This process is implemented by writing a signal processing program, and the parameter settings include a sampling rate of 5 MHz, a Hanning window function, a window length of 1024 points, and an overlap rate of 50%, ensuring the balance of the time resolution and frequency resolution of the frequency-domain analysis. Through the above steps, the frequency-domain analysis of the regional division of the multi-angle reflected acoustic waves of the pear and the extraction of the bandwidth change are completed.

[0018] Step S2: Perform high-low frequency energy ratio integration processing on the regional acoustic wave bandwidth change data among different regions to obtain regional high-low frequency energy ratio integration data; based on the regional high-low frequency energy ratio integration data, perform high-frequency energy attenuation structure analysis among different regions to obtain attenuation structure localization difference data; In the embodiment of the present invention, the ratio calculation between regions is performed on the obtained "regional acoustic wave bandwidth change data", and the integral method is used to calculate the ratio of the total energy between the high-frequency band (2.2–3.0 MHz) and the low-frequency band (1.5–2.2 MHz) in each region. The integral uses the numerical trapezoidal method, the frequency axis is equally divided into 100 sampling points, and the ratio is taken after integrating each segment to form the "regional high-low frequency energy ratio integration data". On this basis, in order to obtain the energy distribution difference of the internal tissue structure of the pear, a high-frequency energy attenuation structure analysis method is used. By calculating the gradient of the high-frequency energy integral difference between adjacent regions, an energy difference gradient field between regions is constructed. The gradient intensity in each direction is convolutionally enhanced using a spatial gradient map, with a Sobel kernel of 3×3 for the enhancement scale, the direction feature with the most obvious energy attenuation is extracted, and combined with the regional spatial distance function, a normalization mapping process is performed on different gradient change directions to obtain the "attenuation structure localization difference data". This data can be specifically described as a 20-dimensional vector, and each dimension represents the high-frequency attenuation gradient value relative to the reference region.

[0019] In another embodiment, for the regional acoustic wave bandwidth change data obtained in step S1, first calculate the ratio of the acoustic wave frequency change rate between different regions. The specific method is to perform differential processing on the bandwidth change data of adjacent regions to obtain the frequency change rate, and then calculate the rate ratio. Based on the rate ratio, perform high-low frequency energy ratio integration processing. Specifically, the bandwidth change data is divided into a high-frequency band (1.2 MHz to 2 MHz) and a low-frequency band (0.5 MHz to 1.2 MHz), and the energy integrals within each frequency band are calculated respectively. The integral uses the Romberg integration method, the integral step size is set to 0.01 MHz, and the integral interval is strictly limited to ensure the integral accuracy. The integral results are used to construct the regional high-low frequency energy ratio integration data. Subsequently, based on this data, high-frequency energy attenuation structure analysis is performed, and a multi-scale localization analysis method is used. Specifically, the high-frequency energy attenuation characteristics at different scales are extracted through wavelet transform, the scale range is set to 1 to 5, the resolution is 0.1, the local attenuation fluctuation frequency band is extracted, and the attenuation aggregation density analysis is performed on the extracted frequency band. The kernel density estimation method is used to calculate the spatial distribution density of the attenuation fluctuation, and the Gaussian kernel is selected for the kernel function, and the bandwidth parameter is set to 0.05. Combining non-uniform complex wavenumber gradient differential processing, complex spectrum construction and first-order complex wavenumber differential are used, and the complex domain phase singular spectrum entropy is calculated through the phase singular spectrum entropy algorithm. The parameter settings include a phase window length of 256 points and a step size of 64 points. Finally, through the calculation of the attenuation acceleration variance, the attenuation structure localization difference data between different regions is obtained.

[0020] Step S3: Based on the localized difference data of the attenuation structure, conduct a simulation evaluation of the increment of pulp viscosity to obtain the increment data of pulp viscosity; based on the increment data of pulp viscosity, conduct an evaluation of pulp softness and toughness to obtain the evaluation data of pulp softness and toughness. In the embodiment of the present invention, a physical simulation method based on structural difference quantification is used to evaluate the change trend of pulp viscosity. Taking the "localized difference data of the attenuation structure" as the input quantity, using the existing experimental data comparison table, establish the corresponding relationship between the pulp damping coefficient and the tissue internal slip coefficient of the pear sample under the specific acoustic wave attenuation structure difference. Use the damping coefficient to represent the shear response delay characteristic of the pulp. Deduce the increment of pulp viscosity through linear fitting weighted difference, with the unit of Pa·s. In actual detection, if the high-frequency energy attenuation gradient of a certain detection area is greater than the threshold of 0.35 (normalized unit), it is determined that the tissue softening trend in this area is obvious. Average the viscosity increment data of each area, and combine the variance between areas to deduce the whole fruit pulp viscosity increment data. Then, according to the established softness and toughness mapping table, the softness and toughness level is divided into five levels according to the viscosity increment interval, corresponding to extremely hard, relatively hard, moderate, relatively soft, and extremely soft respectively. The softness and toughness evaluation is output in a two-dimensional matrix manner, including the softness and toughness level and the mean square error amplitude, forming the "evaluation data of pulp softness and toughness".

[0021] In another embodiment, based on the localized difference data of the attenuation structure obtained in step S2, first conduct an identification of the acoustic wave scattering enhancement gradient. Use a gradient operator to calculate the spatial gradient of the local attenuation difference data. The gradient operator uses the Sobel operator with a window size of 3×3 to calculate the acoustic wave scattering enhancement gradient between different regions. Based on this gradient, simulate the expansion of the cell gaps in the pear, specifically by establishing a cell gap expansion model. Assume the initial width of the cell gap is 5 microns, use the gradient value as the expansion factor, and calculate the width change after the cell gap expands. Subsequently, based on the localized difference data of the attenuation structure, conduct a simulation evaluation of the increment of pulp viscosity. Adopt the associated absorption attenuation measurement mapping method to calculate the pulp absorption attenuation coefficient, and the coefficient range is set to 0.1 to 0.5. Combine the deduction of the regional pulp density difference interval, and the density interval is set to 0.8 to 1.2 g / cm³. Use the shear viscosity increment fitting method, and use the least squares method to fit the pulp shear viscosity increment. The fitting parameters include the shear modulus and the viscosity coefficient. Combine the analysis of the volume viscosity ratio of the pulp to calculate the proportion of the viscous component in the pulp volume, and the proportion range is set to 0.3 to 0.7. Finally, according to the shear viscosity increment fitting data and the volume viscosity ratio, calculate the increment data of pulp viscosity. Based on the increment data of pulp viscosity, use the weighted average method to evaluate the pulp softness and toughness. The weight coefficient is determined according to the viscosity increment and the cell gap expansion data to obtain the evaluation data of pulp softness and toughness.

[0022] Step S4: evaluating the maturity of the pears according to the pulp softness and toughness evaluation data, thereby obtaining the pear maturity evaluation data.

[0023] In an embodiment of the present invention, the "fruit flesh softness and toughness evaluation data" is subjected to maturity archiving and comparison processing. This processing is based on a standard pear maturity data table, which covers the softness and toughness distribution ranges under various maturity levels. During the detection implementation, the softness and toughness mean value and its standard deviation of the current sample are compared with the standard table, and the closest maturity level is determined by the minimum distance matching principle. The "pear maturity evaluation data" is output based on the judgment condition that the softness and toughness mean value falls into the maturity level distribution range and satisfies the standard deviation of no more than 0.1, and finally the maturity level of the pear at the current detection moment is obtained.

[0024] In another embodiment, pear maturity is assessed based on the flesh toughness assessment data obtained in step S3. The toughness assessment data is first mapped to maturity grade intervals, which are divided into unripe (toughness value less than 0.4), mature (toughness value between 0.4 and 0.7), and overripe (toughness value greater than 0.7). A threshold determination method is used to determine the maturity grade of the pear based on the numerical value of the toughness assessment data. Specifically, the toughness assessment data is normalized within a range of 0 to 1 to ensure comparability between different samples. The maturity assessment result is output as a numerical value and grade, with the numerical precision retained to three decimal places, ultimately obtaining the pear maturity assessment data.

[0025] Step S1 includes the following steps: Step S11: Scan the pear at multiple angles using an ultrasonic detector to obtain multi-angle reflected sound waves from the pear; Step S12: performing noise filtering on the pear multi-angle reflected sound waves to obtain the pear multi-angle reflected filtered sound waves; Step S13: performing frequency domain analysis on the pear multi-angle reflected filtered sound waves to generate frequency domain data of the reflected sound waves; Step S14: performing acoustic wave bandwidth change analysis on the regional frequency domain data of the reflected acoustic wave to obtain regional acoustic wave bandwidth change data.

[0026] In an embodiment of the present invention, an ultrasonic detector with a center frequency of 2.25 MHz and a transmitting pulse width of 0.3 microseconds is selected to collect multi-angle reflected sound waves from pears. An ultrasonic transmitting-receiving integrated transducer is arranged inside the detection cavity and evenly distributed spherically around the outside of the pear. The transmitting transducer sequentially transmits ultrasonic pulses in a progressive manner of 15° polar angle and azimuth angle. The receiving transducer synchronously receives the sound wave signals reflected from the inside or surface of the pear. A channel synchronization trigger is used to accurately control the working timing of each transducer to ensure that the reflected signals at all angles cover the entire three-dimensional structural area of the pear. An analog-to-digital converter is used to collect the received signals at a sampling rate of 500 MS / s, and the collected data numbers are marked with their corresponding transmitting angles and receiving angle numbers, thereby obtaining complete multi-angle reflected sound wave data of the pear. The noise filtering process was performed on the multi-angle reflected acoustic wave data obtained from pears. First, a fourth-order Butterworth bandpass filter was used to perform preliminary frequency domain screening on all reflected acoustic wave data. The filter cutoff frequencies were set to 1.8 MHz and 2.7 MHz, respectively, to remove low-frequency environmental background noise and high-frequency electromagnetic interference signals outside the sensor operating frequency. After preliminary filtering, wavelet denoising technology was further used. The Daubechies 4 wavelet function was selected for 5-layer decomposition. The soft threshold compression strategy was used to compress the noise of the high-frequency component coefficients, and the filtered reflected acoustic wave signal was reconstructed. The processed signal retained the main reflection waveform structure while effectively reducing background interference, making the subsequent frequency domain analysis more accurate. The frequency domain analysis of the multi-angle reflected filtered acoustic wave data of pears was performed by regional division. First, the pear surface was divided into 20 equal-area sub-regions according to the spherical coordinate system. The division method was to set the polar angle and azimuth angle to five equal parts to form a spherical grid mapping. The mapping result was used to group each reflected signal according to the region corresponding to the incident and receiving paths. Then, the filtered reflected acoustic wave signal in each region was converted into the frequency domain using fast Fourier transform (FFT). The frequency domain transformation length was set to 2048 points, and the window function used a Hamming window to reduce spectrum leakage. The amplitude spectrum of the transformed frequency domain signal was extracted to form a spectrum data set corresponding to each sub-region. The main frequency peak, energy density distribution range and frequency energy concentration parameters were marked in each spectrum, thereby constructing the frequency domain data of the reflected acoustic wave region, which was used to characterize the frequency domain distribution differences of the acoustic wave reflection characteristics in different spatial regions.Based on the frequency domain data of the reflected sound wave region, the sound wave bandwidth changes in each region are analyzed. First, in each regional spectrum, the part with energy amplitude greater than 80% of the maximum value is selected as the main energy bandwidth interval, and the difference between the starting frequency and the ending frequency of this interval is calculated as the effective bandwidth value. Further, by statistically analyzing the changes in the effective bandwidth size of different regions, its distribution trend in the overall structure is analyzed. In order to eliminate the interference of small fluctuation errors on the judgment of the overall bandwidth change, the 5-point sliding average method is used to smooth the bandwidth data of each region. At the same time, the bandwidth change gradient matrix is constructed by calculating the bandwidth difference gradient between regions. This matrix serves as the key input data for characterizing structural heterogeneity and sound wave propagation heterogeneity, forming the final regional sound wave bandwidth change data.

[0027] Step S2 includes the following steps: Step S21: calculating the sound wave frequency change rate ratios between different regions based on the regional sound wave bandwidth change data to obtain the regional sound wave frequency change rate ratios; Step S22: performing high-low frequency energy ratio integration processing on the regional sound wave bandwidth change data between different regions according to the regional sound wave frequency change rate ratio to obtain regional high-low frequency energy ratio integration data; Step S23: performing high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integral data to obtain regional high-frequency energy attenuation structure data; Step S24: performing multi-scale localized difference analysis between different regions on the regional high-frequency energy attenuation structure data to obtain attenuation structure localized difference data.

[0028] In the embodiment of the present invention, based on the regional sound wave bandwidth change data obtained in step S14, the frequency bandwidth edge point change trend between different regions is quantitatively analyzed and processed. First, the bandwidth value of each region is arranged in order according to the regional number, and the center value of the bandwidth upper boundary frequency and the lower boundary frequency of each region is extracted to form a regional representative frequency sequence. For the representative frequency data in adjacent numbered regions, the frequency change rate sequence is formed by dividing the representative frequency of the previous region by the difference, which is recorded as , then construct a frequency change rate ratio matrix. The calculation method is that the ratio of the frequency change rates of each pair of adjacent regions forms a two-dimensional matrix. The diagonal elements in this matrix represent the frequency change rate of each region itself as 1, and the non-diagonal elements are used to characterize the relative differences in the frequency change rates between regions. Further, mean normalization is performed on this rate ratio matrix to map it between 0 and 1, forming regional acoustic wave frequency change rate ratio data. Use the regional acoustic wave frequency change rate ratio data obtained in step S21 to perform high and low frequency energy integration processing on the regional acoustic wave bandwidth change data. First, in the bandwidth change data, clearly divide the frequency interval range into a low frequency band of 1.5 MHz to 2.2 MHz and a high frequency band of 2.2 MHz to 3.0 MHz. Numerically integrate the spectral density function curves of each region within the two frequency band ranges respectively. The integration method uses the Simpson integration method. By equally dividing the frequency band into 50 small intervals, the spectral amplitudes within each small interval are weighted and accumulated to obtain the high frequency band energy E_high and the low frequency band energy E_low respectively. Then, divide E_high by E_low to form the high and low frequency energy ratio of this region, and adjust the relative weights of the ratios between different regions in combination with the rate ratio data. After weighted processing of the energy ratios of all regions, a unified format regional high and low frequency energy ratio integration data matrix is formed. This matrix has each region as a row and the high and low frequency energy ratios as columns. The comparison data is used for subsequent high frequency energy structure analysis.

[0029] Based on the integrated data of the high-low frequency energy ratio of the regions obtained in step S22, the high-frequency energy attenuation structure between different regions is analyzed. First, the regions with energy ratio significantly deviating from the mean value of the whole region are selected for marking, and the deviation threshold is set at ±15% of the mean value of the whole region. For these significant regions, the high-frequency energy integral gradient value is extracted, and the calculation method is the difference between the ratio of this region and the ratio of its adjacent region divided by the square of the distance between the region centers, obtaining the energy attenuation gradient vector. Then, the gradient vectors of all regions are processed by spatial interpolation, and the inverse distance weighted method is used to construct a three-dimensional energy attenuation field. Next, the main gradient direction of the attenuation direction is extracted from this attenuation field. The method is to obtain the main direction change information through the eigenvalue decomposition of the Hessian matrix, and a local fitting analysis of the energy attenuation trend of this main direction is carried out. The cubic spline function is used to fit the gradient change curve. Finally, the high-frequency energy attenuation amplitude and change trend of each region in the main direction are output, forming a data set of the high-frequency energy attenuation structure of the regions. Based on the high-frequency energy attenuation structure data of the regions in step S23, the multi-scale localization differences between different regions are analyzed. First, the region data are constructed into a three-dimensional coordinate array according to the physical space position. The difference in the mean value of high-frequency energy attenuation within the 10-neighborhood in the large scale, 6-neighborhood in the medium scale, and 3-neighborhood in the small scale of each region is calculated by the three-scale difference method. At the same time, the two-dimensional Gaussian kernel function is used to smooth the weight of the local difference value at each scale. Then, the localized difference values at the three scales are combined to construct a three-channel feature map. The principal component analysis PCA is performed on the feature map to extract the main difference feature dimensions. Then, the analysis of variance method is used to conduct a significance test on the difference distribution at each scale, extracting the significantly changed localized regions, and performing Z-score normalization on the difference values of these regions. Finally, a data matrix of the localized differences of the attenuation structure in a unified format is formed. This matrix records the energy structure inhomogeneity parameters of each region at different scales and is used for subsequent pulp structure correlation analysis.

[0030] Step S22 includes the following steps: Step S221: According to the regional acoustic wave frequency change rate ratio, the acoustic wave high-low frequency change pattern recognition of the acoustic wave bandwidth change data between different regions is carried out to obtain the acoustic wave high-low frequency change pattern data between different regions; Step S222: Perform high-low frequency discrete entropy value regression analysis on the acoustic wave high-low frequency change pattern data between different regions to obtain high-low frequency discrete entropy value regression data; Step S223: Based on the high-low frequency discrete entropy value regression data, perform non-linear wave multi-interpolation on the acoustic wave high-low frequency change pattern data between different regions to obtain high-low frequency non-linear wave interpolation data; Step S224: calculating the variance of the segmented fluctuation slope growth rate on the high-low frequency nonlinear fluctuation interpolation data to obtain the variance of the high-low frequency segmented fluctuation slope growth rate; Step S225: performing high-low frequency energy ratio integration processing on the variance of the high-low frequency segmented fluctuation slope growth rate between different regions according to the Romberg integration method to obtain regional high-low frequency energy ratio integration data.

[0031] In an embodiment of the present invention, based on the regional acoustic wave frequency change rate ratio data and the regional acoustic wave bandwidth change data obtained in step S21, a two-dimensional feature comparison map is constructed to identify the high and low frequency change morphologies of acoustic waves between different regions. First, the bandwidth change data of each region is divided into two parts on the spectrum: a low frequency band of 1.5 MHz to 2.2 MHz and a high frequency band of 2.2 MHz to 3.0 MHz. The energy density change gradients of the frequency change curves in the two frequency bands are extracted respectively, and the change trend weights of the high and low frequency parts of each region are adjusted using the frequency change rate ratio. Then, the second-order differential curvatures of the morphological curves of the two frequency bands are calculated respectively. The curvature change patterns of the high and low frequency bands of different regions are compared with the position offsets of the energy concentration areas. Based on whether the offsets are concentrated at the two ends or the center of the spectrum, the change morphology types are identified, including five types: low frequency type, high frequency increasing type, symmetrical type, asymmetric high frequency contraction type, and multi-peak type. Finally, the change morphology corresponding to each region is recorded in matrix form to form a sound wave high and low frequency change morphology dataset, which is used for further entropy analysis of high and low frequency structural differences. For the high and low frequency change morphological data of the sound waves obtained in step S221, high and low frequency discrete entropy value regression analysis is performed. First, each regional type in the morphological data is numerically encoded, and the frequency energy peak positions of the high and low frequency parts of each region are discretized into 20 frequency nodes according to the frequency gradient distribution density map. The energy value at each node is normalized and a frequency distribution probability vector is constructed. The Shannon entropy definition is used to calculate the discrete entropy values of the high and low frequency bands of each region. Then, a linear regression fitting analysis is performed on the entropy value ratio of the high and low frequency bands between different regions. The fitting variables are the entropy value ratio and the frequency change rate ratio. The regression parameters are solved by the least squares method and the fitting residual distribution is evaluated. The high and low frequency discrete entropy value regression data finally output includes the regression residual, fitting slope and discrete trend index of the entropy value ratio in the high and low frequency bands of each region.

[0032] Based on the high- and low-frequency discrete entropy regression data obtained in step S222, the above-mentioned morphological data is subjected to nonlinear fluctuation polynomial interpolation processing. First, the energy values at 20 frequency points in the high-frequency and low-frequency spectrum intervals of each region are respectively extracted to form a sampling vector. The entropy regression residual is used as a disturbance factor to fit the original data into a fifth-order nonlinear polynomial function. The function coefficients are obtained by the minimum norm approximation method. After the polynomial interpolation is completed, the first-order derivative and second-order derivative sequence of the interpolation function in the entire spectrum range are calculated to construct a fluctuation change trend graph. The interpolated spectrum curve is then compared with the original morphological data for point-to-point error. After confirming that the interpolation error does not exceed 0.03 of the normalized amplitude, high- and low-frequency nonlinear fluctuation interpolation data are formed. This data is used to extract the growth rate trend of frequency change and the slope information of regional energy change. Based on the high- and low-frequency nonlinear fluctuation interpolation data obtained in step S223, the interpolation function is subjected to spectrum segmentation processing. The interpolation frequency band is divided into 10 segments using a fixed bandwidth of 0.15 MHz. The first-order derivative value of each curve segment is extracted as the slope growth rate indicator of the segment. Then, the standard deviation is calculated from the segment slope growth rate values of each segment in each region and the position of the maximum change segment is recorded. The sliding variance window method is used to calculate the growth rate variance of the three consecutive segment slopes. The window size is set to 3 segments. After smoothing the variance sequence of the slope growth rate within the full spectrum range, the high and low frequency fluctuation slope change characteristics of each region are obtained. Finally, a high and low frequency segment fluctuation slope growth rate variance data matrix is formed with the region number as the row and the fluctuation slope growth rate variance of each frequency band as the column for the next energy ratio integral analysis. Based on the high- and low-frequency segmented fluctuation slope growth rate variance data obtained in step S224, the Lumberg integration method is used to integrate the high-frequency and low-frequency slope growth rate variance curves of each region. The Lumberg integration method constructs an integration sequence in an adaptive encrypted node manner. The initial integration spacing is set to 0.05MHz, and the integration accuracy error is controlled within 1e-5. The high-frequency and low-frequency slope growth rate functions are integrated for each region respectively, and the high-frequency integral value is divided by the low-frequency integral value to obtain the energy structure change ratio. This ratio constitutes the core indicator of the regional high- and low-frequency energy ratio integral data. The ratios of all regions are then summarized into a matrix form. At the same time, the weight and error correction parameters of each frequency interval in the integration process are recorded to constitute the final output regional high- and low-frequency energy ratio integral data.

[0033] Step S23 includes the following steps: Step S231: identifying high-frequency energy attenuation fluctuation frequency bands between different regions based on regional high-low frequency energy ratio integral data, and obtaining high-frequency energy attenuation fluctuation frequency bands between different regions; Step S232: performing attenuation concentration density analysis on the high-frequency energy attenuation fluctuations between different regions to obtain the high-frequency energy attenuation concentration density between different regions; Step S233: Perform non-uniform complex wavenumber gradient differentiation on the high-frequency energy attenuation fluctuation frequency band based on the high-frequency energy attenuation aggregation density to obtain high-frequency attenuation complex wavenumber gradient differentiation data; Step S234: Calculate the attenuation acceleration variance between different regions of the high-frequency energy attenuation fluctuation frequency band according to the high-frequency attenuation complex wavenumber gradient differentiation data to obtain the attenuation acceleration variance; Step S235: Perform high-frequency energy attenuation structure analysis between different regions according to the high-frequency attenuation complex wavenumber gradient differentiation data and the attenuation acceleration variance to obtain regional high-frequency energy attenuation structure data.

[0034] In the embodiment of the present invention, based on the regional high-low frequency energy ratio integral data obtained in step S225, focus analysis is performed on the high-frequency band in the spectral data of all regions. The high-frequency band is set to be from 2.2 MHz to 3.0 MHz, and this frequency band is divided into 40 equally spaced sub-bands, each sub-band having a width of 0.02 MHz. The spectral energy density values of each region within this frequency band are extracted to form a frequency band energy sequence. The sequence is smoothed using a five-point moving average to eliminate local minimum perturbations. Then, the energy decline rate between adjacent frequency bands is calculated, and the frequency band intervals with a decline rate exceeding 25% are marked as candidate attenuation fluctuation frequency bands. For the candidate frequency bands, a continuous decline section determination logic is further adopted. It is set that if three or more consecutive frequency bands meet the decline rate threshold and the local energy gradient is negative, it is an effective attenuation fluctuation frequency band. By performing the above logical recognition operations on the spectral data of each region, the high-frequency energy attenuation fluctuation frequency bands of each region are finally extracted and recorded in the form of a matrix with the region number as the row and the start and end boundaries of the frequency band as the columns, forming a high-frequency energy attenuation fluctuation frequency band data set between different regions. Based on the high-frequency energy attenuation fluctuation frequency band data of different regions obtained in step S231, an aggregation density analysis operation is performed on the attenuation frequency band within each region. First, an energy decline gradient sequence is constructed for the energy density values within the attenuation frequency band interval of each region. The first-order difference of this sequence is calculated, and the number and length of the negative value segments are statistically analyzed. The local aggregation degree is defined as the number of negative value segments multiplied by the average length. Then, all local aggregation degrees are normalized to unify the data scale. Then, within each region, the local strongest attenuation point is found through a peak search algorithm as the aggregation center, and boundary points are searched by expanding in the directions of increasing and decreasing frequencies on both sides of the aggregation center. The expansion stop condition is that the energy change rate of two consecutive points is less than 2%. The aggregation boundary range is obtained, and the total value of the energy density decline within this boundary range is divided by the frequency band width to form the high-frequency energy attenuation aggregation density value. Finally, the aggregation density values of all regions are stored in a three-dimensional array, and the dimensions correspond to the region number, the frequency band number, and the aggregation density value respectively, forming a high-frequency energy attenuation aggregation density data set between different regions.

[0035] Based on the high-frequency energy attenuation clustering density data obtained in step S232, perform non-uniform complex wavenumber gradient differential processing on the frequency band. First, convert the energy density spectrum in the high-frequency attenuation frequency band in each region into a complex frequency spectrum representation form. The real part is the actual energy density value, and the imaginary part is composed of the corresponding phase response function obtained through Hilbert transform. After obtaining the complex frequency spectrum, use the five-point central difference method to calculate its first-order complex wavenumber gradient value. Further, use the variable step-size difference method to calculate the change rate of this gradient in the frequency spectrum direction to form a second-order complex wavenumber differential matrix. Perform non-uniformity analysis on the differential matrix. The judgment criterion is that the region where the difference between the local maximum and minimum values exceeds twice the standard deviation of the mean is defined as the non-uniform high-variation interval. Then, extract the data slices of this interval to construct non-uniform complex wavenumber gradient differential data. All processing is carried out under the condition of a frequency accuracy of 0.005 MHz. Finally, output the three-dimensional tensor data of the complex wavenumber gradient differential indexed by the region number and the frequency band number.

[0036] According to the high-frequency attenuation complex wavenumber gradient differential data in step S233, calculate the attenuation acceleration variance in each region within the attenuation frequency band. Define the acceleration as the change rate of the complex wavenumber gradient, that is, the second derivative value. Extract the complex wavenumber gradient differential values in the frequency spectrum sequence of each region, calculate its local acceleration, that is, the slope change rate sequence. Segment this sequence according to each 0.05 MHz frequency width, calculate the statistical variance of each acceleration sequence segment and store it as the segmented acceleration variance value. At the same time, record the frequency center value of this segment for subsequent structure positioning. Then, perform Z-score normalization processing on the acceleration variances of all frequency bands, and use the local maximum extraction algorithm to extract the significant variation segments. This part of the data forms the attenuation acceleration variance matrix, and record the frequency band segmented variance and significant change index corresponding to each region for the subsequent reconstruction of the energy structure map. Integrate the high-frequency attenuation complex wavenumber gradient differential data in step S233 and the attenuation acceleration variance data in step S234 to perform high-frequency energy attenuation structure analysis between different regions. First, fuse the complex wavenumber gradient differential matrix and the acceleration variance matrix in the corresponding dimensions, and perform convolution sliding accumulation processing on it according to the interval number on the frequency axis. The width of the convolution kernel is set to 3 frequency band intervals, and the step size is 1. Extract the average gradient direction change value and the variance average value within the convolution window to form the local structure change comparison quantity. Perform principal component analysis on all sliding window data to extract the main structure change direction, and then construct the high-frequency energy attenuation structure map of each region through two-dimensional tensor mapping. The color depth in the map represents the local change intensity, and the color gradient direction represents the main direction of the complex wavenumber gradient. Finally, output the structure map matrix and combine it with the regional physical coordinate number to form the regional high-frequency energy attenuation structure data.

[0037] Step S233 includes the following steps: Perform non-uniformity analysis on the frequency decay nodes of the aggregated center waveform frequency of the high-frequency energy attenuation fluctuation frequency band based on the aggregated density of high-frequency energy attenuation to obtain the non-uniformity data of the frequency decay nodes; Perform complex spectrum construction processing on the high-frequency energy attenuation fluctuation frequency band according to the non-uniformity data of the frequency decay nodes to obtain a non-uniformly distributed complex spectrum matrix; Perform first-order complex wavenumber differential processing on the non-uniformly distributed complex spectrum matrix to obtain complex wavenumber gradient change data; Calculate the complex domain phase singular spectrum entropy for the complex wavenumber gradient change data to obtain the complex domain phase singular spectrum entropy; Perform non-uniformity complex wave gradient differential processing according to the complex domain phase singular spectrum entropy to obtain high-frequency attenuation complex wavenumber gradient differential data.

[0038] In the embodiment of the present invention, the peak points of the decreasing frequency-concentrated energy in each region are extracted from the high-frequency energy attenuation aggregated density data obtained in step S232 as the initial aggregation centers. The frequency range is set from 2.35 MHz to 2.85 MHz. An extended window of 5 sub-bands on each side is constructed with the aggregation center as the base point within this range. The width of the sub-band is 0.02 MHz. Analyze the spectrum within the window. Determine the frequency decay nodes by detecting whether the energy density change rate between two consecutive frequency bands exceeds the 15% threshold. Then combine the energy decrease rate, frequency spacing, and second derivative value of the spectrum shape at all nodes into a feature vector matrix. Use the normalization index constructed by the standard deviation and the mean to evaluate the degree of change between these nodes, and then calculate the degree of non-uniformity between the nodes. Use the mean value of the product of the absolute value of the local energy density gradient change and the change frequency difference within the non-uniformity fluctuation range as the non-uniformity index of the frequency decay nodes in each region, and finally generate a non-uniformity data matrix of the frequency decay nodes. When performing complex spectrum construction processing on the high-frequency energy attenuation fluctuation frequency band according to the non-uniformity data of the frequency decay nodes, first perform complex domain reconstruction on each frequency point within the attenuation frequency band. Use the energy density value of the actual spectrum data as the real part, and use the Hilbert transform to calculate the instantaneous phase response corresponding to each frequency point as the imaginary part to construct a complex spectrum signal. Sample the complex spectrum signal at a resolution of 0.005 MHz within the frequency range of 2.35 MHz to 2.85 MHz to construct a two-dimensional complex spectrum matrix. The horizontal axis of the matrix is the frequency point, and the vertical axis is the real and imaginary components of the complex value. Then perform complex signal normalization on each column in the matrix to limit the modulus length of all complex frequency vectors between 0 and 1, ensuring that subsequent differential calculations are not distorted due to non-linear amplification of the amplitude. The non-uniformly distributed complex spectrum matrix obtained by this processing accurately characterizes the coupling relationship between the phase and amplitude during the frequency decay process.

[0039] When performing first-order complex wavenumber differentiation on a non-uniformly distributed complex spectrum matrix, the five-point central difference method is used to calculate the complex derivative for each frequency point of the complex spectrum matrix. For each complex value in the frequency point sequence, taking the two adjacent points before and after as the base points, the first-order differences of its real and imaginary parts in the frequency direction are calculated and combined into a complex derivative value, that is, the complex wavenumber gradient change data. The differential calculation strictly limits the frequency step size to 0.005 MHz. After executing on the entire frequency band, a one-dimensional complex wavenumber gradient vector is formed and stored in matrix form according to the region number, constituting a complete complex wavenumber gradient change data set. When calculating the phase singular spectrum entropy in the complex domain for the complex wavenumber gradient change data, first calculate the instantaneous phase change value for each frequency point in the complex wavenumber gradient vector. The arctangent function is used to convert the real and imaginary parts of the complex number into a sequence of phase angles. Then, a sliding window with a length of 5 frequency points is divided on the entire frequency band. The distribution probability of the phase angle difference is statistically calculated in each window, and the entropy value within the window is calculated based on this distribution and its change trend is recorded. Finally, a singular spectrum entropy sequence of the phase in the complex domain is formed. Then, the peak points, slope mutation points, and average entropy values of the entropy sequence are normalized and compared, and a local singularity index is constructed to identify the complexity of the phase information structure in different frequency bands. This calculation process is independently executed in each region, and the data of all regions are summarized to form a phase singular spectrum entropy matrix in the complex domain. When performing non-uniform complex wave gradient differential processing based on the phase singular spectrum entropy in the complex domain, the significant mutation points in the singular spectrum entropy matrix are used as the non-uniform high-variation frequency reference points. Symmetric frequency band windows are constructed around these points, and the corresponding complex wavenumber gradient change data are re-extracted within the window range. The central difference method is used to further calculate its local second derivative to construct a complex wavenumber second-order gradient change sequence. The non-uniformity degree of this sequence is analyzed, and the difference between the maximum slope change value and the minimum slope change value is used as the non-uniformity index. Then, combined with the change slope of the singular spectrum entropy and the second-order gradient change value, weighted normalization processing is performed to output the final high-frequency attenuation complex wavenumber gradient differential data.

[0040] Step S3 includes the following steps: Step S31: Based on the local attenuation structure difference data, identify the acoustic wave scattering enhancement gradient between different regions to obtain the acoustic wave scattering enhancement gradient between different regions; Step S32: According to the acoustic wave scattering enhancement gradient between different regions, perform a simulation evaluation of the expansion of the pear cell gap to obtain the cell gap expansion data; Step S33: Based on the local attenuation structure difference data, perform a simulation evaluation of the increase in pulp viscosity to obtain the pulp viscosity increase data; Step S34: Based on the cell gap expansion data and the pulp viscosity increase data, perform a pulp softness and toughness evaluation to obtain the pulp softness and toughness evaluation data.

[0041] As an example of the present invention, refer toFigure 2 As shown, in this example, step S3 includes: Step S31: identifying the acoustic wave scattering enhancement gradients between different regions based on the attenuation structure localization difference data to obtain the acoustic wave scattering enhancement gradients between different regions; In an embodiment of the present invention, based on the localized difference data of the attenuation structure obtained in step S24, the energy change trend in the acoustic wave reflection path of each region is analyzed. First, the frequency analysis range is set to 2.2 MHz to 3.0 MHz, and the frequency band is divided into 40 frequency sub-bands of equal width. The complex wave number gradient value and the attenuation acceleration variance value in each sub-band are extracted respectively. By jointly fitting the complex wave directionality change and the energy decrease trend of each sub-band, the local amplitude mutation point of the acoustic wave scattering is extracted using the spatial derivative enhancement algorithm. The scattering enhancement coefficient is calculated for each region as a unit. The coefficient is the product of the energy change rate per unit frequency and the spatial direction gradient. The scattering enhancement coefficients of all regions are further constructed into a two-dimensional matrix, where the matrix rows represent the spatial scanning angles and the columns represent the frequency sub-band numbers. The local maximum retrieval algorithm is used to identify the scattering enhancement gradient map formed by the enhancement mutation band, and finally the acoustic wave scattering enhancement gradient data between different regions are output.

[0042] Step S32: performing a simulation evaluation of the pear intercellular expansion based on the acoustic wave scattering enhancement gradients between different regions to obtain intercellular expansion data; In an embodiment of the present invention, according to the acoustic scattering enhancement gradient data obtained in step S31, the range of significantly enhanced local scattering intensity in each region is identified in the frequency space domain. The enhanced region is strongly correlated with the acoustic wave interface reflection behavior of the cell microstructure. The reflectivity improvement interval within 1.5 mm of each enhanced region is calculated, and the morphological change of the reflection source is calculated in combination with the change amplitude of the scanning angle. The local acoustic wave phase delay regression analysis algorithm is used to reversely reconstruct the acoustic wave propagation delay change trend in different regions. The expansion trend of the cell gap is simulated by constructing a propagation path length change model. Using parameters related to the sound velocity and cell tissue density in known materials, the sound velocity is set to 1540 m / s, the time difference is converted into a spatial scale difference, and the intercellular structure displacement is calculated by a morphological regression model based on multi-angle data fusion. A cell gap expansion simulation curve is formed in each region, and a three-dimensional data body is constructed using the region number and output as cell gap expansion data.

[0043] Step S33: performing a simulation evaluation of the pulp viscosity increment based on the attenuation structure localization difference data to obtain pulp viscosity increment data; In the embodiment of the present invention, based on the attenuation structure localization difference data obtained in step S24, the frequency attenuation density map and the reflection structure density map of each region are fitted and integrated, the change value of the absorption power density per unit volume in the high-frequency energy concentration attenuation band of each region is calculated, the correlation calculation is carried out between the change value and the local complex wave number gradient change rate, and the absorption capacity difference value per unit volume is extracted as the main determination basis for the change of the viscous characteristics inside the pulp. Further, the Laplace inverse transform is used to construct the local impedance mapping of the energy attenuation distribution to the pulp structure. Using the known mechanical parameters of the pulp tissue, such as the density of 1020 kg / m³ and the viscosity coefficient of 0.45 Pa·s, the change amount of the shear deformation resistance in the pulp region is inversely deduced according to the energy attenuation rate, and it is normalized with the total amount of reflected energy in this region to obtain the pulp viscosity increment coefficient. Finally, the viscosity increments of all regions are tensor-encoded to form a pulp viscosity increment data set for subsequent structural mechanics characteristic evaluation.

[0044] Step S34: Based on the cell gap expansion data and the pulp viscosity increment data, the pulp softness and toughness are evaluated to obtain the pulp softness and toughness evaluation data.

[0045] In the embodiment of the present invention, the cell gap expansion data obtained in step S32 and the pulp viscosity increment data obtained in step S33 are comprehensively considered. The dimension alignment processing is performed on the two data sets, the region numbers and the frequency sub-segment numbers are uniformly mapped to the lattice coordinates in the three-dimensional space, the polynomial interpolation is used to construct the pulp local structure volume change surface, the shear modulus of each region unit volume on this surface is reconstructed, the relationship between its stress change rate and the local gap increment is calculated, and the static compression modulus data of the pear pulp sample in the actual measurement is used as a reference. The reference elastic modulus is set to 1.6 MPa, the relative ratio calculation of the stress response of each region is carried out, and then the comprehensive softness and toughness score is calculated according to the deformation response and the damping growth value. The regional internal softness and toughness integral value is calculated by using the per-region integration algorithm, and then the integral values of all regions are constructed into a three-dimensional space structure feature map to output the pulp softness and toughness evaluation data.

[0046] Step S33 includes the following steps: Step S331: Based on the attenuation structure localization difference data, the pulp associated absorption attenuation measurement mapping is carried out to obtain the pulp associated absorption attenuation measurement data; Step S332: Based on the pulp associated absorption attenuation measurement data, the regional pulp density difference interval deduction is carried out to obtain the regional pulp density difference interval; Step S333: According to the pulp associated absorption attenuation measurement data and the regional pulp density difference interval, the pulp shear viscosity increment fitting is carried out to obtain the pulp shear viscosity increment fitting data; Step S334: Perform pulp volume viscosity ratio analysis based on the pulp-associated absorption attenuation measurement data, the pulp shear viscosity increment fitting data, and the pulp density difference interval to obtain the pulp volume viscosity ratio; Step S335: Perform a simulated evaluation of the pulp viscosity increment based on the pulp shear viscosity increment fitting data and the pulp volume viscosity ratio to obtain the pulp viscosity increment data.

[0047] As an example of the present invention, refer to Figure 3 As shown, in this example, step S33 includes: Step S331: Perform pulp-associated absorption attenuation measurement mapping based on the attenuation structure localization difference data to obtain the pulp-associated absorption attenuation measurement data; In the embodiment of the present invention, based on the attenuation structure localization difference data obtained in step S24, by performing frame-by-frame integration extraction of the energy attenuation rate in each scanning area within the frequency band range of 2.5 MHz to 3.0 MHz, constructing a regional frequency domain attenuation mapping diagram, using the regional main frequency energy transfer density distribution function to calculate the sound energy dissipation value per unit volume, adjusting and correcting the energy distribution balance of different regions in combination with the scattering enhancement gradient distribution curve, using the Brown distribution noise elimination method to eliminate the non-pulp tissue reflection interference component, and then using the matrix linear mapping method to establish the correlation relationship between energy attenuation and local tissue absorption capacity, and finally forming a quantization matrix of absorption attenuation values corresponding to each spatial coordinate as the pulp-associated absorption attenuation measurement data.

[0048] Step S332: Deduce the regional pulp density difference interval based on the pulp-associated absorption attenuation measurement data to obtain the regional pulp density difference interval; In the embodiment of the present invention, based on the pulp-associated absorption attenuation measurement data obtained in step S331, project the two-dimensional regional attenuation matrix onto the density change interval mapping space, fit the non-linear relationship curve between the energy dissipation rate and the tissue physical density, set the pear pulp sample with a reference density of 1.02 g / cm³ as the benchmark, perform Gaussian fitting with its attenuation coefficient of 0.015 dB / mm as the center point, construct a density distribution interval mapping table by analyzing the corresponding relationship between each attenuation section and the known density standard, and then estimate the density distribution of all regions according to this mapping table, extract the maximum and minimum ranges of the density values of each region to construct a density difference matrix, and obtain the regional pulp density difference interval.

[0049] Step S333: Perform pulp shear viscosity increment fitting based on the pulp-associated absorption attenuation measurement data and the regional pulp density difference interval to obtain the pulp shear viscosity increment fitting data; In the embodiment of the present invention, according to the pulp-associated absorption attenuation measurement data obtained in step S331 and the regional pulp density difference interval obtained in step S332, the density difference interval of the center point of each region is selected as the fitting parameter boundary, and a multivariate fitting function is used to perform bivariate fitting on the absorption value and the density interval. The minimum value of the second derivative is used as the stable point of the shear viscosity increment change. The least squares method is used to estimate the relationship between the acoustic wave viscous dissipation per unit area and the tissue stress difference at this point. Combining with the pulp shear deformation response curve obtained in the experiment, a linearized increment model is established for each interval to form pulp shear viscosity increment fitting data for subsequent volume scale transformation.

[0050] Step S334: Perform pulp volume viscosity ratio analysis according to the pulp-associated absorption attenuation measurement data, the pulp shear viscosity increment fitting data, and the pulp density difference interval to obtain the pulp volume viscosity ratio. In the embodiment of the present invention, the pulp-associated absorption attenuation measurement data obtained in step S331, the pulp shear viscosity increment fitting data obtained in step S333, and the pulp density difference interval obtained in step S332 are input into a three-dimensional tensor fitting function. By constructing a change field of energy absorption capacity at the volume scale for each region based on the tensor core expansion algorithm, setting the unit analysis volume to 1 mm³, calculating the product partial derivative of the energy absorption change and the density perturbation and viscosity increment within this volume, and using the composite triangular grid integration method to obtain the shear viscosity ratio per unit volume of each region, finally forming pulp volume viscosity ratio data.

[0051] Step S335: Perform a simulation evaluation of the pulp viscosity increment according to the pulp shear viscosity increment fitting data and the pulp volume viscosity ratio to obtain pulp viscosity increment data.

[0052] In the embodiment of the present invention, according to the pulp shear viscosity increment fitting data obtained in step S333 and the pulp volume viscosity ratio data obtained in step S334, the local shear response rate and energy dissipation efficiency in the 3 MHz frequency band of each region are respectively extracted. After normalizing the two, they are constructed into a two-dimensional function curve atlas. The Bessel third-order curve is used to perform smooth fitting on the viscosity change surface, the impedance change in the energy propagation path at high frequencies is calculated by piecewise integration, the viscosity change increment per unit thickness of each region is extracted, and weighted integration is performed in combination with the distribution density of the regional difference structure. Finally, the pulp viscosity increment data is output and marked in the spatial region distribution map for subsequent comprehensive evaluation of softness and toughness.

[0053] The present invention also provides an agricultural product maturity detection system for performing the agricultural product maturity detection method as described above. The agricultural product maturity detection system includes: The acoustic wave bandwidth variation analysis module is used to scan the pears at multiple angles using an ultrasonic detector, and then perform frequency domain analysis on the regional division to generate regional frequency domain data of the reflected acoustic wave; the acoustic wave bandwidth variation analysis is performed on the regional frequency domain data of the reflected acoustic wave to obtain regional acoustic wave bandwidth variation data; The high-frequency energy attenuation structure analysis module is used to integrate the high-to-low-frequency energy ratio between different regions based on the regional acoustic wave bandwidth change data to obtain regional high-to-low-frequency energy ratio integral data; based on the regional high-to-low-frequency energy ratio integral data, the high-frequency energy attenuation structure analysis between different regions is performed to obtain localized difference data of the attenuation structure; The pulp softness and toughness evaluation module is used to simulate and evaluate the pulp viscosity increment based on the attenuation structure localization difference data to obtain the pulp viscosity increment data; and to evaluate the pulp softness and toughness based on the pulp viscosity increment data to obtain the pulp softness and toughness evaluation data; The maturity evaluation module is used to evaluate the maturity of pears based on the flesh softness and toughness evaluation data, thereby obtaining the pear maturity evaluation data.

[0054] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for detecting the maturity of agricultural products, characterized in that, Including the following steps: Step S1: Use an ultrasonic detector to scan the pear from multiple angles, and then perform regional division frequency-domain analysis to generate frequency-domain data of the reflected sound wave region; perform analysis on the change of the sound wave bandwidth of the frequency-domain data of the reflected sound wave region to obtain regional sound wave bandwidth change data; Step S2: Perform high-low frequency energy ratio integration processing between different regions on the regional sound wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; based on the regional high-low frequency energy ratio integration data, perform analysis on the high-frequency energy attenuation structure between different regions to obtain attenuation structure localization difference data; Step S3: Based on the attenuation structure localization difference data, perform simulation evaluation on the increment of pulp viscosity to obtain pulp viscosity increment data; Based on the pulp viscosity increment data, perform evaluation on the softness and toughness of the pulp to obtain pulp softness and toughness evaluation data; Step S4: According to the pulp softness and toughness evaluation data, perform evaluation on the maturity of the pear to obtain pear maturity evaluation data.

2. The agricultural product maturity detection method according to claim 1, wherein Step S1 includes the following steps: Step S11: Use an ultrasonic detector to scan the pear from multiple angles to obtain multi-angle reflected sound waves of the pear; Step S12: Perform noise filtering processing on the multi-angle reflected sound waves of the pear to obtain multi-angle reflected filtered sound waves of the pear; Step S13: Perform regional division frequency-domain analysis on the multi-angle reflected filtered sound waves of the pear to generate frequency-domain data of the reflected sound wave region; Step S14: Perform analysis on the change of the sound wave bandwidth of the frequency-domain data of the reflected sound wave region to obtain regional sound wave bandwidth change data.

3. The agricultural product maturity detection method according to claim 1, characterized in that Step S2 includes the following steps: Step S21: Calculate the ratio of the sound wave frequency change rate between different regions on the regional sound wave bandwidth change data to obtain the regional sound wave frequency change rate ratio; Step S22: According to the regional sound wave frequency change rate ratio, perform high-low frequency energy ratio integration processing between different regions on the regional sound wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; Step S23: Based on the regional high-low frequency energy ratio integration data, perform analysis on the high-frequency energy attenuation structure between different regions to obtain regional high-frequency energy attenuation structure data; Step S24: Perform multi-scale localization difference analysis between different regions on the regional high-frequency energy attenuation structure data to obtain attenuation structure localization difference data.

4. The agricultural product maturity detection method according to claim 3, characterized in that Step S22 includes the following steps: Step S221: According to the regional sound wave frequency change rate ratio, perform identification of the high-low frequency change pattern of the sound wave between different regions on the regional sound wave bandwidth change data to obtain the high-low frequency change pattern data of the sound wave between different regions; Step S222: Perform high-low frequency discrete entropy value regression analysis on the high-low frequency change pattern data of the sound wave between different regions to obtain high-low frequency discrete entropy value regression data; Step S223: Based on the high-low frequency discrete entropy value regression data, perform non-linear wave multi-term interpolation on the high-low frequency change pattern data of the sound wave between different regions to obtain high-low frequency non-linear wave interpolation data; Step S224: Calculate the variance of the growth rate of the segmented wave slope of the high-low frequency non-linear wave interpolation data to obtain the high-low frequency segmented wave slope growth rate variance; Step S225: Perform high-low frequency energy ratio integration processing on the high-low frequency segmented fluctuation slope growth rate variance between different regions according to the Romberg integration method to obtain regional high-low frequency energy ratio integration data.

5. The agricultural product maturity detection method according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Identify the high-frequency energy attenuation fluctuation frequency bands between different regions based on the regional high-low frequency energy ratio integration data to obtain the high-frequency energy attenuation fluctuation frequency bands between different regions; Step S232: Conduct attenuation clustering density analysis on the high-frequency energy attenuation fluctuations between different regions to obtain the high-frequency energy attenuation clustering density between different regions; Step S233: Perform non-uniform complex wave number gradient differential processing on the high-frequency energy attenuation fluctuation frequency bands based on the high-frequency energy attenuation clustering density to obtain high-frequency attenuation complex wave number gradient differential data; Step S234: Calculate the attenuation acceleration variance between different regions for the high-frequency energy attenuation fluctuation frequency bands according to the high-frequency attenuation complex wave number gradient differential data to obtain the attenuation acceleration variance; Step S235: Analyze the high-frequency energy attenuation structure between different regions based on the high-frequency attenuation complex wave number gradient differential data and the attenuation acceleration variance to obtain regional high-frequency energy attenuation structure data.

6. The method for detecting the maturity of agricultural products according to claim 5, characterized in that, Step S233 includes the following steps: Perform non-uniformity analysis of the frequency decay nodes of the clustering center waveform frequency for the high-frequency energy attenuation fluctuation frequency bands based on the high-frequency energy attenuation clustering density to obtain frequency decay node non-uniformity data; Perform complex frequency spectrum construction processing on the high-frequency energy attenuation fluctuation frequency bands according to the frequency decay node non-uniformity data to obtain a non-uniformly distributed complex frequency spectrum matrix; Perform first-order complex wave number differential processing on the non-uniformly distributed complex frequency spectrum matrix to obtain complex wave number gradient change data; Calculate the complex domain phase singular spectrum entropy for the complex wave number gradient change data to obtain the complex domain phase singular spectrum entropy; Perform non-uniform complex wave gradient differential processing according to the complex domain phase singular spectrum entropy to obtain high-frequency attenuation complex wave number gradient differential data.

7. The method for detecting the maturity of agricultural products according to claim 1, wherein Step S3 includes the following steps: Step S31: Identify the acoustic wave scattering enhancement gradient between different regions based on the attenuation structure localization difference data to obtain the acoustic wave scattering enhancement gradient between different regions; Step S32: Conduct a simulation evaluation of the expansion of pear cell gaps according to the acoustic wave scattering enhancement gradient between different regions to obtain cell gap expansion data; Step S33: Conduct a simulation evaluation of the increase in pulp viscosity based on the attenuation structure localization difference data to obtain pulp viscosity increase data; Step S34: Evaluate the softness and toughness of the pulp based on the cell gap expansion data and the pulp viscosity increase data to obtain pulp softness and toughness evaluation data.

8. The agricultural product maturity detection method according to claim 7, characterized in that Step S33 includes the following steps: Step S331: Perform pulp-related absorption attenuation measurement mapping based on the attenuation structure localization difference data to obtain pulp-related absorption attenuation measurement data; Step S332: Deduce the regional pulp density difference interval based on the pulp-related absorption attenuation measurement data to obtain the regional pulp density difference interval; Step S333: Fit the pulp shear viscosity increment according to the pulp-related absorption attenuation measurement data and the regional pulp density difference interval to obtain pulp shear viscosity increment fitting data; Step S334: Perform pulp volume viscosity ratio analysis based on pulp-associated absorption attenuation measurement data, pulp shear viscosity increment fitting data, and pulp density difference intervals to obtain the pulp volume viscosity ratio; Step S335: Perform simulated evaluation of pulp viscosity increment based on the pulp shear viscosity increment fitting data and the pulp volume viscosity ratio to obtain pulp viscosity increment data.

9. An agricultural product maturity detection system, characterized in that, An agricultural product maturity detection system for implementing the agricultural product maturity detection method according to claim 1, the system comprising: An acoustic wave bandwidth change analysis module, configured to perform multi-angle scanning on a pear through an ultrasonic detector, and then perform regional division frequency domain analysis to generate reflected acoustic wave regional frequency domain data; perform acoustic wave bandwidth change analysis on the reflected acoustic wave regional frequency domain data to obtain regional acoustic wave bandwidth change data; A high-frequency energy attenuation structure analysis module, configured to perform high-low frequency energy ratio integration processing between different regions on the regional acoustic wave bandwidth change data to obtain regional high-low frequency energy ratio integration data; perform high-frequency energy attenuation structure analysis between different regions based on the regional high-low frequency energy ratio integration data to obtain attenuation structure localization difference data; A pulp softness and toughness evaluation module, configured to perform simulated evaluation of pulp viscosity increment based on the attenuation structure localization difference data to obtain pulp viscosity increment data; perform pulp softness and toughness evaluation based on the pulp viscosity increment data to obtain pulp softness and toughness evaluation data; A maturity evaluation module, configured to perform pear maturity evaluation according to the pulp softness and toughness evaluation data, so as to obtain pear maturity evaluation data.

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