A multi-dimensional tricholoma quality grading detection device

Abstract

The present application belongs to the field of pine mushroom multi-dimensional detection and quality classification detection equipment, and provides a multi-dimensional pine mushroom quality grading detection device, which comprises: an impedance detection sub-device, a hyperspectral detection sub-device, an electronic nose detection sub-device, and a data processing station; the impedance detection sub-device comprises: a pine mushroom information acquisition module; the pine mushroom information acquisition module comprises: a detection electrode sheet, a spring, and a pressure sensor patch. The present application realizes clamping and surface data acquisition of the detection target pine mushroom through the impedance detection sub-device, and improves the ductility and stability of the impedance detection sub-device by using the spring and the rotatable detection electrode sheet; the pine mushroom shape and impedance can be detected simultaneously through the pressure sensor patch and the detection electrode sheet, improving the detection efficiency; multi-dimensional nondestructive detection of the pine mushroom shape, smell, and composition can be realized by combining hyperspectral and electronic nose detection; the whole process from fresh to rotten of the pine mushroom can be monitored in real time through the data processing station.

Description

Technical Field

[0001] The present invention relates to the field of multi-dimensional detection and quality classification detection equipment for matsutake mushrooms, and in particular to a multi-dimensional matsutake mushroom quality grading detection device. Background Art

[0002] Matsutake is a precious wild edible mushroom. Due to factors such as its growing environment, the quality of individual mushrooms often varies significantly, directly impacting their market value. Therefore, they are graded and screened before sale. Traditional manual and physical / chemical testing methods can only screen based on size. However, the quality of matsutake mushrooms is not only determined by size but also by multiple factors, including appearance, chemical composition, odor, and insect pest residue. A single testing method often only captures a single dimension of information, while the comprehensive quality of matsutake requires cross-validation across multiple indicators.

[0003] The key to adopting multi-dimensional detection technology lies in combining the strengths of multiple methods to achieve comprehensive testing of multiple key qualities of matsutake. While each individual technology has its advantages and disadvantages, they also have significant limitations. For example, impedance testing effectively detects internal structural characteristics by analyzing changes in conductivity and resistance within the mushroom. However, impedance measurement is less effective for complex shapes and is easily affected by sample size and environmental factors. It also cannot assess external qualities such as odor or appearance. Hyperspectral imaging technology can analyze the appearance, color, and some internal components of matsutake by capturing light reflections at different wavelengths. However, spectral technology is less effective for samples with surface contamination and cannot accurately assess internal insect infestations or holes. Electronic nose testing uses the detection of volatile organic compounds in matsutake to determine freshness, the presence of insect infestations, and decay. However, electronic noses can only detect odor-related indicators, making it difficult to obtain information about appearance and internal structure. They are also easily affected by environmental changes such as temperature and humidity, increasing the possibility of misjudgment. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a multi-dimensional matsutake quality grading detection device, which realizes the clamping and surface data collection of the detection target matsutake through an impedance detection sub-device, and utilizes springs and rotatable detection electrode sheets to improve the ductility and stability of the impedance detection sub-device; realizes the simultaneous detection of the shape and impedance of matsutake through pressure sensing patches and detection electrodes, thereby improving the detection efficiency; realizes multi-dimensional non-destructive detection of the shape, smell and composition of matsutake through the combination of hyperspectral and electronic nose detection; and monitors the entire process of matsutake from freshness to corruption in real time through a data processing station.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A multi-dimensional matsutake quality grading detection device comprises: an impedance detection sub-device, a hyperspectral detection sub-device, an electronic nose detection sub-device, and a data processing station; the impedance detection sub-device comprises: a back-end circuit, a receiving wire, an excitation wire, and a matsutake information acquisition module; the matsutake information acquisition module comprises: an outer ring, a rotatable detection electrode sheet, a spring, a pressure sensor output wire, a first electrode connector wire, a second electrode connector wire, and a pressure sensing patch; the detection electrode sheet comprises: a rotating disk, a rectangular electrode sheet, a reserved hole, and a fixed point;

[0007] The data processing station is connected to the impedance detection sub-device, the hyperspectral detection sub-device and the electronic nose detection sub-device respectively; the back-end circuit is connected to the second electrode connector wire through the excitation wire; the first electrode connector wire and the pressure sensor output wire are respectively connected to the back-end circuit through a receiving wire; the back-end circuit is connected to the data processing station; the six pressure sensing patches are evenly fixed on the outer wall of the outer ring; a spring is fixed on the inner side of each pressure sensing patch; a rotating disk is fixed on the end of each spring away from the outer ring; the rotating disk is attached to and fixed on the surface of the rectangular electrode sheet; a reserved hole is provided at each end of the rectangular electrode sheet; each reserved hole is provided with a fixed point; adjacent detection electrode sheets are connected through the first electrode connector wire and the second electrode connector wire; the first electrode connector wire and the second electrode connector wire respectively pass through the two reserved holes of each detection electrode sheet and are fixed on the fixed points corresponding to the reserved holes; the pressure sensor output wire is connected to each pressure sensing patch;

[0008] The second electrode connector wire is used to transmit the acquisition excitation output by the back-end circuit to the detection electrode sheet; the detection electrode sheet is used to apply the acquisition excitation to the surface of the target detection matsutake mushroom; the first electrode connector wire is used to transmit the electrical feedback information of the detection electrode sheet to the back-end circuit through the receiving wire; the pressure sensor output wire is used to transmit the mechanical feedback information output by the pressure sensing patch to the back-end circuit through the receiving wire; the back-end circuit is used to transmit the acquisition excitation to the second electrode connector wire through the excitation wire, receive and pre-process the electrical feedback information and the mechanical feedback information, obtain acquisition data, and transmit the acquisition data to the data processing station; the hyperspectral detection sub-device is used to collect hyperspectral data of the target detection matsutake mushroom and transmit the hyperspectral data to the data processing station; the electronic nose detection sub-device is used to collect odor information of the target detection matsutake mushroom and transmit the odor information to the data processing station; the data processing station is used to perform multi-dimensional freshness detection and real-time monitoring of the target detection matsutake mushroom based on the acquisition data, the hyperspectral data, and the odor information.

[0009] Preferably, the back-end circuit is embedded with: a microprocessor unit, a constant current source signal generating unit, a front-end measuring unit, a response signal collecting and processing unit and an RS232 interface circuit unit;

[0010] The constant current source signal generating unit is used to generate the acquisition excitation; the front-end measurement unit is used to amplify, filter and suppress noise on the electrical feedback information and the mechanical feedback information to obtain the input signal; the response signal acquisition and processing unit is used to perform data analysis on the input signal to obtain the acquired data; the RS232 interface circuit unit is used to send the acquired data to the data processing station; the microprocessor unit is used to control the constant current source signal to generate the acquisition excitation and control the RS232 interface circuit unit to send the acquired data to the data processing station.

[0011] Preferably, the data processing station has an embedded multi-dimensional comprehensive quality scoring system; the multi-dimensional comprehensive quality scoring system includes: a first quality evaluation subsystem, a second quality evaluation subsystem, a third quality evaluation subsystem, and a multi-dimensional comprehensive quality scoring subsystem; the first quality evaluation subsystem includes: a matsutake cross-section detection model, a matsutake column uniformity detection model, a matsutake freshness detection model, and an impedance-based quality parameter calculation model;

[0012] The matsutake cross-section detection model is used to calculate the matsutake cross-section area of ​​the target detected matsutake using the pressure sensing patch; the matsutake column uniformity detection model is used to calculate the matsutake column uniformity data of the target detected matsutake using the pressure sensing patch; the matsutake freshness detection model is used to calculate the initial freshness judgment data of the target detected matsutake using the detection electrode sheet; the quality parameter calculation model is used to perform data fusion on the matsutake cross-section area, the matsutake column uniformity data and the initial freshness judgment data to obtain the matsutake quality impedance score; the second quality evaluation subsystem is used to obtain the matsutake quality hyperspectral score using the hyperspectral detection sub-device; the third quality evaluation subsystem is used to obtain the matsutake quality electronic nose score using the electronic nose detection sub-device; the multi-dimensional comprehensive quality scoring subsystem is used to perform data fusion on the matsutake quality impedance score, the matsutake quality hyperspectral score and the matsutake quality electronic nose score to obtain the overall matsutake quality score.

[0013] Preferably, the calculation process of the cross-sectional area of ​​the matsutake mushroom and the uniformity data of the matsutake mushroom column includes:

[0014] Using the pressure sensing patch to collect pressure data of the target detection matsutake mushroom to obtain six-position pressure values;

[0015] The radius is calculated by using the six-position pressure values ​​and the probe design parameters of the pressure sensing patch to obtain the six-position matsutake radius data;

[0016] The polygonal interpolation method is used to fit the six-dimensional matsutake radius data to obtain the cross-sectional area of ​​the matsutake;

[0017] The standard deviation of the six-dimensional matsutake radius data is calculated and normalized to obtain the matsutake column uniformity data.

[0018] Preferably, the calculation process of the initial freshness judgment data includes:

[0019] Using the detection electrode sheet to collect electrical data of the target detection matsutake mushroom to obtain the electrical feedback information;

[0020] Calculating the real part orthogonal anisotropy characteristic parameter and the imaginary part orthogonal anisotropy characteristic parameter of the electrical feedback information by using a difference method to obtain an orthogonal anisotropy characteristic vector;

[0021] Extracting the angle value and weight value of the orthotropic eigenvector;

[0022] Constructing a time series set, a characteristic signal sample matrix and a mapping function based on the angle value and the weight value;

[0023] Calculating morphological characteristic parameters and integral values ​​using the angle value and the weight value;

[0024] Based on the time series set, the characteristic signal sample matrix and the mapping function, freshness judgment is performed using the morphological characteristic parameters and the integral value to obtain the initial freshness judgment data.

[0025] Preferably, the weight value calculation process includes:

[0026] Adding the orthotropic eigenvector to the vector diagram to obtain vector coordinates;

[0027] Calculating the angle between the vector coordinates and the 45° standard line to obtain angle data;

[0028] The weight value is obtained by performing weight calculation using the angle data; the calculation formula of the weight value is: ; where q = 1, 2, 3; is the weight value corresponding to the qth orthogonal anisotropic eigenvector; is the angle data corresponding to the qth orthogonal anisotropic eigenvector; 、 as well as These are the angle data corresponding to the first, second and third orthogonal anisotropic eigenvectors respectively.

[0029] Preferably, the process of constructing the time series set includes:

[0030] Obtaining impedance amplitude samples based on the orthogonal anisotropic eigenvector; the calculation formula for obtaining the impedance amplitude samples is:

[0031] ;

[0032] in, is the impedance amplitude sample; is the dimension; is the time series set; Represents mapping; is the weight value at the i-th time point; and are the impedance value and phase value collected at the i-th time point respectively; X and Y are the impedance data set and phase data set respectively; and are the start and end time of the time series respectively;

[0033] A phase sample is obtained based on the orthogonal anisotropic eigenvector; the calculation formula of the phase sample is:

[0034]

[0035] in, is the phase sample; 、 are data points corresponding to the impedance value and the phase value respectively; and are the nth ZI class and Phase-like points; is the phase transformation function; and For passing The mth ZI class and Phase-like points;

[0036] Data mapping is performed on the impedance amplitude samples and the phase samples to obtain the time series set; the calculation formula of the time series set is:

[0037] P: ;

[0038] in, is the set of time series in a feature space with a dimension d greater than 10; is the time series set; is the element located at (c, j) in the matrix corresponding to the time series set.

[0039] Preferably, the calculation process of the Matsutake quality impedance score includes:

[0040] The cross-sectional area of ​​the matsutake mushroom, the uniformity data of the matsutake mushroom column, and the initial freshness judgment data are standardized to obtain standard cross-sectional area data, standard uniformity data, and standard freshness data; the calculation formulas for the standard cross-sectional area data, the standard uniformity data, and the standard freshness data are respectively: 、 as well as ;in, 、 as well as They are respectively the standard cross-sectional area data, the standard uniformity data and the standard freshness data; 、 as well as They are respectively the cross-sectional area of ​​the matsutake mushroom, the uniformity data of the matsutake mushroom column and the initial freshness judgment data; and are the maximum and minimum values ​​of the cross-sectional area of ​​the matsutake mushroom, respectively; and are respectively the maximum and minimum values ​​of the freshness initial judgment data;

[0041] importance data is obtained by assigning importance weights to the standard cross-sectional area data, the standard uniformity data and the standard freshness data; the importance data satisfies the condition: ; wherein, , and are the importance weights of the standard cross-sectional area data, the standard uniformity data and the standard freshness data, respectively;

[0042] The standard cross-sectional area data, the standard uniformity data and the standard freshness data are weighted and added using the importance data to obtain the truffle quality impedance score.

[0043] Preferably, the process of obtaining the truffle quality hyperspectral score and the truffle quality electronic nose score comprises:

[0044] Optical data of the target detection truffle is collected using the hyperspectral detection sub-device to obtain the hyperspectral data;

[0045] Chemical data of the target detection truffle is collected using the electronic nose detection sub-device to obtain the odor information;

[0046] The covariance of the hyperspectral data and the odor information is calculated respectively to obtain a first covariance matrix and a second covariance matrix;

[0047] Principal component extraction is performed according to the first covariance matrix and the second covariance matrix to obtain first principal component data and second principal component data;

[0048] A prediction model is established using the first principal component data and the second principal component data respectively to obtain a hyperspectral prediction model and an electronic nose prediction model;

[0049] The truffle quality hyperspectral score and the truffle quality electronic nose score are obtained by performing score prediction using the hyperspectral prediction model and the electronic nose prediction model respectively.

[0050] Preferably, the process of obtaining the truffle quality overall score comprises:

[0051] The truffle quality impedance score, the truffle quality hyperspectral score and the truffle quality electronic nose score are standardized respectively to obtain impedance standardized scores, hyperspectral standardized scores and electronic nose standardized scores; the calculation formula of the impedance standardized scores, the hyperspectral standardized scores and the electronic nose standardized scores is: ; wherein, ; is the truffle quality impedance score; providing a hyperspectral score for the quality of the matsutake mushroom; scoring the quality of the matsutake mushrooms by electronic nose; 、 、 They are the impedance normalized score, the hyperspectral normalized score and the electronic nose normalized score; and are the maximum and minimum values ​​of the Matsutake quality impedance score, respectively; and are the maximum and minimum values ​​of the Matsutake quality hyperspectral score, respectively; and are the maximum and minimum values ​​of the electronic nose score of the matsutake quality, respectively;

[0052] Determine the determination weights of the impedance normalized score, the hyperspectral normalized score, and the electronic nose normalized score to obtain determination weight data; the determination weight data satisfies the following conditions: ;in, 、 as well as The determination rights of the impedance normalized score, the hyperspectral normalized score and the electronic nose normalized score respectively;

[0053] The score is calculated based on the determined weight data to obtain the overall quality score of the matsutake mushroom. The calculation formula for the overall quality score of the matsutake mushroom is: ;in, Provide an overall score for the quality of the matsutake mushrooms.

[0054] The present invention discloses the following technical effects:

[0055] The present invention provides a multi-dimensional matsutake quality grading detection device. Through the special structure of the impedance detection sub-device, the problem of excessive collection of irrelevant information during the data collection process is solved, and the simultaneous detection of surface size and impedance is realized; through the impedance detection sub-device and the back-end circuit, the lossy defects of conventional technology are solved, and non-destructive collection of matsutake freshness data is realized; through the impedance detection sub-device, the hyperspectral detection sub-device and the electronic nose detection sub-device, the shortcoming of the conventional technology of single detection direction is solved, and multi-dimensional detection of matsutake is realized; through the data processing station, the defect of conventional technology that can only detect once is solved, and the monitoring of the entire process of matsutake from freshness to corruption is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 A schematic diagram of the structure of a multi-dimensional matsutake quality grading detection device provided by an embodiment of the present invention;

[0058] Figure 2 A structural diagram of a matsutake detection sub-device provided in an embodiment of the present invention;

[0059] Figure 3 A diagram of the local device architecture of a detection electrode sheet provided by an embodiment of the present invention;

[0060] Figure 4 A comparison of the maximum and minimum detection dimensions of the impedance detection sub-device provided by an embodiment of the present invention, and a schematic diagram of detection of irregularly shaped matsutake mushrooms;

[0061] Figure 5 A schematic diagram of the back-end circuit structure provided by an embodiment of the present invention;

[0062] Figure 6 This is a flowchart of the operation of the multi-dimensional comprehensive quality scoring system provided by an embodiment of the present invention.

[0063] Description of reference numerals:

[0064] 1-Impedance detection sub-device, 11-Outer ring, 12-Detection electrode sheet, 121-Rotating disk, 122-Rectangular electrode sheet, 123-Reserved hole, 124-Fixed point, 13-Spring, 14-Pressure sensor output wire, 15-First electrode connector wire, 16-Second electrode connector wire, 17-Pressure sensor patch, 2-Back-end circuit, 3-Receiving wire, 4-Excitation wire, 5-Hyperspectral detection sub-device, 6-Electronic nose detection sub-device, 7-Data processing station. DETAILED DESCRIPTION

[0065] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0066] The purpose of the present invention is to provide a multi-dimensional matsutake quality grading detection device, which realizes the clamping and surface data collection of the detection target matsutake through an impedance detection sub-device, and utilizes springs and rotatable detection electrode sheets to improve the ductility and stability of the impedance detection sub-device; realizes the simultaneous detection of the shape and impedance of matsutake through pressure sensing patches and detection electrodes, thereby improving the detection efficiency; realizes multi-dimensional non-destructive detection of the shape, smell and composition of matsutake through the combination of hyperspectral and electronic nose detection; and monitors the entire process of matsutake from freshness to corruption in real time through a data processing station.

[0067] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] Figure 1 This is a schematic diagram of the structure of a multi-dimensional matsutake quality grading detection device provided by an embodiment of the present invention. Figure 2 This is a structural diagram of a matsutake detection sub-device provided in an embodiment of the present invention. Figure 3 This is a diagram of the local device architecture of the detection electrode sheet 12 provided in an embodiment of the present invention, such as Figure 1 、 Figure 2 as well as Figure 3 As shown, the present invention provides a multi-dimensional matsutake quality grading detection device, comprising: an impedance detection sub-device 1, a hyperspectral detection sub-device 5, an electronic nose detection sub-device 6, and a data processing station 7; the impedance detection sub-device 1 comprises: a back-end circuit 2, a receiving wire 3, an excitation wire 4, and a matsutake information acquisition module; the matsutake information acquisition module comprises: an outer ring 11, a rotatable detection electrode sheet 12, a spring 13, a pressure sensor output wire 14, a first electrode connector wire 15, a second electrode connector wire 16, and a pressure sensing patch 17; the detection electrode sheet 12 comprises: a rotating disk 121, a rectangular electrode sheet 122, a reserved hole 123, and a fixed point 124;

[0069] The data processing station 7 is connected to the impedance detection sub-device 1, the hyperspectral detection sub-device 5, and the electronic nose detection sub-device 6 respectively; the back-end circuit 2 is connected to the second electrode connector wire 16 via the excitation wire 4; the first electrode connector wire 15 and the pressure sensor output wire 14 are respectively connected to the back-end circuit 2 via a receiving wire 3; the back-end circuit 2 is connected to the data processing station 7; six pressure sensing patches 17 are evenly fixed on the outer wall of the outer ring 11; a spring 13 is fixed on the inner side of each pressure sensing patch 17; and a rotating disk 12 is fixed to the end of each spring 13 away from the outer ring 11. 1; a rotating disk 121 is attached and fixed to the surface of a rectangular electrode sheet 122; a reserved hole 123 is provided at each end of the rectangular electrode sheet 122; each reserved hole 123 is provided with a fixed point 124; adjacent detection electrode sheets 12 are connected by a first electrode connector wire 15 and a second electrode connector wire 16; the first electrode connector wire 15 and the second electrode connector wire 16 respectively pass through the two reserved holes 123 of each detection electrode sheet 12 and are fixed on the fixed points 124 corresponding to the reserved holes 123; the pressure sensor output wire 14 is connected to each pressure sensing patch 17;

[0070] The second electrode connector wire 16 is used to transmit the acquisition excitation output by the back-end circuit 2 to the detection electrode sheet 12; the detection electrode sheet 12 is used to apply the acquisition excitation to the surface of the target detection matsutake; the first electrode connector wire 15 is used to transmit the electrical feedback information of the detection electrode sheet 12 to the back-end circuit 2 through the receiving wire 3; the pressure sensor output wire 14 is used to transmit the mechanical feedback information output by the pressure sensing patch 17 to the back-end circuit 2 through the receiving wire 3; the back-end circuit 2 is used to transmit the acquisition excitation to the second electrode connector wire 16 through the excitation wire 4, receive and pre-process the electrical feedback information and mechanical feedback information, obtain acquisition data, and transmit the acquisition data to the data processing station 7; the hyperspectral detection sub-device 5 is used to collect hyperspectral data of the target detection matsutake, and transmit the hyperspectral data to the data processing station 7; the electronic nose detection sub-device 6 is used to collect odor information of the target detection matsutake, and transmit the odor information to the data processing station 7; the data processing station 7 is used to perform multi-dimensional freshness detection and real-time monitoring of the target detection matsutake based on the acquisition data, hyperspectral data and odor information.

[0071] Preferably, the back-end circuit 2 is embedded with: a microprocessor unit, a constant current source signal generating unit, a front-end measuring unit, a response signal acquisition and processing unit, and an RS232 interface circuit unit;

[0072] The constant current source signal generation unit is used to generate the acquisition excitation; the front-end measurement unit is used to amplify, filter and noise suppress the electrical feedback information and the mechanical feedback information to obtain the input signal; the response signal acquisition and processing unit is used to analyze the input signal to obtain the acquisition data; the RS232 interface circuit unit is used to send the acquisition data to the data processing station 7; and the microprocessor unit is used to control the constant current source signal generation unit to generate the acquisition excitation and control the RS232 interface circuit unit to send the acquisition data to the data processing station 7.

[0073] Preferably, the data processing station 7 is embedded with a multi-dimensional comprehensive quality scoring system; the multi-dimensional comprehensive quality scoring system comprises: a first quality evaluation subsystem, a second quality evaluation subsystem, a third quality evaluation subsystem and a multi-dimensional comprehensive quality scoring subsystem; the first quality evaluation subsystem comprises: a truffle cross-section detection model, a truffle column uniformity detection model, a truffle freshness detection model and a quality parameter calculation model based on impedance;

[0074] The truffle cross-section detection model is used to calculate the truffle cross-section area of the target detection truffle by using the pressure sensing patch 17; the truffle column uniformity detection model is used to calculate the truffle column uniformity data of the target detection truffle by using the pressure sensing patch 17; the truffle freshness detection model is used to calculate the freshness preliminary judgment data of the target detection truffle by using the detection electrode sheet 12; and the quality parameter calculation model is used to data fuse the truffle cross-section area, the truffle column uniformity data and the freshness preliminary judgment data to obtain the truffle quality impedance score; the second quality evaluation subsystem is used to obtain the truffle quality hyperspectral score by using the hyperspectral detection sub-device 5; the third quality evaluation subsystem is used to obtain the truffle quality electronic nose score by using the electronic nose detection sub-device 6; and the multi-dimensional comprehensive quality scoring subsystem is used to data fuse the truffle quality impedance score, the truffle quality hyperspectral score and the truffle quality electronic nose score to obtain the truffle quality overall score.

[0075] Preferably, the calculation process of the truffle cross-section area and the truffle column uniformity data comprises:

[0076] The pressure data of the target detection truffle is acquired by using the pressure sensing patch 17 to obtain six-position pressure values;

[0077] The radius is calculated by using the six-position pressure values and the probe design parameters of the pressure sensing patch 17 to obtain six-position truffle radius data;

[0078] The six-position truffle radius data is fitted by using the polygon interpolation method to obtain the truffle cross-section area;

[0079] The standard deviation of the six-position truffle radius data is calculated, and the standard deviation is normalized to obtain the truffle column uniformity data.

[0080] Preferably, the calculation process of the initial freshness judgment data includes:

[0081] The detection electrode sheet 12 is used to collect electrical data of the target detection matsutake mushroom to obtain electrical feedback information;

[0082] The real part orthotropic characteristic parameters and the imaginary part orthotropic characteristic parameters of the electrical feedback information are calculated by using the difference method to obtain the orthotropic characteristic vector;

[0083] Extract the angle value and weight value of the orthotropic eigenvector;

[0084] Construct a time series set, a characteristic signal sample matrix and a mapping function based on the angle value and the weight value;

[0085] Calculate the morphological characteristic parameters and integral values ​​using the angle value and weight value;

[0086] Based on the time series set, characteristic signal sample matrix and mapping function, freshness judgment is performed using morphological characteristic parameters and integral values ​​to obtain the initial freshness judgment data.

[0087] Preferably, the weight value calculation process includes:

[0088] Put the orthotropic eigenvector into the vector graph to get the vector coordinates;

[0089] Calculate the angle between the vector coordinates and the 45° standard line to obtain the angle data;

[0090] The angle data is used to calculate the weight to obtain the weight value; the calculation formula of the weight value is: ; where q = 1, 2, 3; is the weight value corresponding to the qth orthogonal anisotropic eigenvector; is the angle data corresponding to the qth orthotropic eigenvector; 、 as well as These are the angle data corresponding to the 1st, 2nd and 3rd orthogonal anisotropic eigenvectors respectively.

[0091] Preferably, the process of constructing a time series set includes:

[0092] The impedance amplitude samples are obtained based on the orthogonal anisotropic eigenvectors. The calculation formula for obtaining the impedance amplitude samples is:

[0093] ;

[0094] in, is the impedance amplitude sample; is the dimension; is a time series set; is a representative mapping; is a weight value of the i th time point; and are impedance values and phase values collected at the i th time point, respectively; X and Y are impedance data set and phase data set, respectively; and are start time and end time of the time series, respectively;

[0095] The phase sample is obtained based on the orthogonal anisotropic characteristic vector; the calculation formula of the phase sample is:

[0096]

[0097] wherein, is the phase sample; , are data points corresponding to the impedance values and the phase values, respectively; and are the n th ZI class and the n th ZI class phase point collected, respectively; is a phase transformation function; and are the m th ZI class and the m th ZI class phase point after the transformation;

[0098] Data mapping is performed on the impedance amplitude sample and the phase sample to obtain a time series set; the calculation formula of the time series set is:

[0099] P: ;

[0100] wherein, is a time series set in a feature space with a dimension d greater than 10; is a time series set; is an element located at (c, j) in a matrix corresponding to the time series set.

[0101] Preferably, the calculation process of the quality impedance score of the Tricholoma matsutake includes:

[0102] The cross-sectional area of the Tricholoma matsutake, the uniformity data of the Tricholoma matsutake column, and the initial judgment data of the freshness are standardized to obtain standard cross-sectional area data, standard uniformity data, and standard freshness data; the calculation formulas of the standard cross-sectional area data, the standard uniformity data, and the standard freshness data are: , and ; wherein, , and ​​​They are standard cross-sectional area data, standard uniformity data and standard freshness data; 、 as well as They are the cross-sectional area of ​​​​Matsutake mushrooms, the uniformity of Matsutake mushroom columns, and the initial judgment data of freshness; and are the maximum and minimum values ​​of the cross-sectional area of ​​Matsutake, respectively; and are the maximum and minimum values ​​of the initial freshness judgment data respectively;

[0103] The importance weights of the standard cross-sectional area data, the standard uniformity data, and the standard freshness data are assigned to obtain the importance data; the importance data satisfies the following conditions: ;in, 、 as well as are the importance weights of standard cross-sectional area data, standard uniformity data, and standard freshness data respectively;

[0104] Importance data was used to perform weighted summation on the standard cross-sectional area data, standard uniformity data, and standard freshness data to obtain the Matsutake quality impedance score.

[0105] Preferably, the process of obtaining the high-spectral score of Matsutake quality and the electronic nose score of Matsutake quality includes:

[0106] The hyperspectral detection sub-device 5 is used to collect optical data of the target detection matsutake mushroom to obtain hyperspectral data;

[0107] The electronic nose detection sub-device 6 is used to collect chemical data of the target detection matsutake mushroom to obtain odor information;

[0108] Calculate the covariance of the hyperspectral data and the odor information respectively to obtain the first covariance matrix and the second covariance matrix;

[0109] Perform principal component extraction according to the first covariance matrix and the second covariance matrix to obtain first principal component data and second principal component data;

[0110] The first principal component data and the second principal component data are used to establish prediction models respectively, thereby obtaining a hyperspectral prediction model and an electronic nose prediction model;

[0111] The hyperspectral prediction model and electronic nose prediction model were used for score prediction, respectively, to obtain the hyperspectral score of Matsutake quality and the electronic nose score of Matsutake quality.

[0112] Preferably, the process of obtaining the overall quality score of Matsutake mushrooms includes:

[0113] The impedance score, hyperspectral score, and electronic nose score of Matsutake quality were standardized to obtain the impedance standardized score, hyperspectral standardized score, and electronic nose standardized score. The calculation formulas for the impedance standardized score, hyperspectral standardized score, and electronic nose standardized score are as follows: ; Among them, e ; Impedance score for matsutake quality; High-spectral scoring of matsutake quality; electronic nose scoring of matsutake quality; 、 、 They are impedance normalized score, hyperspectral normalized score, and electronic nose normalized score; and are the maximum and minimum values ​​of the Matsutake quality impedance score, respectively; and are the maximum and minimum values ​​of the Matsutake quality hyperspectral score, respectively; and are the maximum and minimum values ​​of the electronic nose score of Matsutake quality;

[0114] Determine the determination weights of the impedance normalized score, the hyperspectral normalized score, and the electronic nose normalized score to obtain determination weight data; the determination weight data satisfies the following conditions: ;in, 、 as well as These are the determination weights of impedance normalized score, hyperspectral normalized score, and electronic nose normalized score;

[0115] The scores are calculated based on the weighted data to obtain the overall quality score of the Matsutake mushrooms. The calculation formula for the overall quality score of the Matsutake mushrooms is: ;in, Provide an overall rating for the quality of the matsutake mushrooms.

[0116] refer to Figure 2 The detection electrode head is connected to each adjacent electrode head via a first electrode connector wire 15 and a second electrode connector wire 16 to form an electrode array. The first electrode connector wire 15 and the second electrode connector wire 16 are both located at the junction of the head and tail of the electrode array. The detection electrode sheet 12 and the spring 13 are connected via a rotating disk 121 to form a detection electrode head. The evenly surrounded group of detection electrode heads constitutes an electrode array. The spring 13 is used to provide elasticity for the electrode array. The left end of the rotating disk 121 is fixedly connected to the spring 13, and the right end is connected to the detection electrode sheet 12 so that it can rotate relative to it. The rear end of the spring 13 contacts the pressure sensing patch 17, so that the pressure generated each time the spring 13 contracts can be detected by the pressure sensing patch 17.

[0117] Furthermore, the multi-dimensional comprehensive quality scoring system includes: an impedance-based quality assessment subsystem (first quality assessment subsystem), a hyperspectral-based quality assessment subsystem (second quality assessment subsystem), an electronic nose-based quality assessment subsystem (third quality assessment subsystem), and a multi-dimensional comprehensive quality scoring subsystem. The impedance-based quality assessment subsystem includes: a matsutake cross-section detection model, a matsutake column uniformity detection model, a matsutake freshness detection model, and an impedance-based quality parameter calculation model. The input information of the matsutake cross-section detection model and the matsutake uniformity detection model is obtained by pressure sensing patch 17; the matsutake freshness detection model is obtained by detection electrode sheet 12. The hyperspectral-based quality assessment subsystem and the electronic nose-based quality assessment subsystem obtain information through a hyperspectral detection device and an electronic nose detection device, respectively.

[0118] refer to Figure 2 , the inner part of the outer ring 11 is evenly attached with detection electrode heads, the upper end of the detection electrode piece 12 is connected by the first electrode head wire, and the lower end is connected by the second electrode connecting head wire 16, and adjacent detection electrode pieces 12 are connected in series. Pressure sensing patches 17 are evenly attached to the outside of the outer ring 11, and adjacent pressure sensing patches 17 are connected in series by the pressure sensor output wire 14, and the series wires have sufficient margin to meet the length change caused by the deformation of the spring 13. The spring 13 is connected between the outer ring 11 and the detection electrode piece 12. When the product is detected, the spring 13 contracts, and the detection electrode piece 12 is attached to the pine mushroom to be detected. The second electrode connecting head wire 16 is passed through After the current applied by the excitation wire 4 releases the excitation signal, it is transmitted back to the back-end circuit 2 through the first electrode head wire and the receiving wire 3. After processing, the impedance information of the tested matsutake is obtained and then input into the data processing station 7. According to the preset model, the freshness of the matsutake can be detected. At the same time, the pressure generated by the contraction of the spring 13 is applied to the pressure sensing patch 17. After processing, the pressure information of the tested matsutake is obtained. The pressure sensing patch 17 transmits the pressure signal back to the back-end circuit 2 through the pressure sensor output wire 14. After processing, the pressure information of the tested matsutake is obtained and then input into the data processing station 7. According to the preset model, the cross-sectional area and the uniformity of the matsutake column can be detected. By moving the tested object, the detection information of multiple cross sections can be obtained, thereby more specifically determining the status information of the tested matsutake. It can ensure that it is in optimal working conditions in real time. This unique design combines efficiency and convenience, making the detection sub-device more effective in practical applications.

[0119] refer to Figure 3The left end of the rotating disk 121 is fixedly connected to the spring 13, and the right end is connected to the electrode sheet so that it can rotate relatively. Reserved holes 123 are provided at the upper and lower symmetrical positions of the electrode sheet so that the wires can connect the electrode sheets in series. A fixed point 124 is set next to the reserved hole 123, and the wire is fixed to this point by insulating glue, which is conducive to the uniform distribution of current in the object being measured to avoid unnecessary disturbance.

[0120] refer to Figure 4 The left side shows the minimum dimension that the detection sub-device can measure when spring 13 is relaxed, and the right side shows the maximum dimension that the detection sub-device can measure when spring 13 is contracted. The bottom side shows an illustration of the detection of an irregularly shaped crop (matsutake). The top views in these two extreme states indicate that the detection sub-device is adaptable to target crops of varying sizes. The measurable dimensions of the detection sub-device in these two extreme states vary significantly, indicating that the detection sub-device has a large measurement range. To ensure the stability of the measurement device's installation position and maximize the contact area for detection, the detection sub-device can be installed where the cross-section of the target matsutake mushroom has its largest dimension. This installation not only improves the detection sub-device's measurement accuracy but also enhances its overall stability, ensuring the accuracy and reliability of the measurement results. The probe of the detection sub-device includes 6 detection electrode pieces 12 to form an electrode group. Due to the symmetry of the detection sub-device and the distribution angle of the electrode pieces is 60°, the complex impedance value obtained can be defined as Zvm, and m is a value of 1 to 6; since the designed group detection electrode detection heads are independent of each other, they can show different compression amounts for the unevenness of the surface of the detected object. The detection electrode piece 12 is installed through a rotating disk 121, so the detection electrode piece 12 can fix the detected object at different angles, which can meet the detection requirements of irregularly shaped matsutake mushrooms.

[0121] refer to Figure 5 Back-end circuit 2 includes a microprocessor unit, a constant current source signal generator unit, a front-end measurement unit, a response signal acquisition and processing unit, and an RS232 interface circuit unit. In the constant current source signal generator unit, the two differential excitation signals output by the constant current source are fed into the internal resistance network under test or the external object under test via a signal switching module. The collected response voltage signal is processed as follows: after amplification by the program global area (PGA), it enters the analog-to-digital converter (ADC), where it is converted into a digital signal and stored in the static random access memory (SRAM). After acquisition, the microcontroller reads the voltage and current signal data stored in the SRAM for preliminary processing. The results are then transmitted to the host computer (data processing station 7) via the RS232 communication module. This completes a high-performance bioelectrical impedance measurement process.

[0122] refer to Figure 6 The calculation process of the cross-sectional area and the uniformity of the Matsutake column includes:

[0123] 1) Pressure distribution measurement: The pressure value recorded by each detection electrode 12 , ,…, ,The pressure value reflects the shape information of Matsutake mushrooms at different positions.

[0124] 2) Determination of electrode position: Since the electrodes of the probe are evenly distributed on a circular outer ring, the electrode position can be expressed as an angle in the polar coordinate system. and distance r (i.e., the outer ring radius), where =1, 2,…, 6.

[0125] 3) Calculate the relationship between pressure and radius: the pressure measured by each electrode You can compare the "radius" of the matsutake in this direction Related. By the compression of spring 13 With the known probe design parameters, the radius of the matsutake at each electrode position can be calculated Usually, the relationship between pressure and radius can be obtained by the spring force formula: ;

[0126] Where r is the initial radius of the probe, k is the spring constant, is the compression amount of spring 13.

[0127] 4) Cross-sectional area calculation: With the "radius" of each electrode position , ,…, After that, these points can be used to reconstruct the cross-sectional outline of the matsutake. Using polygon interpolation, a smooth polygon is fitted to represent the cross-sectional boundary. The area A of the polygon is calculated as the cross-sectional area of ​​the matsutake:

[0128]

[0129] here( , ) is the point converted to Cartesian coordinates, n is the number of electrodes, ( , )=( , ).

[0130] 5) Uniformity calculation: Based on the calculated radius , ,…, Average value , then calculate the standard deviation , standard deviation The smaller the size, the more uniform the Matsutake column. The calculation formula is as follows:

[0131]

[0132] The standard deviation Normalization is performed and converted into a score between 0 and 1 to indicate uniformity, where 0 indicates completely uneven and 1 indicates completely uniform.

[0133] The calculation formula for uniformity is as follows:

[0134]

[0135] in, is the maximum allowed standard deviation set in the experiment.

[0136] Furthermore, the method for detecting the freshness of Matsutake mushrooms includes:

[0137] 1) Due to the inhomogeneity of the object being tested, it is necessary to calculate the weights of the three orthogonal electrode groups to make the measurement results more reliable. The difference method is used to calculate the real part orthogonal anisotropy characteristic parameters ROACPS at a frequency of 2000Hz. o and imaginary orthotropic anisotropic characteristic parameters IOACPS p :

[0138] ROACPSo:ROACPSo

[0139] IOACPSp:IOACPSp=2

[0140] Zvm and Zvs are the impedance values ​​of two different electrodes. To ensure orthogonality, choose opposite electrodes, such as Z1 and Z4, Z2 and Z5, and Z3 and Z6;

[0141] The three sets of orthotropic characteristic parameters are input into the vector diagram to obtain their vectors. , and set the initial position of its vector at the dot, the angle between the vector and the 45° standard line can be obtained , the weight of the orthotropic characteristic parameters can be calculated: ; is the weight value of the orthotropic characteristic parameter; q=1, 2, 3.

[0142] 2) Construction Time series collection of dimensional impedance characteristic data , which represents the impedance value of the fruit at different frequencies or time points;

[0143] Construct the characteristic signal sample matrix S and its mapping functions f(x) and f(y). These two functions can represent the relationship between the fruit impedance characteristic data and its freshness.

[0144] Impedance magnitude sample: , these samples represent the impedance amplitude data collected from time t0 to t1. Each sample is In dimensional space, the amplitude characteristics composition;

[0145] Phase Sample: , these samples contain the time from t0 to Impedance phase data for a period of time, each sample is a tuple (x, y). Each tuple (x, y) represents an impedance phase data point within a given time period, where x is the input feature (time, frequency, etc.) and y is the corresponding measured output (phase value). The set of these tuples covers all impedance phase changes from t0 to t1. x represents the input measurement data point. In impedance phase detection, it is a physical quantity associated with the impedance data point between time t0 and t1. y represents the output value corresponding to the input data x, which is an instance phase value that represents the characteristics of the phase change between t0 and t1.

[0146] Data processing P: ; Through data processing P, the impedance amplitude and phase data are mapped to and In the d-dimensional space, the morphological feature parameter k is obtained, and the parameter k is a d×d dimensional matrix.

[0147] 3) Based on the amplitude and phase angle features extracted from the impedance amplitude and phase data, the extracted amplitude and phase angle features are normalized so that the data are on the same scale for subsequent analysis; the amplitude features and phase angle features are fused to form a comprehensive feature vector, and the various statistical eigenvalues ​​are combined into a vector to obtain a morphological feature deformation parameter, denoted as "K": ; Indicates the nth freshness level; n is the number of freshness levels; The impedance amplitude and phase samples are then integrated to further represent the freshness level of the object being tested, which is recorded as "L": , ; Indicates the time period arrive Integration result of internal impedance amplitude samples; Indicates the time period arrive Integration result of internal impedance phase sample; and Both are weight coefficients used to adjust the influence of impedance amplitude and phase in calculating the freshness level. Based on the above steps, the initial detection model is obtained.

[0148] 4) Input the overall integrated signal into the initial detection model to verify the feasibility of the model, and operate according to the freshness feature recognition and division rules. This process can realize the calculation of the freshness level of the detected object, where " " represents the key stress monitoring parameter signal, and "D" represents the final freshness level of the detected object: .

[0149] Specifically, the step of fusing the data of the cross-sectional area of ​​the matsutake mushroom, the uniformity of the matsutake mushroom column, and the freshness of the matsutake mushroom includes:

[0150] 1) Standardize the cross-sectional area, column uniformity, and freshness of the matsutake mushrooms to make them easier to compare and integrate on the same scale. The standardization formula is as follows:

[0151]

[0152]

[0153]

[0154] 2) Assign weights to each indicator based on its importance in practical applications. Assume that the weights of cross-sectional area, uniformity, and freshness are 、 、 These weights can be set based on experimental experience or the advice of domain experts and must meet the following requirements:

[0155]

[0156] The standardized indicators are weighted and summed according to the weights to obtain a comprehensive score based on impedance. :

[0157]

[0158] Furthermore, the calculation steps of the quality assessment subsystem based on hyperspectral detection include:

[0159] 1) First, calculate the covariance matrix of the hyperspectral data , used to analyze the correlation between each band. The covariance matrix formula is as follows:

[0160]

[0161] in, is the spectral reflectance of the i-th sample, is the mean vector of the samples, and N is the number of samples. Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors. ,in, are the eigenvectors of the covariance matrix, The eigenvalues ​​and eigenvectors represent the main directions of the data, and the eigenvalues ​​represent the variance information contained in these directions. We select several principal components with larger eigenvalues ​​(usually the principal components that can explain more than 90% of the variance) and project the high-dimensional original data into the new space constructed by these principal components, thereby achieving dimensionality reduction: ,in, is the data matrix after dimensionality reduction, is the original hyperspectral data matrix, is the eigenvector matrix.

[0162] 2) Using the extracted principal component data, a prediction model (such as a regression model or classification model) can be further established to predict the specific quality indicators of Matsutake. For example, the moisture content and degree of decay of Matsutake can be predicted using a linear regression model. Comprehensive scoring based on hyperspectral analysis :

[0163]

[0164] in, It is a comprehensive H score based on hyperspectral, , …, is the principal component, , is the regression coefficient.

[0165] Specifically, the calculation steps of the quality assessment subsystem based on electronic nose detection are similar to those of the quality assessment subsystem based on hyperspectral detection, including:

[0166] 1) Use an electronic nose device to collect matsutake odor data to form a data matrix X. Use principal component analysis (PAC) to establish a regression model between the electronic nose data and matsutake quality, and calculate the covariance matrix of the collected data. :

[0167]

[0168] Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue λ e and the eigenvector V e .

[0169] 2) Select the first k principal components P according to the size of the eigenvalue e C e1 , P e Ce2 ,…,P e C ek , these principal components can retain most of the variance information. Based on the scores and weights of the principal components, the comprehensive score based on the electronic nose is calculated. :

[0170] Among them, is a comprehensive score based on the electronic nose, is the weight of the corresponding principal component, which can be determined according to the variance contribution rate of each principal component.

[0171] Furthermore, the calculation steps of the multi-dimensional comprehensive quality scoring subsystem include:

[0172] 1) First, the impedance-based comprehensive score obtained above is Comprehensive scoring based on hyperspectral Comprehensive scoring based on electronic nose To standardize: ; Wherein, e=1, 2, 3, respectively represent the comprehensive scores based on impedance, hyperspectral, and electronic nose; Impedance score for matsutake quality; High-spectral scoring of matsutake quality; electronic nose scoring of matsutake quality; 、 、 They are impedance normalized score, hyperspectral normalized score and electronic nose normalized score and are the maximum and minimum values ​​of the Matsutake quality impedance score, respectively; and are the maximum and minimum values ​​of the Matsutake quality hyperspectral score, respectively; and are the maximum and minimum values ​​of the electronic nose score of Matsutake quality, respectively.

[0173] 2) Determine the weight for each score based on the importance or relevance of different detection methods , , These weights can be determined by expert evaluation, experimental results, or accuracy metrics of each method, ensuring that their sum is 1: + + =1, calculate the comprehensive quality score according to the set weight :

[0174]

[0175] Through this comprehensive evaluation method, the overall quality of Tricholoma matsutake can be effectively evaluated, and scientific basis can be provided for subsequent storage, sales or processing.

[0176] Preferably, according to the value of the comprehensive score Q, the quality of Tricholoma matsutake can be divided into different grades. Threshold values can be set according to experimental data or industry standards to divide quality grades, for example: excellent quality: Q>0.8; better quality: 0.6<Q≤0.8; general quality: 0.4<Q≤0.6; poor quality: Q≤0.4. Through the method of data fusion, the cross-sectional area, uniformity of the column and freshness of Tricholoma matsutake are integrated into a comprehensive quality score Q. This score can provide a scientific basis for the grading of Tricholoma matsutake quality and can reflect the overall quality of Tricholoma matsutake. The larger the cross-sectional area, the higher the uniformity, and the fresher the Tricholoma matsutake, the higher the comprehensive score Q, and the better the quality.

[0177] The beneficial effects of the present application are as follows:

[0178] The present application realizes clamping and surface data acquisition of the detection target Tricholoma matsutake through the impedance detection sub-device, and improves the extensibility and stability of the impedance detection sub-device by using a spring and a rotatable detection electrode sheet; through the pressure sensing patch and the detection electrode sheet, the shape and impedance of Tricholoma matsutake can be detected simultaneously, improving the detection efficiency; through the combination of hyperspectral and electronic nose detection, multi-dimensional non-destructive detection of the shape, odor and composition of Tricholoma matsutake can be realized; through the data processing station, the whole process from fresh to rotten of Tricholoma matsutake can be monitored in real time.

[0179] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be mutually referred to.

[0180] The principles and implementation modes of the present application are described by applying specific examples herein, and the above description of the embodiments is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A multi-dimensional matsutake quality grading detection device, characterized in that: include: Impedance detection sub-device, hyperspectral detection sub-device, electronic nose detection sub-device and data processing station; The impedance detection sub-device includes: a back-end circuit, a receiving wire, an excitation wire, and a matsutake information acquisition module; the matsutake information acquisition module includes: an outer ring, a rotatable detection electrode sheet, a spring, a pressure sensor output wire, a first electrode connector wire, a second electrode connector wire, and a pressure sensing patch; the detection electrode sheet includes: a rotating disk, a rectangular electrode sheet, a reserved hole, and a fixed point; The data processing station is connected to the impedance detection sub-device, the hyperspectral detection sub-device and the electronic nose detection sub-device respectively; the back-end circuit is connected to the second electrode connector wire through the excitation wire; the first electrode connector wire and the pressure sensor output wire are respectively connected to the back-end circuit through a receiving wire; the back-end circuit is connected to the data processing station; the six pressure sensing patches are evenly fixed on the outer wall of the outer ring; a spring is fixed on the inner side of each pressure sensing patch; a rotating disk is fixed on the end of each spring away from the outer ring; the rotating disk is attached to and fixed on the surface of the rectangular electrode sheet; a reserved hole is provided at each end of the rectangular electrode sheet; each reserved hole is provided with a fixed point; adjacent detection electrode sheets are connected through the first electrode connector wire and the second electrode connector wire; the first electrode connector wire and the second electrode connector wire respectively pass through the two reserved holes of each detection electrode sheet and are fixed on the fixed points corresponding to the reserved holes; the pressure sensor output wire is connected to each pressure sensing patch; The second electrode connector wire is used to transmit the acquisition excitation output by the back-end circuit to the detection electrode sheet; the detection electrode sheet is used to apply the acquisition excitation to the surface of the target detection matsutake mushroom; the first electrode connector wire is used to transmit the electrical feedback information of the detection electrode sheet to the back-end circuit through the receiving wire; the pressure sensor output wire is used to transmit the mechanical feedback information output by the pressure sensing patch to the back-end circuit through the receiving wire; the back-end circuit is used to transmit the acquisition excitation to the second electrode connector wire through the excitation wire, receive and pre-process the electrical feedback information and the mechanical feedback information, obtain acquisition data, and transmit the acquisition data to the data processing station; the hyperspectral detection sub-device is used to collect hyperspectral data of the target detection matsutake mushroom and transmit the hyperspectral data to the data processing station; the electronic nose detection sub-device is used to collect odor information of the target detection matsutake mushroom and transmit the odor information to the data processing station; the data processing station is used to perform multi-dimensional freshness detection and real-time monitoring of the target detection matsutake mushroom based on the acquisition data, the hyperspectral data, and the odor information.

2. A multi-dimensional matsutake quality grading detection device according to claim 1, characterized in that: The back-end circuit is embedded with: a microprocessor unit, a constant current source signal generating unit, a front-end measuring unit, a response signal acquisition and processing unit, and an RS232 interface circuit unit; The constant current source signal generating unit is used to generate the acquisition excitation; The front-end measurement unit is used to amplify, filter and suppress noise of the electrical feedback information and the mechanical feedback information to obtain an input signal; The response signal acquisition and processing unit is used to perform data analysis on the input signal to obtain the acquired data; the RS232 interface circuit unit is used to send the acquired data to the data processing station; the microprocessor unit is used to control the constant current source signal to generate the acquisition excitation and control the RS232 interface circuit unit to send the acquired data to the data processing station.

3. A multi-dimensional matsutake quality grading detection device according to claim 1, characterized in that: The data processing station has an embedded multi-dimensional comprehensive quality scoring system; the multi-dimensional comprehensive quality scoring system includes: a first quality evaluation subsystem, a second quality evaluation subsystem, a third quality evaluation subsystem, and a multi-dimensional comprehensive quality scoring subsystem; the first quality evaluation subsystem includes: a matsutake cross-section detection model, a matsutake column uniformity detection model, a matsutake freshness detection model, and an impedance-based quality parameter calculation model; The matsutake cross-section detection model is used to calculate the matsutake cross-section area of ​​the target detected matsutake using the pressure sensing patch; the matsutake column uniformity detection model is used to calculate the matsutake column uniformity data of the target detected matsutake using the pressure sensing patch; the matsutake freshness detection model is used to calculate the initial freshness judgment data of the target detected matsutake using the detection electrode sheet; the quality parameter calculation model is used to perform data fusion on the matsutake cross-section area, the matsutake column uniformity data and the initial freshness judgment data to obtain the matsutake quality impedance score; the second quality evaluation subsystem is used to obtain the matsutake quality hyperspectral score using the hyperspectral detection sub-device; the third quality evaluation subsystem is used to obtain the matsutake quality electronic nose score using the electronic nose detection sub-device; the multi-dimensional comprehensive quality scoring subsystem is used to perform data fusion on the matsutake quality impedance score, the matsutake quality hyperspectral score and the matsutake quality electronic nose score to obtain the overall matsutake quality score.

4. A multi-dimensional matsutake quality grading detection device according to claim 3, characterized in that: The calculation process of the cross-sectional area of ​​the matsutake mushroom and the uniformity data of the matsutake mushroom column includes: Using the pressure sensing patch to collect pressure data of the target detection matsutake mushroom to obtain six-position pressure values; The radius is calculated by using the six-position pressure values ​​and the probe design parameters of the pressure sensing patch to obtain the six-position matsutake radius data; The polygonal interpolation method is used to fit the six-dimensional matsutake radius data to obtain the cross-sectional area of ​​the matsutake; The standard deviation of the six-dimensional matsutake radius data is calculated and normalized to obtain the matsutake column uniformity data.

5. A multi-dimensional matsutake quality grading detection device according to claim 3, characterized in that: The calculation process of the initial freshness judgment data includes: Using the detection electrode sheet to collect electrical data of the target detection matsutake mushroom to obtain the electrical feedback information; Calculating the real part orthogonal anisotropy characteristic parameter and the imaginary part orthogonal anisotropy characteristic parameter of the electrical feedback information by using a difference method to obtain an orthogonal anisotropy characteristic vector; Extracting the angle value and weight value of the orthotropic eigenvector; Constructing a time series set, a characteristic signal sample matrix and a mapping function based on the angle value and the weight value; Calculating morphological characteristic parameters and integral values ​​using the angle value and the weight value; Based on the time series set, the characteristic signal sample matrix and the mapping function, freshness judgment is performed using the morphological characteristic parameters and the integral value to obtain the initial freshness judgment data.

6. A multi-dimensional matsutake quality grading detection device according to claim 5, characterized in that: The weight value calculation process includes: Adding the orthotropic eigenvector to the vector diagram to obtain vector coordinates; Calculating the angle between the vector coordinates and the 45° standard line to obtain angle data; The weight value is obtained by performing weight calculation using the angle data; the calculation formula of the weight value is: ; where q = 1, 2, 3; is the weight value corresponding to the qth orthogonal anisotropic eigenvector; is the angle data corresponding to the qth orthogonal anisotropic eigenvector; 、 as well as These are the angle data corresponding to the first, second and third orthogonal anisotropic eigenvectors respectively.

7. A multi-dimensional matsutake quality grading detection device according to claim 5, characterized in that: The construction process of the time series set includes: Obtaining impedance amplitude samples based on the orthogonal anisotropic eigenvector; the calculation formula for obtaining the impedance amplitude samples is: ； in, is the impedance amplitude sample; is the dimension; is the time series set; Represents mapping; is the weight value at the i-th time point; and are the impedance value and phase value collected at the i-th time point respectively; X and Y are the impedance data set and phase data set respectively; and are the start and end time of the time series respectively; A phase sample is obtained based on the orthogonal anisotropic eigenvector; the calculation formula of the phase sample is: ； in, is the phase sample; 、 are data points corresponding to the impedance value and the phase value respectively; and are the nth ZI class and Phase-like points; is the phase transformation function; and For passing The mth ZI class and Phase-like points; Data mapping is performed on the impedance amplitude samples and the phase samples to obtain the time series set; the calculation formula of the time series set is: P： ； in, is the set of time series in a feature space with a dimension d greater than 10; is the time series set; is the element located at (c, j) in the matrix corresponding to the time series set.

8. A multi-dimensional matsutake quality grading detection device according to claim 3, characterized in that: The calculation process of the Matsutake quality impedance score includes: The cross-sectional area of ​​the matsutake mushroom, the uniformity data of the matsutake mushroom column, and the initial freshness judgment data are standardized to obtain standard cross-sectional area data, standard uniformity data, and standard freshness data; the calculation formulas for the standard cross-sectional area data, the standard uniformity data, and the standard freshness data are respectively: 、 as well as ;in, 、 as well as They are respectively the standard cross-sectional area data, the standard uniformity data and the standard freshness data; 、 as well as They are respectively the cross-sectional area of ​​the matsutake mushroom, the uniformity data of the matsutake mushroom column and the initial freshness judgment data; and are the maximum and minimum values ​​of the cross-sectional area of ​​the matsutake mushroom, respectively; and are respectively the maximum and minimum values ​​of the freshness initial judgment data; Importance weights are assigned to the standard cross-sectional area data, the standard uniformity data, and the standard freshness data to obtain importance data; the importance data satisfies the following conditions: ;in, 、 as well as are the importance weights of the standard cross-sectional area data, the standard uniformity data, and the standard freshness data respectively; The importance data is used to perform weighted summation on the standard cross-sectional area data, the standard uniformity data, and the standard freshness data to obtain the matsutake quality impedance score.

9. A multi-dimensional matsutake quality grading detection device according to claim 3, characterized in that: The process of obtaining the matsutake quality hyperspectral score and the matsutake quality electronic nose score includes: Using the hyperspectral detection sub-device to collect optical data of the target detection matsutake mushroom to obtain the hyperspectral data; Using the electronic nose detection sub-device to collect chemical data of the target detection matsutake mushroom to obtain the odor information; Calculating the covariance of the hyperspectral data and the odor information respectively to obtain a first covariance matrix and a second covariance matrix; Performing principal component extraction based on the first covariance matrix and the second covariance matrix to obtain first principal component data and second principal component data; Establishing prediction models using the first principal component data and the second principal component data respectively to obtain a hyperspectral prediction model and an electronic nose prediction model; The hyperspectral prediction model and the electronic nose prediction model are respectively used to perform score prediction to obtain the matsutake quality hyperspectral score and the matsutake quality electronic nose score.

10. A multi-dimensional matsutake quality grading detection device according to claim 3, characterized in that: The process of obtaining the overall score of the Matsutake quality includes: The impedance score of the matsutake quality, the hyperspectral score of the matsutake quality, and the electronic nose score of the matsutake quality were standardized to obtain an impedance standardized score, a hyperspectral standardized score, and an electronic nose standardized score; the calculation formulas for the impedance standardized score, the hyperspectral standardized score, and the electronic nose standardized score were as follows: ; Among them, e ; Score the quality impedance of the matsutake mushroom; providing a hyperspectral score for the quality of the matsutake mushroom; scoring the quality of the matsutake mushrooms by electronic nose; 、 、 They are the impedance normalized score, the hyperspectral normalized score and the electronic nose normalized score; and are the maximum and minimum values ​​of the Matsutake quality impedance score, respectively; and are the maximum and minimum values ​​of the Matsutake quality hyperspectral score, respectively; and are the maximum and minimum values ​​of the electronic nose score of the matsutake quality, respectively; Determine the determination weights of the impedance normalized score, the hyperspectral normalized score, and the electronic nose normalized score to obtain determination weight data; the determination weight data satisfies the following conditions: ;in, 、 as well as The determination rights of the impedance normalized score, the hyperspectral normalized score and the electronic nose normalized score respectively; The score is calculated based on the determined weight data to obtain the overall quality score of the matsutake mushroom. The calculation formula for the overall quality score of the matsutake mushroom is: ;in, Provide an overall score for the quality of the matsutake mushrooms.

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