Moisture analysis and detection method and system based on tea processing

Abstract

The invention relates to the technical field of moisture detection, in particular to a moisture analysis and detection method and system based on tea processing, and the method specifically comprises the following steps: collecting multi-source heterogeneous data in the processing process of a tea frying machine in real time, and calculating a dynamic moisture distribution value; geometric deformation parameters of the blades in the constant-speed stir-frying stage are obtained, and a water permeation resistance coefficient based on the leaf crimpness is constructed; obtaining blade surface stress spectrum data at a non-uniform speed stage, and calculating a damage accumulation factor based on a microcrack growth rate; a comprehensive vulnerability index is generated in combination with a thermal-mechanical coupling effect, and the tea processing quality state is identified; and dynamically optimizing the stir-frying track according to the tea processing quality state, and outputting a staged time-varying regulation and control strategy. The problems that in an existing tea leaf processing moisture detection system, processing parameter adjustment lags behind, and the risk of leaf breaking is aggravated are solved.

Description

Technical Field

[0001] This invention relates to the field of moisture detection technology, and is a method and system for moisture analysis and detection based on tea processing. Background Technology

[0002] In tea processing, the coupling effect of moisture migration and mechanical force directly affects the morphological integrity and quality stability of tea leaves. Existing roasting processes rely heavily on empirical parameter settings, lacking precise analysis of the dynamic coupling of multiple physical fields, leading to high leaf breakage rates and uneven quality. Current technologies often employ offline sampling or static monitoring with a single sensor (such as a temperature and humidity probe), failing to capture the spatiotemporal heterogeneity of temperature gradients, pressure distribution, and leaf deformation during processing. Especially during high-speed roasting, uneven heating causes localized glass transitions in the tea leaves, obstructing moisture diffusion paths. The synergistic effect of mechanical stress and thermal expansion induces microcrack propagation, accelerating structural damage. While existing technologies attempt to introduce infrared thermometry or pressure sensing, they fail to construct a dynamic correlation model of temperature-deformation-stress, resulting in large moisture prediction errors and difficulty in timely feedback and control. Furthermore, the deformation characteristics differ significantly between uniform and non-uniform speed stages. Existing methods lack a damage assessment system based on curling degree and microcrack dynamics, causing delays in processing parameter adjustments and exacerbating the risk of leaf breakage. Summary of the Invention

[0003] The technical problem to be solved by this invention is that the existing tea processing moisture detection system causes the adjustment of processing parameters to lag, which increases the risk of leaf breakage. This invention proposes a moisture analysis and detection method and system based on tea processing.

[0004] To achieve the above objectives, the technical solution of the moisture analysis and detection method based on tea processing of the present invention includes the following steps: Step 1: Collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value; Step 2: Obtain the geometric deformation parameters of the leaves during the uniform stirring stage, and construct the water permeability resistance coefficient based on the leaf curling degree; Step 3: Obtain stress spectrum data of the blade surface during the non-uniform velocity stage and calculate the damage accumulation factor based on the microcrack propagation rate; Step 4: Combine the output parameters from Step 2 and Step 3, and generate a comprehensive vulnerability index based on the thermo-mechanical coupling effect to identify the quality status of tea processing; Step 5: Dynamically optimize the stir-frying trajectory based on the quality status of tea processing, and output a phased time-varying control strategy.

[0005] Specifically, step one includes: S11: Real-time capture of the two-dimensional temperature distribution inside the tea-frying chamber using an infrared thermal imager array deployed above the tea-frying chamber; The pressure field on the tea leaves is synchronously collected by a piezoelectric thin film sensor array embedded at the bottom of the tea-frying chamber; Acquire a spatiotemporally synchronized temperature-pressure joint distribution dataset and establish initial baseline data, including glass transition temperature and reference pressure; S12: Introducing temperature and pressure gradient correction terms, constructing a partial differential equation for unsteady moisture diffusion in tea leaves, establishing a dynamic moisture distribution prediction model, and outputting real-time moisture content. ; S13: Extract each spatiotemporal point from the temperature field data. instantaneous temperature and instantaneous pressure Extract initial baseline data and calculate temperature deviation. and pressure deviation ; Based on S12 and S13, a comprehensive moisture fluctuation index for tea processing is generated. .

[0006] The strategy for generating the comprehensive moisture fluctuation index during the tea processing process is as follows: ; in, This is the critical temperature fluctuation threshold. This refers to the maximum pressure range. Specifically, step two includes: S21: A high-frequency laser triangulation instrument is used to scan the surface of the tea leaves in real time to obtain the radius of curvature of the tea leaves. Blade thickness and blade 3D point cloud data; Calculate the surface area of ​​tea leaves using a point cloud reconstruction algorithm. and track its time derivative. ; Generate a time-series dataset of blade deformation. Simultaneously extract key deformation features, specifically: maximum rate of change of curvature. ; S22: Determine the quantitative relationship between steam permeability and blade curvature radius; By introducing a correction term for the rate of change of surface area, the dynamic permeability resistance coefficient is calculated. ; Specifically, step two also includes: S23: The elastic modulus K of tea leaves was determined by a three-point bending test, and the thickness gradient of the tea leaves was calculated simultaneously. and the rate of change of blade thickness The elastic modulus, thickness gradient, and rate of change of leaf thickness of tea leaves are incorporated into the strategy for evaluating the deformation coordination of tea leaves, and the deformation coordination factor is obtained through evaluation. ; Specifically, step three includes: S31: Real-time capture of stress wave signals from tea leaves during the tea-frying process using an acoustic emission sensor array; extraction of the dominant frequency by performing Fast Fourier Transform (FFT) and energy integration on the signals. and energy ; It should be noted that the characteristics of stress waves are directly related to the propagation of microcracks in the blade. The dominant frequency reflects the crack size, while the energy reflects the severity of crack propagation.

[0007] S32: Establish a crack propagation dynamics model, inputting the effective stress and real-time moisture gradient experienced by the tea leaves during processing and stir-frying into the crack propagation dynamics model, and outputting the real-time crack propagation rate of the tea leaves. ; Specifically, step three also includes: S33: Real-time crack propagation rate of tea leaves The dominant frequency of stress wave signals and energy The cumulative damage assessment strategy for tea leaves is incorporated to evaluate and obtain the cumulative damage factor of tea leaves. Specifically, step four includes the following steps: S41: Extract dynamic osmotic resistance coefficient and moisture gradient data of tea leaves during the uniform stirring stage of tea processing; Simultaneously extract damage accumulation factors and deformation coordination factors during the non-uniform stir-frying stage of tea processing; S42: The extracted data is standardized by using the Z-score algorithm; S43: Import the standardized data into the comprehensive assessment strategy for tea fragility to obtain the comprehensive tea fragility index during the tea processing process; The comprehensive assessment strategy for the fragility of tea leaves is as follows: ; in, The comprehensive tea fragility index for the i-th data collection period; 'a' is a subscript, indicating the 'a'th data collection period during the uniform stirring stage of the tea-frying equipment. e is a subscript, indicating the e-th data collection period during the non-uniform stirring stage of the tea-frying equipment; These are sub-items for evaluating the comprehensive vulnerability index of tea leaves during the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment, respectively. Specifically, the stir-frying trajectory is dynamically optimized based on the quality status of tea processing, and a phased time-varying control strategy is output, including: S51: Extract the comprehensive vulnerability index of tea leaves and compare it with the preset tea damage threshold. When the comprehensive vulnerability index of tea leaves is greater than the preset tea damage threshold, start the variable temperature-variable pressure coordinated control and invert the optimal processing temperature and processing pressure based on the current moisture content. S52: Generate an adaptive control time series, dynamically adjust the control time interval through the hyperbolic tangent function, and respond to changes in damage gradient; In addition, the moisture analysis and detection system based on tea processing of the present invention includes the following modules: The module includes a moisture prediction module, a moisture permeability resistance quantification module, a leaf damage accumulation quantification module, a processing quality assessment module, and a processing strategy optimization module. The moisture prediction module is used to collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value. The water penetration resistance quantification module is used to obtain the geometric deformation parameters of the leaves during the uniform stir-frying stage and construct a water penetration resistance coefficient based on the leaf curling degree. The blade damage accumulation quantization module is used to acquire the stress spectrum data of the blade surface during the non-uniform speed stage and calculate the damage accumulation factor based on the microcrack propagation rate. The processing quality assessment module is used to fuse output parameters and combine the thermo-mechanical coupling effect to generate a comprehensive vulnerability index to identify the processing quality status of tea. The processing strategy optimization module dynamically optimizes the stir-frying trajectory based on the tea processing quality status and outputs a phased time-varying control strategy.

[0008] Compared with the prior art, the technical effects of the present invention are as follows: This technical solution enhances the dynamic analysis capability of moisture migration and damage evolution during tea processing through multi-source heterogeneous data fusion and thermo-mechanical coupling modeling. Specifically, it employs an infrared thermal imager and a piezoelectric thin-film sensor array to simultaneously acquire spatiotemporal temperature and pressure distribution data. Combined with the unsteady-state partial differential equation for moisture diffusion, a dynamic moisture prediction model under the synergistic effect of temperature gradient and mechanical stress is constructed, overcoming the limitations of static detection and achieving high-precision tracking of moisture distribution. Furthermore, this invention quantifies the inhibitory effect of leaf curling on steam permeation during the uniform-speed stage and the cumulative damage caused by microcrack propagation during the non-uniform-speed stage through high-frequency laser triangulation. By introducing deformation coordination factors and damage accumulation factors, a multi-dimensional quality assessment system is established. Finally, this invention generates a comprehensive vulnerability index by combining the coupling effect of thermal expansion and mechanical stress, enabling real-time identification of the vulnerable state of local areas of tea leaves. This solves the control lag problem caused by insufficient spatiotemporal resolution in traditional methods, reducing tea breakage rate while improving the quality pass rate. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic flowchart of a moisture analysis and detection method based on tea processing according to the present invention. Figure 2 This is a schematic diagram of the output determination structure of a phased time-varying control strategy according to the present invention; Figure 3 This is a schematic diagram of the structure of a moisture analysis and detection system based on tea processing according to the present invention. Detailed Implementation

[0010] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0011] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0012] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0013] Example 1: like Figure 1 , Figure 2 As shown in the figure, an embodiment of the present invention provides a moisture analysis and detection method based on tea processing, such as... Figure 1 As shown, the specific steps include the following: Step 1: Collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value; S11: Real-time capture of the two-dimensional temperature distribution inside the tea-frying chamber using an infrared thermal imager array deployed above the tea-frying chamber; For example, in this embodiment, the infrared thermal imager has a pixel resolution of ≥320×240, a sampling frequency of 25Hz, covers a wavelength range of 8-14μm, and has a temperature measurement accuracy of ±2%. The pressure field on the tea leaves is synchronously collected by a piezoelectric thin film sensor array embedded at the bottom of the tea-frying chamber; For example, in this embodiment, the piezoelectric thin film sensor has a spatial resolution of 10mm × 10mm and a sampling frequency of 100Hz; Acquire a spatiotemporally synchronized temperature-pressure joint distribution dataset and establish initial baseline data, including glass transition temperature and reference pressure; It should be noted that the temperature-pressure joint distribution dataset provides boundary conditions for the moisture diffusion model, where the temperature field reflects the energy input distribution and the pressure field characterizes the interaction strength between the leaves and the tea-frying chamber.

[0014] S12: Introducing temperature and pressure gradient correction terms, constructing a partial differential equation for unsteady moisture diffusion in tea leaves, establishing a dynamic moisture distribution prediction model, and outputting real-time moisture content. ; For example, in this embodiment, a specific formula for an unsteady water diffusion partial differential equation is provided, which is as follows: ; Wherein, diffusion coefficient Specifically: ; in, The reference diffusivity (calibrated through isothermal and pressure drying experiments, typical value) is used as the reference diffusivity. ); R is the ideal gas constant. In this embodiment, R = 8.314 J / (mol·K). It should be noted that the activating energy of tea cellulose Measured by thermogravimetric analysis (TGA), The pressure sensitivity coefficient of tea leaves is determined by a three-point bending test.

[0015] S13: Extract each spatiotemporal point from the temperature field data. instantaneous temperature and instantaneous pressure Extract initial baseline data and calculate temperature deviation. and pressure deviation ; It should be noted that the temperature offset and pressure offset are obtained by subtracting the instantaneous data from the initial baseline data, and the difference is the offset. Based on S12 and S13, a comprehensive moisture fluctuation index for tea processing is generated. .

[0016] In another specific embodiment, the strategy for generating the comprehensive moisture fluctuation index during tea processing is as follows: ; in, The critical temperature fluctuation threshold is used. It should be noted that the critical temperature fluctuation threshold is determined based on experiments on the thermal damage of tea leaves. In this embodiment, ; The maximum pressure range is determined by the design parameters of the tea-frying machine. For example, in this embodiment... .

[0017] Step 2: Obtain the geometric deformation parameters of the leaves during the uniform stirring stage, and construct the water permeability resistance coefficient based on the leaf curling degree; S21: A high-frequency laser triangulation instrument (wavelength 532nm, sampling rate 500Hz) is used to scan the leaf surface in real time to obtain the radius of curvature of the tea leaves. Blade thickness and blade 3D point cloud data; Calculate the surface area of ​​tea leaves using a point cloud reconstruction algorithm. and track its time derivative. ; Generate a time-series dataset of blade deformation. Simultaneously extract key deformation features, specifically: maximum rate of change of curvature. ; S22: Determine the quantitative relationship between steam permeability and blade curvature radius; For example, the quantitative relationship between vapor permeability and blade curvature radius is determined based on microscopic permeability experiments of the wax layer. In this embodiment, ; By introducing a correction term for the rate of change of surface area, the dynamic permeability resistance coefficient is calculated. ; Exemplarily, in this embodiment, ; in, The reference permeability is the permeability of tea leaves; exemplarily, in this embodiment, the reference permeability is provided as Longjing tea. ; The initial radius of curvature is given. In this embodiment, when the tea leaves are not curled, ; It should be noted that the dynamic permeability resistance coefficient Characterize the inhibitory effect of leaf contraction on steam escape, and use it to quantify the hindering effect of leaf curling on water migration.

[0018] S23: The elastic modulus K of tea leaves was determined by a three-point bending test, and the thickness gradient of the tea leaves was calculated simultaneously. and the rate of change of blade thickness The elastic modulus, thickness gradient, and rate of change of leaf thickness of tea leaves are incorporated into the strategy for evaluating the deformation coordination of tea leaves, and the deformation coordination factor is obtained through evaluation. ; For example, in this embodiment, an implementation method for evaluating the degree of deformation coordination of tea leaves is provided, specifically as follows: ; in, Density of tea leaves; The characteristic length of a tea leaf is taken as the average thickness of the leaf in this embodiment. It should be noted that the numerator term is used to reflect the local stress concentration caused by uneven thickness; the denominator term is used to characterize the synergistic effect of curvature and shrinkage rate (i.e., high-speed shrinkage exacerbates deformation mismatch).

[0019] It should also be noted that the deformation compatibility factor , is the length change rate related to the square root of time, used to reflect the combined effect of blade thickness gradient (spatial change) and deformation rate (temporal change).

[0020] Step 3: Obtain stress spectrum data of the blade surface during the non-uniform velocity stage and calculate the damage accumulation factor based on the microcrack propagation rate; S31: Real-time capture of stress wave signals from tea leaves during the tea-frying process using an acoustic emission sensor array; extraction of the dominant frequency by performing Fast Fourier Transform (FFT) and energy integration on the signals. and energy ; It should be noted that the characteristics of stress waves are directly related to the propagation of microcracks in the blade. The dominant frequency reflects the crack size, while the energy reflects the severity of crack propagation.

[0021] S32: Establish a crack propagation dynamics model, inputting the effective stress and real-time moisture gradient experienced by the tea leaves during processing and stir-frying into the crack propagation dynamics model, and outputting the real-time crack propagation rate of the tea leaves. ; For example, in this embodiment, an output implementation strategy for a crack propagation dynamics model is provided, specifically as follows: ; Where C and m are both tea texture constants; for example, in this embodiment... C represents the material's ability to resist crack propagation and is related to inherent properties such as tea variety and initial moisture content. m is the stress exponent, which describes the sensitivity of effective stress to crack propagation rate. The tea texture constant is calibrated by offline uniaxial compression / tension experiments. The characteristic length of a tea leaf is taken as the average thickness of the leaf in this embodiment. The effective stress on the tea leaves, ; For example, in this embodiment, the nominal stress is used. ; The mechanical stress experienced by tea leaves during tea processing; The coefficient of thermal expansion of tea leaves is used. In this embodiment, the coefficient of thermal expansion of tea leaves is determined in advance by thermomechanical analysis (TMA). The temperature difference at different locations inside or on the surface of the blade is calculated by real-time measurement of the temperature field by an infrared thermal imager (such as the temperature difference between adjacent pixels). E is the Young's modulus of tea leaves, which reflects the ability of tea materials to resist elastic deformation. It is related to the moisture content and fiber structure of the leaves and is determined through material mechanics experiments. The spatial variation rate of moisture content inside tea leaves characterizes the degree of unevenness in moisture distribution (such as the difference in moisture content between the center and the edge of the leaf). It should be noted that the greater the moisture gradient inside the leaf, the greater the stress generated by the difference between dryness and wetness inside the leaf, which accelerates the propagation of cracks. S33: Real-time crack propagation rate of tea leaves The dominant frequency of stress wave signals and energy The cumulative damage assessment strategy for tea leaves is incorporated to evaluate and obtain the cumulative damage factor of tea leaves. For example, in this embodiment, an implementation method for assessing cumulative damage to tea leaves is provided, specifically as follows: ; in, It is a cumulative damage factor for tea leaves; Step 4: Combine the output parameters from Step 2 and Step 3, and generate a comprehensive vulnerability index based on the thermo-mechanical coupling effect to identify the quality status of tea processing; S41: Extract dynamic osmotic resistance coefficient and moisture gradient data of tea leaves during the uniform stirring stage of tea processing; Simultaneously extract damage accumulation factors and deformation coordination factors during the non-uniform stir-frying stage of tea processing; S42: The extracted data is standardized by using the Z-score algorithm; S43: Import the standardized data into the comprehensive assessment strategy for tea fragility to obtain the comprehensive tea fragility index during the tea processing process; In another specific embodiment, the comprehensive assessment strategy for the fragility of tea leaves is as follows: ; in, The comprehensive tea fragility index for the i-th data collection period; 'a' is a subscript, indicating the 'a'th data collection period during the uniform stirring stage of the tea-frying equipment. e is a subscript, indicating the e-th data collection period during the non-uniform stirring stage of the tea-frying equipment; These are sub-items for evaluating the comprehensive vulnerability index of tea leaves during the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment, respectively. For example, in this embodiment, a specific evaluation strategy is provided for the evaluation sub-item of the comprehensive fragility index of tea leaves during the uniform stirring stage of a tea-frying device, specifically as follows: ; in, These are the dynamic permeability resistance coefficients of the leaves in the nth tea detection area during the a-th and a+1-th data collection periods, respectively, in the uniform stirring stage of the tea-frying equipment. These are the leaf moisture gradient data for the nth tea detection area during the a-th and a+1-th data collection periods, respectively, in the uniform stirring stage of the tea-frying equipment. For example, in this embodiment, a specific evaluation strategy is provided for the evaluation sub-item of the comprehensive fragility index of tea leaves during the non-uniform stirring stage of a tea-frying equipment, specifically as follows: ; in, These are the damage accumulation factors of the nth tea detection area during the eth and e+1th data collection periods, respectively, in the non-uniform stirring stage of the tea-frying equipment. These are the deformation coordination factors of the nth tea detection area during the eth and e+1th data acquisition periods, respectively, in the non-uniform stirring stage of the tea-frying equipment. These are the weighting coefficients for the evaluation sub-items of the comprehensive vulnerability index of tea leaves during the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment, respectively. For example, in this embodiment, a strategy for obtaining the weight coefficients of the evaluation sub-items of the comprehensive fragility index of tea leaves in the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment is also provided, specifically as follows: ; in, The unit duration for the data collection period; The standard total tea-frying time for tea-frying equipment; Step 5: Dynamically optimize the stir-frying trajectory based on the quality status of tea processing, and output a phased time-varying control strategy.

[0022] S51: Extract the comprehensive vulnerability index of tea leaves and compare it with the preset tea damage threshold. When the comprehensive vulnerability index of tea leaves is greater than the preset tea damage threshold, start the variable temperature-variable pressure coordinated control and invert the optimal processing temperature and processing pressure based on the current moisture content. For example, in this embodiment, a processing temperature inversion function and a processing pressure inversion function are constructed based on the moisture content, and the optimal processing temperature and optimal processing pressure are derived through the inversion functions; In this embodiment, the coefficients of the processing temperature inversion function and the processing pressure inversion function are determined through orthogonal experiments; For example, in this embodiment, an expression for a processing temperature inversion function is provided, specifically as follows: ;in, The initial temperature is specified by the type of tea. This indicates the key moisture content for moisture control during tea processing. When the moisture content of tea is close to or below this value, the temperature control needs to be significantly reduced to prevent excessive evaporation that could lead to leaf scorching. In this embodiment, ; For example, in this embodiment, an expression for the processing pressure inversion function is also provided, specifically: ;in, The critical moisture threshold represents the lower limit of safety for tea processing. When the moisture content of tea leaves is less than or equal to the critical moisture threshold, the mechanical strength of the leaves decreases sharply due to excessive drying. Pressure regulation (reducing processing pressure) is required to prevent the expansion of microcracks and structural damage.

[0023] S52: Generate an adaptive control time series, dynamically adjust the control time interval through the hyperbolic tangent function, and respond to changes in damage gradient; For example, in this embodiment, an implementation strategy for adaptively controlling time series generation is provided, specifically as follows: ; in, These represent the time points of the k-th and (k-1)-th adjustments, respectively. As the reference time step, The gradient of the comprehensive fragility index for tea is shown. It should be noted that the larger the value, the more drastic the change in the quality state of the tea in a local area during processing (such as rapid evaporation of moisture or stress concentration). Conversely, the smaller the value, the more gradual the change in the state of the tea.

[0024] As a reference gradient, in this embodiment, ; Example 2: like Figure 3 As shown in the figure, an embodiment of the present invention provides a moisture analysis and detection system based on tea processing, such as... Figure 3 As shown, it includes the following modules: The module includes a moisture prediction module, a moisture permeability resistance quantification module, a leaf damage accumulation quantification module, a processing quality assessment module, and a processing strategy optimization module. The moisture prediction module is used to collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value. The water penetration resistance quantification module is used to obtain the geometric deformation parameters of the leaves during the uniform stir-frying stage and construct a water penetration resistance coefficient based on the leaf curling degree. The blade damage accumulation quantization module is used to acquire the stress spectrum data of the blade surface during the non-uniform speed stage and calculate the damage accumulation factor based on the microcrack propagation rate. The processing quality assessment module is used to fuse output parameters and combine the thermo-mechanical coupling effect to generate a comprehensive vulnerability index to identify the processing quality status of tea. The processing strategy optimization module dynamically optimizes the stir-frying trajectory based on the tea processing quality status and outputs a phased time-varying control strategy.

[0025] Example 3: This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the aforementioned moisture analysis and detection method based on tea processing by calling the computer program stored in memory.

[0026] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the moisture analysis and detection method based on tea processing provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0027] Example 4: This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When a computer program runs on a computer device, it causes the computer device to perform the aforementioned moisture analysis and detection method based on tea processing.

[0028] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0029] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0030] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0031] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0032] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0033] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0034] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0035] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0036] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0037] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0038] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for moisture analysis and detection based on tea processing, characterized in that, The method includes: Step 1: Collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value; Step 2: Obtain the geometric deformation parameters of the leaves during the uniform stirring stage, and construct the water permeability resistance coefficient based on the leaf curling degree; Step 3: Obtain stress spectrum data of the blade surface during the non-uniform velocity stage and calculate the damage accumulation factor based on the microcrack propagation rate; Step 4: Combine the output parameters from Step 2 and Step 3, and generate a comprehensive vulnerability index based on the thermo-mechanical coupling effect to identify the quality status of tea processing; Step 5: Dynamically optimize the stir-frying trajectory based on the quality status of tea processing, and output a phased time-varying control strategy.

2. The method for moisture analysis and detection based on tea processing according to claim 1, characterized in that, Step one includes: S11: Real-time capture of the two-dimensional temperature distribution inside the tea-frying chamber using an infrared thermal imager array deployed above the tea-frying chamber; The pressure field on the tea leaves is synchronously collected by a piezoelectric thin film sensor array embedded at the bottom of the tea-frying chamber; Acquire a spatiotemporally synchronized temperature-pressure joint distribution dataset and establish initial baseline data, including glass transition temperature and reference pressure; S12: Introducing temperature and pressure gradient correction terms, constructing a partial differential equation for unsteady moisture diffusion in tea leaves, establishing a dynamic moisture distribution prediction model, and outputting real-time moisture content. ; S13: Extract each spatiotemporal point from the temperature field data. instantaneous temperature and instantaneous pressure Extract initial baseline data and calculate temperature deviation. and pressure deviation ; Based on S12 and S13, a comprehensive moisture fluctuation index for tea processing is generated. ; The strategy for generating the comprehensive moisture fluctuation index during the tea processing process is as follows: ； in, This is the critical temperature fluctuation threshold. This represents the maximum pressure range.

3. The method for moisture analysis and detection based on tea processing according to claim 2, characterized in that, Step two includes: S21: A high-frequency laser triangulation instrument is used to scan the surface of the tea leaves in real time to obtain the radius of curvature of the tea leaves. Blade thickness and blade 3D point cloud data; Calculate the surface area of ​​tea leaves using a point cloud reconstruction algorithm. and track its time derivative. ; Generate a time-series dataset of blade deformation. Simultaneously extract key deformation features, specifically: maximum rate of change of curvature. ; S22: Determine the quantitative relationship between steam permeability and blade curvature radius, introduce a correction term for the rate of change of surface area, and calculate the dynamic permeability resistance coefficient. .

4. The method for moisture analysis and detection based on tea processing according to claim 3, characterized in that, Step two also includes: S23: The elastic modulus K of tea leaves was determined by a three-point bending test, and the thickness gradient of the tea leaves was calculated simultaneously. and the rate of change of blade thickness The elastic modulus, thickness gradient, and rate of change of leaf thickness of tea leaves are incorporated into the strategy for evaluating the deformation coordination of tea leaves, and the deformation coordination factor is obtained through evaluation. .

5. The method for moisture analysis and detection based on tea processing according to claim 4, characterized in that, Step three includes: S31: Real-time capture of stress wave signals from tea leaves during the tea-frying process using an acoustic emission sensor array; extraction of the dominant frequency by performing Fast Fourier Transform and energy integration on the signals. and energy ; S32: Establish a crack propagation dynamics model, inputting the effective stress and real-time moisture gradient experienced by the tea leaves during processing and stir-frying into the crack propagation dynamics model, and outputting the real-time crack propagation rate of the tea leaves. .

6. The method for moisture analysis and detection based on tea processing according to claim 5, characterized in that, Step three also includes: S33: Real-time crack propagation rate of tea leaves The dominant frequency of stress wave signals and energy The cumulative damage assessment strategy for tea leaves was introduced to evaluate and obtain the cumulative damage factor of tea leaves.

7. The method for moisture analysis and detection based on tea processing according to claim 6, characterized in that, Step four includes the following specific steps: S41: Extract dynamic osmotic resistance coefficient and moisture gradient data of tea leaves during the uniform stirring stage of tea processing; Simultaneously extract damage accumulation factors and deformation coordination factors during the non-uniform stir-frying stage of tea processing; S42: The extracted data is standardized by using the Z-score algorithm; S43: Import the standardized data into the comprehensive assessment strategy for tea fragility to obtain the comprehensive tea fragility index during the tea processing process; The comprehensive assessment strategy for the fragility of tea leaves is as follows: ； in, The comprehensive tea fragility index for the i-th data collection period; 'a' is a subscript, indicating the 'a'th data collection period during the uniform stirring stage of the tea-frying equipment. e is a subscript, indicating the e-th data collection period during the non-uniform stirring stage of the tea-frying equipment; These are sub-items for evaluating the comprehensive vulnerability index of tea leaves during the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment, respectively. These are the weighting coefficients for the evaluation sub-items of the comprehensive vulnerability index of tea leaves during the uniform-speed stirring stage and the non-uniform-speed stirring stage of the tea-frying equipment, respectively.

8. The method for moisture analysis and detection based on tea processing according to claim 7, characterized in that, The stirring trajectory is dynamically optimized based on the tea processing quality status, and a phased time-varying control strategy is output, including: S51: Extract the comprehensive vulnerability index of tea leaves and compare it with the preset tea damage threshold. When the comprehensive vulnerability index of tea leaves is greater than the preset tea damage threshold, start the variable temperature-variable pressure coordinated control and invert the optimal processing temperature and processing pressure based on the current moisture content. S52: Generates an adaptive control time series, dynamically adjusting the control time interval through the hyperbolic tangent function to respond to changes in the damage gradient.

9. A moisture analysis and detection system based on tea processing, used to implement the moisture analysis and detection method based on tea processing as described in any one of claims 1-8, characterized in that, The system includes the following modules: The module includes a moisture prediction module, a moisture permeability resistance quantification module, a leaf damage accumulation quantification module, a processing quality assessment module, and a processing strategy optimization module. The moisture prediction module is used to collect multi-source heterogeneous data in real time during the tea frying machine processing, import the data into the comprehensive moisture diffusion model, and calculate the dynamic moisture distribution value. The water penetration resistance quantification module is used to obtain the geometric deformation parameters of the leaves during the uniform stir-frying stage and construct a water penetration resistance coefficient based on the leaf curling degree. The blade damage accumulation quantization module is used to acquire the stress spectrum data of the blade surface during the non-uniform speed stage and calculate the damage accumulation factor based on the microcrack propagation rate. The processing quality assessment module is used to fuse output parameters and combine the thermo-mechanical coupling effect to generate a comprehensive vulnerability index to identify the processing quality status of tea. The processing strategy optimization module dynamically optimizes the stir-frying trajectory based on the tea processing quality status and outputs a phased time-varying control strategy.

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