Edible mushroom ultrasonic freezing and thawing synergistic cell wall breaking system and method

Abstract

The invention relates to the technical field of cell wall breaking, and discloses an edible mushroom ultrasonic freeze-thaw synergistic cell wall breaking system and method.The system comprises a sample parameter collection module, an ultrasonic freeze-thaw parameter control module, a synergistic wall breaking treatment module, a wall breaking effect evaluation module, a dynamic optimization module and a result processing and output module; initial cell parameter data and environmental parameter data of edible mushroom samples are collected, data preprocessing and standardized format conversion are carried out, the accuracy and consistency of the parameter data are guaranteed, meanwhile, initialization setting of ultrasonic treatment parameters and freeze-thaw cycle parameters is carried out based on the parameter data, and the accuracy and the consistency of the parameters are guaranteed. Optimized parameters are generated through a frequency matching algorithm and a parameter mapping model, and pertinence and reliability of parameter setting are improved; through ultrasonic and freeze thawing cooperative control parameter matching processing, a matching matrix is established, cooperative control parameters are generated by adopting a cooperative optimization algorithm, and cooperative processing of edible mushroom samples is realized.

Description

Technical Field

[0001] This invention relates to the field of cell wall disruption technology, specifically to a system and method for ultrasonic freeze-thaw synergistic cell wall disruption of edible fungi. Background Technology

[0002] Cell disruption technology uses physical and mechanical means such as high-frequency vibration and high-speed shearing and extrusion to destroy the cell wall structure of organisms such as plants and fungi in order to release internal active ingredients. This technology is divided into two main categories: mechanical and non-mechanical methods. High-frequency pulsed electric field technology has become a research hotspot in the field of biological extraction due to its selective release of small molecule products. Microwave cell disruption method is mainly used in laboratory settings due to the limitations of equipment cost.

[0003] Currently, in the cell wall disruption technology for edible fungi, the fixed processing parameters and lack of adaptive initialization based on the cell parameters and environmental parameters of the edible fungi sample make it impossible to optimize the ultrasonic and freeze-thaw cycle parameters for different sample characteristics, resulting in low cell wall disruption efficiency and unstable results.

[0004] Therefore, a system and method for ultrasonic freeze-thaw synergistic cell disruption of edible fungi are proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a system and method for edible fungi with ultrasonic freeze-thaw synergistic cell disruption, which solves the problem mentioned in the background that the ultrasonic and freeze-thaw cycle parameters cannot be optimized for different sample characteristics, resulting in low cell disruption efficiency and unstable effects.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an ultrasonic freeze-thaw synergistic cell wall disruption system and method for edible fungi, the method comprising the following steps: S1. Collect initial cell parameter data and environmental parameter data of edible fungi samples; S2. Based on the initial cell parameter data and environmental parameter data, initialize the ultrasonic processing parameters and generate ultrasonic processing parameter configuration data; S3. Based on the initial cell parameter data and environmental parameter data, initialize the freeze-thaw cycle parameters and generate freeze-thaw cycle parameter configuration data; S4. Perform ultrasonic and freeze-thaw coordinated control parameter matching processing based on the ultrasonic processing parameter configuration data and freeze-thaw cycle parameter configuration data to generate coordinated control parameter matching data. S5. Based on the coordinated control parameter matching data, perform ultrasonic freeze-thaw coordinated cell wall disruption on edible fungi samples, monitor the cell wall disruption status in real time and generate cell wall disruption status monitoring data. S6. Calculate the cell wall disruption effect evaluation parameters based on the cell wall disruption state monitoring data, and generate cell wall disruption effect evaluation parameter data; S7. Based on the cell wall disruption effect evaluation parameter data, dynamically adjust the ultrasonic treatment parameters and freeze-thaw cycle parameters to generate optimized ultrasonic freeze-thaw synergistic cell wall disruption control data. S8. Based on the optimized ultrasonic freeze-thaw coordinated cell wall disruption control data, perform final cell wall disruption processing on the edible fungi samples and output the cell wall disruption result data.

[0007] Preferably, the initial cell parameter data and environmental parameter data of the edible fungi sample collected in step S1 include the following steps: S11. Obtain data on cell wall thickness, cell membrane permeability, and cell contents concentration of edible fungi samples using edible fungi sample collection equipment, and generate initial cell parameter data. S12. Collect temperature, humidity and air pressure data of the edible fungus treatment environment through environmental sensors, and generate environmental parameter data; S13. Import the initial cell parameter data and environmental parameter data into the edible fungus processing and monitoring platform, perform data preprocessing and standardized format conversion, and generate standardized edible fungus sample parameter data.

[0008] Preferably, the initialization setting of the ultrasonic processing parameters in S2 includes the following steps: S21. Obtain standardized edible fungi sample parameter data; S22. Use a frequency matching algorithm to search for ultrasonic frequency, power and action time parameters corresponding to the standardized edible fungi sample parameter data in the ultrasonic parameter database, and generate ultrasonic processing parameter configuration data. S23. Perform redundancy verification and outlier filtering on the ultrasonic processing parameter configuration data to generate verified ultrasonic processing parameter configuration data.

[0009] Preferably, the freeze-thaw cycle parameter initialization setting in S3 includes the following steps: S31. Obtain standardized edible fungi sample parameter data; S32. Based on the freeze-thaw parameter mapping model, the standardized edible fungi sample parameter data is converted into freezing rate, thawing rate and number of cycles parameters of the freeze-thaw cycle, and freeze-thaw cycle parameter configuration data is generated. S33. The stability of the freeze-thaw cycle parameter configuration data is verified by using a numerical simulation algorithm, and the verified freeze-thaw cycle parameter configuration data is generated.

[0010] Preferably, the ultrasonic and freeze-thaw synergistic control parameter matching process in S4 includes the following steps: S41. Obtain the verified ultrasonic processing parameter configuration data and the verified freeze-thaw cycle parameter configuration data. S42. Establish a matching matrix for ultrasound and freeze-thaw synergistic control parameters, and align and combine ultrasound parameters and freeze-thaw parameters according to time series. S43. Use a collaborative optimization algorithm to calculate the optimal time interval and energy distribution ratio between ultrasound and the freeze-thaw process, and generate collaborative control parameter matching data. S44. The coordinated control parameter matching data is dynamically updated in real time.

[0011] Preferably, the ultrasonic freeze-thaw synergistic cell disruption treatment in S5 includes the following steps: S51. Based on the data matching of collaborative control parameters, control the ultrasonic generator and freeze-thaw equipment to process edible fungi samples in a collaborative manner. S52. Real-time data on the degree of cell wall rupture, the rate of release of cell contents, and changes in cell morphology are collected using a cell wall disruption monitoring sensor. S53. Compare the real-time collected data with the preset threshold to generate cell wall breakage status monitoring data; S54. When the monitored data is abnormal, the automatic pause mechanism is triggered and an exception handling log is generated.

[0012] Preferably, the calculation of the cell wall breaking effect evaluation parameters in S6 includes the following steps: S61. Obtain cell wall disruption monitoring data; S62. Calculate the cell wall disruption efficiency index, the content extraction rate index, and the cell integrity loss index; S63. The indicators are weighted and fused using a multi-objective evaluation function to generate cell wall breaking effect evaluation parameter data; S64. Perform trend analysis on the cell wall breaking effect evaluation parameter data and historical data to generate a cell wall breaking effect trend report.

[0013] Preferably, the step S7, adjusting the ultrasonic treatment parameters and freeze-thaw cycle parameters, includes the following steps: S71. Obtain cell wall breaking effect evaluation parameter data and cell wall breaking effect trend report; S72. An adaptive control algorithm is used to dynamically adjust the ultrasonic frequency, power, and freeze-thaw cycle parameters according to the cell wall breaking effect. S73. Perform feasibility verification and boundary constraint checks on the adjusted parameters, and generate optimized ultrasonic freeze-thaw synergistic cell wall disruption control data; S74. Record the optimization process to the parameter adjustment database for subsequent machine learning model training.

[0014] Preferably, the final cell wall disruption process in S8 includes the following steps: S81. Perform the final cell disruption process based on the optimized ultrasonic freeze-thaw synergistic cell disruption control data; S82. Collect cell debris data, content concentration data, and bioactivity data of edible fungi samples after cell wall disruption using the final testing equipment; S83. Integrate and format the collected data to generate cell wall disruption result data; S84. Upload the cell wall disruption results data to the edible fungi processing supervision platform for long-term storage and sharing.

[0015] Preferably, the system includes: The sample parameter acquisition module uses the cell parameter acquisition unit to acquire the initial cell attribute data of the edible fungi sample, the environmental parameter acquisition unit to acquire the state data of the processing environment, and the data preprocessing unit to output standardized edible fungi sample parameter data. The ultrasonic freeze-thaw parameter control module receives the standardized edible fungi sample parameter data, generates ultrasonic processing parameter configuration data through the ultrasonic parameter setting unit, generates freeze-thaw cycle parameter configuration data through the freeze-thaw parameter setting unit, and outputs verified ultrasonic and freeze-thaw initial control data through the parameter verification unit. The collaborative cell wall disruption processing module receives the verified ultrasound and freeze-thaw initial control data, generates collaborative control commands through the collaborative control parameter matching unit, applies physical processing to the edible fungi sample using the ultrasound freeze-thaw execution unit, and collects and outputs cell wall disruption status monitoring data through the real-time status monitoring unit. The cell wall disruption effect evaluation module receives the cell wall disruption status monitoring data, quantifies the cell wall disruption effect indicators through the evaluation parameter calculation unit, evaluates the cell wall disruption process through the trend analysis unit, and outputs cell wall disruption effect evaluation parameter data and trend report through the evaluation matrix generation unit. The dynamic optimization module receives the cell wall disruption effect evaluation parameter data and trend report, optimizes the control parameters of ultrasound and freeze-thaw in real time through the parameter adaptive adjustment unit, verifies the feasibility and effectiveness of the adjusted parameters through the optimization verification unit, and outputs the optimized ultrasound freeze-thaw synergistic cell wall disruption control data through the control command update unit. The result processing and output module receives the optimized control data and performs the final cell wall disruption process. It acquires the cell wall disruption results through the final result acquisition unit, generates standardized cell wall disruption result data using the data integration unit, and outputs the final cell wall disruption effect report through the report generation unit.

[0016] Compared with the prior art, the present invention provides a system and method for synergistic ultrasonic freeze-thaw cell disruption of edible fungi, which has the following beneficial effects: 1. In this invention, initial cell parameter data and environmental parameter data of edible fungi samples are collected, and data preprocessing and standardized format conversion are performed to ensure the accuracy and consistency of the parameter data. At the same time, the ultrasonic processing parameters and freeze-thaw cycle parameters are initialized based on the parameter data. Optimized parameters are generated through frequency matching algorithm and parameter mapping model to improve the pertinence and reliability of parameter settings.

[0017] 2. In this invention, by matching control parameters of ultrasound and freeze-thaw synergy, a matching matrix is ​​established and synergistic control parameters are generated using a synergistic optimization algorithm to achieve synergistic processing of edible fungi samples. At the same time, the cell wall breaking status is monitored in real time. Data is collected by a cell wall breaking monitoring sensor and compared with a preset threshold, so that the cell wall breaking process can be monitored in real time and abnormalities can be handled, ensuring the stability and safety of the cell wall breaking process.

[0018] 3. In this invention, by calculating and analyzing the parameters for evaluating the cell wall disruption effect, the ultrasonic treatment parameters and freeze-thaw cycle parameters are dynamically adjusted. An adaptive control algorithm is used for optimization, and the feasibility of the adjusted parameters is verified. This enables the cell wall disruption process to be adaptively optimized, improving the efficiency and quality of cell wall disruption. Finally, reliable cell wall disruption result data is output through the cell wall disruption treatment. Attached Figure Description

[0019] Figure 1 This is a flowchart of the ultrasonic freeze-thaw synergistic cell wall disruption method for edible fungi according to the present invention; Figure 2 This is a schematic diagram of the ultrasonic freeze-thaw synergistic cell wall disruption system for edible fungi of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Specific embodiment: A system and method for synergistic ultrasonic freeze-thaw cell disruption in edible fungi, the method comprising the following steps: S1. Collect initial cell parameter data and environmental parameter data of edible fungi samples; S2. Based on the initial cell parameter data and environmental parameter data, initialize the ultrasonic processing parameters and generate ultrasonic processing parameter configuration data; S3. Initialize the freeze-thaw cycle parameters based on the initial cell parameter data and environmental parameter data, and generate freeze-thaw cycle parameter configuration data; S4. Based on the ultrasonic processing parameter configuration data and the freeze-thaw cycle parameter configuration data, perform ultrasonic and freeze-thaw coordinated control parameter matching processing to generate coordinated control parameter matching data. S5. Based on the coordinated control parameter matching data, ultrasonic freeze-thaw coordinated cell wall disruption is performed on edible fungi samples, and the cell wall disruption status is monitored in real time and cell wall disruption status monitoring data is generated. S6. Calculate cell wall disruption effect evaluation parameters based on cell wall disruption status monitoring data, and generate cell wall disruption effect evaluation parameter data; S7. Based on the cell wall disruption effect evaluation parameter data, dynamically adjust the ultrasonic treatment parameters and freeze-thaw cycle parameters to generate optimized ultrasonic freeze-thaw synergistic cell wall disruption control data. S8. Based on the optimized ultrasonic freeze-thaw synergistic cell wall disruption control data, perform final cell wall disruption processing on edible fungi samples and output cell wall disruption result data.

[0022] Initial cell parameter data and environmental parameter data of edible fungi samples were collected in S1, including the following steps: S11. Obtain data on cell wall thickness, cell membrane permeability, and cell contents concentration of edible fungi samples using edible fungi sample collection equipment, and generate initial cell parameter data. S12. Collect temperature, humidity and air pressure data of the edible fungus treatment environment through environmental sensors, and generate environmental parameter data; S13. Import the initial cell parameter data and environmental parameter data into the edible fungi processing and monitoring platform, perform data preprocessing and standardized format conversion, and generate standardized edible fungi sample parameter data. First, initial cell parameter data and environmental parameter data are received through the data interface of the edible fungi processing and monitoring platform. This data comes from different sensors and devices, resulting in inconsistent formats. The preprocessing stage includes data cleaning to remove obvious outliers, threshold filtering to set the normal range for cell wall thickness as 0.1-10 micrometers (data outside this range is considered invalid), filling in missing values ​​using historical data mean imputation, and data denoising using a moving average filter with a window size of 5 data points. Standardization involves converting the data to the platform's internal standard JSON format, where each data point includes a timestamp, parameter type, value, and unit fields. Simultaneously, numerical standardization is performed, converting parameters of different dimensions to dimensionless values ​​using the Z-score standardization method, with the formula: ; in This represents the standardized numerical value. Indicates the original parameter value. This represents the mean of the historical data for this parameter. This represents the standard deviation; this process ensures data consistency and processability, providing high-quality input for subsequent steps.

[0023] The initialization settings for ultrasonic processing parameters in S2 include the following steps: S21. Obtain standardized edible fungi sample parameter data; S22. Use a frequency matching algorithm to search for ultrasonic frequency, power and action time parameters corresponding to the standardized edible fungi sample parameter data in the ultrasonic parameter database, and generate ultrasonic processing parameter configuration data. First, the database stores the optimal ultrasonic frequency ranges for different types of edible fungi. This database was constructed using historical experimental data and includes the mapping relationship between frequency, power, treatment time, and cell wall disruption effect. The algorithm uses cosine similarity to calculate the matching degree between the standardized edible fungi sample parameter data and each entry in the database. The specific formula is as follows: Similarity ; Where A represents the parameter vector of the input sample, and B represents the parameter vector in the database; The algorithm searches the database entries with the highest similarity and extracts the corresponding ultrasonic frequency, power and action time as output parameters. If the matching degree is lower than the threshold, the default parameters are enabled and the manual intervention process is triggered. S23. Perform redundancy verification and outlier filtering on the ultrasonic processing parameter configuration data to generate verified ultrasonic processing parameter configuration data. Redundancy checks and outlier filtering are performed on the edible fungi processing monitoring platform via automated scripts. Redundancy checks detect duplicate or contradictory entries in the parameter configuration data, and if the ultrasonic frequency is set multiple times, the algorithm retains the latest value and logs it. Outlier filtering uses a box plot method to calculate the quartiles of the parameter data and define an outlier range. Any parameter value outside this range is considered an anomaly and is replaced with the mean of adjacent normal values ​​and directly removed. This process ensures the robustness of the parameter data and prevents subsequent processing from failing due to data errors.

[0024] The initialization settings for freeze-thaw cycle parameters in S3 include the following steps: S31. Obtain standardized edible fungi sample parameter data; S32. Based on the freeze-thaw parameter mapping model, the standardized edible fungi sample parameter data is converted into freezing rate, thawing rate and number of cycles parameters of the freeze-thaw cycle, and freeze-thaw cycle parameter configuration data is generated. The model training data comes from historical experiments, with standardized edible fungus sample parameter data as input and freeze-thaw cycle parameters as output; the model formula can be expressed as: ; in, Indicates freeze-thaw parameters. Represents the input parameter vector. Represents kernel function mapping, and For model weights and biases; S33. Use numerical simulation algorithm to verify the stability of freeze-thaw cycle parameter configuration data and generate verified freeze-thaw cycle parameter configuration data. The numerical simulation algorithm uses finite element analysis to simulate the effects of freeze-thaw cycles on cells. The algorithm models edible fungus cells as a porous medium and solves the heat conduction and mass transfer equations. The heat conduction equation is expressed as: ; in Indicates temperature. Indicates time, Indicates the thermal diffusivity. This represents the Laplacian operator; the simulation runs in a virtual environment, with freeze-thaw parameters as input and the output being the internal temperature distribution of cells and the formation of ice crystals; the algorithm verifies the stability of the parameters by checking whether there are abrupt changes in temperature gradients or whether the cell rupture risk index exceeds the threshold in the simulation results.

[0025] The matching process for ultrasound and freeze-thaw synergistic control parameters in S4 includes the following steps: S41. Obtain the verified ultrasonic processing parameter configuration data and the verified freeze-thaw cycle parameter configuration data. S42. Establish a matching matrix for ultrasound and freeze-thaw synergistic control parameters, and align and combine ultrasound parameters and freeze-thaw parameters according to time series. The construction of the collaborative control parameter matching matrix is ​​based on time series alignment technology. First, the ultrasonic parameters and freeze-thaw parameters are aligned using millisecond-level timestamps to form a matrix where rows represent time points and columns represent parameter types. The alignment employs a dynamic time warping algorithm to minimize the cumulative distance between the two sequences, as shown in the formula: ; in This represents the cumulative distance between sequences U and V at points i and j. Point and The distance between them This represents the i-th data point in the ultrasound parameter sequence U. This represents the j-th data point in the freeze-thaw parameter sequence V; after alignment, the parameters are combined into a cooperative matrix, which is applicable to subsequent optimization. S43. Use a collaborative optimization algorithm to calculate the optimal time interval and energy distribution ratio between ultrasound and the freeze-thaw process, and generate collaborative control parameter matching data. The collaborative optimization algorithm employs particle swarm optimization, aiming to minimize cell wall disruption time and maximize content extraction rate; the fitness function is defined as follows:

[0026] in This indicates the updated parameter value. Indicates the original parameter value. Indicates the learning rate. The state change is represented by the particle position, which represents the combination of ultrasonic and freeze-thaw parameters. The algorithm iteratively updates the position and velocity to search for the optimal solution. The core parameters of the algorithm are set as follows: Population size: typically set to 20-50 particles; Learning factors: Cognitive factors and social factors typically take values ​​within the range [1.5, 2.5]. Inertia weight: A linear decreasing strategy is adopted, with an initial value of 0.9 and a final value of 0.4. Number of iterations: Depending on the optimization accuracy and efficiency requirements, it is usually set between 100 and 500 iterations; S44. Real-time dynamic updates of the coordinated control parameter matching data.

[0027] The S5 process, involving ultrasonic freeze-thaw synergistic cell disruption, includes the following steps: S51. Based on the coordinated control parameter matching data, the ultrasonic generator and freeze-thaw equipment are controlled to perform coordinated processing of edible fungi samples; the coordinated processing is executed synchronously by the ultrasonic generator and the freeze-thaw equipment. The platform sends control commands to trigger the ultrasonic waves at a specific phase of the freeze-thaw cycle, achieving energy superposition; the processing process records sensor data in real time, ensuring synchronization accuracy at the millisecond level; S52. Real-time data on the degree of cell wall rupture, the rate of release of cell contents, and changes in cell morphology are collected using a cell wall disruption monitoring sensor. S53. Compare the real-time collected data with the preset threshold to generate cell wall breakage status monitoring data; the algorithm uses if-else logic: if the data is greater than or equal to the threshold, it is considered normal; otherwise, a warning is triggered. S54. When the monitored data is abnormal, the automatic pause mechanism is triggered and an exception handling log is generated.

[0028] The calculation of parameters for evaluating the cell wall breaking effect in S6 includes the following steps: S61. Obtain cell wall disruption monitoring data; S62. Calculate the cell wall disruption efficiency index, the content extraction rate index, and the cell integrity loss index; Cell wall disruption efficiency index: ; in Indicator of cell wall disruption efficiency. This indicates the number of cells identified as having undergone cell wall disruption at the end of the entire treatment cycle. This indicates the total number of cells in the sample; Content extraction rate metric: ; in This indicates the content extraction rate metric. This indicates the concentration of released contents detected in the treatment solution. This indicates the theoretical total concentration of the contents; Indicators of cell integrity loss: ; in Indicators representing cell integrity loss This represents the average projected area of ​​cells in the treated sample. This represents the average projected area of ​​cells in the original sample before treatment. S63. Use a multi-objective evaluation function to weight and fuse the indicators to generate cell wall breaking effect evaluation parameter data; The weighted fusion uses a linear weighted sum, and the formula is: Overall score = ; in Indicates each indicator, Indicates weight, Represents the total number of indicators involved in the evaluation; S64. Perform trend analysis on the cell wall breaking effect evaluation parameter data and historical data. The trend analysis uses the time series model ARIMA to generate a cell wall breaking effect trend report.

[0029] Adjusting the ultrasonic treatment parameters and freeze-thaw cycle parameters in S7 includes the following steps: S71. Obtain cell wall breaking effect evaluation parameter data and cell wall breaking effect trend report; S72. An adaptive control algorithm is used to dynamically adjust the ultrasonic frequency, power, and freeze-thaw cycle parameters according to the cell wall breaking effect. The adaptive control algorithm uses a PID controller to dynamically adjust parameters to reduce evaluation errors; the algorithm formula is: ; in This indicates the value of the control output at time t. This represents the value of the error signal at time t. Represents the proportional gain coefficient. Represents the integral gain coefficient. Represents the differential gain coefficient; S73. Perform feasibility verification and boundary constraint checks on the adjusted parameters, and generate optimized ultrasonic freeze-thaw synergistic cell wall disruption control data; S74. Record the optimization process to the parameter adjustment database for subsequent machine learning model training.

[0030] The final cell disruption process in S8 includes the following steps: S81. Perform the final cell disruption process based on the optimized ultrasonic freeze-thaw synergistic cell disruption control data; S82. Collect cell debris data, content concentration data, and bioactivity data of edible fungi samples after cell wall disruption using the final testing equipment; S83. Integrate and format the collected data to generate cell wall disruption result data; S84. Upload the cell wall disruption results data to the edible fungi processing supervision platform for long-term storage and sharing.

[0031] The system includes: The sample parameter acquisition module uses the cell parameter acquisition unit to acquire the initial cell attribute data of the edible fungi sample, the environmental parameter acquisition unit to acquire the state data of the processing environment, and the data preprocessing unit to output standardized edible fungi sample parameter data. The ultrasonic freeze-thaw parameter control module receives standardized edible fungi sample parameter data, generates ultrasonic processing parameter configuration data through the ultrasonic parameter setting unit, generates freeze-thaw cycle parameter configuration data through the freeze-thaw parameter setting unit, and outputs verified ultrasonic and freeze-thaw initial control data through the parameter verification unit. The collaborative cell wall disruption processing module receives the verified initial control data of ultrasound and freeze-thaw, generates collaborative control commands through the collaborative control parameter matching unit, applies physical treatment to the edible fungi sample using the ultrasound freeze-thaw execution unit, and collects and outputs cell wall disruption status monitoring data through the real-time status monitoring unit. The cell wall disruption effect evaluation module receives cell wall disruption status monitoring data, quantifies cell wall disruption effect indicators through the evaluation parameter calculation unit, evaluates the cell wall disruption process through the trend analysis unit, and outputs cell wall disruption effect evaluation parameter data and trend report through the evaluation matrix generation unit. The dynamic optimization module receives cell wall disruption effect evaluation parameter data and trend reports, optimizes the control parameters of ultrasound and freeze-thaw in real time through the parameter adaptive adjustment unit, verifies the feasibility and effectiveness of the adjusted parameters through the optimization verification unit, and outputs the optimized ultrasound freeze-thaw synergistic cell wall disruption control data through the control command update unit. The results processing and output module receives optimized control data and performs the final cell wall disruption process. It acquires cell wall disruption results through the final result acquisition unit, generates standardized cell wall disruption result data through the data integration unit, and outputs the final cell wall disruption effect report through the report generation unit.

[0032] The operating steps of this system and method are as follows: First, the method begins with collecting initial cell parameter data and environmental parameter data from edible fungi samples. This step involves acquiring data on cell wall thickness, cell membrane permeability, and cell content concentration using edible fungi sample collection equipment to generate initial cell parameter data. Simultaneously, temperature, humidity, and air pressure data are collected using environmental sensors to generate environmental parameter data. Subsequently, these data are imported into the edible fungi processing and monitoring platform for data preprocessing and standardized format conversion to output standardized edible fungi sample parameter data, ensuring that the parameters are accurate and consistent.

[0033] Next, based on standardized edible fungi sample parameter data, the ultrasonic processing parameters were initialized. A frequency matching algorithm was used to search for matching ultrasonic frequency, power, and action time parameters in the ultrasonic parameter database to generate ultrasonic processing parameter configuration data. Redundancy verification and outlier filtering were performed on this data, and the verified ultrasonic processing parameter configuration data was output. Simultaneously, based on the standardized edible fungi sample parameter data, the freeze-thaw cycle parameters were initialized. Based on the freeze-thaw parameter mapping model, these parameters were converted into freezing rate, thawing rate, and cycle number parameters, generating freeze-thaw cycle parameter configuration data. A numerical simulation algorithm was used to verify the stability of the freeze-thaw cycle parameter configuration data, and the verified freeze-thaw cycle parameter configuration data was output.

[0034] Then, based on the verified ultrasonic processing parameter configuration data and the validated freeze-thaw cycle parameter configuration data, ultrasonic and freeze-thaw coordinated control parameter matching processing is performed to establish a coordinated control parameter matching matrix. The ultrasonic parameters and freeze-thaw parameters are aligned and combined according to the time series. The optimal time interval and energy distribution ratio are calculated using a coordinated optimization algorithm to generate coordinated control parameter matching data, which is then dynamically updated in real time. Based on the coordinated control parameter matching data, edible fungi samples undergo ultrasonic freeze-thaw coordinated cell wall disruption treatment. The ultrasonic generator and freeze-thaw equipment work together, and cell wall disruption monitoring sensors collect data on the degree of cell wall disruption, the rate of release of cell contents, and changes in cell morphology in real time. This data is compared with preset thresholds to generate cell wall disruption status monitoring data, and an automatic pause mechanism and anomaly handling log are triggered when abnormalities are detected.

[0035] Subsequently, based on cell wall disruption monitoring data, parameters for evaluating the disruption effect were calculated, including cell wall disruption efficiency, content extraction rate, and cell integrity loss. A multi-objective evaluation function was used for weighted fusion to generate disruption effect evaluation parameter data. Trend analysis was then performed with historical data to output a disruption effect trend report. Based on the disruption effect evaluation parameter data and trend report, the ultrasonic treatment parameters and freeze-thaw cycle parameters were dynamically adjusted. An adaptive control algorithm was used to optimize the ultrasonic frequency, power, and freeze-thaw cycle parameters. The feasibility of the adjusted parameters was verified and boundary constraints were checked, generating optimized ultrasonic-freeze-thaw coordinated cell wall disruption control data. The optimization process was recorded in the parameter adjustment database for machine learning model training.

[0036] Finally, based on the optimized ultrasonic freeze-thaw synergistic cell disruption control data, the final cell disruption process is executed. Cell debris data, content concentration data, and bioactivity data are collected through final detection equipment. The data is integrated and formatted for output, generating cell disruption result data, which is then uploaded to the edible fungi processing and monitoring platform for long-term storage and sharing. In terms of the system, the edible fungi ultrasonic freeze-thaw synergistic cell disruption system includes a sample parameter acquisition module, an ultrasonic freeze-thaw parameter control module, a synergistic cell disruption processing module, a cell disruption effect evaluation module, a dynamic optimization module, and a result processing and output module. Each module corresponds to a specific methodological step, achieving full automation of the entire process from parameter acquisition, parameter control, synergistic processing, effect evaluation, dynamic optimization, and result output, ensuring a highly targeted and reliable cell disruption process.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for synergistic cell disruption via ultrasonic freeze-thaw cycles in edible fungi, characterized by: The method comprises the following steps: S1, collecting initial cell parameter data and environmental parameter data of edible fungus samples; S2, initializing ultrasonic treatment parameters based on the initial cell parameter data and environmental parameter data, and generating ultrasonic treatment parameter configuration data; S3, initializing freeze-thaw cycle parameters according to the initial cell parameter data and environmental parameter data, and generating freeze-thaw cycle parameter configuration data; S4, performing ultrasonic and freeze-thaw synergistic control parameter matching processing according to the ultrasonic treatment parameter configuration data and freeze-thaw cycle parameter configuration data, and generating synergistic control parameter matching data; S5, performing ultrasonic freeze-thaw synergistic cell wall breaking processing on edible fungus samples based on the synergistic control parameter matching data, monitoring the cell wall breaking state in real time, and generating cell wall breaking state monitoring data; S6, calculating breaking effect evaluation parameters according to the cell wall breaking state monitoring data, and generating breaking effect evaluation parameter data; S7, dynamically adjusting ultrasonic treatment parameters and freeze-thaw cycle parameters according to the breaking effect evaluation parameter data, and generating optimized ultrasonic freeze-thaw synergistic cell wall breaking control data; S8, performing final cell wall breaking processing on edible fungus samples based on the optimized ultrasonic freeze-thaw synergistic cell wall breaking control data, and outputting cell wall breaking result data.

2. The method according to claim 1, wherein the method is characterized by: In S1, collecting initial cell parameter data and environmental parameter data of edible fungus samples, comprising the following steps: S11, obtaining cell wall thickness data, cell membrane permeability data and cell content concentration data of edible fungus samples through edible fungus sample collection equipment, and generating initial cell parameter data; S12, collecting temperature data, humidity data and air pressure data of the edible fungus processing environment through environmental sensors, and generating environmental parameter data; S13, importing the initial cell parameter data and environmental parameter data into the edible fungus processing supervision platform, performing data preprocessing and standardizing format conversion, and generating standardized edible fungus sample parameter data.

3. The method according to claim 1, wherein the method is characterized by: In S2, ultrasonic treatment parameter initialization setting, comprising the following steps: S21, obtaining standardized edible fungus sample parameter data; S22, searching for ultrasonic frequency, power and action time parameters corresponding to the standardized edible fungus sample parameter data in the ultrasonic parameter database using a frequency matching algorithm, and generating ultrasonic treatment parameter configuration data; S23, performing redundancy check and outlier filtering on the ultrasonic treatment parameter configuration data, and generating checked ultrasonic treatment parameter configuration data.

4. The method according to claim 1, wherein the method is characterized by: In S3, freeze-thaw cycle parameter initialization setting, comprising the following steps: S31, obtaining standardized edible fungus sample parameter data; S32, converting the standardized edible fungus sample parameter data into freeze-thaw cycle freezing rate, thawing rate and cycle number parameters based on a freeze-thaw parameter mapping model, and generating freeze-thaw cycle parameter configuration data; S33, performing stability verification on the freeze-thaw cycle parameter configuration data using a numerical simulation algorithm, and generating verified freeze-thaw cycle parameter configuration data.

5. The method according to claim 1, wherein the method is characterized by: In S4, ultrasonic and freeze-thaw synergistic control parameter matching processing, comprising the following steps: S41, obtain the verified ultrasonic treatment parameter configuration data and the verified freeze-thaw cycle parameter configuration data; S42, establish an ultrasonic and freeze-thaw collaborative control parameter matching matrix, align and combine the ultrasonic parameter and the freeze-thaw parameter according to a time sequence; S43, calculate the optimal time interval and energy distribution ratio of the ultrasonic wave and the freeze-thaw process by using a collaborative optimization algorithm, and generate collaborative control parameter matching data; S44, real-time dynamically update the collaborative control parameter matching data.

6. The method according to claim 1, wherein the method is characterized by: The ultrasonic and freeze-thaw collaborative cell wall breaking treatment in S5 includes the following steps: S51, control the ultrasonic generator and the freeze-thaw equipment to collaboratively treat the edible fungus sample based on the collaborative control parameter matching data; S52, real-time collect cell wall breaking degree data, cell content release rate data and cell morphology change data through a cell wall breaking monitoring sensor; S53, compare the real-time collected data with a preset threshold to generate cell wall breaking state monitoring data; S54, when the monitoring data is abnormal, trigger an automatic pause mechanism and generate an abnormal processing log.

7. The method according to claim 1, wherein the method is characterized by: The wall breaking effect evaluation parameter calculation in S6 includes the following steps: S61, obtain the cell wall breaking state monitoring data; S62, calculate cell wall breaking efficiency indicators, content extraction rate indicators and cell integrity loss indicators; S63, use a multi-objective evaluation function to weight and fuse the indicators to generate wall breaking effect evaluation parameter data; S64, perform trend analysis on the wall breaking effect evaluation parameter data and historical data to generate a wall breaking effect trend report.

8. The method according to claim 1, wherein the method is characterized by: The state adjustment ultrasonic treatment parameter and freeze-thaw cycle parameter in S7 include the following steps: S71, obtain the wall breaking effect evaluation parameter data and the wall breaking effect trend report; S72, use an adaptive control algorithm to dynamically adjust the ultrasonic frequency, power and freeze-thaw cycle parameters according to the wall breaking effect; S73, perform feasibility verification and boundary constraint check on the adjusted parameters to generate optimized ultrasonic and freeze-thaw collaborative cell wall breaking control data; S74, record the optimization process to a parameter adjustment database for subsequent machine learning model training.

9. The method according to claim 1, wherein the method is characterized by: The final cell wall breaking treatment in S8 includes the following steps: S81, perform the final wall breaking treatment based on the optimized ultrasonic and freeze-thaw collaborative cell wall breaking control data; S82, collect cell debris data, content concentration data and biological activity data of the wall broken edible fungus sample through a final detection device; S83, integrate and format the collected data to generate cell wall breaking result data; S84, upload the cell wall breaking result data to an edible fungus processing supervision platform for long-term storage and sharing.

10. An edible mushroom ultrasonic freeze-thaw and cell wall breaking system for implementing the edible mushroom ultrasonic freeze-thaw and cell wall breaking method of any one of claims 1-9, characterized in that: The system includes: A sample parameter acquisition module acquires initial cell attribute data of the edible fungus sample by using a cell parameter acquisition unit, acquires state data of a processing environment by using an environment parameter acquisition unit, and outputs standardized edible fungus sample parameter data through a data preprocessing unit; An ultrasonic freeze-thaw parameter control module receives the standardized edible mushroom sample parameter data, generates ultrasonic treatment parameter configuration data through an ultrasonic parameter setting unit, generates freeze-thaw cycle parameter configuration data using a freeze-thaw parameter setting unit, and outputs verified ultrasonic and freeze-thaw initial control data through a parameter verification unit; A synergistic cell wall breaking processing module receives the verified ultrasonic and freeze-thaw initial control data, generates synergistic control instructions through a synergistic control parameter matching unit, applies physical treatment to the edible mushroom sample using an ultrasonic freeze-thaw execution unit, and collects and outputs cell wall breaking state monitoring data through a real-time state monitoring unit; A cell wall breaking effect evaluation module receives the cell wall breaking state monitoring data, quantifies cell wall breaking effect indicators through an evaluation parameter calculation unit, evaluates the cell wall breaking process using a trend analysis unit, and outputs cell wall breaking effect evaluation parameter data and trend reports through an evaluation matrix generation unit; A dynamic optimization module receives the cell wall breaking effect evaluation parameter data and trend reports, optimizes ultrasonic and freeze-thaw control parameters in real time through a parameter self-adaptive adjustment unit, verifies the feasibility and effectiveness of the adjusted parameters using an optimization verification unit, and outputs optimized ultrasonic freeze-thaw synergistic cell wall breaking control data through a control instruction update unit; A result processing and output module receives the optimized control data and performs final cell wall breaking processing, obtains cell wall breaking results through a final result collection unit, generates standardized cell wall breaking result data using a data integration unit, and outputs a final cell wall breaking effect report through a report generation unit.

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