Liquid nitrogen flow automatic adjusting system and application thereof in abalone preservation

Abstract

The invention relates to the technical field of automatic control of an aquatic product quick-freezing process, in particular to a liquid nitrogen flow automatic adjusting system and application thereof in abalone preservation, and the system comprises a data acquisition module which is used for acquiring physical parameters of a target aquatic product in real time; the thermophysical property resolving module is used for determining the instantaneous thermal conductivity of the aquatic product; the state prediction module is used for predicting and obtaining a next-moment system state including the next-moment core temperature and the next-moment surface temperature; the risk prediction module is used for determining a predicted heat stress risk index at the next moment; the control decision module is used for solving the minimum value of a preset cost function to determine the optimal liquid nitrogen injection intensity by combining the deviation, predicted by the state prediction module, between the core temperature at the next moment and the preset target temperature and the predicted heat stress risk index determined by the risk prediction module; the method gets rid of dependence on an immobilized cooling procedure, and ensures the stability and high quality of the quick-freezing fresh-keeping effect.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of automatic control of a quick-freezing process of aquatic products, in particular to a liquid nitrogen flow automatic regulation system and application thereof in abalone preservation. BACKGROUND

[0002] In the field of low-temperature preservation of aquatic products, especially in precise temperature control applications such as liquid nitrogen quick-freezing, the precision of the control strategy directly determines the preservation quality and economic benefits; the existing quick-freezing control technology generally adopts an open-loop or semi-closed-loop control scheme based on solidification thermal physical parameters, which sets a universal cooling program according to the empirical model of the sample, and cannot adjust in real time according to the individual differences of the processing objects and the dynamic changes in the freezing process.

[0003] This technical path has significant technical bottlenecks. The inherent individual heterogeneity of aquatic products, such as abalone, the differences in size, water content and tissue composition, make the fixed thermal physical parameters unable to accurately reflect the real physical characteristics of any sample, resulting in inaccurate control; during the phase change process, the key parameters such as the thermal conductivity of the sample will change dramatically and nonlinearly, and the static control model cannot capture and adapt to this dynamic process. This inaccurate and non-dynamic control method is difficult to achieve fine adjustment of the cooling rate, and is prone to cause excessive thermal stress and ice crystal recrystallization, causing irreversible damage to the cell structure, thereby affecting the preservation quality and subsequent application value, and also causing unnecessary consumption of liquid nitrogen, increasing the operating cost; therefore, how to break through the limitations of the static model and realize an accurate, dynamic and personalized closed-loop control that can adapt to individual heterogeneity and process dynamics to synergistically improve the preservation quality and energy efficiency is a key technical problem to be solved. SUMMARY

[0004] The purpose of the present application is to provide a liquid nitrogen flow automatic regulation system and its application in abalone preservation, which can adapt to the individual heterogeneity of aquatic products and the dynamic nonlinear characteristics in the phase change process, realize accurate, dynamic and personalized control of the cooling process, and synergistically improve the preservation quality and energy efficiency. Specifically, the technical scheme of the present application is as follows:

[0005] The liquid nitrogen flow automatic regulation system comprises:

[0006] The data acquisition module is used to acquire the physical parameters of the target aquatic product in real time, and the physical parameters include core temperature, surface temperature, tissue impedance modulus and sound wave propagation speed;

[0007] The thermal physical property calculation module is used to determine the instantaneous thermal conductivity of the aquatic product based on the tissue impedance modulus and the sound wave propagation speed acquired by the data acquisition module;

[0008] The state prediction module is used to combine the core temperature and surface temperature obtained by the data acquisition module and the instantaneous thermal conductivity determined by the thermal property calculation module, and use the extended Kalman filter model to predict the system state at the next moment, which includes the core temperature and surface temperature at the next moment.

[0009] The risk prediction module is used to determine the predicted heat stress risk index for the next moment based on the system state at the next moment obtained by the state prediction module.

[0010] The control decision module is used to combine the deviation between the core temperature predicted by the state prediction module and the preset target temperature at the next moment, and the predicted heat stress risk index determined by the risk prediction module, to solve for the minimum value of the preset cost function to determine the optimal liquid nitrogen injection intensity.

[0011] Optionally, the thermal property calculation module is specifically used for:

[0012] The instantaneous ice crystal integral is determined based on the tissue impedance modulus, the preset unfrozen state impedance modulus, and the fully frozen state impedance modulus.

[0013] The instantaneous thermal conductivity is calculated based on a parallel thermal resistance model by combining the instantaneous ice crystal integral, the sound wave propagation velocity, and the preset acoustic-thermal coupling coefficient.

[0014] Optionally, the state prediction module is specifically used for:

[0015] Construct the current system state vector, which includes the core temperature and the surface temperature;

[0016] The current system state vector is predicted by using a nonlinear state transition equation incorporating instantaneous thermal conductivity.

[0017] The prediction results are corrected by using an observation equation that incorporates real-time sensor measurements to output the system state at the next moment.

[0018] Optionally, the risk prediction module is specifically used for:

[0019] The dynamic thermal gradient is calculated and predicted based on the difference between the core temperature and the surface temperature at the next time step.

[0020] Based on the predicted dynamic thermal gradient, the predicted proportional risk term is determined.

[0021] Based on the predicted rate of change of the dynamic thermal gradient, the predicted differential risk term is determined;

[0022] The predicted proportional risk term and the predicted differential risk term are weighted and summed to obtain the predicted heat stress risk index.

[0023] Optionally, the predicted proportional risk term is the ratio of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold.

[0024] Optionally, the predicted differential risk term is a ratio of a predicted rate of change of the dynamic thermal gradient to a preset peak dynamic thermal gradient threshold, multiplied by a preset system thermal response time constant.

[0025] Optionally, the cost function configured in the control decision module includes an operation cost term and a risk penalty term; the operation cost term is directly proportional to the liquid nitrogen injection intensity; and the risk penalty term is determined by:

[0026] comparing the predicted thermal stress risk index with a preset risk reference threshold;

[0027] if the predicted thermal stress risk index is greater than the risk reference threshold, determining the risk penalty term based on a difference between the two;

[0028] if the predicted thermal stress risk index is not greater than the risk reference threshold, determining the risk penalty term as zero. Optionally, the control decision module is specifically used for:

[0029] taking the liquid nitrogen injection intensity as an optimization variable;

[0030] solving the liquid nitrogen injection intensity that minimizes the cost function by a numerical optimization algorithm, and outputting the intensity as an optimal liquid nitrogen injection intensity.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] 1. The present application dynamically calculates the instantaneous thermal conductivity of aquatic products in the freezing process by real-time monitoring of physical quantities such as tissue impedance and sound speed, which overcomes the defects of traditional technologies relying on solidification and universal thermal physical parameters, can accurately reflect the real physical characteristics of different individuals and different freezing stages, provides a reliable dynamic model basis for realizing personalized precise control, and significantly improves the accuracy of control;

[0033] 2. The present application is based on an extended Kalman filter model, which can accurately predict the internal temperature distribution of the sample at the next time according to the current state, and innovatively constructs a risk assessment model that comprehensively considers the amplitude and rate of change of the thermal gradient, realizes the prospective quantification of the future thermal stress damage risk, and effectively avoids cell damage caused by excessive temperature difference, which significantly improves the preservation quality of the sample;

[0034] 3. The present application unifies the preservation quality and the operation cost into an optimal control framework, constructs a cost function including a risk penalty term and an energy consumption cost term, and the control system can automatically find and execute the most energy-saving liquid nitrogen injection strategy under the premise of ensuring the safety of the sample and not triggering the risk threshold, which avoids the waste of liquid nitrogen caused by excessive cooling and significantly reduces the operation cost;

[0035] 4. The application constructs a complete closed-loop control system from real-time sensing, dynamic modeling, future prediction to optimization decision, which can automatically adapt to the individual differences of aquatic products and the nonlinear dynamic changes of thermal properties in the freezing process, providing each sample with an individualized optimal cooling path throughout the process. This high level of adaptability and automation eliminates the dependence on fixed cooling programs, ensuring the stability and high quality of quick-freezing preservation. BRIEF DESCRIPTION OF DRAWINGS

[0036] The application will be further explained in conjunction with the accompanying drawings and examples:

[0037] Figure 1 is a structural diagram of the system of the application. DETAILED DESCRIPTION

[0038] To make the purpose, technical solutions and advantages of the application clearer, the application will be further described in conjunction with specific examples.

[0039] Example 1:

[0040] Please refer to Figure 1 , the liquid nitrogen flow automatic regulation system comprises:

[0041] A data acquisition module is used to acquire physical parameters of the target aquatic product in real time, including core temperature, surface temperature, tissue impedance modulus and sound wave propagation speed.

[0042] A thermal property solving module is used to determine the instantaneous thermal conductivity of the aquatic product based on the tissue impedance modulus and sound wave propagation speed acquired by the data acquisition module.

[0043] A state prediction module is used to predict the next time system state including the next time core temperature and the next time surface temperature by using an extended Kalman filter model, in combination with the core temperature and surface temperature acquired by the data acquisition module and the instantaneous thermal conductivity determined by the thermal property solving module.

[0044] A risk prediction module is used to determine the predicted thermal stress risk index of the next time based on the next time system state obtained by the state prediction module.

[0045] A control decision module is used to solve the minimum value of a preset cost function to determine the optimal liquid nitrogen injection intensity, in combination with the deviation of the next time core temperature predicted by the state prediction module and the preset target temperature, and the predicted thermal stress risk index determined by the risk prediction module.

[0046] This invention provides an automatic liquid nitrogen flow rate regulation system to address the technical problem in existing technologies where the thermophysical parameters are fixed due to the individual heterogeneity of aquatic products, making it impossible to achieve precise, dynamic, and personalized control of the cooling process. This system is applied to the liquid nitrogen quick-freezing and preservation process of abalone. Through a combination of multimodal sensing, dynamic modeling, and predictive control, it achieves optimized control of liquid nitrogen consumption while ensuring preservation quality. The system constitutes a closed-loop technology, including a data acquisition module, a thermophysical property calculation module, a state prediction module, a risk prediction module, and a control decision module.

[0047] The data acquisition module provides real-time, multi-dimensional physical state input for the analysis, prediction, and control of the entire system. This module utilizes high-frequency acoustic transducers deployed on the inner wall of the preservation chamber, thermocouples implanted on the surface of the abalone samples, thermocouples implanted in the sample core, and micro-impedance electrodes implanted in the tissue. This module can synchronously and in real-time acquire four physical parameters of the target aquatic product: core temperature... Surface temperature Tissue impedance modulus and the speed of sound wave propagation These four parameters form the basis for a comprehensive characterization of the internal thermophysical state of the sample.

[0048] The thermophysical property calculation module overcomes the uncertainty of thermodynamic parameters caused by the heterogeneity of biological individuals, dynamically calculating instantaneous thermophysical parameters reflecting the current state of the sample; this module receives tissue impedance modulus values ​​provided by the data acquisition module. With the speed of sound wave propagation The instantaneous thermal conductivity of the sample is calculated in real time using a built-in physical model. Instantaneous thermal conductivity The effective thermal conductivity of an organism at a specific time t is a core parameter that provides accurate and dynamically changing information for predicting subsequent heat transfer processes. It is based on real-time sensor data. and Calculated online;

[0049] The state prediction module is used to accurately predict the future evolution trend of the system based on the current state, providing a decision-making basis for implementing feedforward optimization control. The core of this module is an extended Kalman filter model; it integrates the core temperature data acquired by the data acquisition module. With surface temperature The real-time measured values, and the dynamically changing instantaneous thermal conductivity provided by the thermal property calculation module. This model can accurately predict the core temperature at the next moment. With the surface temperature at the next moment The system state at the next moment ;

[0050] The risk prediction module quantifies complex cell damage risks into a metric that can be used for optimization calculations; this module is based on the system state at the next time step obtained by the state prediction module. By using a risk model that comprehensively considers the magnitude and rate of change of the thermal gradient, the predicted heat stress risk index for the next moment is determined. Predicting the heat stress risk index It refers to the quantitative assessment of the risk of irreversible cell damage to a sample due to excessive internal and external temperature differences or rapid changes within a future control period. It directly incorporates process safety into the scope of control decision-making and is based on the calculation results of predicted temperature conditions.

[0051] The control decision module, as the final execution decision-making link of the entire closed-loop system, dynamically optimizes to determine the best control output. This module weighs two core objectives within a unified cost function: reducing the core temperature predicted by the state prediction module for the next time step. With preset target temperature The deviation between these parameters ensures cooling efficiency; the predicted thermal stress risk index is determined by the risk prediction module. As a constraint, the preservation quality is ensured; ultimately, the optimal liquid nitrogen injection intensity is determined by solving for the minimum value of a preset cost function. ;

[0052] The system may further include a fault diagnosis module, which can monitor the rationality of the data of each sensor in real time through preset thresholds or historical data models. When an abnormal sensor data or communication interruption is detected, the system can automatically switch to a degraded control mode based on the remaining effective sensors, or start a preset safety shutdown procedure to ensure the safety of aquatic products and equipment, thereby improving the overall robustness of the system.

[0053] The embodiment builds a closed loop from real-time sensing, dynamic modeling, future prediction to optimization control through the cooperative work of the above modules; it no longer depends on static and universal thermal physical parameters, but performs personalized parameter calculation and state prediction for each aquatic product at each moment of the cooling process; such design enables the system to accurately foresee and avoid the risk of cell damage caused by excessive thermal gradient, while accurately controlling the liquid nitrogen flow according to actual needs, thereby solving the contradiction between preservation quality and energy efficiency, and realizing high-quality, high-efficiency and personalized automatic control of the quick freezing process; it should be noted that the embodiment uses a core-surface two-point model to represent the overall thermal state of the sample, and aggregates the spatially varying physical parameters into a single instantaneous effective value, which is an effective simplification of the complex three-dimensional physical process under the premise of ensuring the feasibility of real-time control calculation, and is suitable for application scenarios that mainly focus on overall cooling rate and macroscopic temperature gradient.

[0054] Embodiment 2:

[0055] The thermal physical calculation module is specifically used for:

[0056] determining the instantaneous ice crystal volume fraction based on the tissue impedance modulus, the preset unfrozen state impedance modulus and the completely frozen state impedance modulus;

[0057] combining the instantaneous ice crystal volume fraction, the acoustic wave propagation speed and the preset acoustic-thermal coupling coefficient, and calculating the instantaneous thermal conductivity based on the parallel thermal resistance model.

[0058] To specifically describe the thermal physical calculation module described in embodiment 1, the implementation mode of the module aims to improve the accuracy of the calculated instantaneous thermal conductivity ;

[0059] The module calculates the instantaneous ice crystal volume fraction based on the semi-empirical model of the effective medium theory ; the instantaneous ice crystal volume fraction is used to represent the volume percentage of the tissue in which liquid water is converted into solid ice at time t, and it is a key intermediate variable connecting the measurable tissue impedance and the non-directly measurable thermal conductivity, and its calculation method is:

[0060]

[0061] wherein: is the tissue impedance modulus measured at the current time t, and the unit is ohm, which is input in real time by the micro impedance electrode of the data acquisition module;

[0062] is the impedance modulus of the tissue in the completely unfrozen state, and the unit is ohm, which is used as an initial state reference and is measured and obtained before the cooling starts;

[0063] Z0 is the impedance modulus of the tissue in a fully frozen state, in ohm, which serves as the terminal state reference, and is obtained by measurement after the sample is fully frozen or extracted from a reference database;

[0064] is the geometric correction factor, which is a dimensionless parameter determined by offline calibration experiments; to clarify the calibration process, define the calibration data set wherein Zk is the impedance modulus of the representative sample k in a certain frozen state, measured by experiment, is the ice volume fraction in the same state measured by differential scanning calorimetry; by least squares fitting , the parameter γ can be calibrated; this calibration process ensures that the variables and used as input during model operation and the variables used as output are clearly distinguished, ensuring the feasibility of the technical solution;

[0065] To ensure the physical meaning of the instantaneous ice volume fraction , its value range should be between 0 and 1, and the calculation result should be clamped, i.e. if the calculated value is less than 0, take 0, if greater than 1, take 1;

[0066] After obtaining the instantaneous ice volume fraction , to more comprehensively reflect the influence of tissue solidification degree on heat conduction, this embodiment further integrates acoustic information, and calculates the final instantaneous thermal conductivity based on a modified parallel thermal resistance model ; the internal logic is that tissue freezing not only changes the ice-water ratio, but also significantly increases its hardness, and the change in acoustic velocity can well characterize the change in hardness, thereby providing a second-dimensional physical information for the calculation of thermal conductivity, and the calculation method is:

[0067]

[0068] wherein: is the effective thermal conductivity of the tissue at time t, in W / (m⋅K), calculated in the current step and output to the state prediction module;

[0069] and are the reference thermal conductivities of water and ice, respectively, in W / (m⋅K), which are physical constants obtained by consulting standard property data manuals;

[0070] is the volume fraction of ice crystals, dimensionless, which is derived from the calculation in the previous step;

[0071] is the real-time measured sound speed, unit: m / s, which is derived from the real-time input of the high-frequency acoustic transducer of the data acquisition module;

[0072] is the initial sound speed of unfrozen tissue, unit: m / s, which is derived from the measurement before the cooling starts;

[0073] is the acoustic-thermal coupling coefficient, unit: W / (m⋅K) / (m / s), which represents the contribution weight of the sound speed change to the thermal conductivity; this parameter is derived from the multi-physics simulation or joint measurement experiment, and is calibrated by the least square method; to clarify the calibration process, define the calibration data set , where , and are the experimentally measured thermal conductivity, sound speed and ice crystal fraction of the representative sample k in a specific state; by fitting by the least square method, the parameter can be calibrated; this calibration process also ensures the independence of the calibration variables and the model running variables in terms of sign;

[0074] Through this implementation mode, the thermophysical property solving module creatively integrates the sensing data of two different modalities of electricity and sound; by calculating , the easily measured macroscopic quantity of impedance is accurately associated with the microscopic ice crystal generation state; and by introducing the sound speed as a representation of tissue hardness, the classical mixing rule model is modified; this multi-modal data fusion mode improves the robustness and accuracy of the instantaneous thermal conductivity solving, and provides a more reliable physical model basis for subsequent state prediction and control decision.

[0075] Embodiment 3:

[0076] The state prediction module is specifically used for:

[0077] constructing a current system state vector containing the core temperature and the surface temperature;

[0078] adopting a nonlinear state transfer equation incorporating the instantaneous thermal conductivity to predict the current system state vector;

[0079] adopting an observation equation containing the real-time sensor measurement value to correct the prediction result, so as to output the system state at the next time.

[0080] To specifically illustrate the state prediction module described in Embodiment 1, the module uses an extended Kalman filter model to realize accurate prediction of the system state;

[0081] The module constructs a current system state vector containing the core temperature and the surface temperature; the system state vector is a mathematical expression capable of completely describing the thermodynamic state of the system at time t, which serves as the starting point of the iterative calculation of the Kalman filter algorithm, and is derived from the real-time temperature measurement value integrated by the data acquisition module; the vector is defined as , wherein and are the core temperature and the surface temperature at the current time, respectively;

[0082] The module uses a nonlinear state transition equation incorporating the instantaneous thermal conductivity to predict the current system state vector; the technical solution of the present application dynamically incorporates the instantaneous thermal conductivity calculated by the thermal property calculation module into the state transition function based on the discretization of the Fourier heat conduction law as a time-varying parameter; since is a function of temperature, i.e., state , this makes the state transition equation nonlinear, and its mathematical expression is:

[0083]

[0084] wherein is the predicted next-time state, which is derived from the calculation result of the equation; is the current state, which is derived from the filtering output at the last time; is the control input variable of the current control period, which is substituted into the equation as a candidate value when the control decision module is optimized and solved to predict the future state of the system under different control strategies, and is derived from the output of the control decision module; is the instantaneous thermal conductivity provided by the thermal property calculation module; is the process noise vector, and the covariance matrix of the process noise vector is a tunable parameter reflecting the degree of trust in the physical model, which is determined through system identification or empirical tuning;

[0085] The module uses an observation equation containing real-time sensor measurement values to correct the prediction result to output the system state at the next time; to correct the deviation between the model prediction and the actual physical process, the present embodiment uses an observation equation to establish the relationship between the internal state and the sensor measurement value, and the mathematical expression is:

[0086]

[0087] wherein is the actual sensor measurement vector at time t , which is derived from the data acquisition module; is the observation function, which is a linear identity matrix in this case, representing the direct measurement of the core and surface temperatures; is the measurement noise vector, whose covariance matrix is derived from the technical manual of the sensor device; the extended Kalman filter algorithm calculates the Kalman gain between the predicted value and the measured value, and uses the gain to weight and correct the preliminary prediction result, finally outputting an optimal estimated next time system state ;

[0088] By incorporating the dynamically calculated instantaneous thermal conductivity into the state transition equation of the extended Kalman filter as a core parameter, the state prediction module of the embodiment can reflect the dramatic changes in internal heat transfer characteristics caused by sample icing in real time and adaptively, thereby significantly improving the prediction accuracy of the core temperature at the next time; such high-precision prediction capability is a key prerequisite for realizing feedforward, preventive risk control and fine flow regulation.

[0089] Embodiment 4:

[0090] The risk prediction module is specifically used for:

[0091] Based on the difference between the next time core temperature and the next time surface temperature, the predicted dynamic thermal gradient is calculated;

[0092] Based on the predicted dynamic thermal gradient, the predicted proportional risk term is determined;

[0093] Based on the change rate of the predicted dynamic thermal gradient, the predicted differential risk term is determined;

[0094] The predicted proportional risk term and the predicted differential risk term are weighted and summed to obtain the predicted thermal stress risk index;

[0095] The predicted proportional risk term is the ratio of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold value;

[0096] The predicted differential risk term is the ratio of the change rate of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold value, multiplied by the preset system thermal response time constant, i.e. .

[0097] To specifically describe the risk prediction module described in Embodiment 1, the module builds a dynamic evaluation system that can predict and quantify future thermal stress risks;

[0098] The module calculates a predicted dynamic thermal gradient based on a difference between the core temperature at the next time and the surface temperature at the next time; the predicted dynamic thermal gradient The predicted dynamic thermal gradient is used as a basis for evaluating the future thermal stress, and is derived from the system state at the next time output by the state prediction module The calculation is as follows: ;

[0099] The module builds a comprehensive predicted thermal stress risk index calculation model An effective risk assessment not only focuses on the magnitude of the current deviation, but also predicts the speed of the deviation change, so as to realize early warning of the risk; therefore, the embodiment performs weighted summation on the predicted proportional risk term and the predicted differential risk term to obtain the predicted thermal stress risk index, and the calculation method is as follows:

[0100]

[0101] The first term on the right side of the formula is the predicted proportional risk term, and the second term is the predicted differential risk term;

[0102] The predicted proportional risk term is a ratio of the predicted dynamic thermal gradient to a preset peak dynamic thermal gradient threshold, i.e. ; the peak dynamic thermal gradient threshold is a preset upper limit of a safe temperature difference according to the physiological characteristics of biological tissues, which is used as an explicit and quantifiable reference benchmark for risk assessment, and is set by finding the gradient inflection point of rapid increase in cell damage through offline experiments; this proportional term directly reflects the closeness of the predicted temperature difference to the safe upper limit;

[0103] The predicted differential risk term is a ratio of a change rate of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold, multiplied by a preset system thermal response time constant, i.e. ; this differential term is used to warn the mutation risk of the thermal gradient; the system thermal response time constant is an inherent property of the system response speed to thermal disturbance, and is used to unify the physical quantities on both sides of the formula to be dimensionless, and is obtained by step response testing on the system;

[0104] wherein: and are dimensionless weight coefficients for adjusting the relative importance of the magnitude and the trend in the risk assessment, and the values thereof are determined according to the focus on the preservation quality and the efficiency, are selected through simulation optimization, and satisfy ;

[0105] In this way, the risk prediction module builds a prospective, two-dimensional risk assessment model; it not only assesses the size of the thermal gradient at the next moment, but also creatively assesses the trend of the thermal gradient change; this composite risk assessment enables the system to foresee and quantify the potential risks caused by control behavior in advance, providing a basis for seamlessly integrating process safety into subsequent optimal control decisions.

[0106] Embodiment 5:

[0107] The cost function configured in the control decision module includes an operating cost term and a risk penalty term; the operating cost term is proportional to the liquid nitrogen injection intensity; the risk penalty term is determined in the following manner:

[0108] comparing the predicted thermal stress risk index with a preset risk reference threshold;

[0109] if the predicted thermal stress risk index is greater than the risk reference threshold, determining the risk penalty term based on the difference between the two;

[0110] if the predicted thermal stress risk index is not greater than the risk reference threshold, determining the risk penalty term as zero;

[0111] The control decision module is specifically used for:

[0112] taking the liquid nitrogen injection intensity as an optimization variable;

[0113] solving the liquid nitrogen injection intensity that minimizes the cost function through a numerical optimization algorithm, and outputting the intensity as the optimal liquid nitrogen injection intensity.

[0114] To specifically describe the control decision module, the module, through optimal control theory, maximizes energy efficiency under the premise of ensuring safety, and the core is to build and solve a cost function;

[0115] The cost function configured in the control decision module includes an operating cost term and a risk penalty term; the cost function quantifies the pros and cons of the control behavior, i.e., the liquid nitrogen injection intensity , as a single numerical value, which provides a clear optimization target for finding the optimal control strategy, and is constructed based on the penalty function method of optimal control theory; its specific form is:

[0116]

[0117] The physical dimension of the cost function is unified as cost per unit time;

[0118] The operating cost term is ; this term is proportional to the liquid nitrogen injection intensity , where The unit is watt, and its source is the optimization variable of this module; It is the cost coefficient per unit of energy consumption, expressed in Cost / J, and is a preset economic parameter. The purpose of this design is that, under all constraints, the system will tend to select the control scheme with the lowest energy consumption.

[0119] The risk penalty is determined by the following method: the predicted heat stress risk index. Compared with the preset risk reference threshold Comparison; Risk reference threshold This is a preset safety boundary, typically set to 1.0 based on technical requirements or experimental statistics, representing that the risk index has reached an acceptable upper limit; if the predicted heat stress risk index exceeds the risk reference threshold, then... The risk penalty term is then determined based on the difference between the two, and this term is... ;in This is a high-weighted penalty coefficient, measured in Cost / s, and is a preset safety parameter. Its value must be set to ensure that it is significantly greater than the cost factor upon activation, thereby forcing the system to prioritize risk avoidance. If the predicted heat stress risk index is not greater than the risk reference threshold... If the result of the max function is zero, then the risk penalty term will be set to zero.

[0120] The specific working method of this control decision module is as follows: Injecting liquid nitrogen intensity... As optimization variables; the cost function is solved using a numerical optimization algorithm. Minimum liquid nitrogen injection intensity This intensity is then output as the optimal liquid nitrogen injection intensity; because It is about The function, therefore the entire cost function It is about The system can quickly find the function that minimizes the total cost in each control cycle using numerical optimization algorithms such as gradient descent. smallest And send it as an instruction to the liquid nitrogen supply unit;

[0121] By constructing this cost function, the control decision module unifies the two goals of energy saving and quality preservation into an optimization problem; the design of the risk penalty term sets a safety red line for the system; only when the predicted risk is below the threshold, will the system seek the most energy-saving control point within the safety zone; once it predicts that any control behavior may touch or cross this red line, a huge penalty cost will immediately force the optimization algorithm to abandon the behavior and choose a safer control strategy; this decision mechanism based on prediction and optimization realizes the intelligent upgrade from passive response to active avoidance, ensuring that the optimal trade-off between safety and efficiency is always made in the dynamic process; the optimal liquid nitrogen injection intensity The opening signal or pulse width modulation (PWM) signal of the liquid nitrogen flow control valve can be converted through a preset conversion model or lookup table to realize physical regulation of the liquid nitrogen injection power.

[0122] Embodiment 6

[0123] The application provides an application of the liquid nitrogen flow automatic regulation system in abalone preservation;

[0124] In a specific application scenario, live abalone samples or abalone samples just after pretreatment are placed in a preservation cabin with liquid nitrogen injection function; according to the foregoing embodiments, a micro-thermocouple is implanted on the surface and core of the abalone sample, a micro-impedance electrode is implanted in the tissue, and a high-frequency acoustic transducer is arranged on the inner wall of the preservation cabin; in actual deployment, to reduce the influence of invasive measurement on the characteristics of the sample itself, a micro-sensor with extremely small diameter should be selected, and standardized operation should be performed on the implantation position and depth to ensure consistency.

[0125] After the system is started, the data acquisition module measures and records the impedance module value of the completely unfrozen state and the initial sound velocity as reference parameters; the liquid nitrogen injection system starts to work, and the entire automatic regulation system enters a closed-loop control process: the data acquisition module continuously acquires the real-time core temperature , surface temperature , tissue impedance and sound velocity of the abalone; the thermophysical property calculation module receives and , and calculates the instantaneous ice crystal volume fraction and instantaneous thermal conductivity of the current abalone tissue in real time; the state prediction module fuses the measured , and calculated , and accurately predicts the core temperature and surface temperature of the abalone at the next moment through an extended Kalman filter model.; the risk prediction module calculates the predicted dynamic thermal gradient using the predicted temperature state , and further combines the proportional and differential risk models to obtain the predicted thermal stress risk index ; the control decision module compares with the safety threshold , while considering the deviation from the target cooling curve, solves the optimal liquid nitrogen injection intensity by minimizing the cost function ; the instructions are sent to the liquid nitrogen flow control valve to accurately adjust the injection intensity and pulse of liquid nitrogen; this process is executed in a high frequency loop until the abalone core temperature reaches the preset endpoint temperature

[0126] The application of the system to abalone preservation can achieve significant technological progress; due to differences in individual size, water content, and other factors, the thermal physical parameters of abalone also vary; the system can provide personalized cooling control schemes for each abalone individual, dynamically adjust the liquid nitrogen flow, and make the core temperature closely follow the optimal cooling curve; this can not only ensure rapid passage through the maximum ice crystal generation zone, effectively inhibit ice crystal growth, and minimize damage to abalone cell structure, maintaining its taste, flavor, and nutritional value after thawing; but also avoid energy waste and surface cracking risks caused by excessive liquid nitrogen injection in the late cooling stage, ultimately achieving dual improvement of abalone preservation quality and processing economic benefits.

[0127] It should be noted that the above examples are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application.​

Claims

1. A system for automatic regulation of liquid nitrogen flow, characterized in that , comprising: a data acquisition module, configured to acquire physical parameters of the target aquatic product in real time, the physical parameters including: core temperature, surface temperature, tissue impedance modulus and sound wave propagation speed; a thermal property calculation module, configured to determine the instantaneous thermal conductivity of the aquatic product based on the tissue impedance modulus and the sound wave propagation speed acquired by the data acquisition module; a state prediction module, configured to combine the core temperature and the surface temperature acquired by the data acquisition module, and the instantaneous thermal conductivity determined by the thermal property calculation module, and predict the next time system state including the next time core temperature and the next time surface temperature by using an extended Kalman filter model; a risk prediction module, configured to determine the predicted thermal stress risk index at the next time based on the next time system state obtained by the state prediction module; a control decision module, configured to combine the deviation of the next time core temperature predicted by the state prediction module and the preset target temperature, and the predicted thermal stress risk index determined by the risk prediction module, and solve the minimum value of the preset cost function to determine the optimal liquid nitrogen injection intensity.

2. The automatic liquid nitrogen flow regulating system of claim 1, wherein The thermal property calculation module is specifically configured to: determine the instantaneous ice crystal volume fraction based on the tissue impedance modulus, the preset un-frozen state impedance modulus and the completely frozen state impedance modulus; combine the instantaneous ice crystal volume fraction, the sound wave propagation speed and the preset acoustic-thermal coupling coefficient, and calculate the instantaneous thermal conductivity based on a parallel thermal resistance model.

3. The automatic liquid nitrogen flow regulating system of claim 1, wherein The state prediction module is specifically configured to: construct a current system state vector including the core temperature and the surface temperature; predict the current system state vector by using a nonlinear state transition equation with the instantaneous thermal conductivity; correct the prediction result by using an observation equation including real-time sensor measurement values, to output the next time system state.

4. The automatic liquid nitrogen flow regulating system of claim 1, wherein The risk prediction module is specifically configured to: calculate a predicted dynamic thermal gradient based on the difference between the next time core temperature and the next time surface temperature; determine a predicted proportional risk term based on the predicted dynamic thermal gradient; determine a predicted differential risk term based on the change rate of the predicted dynamic thermal gradient; obtain the predicted thermal stress risk index by weighted sum of the predicted proportional risk term and the predicted differential risk term.

5. The automatic liquid nitrogen flow regulating system of claim 4, wherein The predicted proportional risk term is the ratio of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold.

6. The automatic liquid nitrogen flow regulating system of claim 4, wherein The predicted differential risk term is the ratio of the change rate of the predicted dynamic thermal gradient to the preset peak dynamic thermal gradient threshold, multiplied by the preset system thermal response time constant.

7. The automatic liquid nitrogen flow regulating system of claim 1, wherein The cost function configured in the control decision module includes an operation cost term and a risk penalty term; the operation cost term is directly proportional to the liquid nitrogen injection intensity; the risk penalty term is determined by: comparing the predicted thermal stress risk index with a preset risk reference threshold; if the predicted thermal stress risk index is greater than the risk reference threshold, determining the risk penalty term based on the difference between them; if the predicted thermal stress risk index is not greater than the risk reference threshold, determining the risk penalty term as zero.

8. The automatic liquid nitrogen flow regulating system of claim 7, wherein The control decision module is specifically configured to: take the liquid nitrogen injection intensity as an optimization variable; solve the liquid nitrogen injection intensity that minimizes the cost function by a numerical optimization algorithm, and output the intensity as the optimal liquid nitrogen injection intensity.

9. The use of the automatic liquid nitrogen flow regulating system according to any one of claims 1-8 in the preservation of abalones.

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