A modeling method for a temperature prediction model for efficient litchi thawing

Abstract

The application relates to the field of litchi thawing, and specifically provides a modeling method of a temperature prediction model for efficient litchi thawing, which specifically comprises the following steps: step one, selecting frozen litchi as samples; step two, placing all litchi at the same moment in the same thawing environment, and recording temperature data of each litchi at different moments; step three, constructing a heat conduction equation based on laser heating, and obtaining the thermal conductivity of anisotropic litchi by optimizing the heat conduction equation, iteratively optimizing the heat conduction equation according to the measured temperature data and the temperature data predicted by the heat conduction equation, and obtaining the thermal conductivity; step four, obtaining the temperature prediction model according to the thermal conductivity; and step five, optimizing the temperature prediction model. The model constructed by the application can more accurately predict the temperature in the thawing process of the litchi.

Description

Technical Field

[0001] The present invention relates to the field of litchi thawing, and in particular to a modeling method of a prediction model for efficient litchi thawing. Background Art

[0002] Lychees, a specialty fruit from my country's Lingnan region, possess high edible and economic value. However, lychees mature during hot weather and are physiologically active after harvest, making them susceptible to spoilage. Therefore, freezing technology has gradually become an effective method for long-term preservation in the lychee industry. Frozen lychees need to be thawed before consumption, and the sensory quality of the final product is affected by the freezing and thawing process. The thawing process is often accompanied by physical and chemical changes such as fiber structure, protein oxidation, and juice loss.

[0003] Mathematical modeling, as an efficient tool, demonstrates significant advantages in exploring the dynamic changes in crop parameters. Its low cost and rapid computational speed give it enormous potential for application in agricultural research. In particular, when simulating the thawing process of frozen lychees in air, mathematical modeling can reveal the distribution of temperature fields, opening up new avenues for optimizing ambient temperature settings. Researchers such as Guo Jiaming and Wang Xudong have utilized numerical simulation techniques to further explore the field of food freezing and thawing. By precisely measuring the thermophysical properties of lychees, they successfully constructed a lychee freezing model that accurately predicts temperature changes during freezing, providing solid data support for further research into the mechanism of lychee cracking. Furthermore, Uyar, Pitchai, and others successfully predicted the time-dependent temperature profiles of lean beef and mashed potatoes by constructing a three-dimensional microwave thawing model that coupled heat and electromagnetic fields. However, it is worth noting that there is currently no research on numerical simulation of lychee thawing. Furthermore, when constructing models for some anisotropic products, idealized assumptions and the use of approximate parameters can lead to errors, which requires further research and optimization.

[0004] In this context, a litchi model based on heat and mass transfer principles can accurately simulate multidimensional parameters such as food structural properties, sensory attributes, and nutrients. Given that litchi, as a composite heat transfer material, exhibits unique anisotropic thermal conductivity, a laser heating transient analysis technique was first used to accurately measure the thermal conductivity of the litchi composite structure, significantly reducing the impact of the model's heat transfer idealization on the numerical simulation results. Subsequently, a more accurate litchi thawing model was constructed based on the thermal conductivity data obtained through an inverse identification model. However, given the potential inherent discrepancies between model assumptions and actual conditions, the accuracy of current simulation results remains to be improved. To address this challenge, an optimized litchi thawing model construction method was proposed. This method cleverly combines computational fluid dynamics (CFD) technology with a particle swarm optimization (PSO) algorithm. By dynamically adjusting and optimizing model parameters, it effectively narrows the gap between numerical simulation and experimental results, achieving highly accurate predictions of the litchi thawing process. Summary of the Invention

[0005] The main purpose of the present invention is to provide a modeling method for a temperature prediction model for efficient litchi thawing.

[0006] To achieve the above objectives, the present invention adopts a technical solution: a modeling method for a temperature prediction model for efficient litchi thawing, which specifically comprises the following steps:

[0007] Step 1: Select frozen lychees as samples;

[0008] Step 2: Place all lychees in the same thawing environment at the same time, and record the temperature data of each lychee at different times;

[0009] Step 3: Construct a heat conduction equation based on laser heating and optimize the heat conduction equation to obtain the thermal conductivity of the anisotropic litchi. The heat conduction equation is as follows:

[0010]

[0011] Where T represents the laser heating temperature, t represents the laser heating time, and λ x represents the radial thermal conductivity, λ y represents the axial thermal conductivity, ρ represents the density, c represents the specific heat capacity, Q0 represents the laser power, (x0, y0) represents the position of the heating source, and σ represents the width of the laser spot;

[0012] The heat conduction equation is iteratively optimized based on the measured temperature data and the temperature data predicted by the heat conduction equation to obtain the thermal conductivity;

[0013] Step 4: obtaining the temperature prediction model according to the thermal conductivity;

[0014] Step 5: Optimize the temperature prediction model.

[0015] Preferably, the step three specifically includes the following steps:

[0016] Step 31: Laser is directed onto the litchi shell, the litchi pulp, and the litchi core respectively to construct a two-dimensional anisotropic thermal conductivity model. The two-dimensional anisotropic thermal conductivity model in the xy coordinate system is as follows:

[0017]

[0018] Where ρ represents density, c represents specific heat capacity, T represents laser heating temperature, t represents laser heating time, q x represents the amount of laser heating energy in the x direction, q y represents the amount of laser heating energy in the y direction of the litchi;

[0019] In the laser heating step q x and q y The following formulas are used to calculate respectively:

[0020]

[0021]

[0022] λ x represents the component of the anisotropic thermal conductivity tensor in the x direction, λ y represents the component of the anisotropic thermal conductivity tensor in the y direction, represents the temperature change in the x direction, Indicates the change in temperature in the y direction;

[0023] Step 32: When laser heating is performed, the inner boundary condition of the spot radius of the surface of the litchi shell, flesh, and litchi core is a constant heat flow boundary, and the outer boundary of the spot radius is the radiation boundary of the surface to the environment. At this time, the two-dimensional heat conduction equation of the surface of the litchi shell, flesh, and litchi core is:

[0024]

[0025] in, and represents thermal diffusivity, ρ represents material density, c represents specific heat capacity, and Q(x, y, t) is the heat generated by the laser irradiating the litchi at point (x, y) at time t. It is usually expressed as:

[0026]

[0027] Where Q0 represents the laser power, (x0, y0) represents the position of the laser light source, and σ represents the width of the laser spot;

[0028] Step 33: According to formula (3), formula (5), formula (5) and formula (6), the heat conduction equation is obtained:

[0029]

[0030] Step 34: Arrange temperature monitoring points on the surface of litchi shell, pulp and litchi core respectively, obtain the measured temperature rise values ​​of different parts at different times, and record them as (ΔT exp，i}, where i = 1, 2, …, m, and m is the total number of measured time steps;

[0031] Step 35: The measured temperature rise value and the initial value of the thermal conductivity of different parts of the litchi are used as input to generate the thermal conductivity through the particle swarm optimization algorithm. The thermal conductivity generated by the particle swarm optimization algorithm is substituted into formula (5) to perform a transient heat transfer numerical simulation to obtain the simulated values ​​of the surface temperature rise of the litchi shell, the flesh and the litchi core, respectively, which are recorded as {ΔT sim,i};

[0032] Step 36: Construct the optimization objective function. The formula is as follows:

[0033]

[0034] J is the target value output by formula (8);

[0035] Step 37: The {ΔT exp，i} and {ΔT obtained in step 35 sim，i Substitute into formula (8) to obtain J;

[0036] Step 38: When J is greater than or equal to the predetermined value, return to step 35 and continue iterating until J is less than the predetermined value. Each time step 35 is iterated, the measured temperature rise value at the corresponding moment and the thermal conductivity obtained last time are used as the input of the particle swarm optimization algorithm; when J is less than the predetermined value, the thermal conductivity generated by the particle swarm optimization algorithm at this time is used as the thermal conductivity K of the litchi shell, pulp and litchi core. j , j = 1, 2, 3, K1, K2, K3 represent the thermal conductivity of litchi shell, pulp and litchi core respectively.

[0037] Preferably, the step 4 specifically includes the following steps:

[0038] Step 41: Idealize the litchi model into a spherical model, and express the specific heat capacity of the litchi model during the thawing process by the following formula:

[0039]

[0040] Where C lichi (t) refers to the specific heat capacity of litchi at time t, ηwater Refers to the weight loss function of litchi at the initial moment, C constant Refers to the simulated ideal specific heat of litchi without considering moisture, C ice Refers to the specific heat capacity of ice, C water refers to the specific heat capacity of water, L refers to the latent heat absorbed by ice turning into water, and Φ(t) refers to the freezing rate inside the litchi at time t. The formula is as follows;

[0041] Φ(t)=1-t b / T t (10)

[0042] Where, t b Refers to the freezing point, T t Indicates the real-time temperature of litchi at time t;

[0043] Step 42: The calculation formula for the thermal conductivity of the ideal litchi model is as follows:

[0044]

[0045] Where K lichi represents the thermal conductivity of the whole litchi; η j represents the mass proportion of each part of litchi, j = 1, 2, 3;

[0046] Step 43: The density calculation formula of the ideal litchi model is as follows:

[0047]

[0048] Where, ρ lichi represents the density of the ideal model of litchi; η j Indicates the mass proportion of each part of litchi; W j Indicates the weight of each part of the litchi; V j Indicates the volume proportion of each part of litchi;

[0049] Step 44: Construct the heat conduction formula inside the litchi:

[0050]

[0051] Where, ρ lichi represents the density of the ideal model of litchi, C lichi represents the specific heat capacity of the ideal model of litchi, T represents the temperature of each part of litchi, t represents the thawing time, K lichi represents the thermal conductivity of litchi, q represents the internal heat source of litchi;

[0052] Assuming that the litchi is spherical with a radius of r, the heat conduction equation is converted into spherical coordinate form, the formula is as follows:

[0053]

[0054] Step 45: Due to natural convection heat transfer between the litchi and the air in the thawing environment, the total heat exchange Q between the litchi surface and the thawing environment per unit time is calculated using the following formula:

[0055] Q=h·A·((T enveronment -T surface )=q conv A (15),

[0056] Where h is the convective heat transfer coefficient; A is the surface area of ​​the litchi; T surface is the surface temperature of litchi; T enveronment is the thawing environment temperature, q conv is the heat flux density;

[0057] Step 46: The heat absorbed by the surface of the litchi is transferred to the inside of the litchi through the heat conduction process. The heat flux density is related to the normal temperature gradient of the litchi. The formula between the two is:

[0058]

[0059] Among them, K lichi is the thermal conductivity, is the temperature gradient normal to the surface of the litchi, which indicates the rate of change of temperature in the normal direction per unit time;

[0060] Step 47: Combining formula (13), formula (14), formula (15), and formula (16) yields the following formula:

[0061]

[0062] Step 48: Arrange formula (17) and solve it by integration to obtain the following formula:

[0063]

[0064] Where T(t) is the temperature of the center of the litchi at time t, T center，0 is the initial temperature of the litchi center, T enveronment (t) is the thawing environment temperature.

[0065] Preferably, in step 5, if the error between the temperature curve predicted by the litchi thawing model and the actual temperature curve exceeds a predetermined value, a global optimal solution is obtained based on the first and second formulas in the particle swarm optimization algorithm, and the corresponding weight loss function is optimized based on the global optimal solution:

[0066] Weight loss function: η water =W water / W0,

[0067] W waterThe difference between the weight of the litchi before thawing and the weight at the current moment is measured manually after each iteration. Through optimization, η water Less than a predetermined value;

[0068] The first formula is:

[0069]

[0070] Where c1 and c2 represent acceleration constants; r1 and r2 represent random numbers between 0 and 1; w is the inertia weight; represents the weight loss function corresponding to the k-th generation particle i; represents the optimal weight loss value experienced by the kth generation particles; represents the optimal weight loss value of the entire population in the kth generation, Represents the update speed of the parameter;

[0071] The second formula is:

[0072]

[0073] Where, represents the position of the i-th particle in the k+1th generation; represents the position of the i-th particle in the k-th generation; represents the velocity of the i-th particle in the k+1th generation.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] 1. This study successfully predicted the thermal conductivity of the lychee shell, flesh, and various parts by combining a thermal conductivity inversion identification model with a particle swarm optimization algorithm. This method accounts for and accommodates the anisotropic heat transfer characteristics of lychee, making the lychee thawing model more accurate to actual thawing conditions.

[0076] 2. The present invention significantly improves the prediction accuracy of the litchi thawing model by optimizing the model parameters using the particle swarm optimization algorithm, providing important support for the industrial application of litchi thawing technology and promoting the standardization and large-scale development of the litchi thawing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a modeling flowchart; DETAILED DESCRIPTION

[0078] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0079] A modeling method for a temperature prediction model for efficient litchi thawing comprises the following steps:

[0080] Step 1: Select frozen lychees of good variety, size, color and appearance as samples. The size, color and appearance can be determined based on experience.

[0081] Step 2: Weigh the litchi (W0) and separate the litchi shell, litchi pulp, and litchi core. Weigh the litchi shell, litchi pulp, and litchi core separately, and calculate the percentages η1, η2, and η3 of the weight of each part.

[0082] Step 3: placing the litchi in a thawing environment and collecting temperature data of the litchi over time;

[0083] Step 4: Obtain the weight of thawed lychees W4 and calculate the approximate water content W water , moisture content η water ;

[0084] Step 5: Obtaining the thermal conductivity of the anisotropic litchi by laser heating;

[0085] Step 6: Obtaining the temperature prediction model according to the thermal conductivity;

[0086] Step 7: Optimize the temperature prediction model.

[0087] In step 1, after the samples are determined, each sample needs to be labeled in sequence to facilitate subsequent data recording.

[0088] In step 2, a thin film pressure sensor with a measuring range of 2 g to 1000 g is selected to record the weight W0 of the lychees before thawing and the weights W1, W2, and W3 of the lychee shell, lychee pulp, and lychee core, respectively.

[0089] The specific expression of the mass proportion of each part is:

[0090] η j =W j / W0(j=1, 2, 3) (1).

[0091] In step three, the litchi sample was placed in the same external environment for thawing, a patch temperature sensor was attached to the outside of the litchi core, and a PT100 temperature sensor was used to collect the temperature of the litchi core, and the temperature data at each time point was recorded.

[0092] In step 4, a 2g to 1000g thin film pressure sensor is used to weigh the weight W4 of each lychee sample after thawing, and the water content and percentage of each lychee sample are calculated using a weight loss function.

[0093] W water=W0-W4 (2),

[0094] Among them, W water Indicates the weight loss and water content of the litchi sample, W0 indicates the weight of the litchi before thawing, and W4 indicates the weight of the litchi after thawing.

[0095] η water =W water / W0 (3),

[0096] η water Indicates the moisture content of the sample.

[0097] Step 5 specifically includes the following steps:

[0098] Step 51: Laser is directed onto the litchi shell, litchi pulp, and litchi core respectively to construct a two-dimensional anisotropic thermal conductivity model. The two-dimensional anisotropic thermal conductivity model in the xy coordinate system is as follows:

[0099]

[0100] Where ρ represents density, c represents specific heat capacity, T represents laser heating temperature, t represents laser heating time, q x represents the amount of laser heating energy in the x direction, q y represents the amount of laser heating energy in the y direction of the litchi;

[0101] In the laser heating step q x and q y The following formulas are used to calculate respectively:

[0102]

[0103]

[0104] λ x represents the component of the anisotropic thermal conductivity tensor in the x direction, λ y represents the component of the anisotropic thermal conductivity tensor in the y direction, represents the temperature change in the x direction, Indicates the change in temperature in the y direction;

[0105] Step 52: When laser heating is performed, the inner boundary condition of the spot radius of the litchi shell, pulp, and litchi core surface is a constant heat flow boundary, and the outer boundary condition of the spot radius is a radiation boundary of the surface to the environment. At this time, the two-dimensional heat conduction equation of the litchi shell, pulp, and litchi core surface is:

[0106]

[0107] in, and represents thermal diffusivity, ρ represents material density, c represents specific heat capacity, and Q(x, y, t) is the heat generated by the laser irradiating the litchi at point (x, y) at time t. It is usually expressed as:

[0108]

[0109] Where Q0 represents the laser power, (x0, y0) represents the position of the laser light source, and σ represents the width of the laser spot;

[0110] Step 53: According to formula (5) and formula (6), the heat conduction equation is obtained:

[0111]

[0112] Step 54: Arrange temperature monitoring points on the surface of the litchi shell, the pulp and the litchi core respectively, obtain the measured temperature rise values ​​of different parts at different times, and record them as {ΔT exp，i}, where i = 1, 2, ..., m, where m is the total number of measured time steps;

[0113] Step 55: The measured temperature rise value and the initial value of the thermal conductivity of different parts of the litchi are used as input to generate the thermal conductivity through the particle swarm optimization algorithm. The thermal conductivity generated by the particle swarm optimization algorithm is substituted into formula (9) to perform a transient heat transfer numerical simulation to obtain the simulated value of the surface temperature rise of the litchi shell, pulp and litchi core, which is recorded as {ΔT sim，i}, the initial value of thermal conductivity can be obtained based on experience or existing data;

[0114] Step 56: Construct the optimization objective function. The formula is as follows:

[0115]

[0116] J is the target value output by formula (10);

[0117] Step 57: The {ΔT exp，i} and (ΔT obtained in step 55 sim，i Substitute into formula (10) to obtain J;

[0118] Step 58: When J is greater than or equal to the predetermined value, return to step 55 and continue iterating until J is less than the predetermined value. Each time step 55 is iterated, the measured temperature rise value at the corresponding moment and the thermal conductivity λ obtained last time are calculated. x and λ y As the input of the particle swarm optimization algorithm; when J is less than the predetermined value, the thermal conductivity generated by the particle swarm optimization algorithm at this time is used as the thermal conductivity K of the litchi shell, pulp and litchi core j, j = 1, 2, 3, K1, K2, K3 represent the thermal conductivity of litchi shell, pulp and litchi core respectively.

[0119] in,

[0120] The step 6 specifically includes the following steps:

[0121] Step 61: Idealize the litchi model into a spherical model. During the thawing process, the specific heat capacity of the litchi model will change dramatically as the internal ice undergoes a physical change. The formula is as follows:

[0122] C lichi (t) = (1-η water )C constant +Φ(t)η water C ice +(1-Φ(t))η water C water +Lη water (1-Φ(t)) (11),

[0123] Where C lichi (t) refers to the specific heat capacity of litchi at time t during thawing, η water Refers to the weight loss function of litchi at the initial moment, C constant Refers to the simulated ideal specific heat capacity of litchi without considering the water content, which is usually 3710 J / (kg·K); C ice Refers to the specific heat capacity of ice, C water refers to the specific heat capacity of water, L refers to the latent heat absorbed by ice turning into water, and Φ(t) refers to the freezing rate inside the litchi at time t. The formula is as follows;

[0124] Φ(t)=1-t b / T t (12)

[0125] Where, t b Refers to the freezing point; T t represents the temperature of litchi at time t;

[0126] Step 62: The calculation formula for the thermal conductivity of the ideal litchi model is as follows:

[0127]

[0128] Where K lichi represents the thermal conductivity of the whole litchi; η j Indicates the mass proportion of each part of litchi;

[0129] Step 62: The density calculation formula of the ideal litchi model is as follows:

[0130]

[0131] Where, ρ lichi represents the density of the ideal model of litchi; η j Indicates weight fraction; W j Indicates the weight of different parts of litchi; V j Indicates the volume proportion of different parts of litchi;

[0132] Step 63: Construct the heat conduction formula inside the litchi:

[0133]

[0134] Where, ρ lichi represents the density of the ideal model of litchi, C lichi represents the specific heat capacity of the ideal model of litchi, T represents the temperature of each part of litchi, t represents the thawing time, K lichi represents the thermal conductivity of litchi, q represents the internal heat source of litchi, which is ignored here and is 0;

[0135] Assuming that the litchi is spherical with a radius of r, the heat conduction equation is converted into spherical coordinate form, the formula is as follows:

[0136]

[0137] Step 64: Due to natural convection heat transfer between the litchi and the air in the thawing environment, the total heat exchange Q between the litchi surface and the thawing environment per unit time is calculated using the following formula:

[0138] Q=h·A·(T enveronment -T surface )=q conv A (17),

[0139] Where h is the convective heat transfer coefficient, which is generally 20W / (m 2 K); A is the surface area of ​​the litchi; T surface is the surface temperature of litchi; T enveronment Thawing environment temperature, q conv is the heat flux density;

[0140] Step 65: The heat absorbed by the surface of the litchi is transferred to the inside of the litchi through the heat conduction process. The heat flux density is related to the normal temperature gradient of the litchi. The formula between the two is:

[0141]

[0142] Among them, K lichi is the thermal conductivity, is the temperature gradient normal to the surface of the litchi, which indicates the rate of change of temperature in the normal direction per unit time;

[0143] Step 66: Combine formula (15), formula (16), formula (17), and formula (18) to obtain the following formula:

[0144]

[0145] Step 67: Arrange formula (19) and solve it by integration to obtain the following formula:

[0146]

[0147] Where T(t) is the temperature at the center of the litchi, T center,0 is the initial temperature of the litchi center, T enveronment (t) is the thawing environment temperature at time t.

[0148] In step 7, if there is a large error between the temperature curve predicted by the litchi thawing model and the actual temperature curve, the first and second formulas in the particle swarm optimization algorithm (PSO) are used to obtain a global optimal solution. Based on the global optimal solution, the corresponding weight loss function, i.e., the water content, is optimized to improve the accuracy of the litchi thawing model. Represents the weight loss function of the kth generation particle, denoted as make Indicates the update speed of this parameter.

[0149] Weight loss function is water content: η water =W water / W0,W water After each iteration, we manually measure and optimize η water Less than a predetermined value, that is, the moisture in the lychees is retained as much as possible during the thawing process.

[0150] The first formula is:

[0151]

[0152] Where c1 and c2 represent acceleration constants; r1 and r2 represent random numbers between 0 and 1; w is the inertia weight; represents the weight loss function corresponding to the k-th generation particle i; represents the optimal weight loss value experienced by the kth generation particles; Represents the optimal weight loss value of the entire population in the kth generation;

[0153] The second formula is:

[0154]

[0155] Where, represents the position of the i-th particle in the (k+1)th generation; represents the position of the i-th particle in the k-th generation; represents the velocity of the i-th particle in the (k+1)th generation.

[0156] Preferably, in step 7, the temperature curve predicted by the litchi thawing model based on the particle swarm optimization algorithm is compared with the actual temperature curve, and the correlation coefficient (r) and root mean square error (RMSE) are used as model evaluation indicators. The calculation formula of the correlation coefficient is as follows:

[0157]

[0158] Where, X i represents the predicted temperature value of the litchi thawing model; Y i Indicates the actual thawing temperature of litchi in the test set; and are the means of the two sequences respectively, n is the number of thawed litchi;

[0159] The formula for calculating the root mean square error is as follows:

[0160]

[0161] In the formula, r represents the total number of temperature series, X i Y represents the i-th temperature prediction value predicted by the litchi model; i represents the temperature measurement value of litchi i;

[0162] The water content is approximately the variable W in the particle swarm optimization algorithm. water , combined with the model evaluation index root mean square error RMSE to obtain the objective function, the formula is as follows:

[0163]

[0164] Where η j represents the mass ratio of each part of litchi, K j represents the thermal conductivity of each part of litchi, T enveronment Indicates the external thawing environment temperature, W water Indicates the water loss during thawing of lychee, W j Indicates the weight of each part of litchi, V j Indicates the volume of each part of the litchi, C j represents the specific heat capacity of each part of the litchi, r represents the radius of the litchi, Y i Indicates the actual collected temperature point data.

[0165] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A modeling method for a temperature prediction model for efficient litchi thawing, comprising the following steps: Step 1: Select frozen lychees as samples; Step 2: Place all lychees in the same thawing environment at the same time, and record the temperature data of each lychee at different times; Step 3: Construct a heat conduction equation based on laser heating and optimize the heat conduction equation to obtain the thermal conductivity of the anisotropic litchi. The heat conduction equation is as follows: （1）， in, T represents the laser heating temperature, t represents the laser heating time, represents the radial thermal conductivity, represents the axial thermal conductivity, represents density, c represents the specific heat capacity, represents the laser power, Indicates the location of the heating source, Indicates the width of the laser spot; The heat conduction equation is iteratively optimized based on the measured temperature data and the temperature data predicted by the heat conduction equation to obtain the thermal conductivity; Step 4: obtaining the temperature prediction model according to the thermal conductivity; Step 5: Optimize the temperature prediction model; The step three specifically includes the following steps: Step 31: Laser is directed onto the litchi shell, the litchi pulp, and the litchi core respectively to construct a two-dimensional anisotropic thermal conductivity model. The two-dimensional anisotropic thermal conductivity model in the xy coordinate system is as follows: （2）， in, represents the amount of laser heating energy in the x direction of the litchi, represents the amount of laser heating energy in the y direction of the litchi; During the laser heating step and The following formulas are used to calculate respectively: （3）， （4）， represents the temperature change in the x direction, Indicates the change in temperature in the y direction.

2. The modeling method according to claim 1, characterized in that The step three also includes the following steps: Step 32: When laser heating is performed, the inner boundary condition of the spot radius of the surface of the litchi shell, flesh, and litchi core is a constant heat flow boundary, and the outer boundary of the spot radius is the radiation boundary of the surface to the environment. At this time, the two-dimensional heat conduction equation of the surface of the litchi shell, flesh, and litchi core is: （5）， in, and represents the thermal diffusivity, The laser irradiates the litchi at time t ( x,y ) is expressed as: （6）， Step 33: According to formula (3), formula (4), formula (5) and formula (6), the heat conduction equation is obtained: （7）； Step 34: Arrange temperature monitoring points on the surface of litchi shell, pulp and litchi core respectively, obtain the measured temperature rise values ​​of different parts at different times, and record them as ,in , m is the total number of measured time steps; Step 35: Take the measured temperature rise value and the initial value of the thermal conductivity of different parts of the litchi as input, generate the thermal conductivity through the particle swarm optimization algorithm, substitute the thermal conductivity generated by the particle swarm optimization algorithm into formula (5), perform numerical simulation of the transient heat transfer process, and obtain the simulated values ​​of the surface temperature rise of the litchi shell, flesh and litchi core respectively, which are recorded as ; Step 36: Construct the optimization objective function. The formula is as follows: （8）， is the target value output by formula (8); Step 37: The and step 35 Substituting into formula (8), we get ; Step 38: When it is greater than or equal to the predetermined value, return to step 35 and continue iterating until is less than a predetermined value, each time step 35 is iterated, the measured temperature rise value at the corresponding moment and the thermal conductivity obtained last time are used as the input of the particle swarm optimization algorithm; when When it is less than the predetermined value, the thermal conductivity generated by the particle swarm optimization algorithm is used as the thermal conductivity of the litchi shell, pulp and litchi core. , , Represent the thermal conductivity of litchi shell, flesh and litchi core respectively.

3. The modeling method according to claim 2, characterized in that The step 4 specifically includes the following steps: Step 41: Idealize the litchi model into a spherical model, and express the specific heat capacity of the litchi model during the thawing process by the following formula: C lichi (t)=(1-η water )C constant +Φ(t)η water C ice +(1-Φ(t))η water C water +Lη water (1-Φ(t))(9), Where, Refers to the specific heat capacity of litchi at time t, Refers to the weight loss function of litchi at the initial moment, Refers to the simulated ideal specific heat of litchi without considering the water content. The specific heat capacity of ice, The specific heat capacity of water, Refers to the latent heat absorbed by ice melting into water. Refers to the freezing rate inside the litchi at time t, and the formula is as follows: （10）； Where, Refers to the freezing point, Indicates the real-time temperature of litchi at time t; Step 42: The calculation formula for the thermal conductivity of the ideal litchi model is as follows: （11）， Where, Indicates the thermal conductivity of the whole lychee; Indicates the mass proportion of each part of litchi, ; Step 43: The density calculation formula of the ideal litchi model is as follows: （12）， Where, represents the density of the ideal model of litchi; Indicates the mass proportion of each part of litchi; Indicates the weight of each part of the lychee; Indicates the volume proportion of each part of litchi; Step 44: Construct the heat conduction formula inside the litchi: （13）， Where, represents the density of the ideal model of litchi, represents the specific heat capacity of the ideal model of litchi, T represents the temperature of each part of litchi, t represents the thawing time, represents the thermal conductivity of litchi, q represents the internal heat source of litchi; Assuming that the litchi is spherical with a radius of r, the heat conduction equation is converted into spherical coordinate form, the formula is as follows: （14）； Step 45: Due to natural convection heat transfer between the litchi and the air in the thawing environment, the total heat exchange Q between the litchi surface and the thawing environment per unit time is calculated using the following formula: Q=h·A·(T environment -T surface )=q conv ·A (15), Where h is the convective heat transfer coefficient; A is the surface area of ​​the litchi; is the surface temperature of the lychee; is the thawing ambient temperature, is the heat flux density; Step 46: The heat absorbed by the surface of the litchi is transferred to the inside of the litchi through the heat conduction process. The heat flux density is related to the normal temperature gradient of the litchi. The formula between the two is: （16）， in, is the thermal conductivity, is the temperature gradient normal to the surface of the litchi, which indicates the rate of change of temperature in the normal direction per unit time; Step 47: Combining formula (13), formula (14), formula (15), and formula (16) yields the following formula: Step 48: Arrange formula (17) and solve it by integration to obtain the following formula: （18）， in, is the temperature of the litchi center at time t, is the initial temperature of the litchi center, is the thawing environment temperature at time t.

4. The modeling method according to claim 3, characterized in that In step 5, if the error between the temperature curve predicted by the temperature prediction model and the actual temperature curve exceeds a predetermined value, the global optimal solution is obtained based on the first and second formulas in the particle swarm optimization algorithm, and the corresponding weight loss function is optimized based on the global optimal solution: Weight loss function: , The difference between the weight of the litchi before thawing and the current weight is measured manually after each iteration. Through optimization, Less than a predetermined value; The first formula is: （21） Where, and represents the acceleration constant; and Represents a random number between 0 and 1; w is the inertia weight; represents the weight loss function corresponding to the k-th generation particle i; represents the optimal weight loss value experienced by the kth generation particles; Represents the optimal weight loss value of the entire population in the kth generation; The second formula is: （22）， Where, represents the position of the i-th particle in the k+1th generation; represents the velocity of the i-th particle in the k+1th generation.

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