Modeling method of temperature prediction model for efficient litchi thawing

Abstract

The invention relates to the field of litchi thawing, and particularly provides a modeling method of a temperature prediction model for efficient litchi thawing, and the modeling method specifically comprises the following steps: step 1, selecting frozen litchis as a sample; step 2, placing all litchis in the same unfreezing environment at the same moment, and recording temperature data of each litchis at different moments; 3, constructing a heat conduction equation based on laser heating, optimizing the heat conduction equation to obtain the heat conductivity of the anisotropic litchi, and performing iterative optimization on the heat conduction equation according to the actually measured temperature data and the temperature data predicted by the heat conduction equation to obtain the heat conductivity; 4, obtaining a temperature prediction model according to the thermal conductivity; and 5, optimizing the temperature prediction model. The model constructed by the invention can more accurately predict the temperature of the inside of the litchi in an unfreezing process.

Description

Technical Field

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

[0002] Litchi, as a characteristic fruit in the Lingnan region of China, has high edible value and economic value. However, litchi ripens in high-temperature seasons, and its physiological state is active after harvesting, making it prone to spoilage. Therefore, freezing technology has gradually become an effective method for long-term preservation in the litchi industry. Frozen litchi needs to be thawed before consumption, and the sensory quality of the final product is affected by the freezing and thawing processes. Physical and chemical changes such as fiber structure, protein oxidation, and juice loss often occur during the thawing process.

[0003] Mathematical modeling, as an efficient tool, has shown significant advantages in exploring the dynamic changes of crop parameters. Its characteristics of low cost and fast calculation make it have great application potential in the field of agricultural scientific research. Especially when simulating the thawing process of frozen litchi under static air conditions, mathematical modeling can reveal the distribution of the temperature field, opening up a new path for optimizing the setting of environmental temperature. Some scholars, such as Guo Jiaming, Wang Xudong, etc., have used numerical simulation technology to deeply explore the fields of food freezing and thawing. They accurately measured the thermal physical properties of litchi and successfully constructed a litchi freezing model, which can accurately predict the temperature changes during the freezing process, providing solid data support for further exploring the litchi cracking mechanism. In addition, Uyar, Pitchai, etc. have also successfully predicted the temperature curves of lean beef and mashed potatoes changing with time by constructing a three-dimensional microwave thawing model coupling heat and electromagnetic fields. However, it is worth noting that the current numerical simulation research on litchi thawing is still blank. At the same time, when constructing models for some anisotropic products, the use of idealized assumptions and approximate parameters may lead to errors, which need to be improved and optimized in future research.

[0004] In this context, the litchi model constructed based on the principles of heat and mass transfer can accurately realize the numerical simulation of multi-dimensional parameters such as food structural characteristics, sensory attributes and nutrients. Considering that litchi is a composite heat transfer material with unique anisotropic thermal conductivity, the laser heating transient analysis technology was first used to accurately measure the thermal conductivity of the litchi composite structure, thereby significantly reducing the influence of the idealization of model heat transfer on the numerical simulation results. Subsequently, a more accurate litchi thawing model was constructed based on the thermal conductivity data obtained by the inversion identification model. However, considering the inherent deviation between the model assumptions and the actual situation, the accuracy of the current simulation results still has room for further improvement. In order to meet this challenge, an optimized litchi thawing model construction method was proposed. This method cleverly combines computational fluid dynamics (CFD) technology with particle swarm optimization (PSO) algorithm. By dynamically adjusting and optimizing model parameters, the gap between numerical simulation results and experimental results is effectively narrowed, and a high-precision prediction of the litchi thawing process is achieved. 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 technical solution adopted by the present invention is: a modeling method for a temperature prediction model for efficient litchi thawing, which specifically comprises the following steps:

[0007] Step 1, selecting frozen lychees as samples;

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

[0009] Step 3: Construct a heat conduction equation based on laser heating and obtain the thermal conductivity of anisotropic litchi by optimizing the heat conduction equation. 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 axial thermal conductivity, ρ represents density, c represents specific heat capacity, Q 0 represents the laser power, (x 0 ,y 0 ) represents the position of the heating source, 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 transfer equation to obtain the thermal conductivity;

[0013] Step 4. Obtain the temperature prediction model according to the thermal conductivity;

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

[0015] Preferably, the specific steps of Step 3 are as follows:

[0016] Step 31. Shoot lasers on the litchi pericarp, pulp and litchi seed respectively and construct a two-dimensional anisotropic heat conduction model. The two-dimensional anisotropic heat conduction model in the xy coordinate system is as follows:

[0017]

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

[0019] In the step of laser heating, q x and q y are calculated by the following formulas respectively.

[0020]

[0021]

[0022] λ x represents the component of the anisotropic heat conduction tensor in the x direction, λ y represents the component of the anisotropic heat conduction tensor in the y direction. represents the change amount of temperature in the x direction, represents the change amount of temperature in the y direction;

[0023] Step 32. When laser heating, the inner boundary condition of the light spot radius on the surfaces of the litchi pericarp, pulp and litchi seed is a fixed heat flux boundary, and the outside of the light spot radius is a radiation boundary of the surface to the environment. At this time, the two-dimensional heat conduction equation on the surfaces of the litchi pericarp, pulp and litchi seed is:

[0024]

[0025] Among them, and represent the thermal diffusivity, ρ represents the material density, c represents the specific heat capacity, and Q(x, y, t) is the heat of the laser irradiated on the litchi at (x, y) at time t. The general representation form is:

[0026]

[0027] Among them, Q 0represents the laser power, (x 0 , y 0 ) 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), obtain the heat conduction equation:

[0029]

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

[0031] Step 35: Take the measured temperature rise values and the initial values of the thermal conductivities of different parts of the litchi as inputs, generate the thermal conductivities through the particle swarm optimization algorithm, substitute the thermal conductivities generated by the particle swarm optimization algorithm into formula (5), perform numerical simulation of the transient heat transfer process, and respectively obtain the simulated temperature rise values on the surfaces of the litchi shell, pulp and litchi seed, denoted as {ΔT sim,i};

[0032] Step 36: Construct an optimization objective function, and the formula is as follows:

[0033]

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

[0035] Step 37: Substitute {ΔT exp，i} obtained in Step 34 and {ΔT sim，i} obtained in Step 35 into formula (8) to obtain J;

[0036] Step 38: When J is greater than or equal to the predetermined value, return to Step 35 to continue the iteration until J is less than the predetermined value. Each time Step 35 is iterated, take the measured temperature rise value at the corresponding time and the thermal conductivity obtained in the previous time as the inputs of the particle swarm optimization algorithm; when J is less than the predetermined value, take the thermal conductivity generated by the particle swarm optimization algorithm at this time as the thermal conductivities K j , j = 1, 2, 3, K 1 , K 2 , K 3 respectively represent the thermal conductivities of the litchi shell, pulp and litchi seed.

[0037] Preferably, the specific steps of the fourth step are as follows:

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

[0039] In the formula, C lichi (t) represents the specific heat capacity of litchi at time t, η water represents the weight loss function at the initial moment of litchi, C constant represents the simulated ideal specific heat of litchi without considering moisture, C ice represents the specific heat capacity of ice, C water represents the specific heat capacity of water, L represents the latent heat absorbed when ice melts into water, Φ(t) represents the freezing rate inside the litchi at time t, and the formula is as follows; ;

[0040] Φ(t) = 1 - t b / T t (10)

[0041] In the formula, t b represents the freezing point, T t represents the real-time temperature of litchi at time t;

[0042] Step 42: The calculation formula for the thermal conductivity of the ideal litchi model is as follows:

[0043]

[0044] In the formula, K lichi represents the thermal conductivity of the whole litchi; η j represents the mass proportion of each part of the litchi, j = 1, 2, 3;

[0045] Step 43: The calculation formula for the density of the ideal litchi model is as follows:

[0046]

[0047] In the formula, ρ lichi represents the density of the ideal litchi model; η j represents the mass proportion of each part of the litchi; W j represents the weight of each part of the litchi; V j represents the volume proportion of each part of the litchi;

[0048] Step 44: Construct the heat conduction formula inside the litchi:

[0049]

[0050] In the formula, ρ lichi represents the density of the ideal litchi model, C lichi represents the specific heat capacity of the ideal litchi model, T represents the temperature of each part of the litchi, t represents the thawing time, K lichiλ represents the thermal conductivity of litchi, and q represents the internal heat source of litchi;

[0051] Assume that the litchi is spherical with a radius of r. The heat conduction equation is converted into the spherical coordinate form, and the formula is as follows:

[0052]

[0053] Step 45: Since natural convective heat transfer occurs between the litchi and the air in the thawing environment, the total heat exchange quantity Q between the litchi surface and the thawing environment per unit time is calculated by the following formula:

[0054] Q = h·A·((T enveronment -T surface ) = q conv ·A (15),

[0055] where h is the convective heat transfer coefficient; A is the surface area of the litchi; T surface is the litchi surface temperature; T enveronment is the thawing environment temperature, and q conv is the heat flux density;

[0056] Step 46: The heat absorbed by the litchi surface will be 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, and the formula between the two is:

[0057]

[0058] where K lichi is the thermal conductivity, is the temperature gradient in the normal direction of the litchi surface, indicating the change rate of temperature in the normal direction per unit time;

[0059] Step 47: Combining formula (13), formula (14), formula (15) and formula (16) gives the following formula:

[0060]

[0061] Step 48: Rearrange formula (17) and solve it by integration to obtain the following formula:

[0062]

[0063] where T(t) is the temperature at the center of the litchi at time t, T center，0 is the initial temperature at the center of the litchi, and T enveronment (t) is the thawing environment temperature.

[0064] Preferably, in step five, if the error between the temperature curve predicted by the litchi thawing model and the actual temperature curve exceeds a predetermined value, the global optimal solution is obtained based on the first formula and the second formula in the particle swarm optimization algorithm, and the corresponding weight loss function is optimized based on the global optimal solution:

[0065] Weight loss function: η water = W water / W 0 ,

[0066] W water is the difference between the weight of the litchi before thawing and the weight at the current moment, which is manually measured after each iteration. Through optimization, η water is made less than the predetermined value;

[0067] The first formula is:

[0068]

[0069] In the formula, c 1 and c 2 represent acceleration constants; r 1 and r 2 represent random numbers between 0 and 1; w is the inertia weight; represents the weight loss function corresponding to particle i in the k-th generation; represents the optimal weight loss value experienced by particle i in the k-th generation; represents the optimal weight loss value of the entire population in the k-th generation, represents the update speed of this parameter;

[0070] The second formula is:

[0071]

[0072] In the formula, 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.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. By combining the thermal conductivity inversion identification model and the particle swarm optimization algorithm, the present invention successfully predicts the thermal conductivities of the litchi shell, pulp, and different parts. This method takes into account and conforms to the anisotropic characteristics of litchi during the heat transfer process, thus making the litchi thawing model closer to the actual thawing situation.

[0075] 2. By using the particle swarm optimization algorithm to optimize the model parameters, the present invention significantly improves the prediction accuracy of the litchi thawing model, provides important support for the industrial application of litchi thawing technology, and helps the standardization and large-scale development of the litchi thawing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is the modeling flowchart; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

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

[0080] Step 1: Select frozen litchis with the same variety, size, color, and intact appearance as samples. The size, color, and appearance can be determined according to experience.

[0081] Step 2: Weigh the litchi weight (W 0 ) and separate the litchi shell, litchi pulp, and litchi pit of the litchi, weigh the litchi shell, litchi pulp, and litchi pit respectively, and calculate the percentage η 1 , η 2 , η 3 occupied by the weight of each part;

[0082] Step 3: Place the litchi in a thawing environment and collect the temperature data of the litchi changing with time.

[0083] Step 4: Obtain the weight W 4 of the litchi after thawing, and calculate the approximate value W water of the water content and the moisture content η water ;

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

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

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

[0087] In Step 1, after determining the samples, each sample needs to be numbered in sequence for 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 W 0And the weights W of the litchi shell, litchi pulp and litchi seed 1 、W 2 、W 3 。

[0089] The specific expressions for the mass ratio of each part are as follows:

[0090] η j =W j / W 0 (j = 1, 2, 3) (1).

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

[0092] In step four, a thin-film pressure sensor with a range of 2 g to 1000 g is used to weigh the weight W of each thawed litchi sample 4 , and the water content and percentage of each litchi sample are calculated through a weight loss function

[0093] W water =W 0 -W 4 (2),

[0094] where, W water represents the weight loss value and water content value of the litchi sample, W 0 represents the weight of the litchi before thawing, W 4 represents the weight of the litchi after thawing

[0095] η water =W water / W 0 (3),

[0096] η water represents the water content rate of the sample

[0097] Step five specifically includes the following steps:

[0098] Step 51: Laser is respectively irradiated on the litchi fruit shell, pulp and seed, and a two-dimensional anisotropic heat conduction model is constructed. The two-dimensional anisotropic heat conduction model in the xy coordinate system is as follows:

[0099]

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

[0101] In the step of laser heating, q x and q y are calculated respectively using the following formulas:

[0102]

[0103]

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

[0105] Step 52: When laser heating, the inner boundary condition of the spot radius on the surfaces of the litchi pericarp, pulp, and litchi seed is a fixed heat flux 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 on the surfaces of the litchi pericarp, pulp, and litchi seed is:

[0106]

[0107] Among them, and represent the thermal diffusivity, ρ represents the material density, c represents the specific heat capacity, and Q(x, y, t) is the heat of the laser irradiated at the (x, y) position on the litchi at time t. The general representation form is:

[0108]

[0109] Among them, Q 0 represents the laser power, (x 0 , y 0 ) 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: Temperature monitoring points are arranged on the surfaces of the litchi shell, pulp, and litchi seed respectively, and the measured temperature rise values at different times for different parts are obtained and denoted as {ΔT exp，i}, where i = 1, 2,..., m, and m is the total number of measured time steps;

[0113] Step 55: Take the measured temperature rise value and the initial values of the thermal conductivities of different parts of the litchi as inputs, generate the thermal conductivity through the particle swarm optimization algorithm, substitute the thermal conductivity generated by the particle swarm optimization algorithm into Equation (9), conduct numerical simulation of the transient heat transfer process, and obtain the simulated values of the temperature rise on the surfaces of the litchi shell, pulp, and litchi core, denoted as {ΔT sim，i}, and the initial values of the thermal conductivity can be obtained based on experience or existing data;

[0114] Step 56: Construct an optimization objective function, and the formula is as follows:

[0115]

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

[0117] Step 57: Substitute {ΔT exp，i} obtained in Step 54 and (ΔT sim，i} obtained in Step 55 into Equation (10) to obtain J;

[0118] Step 58: When J is greater than or equal to the predetermined value, return to Step 55 to continue the iteration until J is less than the predetermined value. Each time Step 55 is iterated, use the measured temperature rise value at the corresponding moment and the thermal conductivities λ x and λ y obtained in the previous time as the inputs of the particle swarm optimization algorithm; when J is less than the predetermined value, use the thermal conductivity generated by the particle swarm optimization algorithm at this time as the thermal conductivities K j of the litchi shell, pulp, and litchi core, where j = 1, 2, 3, and K 1 、K 2 、K 3 represent the thermal conductivities of the litchi shell, pulp, and litchi core respectively.

[0119] Among them,

[0120] Step 6 specifically includes the following steps:

[0121] Step 61: Idealize the litchi model into a spherical model. During the thawing process of the litchi model, the specific heat capacity will change significantly with the phase change of the internal ice, and 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] In the formula, C lichi (t) represents the specific heat capacity of litchi at time t during thawing, and η water represents the weight loss function of litchi at the initial moment, and C constant represents the simulated ideal specific heat capacity of litchi without considering moisture, usually 3710 J / (kg·K); C ice represents the specific heat capacity of ice, C water represents the specific heat capacity of water, L represents the latent heat absorbed when ice melts into water, Φ(t) represents the freezing rate of litchi at time t inside, and the formula is as follows;

[0124] Φ(t) = 1 - t b / T t (12)

[0125] In the formula, t b represents the freezing point; T t represents the temperature of litchi at time t;

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

[0127]

[0128] In the formula, K lichi represents the thermal conductivity of the whole litchi; η j represents the mass ratio of each part of the litchi;

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

[0130]

[0131] In the formula, ρ lichi represents the density of the litchi ideal model; η j represents the weight fraction; W j represents the weight of different parts of the litchi; V j represents the volume ratio of different parts of the litchi;

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

[0133]

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

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

[0136]

[0137] Step 64: Since natural convective heat transfer occurs between the litchi and the air in the thawing environment, the total heat transfer amount Q between the litchi surface and the thawing environment per unit time is calculated by the following formula:

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

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

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

[0141]

[0142] where K lichi is the thermal conductivity, is the temperature gradient in the normal direction of the litchi surface, indicating the change rate of temperature in the normal direction per unit time;

[0143] Step 66: Combining formula (15), formula (16), formula (17) and formula (18) gives the following formula:

[0144]

[0145] Step 67: Rearrange 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 at the center of the litchi, and T enveronment (t) is the temperature of the thawing environment at time t.

[0148] In step seven, if there is a large error between the temperature curve predicted by the litchi thawing model and the actual temperature curve, the global optimal solution is obtained based on the first formula and the second formula in the particle swarm optimization algorithm (PSO). The corresponding weight loss function, that is, the water content, is optimized based on the global optimal solution to improve the accuracy of the litchi thawing model. Among them, let represent the weight loss function of the k-th generation of particles, denoted as Let represent the update speed of this parameter.

[0149] The weight loss function, that is, the water content: η water = W water / W 0 W water is manually measured after each iteration. Through optimization, η water is made less than the predetermined value, that is, as much water as possible in the litchi is retained during the thawing process.

[0150] The first formula is:

[0151]

[0152] In the formula, c 1 and c 2 represent the acceleration constants; r 1 and r 2 represent random numbers from 0 to 1; w is the inertia weight; represents the weight loss function corresponding to the i-th particle of the k-th generation; represents the optimal weight loss value experienced by the k-th generation of particles; represents the optimal weight loss value of the entire population in the k-th generation;

[0153] The second formula is:

[0154]

[0155] In the formula, 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 seven, 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 the root mean square error (RMSE) are used as the model evaluation indicators. Among them, the calculation formula of the correlation coefficient is as follows:

[0157]

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

[0159] The root mean square error calculation formula is as follows:

[0160]

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

[0162] The approximate water content is the variable W in the particle swarm optimization algorithm water , and the objective function is obtained by combining the model evaluation index root mean square error RMSE. The formula is as follows:

[0163]

[0164] In the formula, η j represents the mass ratio of each part of the litchi, K j represents the thermal conductivity of each part of the litchi, T enveronment represents the external thawing environment temperature, W water represents the water lost during the litchi thawing process, W j represents the weight of each part of the litchi, V j represents 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 represents the temperature point data actually collected.

[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 by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection required 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, selecting frozen lychees as samples; Step 2: placing all lychees in the same thawing environment at the same time, and recording the temperature data of each lychee at different times; Step 3: Construct a heat conduction equation based on laser heating and obtain the thermal conductivity of anisotropic litchi by optimizing the heat conduction equation. The heat conduction equation is as follows: in, T represents the laser heating temperature, t represents the laser heating time, 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; The heat conduction equation is iteratively optimized based on the measured temperature data and the temperature data predicted by the heat transfer 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.

2. The modeling method according to claim 1, characterized in that: The step three specifically includes the following steps: Step 31, irradiate the laser on the litchi shell, the litchi pulp and the litchi core respectively and construct a two-dimensional anisotropic thermal conductivity model. The two-dimensional anisotropic thermal conductivity model in the xy coordinate system is as follows: Where ρ represents density, c represents specific heat capacity, T represents laser heating temperature, t represents laser heating time, and 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; In the laser heating step, x and q y The following formulas are used to calculate respectively: 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 change in temperature in the x direction, Indicates the change in temperature in the y direction; Step 32, when laser heating is performed, the inner boundary condition of the spot radius of the surface of the litchi shell, pulp 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, pulp and litchi core is: in, and represents thermal diffusivity, ρ represents material density, c represents specific heat capacity, Q(x, y, t) is the heat irradiated by the laser at the point (x, y) on the litchi at time t, expressed as: 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; Step 33: According to formula (3), formula (5), formula (5) and formula (6), the heat conduction equation is obtained: 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, where m is the total number of measured time steps; Step 35: Taking the measured temperature rise value and the initial value of the thermal conductivity of different parts of the litchi as input, the thermal conductivity is generated by the particle swarm optimization algorithm, and the thermal conductivity generated by the particle swarm optimization algorithm is substituted into formula (5) to perform a transient heat transfer process numerical simulation, and the simulated values ​​of the surface temperature rise of the litchi shell, the pulp and the litchi core are obtained respectively, which are recorded as {ΔT sim，i }; Step 36: Construct the optimization objective function. The formula is as follows: J is the target value output by formula (8); Step 37: The {ΔT exp，i } and {ΔT obtained in step 35 sim，i Substitute into formula (8) to obtain J; Step 38: When J is greater than or equal to a predetermined value, return to step 35 and continue to iterate until J 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 inputs of the particle swarm optimization algorithm. When J is less than a 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.

3. The modeling method according to claim 2, characterized in that: The step 4 specifically includes the following steps: Step 41, idealizing the litchi model into a spherical model, and expressing 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), In the formula, C lichi (t) refers to the specific heat capacity of litchi at time t, η water 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 the water content, 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; Φ(t)=1-t b / T t (10); Where, t b Refers to the freezing point, T t Indicates the real-time temperature of litchi at time t; Step 42, the calculation formula of thermal conductivity of the ideal model of litchi is as follows: In the formula, K lichi represents the thermal conductivity of the whole litchi; η j Indicates the mass proportion of each part of litchi, j = 1, 2, 3; Step 43: The density calculation formula of the ideal litchi model is as follows: In the formula, 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; Step 44: Construct the heat conduction formula inside the litchi: In the formula, 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; Assuming that the lychee is spherical with a radius of r, the heat conduction equation is converted into spherical coordinates as follows: Step 45: Due to the 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 by the following formula: Q=h·A·(T enveronment -T surface )=q conv ·A (15), 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; 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: Among them, K lichi is the thermal conductivity, is the temperature gradient in the normal direction of the litchi surface, 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), the following formula is obtained: Step 48: Arrange formula (17) and solve it by integration to obtain the following formula: 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 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 litchi thawing model and the actual temperature curve exceeds a predetermined value, a global optimal solution is obtained based on the first formula and the second formula in the particle swarm optimization algorithm, and the corresponding weight loss function is optimized based on the global optimal solution: Weight loss function: η water =W water / W0, W water is the difference between the weight of the litchi before thawing and the weight at the current moment, which is measured manually after each iteration. Through optimization, η water Less than a predetermined value; The first formula is: In the formula, 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; The second formula is: In the formula, 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.

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