Dielectric inversion simulation method and system for microwave hyperthermia of citrus seedlings

Abstract

The invention discloses a dielectric inversion simulation method and system for microwave hyperthermia of citrus nursery stocks, and relates to the technical field of agricultural simulation, and the method comprises the steps: randomly selecting a plurality of target nursery stocks to obtain a measurement sample, and obtaining related measurement data; building a nursery stock simulation model based on related measurement data; constructing a microwave device simulation model and forming a nursery stock heating simulation model with the nursery stock simulation model; acquiring actually measured temperature data of a sample homologous with the measurement sample; setting an initial dielectric parameter, inputting the initial dielectric parameter and the actually measured temperature data into the nursery stock heating simulation model, and performing inversion iteration by taking the minimum root-mean-square error as a target to obtain an optimal dielectric parameter; importing a nursery stock heating simulation model based on the optimal dielectric parameters to obtain simulation temperature data; and performing comparison verification based on the simulation temperature data and the actually measured temperature data, and putting the seedling heating simulation model passing the verification into use. The optimal dielectric constants of different tissues are determined, and accurate prediction of the internal temperature field in the microwave heating process of the citrus nursery stock is realized.

Description

Technical Field

[0001] This invention relates to the field of agricultural simulation technology, and more specifically to a dielectric inversion simulation method and system for microwave thermotherapy of citrus seedlings. Background Technology

[0002] Currently, Huanglongbing (HLB), a quarantine disease caused by bacteria of the genus Candidatus Liberibacter, has become the "number one killer" restricting the sustainable development of the citrus industry. Heat treatment technology has become one of the core technical pathways for the prevention and control of HLB because it can denature and inactivate the pathogen's proteins through high temperature without significantly damaging the plant. Traditional heat treatment methods (hot water, moist steam, hot air) have problems such as poor heating uniformity, high energy consumption, and long treatment cycle. In contrast, microwave heat treatment has advantages such as fast heating speed, low energy consumption, and convenient operation due to its "volume heating" characteristics, and has gradually become a research hotspot in recent years.

[0003] However, the practical application of microwave heat treatment faces two major technical bottlenecks: First, the dynamic distribution of the internal temperature field of seedlings cannot be monitored in real time, leading to blind parameter control and problems such as "insufficient temperature for sterilization" or "excessive temperature damaging seedlings." Second, the growth cycle of citrus seedlings is as long as 6-12 months, and traditional physical experiments require a large number of seedlings and have an experimental cycle of 3-6 months, resulting in high research and development costs and low efficiency, which seriously hinders the large-scale promotion of the technology. Current related technologies cannot effectively support solutions to these problems. On the one hand, although plant dielectric property inversion technology has been used in agriculture for moisture content assessment and pest and disease detection, its research objects are limited to single tissues such as trunks or leaves, without considering the three-dimensional topological structure of the whole plant and its coupling effect on the microwave field distribution, and without establishing a quantitative correlation model between dielectric parameters and microwave heating temperature, making it difficult to serve the precise control of the heat treatment process. On the other hand, agricultural multiphysics simulation technology mainly focuses on teaching and visualization scenarios such as plant morphology reconstruction, root growth, or pest and disease transmission. It generally adopts the default material parameters in the literature, ignores the dynamic changes in dielectric properties, and does not couple the multi-field interaction mechanism of microwave-thermal-biological effects, resulting in limited simulation accuracy and failing to meet the needs of high-precision simulation of microwave thermotherapy processes.

[0004] Therefore, determining the optimal dielectric constant for different tissues and thus accurately predicting the internal temperature field during microwave heating of citrus seedlings is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, this invention is proposed to provide a dielectric inversion simulation method and system for microwave thermotherapy of citrus seedlings to overcome or at least partially solve the above problems. The optimal dielectric constant of different tissues is determined, thereby realizing accurate prediction of the internal temperature field of citrus seedlings during microwave heating.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a dielectric inversion simulation method for microwave thermotherapy of citrus seedlings, comprising: Multiple target seedlings were randomly selected to obtain measurement samples and relevant measurement data. A seedling simulation model was constructed based on the relevant measurement data; A microwave device simulation model is constructed and combined with the seedling simulation model to form a seedling heating simulation model; Obtain measured temperature data of a sample that is from the same source as the measured sample; The initial dielectric parameters are set and input together with the measured temperature data into the seedling heating simulation model. With the goal of minimizing the root mean square error, the optimal dielectric parameters are obtained by inversion and iteration. Based on the optimal dielectric parameters, the seedling heating simulation model is imported to obtain relevant simulation temperature data. The simulation temperature data was compared and verified with the measured temperature data, and the verified seedling heating simulation model was put into application.

[0008] In another embodiment, the method for acquiring the relevant measurement data is as follows: A predetermined number of citrus seedlings that meet the requirements are randomly selected as the target seedlings; Multiple leaf and branch samples were selected as measurement samples for each target seedling. Based on the leaf samples and the branch samples, the average value of each individual plant was taken after repeated measurements, and then the overall average value of the set number was taken to obtain the corresponding leaf measurement data and branch measurement data. The leaf measurement data and the branch measurement data are used together as the relevant measurement data.

[0009] In another embodiment, the leaf measurement data includes: the length of the main vein of the leaf, the maximum width of the leaf, the length of the leaf petiole, and the diameter of the leaf petiole; The branch measurement data includes: diameter at the base of the branch, diameter at the middle of the branch, diameter at the top of the branch, and length of the branch.

[0010] In another embodiment, the method for constructing the seedling simulation model is as follows: Based on the blade measurement data, a blade simulation model was constructed using the "contour parameterization + semi-model mirroring" technique. Based on the measured data of the branches, a branch simulation model was constructed using the "branch structure parameterization + variable cross-section lofting" technique. The leaf simulation model is imported and assembled into the branch simulation model through feature constraints to obtain the seedling simulation model.

[0011] In another embodiment, the method for constructing the microwave device simulation model is as follows: Draw the cavity and symmetrically draw multiple waveguide ports on both sides of the cavity, and draw a turntable at the bottom of the cavity to complete the construction of the microwave device simulation model; The method for constructing the seedling heating model is as follows: The seedling simulation model is set in the microwave device simulation model, and the electromagnetic thermal effect coupling term is enabled. Material parameters, initial conditions and boundary conditions are set, and the solver is set to transient solution to obtain the seedling heating model.

[0012] In another embodiment, the method for acquiring the measured temperature data is as follows: Leaves and branches of the same origin as the measured samples were obtained as control samples; The control sample was placed in a microwave device and heated, and the temperature data for a preset time was recorded as the measured temperature data. The measured temperature data includes: measured temperature data of leaves and measured temperature data of branches.

[0013] In another embodiment, the method for obtaining the optimal dielectric parameter is as follows: The measured temperature curve is obtained based on the measured temperature data; Based on the measured temperature curve and the initial dielectric parameters, the seedling heating model is input, and the initial dielectric parameters are iteratively adjusted using an interior point optimization algorithm with the goal of minimizing the root mean square error between the simulated temperature and the measured temperature. The current dielectric parameter corresponding to the point where the root mean square error is less than a set threshold is taken as the current optimal parameter. The inversion is repeated multiple times to obtain multiple current optimal parameters, and the average value is taken as the optimal dielectric parameter. The optimal dielectric parameters include: the optimal leaf dielectric constant and the optimal branch dielectric constant.

[0014] In another embodiment, the comparison verification specifically includes: The relevant simulated temperature data includes: leaf simulated temperature data, branch simulated temperature data, and simulated temperature data of each temperature measurement point. The goodness of fit and root mean square error are calculated based on the measured temperature data of the leaves and branches, corresponding to the simulated temperature data of the leaves and branches. The verification is successful when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. Based on the above verification process, the leaf simulation model and the branch simulation model in the seedling heating simulation model are verified respectively. If the verification is successful, it indicates that the constructed leaf simulation model and branch simulation model meet the accuracy requirements. Under different power conditions, the measured temperature data of temperature measurement points at different locations on seedlings of the same origin as the measured sample were obtained; Based on the comparison between the measured temperature data and the simulated temperature data of the temperature measurement point, the verification is successful when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. The accuracy of the seedling simulation model under different power levels was verified to meet the requirements. After all the above verifications were passed, the seedling heating simulation model was used as a simulation test platform and put into application.

[0015] In another embodiment, the input is used for, specifically including: Based on the input of different microwave cavity structure parameters into the simulation test platform, the optimal cavity structure with the highest temperature uniformity was obtained through simulation screening. Based on the optimal cavity structure, different power-duration combinations are simulated and matched with the pathogen inactivation temperature threshold to obtain precise thermotherapy parameters; Based on the optimal cavity structure and the precise thermotherapy parameters, the device was transformed into a practical device and then put into application after iterative adjustments through field trials.

[0016] Secondly, embodiments of the present invention provide a dielectric inversion simulation system for microwave hyperthermia of citrus seedlings, comprising: a measurement data acquisition module, a simulation model construction module, an optimal parameter acquisition module, a simulation data acquisition module, and a model verification application module; The measurement data acquisition module is used to randomly select multiple target seedlings to obtain measurement samples and acquire relevant measurement data; The simulation model construction module is used to construct a seedling simulation model based on the relevant measurement data; construct a microwave device simulation model and combine it with the seedling simulation model to form a seedling heating simulation model; The optimal parameter acquisition module is used to acquire the measured temperature data of a sample that is from the same source as the measured sample; set the initial dielectric parameter and input it together with the measured temperature data into the seedling heating simulation model; and obtain the optimal dielectric parameter by inversion iteration with the goal of minimizing the root mean square error. The simulation data acquisition module is used to import the seedling heating simulation model based on the optimal dielectric parameters to obtain relevant simulation temperature data. The model verification application module is used to verify the simulated temperature data by comparing it with the measured temperature data, and then put the verified seedling heating simulation model into application.

[0017] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a dielectric inversion simulation method and system for microwave thermotherapy of citrus seedlings, which has the following beneficial effects: 1. Significantly reduce experimental costs: Traditional parameter optimization requires 30-50 seedlings, while this invention only requires 3 seedlings for initial modeling to obtain verification data. Subsequent parameter optimization is completed entirely through simulation, reducing seedling consumption by more than 90%, thereby significantly reducing resource waste and R&D costs.

[0018] 2. Significantly shortens the research cycle: The parameter optimization cycle of traditional physical experiments can take up to 3-6 months (including seedling cultivation time). This invention can complete the simulation and screening of more than 20 parameter combinations in 5-7 days through simulation models, improving efficiency by more than 90% and accelerating the iteration and promotion of technology.

[0019] 3. Achieve accurate temperature field prediction: It solves the problem of difficulty in real-time monitoring of the temperature field of the phloem and xylem inside seedlings during microwave heating. The model can accurately output the dynamic temperature distribution of different parts, greatly reducing the temperature prediction error, providing a scientific basis for precise control of thermotherapy parameters, and significantly improving the sterilization success rate.

[0020] 4. This invention constructs a three-dimensional model based on measured morphological parameters to restore the true structure of seedlings; by physically removing the soil and excluding soil factors in the modeling, the interference of dielectric property fluctuations on model accuracy is avoided, thus improving the reliability of simulation.

[0021] 4. Improved thermotherapy effect and seedling survival rate: Through simulation optimization and selection of parameter combinations, the internal temperature of seedlings can be stabilized in the sterilization range of 48-54℃, the temperature difference between different parts is ≤5℃, and the yellowing rate of leaves is ≤10%, which takes into account both the control effect and the safety of seedling growth, and solves the problems of "uneven temperature and easy damage to seedlings" in traditional thermotherapy.

[0022] 5. High scalability and wide application scenarios: This invention forms a general process of "morphological measurement → modeling → inversion → verification → optimization". By adjusting morphological parameters and inverting the dielectric constant of corresponding tissues, it can be adapted to microwave thermotherapy simulation of different citrus varieties (Wogan, navel orange, etc.) and other agricultural and forestry plants. The technology has high reusability and provides a general tool for the prevention and control of diseases and pests in multiple fields, with broad application prospects. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0024] Figure 1 This is a flowchart of a dielectric inversion simulation method for microwave thermotherapy of citrus seedlings provided in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram comparing the simulated blade temperature and the measured blade temperature curves provided in this embodiment of the invention.

[0026] Figure 3 This is a schematic diagram comparing the simulated temperature of branches with the measured temperature of branches provided in this embodiment of the invention.

[0027] Figure 4 This is a schematic diagram comparing the measured temperature data and the simulated temperature data curve of the 150W temperature measuring point provided in this embodiment of the invention.

[0028] Figure 5 This is a schematic diagram comparing the measured temperature data and the simulated temperature data curve of the 250W temperature measuring point provided in this embodiment of the invention.

[0029] Figure 6 This is a schematic diagram comparing the measured temperature data of the 350W temperature measuring point and the simulated temperature data curve of the temperature measuring point provided in the embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram comparing the measured temperature data and the simulated temperature data curve of the 450W temperature measuring point provided in this embodiment of the invention.

[0031] Figure 8 This is a schematic diagram comparing the measured temperature data and the simulated temperature data curve of the 550W temperature measuring point provided in this embodiment of the invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1 like Figure 1As shown, this embodiment of the invention discloses a dielectric inversion simulation method for microwave thermotherapy of citrus seedlings, including the following steps. For ease of description, these steps are numbered S1 to S7, and these numbers are not used to limit the sequential relationship between the various steps of this invention: S1 randomly selects multiple target seedlings to obtain measurement samples and acquires relevant measurement data.

[0034] Furthermore, the relevant measurement data acquisition method is as follows: A set number of citrus seedlings that meet the requirements are randomly selected as target seedlings; Multiple leaf and branch samples were selected as measurement samples for each target seedling. Based on repeated measurements of leaf and branch samples, the average value of each individual plant was taken, and then the overall average value of a set number of samples was taken to obtain the corresponding leaf and branch measurement data. Leaf measurement data and branch measurement data are used together as relevant measurement data.

[0035] Furthermore, in this embodiment, three robust Wogan mandarin orange seedlings, each 60cm tall and 6 months old, planted in an insect-proof net house and managed with conventional water and fertilizer, were randomly selected as target seedlings; for each seedling, 10 new leaves, 10 old leaves, and 3 sections of the middle branch of the main trunk (15cm in length) were selected as measurement samples.

[0036] Furthermore, the measuring equipment uses an electric ruler with an accuracy of 0.1 cm; the leaf measurement data includes: the length of the main vein of the leaf, the maximum width of the leaf, the length of the leaf petiole, and the diameter of the leaf petiole; the branch measurement data includes: the diameter of the branch base, the diameter of the branch middle, the diameter of the branch tip, and the branch length.

[0037] Furthermore, in this embodiment, each sample was measured three times. The average value of a single plant was taken, and then the average value of the three plants was taken as a whole. The final values ​​obtained were: leaf main vein length 7.0±0.2cm, leaf maximum width 3.0±0.1cm, leaf petiole length 1.0±0.05cm, and leaf petiole diameter 0.3±0.02cm. The same method was used to measure the branches. The average value of a single plant was taken, and then the average value of the three plants was taken as a whole. The results were: branch base diameter 1.0±0.08cm, branch middle diameter 0.8±0.05cm, branch tip diameter 0.5±0.03cm, and branch length 15.0±0.5cm. During data processing, outliers (deviations from the mean ± 2 standard deviations) were removed using the Laida criterion to ensure data reliability.

[0038] S2 constructs a seedling simulation model based on relevant measurement data.

[0039] Furthermore, the method for constructing the seedling simulation model is as follows: A blade simulation model was constructed using the "contour parameterization + semi-model mirroring" technique based on blade measurement data. A branch simulation model was constructed based on the branch measurement data using the "branch structure parameterization + variable cross-section lofting" technique. The leaf simulation model is imported into the branch simulation model through feature constraints and assembled to obtain the seedling simulation model.

[0040] Furthermore, since the dielectric constant of soil fluctuates greatly and is prone to introducing model errors, soil factors are excluded during the modeling process.

[0041] Furthermore, the method for constructing the blade simulation model is as follows: Draw a two-dimensional coordinate system with the X-axis representing the main leaf vein axis and the Y-axis representing the perpendicular bisector of the main leaf vein; Draw the half-outline of the leaf, the cross-section of the leaf petiole, and the cross-section of the main vein of the leaf in a two-dimensional coordinate system; After generating the main vein based on the stretching boss of the main vein section of the leaf and merging it with the petiole Boolean, the complete two-dimensional outline of the leaf is obtained by mirroring the half-contour with the vertical line of the main vein as the axis of symmetry. The blade's two-dimensional outline is stretched using the lofting boss function to generate a three-dimensional solid blade. By assigning materials to the three-dimensional solid blade, a blade simulation model is obtained.

[0042] Furthermore, in this embodiment, the blade material includes a density of 1100 kg / m³ and a thermal conductivity of 0.2 W / (m³). K) Furthermore, the method for constructing the branch simulation model is as follows: The stretching boss generates the main branch; Plot the growth trajectory of three lateral branches using spline curves; The 3D solid of the side branches is generated by sweeping along the trajectory with a circular cross section. The main branches and side branches are merged by Boolean to eliminate the gaps in the structure, and the branches are assigned a material to obtain the simulation model of the branches.

[0043] Furthermore, in this embodiment, the main branch is 40cm long, with a base diameter of 1.0cm and a top diameter of 0.6cm; the lateral branches have a growth trajectory length of 12-15cm and an angle of 30° to 45° with the main branch; the circular cross-section has a base diameter of 0.8cm and a top diameter of 0.3cm; the branch material includes a density of 1000kg / m³ and a thermal conductivity of 0.06W / (m³). K).

[0044] Furthermore, the method for constructing the seedling simulation model is as follows: Create a new assembly file, import the branch simulation model, and arrange one lateral branch every 5-8cm according to the leaf sequence pattern to assemble the lateral branches in an array. The leaf simulation model was imported and the leaf assembly was achieved by using the feature constraint that the petiole and the lateral branch surface overlapped. Each lateral branch was equipped with 4-6 leaves, and the total number of leaves was the average of the measured values ​​of 3 seedlings. Define the material appearance and rough texture, set the leaves to green and glossy texture, and complete the construction of the seedling simulation model.

[0045] S3 constructs a microwave device simulation model and combines it with the seedling simulation model to form a seedling heating simulation model.

[0046] Furthermore, the method for constructing the microwave device simulation model is as follows: Draw the cavity and symmetrically draw multiple waveguide ports on both sides of the cavity. Draw a turntable at the bottom of the cavity to complete the construction of the microwave device simulation model.

[0047] Furthermore, in this embodiment, the microwave device modeling is based on COMSOL Multiphysics 6.3, restoring the core structure of the CNWB-6X microwave device. The width, thickness, and height of the cavity are drawn to be 700mm, 600mm, and 100mm respectively. Four rectangular waveguide ports with dimensions of 50mm × 80mm are symmetrically drawn on both sides of the cavity, and the spacing between each waveguide port is set to 150mm. A turntable with a diameter of 300mm and a thickness of 10mm is drawn at the bottom of the cavity, thus completing the modeling of the microwave device simulation model.

[0048] Furthermore, the method for constructing the seedling heating model is as follows: The seedling simulation model was set in the microwave device simulation model, and the electromagnetic thermal effect coupling term was enabled. Material parameters, initial conditions and boundary conditions were set, and the solver was set to transient solution to obtain the seedling heating model.

[0049] Furthermore, in COMSOL Multiphysics 6.3, enable the "Microwave Heating" module, build a coupled physical field, select "Electromagnetic Wave, Frequency Domain" and "Solid Heat Transfer" for the physical field, and enable the "Electromagnetic Thermal Effect" coupling item.

[0050] Furthermore, the material parameters include: leaf material parameters and branch material parameters; The blade material parameters include: initial relative permittivity of 10⁻⁶ J × 2, density of 1100 kg / m³, and thermal conductivity of 0.2 W / (m³). K), specific heat capacity under constant pressure 3700 J / (kg) K), electrical conductivity 0 S / m and relative magnetic permeability 1; The material parameters of the branches and trunks include: an initial relative permittivity of 7-j×3, a density of 1000 kg / m³, and a thermal conductivity of 0.06 W / (m³). K), specific heat capacity under constant pressure 2200 J / (kg) K), electrical conductivity 0 S / m, relative permeability 1.

[0051] Furthermore, the initial conditions were set as follows: the global electric field strength was 0 V / m, the magnetic field strength was 0 A / m, the initial value of the microwave port excitation was 0 V, the global initial temperature was 299.15 K (26 °C), and there was no temperature gradient. T=0.

[0052] Furthermore, the boundary conditions are as follows: the cavity wall and the inner wall of the waveguide are set as ideal electric conductors, and the normal component of the electric field intensity is 0; the waveguide is set as port excitation, and the mode type is rectangular.

[0053] Furthermore, the solver is set to transient solution, with a time range of 0-60 seconds, an initial time step of 0.1 seconds, a minimum of 0.01 seconds, and a maximum of 0.5 seconds; The residual threshold is set to 0.0001, and the maximum number of iterations is set to 5000. An adaptive time step is used: the step size is increased when the residual is below 0.0001 and decreased when it is above 0.001; In the iterative algorithm, the electromagnetic field adopts the finite element method with a tetrahedral mesh type: the mesh size of the seedling area is 5mm and the mesh size of the device area is 10mm. The temperature field adopts the finite volume method, and the maximum number of iterations is 5000.

[0054] S4 acquires the measured temperature data of a sample from the same source as the measured sample.

[0055] Furthermore, the method for obtaining the measured temperature data is as follows: Leaves and branches of the same origin as the measured samples were obtained as control samples; The control sample was placed in a microwave device and heated, and the temperature data for a preset time was recorded as the measured temperature data. The measured temperature data includes: measured temperature data of leaves and measured temperature data of branches.

[0056] Furthermore, during the acquisition of measured temperature data, leaf samples from the same source as those used in modeling were selected: main vein length 7.0±0.2cm and branch samples: diameter 0.8±0.05cm. After removing the soil, the samples were fixed to the center of the turntable with high-temperature resistant tape. The microwave device was started, with the power set to 250W, the working frequency 2.45GHz, and the turntable speed 30r / min. A fiber optic thermometer was used, with the probe inserted 1cm from the main vein into the middle of the leaf and the middle of the branch, to a depth of 0.3cm, corresponding to the phloem position. Temperature data from 0 to 60 seconds were recorded as measured temperature data.

[0057] Furthermore, the fiber optic thermometer used in this embodiment is model FOT-M20, with a temperature measurement range of -40℃ to 210℃, an accuracy of 0.1℃, and a sampling frequency of 1 time / second.

[0058] S5 sets the initial dielectric parameters and inputs them together with the measured temperature data into the seedling heating simulation model. With the goal of minimizing the root mean square error, it performs inversion iteration to obtain the optimal dielectric parameters.

[0059] Furthermore, the method for obtaining the optimal dielectric parameters is as follows: Obtain the measured temperature curve based on the measured temperature data; Based on the measured temperature curve and the initial dielectric parameters input into the seedling heating model, the initial dielectric parameters are iteratively adjusted using an interior point optimization algorithm with the goal of minimizing the root mean square error between the simulated temperature and the measured temperature. The current dielectric parameter is taken as the current optimal parameter when the root mean square error is less than the set threshold. Repeat the inversion multiple times to obtain multiple current optimal parameters and take the average value as the optimal dielectric parameter; The optimal dielectric parameters include the optimal leaf dielectric constant and the optimal branch dielectric constant.

[0060] Furthermore, relying on the "Optimization Module" of COMSOL Multiphysics 6.3, the interior-point optimization algorithm (IPOPT) is used to solve for the dielectric constant in reverse. The objective function is defined as minimizing the root mean square error (RMSE) between the simulated and measured temperature curves. The objective function expression is as follows: ; in, i Indicates the symbol of the observation point. n Indicates the number of observation points. R i and M i These represent the measured temperature value and the simulated temperature value, respectively.

[0061] Furthermore, during the inversion iteration process, the measured temperature curves are first imported into the COMSOL Multiphysics 6.3 optimization module in the seedling heating model; Set the initial dielectric parameters: 10-j×2 for leaves and 7-j×3 for branches; The interior point optimization algorithm IPOPT is started. With the goal of minimizing the root mean square error between the simulated temperature and the measured temperature, the real and imaginary parts of the dielectric constant are automatically iterated and fine-tuned. The adjusted parameters are then input into the simulation model in real time to calculate the temperature deviation. The current simulated and measured temperature values ​​are automatically calculated and output. The iteration continues until the root mean square error RMSE ≤ 0.0001, at which point it stops. The current dielectric constant is recorded. The inversion is repeated 3 times, and the average of the 3 sets of dielectric constants is taken as the optimal dielectric parameter. The optimal dielectric parameters include: the optimal blade dielectric constant is 10.0617. -j×0.9856The optimal branch dielectric constant is 7.5015. -j×7.9711 .

[0062] S6 imports the seedling heating simulation model based on the optimal dielectric parameters and obtains the relevant simulation temperature data.

[0063] Furthermore, the relevant simulated temperature data includes: simulated temperature data of leaves, simulated temperature data of branches and trunks, and simulated temperature data of each temperature measurement point.

[0064] Furthermore, based on the optimal dielectric parameters, the simulation models of the leaves and branches were imported respectively. The microwave power was set to 250W, and the heating time was 60 seconds. The simulation was run to obtain the corresponding simulated temperature data of the leaves and branches. The optimal dielectric parameters were imported into the seedling simulation model. The power gradient was set to 150W, 250W, 350W, 450W and 550W. Each power group was repeated 3 times. The temperature measurement point was set at the top of the seedling, 5cm away from the top. One fiber optic probe was inserted into the phloem, the middle (midpoint of the trunk, phloem) and the bottom (5cm away from the base, phloem) to obtain the simulated temperature data of each temperature measurement point under different power.

[0065] S7 verified the seedling heating simulation model by comparing simulated temperature data with measured temperature data, and put the verified seedling heating simulation model into application.

[0066] Furthermore, the comparative verification specifically includes: Single-component validation: Based on the correspondence between measured temperature data of leaves and branches and simulated temperature data of leaves and branches, the goodness of fit and root mean square error are calculated. The verification is successful when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. Based on the above verification process, the leaf simulation model and branch simulation model in the seedling heating simulation model were verified respectively. If the verification is successful, it indicates that the constructed leaf simulation model and branch simulation model meet the accuracy requirements.

[0067] Whole-plant verification: Under different power conditions, the measured temperature data of temperature measurement points at different locations of the same seedlings as the measurement sample were obtained; Based on the comparison between the measured temperature data and the simulated temperature data of the temperature measurement points, the verification is passed when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. The accuracy of the seedling simulation model under different power levels was verified to meet the requirements. After all the above verifications were passed, the seedling heating simulation model was used as a simulation test platform and put into application.

[0068] Furthermore, the first threshold is set to 98.5%, and the second threshold is set to 3% of the average measured temperature.

[0069] Furthermore, the goodness of fit R 2 Specifically: ; in, x This indicates the measured temperature. y Indicates the simulated temperature. This represents the average value of the measured temperatures. This represents the average value of the simulated temperature.

[0070] Furthermore, in this embodiment, the verification results of the blade simulation model are shown in Table 1: Table 1 Validation results of the blade simulation model

[0071] The comparison chart of simulated and measured blade temperatures is shown below. Figure 2 As shown in Table 1, the quantitative indicators show that the goodness of fit (R²) of the leaf inversion reaches 98.7%, satisfying the condition of R² ≥ 98.5%. Furthermore, the mean root mean square error (RMSE) of the leaf is 2.93 K, which is far less than 3% (9.72 K) of the mean measured temperature, indicating that the error is within a reasonable and controllable range. Figure 2 In the simulation, multiple sets of simulated temperature curves (including the mean) and measured temperature curves showed a complete overlap in trend and minimal numerical difference within 0-60s, intuitively matching the temperature change process and confirming the accuracy and effectiveness of the blade simulation model.

[0072] Furthermore, in this embodiment, the verification results of the branch simulation model are shown in Table 2: Table 2 Validation results of the branch simulation model

[0073] The comparison chart of simulated temperature and measured temperature curves of branches and trunks is shown below. Figure 3 As shown in Table 2, quantitatively, the goodness of fit R² for branch inversion reaches 99.5%, satisfying the condition R² ≥ 98.5%. Furthermore, the mean root mean square error (RMSE) of the branches is 2.67 K, far less than 3% (9.59 K) of the mean measured temperature, indicating the error is within a reasonable and controllable range. Figure 3 In the simulation, multiple sets of simulated temperature curves (including the mean) and measured temperature curves showed a complete overlap in trend and minimal numerical difference within 0-60s, intuitively matching the temperature change process and confirming the accuracy and effectiveness of the branch simulation model.

[0074] Furthermore, in this embodiment, during whole-plant verification, the power gradient was set to 150W, 250W, 350W, 450W, and 550W, with each power group repeated three times. Temperature measurement points were set at the top (5cm from the top, phloem), middle (midpoint of the trunk, phloem), and bottom (5cm from the base, phloem), with one fiber optic probe inserted at each point. The verification results of the seedling simulation model corresponding to the whole seedling under different power levels are shown in Table 3. Table 3. Validation results of the seedling simulation model under different power levels.

[0075] Based on the data in Table 3, it can be seen that the accuracy and effectiveness of the seedling simulation model of the present invention are reflected in the high fitting degree and low error within the effective power range: in the target power range of 150W-450W, the goodness of fit R² of each part (top / middle / bottom) of the whole seedling is above 98.4%, the root mean square error RMSE is much less than "3% of the average measured temperature", and all meet the fitting requirements, indicating that the simulation and the actual measurement are extremely closely matched. When the power increased to 550W, the R² dropped significantly and the RMSE exceeded 3% of the measured temperature average, failing to meet the fitting requirements. This not only demonstrates the model's accuracy within the effective power range of 150W-450W, but also clarifies its applicable boundaries, further confirming the model's reliability and effectiveness.

[0076] The comparison curves of measured temperature data and simulated temperature data at different power levels are shown in the figure below. Figures 4-8 As shown, combined with Figures 4-8 The fitting performance of the seedling simulation model of this invention under different power levels can be seen intuitively: at 150W ( Figure 4 ), 250W Figure 5 ), 350W Figure 6 ), 450W Figure 7 In the effective power range, the simulated temperature curves (different colored lines) in each figure are completely consistent with the trends of the measured temperature curves. The curves are highly consistent and the numerical differences are minimal. This directly corresponds to the quantitative results of "high goodness of fit and controllable error" under these power levels in Table 3, demonstrating that the model meets the fitting requirements. At 550W ( Figure 8 When the measured temperature curve deviates significantly from the simulated temperature curve, the curve fit decreases drastically, which corresponds to the conclusion in Table 3 that "the fitting requirements are not met" under this power, and also clarifies the applicable power boundary of the model.

[0077] Furthermore, the investment is used for, specifically including: Based on the simulation test platform, different microwave cavity structure parameters were input, and the optimal cavity structure with the highest temperature uniformity was obtained through simulation screening. Based on the optimal cavity structure, different power-duration combinations are simulated and matched with the pathogen inactivation temperature threshold to obtain precise thermotherapy parameters; Based on the optimal cavity structure and precise thermotherapy parameters, the device was transformed into a practical device and then put into application after iterative adjustments through field trials.

[0078] Example 2 Based on the same inventive concept, this invention also provides a dielectric inversion simulation system for microwave hyperthermia of citrus seedlings, comprising: a measurement data acquisition module, a simulation model construction module, an optimal parameter acquisition module, a simulation data acquisition module, and a model verification application module; The measurement data acquisition module is used to randomly select multiple target seedlings to obtain measurement samples and acquire relevant measurement data. The simulation model building module is used to build a seedling simulation model based on relevant measurement data; and to build a microwave device simulation model and combine it with the seedling simulation model to form a seedling heating simulation model. The optimal parameter acquisition module is used to acquire the measured temperature data of samples from the same source as the measured sample; the initial dielectric parameter is set and input together with the measured temperature data into the seedling heating simulation model, and the optimal dielectric parameter is obtained through inversion iteration with the goal of minimizing the root mean square error; The simulation data acquisition module is used to import the seedling heating simulation model based on the optimal dielectric parameters and obtain relevant simulation temperature data. The model validation application module is used to compare and validate simulated temperature data with measured temperature data, and then put the validated seedling heating simulation model into application.

[0079] Furthermore, in this embodiment, the functional implementation methods of each functional module correspond one-to-one with the methods described above, and will not be repeated here.

[0080] Example 3 Based on the same inventive concept, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores instructions, characterized in that the instructions are loaded and executed by the processor to implement a dielectric inversion simulation method for microwave hyperthermia of citrus seedlings as in Example 1.

[0081] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes the program stored in the memory, it can implement a dielectric inversion simulation method for microwave thermotherapy of citrus seedlings, as shown in Example 1.

[0082] The electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute the dielectric inversion simulation method for microwave hyperthermia of citrus seedlings as described in Embodiment 1.

[0083] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0084] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0085] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dielectric inversion simulation method for microwave thermotherapy of citrus seedlings, characterized in that, include: Multiple target seedlings were randomly selected to obtain measurement samples and relevant measurement data. A seedling simulation model was constructed based on the relevant measurement data; A microwave device simulation model is constructed and combined with the seedling simulation model to form a seedling heating simulation model; Obtain measured temperature data of a sample that is from the same source as the measured sample; The initial dielectric parameters are set and input together with the measured temperature data into the seedling heating simulation model. With the goal of minimizing the root mean square error, the optimal dielectric parameters are obtained by inversion and iteration. Based on the optimal dielectric parameters, the seedling heating simulation model is imported to obtain relevant simulation temperature data. The simulation temperature data was compared and verified with the measured temperature data, and the verified seedling heating simulation model was put into application.

2. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 1, characterized in that, The method for obtaining the relevant measurement data is as follows: A predetermined number of citrus seedlings that meet the requirements are randomly selected as the target seedlings; Multiple leaf and branch samples were selected as measurement samples for each target seedling. Based on the leaf samples and the branch samples, the average value of each individual plant was taken after repeated measurements, and then the overall average value of the set number was taken to obtain the corresponding leaf measurement data and branch measurement data. The leaf measurement data and the branch measurement data are used together as the relevant measurement data.

3. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 2, characterized in that, The leaf measurement data includes: leaf main vein length, leaf maximum width, leaf petiole length, and leaf petiole diameter; The branch measurement data includes: diameter at the base of the branch, diameter at the middle of the branch, diameter at the top of the branch, and length of the branch.

4. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 3, characterized in that, The method for constructing the seedling simulation model is as follows: Based on the blade measurement data, a blade simulation model was constructed using the "contour parameterization + semi-model mirroring" technique. Based on the measured data of the branches and trunks, a branch and trunk simulation model was constructed using the "branch structure parameterization + variable cross-section lofting" technique. The leaf simulation model is imported and assembled into the branch simulation model through feature constraints to obtain the seedling simulation model.

5. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 4, characterized in that, The method for constructing the microwave device simulation model is as follows: Draw the cavity and symmetrically draw multiple waveguide ports on both sides of the cavity, and draw a turntable at the bottom of the cavity to complete the construction of the microwave device simulation model; The method for constructing the seedling heating model is as follows: The seedling simulation model is set in the microwave device simulation model, and the electromagnetic thermal effect coupling term is enabled. Material parameters, initial conditions and boundary conditions are set, and the solver is set to transient solution to obtain the seedling heating model.

6. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 5, characterized in that, The method for obtaining the measured temperature data is as follows: Leaves and branches of the same origin as the measured samples were obtained as control samples; The control sample was placed in a microwave device and heated, and the temperature data for a preset time was recorded as the measured temperature data. The measured temperature data includes: measured temperature data of leaves and measured temperature data of branches.

7. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 6, characterized in that, The method for obtaining the optimal dielectric parameters is as follows: The measured temperature curve is obtained based on the measured temperature data; Based on the measured temperature curve and the initial dielectric parameters, the seedling heating model is input, and the initial dielectric parameters are iteratively adjusted using an interior point optimization algorithm with the goal of minimizing the root mean square error between the simulated temperature and the measured temperature. The current dielectric parameter corresponding to the point where the root mean square error is less than a set threshold is taken as the current optimal parameter. The inversion is repeated multiple times to obtain multiple current optimal parameters, and the average value is taken as the optimal dielectric parameter. The optimal dielectric parameters include: the optimal leaf dielectric constant and the optimal branch dielectric constant.

8. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 7, characterized in that, The comparative verification specifically includes: The relevant simulated temperature data includes: leaf simulated temperature data, branch simulated temperature data, and simulated temperature data of each temperature measurement point. The goodness of fit and root mean square error are calculated based on the measured temperature data of the leaves and branches, corresponding to the simulated temperature data of the leaves and branches. The verification is successful when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. Based on the above verification process, the leaf simulation model and the branch simulation model in the seedling heating simulation model are verified respectively. If the verification is successful, it indicates that the constructed leaf simulation model and branch simulation model meet the accuracy requirements. Under different power conditions, the measured temperature data of temperature measurement points at different locations on seedlings of the same origin as the measured sample were obtained; Based on the comparison between the measured temperature data and the simulated temperature data of the temperature measurement point, the verification is successful when the goodness of fit is greater than or equal to the first threshold and the root mean square error is less than the second threshold. The accuracy of the seedling simulation model under different power levels was verified to meet the requirements. After all the above verifications were passed, the seedling heating simulation model was used as a simulation test platform and put into application.

9. The dielectric inversion simulation method for microwave thermotherapy of citrus seedlings according to claim 8, characterized in that, The investment is used for, specifically, including: Based on the input of different microwave cavity structure parameters into the simulation test platform, the optimal cavity structure with the highest temperature uniformity was obtained through simulation screening. Based on the optimal cavity structure, different power-duration combinations are simulated and matched with the pathogen inactivation temperature threshold to obtain precise thermotherapy parameters; Based on the optimal cavity structure and the precise thermotherapy parameters, the device was transformed into a practical device and then put into application after iterative adjustments through field trials.

10. A dielectric inversion simulation system for microwave hyperthermia of citrus seedlings, used to execute the dielectric inversion simulation method for microwave hyperthermia of citrus seedlings as described in any one of claims 1-9, characterized in that, include: The system includes a measurement data acquisition module, a simulation model construction module, an optimal parameter acquisition module, a simulation data acquisition module, and a model verification and application module. The measurement data acquisition module is used to randomly select multiple target seedlings to obtain measurement samples and acquire relevant measurement data; The simulation model construction module is used to construct a seedling simulation model based on the relevant measurement data; construct a microwave device simulation model and combine it with the seedling simulation model to form a seedling heating simulation model; The optimal parameter acquisition module is used to acquire the measured temperature data of a sample that is from the same source as the measured sample; set the initial dielectric parameter and input it together with the measured temperature data into the seedling heating simulation model; and obtain the optimal dielectric parameter by inversion iteration with the goal of minimizing the root mean square error. The simulation data acquisition module is used to import the seedling heating simulation model based on the optimal dielectric parameters to obtain relevant simulation temperature data. The model verification application module is used to verify the simulated temperature data by comparing it with the measured temperature data, and then put the verified seedling heating simulation model into application.

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