Intelligent temperature and humidity regulation and control system and method for tobacco transportation

Abstract

The invention relates to the technical field of tobacco logistics and intelligent control, and discloses an intelligent temperature and humidity regulation and control system and method for tobacco transportation, and the system comprises a multi-dimensional environment sensing module, a self-adaptive strategy generation module and a closed-loop efficiency verification module. The method comprises the following steps: capturing three-dimensional space parameters in a transportation carrier in real time through distributed sensing nodes, outputting a structured environment situation matrix, and providing a space-time reference for a regulation and control decision; after receiving the environmental perception data, executing dynamic strategy optimization, establishing a transportation stage-spatial position two-dimensional regulation and control strategy library, processing sensitivity differences of tobacco leaves in different regions by adopting an asymmetric weighting algorithm, and generating a regulation and control scheme containing a gradient regulation instruction; real-time evaluation and strategy iteration of the regulation and control effect are achieved, microenvironment response detection is deployed, tobacco physical characteristic change data are collected, and a triangular verification model is constructed. The method effectively controls moisture content distribution, inhibits browning and mildew risks, and maximally retains tobacco leaf quality characteristics.

Description

Technical Field

[0001] The present invention relates to the field of tobacco logistics and intelligent control technology, and in particular to an intelligent temperature and humidity control system and method for tobacco leaf transportation. Background Art

[0002] As an important economic crop, temperature and humidity control during tobacco transportation is crucial for quality assurance. During tobacco transportation, changes in temperature and humidity will directly affect the chemical composition and physical properties of the tobacco leaves, which in turn affect their processing performance and final product quality. However, existing temperature and humidity control technologies for tobacco transportation have many shortcomings. Traditional methods usually rely on simple constant temperature and humidity equipment or manual experience adjustment. These methods are difficult to cope with complex and changing transportation environments, especially when transporting over long distances and in multiple climate zones, and exhibit problems such as low control accuracy and slow response speed. In addition, the existing system lacks intelligent analysis and dynamic control capabilities, and is unable to make precise adjustments based on real-time environmental data, resulting in tobacco leaves being prone to quality problems such as mildew and cracking during transportation.

[0003] From a technical perspective, the shortcomings of existing temperature and humidity control systems are mainly reflected in the following aspects: First, traditional control models are mostly based on static parameter settings, such as control through fixed temperature and humidity, without considering dynamic changing factors in the transportation environment, such as the impact of external temperature fluctuations and humidity change rate on the internal environment; Second, existing systems generally lack dynamic monitoring and feedback mechanisms for the characteristics of tobacco leaves themselves (such as moisture content and hygroscopicity coefficient), resulting in a disconnect between control strategies and actual needs; Finally, existing technologies rarely involve complex mathematical modeling and optimization algorithms, such as introducing nonlinear dynamic equations to describe the laws of temperature and humidity changes, and combining them with optimal control theory to design control strategies.

[0004] Further analysis revealed that existing technologies also have obvious shortcomings in multivariable coupling control. For example, in the process of temperature and humidity control, there is a strong coupling relationship between temperature and humidity. However, existing systems often ignore this coupling relationship, resulting in poor control effects. In addition, existing technologies fail to fully consider the impact of various external interference factors (such as vibration frequency, airflow velocity, etc.) on the temperature and humidity field distribution during transportation, and lack the ability to model and optimize field distribution based on partial differential equations.

[0005] In response to the above problems, it is urgent to develop a tobacco transportation temperature and humidity management system that can achieve intelligent and refined regulation. Summary of the Invention

[0006] The main purpose of the present invention is to provide an intelligent temperature and humidity control system and method for tobacco leaf transportation, so as to solve the problem that the existing temperature and humidity control technology for tobacco leaf transportation in the prior art has significant deficiencies in dynamic modeling, multivariable coupling analysis and intelligent control, and is difficult to meet the needs of high-quality tobacco leaf transportation.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent temperature and humidity control system for tobacco leaf transportation, comprising:

[0009] A multi-dimensional environmental perception module is used to capture the three-dimensional spatial parameters of the transport vehicle in real time through distributed sensor nodes; it outputs a structured environmental situation matrix to provide a spatiotemporal benchmark for control decisions;

[0010] The adaptive strategy generation module is used to perform dynamic strategy optimization after receiving environmental perception data, establish a two-dimensional control strategy library for transportation stage and spatial location, use an asymmetric weighted algorithm to process the sensitivity differences of tobacco leaves in different locations, and generate a control plan including gradient adjustment instructions;

[0011] The closed-loop effectiveness verification module is used to achieve real-time evaluation of regulatory effects and strategy iteration, deploy microenvironmental response detection, collect data on changes in tobacco leaf physical properties, build a triangular verification model of regulatory input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

[0012] As a further improvement of the present invention, the internal three-dimensional spatial parameters include: gas molecule permeability monitoring, detecting the impact of changes in the permeability of packaging materials on the moisture content of tobacco leaves; thermal flow field reconstruction, identifying local temperature anomaly areas based on thermopile arrays, microbial activity prediction, and calculating the mold risk index through volatile organic compound spectrum characteristics; outputting a structured environmental situation matrix to provide a spatiotemporal benchmark for regulatory decisions.

[0013] As a further improvement of the present invention, the multi-dimensional environment perception module includes:

[0014] The heterogeneous data fusion submodule is used to register the original three-dimensional spatial parameters captured by distributed sensor nodes and establish the topological relationship between each monitoring point through the carrier's three-dimensional coordinate system. The gas molecular permeability data is spatially coupled with the thermal flow field reconstruction results. When the thermopile in a certain area detects an abnormal temperature gradient, it automatically triggers the cross-verification of the packaging material in that location, forming a temperature-permeability correlation layer.

[0015] The risk situation deduction submodule is used to analyze critical points in the associated layers. When an area experiences both increased air permeability and a local temperature rise, the volatile organic compound (VOC) spectrum characteristics are given a higher weight. This automatically labels the area as a high-risk area for mold and mildew, and generates a timestamp-equipped risk diffusion prediction trajectory in the environmental matrix. This risk diffusion prediction trajectory is updated in real time as the thermal flow field changes dynamically.

[0016] The matrix structured encapsulation submodule is used to output a structured environmental situation matrix containing a three-layer nested structure; the base layer retains the time series of the original sensor data, the middle layer stores the cross-validation results after spatial registration, and the decision layer integrates all location risk level labels and expected evolution trends.

[0017] As a further improvement of the present invention, the adaptive strategy generation module includes:

[0018] The topology map formation submodule is used to analyze the three-dimensional spatial parameters in the environmental situation matrix, spatially superimpose the gas permeability monitoring data and the thermal flow field reconstruction results to form a tobacco leaf state topology map with weight distribution, mark the current temperature and humidity absolute values of each location, and calculate the dynamic sensitivity coefficient of different areas based on the microbial activity prediction data;

[0019] The priority sequence generation submodule is used to classify and identify transport stages based on a two-dimensional strategy library and perform asymmetric analysis of heat accumulation areas. It extracts the temperature anomaly profile identified by the thermopile array and combines it with the mold risk gradient derived from the volatile organic compound spectrum characteristics to generate a spatial priority sequence based on hexahedral units. Each unit in the spatial priority sequence is assigned three sets of adjustment weights: the permeability compensation coefficient, the heat flow buffer factor, and the bioactivity inhibition parameter.

[0020] The instruction set generation submodule is used to target high-permeability areas using a spatiotemporal staggered regulation mechanism. It calculates the moisture migration rate based on the packaging material's air permeability curve, then constructs a step function for progressive dehumidification using the predicted value of microbial activity as a constraint. The thermal flow field reconstruction data is continuously fed back and regulated to ensure that the local dehumidification rate does not exceed the thermodynamic carrying capacity threshold of the adjacent area. The generated control scheme is presented as a multidimensional instruction set.

[0021] As a further improvement of the present invention, the multi-dimensional instruction set of the instruction set generation submodule includes a directional airflow guidance path for each hexahedral unit in the spatial dimension, and forms a pulsed adjustment timing based on the transportation stage division in the time dimension; all gradient instructions are embedded in the verification interface of the expected effect prediction matrix, so that the closed-loop verification module can directly compare the deviation between the actual physical property changes and the preset response curve.

[0022] As a further improvement of the present invention, the topology map forming submodule includes:

[0023] The heat map construction unit is used to analyze the volatile organic compound spectrum characteristic data in the three-dimensional characteristic parameters, extract the characteristic peak groups that are strongly correlated with microbial metabolic activity, and construct a three-dimensional activity heat map based on the concentration gradient distribution. After the three-dimensional activity heat map is spatially aligned with the gas permeability monitoring data, an activity-permeability coupling matrix is formed within each hexahedral unit of the tobacco leaf state topology map;

[0024] The region identification unit is used to perform dual-channel analysis on each cell in the activity-permeability coupling matrix. In the time channel, it tracks the time-varying patterns of characteristic peak groups and calculates their correlation with the transport stage classification identifier. In the spatial channel, it compares the activity gradient differences between adjacent cells and, combined with the temperature anomaly profile data provided by the thermopile array, identifies potential areas of thermal-biological coupling effects.

[0025] The element generation unit is used to generate the calculation elements of the dynamic sensitivity coefficient for each unit, including the activity fluctuation index, the penetration impact factor, and the thermal disturbance weight. The three calculation elements are synthesized according to the proportional coefficient determined by the transportation stage classification identification, and the final output is the dynamic sensitivity coefficient matrix that changes with time and spatial position.

[0026] As a further improvement of the present invention, the dynamic sensitivity coefficient matrix of the element generation unit has two key characteristics: its numerical range is positively correlated with the mildew risk gradient, and its spatial distribution pattern has a definite mapping relationship with the thermal flow field reconstruction result; so that the generated priority sequence reflects the response differences of different locations to the control strategy.

[0027] As a further improvement of the present invention, the instruction set generation submodule includes:

[0028] The moisture migration rate calculation unit is used to analyze the characteristic parameters of the packaging material's air permeability curve and extract its permeability variation pattern under different temperature and humidity conditions. Combined with the local temperature gradient distribution in the thermal flow field reconstruction data, it establishes a dynamic response relationship between the material's air permeability and environmental parameters. The water vapor transmission potential energy difference of each hexahedral unit at a specific transportation stage is calculated. This water vapor transmission potential energy difference, together with the gas permeability monitoring data, determines the strength of the moisture migration path between units.

[0029] The dehumidification step function construction unit uses the water migration rate as an input parameter and couples it with the bioactivity inhibition parameter provided by the priority sequence generation submodule for analysis. The migration rate is normalized using a dynamic sensitivity coefficient matrix to obtain the dehumidification demand intensity spectrum of each unit in the time dimension. Based on the time resolution determined by the transport stage classification identifier, the continuous demand intensity spectrum is discretized into dehumidification steps with different amplitudes. The amplitude of each step is dynamically balanced with the heat flow buffering factor.

[0030] A multi-dimensional instruction set generation unit is used to map the discretized results of the step function onto a hexahedral unit grid defined by a spatial priority sequence. For each unit, the intensity parameters of the directional airflow guidance path are adjusted according to its permeability compensation coefficient, while the duration of the airflow action is segmented and controlled in combination with a pulsed adjustment sequence. The control parameters of all units are collaboratively optimized according to the mildew risk gradient, ultimately forming a composite instruction set that includes spatial path planning and timing control.

[0031] As a further improvement of the present invention, the control scheme of the adaptive strategy generation module includes: implementing progressive dehumidification for high permeability areas and starting directional airflow guidance for heat accumulation areas; the output strategy package includes an execution parameter sequence and an expected effect prediction matrix.

[0032] To achieve the above object, the present invention also provides the following technical solutions:

[0033] An intelligent temperature and humidity control method for tobacco leaf transportation, which is applied to the intelligent temperature and humidity control system for tobacco leaf transportation, comprises the following steps:

[0034] Capture the three-dimensional spatial parameters inside the transport vehicle in real time through distributed sensor nodes; output a structured environmental situation matrix to provide a spatiotemporal benchmark for control decisions;

[0035] After receiving environmental perception data, dynamic strategy optimization is performed to establish a two-dimensional control strategy library for transportation stage and spatial location. An asymmetric weighted algorithm is used to process the sensitivity differences of tobacco leaves in different locations, and a control plan containing gradient adjustment instructions is generated. The output strategy package includes the execution parameter sequence and the expected effect prediction matrix.

[0036] Realize real-time evaluation of regulatory effects and strategy iteration, deploy microenvironment response detection, collect data on changes in physical properties of tobacco leaves, build a triangular verification model of regulatory input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

[0037] The present invention achieves precise control and dynamic optimization of the temperature and humidity environment during tobacco leaf transportation. A multi-dimensional environmental perception module acquires layered environmental parameters within the transport carrier, forming a structured environmental situation matrix that provides a precise spatiotemporal benchmark for subsequent regulation. An adaptive strategy generation module establishes a two-dimensional control strategy for the transport stage and spatial location based on the perceived data. Using an asymmetric weighted algorithm to process the differences in tobacco leaf characteristics in different regions, it generates a targeted control scheme containing gradient adjustment instructions. A closed-loop effectiveness verification module evaluates the control effect in real time, using a triangular verification model to analyze the correlation between control input, environmental response, and tobacco leaf status, and continuously optimizes the strategy library through a residual backpropagation mechanism. The three modules operate in synergy, forming a closed-loop control system from environmental perception to strategy generation to effect verification. This ensures that tobacco leaves are always in the optimal temperature and humidity environment during transportation, effectively controls moisture content distribution, inhibits the risk of browning and mildew, and maximizes the retention of tobacco leaf quality characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a functional module diagram of an embodiment of the intelligent temperature and humidity control system for tobacco transportation of the present invention;

[0039] Figure 2 This is a functional module diagram of a multi-dimensional environment perception module in one embodiment of the intelligent temperature and humidity control system for tobacco transportation of the present invention;

[0040] Figure 3 This is a functional module diagram of an adaptive strategy generation module in one embodiment of the intelligent temperature and humidity control system for tobacco transportation of the present invention;

[0041] Figure 4 This is a functional module diagram of a closed-loop performance verification module in one embodiment of the intelligent temperature and humidity control system for tobacco transportation of the present invention;

[0042] Figure 5 This is a schematic flow chart of the steps of an embodiment of the intelligent temperature and humidity control method for tobacco leaf transportation of the present invention;

[0043] Figure 6 This is a schematic structural diagram of an embodiment of an electronic device of the present invention;

[0044] Figure 7 This is a schematic structural diagram of an embodiment of a storage medium of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] The terms "first", "second" and "third" in the present invention are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present invention (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0047] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] like Figure 1 As shown, this embodiment provides an embodiment of an intelligent temperature and humidity control system for tobacco leaf transportation. In this embodiment, the intelligent temperature and humidity control system for tobacco leaf transportation specifically includes:

[0049] A multi-dimensional environmental perception module is used to capture the three-dimensional spatial parameters of the transport vehicle in real time through distributed sensor nodes. These include: gas molecular permeability monitoring to detect the impact of changes in packaging material permeability on tobacco leaf moisture content; thermal flow field reconstruction to identify local temperature anomalies based on thermopile arrays; microbial activity prediction to infer mold risk indexes based on volatile organic compound spectral characteristics; and output of a structured environmental situation matrix to provide a spatiotemporal benchmark for regulatory decision-making.

[0050] The adaptive strategy generation module is used to perform dynamic strategy optimization after receiving environmental perception data, establish a two-dimensional control strategy library for transportation stage and spatial location, use an asymmetric weighted algorithm to process the sensitivity differences of tobacco leaves in different locations, and generate a control plan with gradient adjustment instructions, including: implementing progressive dehumidification in high-permeability areas and initiating directional airflow guidance in heat accumulation areas; the output strategy package includes the execution parameter sequence and the expected effect prediction matrix;

[0051] The closed-loop effectiveness verification module is used to achieve real-time evaluation of regulatory effects and strategy iteration, deploy microenvironmental response detection, collect data on changes in tobacco leaf physical properties, build a triangular verification model of regulatory input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

[0052] Preferably, this embodiment realizes the precise control and dynamic optimization of the temperature and humidity environment during tobacco leaf transportation; through the multi-dimensional environmental perception module, the layered environmental parameters inside the transport carrier are obtained to form a structured environmental situation matrix, providing an accurate spatiotemporal benchmark for subsequent control; the adaptive strategy generation module establishes a two-dimensional control strategy for the transportation stage and spatial position based on the perception data, processes the characteristic differences of tobacco leaves in different regions through an asymmetric weighted algorithm, and generates a targeted control scheme containing gradient adjustment instructions; the closed-loop effectiveness verification module evaluates the control effect in real time, uses a triangular verification model to analyze the correlation between control input, environmental response and tobacco leaf status, and continuously optimizes the strategy library through the residual back propagation mechanism. The three modules work together to form a closed-loop control system from environmental perception to strategy generation to effect verification, ensuring that tobacco leaves are always in the optimal temperature and humidity environment during transportation, effectively controlling the moisture content distribution, suppressing the risk of browning and mildew, and retaining the quality characteristics of tobacco leaves to the maximum extent.

[0053] Furthermore, if Figure 2 As shown, the multi-dimensional environment perception module specifically includes:

[0054] The heterogeneous data fusion submodule is used to register the original three-dimensional spatial parameters captured by distributed sensor nodes and establish the topological relationship between each monitoring point through the carrier's three-dimensional coordinate system. The gas molecular permeability data is spatially coupled with the thermal flow field reconstruction results. When the thermopile in a certain area detects an abnormal temperature gradient, it automatically triggers the cross-verification of the packaging material in that location, forming a temperature-permeability correlation layer.

[0055] The risk situation deduction submodule is used to analyze critical points in the associated layers. When an area experiences both increased air permeability and a local temperature rise, the volatile organic compound (VOC) spectrum characteristics are given a higher weight. This automatically labels the area as a high-risk area for mold and mildew, and generates a timestamp-equipped risk diffusion prediction trajectory in the environmental matrix. This risk diffusion prediction trajectory is updated in real time as the thermal flow field changes dynamically.

[0056] The matrix structured encapsulation submodule is used to output a structured environment situation matrix containing a three-layer nested structure;

[0057] Among them, the base layer retains the time series of the original sensor data, the intermediate layer stores the cross-validation results after spatial registration, and the decision layer integrates all location risk level labels and expected evolution trends.

[0058] Preferably, the multi-dimensional environmental perception module of this embodiment constructs a complete dynamic monitoring system for the tobacco transportation environment through the synergistic effect of three sub-modules: heterogeneous data fusion, risk situation deduction, and matrix structured encapsulation. By establishing a topological relationship in a three-dimensional coordinate system, spatial coupling analysis of gas permeability and thermal flow field data is achieved, so that temperature anomaly areas can automatically trigger packaging material performance verification in the corresponding locations, forming an environmental parameter layer with physical correlation. Based on multi-parameter correlation analysis, the system can identify the critical state where increased permeability and local temperature rise co-occur, and by dynamically adjusting the volatile organic compound monitoring weights, achieve early identification and spatial positioning of mold risks. The resulting three-layer matrix structure not only retains the temporal characteristics of the original data, but also integrates spatial verification results and risk prediction information, providing a multi-dimensional decision-making basis for subsequent control strategies, including environmental status, evolution trends, and risk levels. The real-time update of risk diffusion trajectories as the thermal flow field changes enables the system to track environmental status, ensuring that the output matrix always reflects the current and latest transportation environment status.

[0059] In summary, this embodiment realizes the complete conversion process from raw data collection to structured environmental situation output through the above functions, providing a precise environmental status representation basis for the intelligent control system.

[0060] Furthermore, if Figure 3 As shown, the adaptive strategy generation module specifically includes:

[0061] The topology map formation submodule is used to analyze the three-dimensional spatial parameters in the environmental situation matrix, spatially superimpose the gas permeability monitoring data and the thermal flow field reconstruction results to form a tobacco leaf state topology map with weight distribution, mark the current temperature and humidity absolute values of each location, and calculate the dynamic sensitivity coefficient of different areas based on the microbial activity prediction data;

[0062] The priority sequence generation submodule is used to classify and identify transport stages based on a two-dimensional strategy library and perform asymmetric analysis of heat accumulation areas. It extracts the temperature anomaly profile identified by the thermopile array and combines it with the mold risk gradient derived from the volatile organic compound spectrum characteristics to generate a spatial priority sequence based on hexahedral units. Each unit in the spatial priority sequence is assigned three sets of adjustment weights: the permeability compensation coefficient, the heat flow buffer factor, and the bioactivity inhibition parameter.

[0063] The instruction set generation submodule is used to target high-permeability areas using a spatiotemporal staggered regulation mechanism. This mechanism calculates the moisture migration rate based on the packaging material's air permeability curve. Using the predicted microbial activity value as a constraint, it constructs a step function for progressive dehumidification. Thermal flow field reconstruction data is used for continuous feedback regulation, ensuring that the local dehumidification rate does not exceed the thermodynamic load threshold of adjacent areas. The resulting control scheme is presented as a multidimensional instruction set.

[0064] Among them, the multi-dimensional instruction set includes a directional airflow guidance path for each hexahedral unit in the spatial dimension, and forms a pulsed adjustment timing based on the transportation stage division in the temporal dimension; all gradient instructions are embedded with a verification interface of the expected effect prediction matrix, so that the closed-loop verification module can directly compare the deviation between the actual physical property changes and the preset response curve.

[0065] Preferably, the adaptive strategy generation module of this embodiment constructs a tobacco leaf state topology map through a topology map formation submodule, establishes a mapping relationship between spatial parameters and dynamic sensitivity coefficients, and provides a quantitative basis for regulation. Based on the topology map, the priority sequence generation submodule combines the characteristics of the transportation stage and the risk gradient to generate a spatial priority sequence with three sets of regulation weights, thereby realizing the precise quantitative allocation of regulation intensity at different locations. The instruction set generation submodule utilizes the output results of the first two submodules, combines the moisture migration rate with the microbial activity constraint through a spatiotemporal staggered regulation mechanism, and forms a progressive dehumidification strategy, while ensuring that the dynamic feedback of the thermal flow field data maintains local and global thermodynamic equilibrium; the multidimensional instruction set finally generated is coordinated and optimized in the spatial and temporal dimensions, so that the regulation scheme has precise directional airflow guidance capabilities and timing adaptability.

[0066] To sum up, after the various sub-modules of this embodiment work together, the overall closed-loop logic from environmental perception to strategy generation is realized: the topology map provides basic parameter mapping, the priority sequence determines the control weight distribution, and the instruction set completes the generation of specific execution plans and the embedding of verification interfaces; ensuring that the control strategy not only meets the global needs of the current transportation stage, but also can adaptively optimize the differences in tobacco leaf status in different locations, while providing a quantifiable comparison benchmark for closed-loop verification.

[0067] Furthermore, the topology map forming submodule specifically includes:

[0068] The heat map construction unit is used to analyze the volatile organic compound spectrum characteristic data in the three-dimensional characteristic parameters, extract the characteristic peak groups that are strongly correlated with microbial metabolic activity, and construct a three-dimensional activity heat map based on the concentration gradient distribution. After the three-dimensional activity heat map is spatially aligned with the gas permeability monitoring data, an activity-permeability coupling matrix is formed within each hexahedral unit of the tobacco leaf state topology map;

[0069] The region identification unit is used to perform dual-channel analysis on each cell in the activity-permeability coupling matrix. In the time channel, it tracks the time-varying patterns of characteristic peak groups and calculates their correlation with the transport stage classification identifier. In the spatial channel, it compares the activity gradient differences between adjacent cells and, combined with the temperature anomaly profile data provided by the thermopile array, identifies potential areas of thermal-biological coupling effects.

[0070] The element generation unit is used to generate the calculation elements of the dynamic sensitivity coefficient for each unit, including the activity fluctuation index (time-varying law of the characteristic peak group), the permeability impact factor (gas permeability data in the coupling matrix), and the thermal disturbance weight (derived from the spatial correlation between the temperature anomaly profile and the activity gradient). These three calculation elements are synthesized according to the proportional coefficient determined by the transport stage classification identifier, and the final output is the dynamic sensitivity coefficient matrix that changes with time and spatial position.

[0071] The dynamic sensitivity coefficient matrix has two key characteristics: its numerical range is positively correlated with the mold risk gradient, and its spatial distribution pattern has a definite mapping relationship with the thermal flow field reconstruction results; this enables the generated priority sequence to accurately reflect the response differences of different locations to the control strategies.

[0072] Preferably, the topology map formation submodule of this embodiment fuses the volatile organic compound spectrum characteristics with the gas permeability data through the thermal map construction unit to form an activity-permeability coupling matrix, and establishes the spatial correlation between microbial activity and gas transmission characteristics; the regional identification unit performs spatiotemporal dual-channel analysis on this matrix, extracts the dynamic change patterns and spatial gradient characteristics of characteristic peak groups, and locates the areas with significant thermal-biological interactions in combination with temperature field data, providing a spatiotemporal dimension analysis basis for sensitivity coefficient calculation. Based on the outputs of the first two units, the element generation unit dynamically weights the three elements of activity fluctuation, permeability characteristics, and thermal disturbance according to the transportation stage to generate a dynamic sensitivity coefficient matrix with spatiotemporal continuity. The numerical characteristics of the dynamic sensitivity coefficient matrix are associated with the mold risk gradient, and the spatial distribution corresponds to the thermal flow field reconstruction results, ensuring that it can reflect both the time-varying characteristics of biological activity and match the distribution patterns of the physical field.

[0073] In summary, the three units of this embodiment work together to form a complete computational chain: heat map construction provides a data fusion framework, regional identification extracts key features, and factor generation enables parameter synthesis. The resulting dynamic sensitivity coefficient matrix provides a quantitative basis for the priority sequence generation submodule, enabling it to accurately distinguish the differences in response to regulation in different locations, laying the parameter foundation for the spatiotemporal optimization allocation of multidimensional instruction sets.

[0074] Furthermore, the instruction set generation submodule specifically includes:

[0075] The moisture migration rate calculation unit is used to analyze the characteristic parameters of the packaging material's air permeability curve and extract its permeability variation pattern under different temperature and humidity conditions. Combined with the local temperature gradient distribution in the thermal flow field reconstruction data, it establishes a dynamic response relationship between the material's air permeability and environmental parameters. The water vapor transmission potential energy difference of each hexahedral unit at a specific transportation stage is calculated. This water vapor transmission potential energy difference, together with the gas permeability monitoring data, determines the strength of the moisture migration path between units.

[0076] The dehumidification step function construction unit uses the water migration rate as an input parameter and couples it with the bioactivity inhibition parameter provided by the priority sequence generation submodule for analysis. The migration rate is normalized using a dynamic sensitivity coefficient matrix to obtain the dehumidification demand intensity spectrum of each unit in the time dimension. Based on the time resolution determined by the transport stage classification identifier, the continuous demand intensity spectrum is discretized into dehumidification steps with different amplitudes. The amplitude of each step is dynamically balanced with the heat flow buffering factor.

[0077] A multi-dimensional instruction set generation unit is used to map the discretized results of the step function onto a hexahedral unit grid defined by a spatial priority sequence. For each unit, the intensity parameters of the directional airflow guidance path are adjusted according to its permeability compensation coefficient, while the duration of the airflow action is segmented and controlled in combination with a pulsed adjustment sequence. The control parameters of all units are collaboratively optimized according to the mildew risk gradient, ultimately forming a composite instruction set that includes spatial path planning and timing control.

[0078] Preferably, after the various units of the instruction set generation submodule of this embodiment work together, the dynamic response relationship of the material air permeability established by the moisture migration rate calculation unit is realized as a whole, providing an accurate moisture dynamic distribution data basis, and the output water vapor transmission potential energy difference parameter directly determines the input condition of the dehumidification step function construction unit. The dehumidification step function construction unit realizes the quantitative expression of the dehumidification demand intensity by coupling the moisture migration rate with the biological activity inhibition parameter; the discretized dehumidification step output by it provides a time dimension control benchmark for the multidimensional instruction set generation unit, wherein the dynamic balance between the step amplitude and the heat flow buffer factor ensures thermodynamic stability. The multidimensional instruction set generation unit converts the calculation results of the first two units into executable control strategies through spatial mapping and parameter adjustment, and the composite instruction set it finally generates satisfies simultaneously: precise control of airflow intensity based on the permeability compensation coefficient in the spatial dimension, collaborative optimization timing in accordance with the mildew risk gradient in the time dimension, and overall maintenance of the dynamic balance relationship with the heat flow buffer factor;

[0079] In summary, this embodiment forms a progressive data processing chain, from basic parameter calculation to intermediate function construction, ultimately generating a multidimensional control instruction set with spatial resolution and temporal precision, enabling comprehensive and precise control of tobacco leaf conditions. The output parameters of each unit strictly adhere to the constraints provided by the preceding module (such as bioactivity inhibition parameters and dynamic sensitivity coefficients), ensuring the overall logical consistency and technical coherence of the system.

[0080] Furthermore, the water migration rate calculation unit specifically includes:

[0081] The initial parameter acquisition subunit is used to receive the characteristic parameter set of the packaging material permeability change curve, including the permeability baseline values under different temperature and humidity combinations, synchronously load the local temperature gradient distribution matrix in the thermal flow field reconstruction data, and extract the temperature and humidity environment identification code of the current transportation stage;

[0082] The dynamic response modeling subunit is used to spatially align the permeability baseline value with the temperature gradient at the corresponding location, establish a three-dimensional permeability response surface through an environmental parameter interpolation algorithm, and generate permeability dynamic correction coefficients at the vertices of the hexahedral unit. Based on the corrected unit permeability data and the path impedance value fed back in real time by the gas permeability monitoring equipment, a continuous boundary condition for the water vapor chemical potential is established at the unit interface.

[0083] The potential energy difference calculation subunit is used to integrate the chemical potential gradient along the conduction path between units, introduce the adsorption energy barrier at the material interface as a correction term, and output the standardized transmission potential energy difference parameter.

[0084] Preferably, the moisture migration rate calculation unit of this embodiment constructs a complete calculation link from material properties to environmental dynamic response through the cascade operation of three subunits. Its overall functional significance is reflected in: the initial parameter acquisition subunit integrates the intrinsic characteristics of the material and the environmental monitoring data, ensures that the calculation basic parameters contain both static material properties and dynamic transport environmental variables, and establishes input conditions that match the actual working conditions. The dynamic response modeling subunit spatially aligns the discrete permeability reference value with the continuous temperature field, and the generated permeability response surface quantifies the nonlinear change of material performance with environmental parameters. The dynamic correction coefficient output provides a spatially differentiated permeability distribution for subsequent calculations. The potential energy difference calculation subunit converts the material-environment interaction into a calculable transmission driving force parameter through chemical potential gradient integration and adsorption energy barrier correction. The standardized output result directly characterizes the potential intensity of moisture migration between units.

[0085] In summary, the three-level calculation units of this embodiment jointly realize the dynamic mapping of material permeability parameters from laboratory benchmark conditions to actual transportation environment, the spatial coupling analysis of environmental gradient field and material performance parameters, and the transformation of water vapor transmission driving force from physical mechanism to mathematical representation. The final output transmission potential energy difference parameter provides the only input for the subsequent moisture migration path intensity calculation, forming the basic physical quantity for the generation of humidity control instructions.

[0086] Furthermore, the dehumidification step function construction unit specifically includes:

[0087] The time base division subunit is used to receive the minimum time resolution parameter determined by the transport stage classification identifier, and to generate a non-uniform time division interval sequence using the mutation point of the biological activity inhibition parameter as a natural division boundary;

[0088] The intensity spectrum feature extraction subunit is used to detect extreme points on the normalized dehumidification demand intensity spectrum, identify characteristic intervals where the intensity change rate exceeds the dynamic sensitivity coefficient threshold, and extract the median of the intensity integral in each interval as the candidate amplitude;

[0089] The step amplitude optimization subunit is used to perform a convolution operation on the candidate amplitude and the heat flow buffer factor of the corresponding time period, adjust the amplitude weighting coefficient according to the mildew risk gradient, and output a step amplitude sequence that satisfies the energy conservation constraint; detect the time period when the adjacent step amplitude jump exceeds the permeability compensation coefficient, insert transitional time slices for smoothing, and finally generate a discrete step sequence that conforms to the airflow pulse adjustment timing.

[0090] Preferably, the dehumidification step function construction unit of this embodiment realizes the precise conversion from continuous humidity demand to discrete control instructions through the coordinated operation of three subunits. The time base division subunit establishes a non-uniform time division framework based on the characteristics of the transportation stage and the mutation points of the biological activity parameters to ensure that the discretization process is synchronized with the actual state changes of the goods. The intensity spectrum feature extraction subunit screens the key change intervals through the dynamic sensitivity coefficient threshold, retains the significant features in the original intensity spectrum, and avoids the loss of effective control information in the discretization process. The step amplitude optimization subunit introduces the heat flow buffer factor and the mildew risk gradient as optimization constraints, so that the discrete step sequence can meet the energy conservation requirements and the actual mildew prevention requirements at the same time, and controls the step transition smoothness through the penetration compensation coefficient.

[0091] In summary, this embodiment converts continuous humidity control requirements into discrete, executable time-intensity pairs, ensuring that the discretization preserves the characteristics of the original data while complying with physical system constraints. The output can be directly used to generate timing control parameters for airflow pulse regulation. The resulting discrete step sequence provides a temporal control benchmark for the multidimensional instruction set, enabling spatiotemporal coordinated optimization of humidity control instructions.

[0092] Furthermore, the multi-dimensional instruction set generation unit specifically includes:

[0093] The time series benchmark establishment subunit is used to receive the discrete time-amplitude pair sequence output by the dehumidification step function, determine the basic time slice length according to the transportation stage classification identifier, and establish a time series priority queue with the mold risk gradient as the weight;

[0094] The pulse parameter generation subunit is used to extract the step amplitude as the pulse intensity reference value, calculate the maximum allowable single action duration in combination with the penetration compensation coefficient, and generate a candidate set of pulse widths that meet the heat flow buffer factor constraint;

[0095] The segmented optimization subunit is used to perform energy conservation verification on the pulse parameters of adjacent time slices. When airflow path conflict is detected, the action time period is reallocated according to the spatial priority sequence, and the final segmentation scheme is output that meets the airflow interference restrictions between units.

[0096] Preferably, the multi-dimensional instruction set generation unit of this embodiment achieves precise spatiotemporal matching of humidity control through the coordinated operation of three sub-units; the timing benchmark establishment sub-unit converts the discrete step control requirements into an executable time frame, establishing a timing benchmark structure that matches the dynamic changes in the transportation environment. The pulse parameter generation sub-unit quantifies the abstract humidity control requirements into specific airflow action parameters, ensuring that the intensity and duration of a single action meet the material permeability characteristics. The segmented optimization sub-unit solves the spatiotemporal resource conflict problem when multiple units are controlled in parallel, ensuring that the overall control solution meets the physical constraints of energy conservation.

[0097] In summary, this embodiment converts discrete step sequences into executable time-controlled parameters, coordinates multi-unit airflow paths to avoid interference and conflicts, and ensures that all parameters meet the requirements for infiltration compensation and heat flow buffering. The final output is a composite instruction set containing spatiotemporal coordination parameters, providing a precise execution benchmark for actual control equipment. The parameter transfer and verification of each subunit forms a closed-loop optimization process, ensuring the feasibility of the control solution in both temporal and spatial dimensions.

[0098] Furthermore, if Figure 4 As shown in the figure, the closed-loop effectiveness verification module specifically includes:

[0099] The data acquisition and feature mapping submodule is used to extract the execution parameter sequence output by the adaptive strategy generation module (such as airflow intensity, action duration, and temperature and humidity adjustment gradient), and encode it into a control feature vector according to timestamp and spatial location. It receives real-time monitoring data from the multi-dimensional environmental perception module, including changes in gas permeability, thermal flow field distribution offsets, and microbial activity prediction values, and quantifies them into an environmental dynamic response matrix. Through the microenvironmental response detection of the closed-loop effectiveness verification module, it obtains physical characteristic parameters such as tobacco leaf moisture content, surface temperature, and volatile organic compound release spectrum to construct a state feature set.

[0100] The dynamic correlation modeling submodule is used to establish a nonlinear mapping relationship between control parameters and environmental parameters, analyze the efficiency of airflow guidance paths in eliminating local heat accumulation, or the inhibitory effect of dehumidification gradients on permeability changes; quantify the causal relationship strength between sudden changes in environmental parameters (such as temperature gradient exceeding limits and permeability jumps) and tobacco leaf physical properties (such as moisture content fluctuations and mold index growth); skip environmental intermediary variables and directly verify the equivalence of control instructions (such as the duration of pulsed airflow) and the final state of the tobacco leaf (such as the effect of surface condensation inhibition);

[0101] The residual feedback and model iteration submodule is used to compare the expected effect prediction matrix with the actual tobacco leaf state to generate a three-dimensional residual tensor (control error, environmental response deviation, state offset); based on the residual distribution characteristics, the weight parameters of the strategy library are reversely corrected; and the updated strategy library version is output to form a closed-loop verification chain;

[0102] If the environmental response deviates significantly from expectations, adjust the input weights of thermal flow field reconstruction or permeability monitoring;

[0103] If the tobacco leaf status does not reach the target but the environmental response is normal, the coupling logic of the control instructions and the bioactivity inhibition parameters is optimized.

[0104] Preferably, the closed-loop performance verification module of this embodiment realizes the dynamic optimization and reliability assurance of the control system through the coordinated operation of three submodules; the data acquisition and feature mapping submodule uniformly encodes the control instructions, environmental monitoring data and tobacco leaf state parameters to construct a feature set that can be quantified and analyzed; ensures that the time and space dimensions of the control input, environmental response and tobacco leaf state match to avoid misjudgment caused by data dislocation. The dynamic correlation modeling submodule reveals how the control parameters affect the tobacco leaf state through environmental changes, or directly act on the physical properties of the tobacco leaves; evaluates the actual effects of different control strategies (such as airflow guidance, gradient dehumidification), and identifies effective or redundant control means. The residual feedback and model iteration submodule locates the failure link of the system through three-dimensional residual analysis (control error, environmental deviation, state offset); adjusts the policy library parameters according to the residual distribution, and gives priority to correcting the control logic that deviates significantly from the expected (such as thermal flow field weight correction or biological activity inhibition optimization).

[0105] In summary, this embodiment ensures that the actual execution effect of the control instructions meets expectations and avoids ineffective or excessive control; continuously optimizes the strategy library based on real-time monitoring data to improve the system's adaptability to different transportation environments (temperature and humidity fluctuations, microbial risks); and corrects environmental perception errors or control logic defects through the residual feedback mechanism to maintain stable control of tobacco leaf quality.

[0106] like Figure 5As shown, this embodiment also provides an embodiment of an intelligent temperature and humidity control method for tobacco leaf transportation. In this embodiment, the intelligent temperature and humidity control method for tobacco leaf transportation is applied to the intelligent temperature and humidity control system for tobacco leaf transportation in the above embodiment. The intelligent temperature and humidity control method for tobacco leaf transportation specifically includes the following steps:

[0107] Step S1: Real-time capture of the three-dimensional spatial parameters within the transport vehicle is achieved through distributed sensing nodes. This includes: gas molecular permeability monitoring to detect the impact of changes in packaging material permeability on tobacco leaf moisture content; thermal flow field reconstruction to identify local temperature anomalies based on a thermopile array; microbial activity prediction to estimate the mold risk index based on volatile organic compound (VOC) spectral characteristics; and output of a structured environmental situation matrix to provide a spatiotemporal benchmark for regulatory decision-making.

[0108] Step S2: After receiving environmental perception data, dynamic strategy optimization is performed to establish a two-dimensional control strategy library for transport stage and spatial location. An asymmetric weighted algorithm is used to process the sensitivity differences of tobacco leaves in different locations, and a control plan containing gradient adjustment instructions is generated, including: implementing progressive dehumidification in high permeability areas and initiating directional airflow guidance in heat accumulation areas; the output strategy package includes an execution parameter sequence and an expected effect prediction matrix;

[0109] Step S3: Realize real-time evaluation of control effects and strategy iteration, deploy microenvironment response detection, collect data on changes in tobacco leaf physical properties, build a triangle verification model of control input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

[0110] Preferably, the overall technical effect of the intelligent temperature and humidity control method for tobacco transportation of this embodiment is reflected in the following: constructing a structured environmental situation matrix through the multi-dimensional environmental parameters (gas permeability, heat flow field distribution, microbial activity index) obtained by the distributed sensor network, and performing asymmetric weighted matching with the two-dimensional strategy library of transportation stage-spatial position to generate a gradient control instruction set with temporal and spatial differentiation (progressive dehumidification, directional airflow, etc.); during the execution process, the microenvironment response detection data and the initial prediction matrix are subjected to residual analysis through the triangular verification model to form a dynamic feedback correction of the strategy library parameters, and finally realize the precise closed-loop control of the transportation environment parameters. The synergistic effect of the technical features of this method is reflected in: environmental perception data provides a temporal and spatial benchmark for strategy generation, and the strategy execution effect is optimized through the verification model feedback algorithm weight, forming an iterative optimization loop of perception-decision-verification, ensuring that the physical properties of tobacco leaves (moisture content distribution uniformity, browning inhibition rate, aroma retention rate) are maintained within the optimal threshold range throughout the transportation process.

[0111] like Figure 6As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 1 includes a processor 11 and a memory 12 coupled to the processor 11.

[0112] The memory 12 stores program instructions for implementing the intelligent temperature and humidity control method for tobacco leaf transportation according to any of the above embodiments.

[0113] The processor 11 is used to execute program instructions stored in the memory 12 to perform intelligent temperature and humidity control for tobacco transportation.

[0114] The processor 11 may also be referred to as a CPU (Central Processing Unit). The processor 11 may be an integrated circuit chip having signal processing capabilities. The processor 11 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor may be a microprocessor or any conventional processor.

[0115] Further, Figure 7 This is a schematic diagram of the structure of the storage medium of an embodiment of the present application. The storage medium 2 of the embodiment of the present application stores program instructions 21 that can implement all the above methods, wherein the program instructions 21 can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, server, mobile phone, and tablet.

[0116] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0117] In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of software functional units. The above is only an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

[0118] The above detailed description of the specific embodiments of the invention is intended to be illustrative only, and the present invention is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of the present invention. Therefore, equivalent changes, modifications, and improvements made without departing from the spirit and scope of the present invention are also encompassed within the scope of the present invention.

Claims

1. An intelligent temperature and humidity control system for tobacco transportation, characterized in that: The intelligent temperature and humidity control system for tobacco leaf transportation includes: A multi-dimensional environmental perception module is used to capture the three-dimensional spatial parameters of the transport vehicle in real time through distributed sensor nodes; it outputs a structured environmental situation matrix to provide a spatiotemporal benchmark for control decisions; The adaptive strategy generation module is used to perform dynamic strategy optimization after receiving environmental perception data, establish a two-dimensional control strategy library for transportation stage and spatial location, use an asymmetric weighted algorithm to process the sensitivity differences of tobacco leaves in different locations, and generate a control plan including gradient adjustment instructions; The closed-loop effectiveness verification module is used to achieve real-time evaluation of regulatory effects and strategy iteration, deploy microenvironmental response detection, collect data on changes in tobacco leaf physical properties, build a triangular verification model of regulatory input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

2. The intelligent temperature and humidity control system for tobacco transportation according to claim 1 is characterized in that: Internal three-dimensional spatial parameters include: gas molecular permeability monitoring, detecting the impact of changes in packaging material permeability on tobacco leaf moisture content; thermal flow field reconstruction, identifying local temperature anomaly areas based on thermopile arrays, microbial activity prediction, and calculating the mold risk index through volatile organic compound spectrum characteristics; output of a structured environmental situation matrix to provide a spatiotemporal benchmark for regulatory decisions.

3. The intelligent temperature and humidity control system for tobacco transportation according to claim 1 is characterized in that: Multi-dimensional environmental perception module, including: The heterogeneous data fusion submodule is used to register the original three-dimensional spatial parameters captured by distributed sensor nodes and establish the topological relationship between each monitoring point through the carrier's three-dimensional coordinate system. The gas molecular permeability data is spatially coupled with the thermal flow field reconstruction results. When the thermopile in a certain area detects an abnormal temperature gradient, it automatically triggers the cross-verification of the packaging material in that location, forming a temperature-permeability correlation layer. The risk situation deduction submodule is used to analyze critical points in the associated layers. When an area experiences both increased air permeability and a local temperature rise, the volatile organic compound (VOC) spectrum characteristics are given a higher weight. This automatically labels the area as a high-risk area for mold and mildew, and generates a timestamp-equipped risk diffusion prediction trajectory in the environmental matrix. This risk diffusion prediction trajectory is updated in real time as the thermal flow field changes dynamically. The matrix structured encapsulation submodule is used to output a structured environmental situation matrix containing a three-layer nested structure; the base layer retains the time series of the original sensor data, the middle layer stores the cross-validation results after spatial registration, and the decision layer integrates all location risk level labels and expected evolution trends.

4. The intelligent temperature and humidity control system for tobacco transportation according to claim 1 is characterized in that: Adaptive strategy generation module, including: The topology map formation submodule is used to analyze the three-dimensional spatial parameters in the environmental situation matrix, spatially superimpose the gas permeability monitoring data and the thermal flow field reconstruction results to form a tobacco leaf state topology map with weight distribution, mark the current temperature and humidity absolute values of each location, and calculate the dynamic sensitivity coefficient of different areas based on the microbial activity prediction data; The priority sequence generation submodule is used to classify and identify transport stages based on a two-dimensional strategy library and perform asymmetric analysis of heat accumulation areas. It extracts the temperature anomaly profile identified by the thermopile array and combines it with the mold risk gradient derived from the volatile organic compound spectrum characteristics to generate a spatial priority sequence based on hexahedral units. Each unit in the spatial priority sequence is assigned three sets of adjustment weights: the permeability compensation coefficient, the heat flow buffer factor, and the bioactivity inhibition parameter. The instruction set generation submodule is used to target high-permeability areas using a spatiotemporal staggered regulation mechanism. It calculates the moisture migration rate based on the packaging material's air permeability curve, then constructs a step function for progressive dehumidification using the predicted value of microbial activity as a constraint. The thermal flow field reconstruction data is continuously fed back and regulated to ensure that the local dehumidification rate does not exceed the thermodynamic carrying capacity threshold of the adjacent area. The generated control scheme is presented as a multidimensional instruction set.

5. The intelligent temperature and humidity control system for tobacco transportation according to claim 4 is characterized in that: The multidimensional instruction set of the instruction set generation submodule includes a directional airflow guidance path for each hexahedral unit in the spatial dimension, and forms a pulsed adjustment timing based on the transportation stage division in the temporal dimension; all gradient instructions are embedded in the verification interface of the expected effect prediction matrix, so that the closed-loop verification module can directly compare the deviation between the actual physical property changes and the preset response curve.

6. The intelligent temperature and humidity control system for tobacco transportation according to claim 3 is characterized in that: The topology map formation submodule includes: The heat map construction unit is used to analyze the volatile organic compound spectrum characteristic data in the three-dimensional characteristic parameters, extract the characteristic peak groups that are strongly correlated with microbial metabolic activity, and construct a three-dimensional activity heat map based on the concentration gradient distribution. After the three-dimensional activity heat map is spatially aligned with the gas permeability monitoring data, an activity-permeability coupling matrix is formed within each hexahedral unit of the tobacco leaf state topology map; The region identification unit is used to perform dual-channel analysis on each cell in the activity-permeability coupling matrix. In the time channel, it tracks the time-varying patterns of characteristic peak groups and calculates their correlation with the transport stage classification identifier. In the spatial channel, it compares the activity gradient differences between adjacent cells and, combined with the temperature anomaly profile data provided by the thermopile array, identifies potential areas of thermal-biological coupling effects. The element generation unit is used to generate the calculation elements of the dynamic sensitivity coefficient for each unit, including the activity fluctuation index, the penetration impact factor, and the thermal disturbance weight. The three calculation elements are synthesized according to the proportional coefficient determined by the transportation stage classification identification, and the final output is the dynamic sensitivity coefficient matrix that changes with time and spatial position.

7. The intelligent temperature and humidity control system for tobacco transportation according to claim 6, characterized in that: The dynamic sensitivity coefficient matrix of the element generation unit has two key characteristics: its numerical range is positively correlated with the mold risk gradient, and its spatial distribution pattern has a definite mapping relationship with the thermal flow field reconstruction result; so that the generated priority sequence reflects the response differences of different locations to the control strategy.

8. The intelligent temperature and humidity control system for tobacco transportation according to claim 4 is characterized in that: Instruction set generation submodule, including: The moisture migration rate calculation unit is used to analyze the characteristic parameters of the packaging material's air permeability curve and extract its permeability variation pattern under different temperature and humidity conditions. Combined with the local temperature gradient distribution in the thermal flow field reconstruction data, it establishes a dynamic response relationship between the material's air permeability and environmental parameters. The water vapor transmission potential energy difference of each hexahedral unit at a specific transportation stage is calculated. This water vapor transmission potential energy difference, together with the gas permeability monitoring data, determines the strength of the moisture migration path between units. The dehumidification step function construction unit uses the water migration rate as an input parameter and couples it with the bioactivity inhibition parameter provided by the priority sequence generation submodule for analysis. The migration rate is normalized using a dynamic sensitivity coefficient matrix to obtain the dehumidification demand intensity spectrum of each unit in the time dimension. Based on the time resolution determined by the transport stage classification identifier, the continuous demand intensity spectrum is discretized into dehumidification steps with different amplitudes. The amplitude of each step is dynamically balanced with the heat flow buffering factor. A multi-dimensional instruction set generation unit is used to map the discretized results of the step function onto a hexahedral unit grid defined by a spatial priority sequence. For each unit, the intensity parameters of the directional airflow guidance path are adjusted according to its permeability compensation coefficient, while the duration of the airflow action is segmented and controlled in combination with a pulsed adjustment sequence. The control parameters of all units are collaboratively optimized according to the mildew risk gradient, ultimately forming a composite instruction set that includes spatial path planning and timing control.

9. The intelligent temperature and humidity control system for tobacco transportation according to claim 1, characterized in that: The control scheme of the adaptive strategy generation module includes: implementing progressive dehumidification in high-permeability areas and initiating directional airflow guidance in heat accumulation areas; the output strategy package includes the execution parameter sequence and the expected effect prediction matrix.

10. An intelligent temperature and humidity control method for tobacco leaf transportation, applied to the intelligent temperature and humidity control system for tobacco leaf transportation according to any one of claims 1 to 9, characterized in that: The intelligent temperature and humidity control method for tobacco leaf transportation comprises the following steps: Capture the three-dimensional spatial parameters inside the transport vehicle in real time through distributed sensor nodes; output a structured environmental situation matrix to provide a spatiotemporal benchmark for control decisions; After receiving environmental perception data, dynamic strategy optimization is performed to establish a two-dimensional control strategy library for transportation stage and spatial location. An asymmetric weighted algorithm is used to process the sensitivity differences of tobacco leaves in different locations, and a control plan containing gradient adjustment instructions is generated. The output strategy package includes the execution parameter sequence and the expected effect prediction matrix. Realize real-time evaluation of regulatory effects and strategy iteration, deploy microenvironment response detection, collect data on changes in physical properties of tobacco leaves, build a triangular verification model of regulatory input-environmental response-tobacco leaf status, and update the strategy library weight parameters through the residual backpropagation mechanism.

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