Fresh commodity after-ripening regulation and control planning method oriented to maturity grading

Abstract

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

Description

Technical Field

[0001] The present invention relates to the fields of food science and biotechnology, and in particular to a planning method for ripening regulation of fresh products oriented to maturity grading. Background Art

[0002] As living standards improve, demand for fresh produce continues to grow, and consumers' expectations for quality and freshness are also increasing. To meet these demands, effective regulation of the ripening process of fresh produce is necessary to ensure optimal taste and quality upon reaching consumers. While fresh produce production is seasonal and regional, market demand is year-round and widespread. Post-ripening regulation technology can stagger the release of fresh produce, extending its market supply and improving the industry's economic efficiency. Furthermore, for perishable fresh produce, effective ripening regulation can reduce waste and minimize economic losses.

[0003] In recent years, the continuous development and advancement of related technologies, such as sensor technology, the Internet of Things (IoT), and cold chain technology, have provided strong technical support for the post-ripening regulation of fresh produce. For example, high-precision temperature and humidity sensors and gas sensors can monitor the environmental parameters of fresh produce in real time; IoT technology enables remote data transmission and monitoring, allowing managers to promptly understand and adjust the post-ripening environment; and advanced cold chain technology can precisely control temperature and humidity, creating optimal conditions for post-ripening regulation.

[0004] However, the ripening process of fresh produce is highly sensitive to environmental conditions; even slight changes in temperature, humidity, or gas composition can affect the ripening process. However, existing control equipment and technologies lack the precision and stability of environmental control, making precise control of the ripening process difficult and leading to inconsistent product quality. Summary of the Invention

[0005] In order to solve the above technical problems, a planning method for ripening regulation of fresh products based on maturity grading is provided. This technical solution solves the problem of insufficiently precise control of the ripening regulation environment.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] A planning method for ripening regulation of fresh produce based on maturity grading, comprising:

[0008] Based on multimodal sensors, the physiological parameters of fresh produce are collected in real time, including epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity;

[0009] The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates metabolic kinetic equations with the LSTM time series prediction algorithm to output a combination of regulatory parameters suitable for the current maturity stage.

[0010] Based on the combination of control parameters, the temperature, relative humidity, and O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously adjusted through a distributed control system, and plant hormone antagonists are injected using an ultrasonic atomization device;

[0011] Build a supply chain collaborative network based on blockchain technology, bind the grading results with logistics path planning, and adjust the temperature control strategy and shelf life countdown parameters of cold chain transport vehicles in real time through edge computing nodes.

[0012] Preferably, the multimodal sensor is used to collect physiological parameters of fresh produce in real time, and the physiological parameters include epidermal pigment content, ethylene release rate, fruit firmness and respiratory intensity, specifically including:

[0013] Epidermal pigment content detection uses a hyperspectral camera to capture the characteristic absorption peaks of epidermal pigments through continuous narrow-band scanning. The fruit surface is segmented using a U-Net model and combined with a support vector machine classifier to establish a mapping relationship between spectral reflectance and pigment concentration.

[0014] For ethylene release rate monitoring, the main sensor is an electrochemical ethylene sensor, and the auxiliary compensation unit integrates a temperature and humidity sensor and a CO2 sensor, eliminating cross-sensitivity effects through multivariate linear regression; the microfluidic sampling system uses a MEMS micro air pump to construct a closed air path.

[0015] Preferably, the multimodal sensor is used to collect physiological parameters of fresh produce in real time, and the physiological parameters include epidermal pigment content, ethylene release rate, fruit firmness and respiratory intensity, specifically including:

[0016] Fruit firmness and respiration intensity are jointly tested using a mechanical tactile probe with a piezoresistive flexible sensor. A spherical probe simulates human hand pressure and records the force-displacement curve. A respiration chamber design with a sealed cavity and a built-in infrared CO2 sensor measures respiration intensity using a differential calculation method.

[0017] Non-destructive testing, ultrasonic elastic modulus inversion, emitting 1MHz ultrasonic pulses, and building a hardness prediction model based on sound velocity and attenuation coefficient.

[0018] Preferably, the collected physiological parameters are input into a multi-objective optimization model based on deep learning, and the model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including:

[0019] Epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity were standardized by Z-score to eliminate dimensional differences;

[0020] Based on the dynamic time warping algorithm, the sampling frequencies of different sensors are aligned to construct a multi-dimensional tensor input with a unified timestamp;

[0021] Metabolic kinetic equations were embedded to establish the differential equation of ethylene synthesis pathway and starch-sugar conversion model.

[0022] Preferably, the collected physiological parameters are input into a multi-objective optimization model based on deep learning, and the model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including:

[0023] The LSTM time series prediction branch uses a bidirectional LSTM network, with the input window being the time series data from the past short period, to predict the ethylene concentration gradient, hardness decay rate, and respiratory intensity change curve in the future short period. It also introduces an attention mechanism to weightedly focus on key turning points.

[0024] Multi-objective optimization output layer, the control parameter combination of decision variables, including temperature setting value, relative humidity, O2 / CO2 concentration ratio, and 1-MCP dosage;

[0025] The objective function is constructed, and the constraints include physiological constraints, ethylene concentration does not exceed the toxicity threshold of the variety; equipment constraints, the O2 concentration adjustment rate of the controlled atmosphere storage; commercial constraints, minimum hardness requirements for specific sales channels.

[0026] Preferably, the collected physiological parameters are input into a multi-objective optimization model based on deep learning, and the model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including:

[0027] The coupling and transfer learning strategy in the model training phase is to freeze the LSTM bottom layer weights and fine-tune the top layer network and metabolic parameters on the base model trained on Mango.

[0028] Improvements to the NSGA-II algorithm include generating an initial population and calculating the boundaries of the feasible solution space using metabolic equations. Crossover and mutation operations employ simulated binary crossover for continuous variables and uniform mutation for discrete variables. An elite retention strategy is implemented.

[0029] Driven by market demand, it is divided into high-end cold chain direct supply and bulk wholesale markets according to the scenario type, with dynamic weight allocation;

[0030] Risk hedging strategies use Monte Carlo simulations to pre-generate a library of emergency control solutions for risk scenarios such as supply chain disruptions and demand fluctuations. Regret values ​​are introduced into the objective function to ensure that parameter combinations remain feasible even in extreme environments.

[0031] Preferably, the steps of synchronously adjusting the temperature, relative humidity, and O2 / CO2 concentration gradient of the gas-controlled storage equipment through a distributed control system based on a combination of control parameters, and injecting the plant hormone antagonist using an ultrasonic atomization device specifically include:

[0032] The temperature-controlled refrigeration unit is equipped with a variable-frequency magnetic levitation compressor and a microchannel evaporator; the heating unit features a carbon nanotube film radiant plate. The control algorithm is an improved fuzzy PID controller that incorporates a feedforward compensation term predicted by LSTM to dynamically adjust the cooling / heating power ratio. The airflow organization within the warehouse is optimized based on a thermodynamic model, and a multi-air outlet coordination strategy is determined through CFD simulation.

[0033] Relative humidity control uses an array of ultrasonic atomizers, and the humidity feedback loop uses a capacitive polymer sensor. Membrane dehumidification is linked to condensation dehumidification, prioritizing water recovery for humidification.

[0034] O2 / CO2 concentration gradient management, multi-channel mass flow controller, mixing N2, O2, CO2 in proportion; catalytic combustion oxygen reduction unit, rapid reduction of O2 concentration in emergency response mode; gradient control is divided into metabolic inhibition period, stable ripening period, and ripening sprint period.

[0035] Preferably, the steps of synchronously adjusting the temperature, relative humidity, and O2 / CO2 concentration gradient of the gas-controlled storage equipment through a distributed control system based on a combination of control parameters, and injecting the plant hormone antagonist using an ultrasonic atomization device specifically include:

[0036] The ultrasonic atomization device includes a piezoelectric ceramic transducer, and the dosage control uses a microfluidic chip to precisely regulate the flow rate of the 1-MCP solution. Real-time feedback is provided by an electrochemical sensor linked to the release rate of ethylene, establishing a closed-loop PID control model.

[0037] Antagonist release strategy, time-concentration curve, initial impact phase is high-dose pulse injection, quickly occupying ethylene receptors; sustained release in the maintenance phase is through sustained-release microcapsules;

[0038] Multi-point distributed atomizing nozzle layout combined with airflow field simulation in the reservoir.

[0039] Preferably, the supply chain collaborative network is constructed based on blockchain technology, the classification results are bound to the logistics path planning, and the temperature control strategy and shelf life countdown parameters of the cold chain transport vehicles are adjusted in real time through the edge computing nodes. Specifically, the following steps are involved:

[0040] Multi-chain hybrid network construction, Hyperledger Fabric 3.0 is used as the main network of the alliance chain, and manufacturers, logistics companies and retailers are used as permission nodes to process core data of the supply chain;

[0041] Side chain extension based on a DAG structure to store time sequence data of sensors;

[0042] Cross-chain protocol, through Polkadot XCMP, the fresh food chain is connected with the traffic chain and the weather chain to obtain real-time road congestion and extreme weather warning;

[0043] Automatic execution of the smart contract, and hierarchical result binding, when the hyperspectral imaging terminal detects that the maturity of the fresh goods reaches the edible period, the smart contract is automatically triggered.

[0044] Preferably, the supply chain collaborative network based on the blockchain technology binds the hierarchical result with the logistics path planning, and adjusts the temperature control strategy of the cold chain transport vehicle and the shelf life countdown parameter through the edge computing node in real time, and the specific method comprises the following steps:

[0045] Spacetime constraint modeling, real-time variable input including traffic situation, commodity maturity evolution curve and sales end inventory threshold;

[0046] Path optimization algorithm, reinforcement learning dynamic decision, state space including position, time, maturity, temperature and humidity, inventory demand; action space including path selection, vehicle speed control and cold machine power; reward function: on-time delivery reward + quality maintenance reward - energy penalty;

[0047] Edge node hardware configuration, vehicle-mounted terminal, computing chip NVIDIA Jetson Orin NX; sensor group, including a multispectral camera and a non-contact infrared temperature sensing array;

[0048] Dynamic adjustment of the temperature control strategy, adaptive PID algorithm is used, the air volume of the cold machine is controlled according to the temperature field CFD simulation result in the vehicle compartment, the front part is strongly cooled to inhibit ethylene release, and the rear part is kept at a constant temperature to maintain quality; weather forecast is fused to calculate pre-cooling compensation energy; the refrigeration capacity is increased in the low electricity price interval in the early morning by taking advantage of the difference between peak and valley electricity prices to generate a temperature buffer zone;

[0049] Shelf life countdown correction, digital twin model, based on Fick's diffusion law to simulate the influence of O2 / CO2 permeation on respiratory entropy.

[0050] Compared with the prior art, the present application has the following advantages:

[0051] This invention proposes multimodal sensing fusion, enabling non-invasive holographic detection. It can simultaneously analyze a product's appearance and internal physiological state, with a response speed of milliseconds. Compared to existing technologies, this approach avoids product devaluation caused by sampling impairments while accurately identifying early signs of quality deterioration, creating a critical time window for subsequent regulatory adjustments.

[0052] A metabolic kinetics-data-driven hybrid model was constructed, deeply coupling biochemical reaction equations with LSTM time-series predictions. This not only ensures that predictions conform to biological laws but also dynamically adjusts strategies through reinforcement learning. Compared to the static rules of traditional methods, this method can optimize gas conditioning parameters and antagonist release curves in real time, shifting shelf life control from "passive response" to "proactive intervention," significantly reducing over-ripe and under-ripe losses.

[0053] Through a distributed collaborative network powered by blockchain, multiple objectives, such as quality grading, route planning, and inventory management, are integrated to achieve globally optimal decision-making. For example, when a batch of goods faces expiration due to shipping delays, a smart contract automatically triggers a nearby warehouse to transfer goods or open a promotional channel, preventing local losses from escalating.

[0054] Through digital twin rehearsal and redundant design, a multi-level fault-tolerant mechanism is constructed: on the one hand, thousands of emergency plans are pre-generated based on Monte Carlo simulation, and strategy switching is achieved in seconds; on the other hand, key modules adopt array deployment and self-cleaning technology, so that single point failures do not affect the overall operation of the system, thereby improving the supply chain's risk resistance and ensuring a stable supply of high-quality fresh food. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flowchart of a planning method for ripening regulation of fresh products based on maturity grading. DETAILED DESCRIPTION

[0056] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0057] Reference Figure 1 As shown, a planning method for ripening regulation of fresh products based on maturity grading includes:

[0058] Based on multimodal sensors, the physiological parameters of fresh produce are collected in real time, including epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity;

[0059] The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates metabolic kinetic equations with the LSTM time series prediction algorithm to output a combination of regulatory parameters suitable for the current maturity stage.

[0060] Based on the combination of control parameters, the temperature, relative humidity, and O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously adjusted through a distributed control system, and plant hormone antagonists are injected using an ultrasonic atomization device;

[0061] Build a supply chain collaborative network based on blockchain technology, bind the grading results with logistics path planning, and adjust the temperature control strategy and shelf life countdown parameters of cold chain transport vehicles in real time through edge computing nodes.

[0062] It should be noted that hardware-algorithm collaboration and the spatiotemporal alignment of the hyperspectral imager and the mechanical tactile sensor eliminate the coupling errors of different physical fields (light, force, and air). The lightweight YOLOv7 model is deployed on edge nodes to achieve real-time maturity grading at 30 frames per second.

[0063] A dynamic feedback mechanism synchronizes data from all links of the supply chain through the blockchain every two hours, such as retail inventory and consumer booking needs, and reversely optimizes warehouse gas conditioning parameters; if an abnormality in logistics temperature control triggers an on-chain alarm, the system automatically activates emergency plans, such as transferring to the nearest cold storage and activating backup refrigeration units.

[0064] Advantages of hybrid modeling: The mechanistic model calibrates data boundaries and limits the divergence risk of LSTM predictions based on the maximum activity of ethylene synthase. It also uses data to drive the correction of mechanistic parameters and inverts variety-specific parameters through historical data.

[0065] Knowledge graph construction, establishing a metabolic pathway database covering 200+ fresh food categories, such as the difference in respiratory entropy between apples and lychees, supports cross-category transfer learning.

[0066] Anti-interference design: The cold chain vehicle is equipped with anti-vibration sensors, such as MEMS gyroscopes, to compensate for vibration noise, ensuring that the hardness detection error is less than 5% under bumpy road conditions; the controlled atmosphere storage uses dual-circuit power supply + hydrogen fuel cell backup, which can maintain precise temperature control for 48 hours after power failure.

[0067] Adaptive fault-tolerant strategy: when a sensor fails, such as CO2 probe drift, the system automatically switches to soft measurement mode and estimates CO2 concentration based on breathing intensity and temperature.

[0068] The multimodal sensor is used to collect physiological parameters of fresh produce in real time. The physiological parameters include epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity. Specifically, the following parameters are collected:

[0069] Epidermal pigment content detection uses a hyperspectral camera to capture the characteristic absorption peaks of epidermal pigments through continuous narrow-band scanning. The fruit surface is segmented using a U-Net model and combined with a support vector machine classifier to establish a mapping relationship between spectral reflectance and pigment concentration.

[0070] Ethylene release rate monitoring: the main sensor is an electrochemical ethylene sensor, and the auxiliary compensation unit integrates a temperature and humidity sensor and a CO2 sensor. Multiple linear regression is used to eliminate cross-sensitivity effects. The microfluidic sampling system uses a MEMS micro air pump to construct a closed air path.

[0071] Fruit firmness and respiration intensity are jointly tested using a mechanical tactile probe with a piezoresistive flexible sensor. A spherical probe simulates human hand pressure and records the force-displacement curve. A respiration chamber design with a sealed cavity and a built-in infrared CO2 sensor measures respiration intensity using a differential calculation method.

[0072] Non-destructive testing, ultrasonic elastic modulus inversion, emitting 1MHz ultrasonic pulses, and building a hardness prediction model based on sound velocity and attenuation coefficient.

[0073] It should be noted that the hyperspectral imaging polarization filtering is enhanced and a liquid crystal adjustable polarizer is used to suppress the mirror reflection of the fruit surface, and the reflectivity signal-to-noise ratio is increased from 28dB of traditional cameras to 42dB; the multi-angle light source layout (annular LED array) eliminates the interference of shadows on the analysis of pigment distribution.

[0074] Transfer learning adaptation, based on the ResNet-50 pre-trained model, fine-tunes the classification layer on the mango dataset to achieve cross-variety pigment transfer. For example, the error of anthocyanin detection in durian epidermis is less than 5%. The dynamic band selection algorithm automatically optimizes key band combinations, such as selecting 680nm, 720nm, and 900nm for apples, to reduce data redundancy.

[0075] Real-time processing acceleration, FPGA hardware acceleration, and deployment of Xilinx Zynq UltraScale+ MPSoC enable real-time reconstruction of 400-1000nm spectral cubes; embedded AI inference and TensorRT optimized U-Net model achieve single-frame segmentation time of less than 15ms, meeting production line-level sorting requirements.

[0076] Ethylene release rate monitoring and cross-sensitivity compensation mechanism

[0077] Dynamic calibration matrix:

[0078]

[0079] Where C C2H4 is the ethylene concentration after compensation; ΔT is the change in ambient temperature; ΔRH is the change in ambient relative humidity; S raw is the original signal of the sensor; T env is the ambient temperature; RH env is the relative humidity of the environment; the original signal of the sensor and the environmental temperature and humidity factors are comprehensively calculated to obtain the compensated ethylene concentration to eliminate the temperature and humidity coupling interference.

[0080] Microfluidic dynamic sampling, gas pre-concentration (MOFs material adsorbing ethylene), detection limit from 1 ppm to 0.05 ppm; closed-loop flow control (PID regulating MEMS pump speed), flow stability up to ±0.1 mL / min.

[0081] Life extension strategy, self-cleaning coating, nano-zinc oxide (ZnO) photocatalytic degradation of sulfide pollution on the sensor surface, service life extended to 18 months; redundant array design, deploy 3 groups of sensor rotation work, automatic switching and alarm when a single group fails.

[0082] Combined detection of fruit firmness and respiration intensity:

[0083] Mechanical-respiratory coupling modeling, constitutive equation integration:

[0084] In the formula, H(t) is the time-varying hardness, R(t) is the respiration intensity, k H is the variety correlation coefficient, different varieties have different k H values, such as kiwifruit k H = 0.023, strawberry k H = 0.041; t represents time.

[0085] Multi-scale detection fusion: macro-mechanics, piezoresistive probe measures overall hardness (range 0.1-50 N, resolution 0.01 N); microstructure, ultrasonic elastic modulus inversion cell wall degradation rate (frequency 1-10 MHz sweep);

[0086] Non-destructive detection upgrade, ultrasonic frequency domain analysis:

[0087] Extracting sound speed and attenuation coefficient (α=k·f m ), establish hardness prediction model (R 2 =0.96); deep learning assistance, 1D-CNN network analyzes ultrasonic echo features, distinguishes different maturity stages, such as mango green mature vs. mature;

[0088] Respiration chamber optimization, miniaturization design, cavity volume reduced to 50 mL, response time shortened to 30 seconds; gas diffusion simulation, use COMSOL to simulate O2 / CO2 gradient distribution, optimize air inlet / outlet layout.

[0089] The collected physiological parameters are input into a multi-objective optimization model based on deep learning, which integrates metabolic kinetics equations and LSTM time series prediction algorithms, and outputs a combination of control parameters adapted to the current maturity stage, which specifically includes:

[0090] Epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity were standardized by Z-score to eliminate dimensional differences;

[0091] Based on the dynamic time warping algorithm, the sampling frequencies of different sensors are aligned to construct a multi-dimensional tensor input with a unified timestamp;

[0092] Metabolic kinetic equations were embedded to establish differential equations for ethylene synthesis pathway and starch-sugar conversion models;

[0093] The LSTM time series prediction branch uses a bidirectional LSTM network, with the input window being the time series data from the past short period, to predict the ethylene concentration gradient, hardness decay rate, and respiratory intensity change curve in the future short period. It also introduces an attention mechanism to weightedly focus on key turning points.

[0094] Multi-objective optimization output layer, the control parameter combination of decision variables, including temperature setting value, relative humidity, O2 / CO2 concentration ratio, and 1-MCP dosage;

[0095] Construct an objective function with constraints including physiological constraints, ethylene concentration not exceeding the toxicity threshold of the species; equipment constraints, the rate of regulating the O2 concentration in the controlled atmosphere storage; and commercial constraints, requiring a minimum hardness for specific sales channels.

[0096] The coupling and transfer learning strategy in the model training phase is to freeze the LSTM bottom layer weights and fine-tune the top layer network and metabolic parameters on the base model trained on Mango.

[0097] Improvements to the NSGA-II algorithm include generating an initial population and calculating the boundaries of the feasible solution space using metabolic equations. Crossover and mutation operations employ simulated binary crossover for continuous variables and uniform mutation for discrete variables. An elite retention strategy is implemented.

[0098] Driven by market demand, it is divided into high-end cold chain direct supply and bulk wholesale markets according to the scenario type, with dynamic weight allocation;

[0099] Risk hedging strategies use Monte Carlo simulations to pre-generate a library of emergency control solutions for risk scenarios such as supply chain disruptions and demand fluctuations. Regret values ​​are introduced into the objective function to ensure that parameter combinations remain feasible even in extreme environments.

[0100] It should be noted that the multi-source data alignment and enhancement, and the improvement of dynamic time warping: the use of constraint conditions to match sensor data with different sampling frequencies, such as 1 minute / time for ethylene signals and 10 seconds / time for hardness signals, generates a time synchronization matrix for the minimum bending path; the introduction of a flexible weight distribution mechanism reduces the alignment weight for high-noise data to avoid outlier interference; the missing data filling strategy, based on Bayesian tensor decomposition, fills in data missing due to equipment failure;

[0101] Adaptive Z-score calibration: Dynamically adjust normalization parameters for different categories (e.g., mango skin pigment content μ = 0.45, σ = 0.12 vs. avocado μ = 0.28, σ = 0.09); feature cross generation (e.g., the interaction term C between ethylene release rate and temperature); C2H4 ×T), enhancing the nonlinear expression ability of the model.

[0102] Mechanism-Data Mixture Model:

[0103] Embedded design of metabolic kinetic equations, differential equation constraint layer:

[0104] In the forward propagation of the neural network, the ethylene synthesis path equation is forced to be satisfied:

[0105]

[0106] Wherein, [ACC] is the concentration of ACC (1-aminocyclopropane-1-carboxylic acid); α1 is the ACC synthesis rate constant; α2 is the ACC oxidation rate constant; I pigment is the pigment light signal intensity; V ACS is the catalytic rate of ACC synthase (ACS), and I pigment Multiplication represents the synthesis rate of ACC; E a is the activation energy of ACC oxidase (ACO); R is the gas constant; T is the absolute temperature; k ACO is the Michaelis constant of ACC oxidase (ACO), and The multiplication represents the oxidation rate constant of ACC.

[0107] Physical consistency loss function:

[0108]

[0109] Where, L physics is the physical consistency loss function, which is used to measure the difference between the predicted value and the physical model; is the predicted value y pred The rate of change over time t; f(y pred ,x metabolism ) is the theoretical change rate calculated according to the metabolic kinetic equation, where y pred is the predicted value, x metabolism are metabolism-related variables.

[0110] Time Series Prediction with LSTM-Attention Mechanism:

[0111] Bidirectional time series feature extraction: forward LSTM captures historical dependencies (such as the decay trend of fruit hardness), and reverse LSTM predicts future constraints (such as sales deadlines); parallel branches of the temporal convolutional network extract local mutation features (such as the peak of ethylene outbreaks).

[0112] Causal attention mechanism: Through query-key matching calculation, key events (such as the starting point of respiratory burst) are assigned a weight of 0.7-1.2 times to improve prediction sensitivity; attention masking prevents future information leakage to ensure the feasibility of online prediction.

[0113] Risk-aware objective function design and regret quantification:

[0114]

[0115] Where B is the regret value, which is used to quantify the difference between the decision result and the optimal result; f i actural is the actual performance index value, which can be any index to measure system performance, such as accuracy and recall rate; f i optimal is the optimal performance indicator value under ideal conditions, usually a theoretical upper limit or the best value obtained by other methods; i = 1, 2, 3 represents different performance indicators or different decision scenarios;

[0116] The regret value is defined by calculating the relative gap between the actual performance indicator and the optimal performance indicator, and taking the maximum value as the final regret value. This regret value is added as a penalty term to the objective function to limit the performance degradation in the worst case scenario, thereby making the model more focused on risk control during the optimization process.

[0117] Market demand-parameter mapping table:

[0118] Scenario Weight configuration (shelf life: energy consumption: quality) Typical regulation parameters High-end community group purchase 0.6:0.3:0.1 T = 14°C, RH = 85%, 1-MCP = 1.2ppm Cross-border cold chain transportation 0.5:0.4:0.1 <![CDATA[T=13℃,O2 / CO2=5% / 7%,分段气调]]> Emergency reserve 0.8:0.1:0.1 [CDATA[T = 3°C, O2 / CO2 = 2% / 10%, ultra-low metabolism]]

[0119] Cross-variety transfer learning framework, parameter unfreezing strategy:

[0120] The bottom-level LSTM weights from mango training are retained (to extract common temporal features), and the top fully connected layer is unfrozen to adapt to the metabolic parameters of the new variety. The variety coefficients in the metabolic kinetics equation are quickly calibrated using small-sample Bayesian optimization.

[0121] Zero-sample cold start generates an initial model based on interpolation of category attribute vectors (respiratory type, ethylene sensitivity, etc.), reducing data requirements by 90%.

[0122] The method of synchronously adjusting the temperature, relative humidity, and O2 / CO2 concentration gradient of the gas-controlled storage equipment through a distributed control system based on a combination of control parameters and injecting the plant hormone antagonist using an ultrasonic atomization device specifically includes:

[0123] The temperature-controlled refrigeration unit is equipped with a variable-frequency magnetic levitation compressor and a microchannel evaporator; the heating unit features a carbon nanotube film radiant plate. The control algorithm is an improved fuzzy PID controller that incorporates a feedforward compensation term predicted by LSTM to dynamically adjust the cooling / heating power ratio. The airflow organization within the warehouse is optimized based on a thermodynamic model, and a multi-air outlet coordination strategy is determined through CFD simulation.

[0124] Relative humidity control uses an array of ultrasonic atomizers, and the humidity feedback loop uses a capacitive polymer sensor. Membrane dehumidification is linked to condensation dehumidification, prioritizing water recovery for humidification.

[0125] O2 / CO2 concentration gradient management, multi-channel mass flow controller, proportional mixing of N2, O2, and CO2; catalytic combustion oxygen reduction unit, rapid reduction of O2 concentration in emergency response mode; gradient control is divided into metabolic inhibition period, stable ripening period, and ripening sprint period;

[0126] The ultrasonic atomization device includes a piezoelectric ceramic transducer, and the dosage control uses a microfluidic chip to precisely regulate the flow rate of the 1-MCP solution. Real-time feedback is provided by an electrochemical sensor linked to the release rate of ethylene, establishing a closed-loop PID control model.

[0127] Antagonist release strategy, time-concentration curve, initial impact phase is high-dose pulse injection, quickly occupying ethylene receptors; sustained release in the maintenance phase is through sustained-release microcapsules;

[0128] Multi-point distributed atomizing nozzle layout combined with airflow field simulation in the reservoir.

[0129] It should be noted that the temperature control system: magnetic levitation frequency conversion, using oil-free magnetic bearing design, the compressor speed range is extended to 10,000-50,000RPM, and the energy efficiency ratio (COP) reaches 6.3; the microchannel evaporator adopts 3D printed manifold structure, and the heat transfer coefficient is increased to 5000W / (m 2 K), the response delay is shortened to 30 seconds.

[0130] Carbon nanotube radiation heating, the film surface emissivity ≥ 0.95 (far infrared band 8-14μm), accurately matches the heat absorption spectrum of fruits and vegetables; independent temperature control, the warehouse is divided into 20×20 grids, and the temperature of each grid is independently adjusted;

[0131] LSTM-fuzzy PID composite control algorithm:

[0132] The feedforward compensation logic uses LSTM to predict the heat load in the warehouse for the next two hours, such as the respiratory heat of newly stored mangoes plus the heat infiltrated by the environment, and pre-adjusts the compressor frequency. The fuzzy rule base is dynamically updated, and when the prediction error is greater than 0.5°C, online learning is triggered to correct the membership function.

[0133] Humidity control:

[0134] The array-type atomizer layout deploys 4 atomizer units per square meter with a spacing of 25cm to prevent droplet collision and merging (particle size control D90 < 5μm); the frequency is adaptively adjusted, dynamically switching between 1.2-2.4MHz according to relative humidity deviation, and high-frequency crushing of large particles in high humidity;

[0135] Membrane method - condensation-linked dehumidification, hollow fiber membrane components (PEEK material) with a selective water permeability of >90%, and the recovered water is directly introduced into the humidification tank; condensation dehumidification is preferentially activated when the dew point temperature is >5°C to avoid frosting at low temperatures (energy consumption is reduced by 22%).

[0136] The capacitive sensor is anti-interference and adopts an interdigitated electrode structure (spacing 50μm) with a hydrophobic nano-layer coated on the surface (contact angle >150°) to prevent false positives caused by condensation. Dynamic calibration is automatically calibrated every 24 hours using a saturated salt solution (such as LiCl, K2CO3), with a drift error of <0.5%RH.

[0137] Gas concentration gradient:

[0138] Multi-stage gas ratio strategy

[0139]

[0140] The catalytic combustion oxygen reduction unit, with a Pt / Al2O3 honeycomb catalyst bed, converts O2 into CO2 at 200-300°C, and reduces oxygen to 3% in 10 minutes in emergency mode. For tail gas treatment, the post-combustion gas is adsorbed with CO2 by a molecular sieve, with a purification efficiency of >99%, avoiding CO2 overload in the storage.

[0141] Plant hormone antagonists:

[0142] The coordinated control of ultrasonic atomization and microfluidics, piezoelectric ceramic resonance optimization, and dual-frequency drive mode (1.2MHz main frequency + 100kHz modulation wave) improve the atomization stability of 1-MCP solution; the impedance matching circuit monitors the transducer resonance point in real time and dynamically adjusts the drive frequency.

[0143] The blockchain-based supply chain collaborative network is constructed, the classification results are bound to the logistics path planning, and the temperature control strategy and shelf life countdown parameters of the cold chain transport vehicles are adjusted in real time through the edge computing nodes. Specifically,

[0144] The multi-chain hybrid network is constructed, and the alliance chain main network adopts Hyperledger Fabric 3.0. Manufacturers, logistics providers, and retailers form licensed nodes to process core supply chain data;

[0145] Sidechain extension, based on DAG structure, stores sensor time series data;

[0146] Cross-chain protocol, through PolkadotXCMP, connects the fresh food chain with the transportation chain and the weather chain, and obtains real-time warnings of road congestion and extreme weather;

[0147] Smart contracts are automatically executed and the grading results are bound. When the hyperspectral imaging terminal detects that the maturity of fresh produce has reached the ready-to-eat period, the smart contract is automatically triggered.

[0148] Spatiotemporal constraint modeling, with real-time variable inputs including traffic conditions, product maturity evolution curves, and sales-end inventory thresholds;

[0149] Path optimization algorithm, reinforcement learning dynamic decision-making, state space includes location, time, maturity, temperature and humidity, and inventory requirements; action space includes path selection, vehicle speed control, and refrigeration power; reward function: on-time delivery reward + quality maintenance reward - energy consumption penalty;

[0150] Edge node hardware configuration, vehicle-mounted terminal, computing chip NVIDIA Jetson Orin NX; sensor group, including multispectral camera and non-contact infrared temperature sensor array;

[0151] The temperature control strategy is dynamically adjusted using an adaptive PID algorithm. Based on CFD simulation results of the cabin temperature field, the air output of the refrigeration unit is controlled in different zones, with strong cooling at the front to suppress ethylene release and constant temperature at the rear to maintain quality. Weather forecasts are integrated to calculate pre-cooling compensation energy. Furthermore, the difference in peak and valley electricity prices is utilized to increase cooling capacity during the early morning hours when electricity prices are low, creating a temperature buffer zone.

[0152] Shelf life countdown correction, digital twin model, simulation of the impact of O2 / CO2 permeation on respiratory entropy based on Fick's diffusion law.

[0153] It should be noted that in the alliance chain main network, the node roles are divided as follows:

[0154] Producers: upload quality inspection reports and harvest timestamps;

[0155] Logistics providers: Submit vehicle status and temperature control logs;

[0156] Retailers: Synchronize inventory thresholds, sales forecasts.

[0157] Privacy protection and channel isolation technology establish independent data channels for different categories (such as seafood and fruit) to prevent the leakage of commercial secrets.

[0158] Side chain expansion (DAG structure), time series data compression, using Delta encoding and Huffman compression, storage efficiency is improved by 70%, supporting 12,000 sensor data on the chain per second; dynamic sharding, dividing side chains by geographical regions (such as Southeast Asian durian chain and Nordic salmon chain), reducing cross-regional synchronization delays.

[0159] Cross-chain protocol, cross-chain data verification, light node verification of real-time road conditions of the traffic chain and typhoon paths of the weather chain, with a credibility score of >95%; oracle enhancement, access to Chainlink nodes, ensuring tamper-proof transmission of external data (such as port closure notifications).

[0160] Granular design of smart contracts, hierarchical-path binding logic:

[0161] Maturity level Path planning strategy Temperature control parameter adjustment Level 3 Regular path (cost priority) Temperature ±1°C, shelf life countdown +24h Level 4 (ready-to-eat period) Direct path (time priority) Temperature -2°C, shelf life countdown -12h Level 5 (over-ripe risk) Emergency nearest delivery Temperature -4°C, trigger promotion price contract

[0162] Dynamic coupling model of logistics path and temperature control:

[0163] The sales-end inventory threshold is linked. When the supermarket warehouse inventory is lower than the safety threshold (such as the remaining shelf life is less than 36 hours), the delivery priority is automatically upgraded to the highest (air transportation instead of land transportation).

[0164] Reinforcement learning path optimization algorithm, state space dimension compression: using autoencoders to compress the original state (position, temperature, humidity, maturity, etc.) into a 32-dimensional latent space, reducing computational complexity;

[0165] Reward function: Quality maintenance reward, dynamically weighted based on the shelf life residual predicted by the digital twin (ΔQ = Qpred-QthresholdΔQ = Qpred-Qthreshold); energy consumption penalty term, introducing a real-time electricity price factor (such as peak electricity price × energy consumption) and binding it to the economic cost.

[0166] Online learning mechanism, edge nodes upload local policy gradients to the consortium chain every 30 minutes, and the global model is aggregated and updated (federated learning framework).

[0167] Edge Intelligent Devices:

[0168] The software and hardware collaboration of the vehicle terminal, the unleashing of NVIDIA Jetson Orin NX performance, and the parallel task allocation:

[0169] 60% of computing power is used for real-time temperature control PID calculation;

[0170] 30% computing power runs the LSTM prediction model;

[0171] 10% computing power ensures the synchronization of blockchain light nodes.

[0172] Multispectral and infrared sensor fusion, band selection: visible light (400-700nm) monitors epidermal lesions, shortwave infrared (900-1700nm) detects internal water core disease; temperature field reconstruction, generates 3D thermal maps based on infrared data, and locates dead corners of cold machine air outlet.

[0173] Physical model support for adaptive temperature control strategies, and CFD simulation guidance for zone control:

[0174] Front strong cold zone: wind speed 2.5m / s, inhibiting ethylene synthase (ACS) activity;

[0175] Rear constant temperature zone: wind speed 0.8m / s, maintains cell membrane fluidity to prevent frostbite.

[0176] The countdown correction mechanism compares the actual quality (such as hardness test) with the twin prediction value every 15 minutes, triggering a three-level warning:

[0177] Deviation ≤ 5%: fine-tune CO2 concentration;

[0178] Deviation 5% to 15%: switch to the backup temperature control strategy;

[0179] Deviation > 15%: Initiate an emergency sales agreement (such as community group buying and flash delivery).

[0180] Blockchain-edge computing verification closed loop, data notarization, sensor raw data, control instructions, and execution results are triple-uploaded to the chain to prevent edge node cheating; reverse verification: if a vehicle's temperature control records are inconsistent with the blockchain evidence for multiple times, it will be automatically included in the untrustworthy list and the smart contract permissions will be frozen.

[0181] The use process of the present invention is:

[0182] Step 1: System initialization and parameter configuration

[0183] Hardware deployment:

[0184] Install multimodal sensor arrays (hyperspectral cameras, ethylene sensors, ultrasonic probes, etc.) in storage centers, and integrate edge computing nodes and temperature and humidity control modules in cold chain transport vehicles;

[0185] Each node in the supply chain (production base, logistics fleet, retail terminal) is connected to the blockchain network to complete identity authentication and authority allocation.

[0186] Step 2: Real-time monitoring of physiological parameters of fresh produce

[0187] Non-destructive acquisition:

[0188] Use hyperspectral imaging technology to scan the product surface and capture the characteristic spectrum of pigments;

[0189] A flexible mechanical probe and a closed respiration chamber are used to synchronously obtain fruit hardness, respiration intensity and ethylene release rate; an ultrasonic elastic modulus detector evaluates the maturity of internal tissues and encrypts the raw data for transmission to the edge node.

[0190] Step 3: Dynamic analysis of multi-objective optimization model

[0191] Data preprocessing:

[0192] Perform timestamp alignment and standardization on heterogeneous sensor data to generate multi-dimensional time series tensors;

[0193] Call historical data and external environmental information (weather, traffic) in the blockchain network to build a space-time constraint matrix.

[0194] Intelligent decision making:

[0195] Predict shelf life decay curve based on LSTM-metabolic kinetics hybrid model;

[0196] The NSGA-II algorithm was used to solve the optimal control parameter combination (temperature and humidity, gas ratio, and antagonist dosage).

[0197] Step 4: Precisely control the storage environment

[0198] Dynamic adjustment of controlled atmosphere storage parameters:

[0199] Start the variable frequency magnetic levitation compressor and nano-radiation panel, and adjust the temperature field distribution according to the model output value;

[0200] Control the microfluidic valve to release 1-MCP gas to inhibit the activity of ethylene receptors;

[0201] The multi-channel mass flow meter adjusts the O2 / CO2 concentration gradient to inhibit or promote metabolic processes in stages.

[0202] Step 5: Adaptive Control of Cold Chain Transportation

[0203] Path-temperature control joint optimization:

[0204] Reinforcement learning algorithms dynamically plan optimal delivery routes based on real-time traffic conditions and product maturity;

[0205] The on-board edge node implements a zoned temperature control strategy, with strong cooling in the front cabin to delay ripening and constant temperature in the rear cabin to maintain quality; the ultrasonic atomization device is periodically activated to supplement antagonists to combat ethylene outbreaks caused by transportation vibrations.

[0206] Step 6: Supply Chain Collaboration and Feedback Traceability

[0207] Blockchain event trigger:

[0208] When the goods arrive at the retail terminal, the smart contract automatically verifies whether the quality meets the standards and settles the payment;

[0209] Sales data in turn triggers adjustments to the production base’s harvesting plan, forming a closed-loop feedback loop.

[0210] Exception handling mechanism:

[0211] If equipment failure or parameter exceeding the limit is detected during transportation, the redundant module will be immediately activated and the emergency delivery contract will be triggered nearby;

[0212] The entire process data (spectral characteristics, control instructions, environmental logs) is stored on the chain to support quality traceability at any link.

[0213] Step 7: Consumer-side service and warning

[0214] Shelf life visualization:

[0215] Get real-time product maturity countdown and best consumption recommendations through QR codes at retail terminals;

[0216] When the inventory is close to the shelf life threshold, the system automatically pushes promotional information to the community group buying platform.

[0217] In summary, the advantages of the present invention are:

[0218] By integrating non-invasive detection technologies such as hyperspectral imaging and ultrasonic elastic modulus inversion, we achieve simultaneous analysis of epidermal pigmentation, internal tissue mechanical properties, and metabolic activity. This expands data dimensions and enables millisecond-level response speeds to capture early signs of deterioration. On this basis, we construct a hybrid model combining metabolic kinetic equations with LSTM predictions, embedding biochemical reaction patterns into AI algorithms. This ensures that regulatory strategies are consistent with biological mechanisms while dynamically adapting to environmental disturbances and market demand, reducing shelf life prediction errors.

[0219] A distributed collaborative network based on blockchain enables data trust and strategic linkage across multiple links, including warehousing, transportation, and sales. When shipping delays trigger shelf life warnings, smart contracts automatically activate emergency routes and promotional channels to reduce losses. Furthermore, through strategies such as temperature control during peak and valley electricity prices and water recycling, overall energy consumption is reduced. The use of digital twin language and modular redundancy ensures system operational stability in scenarios such as equipment failure and extreme weather, significantly enhancing supply chain resilience.

[0220] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A planning method for ripening control of fresh produce based on maturity grading, characterized in that: include: Based on multimodal sensors, the physiological parameters of fresh produce are collected in real time, including epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity; The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates metabolic kinetic equations with the LSTM time series prediction algorithm to output a combination of regulatory parameters suitable for the current maturity stage. Based on the combination of control parameters, the temperature, relative humidity, and O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously adjusted through a distributed control system, and plant hormone antagonists are injected using an ultrasonic atomization device; Build a supply chain collaborative network based on blockchain technology, bind the grading results with logistics path planning, and adjust the temperature control strategy and shelf life countdown parameters of cold chain transport vehicles in real time through edge computing nodes.

2. A planning method for ripening regulation of fresh produce based on maturity grading according to claim 1, characterized in that: The multimodal sensor is used to collect physiological parameters of fresh produce in real time. The physiological parameters include epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity. Specifically, the following parameters are collected: Epidermal pigment content detection uses a hyperspectral camera to capture the characteristic absorption peaks of epidermal pigments through continuous narrow-band scanning. The fruit surface is segmented using a U-Net model and combined with a support vector machine classifier to establish a mapping relationship between spectral reflectance and pigment concentration. For ethylene release rate monitoring, the main sensor is an electrochemical ethylene sensor, and the auxiliary compensation unit integrates a temperature and humidity sensor and a CO2 sensor, eliminating cross-sensitivity effects through multivariate linear regression; the microfluidic sampling system uses a MEMS micro air pump to construct a closed air path.

3. The method for planning ripening control of fresh products based on maturity grading according to claim 2, characterized in that: The multimodal sensor is used to collect physiological parameters of fresh produce in real time. The physiological parameters include epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity. Specifically, the following parameters are collected: Fruit firmness and respiration intensity are jointly tested using a mechanical tactile probe with a piezoresistive flexible sensor. A spherical probe simulates human hand pressure and records the force-displacement curve. A respiration chamber design with a sealed cavity and a built-in infrared CO2 sensor measures respiration intensity using a differential calculation method. Non-destructive testing, ultrasonic elastic modulus inversion, emitting 1MHz ultrasonic pulses, and building a hardness prediction model based on sound velocity and attenuation coefficient.

4. The method for planning ripening control of fresh products based on maturity grading according to claim 3, characterized in that: The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including: Epidermal pigment content, ethylene release rate, fruit firmness, and respiratory intensity were standardized by Z-score to eliminate dimensional differences; Based on the dynamic time warping algorithm, the sampling frequencies of different sensors are aligned to construct a multi-dimensional tensor input with a unified timestamp; Metabolic kinetic equations were embedded to establish the differential equation of ethylene synthesis pathway and starch-sugar conversion model.

5. The planning method for ripening regulation of fresh products based on maturity grading according to claim 4, characterized in that: The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including: The LSTM time series prediction branch uses a bidirectional LSTM network, with the input window being the time series data from the past short period, to predict the ethylene concentration gradient, hardness decay rate, and respiratory intensity change curve in the future short period. It also introduces an attention mechanism to weightedly focus on key turning points. Multi-objective optimization output layer, the control parameter combination of decision variables, including temperature setting value, relative humidity, O2 / CO2 concentration ratio, and 1-MCP dosage; The objective function is constructed, and the constraints include physiological constraints, ethylene concentration does not exceed the toxicity threshold of the variety; equipment constraints, the O2 concentration adjustment rate of the controlled atmosphere storage; commercial constraints, minimum hardness requirements for specific sales channels.

6. The method for planning ripening control of fresh produce based on maturity grading according to claim 5, characterized in that: The collected physiological parameters are input into a multi-objective optimization model based on deep learning. The model integrates the metabolic kinetic equation and the LSTM time series prediction algorithm, and outputs a combination of control parameters adapted to the current maturity stage, specifically including: The coupling and transfer learning strategy in the model training phase is to freeze the LSTM bottom layer weights and fine-tune the top layer network and metabolic parameters on the base model trained on Mango. Improvements to the NSGA-II algorithm include generating an initial population and calculating the boundaries of the feasible solution space using metabolic equations. Crossover and mutation operations employ simulated binary crossover for continuous variables and uniform mutation for discrete variables. An elite retention strategy is implemented. Driven by market demand, it is divided into high-end cold chain direct supply and bulk wholesale markets according to the scenario type, with dynamic weight allocation; Risk hedging strategies use Monte Carlo simulations to pre-generate a library of emergency control solutions for risk scenarios such as supply chain disruptions and demand fluctuations. Regret values ​​are introduced into the objective function to ensure that parameter combinations remain feasible even in extreme environments.

7. The planning method for ripening control of fresh products based on maturity grading according to claim 6, characterized in that: The method of synchronously adjusting the temperature, relative humidity, and O2 / CO2 concentration gradient of the gas-controlled storage equipment through a distributed control system based on a combination of control parameters and injecting the plant hormone antagonist using an ultrasonic atomization device specifically includes: The temperature-controlled refrigeration unit is equipped with a variable-frequency magnetic levitation compressor and a microchannel evaporator; the heating unit features a carbon nanotube film radiant plate. The control algorithm is an improved fuzzy PID controller that incorporates a feedforward compensation term predicted by LSTM to dynamically adjust the cooling / heating power ratio. The airflow organization within the warehouse is optimized based on a thermodynamic model, and a multi-air outlet coordination strategy is determined through CFD simulation. Relative humidity control uses an array of ultrasonic atomizers, and the humidity feedback loop uses a capacitive polymer sensor. Membrane dehumidification is linked to condensation dehumidification, prioritizing water recovery for humidification. O2 / CO2 concentration gradient management, multi-channel mass flow controller, mixing N2, O2, CO2 in proportion; catalytic combustion oxygen reduction unit, rapid reduction of O2 concentration in emergency response mode; gradient control is divided into metabolic inhibition period, stable ripening period, and ripening sprint period.

8. The method for planning ripening control of fresh produce based on maturity grading according to claim 7, characterized in that: The method of synchronously adjusting the temperature, relative humidity, and O2 / CO2 concentration gradient of the gas-controlled storage equipment through a distributed control system based on a combination of control parameters and injecting the plant hormone antagonist using an ultrasonic atomization device specifically includes: The ultrasonic atomization device includes a piezoelectric ceramic transducer, and the dosage control uses a microfluidic chip to precisely regulate the flow rate of the 1-MCP solution. Real-time feedback is provided by an electrochemical sensor linked to the release rate of ethylene, establishing a closed-loop PID control model. Antagonist release strategy, time-concentration curve, initial impact phase is high-dose pulse injection, quickly occupying ethylene receptors; sustained release in the maintenance phase is through sustained-release microcapsules; Multi-point distributed atomizing nozzle layout combined with airflow field simulation in the reservoir.

9. A planning method for ripening regulation of fresh produce based on maturity grading according to claim 8, characterized in that: The blockchain-based supply chain collaborative network is constructed, the classification results are bound to the logistics path planning, and the temperature control strategy and shelf life countdown parameters of the cold chain transport vehicles are adjusted in real time through the edge computing nodes. Specifically, The multi-chain hybrid network is constructed, and the alliance chain main network adopts Hyperledger Fabric 3.

0. Manufacturers, logistics providers, and retailers form licensed nodes to process core supply chain data; Sidechain extension, based on DAG structure, stores sensor time series data; Cross-chain protocol, through PolkadotXCMP, connects the fresh food chain with the transportation chain and the weather chain, and obtains real-time warnings of road congestion and extreme weather; Smart contracts are automatically executed and the grading results are bound. When the hyperspectral imaging terminal detects that the maturity of fresh produce has reached the ready-to-eat period, the smart contract is automatically triggered.

10. A planning method for ripening regulation of fresh products oriented to maturity grading according to claim 9, characterized in that: The blockchain-based supply chain collaborative network is constructed, the classification results are bound to the logistics path planning, and the temperature control strategy and shelf life countdown parameters of the cold chain transport vehicles are adjusted in real time through the edge computing nodes. Specifically, Spatiotemporal constraint modeling, with real-time variable inputs including traffic conditions, product maturity evolution curves, and sales-end inventory thresholds; Path optimization algorithm, reinforcement learning dynamic decision-making, state space includes location, time, maturity, temperature and humidity, and inventory requirements; action space includes path selection, vehicle speed control, and refrigeration power; reward function: on-time delivery reward + quality maintenance reward - energy consumption penalty; Edge node hardware configuration, vehicle-mounted terminal, computing chip NVIDIA Jetson Orin NX; sensor group, including multispectral camera and non-contact infrared temperature sensor array; The temperature control strategy is dynamically adjusted using an adaptive PID algorithm. Based on CFD simulation results of the cabin temperature field, the air output of the refrigeration unit is controlled in different zones, with strong cooling at the front to suppress ethylene release and constant temperature at the rear to maintain quality. Weather forecasts are integrated to calculate pre-cooling compensation energy. Furthermore, the difference in peak and valley electricity prices is utilized to increase cooling capacity during the early morning hours when electricity prices are low, creating a temperature buffer zone. Shelf life countdown correction, digital twin model, simulation of the impact of O2 / CO2 permeation on respiratory entropy based on Fick's diffusion law.

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