Physical ripening technology ai data model simulation system

Abstract

The invention proposes a physical ripening technology ai data model simulation system, and relates to the technical field of artificial intelligence, and the system comprises a data collection module which is used for collecting various environmental parameter data related to physical ripening, including but not limited to temperature, humidity, gas concentration, illumination intensity, and initial state data of a ripened object; and the AI data processing and modeling module is used for constructing a mathematical model of a physical ripening process by applying an artificial intelligence algorithm based on the data collected by the data acquisition module, and the model can simulate the ripening process of the ripening object under different environmental parameter combinations. According to the physical ripening technology ai data model simulation system provided by the invention, the data acquisition module, the AI data processing and modeling module, the simulation display module and the control feedback module are arranged, so that the maturity information of fruits can be acquired in real time, and the maturity condition of the fruits can be judged according to the acquired data information; and the ripening condition of the fruits can be accurately monitored.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a physical ripening technology AI data model simulation system. Background Art

[0002] In modern agriculture and related industries, physical ripening technology is of great significance for the efficient processing and quality assurance of fruits, agricultural products, etc. Traditional physical ripening methods rely more on experience and lack precise control and scientific simulation methods.

[0003] From the perspective of ripening environmental parameter control, previous ripening processes have found it difficult to accurately regulate key environmental factors such as temperature, humidity, gas concentration (such as ethylene concentration) and light intensity. For example, in fruit ripening, temperatures that are too high or too low may lead to uneven ripening and quality degradation. Traditional methods rely solely on simple temperature control equipment and are unable to dynamically adjust according to the needs of the fruit at different stages. In terms of humidity, there is a lack of precise monitoring and regulation, which can easily lead to mold caused by excessive humidity, or dehydration and shriveling of the fruit due to excessive humidity. As an important ripening gas, ethylene concentration control is also extremely critical. Traditional ripening methods often cannot accurately maintain an appropriate ethylene concentration, which affects the ripening effect. Light intensity has a significant impact on the color and quality of certain fruits, but traditional ripening processes rarely achieve precise management of light intensity. In monitoring the objects being ripened, existing technologies find it difficult to fully and accurately obtain their initial state data. In the past, the maturity indicators of fruits were mostly judged through manual experience, lacking objective and quantitative detection methods, resulting in inaccurate assessments of fruit maturity and affecting the formulation of ripening plans. Changes in fruit size and weight during the ripening process can reflect its internal physiological changes, but traditional methods cannot monitor these parameters in real time and accurately. Summary of the Invention

[0004] Based on the technical problems existing in the background technology, the present invention proposes a physical ripening technology AI data model simulation system.

[0005] The present invention proposes a physical ripening technology AI data model simulation system, comprising: Data acquisition module: used to collect various environmental parameter data related to physical ripening, including but not limited to temperature, humidity, gas concentration, light intensity, and the initial state data of the ripened object; AI data processing and modeling module: Based on the data collected by the data acquisition module, an artificial intelligence algorithm is used to construct a mathematical model of the physical ripening process. The model can simulate the ripening process of the ripening object under different combinations of environmental parameters; Simulation display module: presents the simulation results of the model constructed by the AI ​​data processing and modeling module in a visual manner, including dynamic display of the ripening process, status display of key time nodes, and comparative display of ripening effects under different parameter conditions; Control feedback module: Generates control instructions for the physical ripening equipment based on the results presented by the simulation display module. At the same time, it can receive the actual operating data of the physical ripening equipment and feed it back to the data acquisition module and AI data processing and modeling module to optimize and adjust the model.

[0006] Preferably, the data acquisition module adopts an adaptive data acquisition strategy, which can automatically adjust the frequency and accuracy of data acquisition according to different stages of the physical ripening process; In the early stages of ripening, basic data is collected at a lower frequency; as the ripening process progresses, when key indicators show significant changes, the frequency and accuracy of data collection are automatically increased.

[0007] Preferably, the AI ​​data processing and modeling module has a model self-update function. When the system detects new physical ripening experimental data or abnormal ripening cases in actual production, it automatically incorporates the new data into the model training and re-optimizes the model parameters.

[0008] Preferably, the simulation display module adopts a multimodal display method, including graphics, image display, sound simulation and tactile feedback. The tactile feedback is provided through a wearable device, allowing the operator to feel the simulated changes in fruit hardness and tactile information.

[0009] Preferably, the control feedback module is provided with a fault self-diagnosis and early warning submodule. When it detects abnormal fluctuations in the operating data of the physical ripening equipment or deviations from the simulation results beyond a preset range, it automatically analyzes the possible causes of the fault, sends an early warning message to the operator, and provides fault solution suggestions.

[0010] Preferably, the system has cross-platform operation capabilities, can run smoothly on devices with different operating systems and different hardware architectures, and can maintain consistency in simulation performance.

[0011] Preferably, the data acquisition module is equipped with a data encryption transmission function. During the process of transmitting data from the sensor to the system core processing unit, an encryption algorithm is used to encrypt the data to prevent the data from being stolen or tampered with, thereby ensuring the security and integrity of the data.

[0012] Preferably, the AI ​​data processing and modeling module adopts transfer learning technology, which can draw on existing model parameters and knowledge in similar fields when constructing a new physical ripening model, quickly initialize the model, and reduce training time and data requirements.

[0013] Preferably, the simulation display module supports multi-person collaborative operation. Multiple operators can connect through the network, log into the system at the same time, perform interactive operations in the same simulation scenario, and jointly discuss different ripening plans and collaboratively adjust observation perspectives.

[0014] Preferably, it also includes an energy consumption optimization module, which monitors the energy consumption of the system hardware equipment in real time during the simulation process, dynamically adjusts the operating power of the equipment according to the priority and real-time load of the simulation task, and minimizes the system energy consumption while ensuring the simulation performance.

[0015] The physical ripening technology AI data model simulation system proposed in the present invention has the following beneficial effects: through the set data acquisition module, AI data processing and modeling module, simulation display module and control feedback module, it can collect fruit maturity information in real time, and judge the maturity of the fruit based on the collected data information, and can accurately monitor the maturity of the fruit. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the overall structure of the AI ​​data model simulation system for physical ripening technology proposed in this invention. DETAILED DESCRIPTION

[0017] Reference Figure 1 The present invention proposes a physical ripening technology AI data model simulation system, including: Data acquisition module: used to collect various environmental parameter data related to physical ripening, including but not limited to temperature, humidity, gas concentration (such as ethylene concentration), light intensity, etc., as well as the initial state data of the ripening object, such as the maturity index, size, weight and other information of the fruit. The data acquisition module includes a temperature sensor for accurately measuring the ripening environment temperature, such as a high-precision thermistor sensor; a humidity sensor, such as a capacitive humidity sensor, with a measurement accuracy of up to ±2%RH, used to obtain humidity data; an ethylene concentration sensor, which uses an electrochemical sensor to accurately monitor ethylene concentration, which is crucial for fruit ripening; and a light intensity sensor for measuring light intensity to ensure the collection of light condition data for the ripening environment; a fruit maturity index sensor: a near-infrared spectral sensor, which assesses the maturity of the fruit by detecting the fruit's absorption of near-infrared light of different wavelengths; a size measurement device: a laser ranging sensor or a 3D camera, which can measure the size of the fruit; and a weight sensor: a strain gauge weighing sensor, which is used to measure the weight of the fruit.

[0018] AI Data Processing and Modeling Module: Uses artificial intelligence algorithms to construct a mathematical model of the physical ripening process. This model can simulate the maturation process of ripening objects under different combinations of environmental parameters. This module relies on a series of physical devices to operate. In terms of hardware computing, it is equipped with high-performance servers with multi-core central processing units (CPUs), such as Intel Xeon series processors, which can process massive amounts of data in parallel and provide powerful computing power for complex artificial intelligence algorithm operations. At the same time, it is equipped with a professional graphics processing unit (GPU). Based on the data collected by the data acquisition module, it uses artificial intelligence algorithms to construct a mathematical model of the physical ripening process. This model can simulate the maturation process of ripening objects under different combinations of environmental parameters. When constructing the physical ripening mathematical model, for computationally intensive tasks such as convolutional neural networks (CNN) for extracting fruit image features and recurrent neural networks (RNN) for processing time series data (such as the impact of time-changing environmental parameters on the ripening process), GPUs can greatly accelerate computing speed, significantly shorten model training time, and improve modeling efficiency.

[0019] Simulation display module: The simulation results of the model constructed by the AI ​​data processing and modeling module are presented in a visual way, including the dynamic display of the ripening process, the status display of key time nodes, and the comparative display of ripening effects under different parameter conditions. In terms of the dynamic display of the ripening process, advanced 3D modeling and animation rendering technology is used, relying on professional graphics engines such as Unity or Unreal. Engine, for the physical ripening simulation of the fruit, starting from the initial green state of the fruit, as the simulation time progresses, the gradual change of the fruit color is rendered in real time, for example, from green to mature red or yellow; the change of the texture of the fruit surface is simulated, from smooth to gradually with subtle wrinkles or spots; at the same time, by dynamically simulating the expansion or contraction of the fruit, the impact of the material changes inside it due to ripening on its appearance is shown. This process is presented on a high-resolution display, such as an LCD screen, to ensure that the operator can clearly observe every detail change; the status display of key time nodes is based on data annotation and close-up technology. When the simulation reaches the set key time node, such as the peak of ethylene release, the moment when the fruit hardness changes significantly, etc., the system automatically pauses the simulation and marks the time node with eye-catching colors and text. At the same time, a close-up display of the key parts of the fruit is performed, such as by zooming in on the part to show the changes in the fruit epidermal cell structure due to ripening at that time node; different colors of light and shadow effects are used to highlight the sugar content inside the fruit. The distribution of starch accumulation or starch conversion areas will be displayed together with the corresponding environmental parameter data, such as the temperature, humidity, ethylene concentration, etc., in the form of a table or bar chart on one side of the screen, which is convenient for operators to conduct correlation analysis. For the comparative display of ripening effects under different parameter conditions, the simulation display module supports split-screen display function, which can display up to four screens simultaneously. Operators can freely choose different parameter combinations for comparison. For example, the ripening effect under high temperature, high humidity and high ethylene concentration conditions is displayed on one screen; the situation under low temperature, low humidity and low ethylene concentration conditions is displayed on another screen. Through side-by-side comparison, the influence of different parameters on the fruit ripening speed and final quality (such as sweetness and color uniformity) is clearly presented. At the same time, the system provides data comparison and analysis charts, such as line charts showing the changing trend of fruit maturity over time under different parameters, and pie charts comparing the proportion of various quality indicators of fruits under different parameter combinations, which help operators more intuitively understand the relationship between parameters and ripening effects, so as to make scientific decisions quickly.

[0020] Control feedback module: Based on the results presented by the simulation display module, it generates control instructions for the physical ripening equipment. At the same time, it can receive the actual operation data of the physical ripening equipment and feedback it to the data acquisition module and the AI ​​data processing and modeling module to optimize and adjust the model. This infrastructure builds a complete physical ripening simulation system. The data acquisition module comprehensively collects data to lay a solid foundation for accurate modeling and improve the restoration of the actual ripening scene; the AI ​​data processing and modeling module uses artificial intelligence algorithms to efficiently and accurately simulate complex ripening processes, greatly improving the accuracy of ripening effect prediction compared to traditional empirical ripening; the simulation display module is presented in a visual way, which facilitates operators to make quick decisions; the control feedback module realizes the interaction between the simulation system and the actual equipment, forming a closed-loop optimization system, and improving the intelligence and precision of physical ripening. The control feedback module: As the key hub connecting the virtual simulation and the actual physical ripening equipment in the physical ripening simulation system, it shoulders multiple core tasks. Based on the detailed results presented by the simulation display module, this module uses built-in precision control algorithms to generate extremely accurate control instructions that are suitable for physical ripening equipment. At the hardware level, the control feedback module is equipped with a high-performance programmable logic controller (PLC). According to the preset control strategy, it generates various control signals. These signals are connected to the actuators in the physical ripening equipment through dedicated signal transmission cables, such as the power control terminal of the heating element, the motor speed regulator of the ventilation equipment, and the flow control valve of the gas injection device. This enables precise control of key environmental parameters such as temperature, humidity, and gas concentration. At the same time, the control feedback module also has the ability to receive real-time data. Through industrial-grade communication interfaces, it is closely connected to various sensors in the physical ripening equipment. These sensors monitor the equipment's operating data in real time, including but not limited to actual temperature and humidity values, the real-time concentration of gases such as ethylene, and the equipment's operating status (such as whether the heating equipment is working properly and the speed of the ventilation equipment). The large amount of equipment operating data obtained is transmitted to the control feedback module in a high-speed and stable manner. During the data processing and feedback stage, the control feedback module performs preliminary filtering and preprocessing on the received actual operating data to remove possible noise interference and ensure data accuracy and reliability.Subsequently, these data are fed back in two ways: one part is directly transmitted to the data acquisition module and integrated with the environmental parameters and initial state data of the ripening objects originally collected by the module. This integration process makes the data owned by the data acquisition module more comprehensive and real-time, providing a more solid data foundation for subsequent accurate modeling, and further improving the degree of restoration of the actual ripening scene. The other part of the data is transmitted to the AI ​​data processing and modeling module. The intelligent algorithm in this module will conduct an in-depth comparison and analysis of these actual operating data and simulation results. When a deviation is found between the two, the algorithm will automatically trigger the model optimization process. For example, if The actual fruit ripening speed is too slow compared to the simulation prediction. The AI ​​algorithm may adjust the parameters in the model regarding the impact of temperature, humidity and ethylene concentration on the ripening speed, and retrain the model to improve the accuracy of the model's simulation of the actual ripening process. Through this closed-loop optimization mechanism, with the continuous feedback of actual operation data and continuous optimization of the model, the entire physical ripening simulation system can adapt to different ripening tasks and actual production environments more intelligently and accurately, effectively avoiding the problem of unstable ripening effects caused by the lack of real-time feedback and precise regulation in traditional empirical ripening, and providing a reliable and efficient control and optimization solution for the physical ripening process.

[0021] The data acquisition module adopts an adaptive data acquisition strategy, which can automatically adjust the frequency and accuracy of data acquisition according to the different stages of the physical ripening process. In the early stage of ripening, basic data is collected at a lower frequency. The physical ripening process is relatively stable and the parameters change relatively slowly. At this time, in order to avoid unnecessary resource consumption, the data acquisition module collects basic data at a lower frequency. Specifically, the temperature sensor collects data every 5 minutes, the humidity sensor and ethylene concentration sensor collect data every 10 minutes, and the light intensity sensor records data every 15 minutes; as the ripening process progresses, when key indicators change significantly, the data collection frequency and accuracy are automatically increased. The adaptive data acquisition strategy avoids the waste of resources caused by high-frequency and high-precision acquisition throughout the process, while ensuring that sufficiently detailed data is obtained in the key stage of physical ripening. As the ripening process progresses, when the system detects significant changes in key indicators, the adaptive data acquisition strategy automatically switches to high-frequency and high-precision acquisition mode. The basis for determining changes in key indicators is a complex algorithmic model that comprehensively considers multiple factors. Taking fruit ripening as an example, a significant change in key indicators is determined when the fruit's color begins to change significantly, its firmness changes significantly, or its ethylene release increases sharply. At this time, the temperature sensor's acquisition frequency is increased to once per second, with an accuracy of ±0.05°C. A higher-precision platinum resistance temperature sensor is used to ensure that subtle temperature fluctuations can be accurately captured. The humidity sensor's acquisition frequency is increased to once every two minutes, with an accuracy of ±1%RH, using a more advanced capacitive humidity sensor. The ethylene concentration sensor is tested once per minute, with an accuracy of ±0.05ppm, using a higher-performance electrochemical sensor. Simultaneously, the light intensity sensor's acquisition frequency is increased to once per minute to obtain more real-time light data. Reducing the amount of data collected initially reduces system resource consumption. In critical stages, increasing the acquisition frequency and accuracy can capture subtle changes, providing the AI ​​data model with more timely and accurate data, further improving the simulation system's adaptability and accuracy to complex ripening processes.

[0022] The AI ​​data processing and modeling module features a self-updating model function. When the system detects new physical ripening experimental data or abnormal ripening cases in actual production, it automatically incorporates the new data into model training and re-optimizes model parameters, eliminating the need for manual intervention to initiate the update process. This self-updating function enables the system to keep up with the latest data changes in the field of physical ripening in real time. Whether it is new experimental results or special circumstances in actual production, the system can automatically absorb new data for optimization, ensuring that the AI ​​data model is always in the best simulation state. This greatly reduces manual maintenance costs and enables the simulation system to quickly adapt to new ripening scenarios and needs, improving the accuracy and practicality of the simulation. When new data arrives, the algorithm does not completely retrain the entire model. Instead, it incorporates the knowledge contained in the new data into the existing model through parameter fine-tuning strategies. For example, for physical ripening models built based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs), incremental learning algorithms can identify parts of the new data that are similar or different from the existing model features and adjust the model's weights and bias parameters accordingly. In actual operation, when a new fruit ripening experiment is detected, the algorithm can automatically adjust the model's weights and bias parameters accordingly. When data shows that the fruit ripening cycle under specific lighting conditions differs significantly from the previous model predictions, the incremental learning algorithm focuses on analyzing the lighting-related feature dimensions in the new data and optimizes the parameters of the convolutional and fully connected layers in the model that process lighting information. During the model re-optimization process, automatic hyperparameter adjustment techniques are used, such as Bayesian optimization algorithms or random search algorithms. (The Bayesian optimization algorithm, based on Bayes' theorem, transforms the hyperparameter optimization problem into a probabilistic inference problem. It first makes a prior distribution assumption about the hyperparameter space, then continuously samples and evaluates model performance in the hyperparameter space, using Bayes' theorem to update the posterior distribution of the hyperparameters.)Specifically, it uses a proxy model (such as a Gaussian process) to approximate the objective function. The proxy model can predict the distribution of the value of the objective function at other points based on the existing sampling points. At the same time, it uses the acquisition function to balance exploration (trying in unsampled areas) and utilization (selecting points that may make the objective function optimal), so as to gradually find the optimal hyperparameter combination. For example, when training a neural network, the Bayesian optimization algorithm can intelligently select the next hyperparameter combination to be tried based on the performance of different hyperparameter combinations such as learning rates and number of layers tried before, avoiding blind search; the random search algorithm is a simple and direct hyperparameter search method, which randomly generates hyperparameter combinations in a preset hyperparameter space, and performs model training and evaluation on each randomly generated combination. Similarly, it accumulates model performance data under different hyperparameter combinations, and then selects the hyperparameter combination with the best performance as the result. Although random search seems blind, due to its randomness, it can explore extensively in the hyperparameter space and may find some optimal solutions that are difficult to find through other deterministic algorithms. For example, in a complex hyperparameter space, random search may accidentally sample some remote but high-performance hyperparameter areas, thereby finding better hyperparameter settings for the model. Searching within a preset hyperparameter space, looking for the hyperparameter combination that best suits the current new data and model state, such as learning rate, number of iterations, number of hidden layer neurons, etc. This process can further improve the model's adaptability to new data and ensure that the model can achieve optimal performance after updating.

[0023] The simulation display module adopts a multimodal display method. In addition to conventional graphics and image displays, it also combines sound simulation, such as simulating the subtle popping sounds and gas release sounds that may be produced during the ripening process of the fruit, as well as tactile feedback. Through wearable devices or special operating handles, the operator can feel the simulated tactile information such as the change in fruit hardness. The tactile feedback adopts the following technologies, such as vibration feedback technology: it can be achieved with the help of vibration motors. Small eccentric rotating mass (ERM) vibration motors or linear resonant actuators (LRA) are more commonly used. For example, these vibration motors are embedded in operating handles or wearable devices (such as smart gloves). When simulating changes in fruit hardness, by controlling the different vibration frequencies and amplitudes of the motor, the operator can feel the vibration of the corresponding intensity, simulating changes in fruit hardness. For example, when the hardness of the fruit increases, the vibration motor generates stronger and more intensive vibration feedback to the operator; force feedback technology: a force feedback device is required, such as some professional force feedback handles, which have built-in motors and transmission mechanisms. When the simulated operator squeezes the fruit to feel the hardness, the handle can generate through the motor according to the simulation data. The reverse force allows the operator to feel resistance, simulating the hardness and elasticity of the fruit. In physical reality, in addition to force feedback handles, exoskeleton-style force feedback devices could be worn on the operator's limbs to provide a more comprehensive force feedback experience when simulating actions such as grasping and touching the fruit. Electrotactile feedback technology: This technology uses electric current to stimulate human skin to produce tactile perception. It requires electrical stimulation electrodes and corresponding electrical signal control circuits. These electrodes are integrated into the skin-contact area of ​​wearable devices, such as smart bracelets and patches. By precisely controlling the intensity, frequency, and waveform of the electrical signal, different tactile sensations can be simulated. For example, the texture of a fruit surface can be simulated using different electrical stimulation patterns, allowing the operator to feel subtle bumps and depressions. Multimodal presentation greatly enriches the operator's perception of the physical ripening simulation process. Sound simulation and tactile feedback allow the operator to experience the ripening process from multiple sensory perspectives, providing a deeper and more comprehensive understanding of the ripening details compared to purely visual presentations. This immersive experience helps operators more accurately judge the ripening effect and improves the accuracy and scientific nature of the evaluation of different ripening solutions.

[0024] The control feedback module is equipped with a fault self-diagnosis and early warning submodule. When it detects abnormal fluctuations in the operating data of the physical ripening equipment or deviations from the simulation results that exceed the preset range, it automatically analyzes the possible causes of the fault. Common abnormalities include: Abnormal temperature fluctuations: A sudden temperature rise: This could be due to a heating element failure, such as a shorted heating wire, which results in an abnormal increase in heating power and a rapid rise in the ripening environment temperature. Alternatively, a temperature sensor failure could be causing a false alarm indicating an excessively high temperature.

[0025] Persistently low temperatures: This could indicate a problem with the heating system, such as aging or damaged heating elements that are malfunctioning, or incorrect temperature control system parameter settings, resulting in insufficient heating power. Additionally, damage to the insulation layer and rapid heat loss can make it difficult to maintain a normal ripening temperature.

[0026] Abnormal humidity fluctuations: Excessive humidity: This could be caused by a faulty humidity sensor, which may be giving erroneously high humidity readings. Alternatively, the humidification system may be out of control, such as a faulty humidifier valve that remains open, continuously injecting moisture into the ripening environment and causing excessive humidity. Additionally, a poor ventilation system that fails to remove excess moisture can also contribute to elevated humidity.

[0027] Low humidity: This could be caused by a malfunctioning humidifier, which is not working properly and cannot provide enough moisture for the ripening environment. Alternatively, the ventilation system may be too strong, which is causing excessive moisture to be removed from the environment, resulting in a drop in humidity.

[0028] Abnormal fluctuations in gas concentration: Ethylene concentration is too high: This could be due to a malfunction in the ethylene generator, which is continuously overproducing ethylene gas. Or it could be due to a malfunction in the ethylene gas detection sensor, which is falsely reporting an excessively high ethylene concentration.

[0029] Low oxygen concentration: This could be caused by a problem with the sealing system, resulting in poor exchange between the ripening environment and the outside air, preventing timely oxygen replenishment. Alternatively, during the ripening process, the fruit may be breathing too vigorously, consuming oxygen too quickly, while the oxygen supply system fails to adjust in time, causing a drop in oxygen concentration. This system also sends an early warning message to the operator and provides troubleshooting suggestions. The fault self-diagnosis and early warning submodule greatly improves the stability and reliability of the physical ripening equipment. When signs of equipment failure appear, it can quickly locate the problem and promptly notify the operator, avoiding production losses caused by undetected faults. At the same time, the solution suggestions provided provide guidance for operators to quickly resolve the problem, shortening equipment downtime and improving production efficiency.

[0030] This system has cross-platform operation capabilities and can run smoothly on devices with different operating systems (including but not limited to Windows, Linux, Android, iOS) and different hardware architectures (such as x86, ARM, etc.), while maintaining consistency in simulation performance. The cross-platform operation capability enables the simulation system to adapt to the device environments of different users. Whether on a professional computer in the laboratory or on a mobile device at the production site, users can easily use the system, expanding the application scope of the system, improving the system's usability and popularity, and facilitating operators in different scenarios to perform physical ripening simulation and control.

[0031] The data acquisition module is equipped with a data encryption transmission function. In the process of transmitting data from the sensor to the core processing unit of the system, advanced encryption algorithms (such as AES-256) are used to encrypt the data to prevent data from being stolen or tampered with, ensuring the security and integrity of the data. The data encryption transmission function ensures the security of physical ripening-related data during transmission. At a time when data sensitivity is increasing, especially data involving production processes and commercial secrets, encrypted transmission can effectively resist external attacks and prevent data leakage or malicious tampering. This provides strong support for the stable operation of the system and the protection of corporate commercial interests, ensuring the reliability of AI models and simulation results built based on these data.

[0032] The AI ​​data processing and modeling module uses transfer learning technology. When constructing a new physical ripening model, it can draw on the model parameters and knowledge of existing similar fields (such as biological growth simulation, chemical reaction kinetics simulation, etc.), quickly initialize the model, and reduce training time and data requirements. Transfer learning technology enables the system to stand on the shoulders of existing research in related fields when constructing the physical ripening model. By drawing on model parameters and knowledge in similar fields, it greatly shortens the model training cycle and reduces dependence on large amounts of physical ripening raw data. Even when the cost of obtaining physical ripening data is high or the amount of data is limited, it can still quickly build a high-quality simulation model, thereby improving the application flexibility and efficiency of the system.

[0033] The simulation display module supports multi-person collaborative operation. Multiple operators can connect through the network, log in to the system at the same time, and perform interactive operations in the same simulation scenario, such as jointly discussing different ripening plans, collaboratively adjusting observation perspectives, etc. The multi-person collaborative operation function promotes the application of teamwork in physical ripening simulation. In actual production or research, personnel with different professional backgrounds (such as agricultural experts, engineers, technical workers, etc.) can participate in the simulation process at the same time and jointly discuss the best ripening plan. This collaborative method can give full play to the respective advantages of team members, improve the comprehensiveness and scientificity of decision-making, and also facilitate knowledge sharing and experience exchange, thereby enhancing the entire team's understanding and application level of physical ripening technology.

[0034] The system is equipped with an energy consumption optimization module, which monitors the energy consumption of system hardware devices (such as servers, sensors, etc.) in real time during the simulation process. According to the priority of the simulation task and the real-time load, the operating power of the equipment is dynamically adjusted to minimize the system energy consumption while ensuring the simulation performance. The energy consumption optimization module conforms to the development concept of green energy conservation. During the long-term operation of the system, by real-time monitoring and dynamic adjustment of the equipment power, it can effectively reduce energy consumption and reduce operating costs. At the same time, reasonable energy consumption management helps to extend the service life of the hardware equipment, improve the stability and sustainability of the system, and make the physical ripening simulation system more energy-efficient and environmentally friendly while running efficiently. The main devices used are: power sensors: used to measure the real-time power consumption of hardware devices; smart meters: installed on the main power supply line of the system and the branch power supply lines of each major hardware device. Smart meters can not only measure power consumption, but also monitor parameters such as voltage, current, and power factor to provide comprehensive data for energy consumption analysis; sensor nodes: for some distributed sensor devices, low-power sensor nodes can be used to monitor energy consumption; intelligent power management module: including dynamic voltage and frequency scaling (DVFS) chips, such as Intel's VRM (Voltage Regulator Energy Optimization Module (EEM) chips automatically adjust the supply voltage and operating frequency of core components such as the server CPU and GPU according to the system load. When the analog task load is low, the voltage and frequency are reduced to reduce power consumption. When the load is high, the voltage and frequency are increased to ensure performance. Thyristor voltage regulators are used to adjust the power of linear loads such as heating equipment and lighting equipment. Variable frequency drives are used for power regulation of motor equipment such as fans and water pumps. Microcontrollers serve as the core control unit of the energy optimization module, such as the STM32 series microcontrollers. Data acquisition cards are used to convert energy consumption data in the form of analog signals into digital signals for processing by the microcontroller. Communication modules enable data communication within the energy optimization module and between the module and other modules in the system.

[0035] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A physical ripening technology AI data model simulation system, characterized by: include: Data acquisition module: used to collect various environmental parameter data related to physical ripening, including but not limited to temperature, humidity, gas concentration, light intensity, and the initial state data of the ripened object; AI data processing and modeling module: Based on the data collected by the data acquisition module, an artificial intelligence algorithm is used to construct a mathematical model of the physical ripening process. The model can simulate the ripening process of the ripening object under different combinations of environmental parameters; Simulation display module: presents the simulation results of the model constructed by the AI ​​data processing and modeling module in a visual manner, including dynamic display of the ripening process, status display of key time nodes, and comparative display of ripening effects under different parameter conditions; Control feedback module: Generates control instructions for the physical ripening equipment based on the results presented by the simulation display module. At the same time, it can receive the actual operating data of the physical ripening equipment and feed it back to the data acquisition module and AI data processing and modeling module to optimize and adjust the model.

2. A physical ripening technology AI data model simulation system according to claim 1, characterized in that: The data acquisition module adopts an adaptive data acquisition strategy, which can automatically adjust the frequency and accuracy of data acquisition according to the different stages of the physical ripening process; In the early stages of ripening, basic data are collected at a lower frequency; As the ripening process progresses, when key indicators show significant changes, the frequency and accuracy of data collection will be automatically increased.

3. A physical ripening technology AI data model simulation system according to claim 1, characterized in that: The AI ​​data processing and modeling module has a model self-update function. When the system detects new physical ripening experimental data or abnormal ripening cases in actual production, it automatically incorporates the new data into the model training and re-optimizes the model parameters.

4. A physical ripening technology AI data model simulation system according to claim 1, characterized in that: The simulation display module adopts a multimodal display method, including graphics, image display, sound simulation and tactile feedback. The tactile feedback is provided through wearable devices, allowing operators to feel the simulated changes in fruit hardness and tactile information.

5. A physical ripening technology AI data model simulation system according to claim 1, characterized in that: The control feedback module is equipped with a fault self-diagnosis and early warning submodule. When it detects abnormal fluctuations in the operating data of the physical ripening equipment or deviations from the simulation results beyond a preset range, it automatically analyzes the possible causes of the fault, sends an early warning message to the operator, and provides fault solution suggestions.

6. A physical ripening technology AI data model simulation system according to claim 1, characterized in that: The system has cross-platform operation capabilities and can run smoothly on devices with different operating systems and different hardware architectures, while maintaining consistency in simulation performance.

7. The physical ripening technology AI data model simulation system according to claim 1 is characterized in that: The data acquisition module is equipped with a data encryption transmission function. During the data transmission from the sensor to the system core processing unit, an encryption algorithm is used to encrypt the data to prevent the data from being stolen or tampered with, thereby ensuring the security and integrity of the data.

8. The physical ripening technology AI data model simulation system according to claim 1 is characterized in that: The AI ​​data processing and modeling module uses transfer learning technology. When building a new physical ripening model, it can draw on existing model parameters and knowledge in similar fields, quickly initialize the model, and reduce training time and data requirements.

9. The physical ripening technology AI data model simulation system according to claim 1 is characterized in that: The simulation display module supports multi-person collaborative operation. Multiple operators can connect through the network and log into the system at the same time to interact in the same simulation scene, jointly discuss different ripening plans and collaboratively adjust observation perspectives.

10. The physical ripening technology AI data model simulation system according to claim 1, characterized in that: It also includes an energy consumption optimization module, which monitors the energy consumption of system hardware devices in real time during the simulation process, and dynamically adjusts the operating power of the equipment according to the priority and real-time load of the simulation task, so as to minimize the system energy consumption while ensuring the simulation performance.

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