Microenvironment constant temperature and humidity system for tea storage

Abstract

The invention discloses a microenvironment constant temperature and humidity system for tea storage, and relates to the technical field of tea storage, the system comprises a sensor array module used for performing real-time data acquisition on temperature, humidity and volatile component change in a tea storage microenvironment by deploying a high-precision sensor array, the data acquisition module is used for acquiring an original data set for storing environment parameters and tea state feedback to obtain initial monitoring information, and the data analysis module is used for performing feature extraction on the collected environment parameters and the tea state feedback by adopting a pre-established data analysis model according to the initial monitoring information, analyzing a quality change rule and sending the quality change rule to the server. Determining the chemical component change trend of the tea in the current storage stage; according to the microenvironment constant-temperature and constant-humidity system for tea storage, the constant-temperature and constant-humidity system integrating dynamic monitoring, data analysis and parameter self-adaptive adjustment is designed, a tea quality change curve is formed, and closed-loop management is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of tea storage, and in particular to a microenvironment constant temperature and humidity system for tea storage. Background Art

[0002] As an agricultural product sensitive to storage conditions, tea quality directly impacts its market value and consumer experience. Scientific storage techniques are crucial for preserving tea's aroma, taste, and nutritional content. Microenvironmental control has become a key focus in the industry, especially during long-term storage. Constant temperature and humidity systems simulate ideal storage conditions to safeguard tea quality.

[0003] However, existing storage methods rely heavily on static environmental control, making them inadequate for adapting to the dynamic demands of tea quality over time. Traditional equipment typically only provides fixed temperature and humidity settings, lacking real-time monitoring and feedback on the tea's actual condition. This leads to unstable storage performance, and even high-quality tea can deteriorate due to environmental fluctuations. Current solutions are limited in that most systems focus solely on maintaining environmental parameters, ignoring the dynamic changes in tea quality. Manual regular testing is time-consuming and labor-intensive, with delayed results, making it difficult to guide real-time storage parameter adjustments. This static management approach fails to address the varying temperature and humidity requirements of tea during different storage stages, thus impacting long-term quality stability. More critically, existing systems lack automated quality assessment mechanisms, preventing the generation of continuous quality change data and hindering the scientific evaluation and optimization of storage performance. The core challenge lies in accurately monitoring subtle changes in tea quality. The complex chemical composition of tea during storage requires real-time monitoring through sensors or detection technologies, but existing technologies struggle to achieve high-precision, low-cost dynamic monitoring within microenvironments. The resulting challenge is translating this monitoring data into actionable parameter adjustment strategies. Without a scientific analysis and feedback mechanism, simple monitoring data is difficult to form an effective closed-loop control, resulting in the system's inability to adaptively adjust temperature and humidity based on the tea's condition. Furthermore, the lack of a continuous quality change curve makes it difficult for users to intuitively understand storage effects, limiting the system's intelligence and practicality. Summary of the Invention

[0004] The purpose of the present invention is to provide a microenvironment constant temperature and humidity system for tea storage, and to design a constant temperature and humidity system that integrates dynamic monitoring, data analysis and parameter adaptive adjustment to form a tea quality change curve and realize closed-loop management.

[0005] To achieve the above object, the present invention provides the following technical solution: a microenvironment constant temperature and humidity system for tea storage, the system comprising: The sensor array module is used to collect real-time data on changes in temperature, humidity, and volatile components in the tea storage microenvironment by deploying a high-precision sensor array, obtaining a raw data set of storage environment parameters and tea status feedback to obtain initial monitoring information; The data analysis module is used to extract features from the collected environmental parameters and tea status feedback based on the initial monitoring information using a pre-established data analysis model, analyze the quality change pattern, and determine the chemical composition change trend of the tea during the current storage stage; The visualization and judgment module is used to obtain visualization results based on the trend of chemical composition changes. Based on the visualization results, combined with the closed-loop management mechanism, the continuous change curve is compared with historical data. If abnormal fluctuations are found, the data analysis model is triggered to recalculate and determine whether the quality change pattern needs to be updated; The adaptive control module is used to feed back the latest analysis results to the adaptive control module based on the updated quality change rules through intelligent system design, obtain new adjustment parameters, and determine the optimization direction of storage environment parameters.

[0006] Preferably, obtaining visualization results according to the chemical composition change trend includes: Based on the changing trend of chemical composition, a quality assessment module is built to compare the analysis results with the preset threshold range. If the changing trend exceeds the threshold range, an adjustment demand signal is generated to determine the need to optimize the storage environment parameters.

[0007] Preferably, obtaining visualization results according to the chemical composition change trend further includes: According to the adjustment demand signal and combined with the adaptive control logic, the specific parameter values ​​of temperature and humidity adjustment are calculated, the target environment setting values ​​are obtained, and the storage conditions suitable for the current state of the tea are determined.

[0008] Preferably, obtaining visualization results according to the chemical composition change trend further includes: For the target environment setting value, the execution unit of the constant temperature and humidity system updates the stored environmental parameters in real time, obtains the adjusted environmental status data, and determines whether it meets the target setting range.

[0009] Preferably, obtaining visualization results according to the chemical composition change trend further includes: Based on the adjusted environmental status data, new tea status feedback information is continuously collected through the sensor array to obtain updated quality change data to determine whether the storage effect is stable.

[0010] Preferably, obtaining visualization results according to the chemical composition change trend further includes: Based on the updated quality change data, a continuous change curve is drawn to record the dynamic performance of tea quality in different storage stages and obtain visual results.

[0011] Preferably, the latest analysis result is fed back to the adaptive control module to obtain new adjustment parameters and determine the optimization direction of the storage environment parameters. The specific formula is: ; in, Indicates the current adjustment amount, used to control temperature or humidity adjustment. Indicates the current quality index of tea leaves. Represents the current environment variable vector, represents the coupled dynamic evaluation function of environmental quality, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight, represents the coupling stability trend control weight, Indicates time.

[0012] Preferably, the environmental quality coupled dynamic evaluation function The specific expression formula is: ; in, represents the coupled dynamic evaluation function of environmental quality, Indicates the current quality index of tea leaves. Indicates the current A vector of environment variables, Indicates the total number of environmental variables involved in regulation, Represents the environment variable index, Indicates time.

[0013] Preferably, the environmental sensitivity control weight The specific calculation formula is: = ; in, represents the environmental sensitivity control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval, Indicates taking the maximum value, that is, the quality degradation detector.

[0014] Preferably, the quality acceleration trend control weight The specific calculation formula is: = ; in, Indicates the quality acceleration trend control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval; The coupled stability trend regulation weight =1- - ; in, represents the coupling stability trend control weight, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This microenvironment constant temperature and humidity system for tea storage uses a high-precision sensor array to collect real-time tea storage microenvironment data. It then uses a pre-established data analysis model to extract features and analyze quality changes. A quality assessment module determines whether the storage environment needs to be adjusted. Adaptive control logic is then used to calculate target environmental parameters, which are updated in real time via an execution unit. The system continuously collects feedback on tea status and plots a quality change curve. The results are compared with historical data to trigger model recalculation, and the analysis results are fed back to the control module to optimize the storage environment. This system enables intelligent monitoring and dynamic adjustment throughout the tea storage process, effectively ensuring tea quality stability and improving storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a system connection diagram of the present invention; Figure 2 Schematic diagram of the refrigeration and drying module in the constant temperature and humidity system for tea storage microenvironment.

[0017] In the figure, 1. Water-spraying air-cooled evaporator; 2. Capillary tube; 3. Dry filter; 4. Condenser; 5. Compressor. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0019] like Figure 1As shown, the present invention provides a technical solution: a microenvironment constant temperature and humidity system for tea storage, the system comprising: The sensor array module is used to collect real-time data on changes in temperature, humidity, and volatile components in the tea storage microenvironment by deploying a high-precision sensor array, obtaining a raw data set of storage environment parameters and tea status feedback to obtain initial monitoring information; The data analysis module is used to extract features from the collected environmental parameters and tea status feedback based on the initial monitoring information using a pre-established data analysis model, analyze the quality change pattern, and determine the chemical composition change trend of the tea during the current storage stage; The visualization and judgment module is used to obtain visualization results based on the trend of chemical composition changes. Based on the visualization results, combined with the closed-loop management mechanism, the continuous change curve is compared with historical data. If abnormal fluctuations are found, the data analysis model is triggered to recalculate and determine whether the quality change pattern needs to be updated; The adaptive control module is used to feed back the latest analysis results to the adaptive control module based on the updated quality change rules through intelligent system design, obtain new adjustment parameters, and determine the optimization direction of storage environment parameters.

[0020] This system utilizes a modular structure, forming a closed-loop feedback loop from data acquisition to intelligent control. The sensor array module, deployed within the tea storage device, uses high-precision temperature and humidity sensors and gas detection equipment such as an electronic nose to collect real-time data on ambient temperature, humidity, and the concentration of volatile organic compounds (VOCs) in tea leaves (such as aldehydes and esters) in the air, generating a raw data set. The data analysis module processes the collected data using multivariate data analysis algorithms, such as principal component analysis (PCA), support vector machines (SVM), or time series regression models, to identify key influencing factors and patterns of change, and to determine trends in the evolution of tea chemical composition (such as the oxidation rate of tea polyphenols and the decline in aromatic compound concentrations). The visualization and judgment module uses a graphical interface to present analysis results in the form of graphs and heat maps, monitoring tea quality changes in real time and comparing them with historical high-quality sample data. Identifying any abnormal deviations triggers model retraining and correction, ensuring accurate and effective tracking of quality changes. The adaptive control module adjusts the operating parameters of the constant temperature and humidity equipment (such as the thermostat setting temperature, the humidifier operating frequency, etc.) based on the updated analysis results to maintain the optimal microenvironment conditions, thereby achieving long-term dynamic protection of the tea storage quality.

[0021] This system achieves precise adjustment of the tea storage environment and quality assurance through a closed-loop mechanism of "collection-analysis-judgment-control". The specific workflow is as follows: First, during the data acquisition phase, the sensor array module consists of multiple high-precision temperature and humidity sensors and a multi-channel electronic nose. These sensors synchronously sample the temperature and relative humidity of the storage environment, as well as the concentrations of various tea volatile organic compounds, every ten seconds, forming a structured dataset. This module not only captures subtle changes in climatic conditions in real time, but also effectively monitors fluctuations in key aroma components such as aldehydes, esters, and alcohols.

[0022] The data analysis module then preprocesses the raw data. This includes normalizing each parameter by calculating its deviation from the historical average and adjusting it uniformly based on the historical standard fluctuation range. This eliminates analytical bias caused by dimensional differences in each data dimension and improves the stability of subsequent model analysis. The historical average and standard fluctuation range are typically calculated from a seven-day stable sample of data collected during the initial system operation to ensure statistical significance.

[0023] After preprocessing, the system introduces a feature extraction mechanism. Its core is to simplify the data structure and extract variable combinations that represent key trends. This process is typically based on principal component analysis or clustering algorithms, using existing training data to create a model. Subsequently, regression analysis is used to map changes in tea quality to environmental factors. Based on this model, the system can assess changes in tea quality indicators in real time, such as the degree of aromatic retention or the level of oxidation activity.

[0024] To ensure the reliability of evaluation results, the system incorporates an anomaly detection mechanism. Within each sliding time window (for example, collecting data from the past six hours every hour), the system compares the actual quality results with the model's predictions, calculating the difference trend between the two to determine if there are any abnormal deviations. If the average difference exceeds a certain threshold, a model retraining mechanism is triggered. This threshold is set during the model initialization phase based on the historical error distribution, typically taking the average error of all training samples plus one standard deviation, to ensure that anomaly detection is both sensitive and non-overreactive.

[0025] Once an anomaly is confirmed, the system automatically reloads the analysis model, readjusts the analysis parameters based on the latest data, and provides real-time feedback to the control module. The adaptive control module dynamically adjusts the temperature and humidity setpoints based on the changing trends in the analysis results. For example, if quality indicators indicate an intensified oxidation reaction, the control system automatically lowers the ambient temperature setpoint and increases the humidity to slow the reaction. The specific adjustment magnitude is calculated based on the rate of change in the reaction trend, calculated by dividing the magnitude of the change in the quality indicator by the duration over the past few hours. Furthermore, to ensure smooth response of the control system, the control sensitivity parameter is pre-set, typically determined based on environmental test results and typically maintained between 0.5 and 2. Finally, the control module transmits all parameter adjustment commands to the environmental control equipment, automatically controlling the operating status of the heating unit, humidifier, and exhaust system to achieve precise temperature and humidity regulation and maintain the microenvironmental conditions optimal for the current tea condition. This closed-loop control process automatically runs and updates during each sampling cycle, ensuring timely system response and precise adjustment.

[0026] The present invention achieves in-depth monitoring and intelligent adjustment of the tea storage environment and the evolution of its intrinsic quality by introducing high-precision sensing technology and multi-dimensional data analysis. Compared with the traditional method that relies on manual experience and judgment, this system significantly improves the response speed and accuracy of environmental regulation, and effectively avoids the deterioration of tea quality due to environmental fluctuations. At the same time, it adopts closed-loop management and adaptive control mechanisms, with intelligent decision-making and continuous optimization capabilities, so that the tea is always in ideal conditions during storage, which helps to maintain the stability of its aroma, taste and color, thereby enhancing the commodity value and storage safety. In addition, the system is also scalable and suitable for the storage needs of different types and grades of tea.

[0027] Obtaining visualization results based on the changing trends of chemical composition involves building a quality assessment module to compare the analysis results with the preset threshold range. If the changing trend exceeds the threshold range, an adjustment demand signal is generated to determine the need to optimize the storage environment parameters.

[0028] This implementation introduces a quality assessment module, building upon the existing visualization and judgment module, to identify, determine, and provide visual early warning of chemical composition trends. First, the chemical composition trend information output by the data analysis module is fed into the quality assessment module. Based on pre-established quality evaluation criteria, this module analyzes the change curves of key chemical components, such as aromatics, tea polyphenols, and amino acids, and compares them against a set of expected ranges. Preset threshold ranges are statistically derived from a large number of experimental samples and typically include upper and lower limits for each indicator's variation during storage of high-quality tea samples. For example, the rate of decrease in aromatic concentration should not exceed 20% within three days, and the change in amino acid content should not exceed 10%. If the current trend of a particular indicator continuously deviates from and breaks through this range, it is identified as an abnormal trend. The system then automatically generates an adjustment request signal, which prompts the adaptive control module to recalculate and optimize environmental parameters to prevent further quality degradation. The assessment results are also output through a visual interface as graphs, warnings, or color changes, facilitating intuitive identification and intervention by operators.

[0029] This implementation introduces a quality assessment module to quantify and visualize the chemical composition trends provided by the data analysis module. Its core workflow includes the following stages: First, the system processes the environmental and chemical composition data collected from the sensor array in the data analysis module, extracting the concentration changes of key components (such as aromatic compounds, tea polyphenols, and amino acids) within each monitoring cycle and calculating their rate of change based on the sampling period. The sampling period is generally set to every ten minutes.

[0030] The system then establishes a comprehensive evaluation mechanism, weighting and aggregating the changing trends of multiple components according to their impact on tea quality, to form an indicator that describes the overall state of quality change. The impact weight of each component is determined during the development phase through expert evaluation and scoring, empirical analysis, or regression fitting, and the sum of the weights is maintained as one. This ensures that each key component is appropriately weighted in the overall assessment, reflecting its actual impact on quality.

[0031] Next, to determine whether these changes are within the normal fluctuation range, the system needs to set a set of reference thresholds. These thresholds are based on a large amount of laboratory data and monitoring results of high-quality tea samples during storage in a controlled environment. Specifically, the average rate of change of each component in the historical data per unit time is used as the base value, and the standard fluctuation range of this data over multiple cycles is calculated. Then, the warning range is set according to the safety tolerance requirements. Generally, the safety tolerance value is between 1.5 and 2 times the standard fluctuation range, which can cover most normal changes and detect abnormal trends in a timely manner.

[0032] During actual operation, the system compares the currently monitored trends of each component with the normal range of variation set above. When the current rate of change of one or more components continues to exceed its normal range, it is determined to be an abnormal trend. In addition, the system calculates the current status of the overall quality index and determines whether it is in a warning or abnormal state. For example, if the index value deviates from its reference interval by more than the preset mild warning range, the system will issue a warning prompt; if the deviation is large or lasts for a long time, the system will automatically issue an optimization suggestion signal.

[0033] Once the system determines that there is an environmental anomaly or a significant trend in quality fluctuations, the optimization recommendation signal will be transmitted to the adaptive control module, prompting it to adjust the current parameters such as temperature, humidity or gas circulation. The adjustment direction and recommended amplitude are determined by the evaluation module based on factors such as the current abnormal component, its rate of change, degree of deviation, and historical response effects. At the same time, the system will also synchronously generate visual results on the graphical user interface. Common forms include multi-component trend curves, comprehensive score heat maps, abnormal reminder color flashing or icon changes, etc., which are convenient for operators to intuitively identify the current storage status and decide whether to perform manual intervention based on actual needs.

[0034] The entire process is updated every ten minutes to ensure that the evaluation results are real-time and continuous, and that timely feedback and adjustment decisions can be made at the early stages of fluctuations in tea quality.

[0035] This implementation introduces a quality assessment module, which not only allows the system to visually present the changes in the chemical composition of tea, but also enables real-time judgment based on quantitative thresholds, thus possessing strong early warning and adaptive capabilities. Compared with the traditional method of relying solely on manual experience to judge the trend of change, this method significantly improves the scientific nature and accuracy of the judgment, and can respond to problems in the early stages, preventing the quality of tea from deteriorating due to environmental discomfort. In addition, the visual feedback interface improves the efficiency of human-computer interaction, helps operators quickly understand the system status and make decisions, and improves the practicality and ease of operation of the system in actual applications.

[0036] Obtaining visualization results based on the changing trends of chemical composition also includes calculating the specific parameter values ​​for temperature and humidity adjustment based on the adjustment demand signal and combining it with adaptive control logic, obtaining the target environment setting values, and determining storage conditions suitable for the current state of the tea.

[0037] In this embodiment, upon identifying a deviation in tea quality indicators, the system triggers an optimization recommendation signal through visualization results. This signal serves as an input to the control chain and, through interaction with the control logic model in the adaptive control module, further calculates specific temperature and humidity adjustment parameters. First, the system assesses the sensitivity of the abnormal component to environmental factors based on its rate of change, direction, and severity. For example, if the decline in tea polyphenols intensifies, and they are known to be more sensitive to temperature changes than humidity, temperature adjustment is prioritized. The system then accesses the environmental response database matching the current tea variety and, combined with the gain factors in the control strategy, calculates the required temperature and humidity values ​​to be adjusted upward or downward. This calculation takes into account the expected effect of the adjustments, system response time, and device control capabilities. Finally, the resulting parameters are used to set target environmental conditions, such as adjusting the temperature from 25°C to 23°C and the humidity from 65% to 68%. These target values ​​become the new set points appropriate for the current tea condition and are immediately transmitted to the environmental control equipment via control commands.

[0038] Specifically, this implementation describes how the adaptive control module automatically calculates optimal adjustment values ​​for environmental parameters based on tea quality trends and generated adjustment demand signals, generating target temperature and humidity set points appropriate for the current tea condition. The entire process encompasses five technical steps: abnormal input analysis, component sensitivity identification, parameter adjustment calculation, setpoint boundary constraints, and control output feedback.

[0039] First, during the abnormality analysis input phase, the system receives an adjustment request signal from the front-end quality assessment module. This signal contains three core pieces of information: 1. The specific chemical component experiencing abnormal fluctuations, such as a significant decrease in aroma content; 2. The direction of change, such as whether it's increasing or decreasing; and 3. The rate of change and the magnitude of the deviation. For example, if the system identifies a component that has decreased by 4 units within an hour, while the standard allowable range is only 2 units, it will be considered to have deviated by 2 units from the normal range.

[0040] Next, based on the type of abnormal component, the system retrieves the component's response coefficient to changes in temperature and humidity from the environmental response database. These response coefficients are obtained during the experimental phase using the controlled variable method. Specifically, in a closed environment, the temperature or humidity is adjusted by just one unit and the change in concentration of the component is observed. This allows the degree of influence of temperature and humidity to be determined separately. For example, aromatic substances are generally more sensitive to temperature than humidity. For example, a 1°C increase in temperature results in a 0.8-unit decrease in the concentration of the component, while a 1% increase in humidity results in only a 0.4-unit decrease.

[0041] Next, during the parameter adjustment calculation phase, the system divides the deviation by the corresponding sensitivity coefficient to infer the theoretically required temperature and humidity adjustments. For example, if a component's deviation is 2 units and its sensitivity to temperature is 0.8 units per degree Celsius, the system would initially determine that the temperature should be lowered by 2.5 degrees Celsius to return to normal. If the humidity sensitivity is 0.5 units per one hundredth, the humidity would need to be increased by 4%. Through this proportional inverse calculation, the system quickly arrives at preliminary optimization recommendations for temperature and humidity.

[0042] The system then enters the setpoint boundary constraint phase. Due to the limited control capabilities of storage devices, environmental setpoints must fall within a safe range. For example, the system specifies a temperature control range of 20 to 30 degrees Celsius and a humidity range of 60 to 80 percent. Furthermore, to prevent drastic environmental fluctuations, the system limits each adjustment to no more than 5 degrees Celsius or 10 percent humidity. If the calculated result exceeds this range, the system automatically clips it to the closest controllable value and marks the adjustment as a "limited adjustment."

[0043] Finally, during the control output feedback phase, the adaptive control module issues commands through the control interface based on the finalized setpoints to adjust the operating status of the heater, humidifier, dehumidifier, or ventilation module. During the actual adjustment process, the system is set to recheck quality changes and environmental parameters every ten minutes to determine whether the previous adjustment has achieved the expected results. If the fluctuation range has not returned to normal, the system will automatically adjust the target setpoint in the next round of iterations, forming a complete closed-loop adaptive control system.

[0044] In this embodiment, the setting of key parameters has a clear basis and scientific calculation logic. First, the abnormal deviation amplitude refers to the difference between the currently detected rate of change of the chemical component concentration and the historical normal maximum rate of change. This value is statistically derived by the quality assessment module based on a large number of high-quality tea sample storage experimental data, and has significant representativeness and reference value. Secondly, the component response coefficient is used to measure the sensitivity of various chemical components to changes in temperature and humidity. Usually, through the control variable method, the temperature and humidity parameters are adjusted one by one in a specific experimental environment and the component concentration response results are recorded to obtain the temperature and humidity response ratio of each component. Thirdly, in order to ensure the safety and stability of the adjustment, the system sets boundary constraint values: the temperature setting range is generally 20 to 30 degrees Celsius, the humidity setting range is 60% to 80%, and the maximum amplitude of a single adjustment is limited to no more than 5 degrees Celsius and 10% humidity, respectively, to prevent drastic fluctuations in tea quality due to excessive environmental changes. Finally, to ensure the system has continuous adjustment capabilities and response speed, the adaptive control module uses a control cycle of every 10 minutes to regularly evaluate the adjustment effect and dynamically optimize subsequent set values, thereby building an efficient, precise and sustainable intelligent microenvironment control system.

[0045] Obtaining visualization results based on the changing trends of chemical composition also includes updating the stored environmental parameters in real time for the target environmental setting values ​​through the execution unit of the constant temperature and humidity system, obtaining the adjusted environmental status data, and judging whether it meets the target setting range.

[0046] After calculating the target environmental parameters, this implementation activates the execution unit of the constant temperature and humidity system to adjust the environment in real time and dynamically verify the adjustment results. First, the control module sends the target setpoints to the execution unit, which includes the heater, cooling device, humidifier, dehumidifier, and air circulation module. Upon receiving the instructions, the execution unit immediately adjusts the corresponding control components, such as adjusting the heater power output to change the temperature or controlling the humidifier on-time to increase the humidity. During the adjustment process, the sensor array continuously monitors the actual temperature and humidity of the storage space and returns the collected parameters in real time as feedback data to the control module. The control module compares the deviation between the current environmental state and the target set range. If the deviation is within the allowable error tolerance, the control module confirms that the environmental adjustment is complete. If the deviation exceeds the tolerance standard (for example, ±0.5 degrees Celsius or ±2% humidity), the control module continues to adjust until the preset conditions are met. This control process forms an automated, real-time closed-loop adjustment system, ensuring that the target setpoints are not only accurately calculated but also effectively achieved and maintained within the physical space.

[0047] Obtaining visualization results based on the changing trends of chemical composition also includes updating the stored environmental parameters in real time for the target environmental setting values ​​through the execution unit of the constant temperature and humidity system, obtaining the adjusted environmental status data, and judging whether it meets the target setting range.

[0048] This implementation transmits the target temperature and humidity setpoints calculated by the system to the actuator unit of the constant temperature and humidity system in real time. It also continuously collects environmental status data during the adjustment process to determine whether the actual environment has reached the expected set range. The entire process includes five technical steps: target value transmission, actuator module response, real-time sampling feedback, adjustment amount evaluation, and achievement determination.

[0049] First, the adaptive control module generates target environmental parameters for the current stage, including target temperature and humidity. For example, based on the quality assessment results, the system determines that the optimal temperature for tea leaves is 23 degrees Celsius and the humidity is 68 percent. These target values ​​are transmitted via the internal control bus to the constant temperature and humidity execution unit, which includes subsystems such as the heater, cooling device, humidifier, dehumidifier, and fan module.

[0050] After receiving the set instructions, the execution unit starts the corresponding control device according to the current environmental status. For example, when the actual temperature is two degrees Celsius higher than the target temperature, the system will instruct the cooling device to continue running; if the current humidity is four percentage points lower than the target humidity, the system will instruct the humidifier to work. At this time, the system needs to determine the required running time based on the response rate of the device. For example, the cooling device can reduce the temperature by 0.5 degrees Celsius per minute, and the humidifier can increase the humidity by one percentage point per minute. Based on the proportional relationship between the actual deviation value and the device response rate, the system can estimate the operating time of each device. In the above example, in order to reduce the temperature by two degrees Celsius, the cooling device needs to run continuously for four minutes; to increase the humidity by four percentage points, the humidifier needs to run continuously for four minutes.

[0051] During the adjustment process, the system collects environmental data every 30 seconds, including real-time temperature and humidity. The sampled data is compared with the target value to calculate the current deviation. The system sets a temperature deviation tolerance of ±0.5 degrees Celsius and a humidity deviation tolerance of ±2 percentage points. If the sample results show that both temperature and humidity are within the allowable error range, the system records the sampling as a successful adjustment. If three consecutive samplings meet the conditions, the adjustment is considered successful. If either value exceeds the tolerance after two consecutive samplings, the system continues adjustment and enters the next control cycle.

[0052] If the system fails to meet the conditions for five consecutive samplings, meaning that the adjustment is ineffective for two and a half minutes, the system will enter the abnormality detection mechanism. At this time, the self-test process will be initiated to check for sensor failure, delayed response of the actuator module, or physical failure, and an alarm will be issued or a request for intervention from the manual system will be issued.

[0053] Through the above closed-loop control process, the system realizes intelligent control of the entire process from the issuance of target values ​​to physical adjustment feedback, ensuring that the tea storage environment always remains in the temperature and humidity range that is most favorable to quality.

[0054] This implementation method realizes a complete closed-loop control from parameter calculation to physical environment adjustment. Through the precise linkage and real-time feedback mechanism of the execution units, the system can quickly adjust the tea storage environment to a suitable state in a relatively short period of time, ensuring that the set goals are effectively implemented. Compared with the traditional "set-wait" operation mode, this method has higher environmental stability and adjustment efficiency, and is especially suitable for the storage needs of high-end tea that is extremely sensitive to temperature and humidity. At the same time, real-time data monitoring enables the system to have self-diagnosis capabilities, and can promptly prompt or trigger alarms when adjustments are abnormal, helping to prevent environmental out-of-control due to equipment failure or adjustment failure.

[0055] Obtaining visualization results based on the changing trends of chemical composition also includes continuously collecting new tea status feedback information through the sensor array based on the adjusted environmental status data, obtaining updated quality change data, and determining whether the storage effect is stable.

[0056] In this embodiment, after the environmental parameters are adjusted, the system continues to rely on the sensor array to continuously monitor key indicators in the tea storage environment to evaluate the actual response effect of the adjusted tea and determine whether the storage effect has reached a stable state.

[0057] First, the system is set up to sample environmental and composition data every ten minutes. Each sample contains multiple parameters, including temperature, humidity, oxygen concentration, carbon dioxide concentration, and the concentrations of several key chemical components, typically measured in milligrams per cubic meter. Commonly monitored chemical components include aromatic volatiles, tea polyphenols, and amino acids, which are important reference indicators for evaluating tea quality.

[0058] The system then divides the change in concentration between two consecutive samples by the time interval between them to calculate the rate of change per unit time for each component. For example, if the concentration of a particular aromatic substance decreases by two units over ten minutes, its rate of change is 0.2 units per minute. The sign of this rate of change indicates whether the component is increasing or decreasing, while the absolute value indicates the severity of the change.

[0059] In order to determine whether the current quality is stable, the system continuously records all sampling data within a set sliding time window (for example, the last two hours) and calculates the average change rate and change amplitude of each component. The system sets a change rate threshold for each component based on the stable storage characteristics shown by previous high-quality samples. This threshold is generally equal to the historical average change rate plus its fluctuation amplitude multiplied by the safety adjustment factor. This coefficient generally takes a value between 1.5 and 2, and is used to ensure that it is neither too sensitive nor unable to capture anomalies in a timely manner. For example, if in the historical stable sample, the hourly change rate of the aromatic component is a decrease of 0.2 units and the fluctuation amplitude is 0.1 units, the system can set the threshold to 0.35 units per hour.

[0060] If the rate of change in the actual monitoring data remains below the threshold for several consecutive sampling periods, and the magnitude of changes in each component does not fluctuate dramatically, the current storage effect can be determined to be stable. Conversely, if certain components show a continuous upward or downward trend and exceed the threshold for three consecutive sampling periods, or if the magnitude of their changes significantly exceeds the normal range, the system will determine the current state as unstable and immediately trigger the feedback mechanism, re-entering the adaptive control process.

[0061] In addition, to enhance the comprehensiveness and intuitiveness of judgments, the system weights and integrates the rates of change of all ingredients to form a scoring index representing the current quality stability. Each ingredient is weighted according to its importance to sensory attributes such as taste, aroma, and color. For example, aromatic substances are weighted at 40%, amino acids at 30%, tea polyphenols at 20%, and other indicators at 10%. The lower the overall score, the more stable the environment. A score above 2.5 indicates a failed adjustment, and a score below 1 indicates extreme stability. The scoring results are displayed in real time in the visualization module, and a trend chart is formed to facilitate subsequent judgments and adjustments by operators or the system.

[0062] This implementation effectively addresses the disconnect between the environmental state and the tea leaves' physical condition in traditional constant temperature and humidity systems by continuously tracking their actual quality after environmental adjustments. This represents a technological leap from "environmental control" to "quality-driven" development. The system not only monitors the physical effects of adjustments during the entire process but also verifies their actual impact on tea quality in real time, further enhancing the scientific and targeted nature of adjustments, helping to reduce the adverse effects of adjustment errors on quality and improving the overall intelligence of the storage system.

[0063] Obtaining visualization results based on the changing trends of chemical composition also includes drawing continuous change curves for the updated quality change data, recording the dynamic performance of tea quality at different storage stages, and obtaining visualization results.

[0064] In this implementation, the system uses continuously collected tea quality change data to create a dynamic time-varying curve, visually reflecting the quality evolution of tea at different storage stages. The entire process involves five technical steps: data organization, curve construction, segmented change identification, curve smoothing, and visualization.

[0065] First, the system records the concentrations of key tea chemical components at the current point in time, with a sampling cycle of ten minutes. Common components include aromatic compounds, polyphenols, and amino acids. This data is organized chronologically into a time series. In each time series, one sample corresponds to a sampling value at a specific point in time. All samples are numbered and arranged sequentially according to their time tags to construct trend analysis.

[0066] Next, the system maps the sampling points to the graphic coordinate space in sequence with time on the horizontal axis and component concentration on the vertical axis, and draws independent change curves according to the component divisions. In order to avoid severe jittering of the curve due to fluctuations in individual samples, the system introduces the exponentially weighted moving average method to smooth the original data. The core idea of ​​this method is to assign a certain weight to each new sampling value, and combine it with the smoothed value of the previous time point, using a larger weight to retain historical trends and a smaller weight to absorb new changes, thereby achieving a smooth transition. The weight coefficient is set by the user according to the sensitivity of component fluctuations, generally between 0.6 and 0.9. Lower weights are suitable for components that are sensitive to mutations, and higher weights are suitable for components that change smoothly.

[0067] After completing the preliminary curve drawing, the system divides the entire curve into stages. The basis for division is to examine the overall change amplitude and direction of the curve over a period of time. Taking two hours as an evaluation segment, the system counts the difference between the maximum and minimum values ​​in the segment, and then divides the difference by the total length of the time period to obtain the average rate of change per unit time. If the rate is less than the preset stability threshold, for example, the concentration change does not exceed 0.1 units per hour, then the stage is marked as a "stable period"; if it is greater than a change rate of 0.3 units per hour, it is marked as a "fluctuation period" or "abnormal period". The above thresholds are obtained through statistical analysis of the curve data of historical high-quality tea samples, usually using the mean plus one to two times the standard deviation as the dividing line to ensure that both normal fluctuations are reflected and abnormalities are discovered in a timely manner.

[0068] To enhance presentation, the system supports multi-component overlay plotting. For example, the concentration curves for aromatic components, tea polyphenols, and amino acids can be displayed superimposed on the same coordinate system, with different curves distinguished by different colors and line types, such as solid, dashed, or dotted lines. The system can also overlay environmental parameter curves, such as temperature and humidity trend curves over time, to facilitate causal analysis between environmental conditions and quality fluctuations.

[0069] Furthermore, at key inflection points, such as where the curve transitions from rising to falling or where a sudden increase occurs, the system automatically adds labels indicating the current concentration value, relative fluctuation rate, and time period. These labels help users quickly identify critical moments in tea quality changes. The system also sets warning thresholds. For example, when the aroma concentration falls below the set lower limit of 3 mg / m³, a red shaded area is automatically added to the graph to indicate risk.

[0070] Finally, the system renders the change curve in real time on the user terminal device through a graphical interface, and supports exporting it as static image files such as PNG and PDF, or structured data files such as CSV for further report generation or expert analysis.

[0071] This implementation provides a graphical representation of tea quality evolution, enabling real-time understanding of the current tea quality status and enabling tracing and analysis of historical evolution, enhancing the system's interpretability and operational transparency. By displaying phased curves, operators can identify quality change characteristics and risk points at each stage, predict tea storage trends, optimize environmental management strategies, and improve overall quality control.

[0072] Feedback the latest analysis results to the adaptive control module to obtain new adjustment parameters and determine the optimization direction of storage environment parameters. The specific formula is: ; in, Indicates the current adjustment amount, used to control temperature or humidity adjustment. Indicates the current quality index of tea leaves. Represents the current environment variable vector, represents the coupled dynamic evaluation function of environmental quality, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight, represents the coupling stability trend control weight, Indicates time.

[0073] In this embodiment, the system incorporates a dynamic adaptive optimization control model. The latest quality analysis results are fed into the adaptive control module as feedback signals, automatically calculating the control variables used to adjust temperature or humidity, thereby achieving intelligent optimization of the microenvironment. This optimization control model comprehensively considers factors such as the sensitivity of tea quality to environmental variables, the acceleration trend of quality changes, and the impact of environmental stability on quality fluctuations. Specifically, the system first uses sensors to collect current tea quality indicators and environmental state parameters. Using an intelligent algorithm, it calculates the partial derivatives of the quality indicators with respect to environmental variables, reflecting the sensitivity of quality to environmental changes. Next, it calculates the second-order derivative of quality changes to capture the acceleration of change, which is used to determine whether quality is rapidly deteriorating. It also evaluates the impact trend of environmental changes on the scoring function, thereby constructing a complete control function. Based on the control variables output by this function, the system dynamically adjusts the operating state of the temperature and humidity control equipment, achieving high-precision closed-loop control of environmental parameters. Various control weights (such as environmental sensitivity weight, quality trend weight, and scoring stability weight) can be flexibly configured based on the tea type and target storage strategy, ensuring the optimal balance between stability and responsiveness in the control strategy.

[0074] The present invention significantly improves the refinement, intelligence and dynamic adaptability of tea storage environment control by designing a feedback optimization control function. Compared with the traditional control method that relies on a single parameter setting or a fixed threshold, this system can automatically identify the quality change trend according to the real-time feedback of tea quality, accurately calculate the adjustment amount, and realize personalized and multi-dimensional environmental control in a true sense. Especially when faced with sudden changes in tea quality, external environmental disturbances or storage requirements of different types of tea, the system can respond in time and make stable adjustments, greatly reducing the risk of quality loss. At the same time, the coupling mechanism of the three control indicators makes the adjustment process both stable and forward-looking, avoiding the problems of over-adjustment or delayed response. The system has good scalability and is suitable for multi-scenario storage management of multiple types of products such as green tea, black tea, and dark tea. It has important application value in high-end tea storage, fine quality control, and full life cycle management, further enhancing the market competitiveness and brand value of tea products.

[0075] In this embodiment, Indicates the current regulation amount, which is the command variable output by the system for controlling temperature or humidity regulation. Its value is calculated by the optimization control model and the unit is degrees Celsius per minute or relative humidity percentage per minute; This index represents the current quality of tea, reflecting the overall quality of tea during its current storage phase. It is typically obtained by extracting features from multiple sensor data (such as the degree of polyphenol oxidation and the content of aromatic components) and scoring them using a quality model. The value range is a standardized percentage score. Indicates the current environmental variable values, such as temperature, humidity, oxygen concentration, etc., which are collected in real time by environmental sensors and are important external factors affecting the quality of tea; It represents the comprehensive evaluation function of environmental quality, reflecting the direction and intensity of the impact of current environmental changes on the future trend of tea quality. It is usually derived by a time series model or prediction function based on a comprehensive evaluation of historical fluctuations and current status. It represents the environmental sensitivity control weight, which is used to measure the sensitivity of tea quality to changes in environmental variables. The value can be determined through sensitivity analysis or empirical regression modeling and usually ranges from 0.5 to 1.5; Indicates the quality acceleration trend control weight, which is used to evaluate the "inertia effect" of quality changes. Its value is determined by the stability of quality fluctuations and is recommended to be set between 0.1 and 0.5; Represents the stability trend control weight, which is used to adjust the system The responsiveness of the rate of change is determined by the trend analysis of the residual error of historical score changes and is usually set to 0.2 to 0.6; Δ Represents the change value of the evaluation function in the current sampling period, which is The difference between the previous moment Ψ and the previous moment; Δt represents the sampling period in seconds, typically set between 600 and 1800 seconds, depending on the system's operating frequency. These parameters collectively form the core of the control function, enabling the system to stably output a highly adaptable control strategy under multi-dimensional input conditions.

[0076] In practical applications, the system conducted experimental data collection and dynamic adjustment tests on different types of tea samples, forming a comprehensive data support foundation. Taking a batch of Longjing green tea as an example, under initial storage conditions of 25 degrees Celsius and 60% humidity, the system collected tea quality indicators Q(t), environmental variables E(t), and environmental status scores Ψ(t) every 10 minutes for a total of 72 consecutive hours. The experimental results showed that when the ambient temperature rose by more than 1.5 degrees Celsius, the Q(t) score dropped significantly, falling from 91 to 84 within 48 hours. Analysis showed that its sensitivity coefficient λ to temperature changes was approximately 1.2, indicating that this type of green tea is sensitive to high temperatures. However, when the humidity remained within 65%, the quality score remained stable, and a θ control weight of around 0.35 was optimal for ensuring score stability. By analyzing the second-order variation trend of Q(t), the system identified that a setting of η to 0.25 effectively prevented a rapid decline in the tea polyphenols score during the accelerated oxidation phase. When Ψ(t) decreased by more than 3 units within 8 hours, and ΔΨ(t) / Δt exceeded 0.006 units per second, the system initiated an active cooling mechanism, adjusting u(t) by 0.4 degrees Celsius per minute. After 15 minutes of continuous operation, the Q(t) score recovered to above 87 and stabilized, validating the model's responsiveness and accuracy in responding to rapid quality degradation. Multiple rounds of testing demonstrated that the optimized control formula exhibited excellent adaptability and effectiveness across different tea types (e.g., green tea, black tea, and oolong tea) and diverse environmental disturbances. The average adjustment response time was kept within 20 minutes, and the quality score volatility decreased by over 35% compared to the uncontrolled state, fully demonstrating the feasibility and effectiveness of the control model in actual tea storage microenvironments.

[0077] Environmental quality coupled dynamic evaluation function The specific expression formula is: ; in, represents the coupled dynamic evaluation function of environmental quality, Indicates the current quality index of tea leaves. Indicates the current A vector of environment variables, Indicates the total number of environmental variables involved in regulation, Represents the environment variable index, Indicates time.

[0078] Based on comprehensive dynamic evaluation function of environmental quality This function evaluates the interaction between tea quality and storage environment and dynamically adjusts environmental parameters to ensure the best storage state of tea. and various environment variables , combined with the sensitivity relationship between environmental factors and tea quality, the system calculates the comprehensive impact of changes in environmental variables on tea quality. Specifically, the system collects tea quality and environmental data through sensors at each time point, and calculates the rate of change between the current tea quality and environmental variables. By weighted summation of the rates of change of each environmental variable, the system can evaluate the trend of the environment's impact on tea quality in real time, adjust environmental parameters such as temperature, humidity, and oxygen concentration, and ensure that quality changes are within the expected range. The system's adaptive adjustment mechanism enables it to flexibly adjust environmental control strategies according to different tea types and storage conditions, thereby optimizing the storage effect of tea and extending the shelf life of tea.

[0079] The microenvironmental constant temperature and humidity system provided in this embodiment can intelligently adjust the storage environment based on real-time feedback from tea quality and environmental variables, maintaining tea under optimal storage conditions. Compared to traditional single-environment control methods, this system offers more refined and dynamic control capabilities, flexibly responding to changes in tea quality during different storage stages and making rapid adjustments based on fluctuations in environmental factors. By introducing a comprehensive evaluation function, Ψ(t), the system not only optimizes the regulation of various environmental parameters but also minimizes the negative impact of environmental changes on tea quality during storage, reducing quality fluctuations and waste, and enhancing tea's market competitiveness. Furthermore, the system supports customized adjustments based on the different characteristics of tea leaves, offering strong adaptability and flexibility, and demonstrating excellent performance across a wide range of tea types (such as green tea, black tea, and white tea) and under varying storage conditions. The application of this technology will help improve tea preservation technology, extend the shelf life of tea, and provide important technical support for the sustainable development of the tea industry.

[0080] In this embodiment, It represents the comprehensive dynamic evaluation value of environmental quality. It is a comprehensive evaluation result calculated by the system based on the interaction between tea quality and environmental factors. It reflects the matching degree between tea quality and storage environment changes. It represents the current quality index of tea. It is a quantitative indicator to measure the quality of tea in its current storage state. It is usually obtained by analyzing the concentrations of various volatile components, polyphenols and other key chemical components in tea. Represents the value of the iiith environmental variable at the current moment, such as temperature, humidity, oxygen concentration, etc. These variables directly affect the storage quality and changes of tea. It represents the sensitivity of tea quality to the change of the i-th environmental variable, reflecting the response degree of tea quality to the change of the environmental variable. This sensitivity can be obtained by fitting historical experimental data or calculating based on the experimental response of tea to different environmental conditions. It represents the rate of change of the i-th environmental variable over time, and measures the speed at which environmental factors change, such as the rate of temperature rise or humidity change. It indicates the total number of environmental variables involved in regulation, usually including temperature, humidity, airflow and other environmental factors. The system is flexibly configured according to needs. Time represents the time variable of system operation, usually measured in seconds or minutes, reflecting the real-time dynamics of the adjustment process. All of these parameters are determined based on long-term experimental data and real-time monitoring of system feedback, ensuring that each adjustment accurately responds and optimizes the environment during tea storage to maximize the stability of tea quality.

[0081] Taking a batch of green tea stored in a constant temperature and humidity environment as an example, the system uses sensors to collect real-time environmental parameters such as temperature, humidity, and oxygen concentration, and compares and analyzes these data with tea quality indicators (such as tea polyphenols, aromatic substances, etc.). The experimental results show that in the early stage of storage, when the ambient temperature is maintained at 25 degrees Celsius and the humidity is maintained at 60%, the quality indicators of tea are The tea quality score remained stable and above 90 points. However, as the storage time went on, if the temperature fluctuated by more than 1 degree Celsius, the tea quality score dropped by about 6 points within 48 hours, and the concentration of aromatic substances dropped by 0.2 units per hour, resulting in an increase in quality fluctuations. According to the sensitivity coefficient in the formula The system can quickly identify the impact of temperature changes on quality scores and Adjust the temperature control equipment in time to restore to the preset optimal storage state. The calculation results successfully realized the dynamic adjustment of multiple environmental parameters such as temperature, humidity and oxygen concentration. In the subsequent experiment of 72 hours, the quality score of tea was stable at more than 85 points, and the fluctuation range was controlled within 3%, which significantly improved the storage stability of tea. Through experimental verification, the system can adaptively adjust the storage conditions according to the storage characteristics of different teas and environmental fluctuations to ensure that the quality of tea is maintained in the best state. These data support the practical operability of the proposed formula, and prove that the optimization and control method has high accuracy and reliability in practical applications, and can significantly improve the storage effect and quality stability of tea.

[0082] Environmental sensitivity control weight The specific calculation formula is: = ; in, represents the environmental sensitivity control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval, Indicates taking the maximum value, that is, the quality degradation detector.

[0083] Using a dynamic optimization adjustment mechanism, the weight is adjusted by calculating the environmental sensitivity , adjust the temperature and humidity control strategy in real time to ensure that the tea maintains high quality in the best storage environment. This calculation is based on the current quality index of the tea The difference between the current quality index Q(t−Δt) and the previous moment's quality index is combined with the sensitivity of environmental changes to tea quality to dynamically generate a control factor λ, which is used to adjust the environmental control system. Specifically, when the tea quality index Q(t) shows a downward trend, the system calculates the difference between the current quality and the quality at the previous moment, and generates an adjustment coefficient based on this difference. This coefficient is corrected by the maximum value function in the formula to ensure that it does not over-adjust and avoid excessive quality fluctuations due to frequent adjustments. The parameter Δt represents the time interval from the quality sampling. The system monitors the quality fluctuations of tea in real time and uses smoothing to prevent short-term natural fluctuations from affecting the adjustment decision. The denominator in the formula is normalized so that the calculated λ value does not over-amplify the adjustment response due to excessive quality fluctuations, ensuring the smoothness and stability of the adjustment.

[0084] This implementation introduces a dynamic sensitivity adjustment weight λ, enabling the system to adaptively adjust environmental parameters based on real-time fluctuations in tea quality, avoiding the drawbacks of over-adjustment in traditional environmental control methods. Compared to fixed adjustment methods, the system accurately responds to changes in tea quality and promptly optimizes the environment, ensuring that tea maintains a stable quality throughout storage. By accurately capturing and adjusting for tea quality fluctuations, the system significantly reduces the adverse effects of environmental changes on tea quality and extends the shelf life of tea. Experimental data validates the system's performance in practical applications. These data show that when temperature and humidity fluctuate significantly, the tea quality score Q(t) decreases within a short period of time. The product of the difference Q(t−Δt) and the time interval Δt directly affects the adjustment coefficient λ. The adjusted environmental control parameters significantly reduce the magnitude of tea quality fluctuations. For example, in one set of experiments, when the ambient temperature increased from 26°C to 30°C, the system automatically reduced the temperature increase rate based on the calculated adjustment coefficient λ, resulting in a mere 2.5% drop in the tea quality score, without excessive quality loss. The system maintained a stable tea quality score of over 90 points within 60 hours, demonstrating the efficiency and accuracy of the regulation mechanism in complex environmental changes.

[0085] Changes in the tea quality score Q(t) are directly related to environmental parameters such as temperature and humidity. Experimental data show that when the ambient temperature fluctuates by more than 2 degrees Celsius within 24 hours, the tea quality score is significantly affected. In particular, when the humidity fluctuates by more than 10%, the quality score declines even more rapidly. The system uses the adjustment coefficient λ calculated using the provided adjustment formula to control temperature and humidity fluctuations within acceptable levels during the adjustment process, effectively preventing a sharp decline in tea quality. In a 48-hour experiment, the system's dynamic adjustments kept the tea quality score fluctuations within 3%, and throughout the entire storage period, the tea quality score remained above 85 points, demonstrating the effectiveness and feasibility of this optimized control mechanism in actual storage environments.

[0086] Quality acceleration trend control weight The specific calculation formula is: = ; in, Indicates the quality acceleration trend control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval; The coupled stability trend regulation weight =1- - ; in, represents the coupling stability trend control weight, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight.

[0087] In this embodiment, the system dynamically adjusts the environmental control strategy by calculating the accelerated adjustment sensitivity control weight η of the tea quality to adapt to the rapid changes in tea quality. Specifically, the system calculates an accelerated adjustment factor η based on the change range between the quality index Q(t) of the tea at the current time point t and the quality index Q(t-Δt) at the previous moment t-Δt. When the quality of tea fluctuates greatly, the system adjusts the control weight in real time according to the fluctuation value, and responds quickly to environmental changes to avoid further decline in quality. The calculation formula of η is the absolute value of the difference between the current quality score and the previous sampling point, and then normalized by the current quality score to ensure that the adjustment range does not exceed the maximum limit. In order to further improve the accuracy of the control, the system also introduces a stability control weight θ to ensure rapid response of environmental control while avoiding quality fluctuations caused by over-adjustment. The stability control weight θ is given by the formula =1- - Calculations show that the system maintains a balance between stability and response speed, so as to ensure the stability of tea quality while also being able to respond efficiently to environmental changes.

[0088] The microenvironment constant temperature and humidity system of this embodiment realizes precise control and dynamic adaptation of the tea storage environment by introducing the acceleration adjustment sensitivity control weight η and stability control weight θ. Compared with the traditional static adjustment mode, this system can flexibly adjust the environmental parameters according to the real-time fluctuation of tea quality, so as to maintain the stable quality of tea to the greatest extent. This adjustment mechanism based on real-time feedback not only improves the system response speed, but also effectively avoids the quality loss caused by over-adjustment. In the experiment, when the system faces temperature or humidity fluctuations, it calculates the The system uses the calculated η value to adjust environmental control measures promptly, keeping the fluctuation range of the tea quality score Q(t) within 5%. For example, in one experiment, when the temperature fluctuated by more than 2°C, the system automatically adjusted the temperature control system based on the calculated η value, preventing a drop in the tea quality score and maintaining it above 85 points, thereby maximizing the tea's storage life. Furthermore, through precise stability control, the system ensures that the quality of tea at different storage stages is not affected by excessive adjustments, thereby improving the tea's overall freshness and market competitiveness.

[0089] A set of experiments used Pu'er tea as a sample and stored it in a constant temperature and humidity environment. The experimental results showed that when the system performed environmental control based on the adjustment factors calculated by η and θ, the fluctuation of the quality score of the tea remained within the range of ±3% within 24 hours. In particular, when the ambient temperature rose from 28°C to 32°C, the system avoided the quality score from dropping by more than 5 points by adjusting the weights of η and θ, thereby maintaining the stability of the tea. In the absence of an adjustment mechanism, the quality score dropped by 8 points under the same environmental fluctuations, proving the effectiveness and reliability of the system in practical applications. The experimental results fully support the operability and efficiency of the formula described in claim 10 in a real environment, and verify that it has significant application value in the storage process of tea.

[0090] like Figure 2The figure shows the refrigeration and drying module of the tea storage microenvironment constant temperature and humidity system. The main purpose of this system is to maintain the stability of tea quality by precisely regulating the temperature and humidity in the storage environment. In the figure, the compressor 5 generates a low-temperature airflow through the condenser 4. When the cooling air flows through the capillary tube 2, the temperature is further reduced, allowing the moisture to be effectively condensed. The condensed moisture is processed by the drying filter 3 to remove excess moisture and prevent the humidity in the environment from negatively affecting the quality of the tea. Finally, the airflow after cooling and drying is humidified and circulated by the water-spraying air-cooled evaporator 1 to achieve stable microenvironment control.

[0091] This module effectively regulates the temperature and humidity of the storage space, preventing tea quality loss caused by temperature and humidity fluctuations during storage. Through continuous cooling and drying, the system maintains an ideal microclimate, ensuring that the tea retains its original aroma, flavor, and texture throughout its storage cycle. Experimental data shows that this system can control tea quality fluctuations within a narrow range, reducing the risk of tea quality degradation due to environmental factors, extending the storage period, and enhancing the market competitiveness of tea during storage and transportation.

[0092] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A microenvironment constant temperature and humidity system for tea storage, characterized in that: The system comprises: The sensor array module is used to collect real-time data on changes in temperature, humidity, and volatile components in the tea storage microenvironment by deploying a high-precision sensor array, obtaining a raw data set of storage environment parameters and tea status feedback to obtain initial monitoring information; The data analysis module is used to extract features from the collected environmental parameters and tea status feedback based on the initial monitoring information using a pre-established data analysis model, analyze the quality change pattern, and determine the chemical composition change trend of the tea during the current storage stage; The visualization and judgment module is used to obtain visualization results based on the trend of chemical composition changes. Based on the visualization results, combined with the closed-loop management mechanism, the continuous change curve is compared with historical data. If abnormal fluctuations are found, the data analysis model is triggered to recalculate and determine whether the quality change pattern needs to be updated; The adaptive control module is used to feed back the latest analysis results to the adaptive control module based on the updated quality change rules through intelligent system design, obtain new adjustment parameters, and determine the optimization direction of storage environment parameters.

2. A microenvironment constant temperature and humidity system for tea storage according to claim 1, characterized in that: Obtaining visualization results based on the chemical composition change trend includes: Based on the changing trend of chemical composition, a quality assessment module is built to compare the analysis results with the preset threshold range. If the changing trend exceeds the threshold range, an adjustment demand signal is generated to determine the need to optimize the storage environment parameters.

3. A microenvironment constant temperature and humidity system for tea storage according to claim 2, characterized in that: Obtaining visualization results based on the chemical composition change trend also includes: According to the adjustment demand signal and combined with the adaptive control logic, the specific parameter values ​​of temperature and humidity adjustment are calculated, the target environment setting values ​​are obtained, and the storage conditions suitable for the current state of the tea are determined.

4. A microenvironment constant temperature and humidity system for tea storage according to claim 3, characterized in that: Obtaining visualization results based on the chemical composition change trend also includes: For the target environment setting value, the execution unit of the constant temperature and humidity system updates the stored environmental parameters in real time, obtains the adjusted environmental status data, and determines whether it meets the target setting range.

5. A microenvironment constant temperature and humidity system for tea storage according to claim 4, characterized in that: Obtaining visualization results based on the chemical composition change trend also includes: Based on the adjusted environmental status data, new tea status feedback information is continuously collected through the sensor array to obtain updated quality change data to determine whether the storage effect is stable.

6. A microenvironment constant temperature and humidity system for tea storage according to claim 5, characterized in that: Obtaining visualization results based on the chemical composition change trend also includes: Based on the updated quality change data, a continuous change curve is drawn to record the dynamic performance of tea quality in different storage stages and obtain visual results.

7. The microenvironment constant temperature and humidity system for tea storage according to claim 1, characterized in that: The latest analysis results are fed back to the adaptive control module to obtain new adjustment parameters and determine the optimization direction of the storage environment parameters. The specific formula is: ； in, Indicates the current adjustment amount, used to control temperature or humidity adjustment. Indicates the current quality index of tea leaves. Represents the current environment variable vector, represents the coupled dynamic evaluation function of environmental quality, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight, represents the coupling stability trend control weight, Indicates time.

8. The microenvironment constant temperature and humidity system for tea storage according to claim 7, characterized in that: The environmental quality coupled dynamic evaluation function The specific expression formula is: ； in, represents the coupled dynamic evaluation function of environmental quality, Indicates the current quality index of tea leaves. Indicates the current A vector of environment variables, Indicates the total number of environmental variables involved in regulation, Represents the environment variable index, Indicates time.

9. The microenvironment constant temperature and humidity system for tea storage according to claim 7, characterized in that: The environmental sensitivity control weight The specific calculation formula is: ＝ ； in, represents the environmental sensitivity control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval, Indicates taking the maximum value, that is, the quality degradation detector.

10. The microenvironment constant temperature and humidity system for tea storage according to claim 7, characterized in that: The quality acceleration trend control weight The specific calculation formula is: ＝ ； in, Indicates the quality acceleration trend control weight, Indicates the current quality index of tea leaves. Indicates time, Indicates the quality sampling time interval; The coupled stability trend regulation weight =1- - ; in, represents the coupling stability trend control weight, represents the environmental sensitivity control weight, Indicates the quality acceleration trend control weight.

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