Intelligent controlled atmosphere maintenance equipment and system based on cloud computing

Abstract

The invention discloses intelligent controlled atmosphere maintenance equipment and system based on cloud computing, and relates to the technical field of controlled atmosphere maintenance. Through independent sealing design of a sealing module, in combination with precise gas supply and discharge functions of a gas supply module, and real-time monitoring of concentrations of carbon dioxide, oxygen, nitrogen and the like by a gas monitoring module, the intelligent controlled atmosphere maintenance equipment and system based on cloud computing are provided; independent control over the multi-unit maintenance environment is achieved, the intelligent gas adjusting module dynamically analyzes gas monitoring data based on a cloud data server, and the gas component proportion is automatically adjusted through the gas supply module so that the preset maintenance environment can be maintained; meanwhile, the instruction regulation and control module stores and analyzes historical and real-time data, optimizes a gas supply strategy and dynamically sends a regulation and control instruction according to an early warning value, so that the stability and coordination of the environment are ensured, the problem of cross contamination of gas components is effectively solved, real-time monitoring and intelligent regulation of a multi-unit system are realized, and the safety of the system is improved. The maintenance effect and the quality stability of high-value products are obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of controlled atmosphere maintenance technology, and in particular to intelligent controlled atmosphere maintenance equipment and systems based on cloud computing. Background Art

[0002] With the development of intelligent manufacturing and digital information technology, the application potential of cloud-based intelligent controlled atmosphere equipment and systems in multiple fields is gradually emerging. Traditional controlled atmosphere curing equipment typically uses mechanical or stand-alone control methods, which cannot meet the sophisticated requirements of gas composition, temperature and humidity, and long-term dynamic monitoring for a variety of products in complex environments. However, the introduction of cloud computing technology can realize the collection, analysis, and processing of real-time data from multiple sensors, forming an intelligent and systematic curing solution. Cloud-based intelligent controlled atmosphere systems can significantly improve data transmission and processing efficiency, providing precise solutions for high-quality product curing. For example, in the curing process of high-value products such as coffee beans, Pu'er tea, and cigars, precise control of gas components such as oxygen, nitrogen, and carbon dioxide is particularly important. In addition, by integrating image monitoring and data modeling, these systems can achieve full digital monitoring and early warning control during the curing process.

[0003] The existing technology has the following shortcomings: In curing equipment where multiple products coexist, each enclosed unit may have different gas composition requirements (e.g., coffee requires a nitrogen-rich environment, while Pu'er tea requires an oxygen-rich environment). Due to imprecise regulation of gas supply or leakage, cross-contamination of gas composition between units can occur, severely degrading or even failing the curing effect. Currently, most controlled atmosphere systems focus solely on the stability of a single environment, while ignoring the complexity of systemic coordination within multi-unit equipment. This technology gap is particularly significant in the real-time monitoring and management of multi-point cross-contamination. This cross-contamination issue can directly impact product quality and stability, and can even result in the loss of high-value products. Solutions based on intelligent systems and cloud computing are urgently needed to dynamically balance the gas ratios within multi-unit environments. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent atmosphere conditioning maintenance equipment and system based on cloud computing to solve the shortcomings of the background technology.

[0005] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent gas conditioning and maintenance system based on cloud computing, comprising a sealing module, a gas supply module, a gas monitoring module, an intelligent gas conditioning module and a command control module; Sealed module: It includes three independent storage closed units. The internal space of each unit is used to store different types of products. The closed unit is equipped with gas inlet and outlet valves, temperature and humidity sensors, gas composition monitoring sensors and image cameras. The closed unit adopts an independent sealing design to prevent cross contamination of gas components between units. Gas supply module: equipped with storage devices for oxygen and nitrogen gases, connected to the gas inlet and outlet valves of the closed units, used to supply or discharge gas to each closed unit as needed; Gas monitoring module: connected to the gas composition monitoring sensor, used to collect gas composition data in the closed unit in real time and monitor the concentrations of carbon dioxide, oxygen and nitrogen; Intelligent gas conditioning module: Based on the cloud data server, it communicates with the gas monitoring module and automatically adjusts the gas composition ratio through the gas supply module according to the gas monitoring data of the closed module to maintain the preset gas curing environment; Command control module: used to store the gas composition data uploaded by the gas monitoring module, analyze historical data and real-time data to optimize the gas supply strategy, and send control instructions to the intelligent gas conditioning module according to the warning value to ensure the stability of the maintenance environment in the closed module.

[0006] Preferably, the sealed module includes three independent storage sealed units, specifically: a coffee bean curing unit, a Pu'er tea curing unit and a cigar curing unit.

[0007] Preferably, the gas composition monitoring sensor of the gas monitoring module adopts electrochemical principles combined with optical technology, with a detection range of 0-100% and a detection error controlled within ±0.1%.

[0008] Preferably, the parameter acquisition unit in the instruction control module includes acquiring the abnormal change value of the gas diffusion rate and the environmental dynamic response time.

[0009] Preferably, the gas diffusion rate D is defined as the rate at which the concentration changes with position and time, and its mathematical formula is: Where C is the gas concentration, t is the time, and x is the position coordinate. Due to the non-uniform distribution of gas in the closed cell, a numerical method is used to discretely solve the gas diffusion rate: ;in, is the gas diffusion rate at the i-th position; Δt is the time interval; Δx is the position interval; the diffusion rate of all positions in the unit Calculate the abnormal change value of gas diffusion rate, the expression is: ; Where N is the number of sensors, is the average value of the diffusion rate, and SX is the abnormal change value of the gas diffusion rate.

[0010] Preferably, the normal variation range of the diffusion rate is set based on historical data analysis. When the abnormal variation value of the gas diffusion rate at a certain location exceeds the range, it is judged to be abnormal. When abnormalities occur at multiple locations at the same time, the module triggers an alarm to adjust the gas supply strategy. If the abnormal diffusion rate values ​​are concentrated in a single location, the gas supply directionality and flow rate are adjusted. If multiple locations are abnormal at the same time, the gas supply flow rate is increased or the gas supply time is extended. The command control module feeds back the adjusted strategy to the intelligent gas conditioning module.

[0011] Preferably, the gas monitoring module collects the gas component concentration data in the closed unit in real time, with a sampling frequency of once per second, and the intelligent gas conditioning module records the triggering time of each gas supply or exhaust instruction. , and synchronized to the command control module; set the significant change threshold ΔCthreshold of gas component concentration to determine the starting point of gas response; by analyzing the curve of gas concentration change over time, the dynamic response time It is defined as the time difference from when the control command is triggered to when the gas concentration first reaches the threshold ΔCthreshold: ;in: is the instruction trigger time, is the time point when the gas concentration first reaches the threshold change.

[0012] Preferably, the abnormal change value of the gas diffusion rate and the dynamic response time of the environment are integrated into the machine learning algorithm. By evaluating the relationship between multi-dimensional parameters in real time, an optimized gas control strategy is generated to dynamically adjust the timing and method of gas supply and exhaust, specifically: Input data: abnormal change value of gas diffusion rate, environmental dynamic response time, gas component concentration ;Temperature and humidity data; Use random forest regression models to evaluate nonlinear relationships between multidimensional parameters and generate regulatory strategies; Input features: abnormal changes in gas diffusion rate, environmental dynamic response time, gas concentration change rate, and historical control records; Model output: optimal air supply flow rate, optimal air supply time, and exhaust trigger threshold adjustment suggestions; The importance weight of each feature is calculated using the random forest model. The reward function is aimed at maintaining the stability of the environment, and the expression is: ;in: is the target gas concentration, is the actual gas concentration, Energy wasted in the process of air supply or exhaust, 、 is the weight proportional coefficient, and 、 Both are greater than 0.

[0013] Preferably, the model continuously learns historical data of gas diffusion rate and dynamic response time and continuously optimizes the control strategy, specifically: The command sending unit obtains the current gas concentration based on the evaluation results of the command control module : Real-time concentration of the i-th gas in the closed unit, target gas concentration : The ideal concentration of the i-th gas in the closed unit; Response time delay: The delay time of the gas composition change relative to the control instruction; Calculate the concentration error of each gas based on the input parameters , the expression is: The instruction sending unit generates a control instruction I, including the supply flow rate and the supply time: supply flow rate: ;in, is the proportional adjustment coefficient of the i-th gas; is the current concentration error; is the dynamic response time; gas supply time: ; Where V is the effective volume of the closed unit, is the supply flow rate of the i-th gas; When a high concentration of a certain gas is detected, the command sending unit generates an exhaust command : ;in: is the exhaust volume, The exhaust time is optimized according to the dynamic response of the system; The command sending unit dynamically adjusts the control priority according to the importance and abnormality of the gas, giving priority to adjusting the concentration of the gas that has the greatest impact on the maintenance environment: If the SX shows an abnormal diffusion rate, increase the gas supply rate first. To improve uniformity; if Long, trigger the gas supply command in advance; When multiple gases need to be regulated simultaneously, the comprehensive optimization formula is: ;in: is the comprehensive air supply velocity, is the weight of the regulation of the i-th gas on the j-th gas; The generated control instruction I includes: gas supply flow rate and gas supply time; the instruction sending unit sends the instruction to the intelligent gas control module through the communication module, the intelligent gas control module executes the instruction, and feeds back the real-time control data to the instruction control module for updating and optimization.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, an intelligent gas conditioning and maintenance system based on cloud computing is implemented.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. This invention, through a cloud-based intelligent atmosphere-controlled maintenance system, effectively solves the problem of cross-contamination of gas components in maintenance equipment where multiple products coexist, and achieves precise control of multi-unit environments. Through the independent sealing design of the closed module, the precise supply and exhaust of the gas supply module, the real-time data acquisition of the gas monitoring module, and the coordinated operation of the intelligent atmosphere-controlled module and the command control module, the system can dynamically balance the gas ratio within the closed unit to meet the personalized maintenance requirements of different products (such as a nitrogen-rich environment for coffee and an oxygen-rich environment for Pu'er tea). With the help of machine learning algorithms, the system can evaluate abnormal changes in gas diffusion rate and the dynamic response time of the environment in real time, generate optimized control strategies, and dynamically adjust the timing and method of gas supply and exhaust to ensure the stability and efficiency of the maintenance environment.

[0016] 2. The present invention significantly improves the overall coordination and control efficiency of the multi-unit system, minimizes gas waste and energy consumption, and ensures the independence of the maintenance environment and the stability of product quality. Through the accumulation of historical data and optimization algorithms of the intelligent gas control module, the system can continuously improve the control strategy, making the adjustment more precise and reducing the loss of high-value products due to improper control. In addition, the system uses a random forest model to calculate the importance weights of multi-dimensional parameters, achieving accurate analysis and dynamic optimization of complex environments, enhancing the practicality and reliability of the system in multi-product maintenance scenarios, and providing an innovative solution for the intelligent maintenance of high-value products. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] For examples, see Figure 1 As shown, the cloud computing-based intelligent gas conditioning and maintenance system described in this embodiment includes a sealing module, a gas supply module, a gas monitoring module, an intelligent gas conditioning module, and a command control module; Sealed module: It includes three independent storage closed units. The internal space of each unit is used to store different types of products. The closed unit is equipped with gas inlet and outlet valves, temperature and humidity sensors, gas composition monitoring sensors and image cameras. The closed unit adopts an independent sealing design to prevent cross contamination of gas components between units. Gas supply module: equipped with storage devices for oxygen and nitrogen gases, connected to the gas inlet and outlet valves of the closed units, used to supply or discharge gas to each closed unit as needed; Gas monitoring module: connected to the gas composition monitoring sensor, used to collect gas composition data in the closed unit in real time and monitor the concentrations of carbon dioxide, oxygen and nitrogen; Intelligent gas conditioning module: Based on the cloud data server, it communicates with the gas monitoring module and automatically adjusts the gas composition ratio through the gas supply module according to the gas monitoring data of the closed module to maintain the preset gas curing environment; Command control module: used to store the gas composition data uploaded by the gas monitoring module, analyze historical data and real-time data to optimize the gas supply strategy, and send control instructions to the intelligent gas conditioning module according to the warning value to ensure the stability of the maintenance environment in the closed module.

[0021] Each sealed unit is an independent, sealed structure made of high-strength, leak-proof materials. The interior of the unit is coated with a food-grade coating to ensure safety and durability. Thin sheets of natural materials, such as cedar wood, are placed at the bottom of the unit's inner wall to inhibit pests, absorb ammonia and other harmful gases, and enhance the stability of the sealed unit's environment.

[0022] Each closed unit is equipped with the following functional components: Gas inlet and outlet valves: used to realize the input of oxygen and nitrogen and the discharge of waste gas. The gas inlet and outlet valves are connected to the gas supply module and have high-precision adjustment capabilities, which can independently control the gas environment in the unit; Temperature and humidity sensor: installed inside the sealed unit, used to detect the temperature and humidity inside the unit in real time, and the sensor data is directly uploaded to the cloud server; Gas composition monitoring sensor: monitors the composition ratio of carbon dioxide, oxygen and nitrogen in the unit, collects data in real time through high-precision sensors, and ensures that environmental parameters meet preset requirements; Image camera probe: fixed on the top of the closed unit, it can take images of the maintenance status of the products in the unit at regular intervals every day and upload them to the cloud data server via wireless transmission for storage and analysis.

[0023] Each sealed unit is independently sealed and completely separated by insulating materials to prevent cross-contamination of gas components between units. The air inlet and exhaust channels of the sealed unit are equipped with one-way valves to prevent gas backflow or mixing.

[0024] A "plant medium" is introduced within each enclosed unit to maintain and manage tea, coffee, and cigar products. As a key component of the intelligent controlled atmosphere maintenance system, the plant medium plays an essential role. This plant medium is placed within the maintenance layer, in direct contact with the products being maintained, within the enclosed space and is replaced regularly. It is recommended that the plant medium be replaced every 15 days to ensure maximum effectiveness. The plant medium has two main functions: moisture retention and insect control. For high-value products like tea (especially Pu'er tea), coffee, and cigars, poor maintenance or inadequate conditions can easily breed pests, particularly tea and cigar beetles in Pu'er tea and cigars. These problems can seriously affect product quality and easily lead to consumer dissatisfaction. Plant medium effectively suppresses pests in tea and cigars, ensuring the long-term stability and quality of the products. This maintenance method, which regularly replaces the plant medium, ensures a suitable environment while extending the shelf life of tea, coffee, and cigars, enhancing their market competitiveness.

[0025] Unit 1: Coffee Bean Care Nitrogen is the primary gas in the sealed unit, with temperature controlled at 10-25°C and humidity at 40-60%. A gas sensor monitors carbon dioxide concentrations, automatically venting and replenishing nitrogen when they exceed a preset threshold to slow oxidation and maintain aroma balance.

[0026] Unit 2: Pu'er Tea Maintenance Oxygen is the primary gas in the sealed unit, with the temperature controlled at 18-28°C and the humidity at 50-70%. The system undergoes a weekly overall ventilation to ensure adequate oxygen and promote the aging of the tea leaves.

[0027] Unit 3: Cigar Care The mixture of oxygen and nitrogen within the sealed unit is adjusted according to the cigar's curing requirements, with the temperature maintained at 18-21°C and the humidity at 65-70%. Cameras regularly capture images to monitor the aging state of the cigars, and control them using data models to ensure a stable curing environment.

[0028] The sensors and probes in the closed module upload environmental data to the cloud data server in real time. The server automatically sends control instructions to the air supply module and intelligent gas conditioning module based on the data analysis results to ensure the independence and stability of the environment of each closed unit.

[0029] In this application, through the above design and implementation, the closed module can be widely used in the intelligent maintenance of high-value products such as coffee beans, Pu'er tea, cigars, etc., effectively avoiding gas cross-contamination, and at the same time realizing precise monitoring and dynamic adjustment of the maintenance environment.

[0030] The air supply module includes the following key components: Gas storage device: independent oxygen storage tanks and nitrogen storage tanks, each storage tank is equipped with a high-precision pressure control valve and a safety relief valve to ensure the stability and safety of gas storage.

[0031] Distribution pipeline: Each gas is connected to the gas inlet and outlet valves of each closed unit through an independent delivery pipeline. The pipeline is made of high-pressure resistant and leak-proof materials, and a one-way valve is installed to prevent gas backflow.

[0032] Gas supply control valve group: Each closed unit corresponds to an independent gas supply control valve group, which is used to adjust the gas flow and inlet and outlet time, and can accurately adjust the gas supply according to real-time instructions.

[0033] Based on instructions from the intelligent gas conditioning module, the gas supply module precisely supplies the required gas to each enclosed unit, adjusting the oxygen-to-nitrogen ratio via control valves to meet the curing requirements of different products. If the concentration of carbon dioxide or other gases within a closed unit exceeds a set value, the gas supply module activates its exhaust function, discharging the waste gas through a gas discharge pipe and maintaining the gas balance within the unit. The gas supply module is equipped with a pressure sensor that monitors the pressure in the gas storage tank and delivery pipeline in real time, ensuring the safety and stability of the gas supply process.

[0034] Unit 1: Coffee Bean Care The gas supply module supplies high-purity nitrogen to sealed unit 1 and dynamically adjusts the gas ratio based on sensor data. When the carbon dioxide concentration exceeds a preset range (e.g., over 5%), the system initiates an exhaust process to expel the excess carbon dioxide and replenish nitrogen to restore the maintenance environment.

[0035] Unit 2: Pu'er Tea Maintenance For Containment Unit 2, the air supply module primarily uses oxygen, regularly supplying fresh oxygen at a set ratio. Once a week, the entire unit is ventilated to remove accumulated carbon dioxide. The air supply ratio is dynamically adjusted based on the aging requirements of the Pu'er tea to ensure a stable oxygen concentration of 20%-22%.

[0036] Unit 3: Cigar Care In sealed unit 3, the air supply module regularly delivers a nitrogen-rich mixture based on a maintenance data model to slow oxidation and control humidity. When ammonia concentration rises, the system automatically activates exhaust and replenishes oxygen to purify the environment and maintain stability during the alcoholization process.

[0037] The gas supply module works in conjunction with the intelligent gas conditioning module and the cloud data server to achieve precise gas supply through the following process: Data monitoring: The gas monitoring module collects gas composition data in the closed unit in real time, including the concentrations of oxygen, nitrogen, and carbon dioxide; Instruction sending: The intelligent gas conditioning module sends instructions based on the cloud data analysis results, requiring the gas supply module to adjust the gas supply or exhaust operation; Execution and control: The gas supply module performs specific operations according to instructions and feeds back operation data to the cloud server for optimizing the gas supply model.

[0038] Gas storage tanks and delivery pipelines are equipped with dual safety devices, including overpressure alarms and automatic pressure relief functions, to ensure equipment safety. During the gas supply or discharge process, the system automatically records pressure, gas flow, and environmental data for fault diagnosis and maintenance optimization.

[0039] The gas monitoring module includes the following key components: Gas composition monitoring sensors: Each sealed unit is equipped with a high-precision gas sensor capable of detecting the concentrations of carbon dioxide, oxygen, and nitrogen, respectively, with a detection range of 0-100% and a detection error of ±0.1%. The sensor utilizes electrochemical principles combined with optical technology to provide fast response and high sensitivity.

[0040] Data Acquisition Unit: The gas monitoring module integrates a data acquisition unit to aggregate real-time sensor data and perform preliminary processing (such as filtering and denoising). The acquisition unit has multi-channel capabilities and can simultaneously process gas data from multiple closed cells.

[0041] Communication module: This module uses wireless communication technologies (such as Wi-Fi or LoRa) to transmit collected data to a cloud data server, enabling real-time data upload and storage. The communication module has a disconnection and reconnection function to ensure the continuity and stability of data transmission.

[0042] The gas monitoring module can refresh the detection data once a second and upload the updated carbon dioxide, oxygen and nitrogen concentration values ​​to the cloud server.

[0043] A safe concentration range is set in the system (for example: carbon dioxide concentration 0%-5%, oxygen concentration 18%-22%, nitrogen concentration 78%-80%). When the concentration of a gas exceeds the safe range, the monitoring module immediately triggers an alarm and sends a control instruction to the intelligent gas conditioning module.

[0044] The gas monitoring module is equipped with an automatic calibration function, which can automatically adjust the zero point and sensitivity of the sensor according to environmental changes to ensure data accuracy.

[0045] Unit 1: Coffee Bean Care The gas monitoring module collects the carbon dioxide concentration in the closed unit in real time. When the concentration exceeds the set upper limit (such as 5%), the monitoring module sends a signal to the intelligent gas conditioning module to instruct the exhaust system to start and replenish nitrogen to restore the balanced environment.

[0046] Unit 2: Pu'er Tea Maintenance In the Pu'er tea curing environment, the gas monitoring module focuses on monitoring oxygen concentration, ensuring it remains between 20% and 22%. If the oxygen concentration is insufficient, the monitoring module sends a command to the gas supply module to replenish oxygen to promote the mellowing of the tea leaves.

[0047] Unit 3: Cigar Care The gas monitoring module accurately monitors the concentrations of nitrogen and carbon dioxide, especially in the early stages of ammonia generation. By combining sensor data and historical models, it predicts the generation trend of harmful gases in the environment in advance and sends control signals.

[0048] Collected gas data is uploaded to a cloud server via the communication module for simultaneous analysis along with temperature, humidity, and image monitoring data. The cloud server uses the data uploaded by the gas monitoring module to analyze the changing trends of gas composition within each unit using a data model. It then generates an optimized control plan and transmits it back to the monitoring module. The gas monitoring module stores gas data over a specific timeframe (e.g., 24 hours) to facilitate equipment maintenance and fault analysis.

[0049] The sensor is equipped with a high-efficiency filter to prevent particulate matter or contaminants from affecting sensor sensitivity. The monitoring of key gases (such as oxygen) adopts a dual sensor design to ensure that the monitoring function can still be maintained in the event of a main sensor failure.

[0050] The intelligent gas conditioning module consists of the following main parts: Control processing unit: An embedded chip or industrial-grade computing device used to receive real-time data from the gas monitoring module, run the control algorithm, and send corresponding instructions to the gas supply module.

[0051] Algorithm model library: pre-stores gas composition control models for different maintenance requirements (such as coffee beans, Pu'er tea, cigars, etc.), combines real-time data for dynamic calculation, and generates adaptive control plans.

[0052] Communication interface: Communicates with the gas monitoring module and cloud data server through encrypted communication protocols (such as TLS) to ensure the security and accuracy of data transmission.

[0053] The intelligent gas conditioning module receives data from the gas monitoring module every second, including the concentrations of carbon dioxide, oxygen, and nitrogen, and combines it with the historical analysis data returned by the cloud server to calculate the gas composition ratio required for the current environment.

[0054] The module dynamically adjusts the gas ratio through the gas supply module, such as increasing the oxygen concentration or emitting excess carbon dioxide, to automatically maintain the balance of the environment.

[0055] According to the type of product in the closed unit, the intelligent atmosphere control module calls the corresponding curing model (such as nitrogen-rich environment for coffee and oxygen-rich environment for Pu'er tea) to ensure the curing effect.

[0056] Based on the accumulation of historical data, the module continuously optimizes the control algorithm to make the adjustment process more accurate and efficient, reducing gas waste and energy consumption.

[0057] Unit 1: Coffee Bean Care The intelligent atmosphere control module monitors the carbon dioxide concentration released by coffee beans in real time. When the concentration exceeds a set threshold (e.g., 5%), it sends a command to the air supply module, initiating an exhaust process to release the carbon dioxide while simultaneously replenishing nitrogen to ensure a stable nitrogen content above 90% in the curing environment.

[0058] Unit 2: Pu'er Tea Maintenance During the curing process of Pu'er tea, the intelligent gas conditioning module instructs the gas supply module to add oxygen based on the monitored oxygen concentration (such as below 20%), and adjusts the gas supply time and flow rate to promote the aging process of the tea.

[0059] Unit 3: Cigar Care For cigars, the intelligent atmosphere conditioning module dynamically adjusts the supply of mixed gases by monitoring the ratio of nitrogen and oxygen. When the ammonia concentration in the environment increases, the module instructs the air supply module to discharge the exhaust gas and replenish fresh oxygen to purify the environment and maintain optimal conditions for cigar aging.

[0060] Upload key data in the control process (such as gas supply volume and gas composition ratio adjustment) to the cloud data server in real time to establish a more accurate control model.

[0061] The intelligent gas conditioning module calls on historical data stored in the cloud, and through comparative analysis, predicts the changing trend of the maintenance environment and takes measures in advance, such as adjusting the gas supply time or ratio.

[0062] When the gas composition changes beyond the preset range, the module automatically sends an early warning signal to the cloud server and triggers emergency procedures, such as forced exhaust or suspension of gas supply.

[0063] The intelligent gas conditioning module features a redundant system. If the primary module fails, the backup module immediately takes over, ensuring continuous system operation. The module automatically records the time, operation details, and results of each gas conditioning operation for troubleshooting and maintenance. All communications with the cloud server and other modules are end-to-end encrypted to prevent data leakage or tampering.

[0064] The instruction control module consists of the following key parts: Data storage unit: used to store real-time and historical data from the gas monitoring module, including the concentration of carbon dioxide, oxygen, and nitrogen, temperature and humidity change trends, etc., supporting long-term data retention.

[0065] Parameter acquisition unit: includes obtaining the abnormal change value of gas diffusion rate, which is used to evaluate the uniformity of gas in the closed unit and determine whether the gas supply strategy is reasonable; and obtaining the dynamic response time of the environment, which reflects the delay of the change of gas composition in the closed unit relative to the external control instructions, which is used to optimize the timing of gas supply and exhaust.

[0066] Algorithm processing unit: Integrates machine learning and data analysis algorithms to evaluate the relationship between multidimensional parameters in real time and generate optimized control strategies.

[0067] Instruction sending unit: sends control instructions to the intelligent gas conditioning module based on the evaluation results to perform dynamic adjustment of the gas composition ratio.

[0068] The parameter acquisition unit specifically includes: The gas monitoring module installs gas composition monitoring sensors at multiple locations within the sealed cell. These sensors monitor the concentrations of carbon dioxide, oxygen, and nitrogen in real time, collecting data once per second. The changes in gas concentration at each location over time are recorded as time series data, reflecting the dynamic diffusion of gases within the sealed cell.

[0069] The gas diffusion rate D is defined as the rate at which concentration changes with position and time, and its mathematical formula is: ; Where C is the gas concentration in ppm (parts per million); t is the time in seconds; and x is the position coordinate in meters.

[0070] Since the gas distribution in the closed cell may be non-uniform, a numerical method is used to discretely solve the gas diffusion rate: ;in, is the gas diffusion rate at the i-th position; Δt is the time interval; Δx is the position interval; the diffusion rate of all positions in the unit Calculate the abnormal change value of gas diffusion rate, the expression is: ; Where N is the number of sensors, is the average value of the diffusion rate, and SX is the abnormal change value of the gas diffusion rate.

[0071] Based on historical data analysis, the module sets a normal range for diffusion rate variations. When the abnormal change in the gas diffusion rate at a certain location exceeds this range, it is considered abnormal. If abnormalities occur simultaneously at multiple locations, the module triggers an alarm and records the problem area for subsequent adjustments to the gas supply strategy.

[0072] Air supply strategy optimization: If diffusion rate anomalies are concentrated in a single location, the air supply direction and flow rate are adjusted. If anomalies occur simultaneously in multiple locations, the flow rate is increased or the air supply duration is extended. The command control module feeds the adjusted strategy back to the intelligent gas conditioning module and monitors diffusion rate changes in real time to ensure improvement.

[0073] Environmental dynamic response time refers to the time interval from the receipt of external control instructions to the significant change of gas components (such as oxygen, carbon dioxide, and nitrogen) in a closed unit. It is used to evaluate the agility of the gas control system.

[0074] The gas monitoring module collects the gas component concentration data in the closed unit in real time, such as The sampling frequency is once per second. The intelligent gas conditioning module records the triggering time of each gas supply or exhaust instruction. , and synchronized to the instruction control module.

[0075] Set the significant change threshold ΔCthreshold of gas component concentration (for example, the concentration change reaches 5% of the initial value) to determine the starting point of gas response. By analyzing the curve of gas concentration change over time, the dynamic response time It is defined as the time difference from when the control command is triggered to when the gas concentration first reaches the threshold ΔCthreshold: ;in: is the instruction trigger time, is the time point when the gas concentration first reaches the threshold change.

[0076] The concentration change threshold ΔCthreshold is dynamically adjusted through reinforcement learning algorithms to adapt it to the characteristics of different products and environments. Based on long-term data accumulation, time series prediction algorithms (such as ARIMA models) are used to predict future environmental dynamic response times.

[0077] The abnormal changes in gas diffusion rate and the dynamic response time of the environment are integrated into the machine learning algorithm. By real-time evaluation of the relationship between multidimensional parameters, an optimized gas control strategy is generated to dynamically adjust the timing and method of gas supply and exhaust to ensure the stability and efficiency of the maintenance environment.

[0078] Input data: Abnormal change value of gas diffusion rate, which indicates the abnormal degree of gas distribution uniformity in a closed unit.

[0079] Environmental dynamic response time, which indicates the delay between the change of gas composition and the control instruction. Other auxiliary parameters: gas composition concentration ; Temperature and humidity data; historical control records and effect feedback (such as the time taken to reach the target concentration). Clean the data to remove outliers and redundant data; A random forest regression model is used to evaluate the nonlinear relationship between multidimensional parameters and generate regulatory strategies.

[0080] Input features: abnormal changes in gas diffusion rate, environmental dynamic response time; gas concentration change rate; historical control records (gas supply / exhaust time, gas ratio, etc.).

[0081] Model output: optimal air supply flow rate, optimal air supply time, and exhaust trigger threshold adjustment suggestions.

[0082] A random forest model is used to calculate the importance weight of each feature. For example, if the weight of the abnormal change in gas diffusion rate is high, the air supply flow rate is adjusted first; if the weight of the environmental dynamic response time is high, the air supply is triggered in advance or the exhaust time is extended.

[0083] The reward function aims to maintain the stability of the environment and takes the following factors into consideration: ;in: is the target gas concentration, is the actual gas concentration, Energy wasted in the process of air supply or exhaust, 、 is the weight proportional coefficient, and 、 Both are greater than 0.

[0084] The model continuously learns from historical data on gas diffusion rates and dynamic response times to continuously optimize control strategies. The system monitors the effectiveness of adjustments in real time and uses feedback data as a training set to update the model, ensuring high efficiency and stability in complex environments.

[0085] The instruction sending unit obtains the following inputs based on the evaluation results of the instruction control module: Current gas concentration : Real-time concentration of the i-th gas in the closed unit, target gas concentration : The ideal concentration of the i-th gas in the closed unit.

[0086] Response time delay: The delay time between the change of gas composition and the control instruction.

[0087] Diffusion rate anomaly: reflects the degree of gas distribution unevenness Calculate the concentration error of each gas based on the input parameters , the expression is: The control goal is to supply or exhaust air to Minimize as much as possible to make the gas composition ratio reach the set target: The instruction sending unit generates a control instruction I, which includes two key parameters: gas supply flow rate and gas supply time: Air supply flow rate: ;in, is the proportional adjustment coefficient of the i-th gas (determined by historical data and system optimization algorithm); is the current concentration error; It is the dynamic response time, reflecting the degree of delay.

[0088] Gas supply time: ; Where V is the effective volume of the closed unit, is the supply flow rate of the i-th gas.

[0089] When it is detected that the concentration of a certain gas is too high, the command sending unit generates an exhaust command : ;in: is the exhaust volume, is the exhaust time, which is optimized according to the dynamic response of the system.

[0090] The command sending unit dynamically adjusts the control priority according to the importance and abnormality of the gas. The gas concentration that has the greatest impact on the maintenance environment is adjusted first. For example: If the SX shows an abnormal diffusion rate, increase the air supply rate first. To improve uniformity.

[0091] if Longer, trigger the gas supply command in advance to avoid excessive concentration caused by delay.

[0092] When multiple gases need to be regulated simultaneously, the following formula is used for comprehensive optimization: ;in: is the comprehensive air supply velocity, is the weight of the regulation of the i-th gas on the j-th gas.

[0093] The generated control instruction I includes: gas supply flow rate and gas supply time; for example: The instruction sending unit sends instructions to the intelligent gas conditioning module through the communication module. The intelligent gas conditioning module executes the instructions and feeds back real-time control data (such as gas supply volume and response results) to the instruction control module for updating and optimization.

[0094] In this embodiment, it includes a closed module, a gas supply module, a gas monitoring module, an intelligent gas conditioning module and a command control module. The closed module consists of three independently sealed storage units, each unit is equipped with a gas inlet and outlet valve, a temperature and humidity sensor, a gas composition monitoring sensor and an image camera probe to ensure that the environment between different units is independent. The gas supply module stores oxygen and nitrogen, is connected to the closed unit, and accurately supplies or discharges gas according to demand. The gas monitoring module collects and monitors the concentrations of carbon dioxide, oxygen and nitrogen in real time. The intelligent gas conditioning module is based on cloud data and combines the monitoring data to automatically adjust the gas composition ratio through the gas supply module to maintain the preset maintenance environment. The command control module stores and analyzes historical and real-time data, optimizes the gas supply strategy, and sends instructions to the intelligent gas conditioning module according to the warning value to ensure environmental stability and control efficiency.

[0095] Example 2, an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements a cloud computing-based intelligent gas conditioning maintenance system by executing the computer program.

[0096] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0097] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0098] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. Intelligent atmosphere conditioning and maintenance system based on cloud computing, characterized by: It includes a closed module, a gas supply module, a gas monitoring module, an intelligent gas conditioning module and a command control module; Sealed module: It includes three independent storage sealed units. The internal space of each unit is used to store different types of products. The sealed unit is equipped with gas inlet and outlet valves, temperature and humidity sensors, gas composition monitoring sensors and image camera probes. The sealed unit adopts an independent sealing design to avoid cross contamination of gas components between units. Gas supply module: equipped with storage devices for oxygen and nitrogen gases, connected to the gas inlet and outlet valves of the closed units, and used to supply or discharge gas to each closed unit as needed; Gas monitoring module: connected to the gas composition monitoring sensor, used to collect gas composition data in the closed unit in real time and monitor the concentrations of carbon dioxide, oxygen and nitrogen; Intelligent gas conditioning module: Based on the cloud data server, it communicates with the gas monitoring module, and automatically adjusts the gas component ratio through the gas supply module according to the gas monitoring data of the closed module to maintain the preset gas maintenance environment; Command control module: used to store the gas composition data uploaded by the gas monitoring module, analyze historical data and real-time data to optimize the gas supply strategy, and send control instructions to the intelligent gas conditioning module according to the warning value to ensure the stability of the maintenance environment in the closed module.

2. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 1 is characterized in that: The closed module includes three independent storage closed units, namely: coffee bean curing unit, Pu'er tea curing unit and cigar curing unit.

3. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 1 is characterized in that: The gas composition monitoring sensor of the gas monitoring module adopts electrochemical principle combined with optical technology, with a detection range of 0-100% and a detection error controlled within ±0.1%.

4. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 1 is characterized in that: The parameter acquisition unit in the command control module: includes obtaining the abnormal change value of the gas diffusion rate and the dynamic response time of the environment.

5. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 4 is characterized in that: The gas diffusion rate D is defined as the rate at which concentration changes with position and time, and its mathematical formula is: ; Where C is the gas concentration, t is the time, and x is the position coordinate. Due to the non-uniform distribution of gas in the closed unit, a numerical method is used to discretely solve the gas diffusion rate: ;in, is the gas diffusion rate at the i-th position; Δt is the time interval; Δx is the position interval; the diffusion rate for all positions in the unit Calculate the abnormal change value of gas diffusion rate, the expression is: ; Where N is the number of sensors, is the average value of the diffusion rate, and SX is the abnormal change value of the gas diffusion rate.

6. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 5 is characterized in that: The normal range of variation of the diffusion rate is set based on historical data analysis. When the abnormal change value of the gas diffusion rate at a certain location exceeds the range, it is judged to be abnormal. When abnormalities occur at multiple locations at the same time, the module triggers an alarm to adjust the gas supply strategy. If the abnormal diffusion rate values ​​are concentrated in a single location, adjust the gas supply directionality and flow rate. If multiple locations are abnormal at the same time, increase the gas supply flow rate or extend the gas supply time. The command control module feeds back the adjusted strategy to the intelligent gas conditioning module.

7. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 6 is characterized in that: The gas monitoring module collects the gas component concentration data in the closed unit in real time, with a sampling frequency of once per second. The intelligent gas conditioning module records the triggering time of each gas supply or exhaust instruction. , and synchronized to the command control module; set the significant change threshold ΔCthreshold of the gas component concentration to determine the starting point of the gas response; by analyzing the curve of gas concentration change over time, the dynamic response time It is defined as the time difference from when the control command is triggered to when the gas concentration first reaches the threshold ΔCthreshold: ;in: is the instruction trigger time, is the time point when the gas concentration first reaches the threshold change.

8. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 1, characterized in that: The abnormal change value of gas diffusion rate and the dynamic response time of the environment are integrated into the machine learning algorithm. By evaluating the relationship between multi-dimensional parameters in real time, an optimized gas control strategy is generated to dynamically adjust the timing and method of gas supply and exhaust. Specifically: Input data: abnormal change value of gas diffusion rate, environmental dynamic response time, gas component concentration ; Temperature and humidity data; Use random forest regression models to evaluate nonlinear relationships between multidimensional parameters and generate regulatory strategies; Input features: abnormal change value of gas diffusion rate, environmental dynamic response time, gas concentration change rate and historical control records; Model output: optimal air supply flow rate, optimal air supply time, and exhaust trigger threshold adjustment suggestions; The importance weight of each feature is calculated using the random forest model. The reward function aims to maintain the stability of the environment, and the expression is: ;in: is the target gas concentration, is the actual gas concentration, Energy wasted in the process of supplying or exhausting air. , is the weight proportional coefficient, and , Both are greater than 0.

9. The intelligent atmosphere conditioning maintenance system based on cloud computing according to claim 8, characterized in that: The model continuously learns historical data of gas diffusion rate and dynamic response time, and continuously optimizes the control strategy, specifically: The command sending unit obtains the current gas concentration based on the evaluation results of the command control module. : Real-time concentration of the i-th gas in the closed unit, target gas concentration : The ideal concentration of the i-th gas in the closed unit; Response time delay: The delay time of the gas composition change relative to the control instruction; Calculate the concentration error of each gas based on the input parameters , the expression is: The instruction sending unit generates a control instruction I, including the gas supply flow rate and the gas supply time: gas supply flow rate: ;in, is the proportional adjustment coefficient of the i-th gas; is the current concentration error; is the dynamic response time; gas supply time: ; Where V is the effective volume of the closed unit, is the supply flow rate of the i-th gas; When a high concentration of a certain gas is detected, the command sending unit generates an exhaust command : ;in: is the exhaust volume, The exhaust time is optimized according to the dynamic response of the system; The command sending unit dynamically adjusts the control priority according to the importance and abnormality of the gas, giving priority to adjusting the concentration of the gas that has the greatest impact on the maintenance environment: If the SX shows that the diffusion rate is abnormal, increase the gas supply rate first To improve uniformity; if Long, trigger the gas supply command in advance; When multiple gases need to be regulated at the same time, the comprehensive optimization formula is: ;in: is the comprehensive air supply velocity, is the weight of the regulation of the i-th gas on the j-th gas; The generated control instruction I includes: gas supply flow rate and gas supply time; the instruction sending unit sends the instruction to the intelligent gas control module through the communication module, the intelligent gas control module executes the instruction, and feeds back the real-time control data to the instruction control module for updating and optimization.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, it implements the cloud computing-based intelligent atmosphere conditioning maintenance system described in any one of claims 1 to 9.

Images