Artificial intelligence-based tea constant temperature and humidity fermentation system
By using an AI-based constant temperature and humidity fermentation system for tea, the fermentation process can be monitored and optimized in real time, solving the problems of unstable quality and non-standard production in traditional tea fermentation, and realizing intelligent and standardized tea fermentation production.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-10
- Publication Date
- 2026-05-26
AI Technical Summary
Traditional tea fermentation processes rely on the natural environment and human experience, resulting in unstable quality, long production cycles, and high hygiene risks. Existing constant temperature and humidity equipment cannot sense the state of tea in real time and lacks objective judgment standards, which restricts the standardization of the process and large-scale production.
The system employs an AI-based constant temperature and humidity fermentation system for tea. Through multi-layered collaborative work involving omni-channel perception and data acquisition, AI decision-making and digital twins, precise execution and control, and management and interactive presentation layers, it achieves real-time perception, intelligent diagnosis, and dynamic optimization of the tea fermentation status, providing quantitative judgment and personalized control.
It enables real-time monitoring and intelligent decision-making of the tea fermentation process, improves the stability and yield of high-quality tea, reduces operational complexity, and realizes the digital preservation and inheritance of tea-making techniques.
Smart Images
Figure CN122086172A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent tea processing equipment technology, specifically to an artificial intelligence-based constant temperature and humidity fermentation system for tea. Background Technology
[0002] Traditional teas, such as black tea and Pu'er tea, rely heavily on the natural environment and the experience of tea masters during fermentation. This results in issues such as unstable quality, long production cycles, and high hygiene risks. While existing constant temperature and humidity fermentation equipment has achieved basic closed-loop control of ambient temperature and humidity to some extent, it is essentially still a static or semi-static environmental maintenance device, which has the following drawbacks:
[0003] 1. It can only sense and control the air temperature and humidity in the fermentation chamber, but cannot sense the actual fermentation state of the tea leaves themselves, such as microbial activity and the degree of biochemical transformation. Its control behavior is a passive response to deviations in environmental parameters, rather than actively guiding the fermentation process.
[0004] 2. The fermentation equipment system cannot understand how to adjust parameters, nor can it predict the future transformation trend of tea under the current parameters. The process curve needs to be preset manually and cannot be adaptively optimized according to the differences of each batch of raw materials and the real-time dynamics of fermentation.
[0005] 3. Whether fermentation is complete still depends on manual "seeing, smelling, touching and tasting", lacking objective and quantitative judgment standards, resulting in large quality differences between batches and unstable premium rate.
[0006] 4. The experience of senior tea masters cannot be effectively transformed into a standardized data model that can be replicated and optimized, which restricts the inheritance of craftsmanship and the consistency of quality in large-scale production.
[0007] Therefore, in order to address the above problems, there is an urgent need for an intelligent fermentation system that can sense the state of tea leaves in real time, make intelligent decisions, and precisely control the entire fermentation process, thereby achieving quality orientation, production standardization, and knowledge digitization. Summary of the Invention
[0008] In response to the above situation and to overcome the current technical deficiencies, this invention provides a tea constant temperature and humidity fermentation system based on artificial intelligence. This system can realize the adjustment from environmental control to quality process control, and thus complete the real-time perception, intelligent diagnosis, dynamic optimization and endpoint determination of the fermentation state.
[0009] The technical solution adopted by this invention is as follows: This invention provides an artificial intelligence-based constant temperature and humidity fermentation system for tea, including a global perception and data acquisition layer, an AI decision-making and digital twin layer, a precise execution and control layer, and a management and interactive presentation layer; the global perception and data acquisition layer is used to collect multi-dimensional data from the fermentation environment and the tea itself in real time; the AI decision-making and digital twin layer is used to fuse and analyze the collected data, construct a virtual fermentation model, and generate optimized control strategies; the precise execution and control layer is used to distribute the control strategies generated by the decision-making layer to various execution mechanisms and drive their actions; the management and interactive presentation layer is used to provide system configuration, process monitoring, model management, and visualization interaction functions.
[0010] Furthermore, the AI decision-making and digital twin layer includes a data fusion and governance unit, a fermentation state diagnosis unit, a dynamic process optimization unit, and a process knowledge base unit. The data fusion and governance unit is used to clean, align, and spatiotemporally correlate multi-source heterogeneous data to form a unified "fermentation batch digital thread." The fermentation state diagnosis unit, based on a deep learning model, processes the fused real-time data and outputs quantitative indicators such as "fermentation intensity index" and "flavor precursor conversion rate." The dynamic process optimization unit, built on a reinforcement learning framework, aims to maximize the final tea quality prediction score and calculates and outputs the optimal temperature and humidity setpoints, ventilation strategies, and other control command sequences in real time. The process knowledge base unit stores historical high-quality process cases and corresponding multi-dimensional data, supporting case retrieval and intelligent recommendation of initial process schemes for new batches.
[0011] Furthermore, the precise execution and control layer includes a multivariable collaborative controller and a group of multimodal actuators; the multivariable collaborative controller is used to receive composite instructions from the AI decision layer and decouple them into coordinated control logic for each independent actuator; the group of multimodal actuators includes a precisely modulated constant temperature and humidity unit, independently controlled auxiliary heating / cooling modules, a variable frequency ventilation and fresh air system, and atomizing humidification and condensing dehumidification devices, all of which have status feedback functions.
[0012] Furthermore, the management and interactive presentation layer includes a configuration management unit, a panoramic monitoring unit, a digital twin simulation unit, and a report analysis unit.
[0013] Furthermore, the configuration management unit is used to configure and manage fermentation task parameters, AI model versions, and control thresholds; the panoramic monitoring unit is used to display fermentation environment data, tea body status indicators, AI decision logs, and equipment operating status in real time, and trigger alarms when anomalies occur; the digital twin simulation unit allows process engineers to simulate and preview optimization strategies in a virtual environment by constructing a digital twin synchronized with the physical fermentation process.
[0014] Furthermore, the report analysis unit is used to automatically generate a batch report containing full-process data curves, key node diagnostic records, and process evaluations after fermentation.
[0015] The beneficial effects achieved by the present invention using the above structure are as follows:
[0016] 1. Through the establishment of a full-domain perception and data acquisition layer, especially the tea body perception unit, non-invasive, multi-dimensional real-time monitoring of the physicochemical state of tea itself during fermentation has been achieved for the first time, providing a direct and objective data basis for intelligent decision-making.
[0017] 2. By constructing a closed loop of "perception-diagnosis-optimization" through AI decision-making and digital twin layer, artificial intelligence is deeply integrated into the core of the process, enabling the system to understand the fermentation process, predict future trends, and proactively seek the optimal process path, thus realizing a fundamental shift from "experience-based control" to "model-driven control".
[0018] 3. Through precise execution and control layer, multi-variable collaborative control technology is used to achieve precise, flexible and coordinated regulation of parameters in complex coupled environments, ensuring that the optimization strategies generated by AI can be executed accurately.
[0019] 4. Through the management and interactive presentation layer, digital twin simulation and panoramic visualization provide process engineers with decision support tools and an intuitive operating interface, which not only ensures the intervention of human experience, but also greatly reduces the complexity of operation and the risk of misjudgment.
[0020] 5. The system works collaboratively at all levels, forming a complete autonomous closed loop of "perception-thinking-execution-evaluation". It can achieve personalized fermentation for different raw materials and flavor targets, significantly improve the stability of tea quality, the rate of superior products and production efficiency, and realize the digital accumulation and inheritance of tea-making process knowledge. Attached Figure Description
[0021] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:
[0022] Figure 1 This is a block diagram of the overall architecture of the tea constant temperature and humidity fermentation system based on artificial intelligence of the present invention; Figure 2 This is a schematic diagram of the layout of the global perception and data acquisition layer of the present invention; Figure 3 This is a schematic diagram of the workflow of the AI decision-making and digital twin layer of the present invention; Figure 4 This is a schematic diagram illustrating the principle of the precise execution and control layer of the present invention; Figure 5 This is the interface logic diagram of the management and interaction presentation layer of this invention; Figure 6This is a flowchart of the closed-loop control process of the system of the present invention in response to abnormal fermentation scenarios.
[0023] The system comprises the following components: 1. Full-domain perception and data acquisition layer; 2. AI decision-making and digital twin layer; 3. Precise execution and control layer; 4. Management and interactive presentation layer; 5. Environmental parameter perception unit; 6. Tea body perception unit; 7. Process-aided perception unit; 8. Data fusion and governance unit; 9. Fermentation status diagnosis unit; 10. Dynamic process optimization unit; 11. Process knowledge base unit; 12. Multivariable collaborative controller; 13. Multimodal actuator group; 14. Configuration management unit; 15. Panoramic monitoring unit; 16. Digital twin simulation unit; and 17. Report analysis unit. Detailed Implementation
[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0025] It should be noted that the terms “front,” “back,” “left,” “right,” “up,” and “down” used in the following description refer to the directions shown in the attached diagram, while the terms “inside” and “outside” refer to the directions toward or away from the geometric center of a specific component, respectively.
[0026] As per the instruction manual Figure 1-5 As shown, the technical solution adopted by this invention is as follows: This solution proposes an artificial intelligence-based constant temperature and humidity fermentation system for tea, comprising a global perception and data acquisition layer 1, an AI decision-making and digital twin layer 2, a precise execution and control layer 3, and a management and interaction presentation layer 4.
[0027] The overall perception and data acquisition layer 1 is used to collect multi-dimensional data from the fermentation environment and the tea leaves themselves in real time, specifically including:
[0028] Environmental parameter sensing unit 5: Deployed at multiple representative locations within the fermentation chamber to collect data on air temperature and humidity, CO2 concentration, and O2 concentration.
[0029] Tea body sensing unit 6 includes a distributed temperature sensing network, in which wireless temperature probes are embedded in the tea pile in a three-dimensional grid to transmit temperature data of the core, middle layer and surface layer in real time; a multispectral vision unit, which takes high-definition images of the tea pile surface at regular intervals under specific light sources and analyzes the changes in color HSV value and texture features; and a microenvironment gas sensing unit, which samples and analyzes above the tea pile to monitor the dynamic concentration of characteristic volatile components such as ethanol and ethyl acetate.
[0030] Process Auxiliary Sensing Unit 7: Records the start-up and stop times and operation trajectory of the turner.
[0031] AI decision-making and digital twin layer 2 connects to the global perception and data acquisition layer 1, and is used to fuse and analyze the acquired data, construct a virtual fermentation model, and generate optimized control strategies, specifically including:
[0032] Data Fusion and Governance Unit 8: Timestamp alignment, invalid data removal, and feature value extraction are performed on time series data, image feature data, and operation logs from different sensors to form a structured batch process dataset.
[0033] Fermentation status diagnosis unit 9: Utilizes a trained convolutional neural network model to analyze image features, combines thermal field data, and calculates and outputs the "color conversion index" and "microbial activity thermal index" in real time.
[0034] Dynamic Process Optimization Unit 10: Based on a reinforcement learning model, using a digital twin as the simulation environment, and rewarded with "predicted quality score for the next 24 hours", it continuously explores and outputs the optimal environmental temperature and humidity setpoints and ventilation plan for the current moment.
[0035] Process Knowledge Base Unit 11: Stores complete data and process evaluations of historical successful batches. When a new batch is put into the stack, the system automatically matches similar cases and recommends initial temperature and humidity curves based on the raw material moisture content and grade information.
[0036] The precise execution and control layer 3 connects to the AI decision-making and digital twin layer 2, and is used to distribute and execute the control strategies generated by the decision-making layer, specifically including:
[0037] Multivariable Cooperative Controller 12: Receives instructions such as "increase the core area temperature by 0.5°C and reduce the ambient humidity from 88% to 86%", decomposes and coordinates the action logic and duration of the constant temperature and humidity host, auxiliary heating element and dehumidifier.
[0038] Multimodal actuator group 13: includes variable frequency compressor, PTC heater, ultrasonic humidifier, surface cooler dehumidification module and variable frequency centrifugal fan, etc. All mechanisms are scheduled by the controller and the real-time operating status is fed back.
[0039] The management and interactive presentation layer 4 connects to all other layers and provides system configuration, process monitoring, model management, and visual interaction functions, specifically including:
[0040] Configuration Management Unit 14: Used to set the target flavor type for this fermentation, set the confidence threshold for each AI model, and set alarm conditions.
[0041] Panoramic monitoring unit 15: Dynamically displays a 3D heat map, VOC change trend map, real-time video stream and AI decision suggestions on the central screen, and issues an alarm when the diagnostic index is abnormal.
[0042] Digital Twin Simulation Unit 16: Constructs a virtual model synchronized with physical fermentation. Process engineers can manually adjust the temperature and humidity settings for a future period on this model. The system will simulate and extrapolate the fermentation results for use in scheme verification and teaching.
[0043] Report Analysis Unit 17: After the batch is completed, a graphic report is automatically generated, including key parameter curves, AI diagnostic records, actuator action logs, and comparative analysis with historical high-quality batches. Example:
[0044] Reference manual attached Figure 1-6 As shown, this system was applied to the fermentation process of a batch of ripe Pu-erh tea. 24 hours after being piled, the distributed temperature network of the tea leaf sensing unit 6 showed that the core temperature of the pile rose sharply to 62°C (approaching the "burning" risk threshold), while the microenvironment gas sensing unit detected an abnormal increase in acetic acid concentration. The data was transmitted in real-time to the AI decision-making and digital twin layer 2.
[0045] The fermentation status diagnosis unit 9 judged that "local overheating has increased the risk of rancidity". The dynamic process optimization unit 10 immediately performed a rapid simulation in the model of the digital twin simulation unit 16 and calculated the optimal intervention strategy as: "In the next 2 hours, start strong circulation ventilation (30Hz) to uniformly heat the pile, temporarily lower the ambient temperature setpoint by 2°C, and suspend humidification at the same time".
[0046] After the process engineer quickly confirmed the strategy on the management interface, it was issued to the precise execution and control layer 3. The multivariable coordinating controller 12 precisely coordinated the speed increase of the variable frequency fan, the enhanced cooling of the surface cooler, and the shutdown of the humidifier. After execution, the core temperature steadily dropped to 58°C within 1.5 hours, and the upward trend of acetic acid concentration was curbed.
[0047] The entire control process was completed automatically by the system, and the panoramic monitoring unit 15 clearly displayed the dynamic process of the temperature field returning to normal from anomaly. After fermentation, the report analysis unit 17 generated a report that recorded detailed data on the entire chain of triggering, decision-making, and execution of this event, providing a valuable case for process optimization. Throughout the process, human intervention was limited to supervision and confirmation, greatly reducing labor intensity and avoiding potential quality accidents.
[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, material, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, material, or apparatus.
[0049] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. An artificial intelligence-based constant temperature and humidity fermentation system for tea, comprising a global perception and data acquisition layer, an AI decision-making and digital twin layer, a precise execution and control layer, and a management and interactive presentation layer; characterized in that: The full-domain perception and data acquisition layer is used to collect multi-dimensional data from the fermentation environment and the tea itself in real time; the AI decision-making and digital twin layer is used to fuse and analyze the collected data, construct a virtual fermentation model, and generate optimized control strategies; the precise execution and control layer is used to distribute the control strategies generated by the AI decision-making and digital twin layer to various execution mechanisms and drive their actions. The management and interactive presentation layer is used to provide system configuration, process monitoring, model management, and visual interactive functions.
2. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 1, characterized in that: The global perception and data acquisition layer includes an environmental parameter sensing unit, deployed inside the fermentation chamber, used to collect data on air temperature, air humidity, carbon dioxide concentration, and oxygen concentration.
3. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 2, characterized in that: The overall perception and data acquisition layer also includes: a tea body perception unit, used to directly collect intrinsic data reflecting the fermentation state of tea; and a process-aided perception unit, used to collect time and status data of turning and water spraying processes.
4. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 3, characterized in that: The tea body sensing unit includes: a distributed temperature sensing network, embedded in the tea pile, for monitoring the three-dimensional thermal field distribution of the pile; a multispectral vision unit, for acquiring images of color and texture changes on the tea surface; and a microenvironment gas sensing unit, for monitoring the spectrum of volatile organic compounds around the tea pile.
5. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 1, characterized in that: The AI decision-making and digital twin layer includes: a data fusion and governance unit, used to clean, align and spatiotemporally correlate multi-source heterogeneous data to form a unified fermentation batch digital thread; and a fermentation status diagnosis unit, which processes the fused real-time data based on a deep learning model and outputs quantitative indicators including fermentation intensity index and flavor precursor conversion rate.
6. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 1, characterized in that: The AI decision-making and digital twin layer also includes: a dynamic process optimization unit, built on a reinforcement learning framework, which aims to maximize the final tea quality prediction score and calculates and outputs the optimal temperature and humidity setpoints and ventilation strategy control command sequence in real time; and a process knowledge base unit, which stores historical high-quality process cases and corresponding multi-dimensional data, and supports case retrieval and intelligent recommendation of initial process schemes for new batches.
7. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 1, characterized in that: The precise execution and control layer includes: a multivariable collaborative controller, which receives composite instructions from the AI decision-making and digital twin layers and decouples them into coordinated control logic for each independent actuator; and a multimodal actuator group, including a precisely modulated constant temperature and humidity unit, independently controlled auxiliary heating / cooling modules, a variable frequency ventilation and fresh air system, and atomizing humidification and condensing dehumidification devices, all of which have status feedback functions.
8. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 1, characterized in that, The management and interaction presentation layer includes: The configuration management unit is used to configure and manage fermentation task parameters, AI model versions, and control thresholds; The panoramic monitoring unit is used to display fermentation environment data, tea leaf status indicators, AI decision logs, and equipment operating status in real time, and to trigger alarms when abnormalities occur.
9. The tea constant temperature and humidity fermentation system based on artificial intelligence according to claim 8, characterized in that: The management and interactive presentation layer also includes: a digital twin simulation unit, used to construct a digital twin synchronized with the physical fermentation process, allowing for simulation and effect preview of optimization strategies in a virtual environment; and a report analysis unit, used to automatically generate batch reports containing full-process data curves, key node diagnostic records, and process evaluations after fermentation.