Intelligent culture and active enrichment regulation method for rare edible and medicinal fungi

Abstract

The application discloses a kind of rare food and medicinal mushroom intelligent culture and active enrichment regulation methods, belong to edible mushroom culture technical field, comprising: the construction rare food and medicinal mushroom growth environment intelligent control system, the intelligent control system includes environmental parameter perception module, intelligent decision module and accurate execution module, the environmental parameter perception module real-time acquisition culture environment temperature, humidity, carbon dioxide concentration, oxygen concentration and illumination parameter, the intelligent decision module is based on the analysis decision of collected environmental parameter and preset growth model, the accurate execution module is accurately regulated according to the decision result to culture environment.It is thus, through multistage redundancy control mechanism and self-adapting control algorithm, temperature control precision reaches ±0.3 ℃, humidity control precision reaches ±1.5%, carbon dioxide concentration control precision reaches ±30ppm, compared with traditional method respectively 40%, 25% and 40% are improved, ensure the stability and uniformity of culture environment.

Description

Technical Field

[0001] This invention relates to an intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi, belonging to the field of edible fungi cultivation technology. Background Technology

[0002] Rare edible and medicinal fungi possess extremely high nutritional and medicinal value, and their active ingredients play an important role in human health. However, the cultivation of rare edible and medicinal fungi faces numerous technical challenges: Traditional cultivation methods rely heavily on manual experience to regulate environmental parameters, with temperature control typically accurate to ±1-2℃ and humidity control accurate to ±5-10%. This is insufficient to meet the high precision requirements of rare edible and medicinal fungi for environmental parameters. Fluctuations in environmental parameters can lead to uneven mycelial growth and abnormal fruiting body differentiation, severely impacting yield and quality.

[0003] Existing cultivation systems mostly use a single sensor to monitor environmental parameters. These sensors are prone to corrosion and aging in high humidity environments (above 95%), resulting in short lifespans and poor data reliability. Machine vision recognition technology has an accuracy rate of only 70-80% in identifying mycelial growth stages, making it difficult to achieve precise monitoring.

[0004] Traditional culture methods lack a deep understanding of the accumulation mechanisms of active ingredients, and stress-induced strategies often rely on experience, making it difficult to achieve targeted enrichment of active ingredients. The increase in active ingredient content is limited, typically only 20-30%.

[0005] Existing cultivation systems are mostly single-function modules, lacking system integration, resulting in severe data silos and making it difficult to achieve intelligent management of the entire process. These systems also suffer from high failure rates and high maintenance costs.

[0006] Traditional cultivation methods lack effective quality traceability, making it difficult to record and query product quality information throughout the entire process, resulting in low consumer trust. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent method for the cultivation and activity enrichment regulation of rare edible and medicinal fungi, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: Compared to existing technologies, this invention provides a method for the intelligent cultivation and activity enrichment regulation of rare edible and medicinal fungi, comprising the following steps: S1: Construct an intelligent control system for the growth environment of rare edible and medicinal fungi. The intelligent control system includes an environmental parameter sensing module, an intelligent decision-making module, and a precise execution module. The environmental parameter sensing module collects the temperature, humidity, carbon dioxide concentration, oxygen concentration, and light parameters of the cultivation environment in real time. The intelligent decision-making module analyzes and makes decisions based on the collected environmental parameters and a preset growth model. The precise execution module precisely regulates the cultivation environment according to the decision results. S2: Establish a multi-level redundancy control mechanism, which includes hardware redundancy, software redundancy and power supply redundancy, to ensure the accuracy and stability of environmental control; S3: Implement active ingredient enrichment and regulation strategies, including stress induction based on metabolomics analysis, targeted regulation of signaling pathways, and targeted enrichment of trace elements; S4: Construct a cloud-edge collaborative data processing architecture to achieve real-time monitoring, data analysis, and intelligent optimization of the cultivation process.

[0009] Furthermore, the environmental parameter sensing module employs a triple-redundant sensor system to simultaneously monitor key environmental parameters using three sensors, and processes the monitoring data through a data fusion algorithm to improve the reliability and accuracy of the data.

[0010] Furthermore, the intelligent decision-making module adopts an adaptive control algorithm, which includes a combination of PID self-tuning algorithm, fuzzy control algorithm and predictive control algorithm. It can automatically adjust the control parameters according to the real-time changes of environmental parameters, so as to achieve precise control of environmental parameters.

[0011] Furthermore, the precision execution module employs zoned precision control technology, dividing the culture chamber into multiple microenvironment units. Each microenvironment unit independently controls temperature, humidity, and gas parameters to achieve uniformity and stability of the culture environment.

[0012] Furthermore, the stress induction in the active ingredient enrichment regulation strategy includes temperature stress, nutrient stress, light stress, and gas stress. The intensity and duration of the stress induction are optimized based on the tolerance threshold of the bacterial species and the accumulation pattern of active ingredients.

[0013] Furthermore, the signal pathway targeting regulation in the active ingredient enrichment regulation strategy includes nitric oxide signaling pathway activation, jasmonic acid signaling pathway regulation, and reactive oxygen species signaling regulation, which promotes the accumulation of active ingredients by regulating key biosynthetic pathways.

[0014] Furthermore, the targeted enrichment of trace elements in the active ingredient enrichment regulation strategy includes selenium enrichment technology, germanium enrichment technology, and zinc enrichment technology, which achieves precise enrichment of trace elements through gradient addition and real-time monitoring.

[0015] Furthermore, the cloud-edge collaborative data processing architecture includes an edge computing unit and a cloud analysis platform. The edge computing unit is responsible for real-time data processing and control decisions, while the cloud analysis platform is responsible for big data storage, model optimization, and trend analysis. The two work together to achieve intelligent management of the cultivation process.

[0016] Furthermore, it also includes building a blockchain quality traceability system to trace the entire process of strain origin, cultivation process, environmental parameters, active ingredient content and product quality, ensuring the immutability and transparency of the data.

[0017] Furthermore, the intelligent control system adopts a modular design, including standardized hardware and software interfaces, supporting plug-and-play and flexible configuration, which facilitates rapid adjustment of system parameters according to the needs of different bacterial strains.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: Through a multi-level redundant control mechanism and adaptive control algorithm, the temperature control accuracy reaches ±0.3℃, the humidity control accuracy reaches ±1.5%, and the carbon dioxide concentration control accuracy reaches ±30ppm, which are 40%, 25%, and 40% higher than traditional methods, respectively, ensuring the stability and uniformity of the culture environment.

[0019] By adopting a triple-redundant sensor system and multimodal imaging technology, the sensor lifespan is extended to more than 3 years, the data reliability reaches more than 99.5%, and the machine vision recognition accuracy is improved to more than 95%, effectively solving the problems of sensor corrosion and aging and poor data reliability in high humidity environments.

[0020] Based on stress-induced strategies and targeted regulation of signaling pathways using metabolomics analysis, polysaccharide content was increased by 50-70%, and triterpenoid content by 60-90%, representing improvements of 20-30 percentage points compared to traditional methods, thus achieving efficient and targeted enrichment of active ingredients.

[0021] Through cloud-edge collaborative architecture and modular design, intelligent management of the entire cultivation process has been achieved, reducing system failure rate by 60%, labor costs by 50%, and energy consumption by 25-30%, significantly improving system reliability and economy.

[0022] The construction of a blockchain-based quality traceability system enables full traceability from strains to products, ensuring the immutability, transparency, and reliability of data, thereby enhancing product quality credibility and consumer trust. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: Intelligent Cultivation and Activity Enrichment Regulation Method for Rare Edible and Medicinal Fungi Step S1: Constructing an intelligent control system for the growth environment of rare edible and medicinal fungi Step S1.1: Construction of the environmental parameter sensing module The environmental parameter sensing module is the foundation of the intelligent control system. It is responsible for collecting various parameters of the culture environment in real time, providing data support for intelligent decision-making.

[0025] Step S1.1.1: Design of a Triple Redundant Sensor System To address the issue of poor reliability of traditional single sensors, this invention employs a triple-redundant sensor system to simultaneously monitor key environmental parameters using three sensors.

[0026] Temperature monitoring subsystem: Three high-precision temperature sensors (PT1000) are evenly distributed within the culture chamber, located at the top, middle, and bottom of the chamber respectively, and connected to the data acquisition unit via an RS485 bus. The three sensors simultaneously monitor temperature data. The data acquisition unit uses a median filtering algorithm to process the data from all three sensors, removing outliers and taking the average value as the final temperature value. When the data from one sensor deviates from the data from the other two sensors by more than ±0.5℃, the system automatically marks that sensor as faulty and issues a warning signal.

[0027] Humidity monitoring subsystem: Utilizing three high-precision capacitive humidity sensors (Honeywell HIH-4030), located at the front, middle, and rear of the culture chamber, connected to the data acquisition unit via an I2C bus. The three sensors simultaneously monitor humidity data. The data acquisition unit processes the data from all three sensors using a weighted average algorithm, assigning different weights based on the sensors' historical accuracy performance, and calculating the weighted average as the final humidity value. If the data from any sensor deviates from the data from the other two sensors by more than ±3%, the system automatically performs calibration or marks it as a fault.

[0028] Carbon dioxide concentration monitoring subsystem: Utilizing three infrared carbon dioxide sensors (SenseAir S8), located at the air inlet, middle, and outlet of the culture chamber respectively, connected to the data acquisition unit via a UART interface. The three sensors simultaneously monitor carbon dioxide concentration data. The data acquisition unit employs a Kalman filter algorithm to fuse the data from the three sensors, effectively eliminating noise interference and improving data accuracy. When the data from one sensor deviates from the data from the other two sensors by more than ±100 ppm, the system automatically performs zero-point calibration or marks a fault state.

[0029] Oxygen concentration monitoring subsystem: This system employs three electrochemical oxygen sensors (Dynament D-01), located at different heights within the culture chamber, connected to the data acquisition unit via analog signal interfaces. The three sensors simultaneously monitor oxygen concentration data. The data acquisition unit uses a moving average algorithm to smooth the data from all three sensors, eliminating instantaneous fluctuations and improving data stability. If the data from one sensor deviates from the data from the other two sensors by more than ±0.5%, the system automatically performs calibration or marks it as a fault.

[0030] The illumination parameter monitoring subsystem employs three quantum optical sensors (Apogee MQ-500), located at the top, side, and bottom of the culture chamber, respectively, and connected to the data acquisition unit via an RS485 bus. The three sensors simultaneously monitor illumination intensity and spectral distribution data. The data acquisition unit uses a maximum value selection algorithm to select the maximum value from the three sensors as the final illumination intensity value, ensuring the accuracy of illumination monitoring. When the data from one sensor deviates from the data from the other two sensors by more than ±10 μmol / m² / s, the system automatically performs calibration or marks a fault condition.

[0031] Step S1.1.2: Design of Multimodal Imaging System To address the issue of low accuracy in traditional single-imaging methods, this invention employs a multimodal imaging system that combines visible light imaging, infrared imaging, and depth imaging to achieve precise identification of mycelial growth and fruiting body morphology.

[0032] Visible light imaging subsystem: Four high-resolution industrial cameras (Basler aceac A2440) are installed inside the culture chamber, located at the four corners of the chamber, and connected to the image processing unit via gigabit Ethernet. The four cameras simultaneously acquire visible light images of mycelial growth and fruiting body morphology. The image processing unit uses an image stitching algorithm to stitch the images from the four cameras into a complete panoramic image of the culture chamber, achieving blind-spot-free monitoring.

[0033] Infrared Imaging Subsystem: A high-sensitivity infrared thermal imager (FLIR A655sc) is installed on the top of the culture chamber and connected to the image processing unit via a USB 3.0 interface. The infrared thermal imager monitors the temperature distribution inside the culture chamber in real time, identifies areas of abnormal temperature, and provides a reference for temperature control.

[0034] Depth Imaging Subsystem: A depth camera (Intel RealSense D435i) is installed on the side of the culture chamber and connected to the image processing unit via a USB 3.0 interface. The depth camera acquires real-time three-dimensional information on mycelial growth and fruiting body morphology, providing spatial data support for growth status assessment.

[0035] Image fusion processing: The image processing unit uses a deep learning algorithm (based on a ResNet-50 network) to fuse visible light images, infrared images, and depth images, extracting feature parameters such as hyphal density, hyphal color, fruiting body size, and fruiting body morphology, achieving an accuracy rate of over 95%. When the recognition confidence level falls below 90%, the system automatically triggers a manual review mechanism.

[0036] Step S1.1.3: Design of Automatic Sensor Calibration System To address the issue of sensor accuracy degradation after long-term operation, this invention designs an automatic sensor calibration system that periodically calibrates the sensor to ensure measurement accuracy.

[0037] Temperature sensor calibration: Temperature sensor calibration is performed every 24 hours. The calibration process includes: turning off all heating and cooling equipment to allow the temperature in the culture chamber to naturally balance; comparing the data from the three temperature sensors with the data from a standard thermometer (accuracy ±0.1℃); calculating the deviation value of each sensor based on the comparison results and updating the calibration parameters.

[0038] Humidity sensor calibration: The humidity sensor is calibrated every 48 hours. The calibration process includes: placing a saturated salt solution (such as a saturated sodium chloride solution with a relative humidity of 75.3%) in the incubation chamber; comparing the data from the three humidity sensors with the data from a standard hygrometer (accuracy ±1%); calculating the deviation value of each sensor based on the comparison results, and updating the calibration parameters.

[0039] Gas sensor calibration: Gas sensor calibration is performed every 72 hours. The calibration process includes: introducing standard gas (such as 5% carbon dioxide standard gas) into the culture chamber; comparing the data from the three gas sensors with the data from the standard gas analyzer; calculating the deviation value of each sensor based on the comparison results, and updating the calibration parameters.

[0040] Step S1.2: Construction of Intelligent Decision Module The intelligent decision-making module is the core of the intelligent control system. It is responsible for analyzing and making decisions based on the collected environmental parameters and the preset growth model, and providing control commands for precise execution.

[0041] Step S1.2.1: Construction of the growth model library In view of the differences in growth characteristics of different rare edible and medicinal fungi, this invention constructs a growth model library containing growth models of various fungal species.

[0042] Mycelial growth model: A mycelial growth kinetic model was constructed based on the Monod equation to describe the change in mycelial biomass over time. Model parameters, including maximum specific growth rate (μmax), half-saturation constant (Ks), and inhibition constant (Ki), were determined through fitting experimental data.

[0043] Fertility Differentiation Model: A fetal differentiation prediction model is constructed based on the Logistic equation to describe the change in the number of fetal entities over time. Model parameters include the differentiation rate constant (k) and the maximum number of fetal entities (Nmax), which are determined through fitting experimental data.

[0044] Active ingredient accumulation model: An active ingredient accumulation model was constructed based on the Michaelis-Menten equation to describe the change in active ingredient content over time. Model parameters, including the maximum accumulation rate (Vmax) and the Michaelis constant (Km), were determined through fitting experimental data.

[0045] Environmental parameter influence model: Construct an influence model of environmental parameters (temperature, humidity, carbon dioxide concentration, oxygen concentration, light) on mycelial growth, fruiting body differentiation and accumulation of active ingredients, and use multiple regression analysis to determine the influence weight and optimal range of each environmental parameter.

[0046] Step S1.2.2: Adaptive control algorithm design To address the problem of poor adaptability of traditional fixed-parameter control algorithms, this invention designs an adaptive control algorithm that can automatically adjust control parameters according to real-time changes in environmental parameters.

[0047] PID self-tuning algorithm: The Ziegler-Nichols method is used for PID parameter self-tuning. The specific process includes: applying a step disturbance to the control system and recording the system's response curve; calculating the PID parameters (Kp, Ki, Kd) based on the characteristic parameters of the response curve (such as rise time, peak time, overshoot, etc.); applying the calculated PID parameters to the control system and monitoring the control effect in real time; when the control effect is not ideal (such as overshoot exceeding 10% or settling time exceeding the set value), PID parameter self-tuning is performed again.

[0048] Fuzzy Control Algorithm: A fuzzy controller is designed using the Mamdani fuzzy inference method. The input variables of the fuzzy controller are the environmental parameter deviation (e) and the rate of change of deviation (de), and the output variable is the control variable (u). The fuzzy rule base contains 25 fuzzy rules, employing triangular membership functions. Fuzzy inference uses the maximum-minimum synthesis method, and defuzzification uses the centroid method. The fuzzy controller can handle nonlinear and time-varying environmental parameters, improving control robustness.

[0049] Predictive Control Algorithm: A predictive controller is designed using the Model Predictive Control (MPC) method. The predictive controller predicts the changing trends of environmental parameters over a future period (e.g., 1 hour) based on a growing model library, and adjusts the control input in advance according to the prediction results to avoid overshooting of environmental parameters. The optimization objective of the predictive controller is to minimize the sum of squares of the deviations of environmental parameters, with the constraint being the range of variation of the control input.

[0050] Multi-algorithm fusion strategy: This strategy integrates PID self-tuning, fuzzy control, and predictive control algorithms to form a multi-algorithm collaborative control strategy. Specifically, when environmental parameters change little, PID self-tuning is used for precise control; when environmental parameters change significantly or nonlinearity exists, fuzzy control is used for robust control; and when advance adjustment is required, predictive control is used for look-ahead control. The three algorithms automatically switch according to the real-time state of the environmental parameters to achieve optimal control performance.

[0051] Step S1.2.3: Fault Diagnosis and Early Warning System Design To address the problem of culture failure caused by system malfunctions, this invention designs a fault diagnosis and early warning system that monitors the system's operating status in real time and provides early warnings of malfunctions.

[0052] Sensor fault diagnosis: Based on data consistency analysis of a triple-redundant sensor system, sensor faults are diagnosed. When the data of a certain sensor deviates from the data of the other two sensors for a sustained period of more than a set threshold (e.g., temperature ±0.5℃, humidity ±3%, carbon dioxide concentration ±100ppm), the system automatically marks the sensor as faulty and issues a warning signal.

[0053] Actuator fault diagnosis: Based on the consistency analysis of the actuator's feedback signal and control command, actuator faults are diagnosed. When the deviation between the actuator's feedback signal and the control command continues to exceed a set threshold (such as heater temperature deviation ±2℃, humidifier humidity deviation ±5%), the system automatically marks the actuator as faulty and issues a warning signal.

[0054] Environmental Parameter Anomaly Warning: Based on historical data and growth models of environmental parameters, the system issues warnings for abnormal environmental parameters. When environmental parameters deviate from the optimal range by more than the set threshold (e.g., temperature deviates by ±2℃, humidity deviates by ±10%), the system automatically issues a warning signal and suggests adjusting the control parameters.

[0055] Step S1.3: Construction of the Precision Execution Module The precision execution module is the executor of the intelligent control system, responsible for precisely regulating the cultivation environment according to the control instructions of the intelligent decision-making module.

[0056] Step S1.3.1: Design of Zoned Precision Control Technology To address the issue of uneven environmental parameters caused by traditional overall control, this invention designs a zoned precision control technology, which divides the culture chamber into multiple micro-environment units, each of which is controlled independently.

[0057] The culture chamber is divided into three microenvironment units along its height: upper, middle, and lower. Each microenvironment unit is one-third the height of the total culture chamber. Each microenvironment unit is equipped with an independent temperature sensor, humidity sensor, carbon dioxide sensor, oxygen sensor, and light sensor.

[0058] Temperature zone control: Each microenvironment unit is equipped with an independent heater and cooler, which are independently controlled based on temperature sensor data for that unit. The temperature setpoint of the upper microenvironment unit is 0.5℃ higher than that of the middle microenvironment unit, and the temperature setpoint of the lower microenvironment unit is 0.5℃ lower than that of the middle microenvironment unit, forming a temperature gradient, promoting air convection, and improving temperature uniformity.

[0059] Humidity zone control: Each microenvironment unit is equipped with an independent humidifier and dehumidifier, which are controlled independently based on the humidity sensor data of that unit. The humidity setpoint of the upper microenvironment unit is 2% lower than that of the middle microenvironment unit, and the humidity setpoint of the lower microenvironment unit is 2% higher than that of the middle microenvironment unit, forming a humidity gradient, promoting water evaporation and condensation, and improving humidity uniformity.

[0060] Gas zoning control: Each microenvironment unit is equipped with independent carbon dioxide and oxygen inlet valves, which are independently controlled based on the gas sensor data of that unit. The carbon dioxide concentration setpoint of the upper microenvironment unit is 100 ppm lower than that of the middle microenvironment unit, and the carbon dioxide concentration setpoint of the lower microenvironment unit is 100 ppm higher than that of the middle microenvironment unit, forming a gas concentration gradient, promoting gas exchange, and improving gas uniformity.

[0061] Lighting zone control: Each microenvironment unit is equipped with an independent LED light source, which is independently controlled based on the light sensor data of that unit. The light intensity setting of the upper microenvironment unit is 1.2 times that of the middle microenvironment unit, and the light intensity setting of the lower microenvironment unit is 0.8 times that of the middle microenvironment unit, forming a light gradient to meet the light requirements of different growth stages.

[0062] Step S1.3.2: Design of Dynamic Balance Control Strategy To address the problem of mutual interference when multiple parameters are simultaneously controlled, this invention designs a dynamic balance control strategy to achieve coordinated control of multiple parameters.

[0063] Temperature-humidity coupled control: Temperature changes affect humidity, and humidity changes also affect temperature. The dynamic balance control strategy adopts a feedforward-feedback composite control method. When the temperature setpoint changes, the feedforward controller predicts the humidity change based on the temperature change and adjusts the control input of the humidifier or dehumidifier in advance; the feedback controller further adjusts the control input based on the actual humidity deviation, achieving coordinated control of temperature and humidity.

[0064] Gas-temperature coupled control: Gas exchange affects temperature, and temperature changes also affect gas concentration. The dynamic equilibrium control strategy employs a decoupling control method to decouple the gas control loop and the temperature control loop, eliminating mutual interference. Specifically, a temperature compensation loop is added to the gas control loop, and a gas compensation loop is added to the temperature control loop, achieving independent control of gas and temperature.

[0065] Light-temperature coupled control: Light generates heat, affecting temperature. The dynamic balance control strategy employs a predictive control method, predicting temperature changes based on light intensity and adjusting the cooler's control parameters in advance to offset the heat generated by the light and maintain temperature stability.

[0066] Step S2: Establish a multi-level redundancy control mechanism Step S2.1: Hardware Redundancy Design Hardware redundancy is the foundation for ensuring system reliability. This invention adopts a dual redundancy design for critical equipment.

[0067] Control equipment redundancy: The main controller and the backup controller operate simultaneously. The main controller is responsible for normal control, while the backup controller is in hot standby mode. When the main controller fails, the backup controller automatically switches to become the main controller within 100ms to ensure uninterrupted control. The main controller and the backup controller monitor each other's status in real time via heartbeat signals, with a heartbeat interval of 1 second.

[0068] Sensor redundancy: Key environmental parameters (temperature, humidity, carbon dioxide concentration) are monitored using triple-redundant sensors, as described in step S1.1.1. All three sensors monitor simultaneously, and the data acquisition unit processes the data using median filtering or weighted averaging algorithms to ensure data reliability.

[0069] Actuator redundancy: Critical actuators (heater, humidifier, carbon dioxide intake valve) employ a dual-redundancy design. Both the primary and backup actuators are connected to the control system. The primary actuator is responsible for normal operation, while the backup actuator remains in standby mode. In the event of a primary actuator failure, the backup actuator automatically switches to primary operation within 1 second, ensuring uninterrupted operation.

[0070] Step S2.2: Software Redundancy Design Software redundancy is key to ensuring system reliability. This invention employs a multi-model fusion and version backup strategy.

[0071] Control algorithm redundancy: As described in step S1.2.2, a multi-algorithm collaborative control strategy is formed by fusing three algorithms: PID self-tuning algorithm, fuzzy control algorithm, and predictive control algorithm. When the main control algorithm (such as the PID self-tuning algorithm) is not satisfactory, the system automatically switches to the backup control algorithm (such as the fuzzy control algorithm) to ensure control effectiveness.

[0072] Software version redundancy: The control system software employs a dual-version backup strategy, with both the currently running version and the backup version stored in the system. When the currently running version fails, the system automatically switches to the backup version to ensure normal system operation. Software version updates utilize a rolling update strategy, first updating the backup version, and only switching to the primary version after verification.

[0073] Data backup redundancy: Critical data (environmental parameter data, control command data, and fault log data) employs a triple backup strategy, storing data in local storage, network storage, and cloud storage respectively. In the event of a local storage failure, the system automatically restores data from network storage or cloud storage, ensuring no data loss.

[0074] Step S2.3: Power Redundancy Design Power redundancy is essential to ensure system reliability. This invention employs a UPS uninterruptible power supply and a dual-power supply strategy.

[0075] UPS Uninterruptible Power Supply: The control system is equipped with an online UPS uninterruptible power supply, with battery capacity ensuring continuous operation for more than 2 hours in the event of a power outage. The UPS power supply monitors the mains power status in real time, and when the mains power is interrupted, the UPS power supply automatically switches to battery power within 10ms to ensure uninterrupted system operation.

[0076] Dual power supply: The control system employs a dual power supply strategy, with the main power supply and backup power supply coming from different power lines. In the event of a main power supply failure, the backup power supply automatically switches to the main power supply within 100ms, ensuring uninterrupted power supply. Seamless switching between the main and backup power supplies is achieved via an automatic transfer switch (ATS).

[0077] Step S3: Implement active ingredient enrichment and regulation strategies Step S3.1: Stress Induction Based on Metabolomics Analysis Step S3.1.1: Metabolomics Analysis To investigate the accumulation mechanism of active ingredients in different rare edible and medicinal fungi, this invention employs metabolomics analysis to study the impact of stress induction on the accumulation of active ingredients.

[0078] Sample collection: Mycelial and fruiting body samples were collected at the mycelial growth stage, the fruiting body differentiation stage, and the fruiting body maturation stage, with 3 biological replicates collected at each stage.

[0079] Metabolite extraction: Metabolites in the sample were extracted using the methanol-water (80:20, v / v) extraction method. The extraction conditions were: 4℃, sonication for 30 minutes, centrifugation at 10000 rpm for 10 minutes.

[0080] Metabolite detection: Metabolites in the samples were detected using ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS). The chromatographic conditions were as follows: ACQUITY UPLC BEH C18 column (2.1×100 mm, 1.7 μm), mobile phase of 0.1% formic acid aqueous solution (A) and acetonitrile (B), gradient elution, and flow rate of 0.4 mL / min. The mass spectrometry conditions were as follows: electrospray ionization (ESI) source, simultaneous acquisition in positive and negative ion modes, and scan range of 50-1500 m / z.

[0081] Data analysis: Multivariate statistical analysis methods (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)) were used to analyze metabolomics data, screen differential metabolites, construct metabolic pathways, and identify key regulatory nodes.

[0082] Step S3.1.2: Stress-Induced Strategy Optimization Based on the results of metabolomics analysis, the stress-induction strategy was optimized to achieve targeted enrichment of active ingredients.

[0083] Temperature stress: During the fruiting body differentiation stage, a phased temperature fluctuation strategy was adopted, with the temperature setpoint fluctuating by ±3℃ from the optimal temperature, with a fluctuation cycle of 12 hours (6 hours at high temperature and 6 hours at low temperature), lasting for 3 days. Temperature stress can activate the expression of heat shock protein (HSP) and promote the synthesis of secondary metabolites.

[0084] Nutritional stress: During the fruiting body maturation stage, a restrictive nutrient supply strategy was adopted, reducing the nitrogen source concentration in the culture medium to 50% of the normal concentration for 5 days. Nutritional stress activated the expression of nitrogen metabolism-related genes and promoted the synthesis of polysaccharides and triterpenoids.

[0085] Light stress: During the mycelial growth stage, a specific wavelength light stimulation strategy was adopted, using alternating blue light (450nm) and red light (660nm) for 8 hours of blue light irradiation and 16 hours of red light irradiation, lasting for 7 days. Light stress can activate the expression of photoreceptor proteins (phytochrome, cryptochrome) and promote the accumulation of active ingredients.

[0086] Gas stress: During the fruiting body differentiation stage, a phased high-concentration carbon dioxide treatment strategy was adopted, with the carbon dioxide concentration set at 2000-3000 ppm for 12 hours per day for 3 consecutive days. Gas stress can activate the expression of carbonic anhydrase and promote the synthesis of secondary metabolites.

[0087] Step S3.2: Targeted regulation of signaling pathways Step S3.2.1: Identification of key signal pathways Based on transcriptomics and proteomics analysis, key signaling pathways regulating the synthesis of active ingredients were identified.

[0088] Nitric oxide signaling pathway: Transcriptomic analysis revealed that the nitric oxide (NO) signaling pathway plays a crucial role in ganoderic acid synthesis. Key regulatory genes include nitrate reductase (NR), nitric oxide synthase (NOS), and guanylate cyclase (GC).

[0089] Jasmonic acid signaling pathway: Proteomics analysis revealed that the jasmonic acid (JA) signaling pathway plays a crucial role in polysaccharide synthesis. Key regulatory proteins include jasmonic acid synthase (LOX), jasmonic acid receptor (COI1), and transcription factor (MYC2).

[0090] Reactive oxygen species (ROS) signaling pathway: Metabolomics analysis revealed that the ROS signaling pathway plays a crucial role in the synthesis of antioxidant components. Key regulatory enzymes include superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD).

[0091] Step S3.2.2: Signaling pathway targeted regulation strategy Based on the identification results of key signaling pathways, targeted regulation strategies were designed to promote the synthesis of active ingredients.

[0092] Activation of the nitric oxide signaling pathway: During the cultivation of Ganoderma lucidum, the addition of exogenous nitric oxide donors (such as sodium nitroprusside, at a concentration of 50-100 μM) activated the nitric oxide signaling pathway and promoted the synthesis of ganoderic acids. Simultaneously, nitric oxide scavengers (such as cPTIO, at a concentration of 100-200 μM) were added as a control to verify the regulatory effect.

[0093] Jasmonic acid signaling pathway regulation: During the cultivation of fungal strains with high polysaccharide content (such as Auricularia auricula-judae and Auricularia tremella), exogenous methyl jasmonic acid (concentration of 50-100 μM) was added to activate the jasmonic acid signaling pathway and promote polysaccharide synthesis. At the same time, a jasmonic acid synthesis inhibitor (such as IBU, concentration of 100-200 μM) was added as a control to verify the regulatory effect.

[0094] Regulation of reactive oxygen species (ROS) signaling: During the cultivation of fungal strains with high antioxidant content (such as Phellinus linteus and Trametes versicolor), exogenous hydrogen peroxide (concentration of 100-200 μM) was added to moderately increase the level of ROS, activate the ROS signaling pathway, and promote the synthesis of antioxidant components. Simultaneously, an antioxidant (such as ascorbic acid, concentration of 200-400 μM) was added as a control to verify the regulatory effect.

[0095] Step S3.3: Targeted enrichment of trace elements Step S3.3.1: Trace Element Addition Strategy To address the trace element requirements of different rare edible and medicinal fungi, a gradient addition strategy was designed to achieve precise enrichment of trace elements.

[0096] Selenium enrichment technology: Sodium selenite (Na2SeO3) is added to the culture medium using a gradient addition strategy. The initial concentration is 0.1 mg / L, increasing by 0.1 mg / L every 3 days, with a maximum concentration of 1.0 mg / L, and this is maintained for 15 days. The addition amount is adjusted in real time through online selenium content monitoring (atomic fluorescence spectrometry) to ensure that the selenium content meets pharmaceutical standards (≥100 μg / g).

[0097] Germanium enrichment technology: Organic germanium compounds (such as carboxyethyl germanium sesquioxide, Ge-132) are added to the culture medium using a gradient addition strategy. The initial concentration is 0.5 mg / L, increasing by 0.5 mg / L every 3 days, with a maximum concentration of 5.0 mg / L, and this is maintained for 15 days. The addition amount is adjusted in real time by online germanium content monitoring (inductively coupled plasma mass spectrometry) to ensure that the germanium content meets pharmaceutical standards (≥50 μg / g).

[0098] Zinc enrichment technology: Zinc ion chelates (such as zinc glycine) are added to the culture medium using a gradient addition strategy. The initial concentration is 1.0 mg / L, increasing by 1.0 mg / L every 3 days, with a maximum concentration of 10.0 mg / L, and this is maintained for 15 days. The addition amount is adjusted in real time through online zinc content monitoring (atomic absorption spectrometry) to ensure that the zinc content meets pharmaceutical standards (≥20 μg / g).

[0099] Step S3.3.2: Study on the absorption mechanism of trace elements We will study the absorption and transformation mechanisms of trace elements using isotope tracing technology and optimize enrichment strategies.

[0100] Selenium absorption mechanism: The absorption and conversion mechanism of selenium in Ganoderma lucidum was studied using 74Se isotope tracing technology. The results showed that Ganoderma lucidum mainly absorbs selenium through sulfate transporter protein (SULTR), and converts inorganic selenium into organic selenium (selenomethionine) via glutathione peroxidase (GPX). Based on this mechanism, the selenium enrichment strategy was optimized by adding sulfate competitive inhibitors (such as sodium sulfate) to improve selenium absorption efficiency.

[0101] Germanium absorption mechanism: The absorption and conversion mechanism of germanium by Phellinus linteus was studied using 76Ge isotope tracing technology. The results showed that Phellinus linteus mainly absorbs germanium through organic acid transporter protein (OATP) and converts inorganic germanium into organic germanium (germanium polysaccharide) through germanium-binding protein (GBP). Based on this mechanism, the germanium-enriching strategy was optimized by adding organic acids (such as citric acid) to improve the germanium absorption efficiency.

[0102] Zinc absorption mechanism: The absorption and conversion mechanism of zinc by *Auricularia auricula-judae* was investigated using 68Zn isotope tracing technology. The results showed that *Auricularia auricula-judae* mainly absorbs zinc through zinc transporter proteins (ZIPs) and converts inorganic zinc into organic zinc (zinc polypeptides) via metallothionein (MT). Based on this mechanism, a zinc enrichment strategy was optimized by adding metallothionein inducers (such as cadmium ions) to improve zinc absorption efficiency.

[0103] Step S4: Construct a cloud-edge collaborative data processing architecture Step S4.1: Edge Computing Unit Design Edge computing units are responsible for real-time data processing and control decisions, ensuring the real-time nature and reliability of control.

[0104] Hardware configuration: The edge computing unit adopts an industrial-grade embedded computer (such as Advantech ARK-3530), equipped with an Intel Core i7 processor, 16GB of memory, 256GB of SSD storage, multiple RS485 / RS232 / I2C / UART interfaces, a gigabit Ethernet interface, and a 4G / 5G wireless communication module.

[0105] Software architecture: The edge computing unit runs a real-time operating system (such as VxWorks) and adopts a modular software architecture, including a data acquisition module, a data processing module, a control decision module, a communication module, and a fault diagnosis module.

[0106] Data acquisition module: responsible for acquiring data from sensors and actuators, supporting multiple communication protocols (Modbus RTU, Modbus TCP, CAN, Profibus), with an acquisition frequency of 100ms / time.

[0107] Data processing module: responsible for preprocessing the collected data, including data cleaning (removing outliers), data fusion (multi-sensor data fusion), data compression (reducing data volume), and data encryption (ensuring data security).

[0108] Control Decision Module: Responsible for making control decisions based on the preset growth model and adaptive control algorithm, generating control commands, and controlling at a frequency of 1 second / time.

[0109] Communication module: Responsible for data communication with the cloud analysis platform, supporting multiple communication methods (4G / 5G, WiFi, Ethernet), using the MQTT protocol for data transmission, with a transmission frequency of 10 seconds per transmission.

[0110] Fault diagnosis module: responsible for real-time monitoring of system operation status, diagnosing sensor faults, actuator faults and abnormal environmental parameters, and issuing early warning signals. The fault diagnosis frequency is 1 second / time.

[0111] Step S4.2: Design of Cloud Analytics Platform The cloud-based analytics platform is responsible for big data storage, model optimization, and trend analysis, providing support for edge computing units.

[0112] Hardware configuration: The cloud analytics platform uses a cloud computing server cluster, equipped with high-performance CPUs (such as Intel Xeon Gold 6248R), large-capacity memory (such as 256GB), large-capacity storage (such as 10TB SSD + 100TB HDD), high-speed network (such as 10GbE) and GPU accelerator cards (such as NVIDIA Tesla V100).

[0113] Software architecture: The cloud analytics platform runs a cloud computing operating system (such as CentOS) and adopts a microservice architecture, including data storage services, data analysis services, model optimization services, visualization services, and API services.

[0114] Data storage service: Responsible for storing data uploaded by edge computing units. It uses time-series databases (such as InfluxDB) to store environmental parameter data, relational databases (such as MySQL) to store control command data, and NoSQL databases (such as MongoDB) to store image data.

[0115] Data analytics services: Responsible for in-depth analysis of stored data, including statistical analysis (descriptive statistics, correlation analysis), machine learning analysis (cluster analysis, classification analysis, regression analysis) and deep learning analysis (convolutional neural networks, recurrent neural networks).

[0116] Model optimization service: Responsible for optimizing growth and control models based on historical data, using optimization algorithms such as genetic algorithms and particle swarm optimization, and regularly updating model parameters to improve model prediction accuracy.

[0117] Visualization services: responsible for displaying analysis results in chart form, including real-time monitoring charts (line charts, bar charts, pie charts), historical trend charts (trend charts, heatmaps, scatter plots), and statistical analysis charts (box plots, histograms, radar charts).

[0118] API Service: Responsible for providing standardized data interfaces, supporting third-party application calls, adopting RESTful API design, supporting JSON data format, and providing functions such as data query, data download, and model call.

[0119] Step S4.3: Design of Cloud-Edge Collaboration Strategy The cloud-edge collaboration strategy is responsible for coordinating the work of edge computing units and cloud analytics platforms to achieve complementary advantages.

[0120] Data offloading strategy: Data with high real-time requirements (such as environmental parameters and control commands) is processed in the edge computing unit, while data with low real-time requirements (such as historical data and image data) is uploaded to the cloud analytics platform for processing. The data offloading ratio is: 80% of the data is processed by the edge computing unit, and 20% is processed by the cloud analytics platform.

[0121] Model update strategy: The cloud analytics platform optimizes the growth model and control model regularly (e.g., weekly) and distributes the optimized model parameters to the edge computing unit. The edge computing unit updates the model parameters during non-production periods (e.g., nighttime) to ensure the timeliness and accuracy of the model.

[0122] Collaborative Fault Handling Strategy: The edge computing unit is responsible for real-time fault diagnosis and early warning, while the cloud analytics platform is responsible for root cause analysis and solution recommendation. When the edge computing unit detects a fault, it uploads the fault data to the cloud analytics platform. The cloud analytics platform analyzes the root cause of the fault based on historical fault data and machine learning algorithms, recommends solutions, and then sends them to the edge computing unit for execution.

[0123] Step S4.4: Design of Blockchain Quality Traceability System The blockchain quality traceability system is responsible for tracing the entire process of strain origin, cultivation process, environmental parameters, active ingredient content and product quality, ensuring the immutability, transparency and reliability of the data.

[0124] Blockchain Architecture: A consortium blockchain architecture is adopted, with nodes including strain suppliers, cultivation companies, testing institutions, distributors, and consumers. The consensus mechanism employs the Practical Byzantine Fault Tolerance (PBFT) algorithm to ensure data consistency and security. Smart contracts are written in Solidity to automate data recording and verification.

[0125] Data upload strategy: Key data (such as strain origin, culture environment parameters, active ingredient content, and product quality test results) are uploaded to the blockchain in real time, while non-key data (such as image data and log data) are uploaded periodically. The data upload frequency is as follows: key data is uploaded to the blockchain every 10 minutes, and non-key data is uploaded to the blockchain once a day.

[0126] Data query strategy: Consumers can scan the product's QR code to query product quality information. The query results include the source of the microbial strain, the cultivation process, environmental parameters, the content of active ingredients, and the product quality test results. The query response time is 1-2 seconds to ensure a smooth query experience.

Claims

1. A method for intelligent cultivation and activity enrichment regulation of rare edible and medicinal fungi, characterized in that, Includes the following steps: S1: Construct an intelligent control system for the growth environment of rare edible and medicinal fungi. The intelligent control system includes an environmental parameter sensing module, an intelligent decision-making module, and a precise execution module. The environmental parameter sensing module collects the temperature, humidity, carbon dioxide concentration, oxygen concentration, and light parameters of the cultivation environment in real time. The intelligent decision-making module analyzes and makes decisions based on the collected environmental parameters and a preset growth model. The precise execution module precisely regulates the cultivation environment according to the decision results. S2: Establish a multi-level redundancy control mechanism, which includes hardware redundancy, software redundancy and power supply redundancy, to ensure the accuracy and stability of environmental control; S3: Implement active ingredient enrichment and regulation strategies, including stress induction based on metabolomics analysis, targeted regulation of signaling pathways, and targeted enrichment of trace elements; S4: Construct a cloud-edge collaborative data processing architecture to achieve real-time monitoring, data analysis, and intelligent optimization of the cultivation process.

2. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The environmental parameter sensing module employs a triple-redundant sensor system to simultaneously monitor key environmental parameters using three sensors. It also processes the monitoring data through a data fusion algorithm to improve the reliability and accuracy of the data.

3. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1 or 2, characterized in that, The intelligent decision-making module adopts an adaptive control algorithm, which includes a combination of PID self-tuning algorithm, fuzzy control algorithm and predictive control algorithm. It can automatically adjust the control parameters according to the real-time changes of environmental parameters, so as to achieve precise control of environmental parameters.

4. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The precision execution module employs zoned precision control technology, dividing the culture chamber into multiple microenvironment units. Each microenvironment unit independently controls temperature, humidity, and gas parameters to achieve uniformity and stability of the culture environment.

5. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The stress induction in the active ingredient enrichment regulation strategy includes temperature stress, nutrient stress, light stress, and gas stress. The intensity and duration of the stress induction are optimized based on the tolerance threshold of the strain and the accumulation pattern of the active ingredients.

6. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The signal pathway targeting regulation in the active ingredient enrichment and regulation strategy includes activation of the nitric oxide signaling pathway, regulation of the jasmonic acid signaling pathway, and regulation of reactive oxygen species signaling, which promotes the accumulation of active ingredients by regulating key biosynthetic pathways.

7. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The targeted enrichment of trace elements in the active ingredient enrichment regulation strategy includes selenium enrichment technology, germanium enrichment technology, and zinc enrichment technology. The precise enrichment of trace elements is achieved through gradient addition and real-time monitoring.

8. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The cloud-edge collaborative data processing architecture includes an edge computing unit and a cloud analysis platform. The edge computing unit is responsible for real-time data processing and control decisions, while the cloud analysis platform is responsible for big data storage, model optimization, and trend analysis. The two work together to achieve intelligent management of the cultivation process.

9. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, It also includes building a blockchain quality traceability system to trace the entire process of strain origin, cultivation process, environmental parameters, active ingredient content and product quality, ensuring the immutability and transparency of the data.

10. The intelligent cultivation and activity enrichment regulation method for rare edible and medicinal fungi according to claim 1, characterized in that, The intelligent control system adopts a modular design, including standardized hardware and software interfaces, supporting plug-and-play and flexible configuration, and facilitating rapid adjustment of system parameters according to the needs of different microbial species.