Multi-dimensional monitoring system for edible mushroom culture environment

Abstract

The invention relates to the technical field of edible mushroom culture, in particular to an edible mushroom culture environment multi-dimensional monitoring system, which comprises an environment parameter acquisition module, a dynamic data preprocessing module, a multi-dimensional environment characteristic parameter extraction module, an environment abnormity evaluation module, a regulation and control decision generation module and an execution mechanism control module, the environmental parameter acquisition module acquires an original environmental parameter set; the dynamic data preprocessing module generates preprocessing environment data; the multi-dimensional environment characteristic parameter extraction module is used for extracting multi-dimensional environment characteristic parameters; the environment abnormity evaluation module outputs an evaluation result; the regulation and control decision generation module generates a regulation and control instruction sequence including priority ranking; and the execution mechanism control module converts the regulation and control instruction sequence into an execution mechanism driving signal. According to the method, the response efficiency of environmental regulation and the resource utilization rate are improved, and stable growth of edible mushrooms under variable environmental conditions is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of edible mushroom cultivation, and particularly to a multi-dimensional monitoring system for the edible mushroom cultivation environment. Background Art

[0002] In modern agriculture, especially in the industrialized production of edible mushrooms, the healthy growth of mycelium highly depends on the synergistic action of various factors such as temperature, humidity, carbon dioxide concentration, light intensity, and substrate environment. To achieve high-quality and high-yield edible mushroom cultivation, it is necessary to continuously and accurately monitor and control the cultivation environment. With the development of the Internet of Things and intelligent control technologies, environmental monitoring systems have been initially applied in edible mushroom production, but overall, they still mainly focus on single-point collection and rule-based control, lacking the ability of multi-dimensional information fusion and intelligent response.

[0003] Existing environmental monitoring systems generally have problems such as single perception dimension, weak dynamic recognition ability, and lagging regulation strategies. On the one hand, most systems only collect basic parameters such as temperature and humidity, etc., and cannot comprehensively reflect the multi-level environmental information required for the growth of edible mushrooms. On the other hand, the system lacks the ability of dynamic analysis and modeling of environmental data, and cannot timely judge the specific impact of environmental changes on the growth of mycelium, resulting in the regulation response relying on manual intervention or fixed threshold judgment, and prone to problems such as untimely adjustment and inaccurate regulation, which restricts the intelligent level of edible mushroom production. Summary of the Invention

[0004] The present invention provides a multi-dimensional monitoring system for the edible mushroom cultivation environment, which realizes the full-cycle, multi-dimensional, and intelligent monitoring and control of the edible mushroom growth environment. By introducing two core indicators, namely the mycelium growth inhibition index and the environmental imbalance probability, it realizes the accurate identification and hierarchical response of environmental risks, further improves the real-time performance and coordination of environmental regulation, and thus improves the automation degree and growth stability of edible mushroom cultivation.

[0005] A multi-dimensional monitoring system for the edible mushroom cultivation environment includes an environmental parameter acquisition module, a dynamic data preprocessing module, a multi-dimensional environmental characteristic parameter extraction module, an environmental anomaly assessment module, a regulation decision generation module, and an actuator control module, wherein;

[0006] The environmental parameter acquisition module is configured with a distributed temperature and humidity sensor array, a non-discrete concentration sensor, a spectral adaptive light intensity detection unit, and a substrate metabolite concentration sensor to obtain an original environmental parameter set;

[0007] The dynamic data preprocessing module receives the original environmental parameter set and generates preprocessed environmental data through timestamp alignment and outlier correction;

[0008] The multi-dimensional environmental feature parameter extraction module performs joint time-frequency domain analysis on the preprocessed environmental data, and extracts multi-dimensional environmental feature parameters including temperature fluctuation gradient, humidity change rate, cumulative effect coefficient, and light distribution uniformity;

[0009] The environmental anomaly assessment module inputs the multi-dimensional environmental feature parameters into the growth state prediction model, and outputs assessment results including the hyphal growth inhibition index and the environmental imbalance probability;

[0010] The regulation decision generation module matches the preset regulation rule library according to the assessment results, and generates a regulation instruction sequence including priority sorting;

[0011] The actuator control module converts the regulation instruction sequence into an actuator drive signal, and controls the coordinated actions of the ventilation unit, the humidification device, the LED supplementary light array, and the substrate spraying system.

[0012] Optionally, the environmental parameter acquisition module includes:

[0013] Configure a distributed temperature and humidity sensor array: Deploy multiple temperature and humidity sensors at various positions in the edible mushroom cultivation environment to form a distributed sensor array, and collect temperature and humidity data in the area;

[0014] Configure a non-discrete concentration sensor: Set a non-discrete concentration sensor in the edible mushroom cultivation room to continuously monitor the concentration in the air;

[0015] Configure a spectral adaptive light intensity detection unit: Install a spectral adaptive light intensity detection unit to automatically adjust the adaptability to different light conditions, analyze the wavelength and intensity of the light source, adjust the measurement strategy according to the real-time light environment, and collect light intensity data;

[0016] Configure a substrate metabolite concentration sensor: Embed a substrate metabolite concentration sensor in the edible mushroom culture substrate to continuously monitor the concentration change of metabolites in the substrate;

[0017] Integrate and obtain the original environmental parameter set: Summarize and integrate the environmental parameters collected respectively through wireless or wired networks to generate the original environmental parameter set.

[0018] Optionally, the dynamic data preprocessing module includes:

[0019] Timestamp alignment: Perform timestamp alignment processing on each environmental parameter in the original environmental parameter set;

[0020] Outlier correction: Detect and correct outliers in the received set of original environmental parameters. Use the standard deviation method (Z-score) based on statistics to identify outliers and correct them through interpolation methods;

[0021] Generate preprocessed environmental data: After timestamp alignment and outlier correction, generate preprocessed environmental data.

[0022] Optionally, the multi-dimensional environmental feature parameter extraction module includes:

[0023] Extract temperature fluctuation gradient: The temperature fluctuation gradient is used to measure the rapidity of temperature change in the environment. Analyze temperature data through time series, calculate the temperature change rate at each time point, and obtain its fluctuation characteristics through frequency analysis;

[0024] Extract humidity change rate: The humidity change rate is used to measure the speed of humidity change and reflects the dynamic response of humidity. Calculate the change rate of humidity data and extract periodic characteristics through time-frequency analysis;

[0025] Extract Cumulative effect coefficient: The cumulative effect coefficient reflects The cumulative effect of concentration over time, revealing The impact of concentration change on mycelial growth, by calculating The cumulative and fluctuation effects of concentration to extract Cumulative effect coefficient;

[0026] Extract light distribution uniformity: The light distribution uniformity is used to quantify the uniformity of light in the edible mushroom cultivation environment. Calculate it through the spatial distribution of light intensity and measure the difference in light intensity in different regions;

[0027] Generate multi-dimensional environmental feature parameters: By combining the temperature fluctuation gradient, humidity change rate, Cumulative effect coefficient and light distribution uniformity, form multi-dimensional environmental feature parameters.

[0028] Optionally, the environmental anomaly assessment module includes:

[0029] Growth state prediction: The growth state prediction model evaluates the impact of the current environment on the growth of edible mushrooms through multi-dimensional environmental feature parameters and outputs a mycelial growth inhibition index to measure whether the environment inhibits mycelial growth;

[0030] Environmental imbalance assessment: By analyzing the results of multi-dimensional environmental feature parameters and growth state prediction, calculate the environmental imbalance probability and evaluate whether there is a risk of environmental imbalance in the current environment.

[0031] Optionally, the growth state prediction includes:

[0032] Environmental impact assessment: By using a growth state prediction model, weighted analysis is performed on multi-dimensional environmental characteristic parameters. Combining the sensitivity of each parameter to mycelial growth, the mycelial growth inhibition index of the current environment is calculated. ;

[0033] Judging the degree of inhibition: Compare the mycelial growth inhibition index with a set threshold. When ( Set to 0.65), it is judged that the current environment inhibits mycelial growth. When , it is judged that the current environmental conditions are suitable for normal mycelial growth;

[0034] Outputting the prediction result: Finally, output the mycelial growth inhibition index , as an index to measure the influence degree of the current environment on mycelial growth.

[0035] Optionally, the environmental imbalance assessment includes:

[0036] Receiving evaluation input parameters: Receive multi-dimensional environmental characteristic parameters and the mycelial growth inhibition index as input parameters;

[0037] Constructing a risk assessment factor vector: Combine all input parameters into a joint feature vector ;

[0038] Calculating the probability of environmental imbalance: Based on a logistic regression model, evaluate the joint feature vector and calculate the probability value of the current environment being in an imbalanced state;

[0039] Judging environmental imbalance: Compare the calculated probability of environmental imbalance with a preset threshold (set to 0.7) to judge whether there is a risk of environmental imbalance in the current environment. When , it is considered that there is a risk of environmental imbalance in the current environment. When , it is considered that the environmental state is within the balanced range;

[0040] Outputting the evaluation result: Finally, output the probability of environmental imbalance , which is used to judge whether it is necessary to adjust and intervene in environmental parameters.

[0041] Optionally, the regulation decision-making generation module includes:

[0042] Matching a preset regulation rule library: According to the mycelial growth inhibition index and the probability of environmental imbalance , the received evaluation result, match the coping strategies in the preset regulation rule library. When and , the matching rule is forced intervention, triggering enhanced ventilation, increased humidity or cooling, and homogenized light. When or when, the matching rule is moderate regulation, turn on the medium-speed fan, humidify or dehumidify, and finely adjust the distribution of the LED supplementary light array. When and when, the matching rule is normal maintenance, no regulation is performed, the existing parameters are monitored, and the status data is recorded. In addition, when any environmental characteristic parameter (temperature fluctuation gradient, humidity change rate, cumulative effect coefficient or light distribution uniformity) exceeds its preset safety threshold, even if and are not exceeded, the corresponding matching rules are triggered respectively;

[0043] Generate a regulation instruction sequence: For the matched response rules, extract the corresponding control actions to form a structured regulation instruction sequence;

[0044] Instruction priority sorting: Sort the generated regulation instruction sequence according to the degree of influence on mycelial growth and response speed. The sorting rules include that the parameter with the greatest contribution to the inhibition index is prioritized, the measures for quickly restoring environmental balance are prioritized, and the multi-parameter linkage scenarios are sorted according to the dependency order (such as ventilation first and then dehumidification).

[0045] Optionally, the actuator control module includes:

[0046] Analyze the regulation target and parameters: Analyze the regulation instruction sequence, and extract the control object (such as temperature, humidity, 、light), specific actions (such as turn on, adjust, turn off) and target set values in each instruction;

[0047] Convert to drive control signals: According to the analysis results, call the preset control mapping logic to convert the regulation instructions into low-level control signals, including PWM pulse width modulation, relay trigger, and current control;

[0048] Coordinated control of actuators: According to the instruction priority and response dependency relationship, control the coordinated actions of the ventilation unit, humidification device, LED supplementary light array, and substrate spraying system.

[0049] Advantages of the present invention:

[0050] In the present invention, through the comprehensive perception of key environmental parameters such as temperature, humidity, concentration, light intensity, and substrate metabolites, the distributed sensing and spectral adaptive technology is adopted to improve the monitoring accuracy and coverage breadth, lay a data foundation for the refined management of the edible mushroom growth environment, and effectively solve the problems of single environmental perception dimension and slow response of traditional systems.

[0051] In the present invention, by introducing a dynamic data preprocessing and time-frequency domain joint analysis method, multi-dimensional environmental characteristic parameters are extracted, and an intelligent discrimination mechanism for environmental anomalies is constructed by using a growth state prediction model and a logistic regression evaluation algorithm, which can output the mycelium growth inhibition index and the environmental imbalance probability in real time, improving the dynamic recognition ability and problem warning ability of the environmental state, and ensuring that the system can make rapid and accurate regulation judgments according to the actual growth risks.

[0052] In the present invention, by matching the rule base to generate sorted regulation instructions, and the actuator control module converts them into drive signals, realizing the intelligent linkage control of equipment such as ventilation, humidification, light supplementation, and spraying, significantly improving the response efficiency of environmental regulation and the resource utilization rate, ensuring the stable growth of edible fungi under variable environmental conditions, and improving the level of production intelligence and automation. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a schematic diagram of the system function module of the embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of the regulation decision generation module of the embodiment of the present invention. Detailed Embodiments

[0056] The following will describe the present invention in detail with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; and the drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0057] It should be pointed out that in the specification, it is mentioned that "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. indicate that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. In addition, when combining embodiments to describe specific features, structures or characteristics, implementing such features, structures or characteristics in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0058] Generally, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or can be used to describe a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but rather can alternatively, at least in part depending on the context, allow for the existence of other factors that are not necessarily explicitly described.

[0059] As Figure 1 - Figure 2 shown, a multi-dimensional monitoring system for edible mushroom cultivation environment includes an environmental parameter acquisition module, a dynamic data preprocessing module, a multi-dimensional environmental characteristic parameter extraction module, an environmental anomaly evaluation module, a regulation decision generation module, and an actuator control module, wherein;

[0060] The environmental parameter acquisition module is configured with a distributed temperature and humidity sensor array, a non-discrete concentration sensor, a spectral adaptive light intensity detection unit, and a substrate metabolite concentration sensor to obtain an original set of environmental parameters;

[0061] The dynamic data preprocessing module receives the original set of environmental parameters and generates preprocessed environmental data through timestamp alignment and outlier correction;

[0062] The multi-dimensional environmental characteristic parameter extraction module performs joint time-frequency domain analysis on the preprocessed environmental data to extract multi-dimensional environmental characteristic parameters including temperature fluctuation gradient, humidity change rate, cumulative effect coefficient, and light distribution uniformity;

[0063] The environmental anomaly evaluation module inputs the multi-dimensional environmental characteristic parameters into a growth state prediction model and outputs evaluation results including a mycelium growth inhibition index and an environmental imbalance probability;

[0064] The regulation decision generation module matches the evaluation results with a preset regulation rule library to generate a sequence of regulation instructions including a priority ranking;

[0065] The actuator control module converts the sequence of regulation instructions into actuator drive signals to control the coordinated actions of a ventilation unit, a humidifying device, an LED supplementary lighting array, and a substrate spraying system.

[0066] The environmental parameter acquisition module includes:

[0067] Configure a distributed temperature and humidity sensor array: Deploy multiple temperature and humidity sensors at various positions in the edible mushroom cultivation environment to form a distributed sensor array and collect temperature and humidity data in the area;

[0068] Configure a non-discrete concentration sensor: Set a non-discrete in the edible mushroom cultivation room A concentration sensor that monitors the concentration in the air in real time and continuously of the concentration;

[0069] Configure a spectral adaptive light intensity detection unit: Install a spectral adaptive light intensity detection unit to automatically adjust the adaptability to different lighting conditions. By analyzing the wavelength and intensity of the light source, adjust the measurement strategy according to the real-time lighting environment and collect light intensity data;

[0070] Configure a substrate metabolite concentration sensor: Embed a substrate metabolite concentration sensor (ISFET micro pH sensor) in the edible mushroom culture substrate to monitor the concentration change of metabolites in the substrate in real time;

[0071] Integrate and obtain the original environmental parameter set: Summarize and integrate the environmental parameters collected respectively through a wireless or wired network to generate the original environmental parameter set.

[0072] The dynamic data preprocessing module includes:

[0073] Timestamp alignment: Perform timestamp alignment processing on each environmental parameter in the original environmental parameter set. Let the timestamp of each environmental parameter be , and after alignment, the timestamp of the environmental parameter is unified as , expressed as:

[0074] ;

[0075] Among them, is the aligned timestamp, is the timestamp in the original data, is the reference timestamp;

[0076] Outlier correction: Perform outlier detection and correction on the received original environmental parameter set. Use the statistical standard deviation method (Z-score) to identify outliers and correct them through interpolation method, expressed as:

[0077] ;

[0078] Among them, is the data point, is the mean of the data set, is the standard deviation of the data set, is the standardized score. If , then is regarded as an outlier. Among them, is the standardized score threshold;

[0079] ;

[0080] Among them, is the corrected data value, are the data points adjacent to the outlier before and after;

[0081] Generate preprocessed environmental data: After timestamp alignment and outlier correction, generate preprocessed environmental data.

[0082] The multi-dimensional environmental feature parameter extraction module includes:

[0083] Extract the temperature fluctuation gradient: The temperature fluctuation gradient is used to measure the rapidity of temperature change in the environment. By analyzing temperature data through time series, calculate the temperature change rate at each time point, and then obtain its fluctuation characteristics through frequency analysis, expressed as:

[0084] ;

[0085] where, is the temperature change rate at time time, is the temperature at time time, is the temperature at time time, is the time interval;

[0086] Extract the humidity change rate: The humidity change rate is used to measure the speed of humidity change and reflect the dynamic response of humidity. Calculate the change rate of humidity data and extract periodic characteristics through time-frequency analysis, expressed as:

[0087] ;

[0088] where, is the humidity change rate at time time, is the humidity at time time, is the time interval;

[0089] Extract cumulative effect coefficient: The cumulative effect coefficient reflects the cumulative effect of concentration over time, revealing the impact of concentration change on mycelial growth. Extract the cumulative effect coefficient by calculating the cumulative and fluctuation effects of concentration, expressed as: :

[0090] ;

[0091] where, is the cumulative value at time time, is the time at the moment of concentration, is the time interval;

[0092] ;

[0093] wherein, is the frequency domain value at the frequency , is the original data point in the time series data , is the total number of data points, is the frequency corresponding frequency value, ;

[0094] ;

[0095] wherein, is the cumulative effect coefficient of , is the energy of the frequency component , is the low frequency range;

[0096] Extracting the light distribution uniformity: The light distribution uniformity is used to quantify the uniformity of light in the edible mushroom cultivation environment, calculated through the spatial distribution of light intensity, measuring the difference in light intensity in different regions, expressed as:

[0097] ;

[0098] wherein, is the light distribution uniformity, is the standard deviation of the light intensity distribution, is the mean value of the light intensity;

[0099] Generating multi-dimensional environmental characteristic parameters: By combining the temperature fluctuation gradient, humidity change rate, cumulative effect coefficient and light distribution uniformity, multi-dimensional environmental characteristic parameters are formed, expressed as:

[0100] ;

[0101] wherein, is the multi-dimensional environmental characteristic parameter.

[0102] The environmental anomaly assessment module includes:

[0103] Growth state prediction: The growth state prediction model evaluates the influence of the current environment on the growth of edible mushrooms through multi-dimensional environmental characteristic parameters, and outputs the mycelium growth inhibition index, which is used to measure whether the environment inhibits the growth of mycelium;

[0104] Environmental imbalance assessment: By analyzing the multi-dimensional environmental characteristic parameters and the results of growth state prediction, calculate the environmental imbalance probability to evaluate whether there is a risk of imbalance in the current environment.

[0105] Growth state prediction includes:

[0106] Environmental impact assessment: Through the growth state prediction model, perform weighted analysis on the multi-dimensional environmental characteristic parameters, and combine the sensitivity of each parameter to the mycelial growth to calculate the mycelial growth inhibition index of the current environment , expressed as:

[0107] ;

[0108] Among them, is the mycelial growth inhibition index, is the th environmental characteristic parameter (such as the temperature fluctuation gradient), is the th weight coefficient corresponding to the characteristic parameter, is the temperature fluctuation gradient weight, set to 0.35, is the humidity change rate weight, set to 0.25, is the cumulative effect coefficient weight, set to 0.2, is the light distribution uniformity weight, set to 0.2, is the total number of characteristic parameters;

[0109] Judgment of inhibition degree: Compare the mycelial growth inhibition index with the set threshold. When ( set to 0.65), it is judged that the current environment inhibits the mycelial growth. When , it is judged that the current environmental conditions are suitable for the normal growth of the mycelium;

[0110] Output prediction result: Finally, output the mycelial growth inhibition index , as an index to measure the degree of influence of the current environment on the mycelial growth.

[0111] Environmental imbalance assessment includes:

[0112] Receive evaluation input parameters: Receive multi-dimensional environmental characteristic parameters and the mycelial growth inhibition index as input parameters;

[0113] Construct a risk assessment factor vector: Combine all input parameters into a joint feature vector ;

[0114] Calculating the probability of environmental imbalance: Evaluate the joint feature vector based on the logistic regression model, and calculate the probability value that the current environment is in an imbalanced state, expressed as:

[0115] ;

[0116] where, is the model bias term, is the weight coefficient of the th evaluation factor, is the weight of the temperature fluctuation gradient, set to 1.2, is the weight of the humidity change rate, set to 1, is the weight of the cumulative effect coefficient, set to 0.8, is the weight of the light distribution uniformity, set to 0.6, is the weight of the mycelial growth inhibition index, set to 1.5, is the th joint feature vector, is the probability of environmental imbalance;

[0117] Judging environmental imbalance: Compare the calculated probability of environmental imbalance with the preset threshold (set to 0.7) to determine whether there is a risk of environmental imbalance in the current environment. When , it is considered that there is a risk of environmental imbalance in the current environment. When , it is considered that the environmental state is within the balanced range;

[0118] Outputting the evaluation result: Finally, output the probability of environmental imbalance for determining whether it is necessary to adjust and intervene in the environmental parameters.

[0119] The regulation decision generation module includes:

[0120] Matching the preset regulation rule library: According to the mycelial growth inhibition index and the probability of environmental imbalance , the received evaluation result, match the coping strategies in the preset regulation rule library. When and , the matching rule is forced intervention, triggering enhanced ventilation, increased humidity or cooling, and homogenizing light. When or , the matching rule is moderate regulation, turning on the medium-speed fan, humidifying or dehumidifying, and finely adjusting the distribution of the LED supplementary light array. When and , the matching rule is normal maintenance, without performing regulation, maintaining the existing parameter monitoring, and recording the status data. In addition, when any environmental characteristic parameter (temperature fluctuation gradient, humidity change rate, When the cumulative effect coefficient or light distribution uniformity) exceeds its preset safety threshold, even if and is not exceeded, the corresponding regulation rules are triggered respectively, specifically including:

[0121] Temperature fluctuation regulation rule: When occurs, start the fan group to enhance air circulation. If the temperature rises too fast, turn on the cooling module or sunshade system. If the temperature drops too fast, start the heat supplement device;

[0122] Humidity fluctuation regulation rule: When occurs, start the atomizing humidifier to supplement moisture. If the humidity is too high, start the ventilation and dehumidification mode, fine-tune the humidification intensity or fan speed to maintain humidity balance;

[0123] Cumulative effect regulation rule: When occurs, increase the ventilation frequency, open the exhaust valve / ventilation window for forced ventilation. If ventilation is ineffective, start the fan group to run at maximum power for 1 cycle;

[0124] Light uniformity regulation rule: When occurs, adjust the layout angle and intensity distribution of the LED supplementary light array, increase the supplementary light in the edge area and reduce the intensity in the central area. If it is a fixed lamp holder, adjust the control circuit for power reallocation;

[0125] Generate a regulation instruction sequence: For the matching response rules, extract the corresponding control actions to form a structured regulation instruction sequence;

[0126] Instruction priority sorting: Sort the generated regulation instruction sequence according to the degree of influence on mycelium growth and response speed. The sorting rules include that the parameter with the greatest contribution to the inhibition index is prioritized, the measures to quickly restore environmental balance are prioritized, and the multi-parameter linkage scenarios are sorted according to the dependence order (such as ventilation first and then dehumidification).

[0127] The actuator control module includes:

[0128] Analyze the regulation target and parameters: Analyze the regulation instruction sequence, extract the control object (such as temperature, humidity, , light), specific action (such as turn on, adjust, turn off) and target set value in each instruction;

[0129] Convert to drive control signal: According to the analysis result, call the preset control mapping logic to convert the regulation instruction into a low-level control signal, including PWM pulse width modulation, relay trigger, current control;

[0130] Actuator collaborative control: According to the instruction priority and response dependency, control the collaborative actions of the ventilation unit, humidification device, LED supplementary lighting array, and substrate spraying system.

[0131] This invention covers any alternatives, modifications, equivalent methods, and solutions made within the essence and scope of this invention. For the public to have a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention even without the description of these details. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0132] The above description is only a preferred embodiment of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this invention.

Claims

1. An edible mushroom cultivation environment multi-dimensional monitoring system, characterized in that, It includes an environmental parameter acquisition module, a dynamic data preprocessing module, a multi-dimensional environmental characteristic parameter extraction module, an environmental anomaly assessment module, a regulation decision generation module, and an actuator control module, where; The environmental parameter acquisition module is configured with a distributed temperature and humidity sensor array, a non-discrete concentration sensor, a spectral adaptive light intensity detection unit, and a substrate metabolite concentration sensor to obtain an original set of environmental parameters; The dynamic data preprocessing module receives the original environmental parameter set and generates preprocessed environmental data through timestamp alignment and outlier correction; The multi-dimensional environmental feature parameter extraction module performs joint time-frequency domain analysis on the preprocessed environmental data, and extracts multi-dimensional environmental feature parameters including temperature fluctuation gradient, humidity change rate, cumulative effect coefficient, and illumination distribution uniformity; The environmental anomaly assessment module inputs the multi-dimensional environmental characteristic parameters into the growth state prediction model and outputs an assessment result including the mycelial growth inhibition index and the environmental imbalance probability; The regulation decision generation module matches the preset regulation rule library according to the assessment result and generates a regulation instruction sequence including priority sorting; The actuator control module converts the regulation instruction sequence into an actuator drive signal to control the coordinated actions of the ventilation unit, the humidification device, the LED supplementary light array, and the substrate spraying system.

2. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 1, characterized in that, The environmental parameter acquisition module includes: Configure a distributed temperature and humidity sensor array: Deploy multiple temperature and humidity sensors at various positions in the edible mushroom cultivation environment to form a distributed sensor array and collect temperature and humidity data in the area; Configure non-discrete Concentration sensor: Set up a non-discrete concentration sensor in the edible mushroom cultivation room to continuously monitor the concentration in the air in real time; Configure a spectral adaptive light intensity detection unit: Install a spectral adaptive light intensity detection unit to automatically adjust the adaptability to different light conditions. By analyzing the wavelength and intensity of the light source, adjust the measurement strategy according to the real-time light environment and collect light intensity data; Configure a substrate metabolite concentration sensor: Embed a substrate metabolite concentration sensor in the edible mushroom culture substrate to monitor the concentration change of metabolites in the substrate in real time; Integrate and obtain the original environmental parameter set: Summarize and integrate the environmental parameters collected respectively through wireless or wired networks to generate the original environmental parameter set.

3. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 1, wherein, The dynamic data preprocessing module includes: Timestamp alignment: Perform timestamp alignment processing on each environmental parameter in the original environmental parameter set; Outlier correction: Perform outlier detection and correction on the received original environmental parameter set. Use the standard deviation method based on statistics to identify outliers and perform correction through the interpolation method; Generate preprocessed environmental data: After timestamp alignment and outlier correction, generate preprocessed environmental data.

4. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 1, wherein The multi-dimensional environmental characteristic parameter extraction module includes: Extract the temperature fluctuation gradient: The temperature fluctuation gradient is used to measure the rapidity of temperature change in the environment. Analyze the temperature data through time series, calculate the temperature change rate at each time point, and then obtain its fluctuation characteristics through frequency analysis; Extract the humidity change rate: The humidity change rate is used to measure the speed of humidity change and reflect the dynamic response of humidity. Calculate the change rate of humidity data and extract periodic characteristics through time-frequency analysis; Extraction Cumulative effect coefficient: The cumulative effect coefficient reflects The cumulative effect of concentration over time, revealing The effect of concentration changes on mycelial growth, by calculating The cumulative and fluctuating effects of concentration are extracted Cumulative effect coefficient; Extract the light distribution uniformity: The light distribution uniformity is used to quantify the uniformity of light in the edible mushroom cultivation environment. Calculate it through the spatial distribution of light intensity and measure the light intensity difference in different regions; Multi-dimensional environmental characteristic parameter generation: By combining the temperature fluctuation gradient, humidity change rate, cumulative effect coefficient, and light distribution uniformity, a multi-dimensional environmental characteristic parameter is formed.

5. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 4, characterized in that, The environmental anomaly assessment module includes: Growth state prediction: The growth state prediction model evaluates the impact of the current environment on the growth of edible mushrooms through multi-dimensional environmental characteristic parameters and outputs the mycelial growth inhibition index, which is used to measure whether the environment inhibits the growth of mycelia; Environmental imbalance assessment: By analyzing the results of multi-dimensional environmental characteristic parameters and growth state prediction, calculate the environmental imbalance probability to evaluate whether there is a risk of imbalance in the current environment.

6. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 5, wherein The growth state prediction includes: Environmental impact assessment: Perform weighted analysis on multi-dimensional environmental characteristic parameters through a growth state prediction model, and combine the sensitivity of each parameter to the mycelial growth to calculate the mycelial growth inhibition index of the current environment ; Determine the degree of inhibition: Compare the mycelial growth inhibition index with the set threshold. When occurs, it is determined that the current environment inhibits the growth of mycelia. When occurs, it is determined that the current environmental conditions are suitable for the normal growth of mycelia; Output prediction result: The final output is the hyphal growth inhibition index , which is used as an indicator to measure the degree of influence of the current environment on hyphal growth.

7. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 6, wherein The environmental imbalance assessment includes: Receiving assessment input parameters: Receive multi-dimensional environmental characteristic parameters and the mycelium growth inhibition index as input parameters; Construct a risk assessment factor vector: Combine all input parameters into a joint feature vector ; Calculating the environmental imbalance probability: Based on the logistic regression model, evaluate the joint feature vector and calculate the probability value that the current environment is in an imbalanced state; Environmental imbalance determination: The calculated environmental imbalance probability is compared with a preset threshold to determine whether there is a risk of imbalance in the current environment. When , it is considered that there is a risk of imbalance in the current environment. When , it is considered that the environmental state is within the balanced range; Output the evaluation result: the final output probability of environmental imbalance , which is used to determine whether it is necessary to adjust and intervene in environmental parameters.

8. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 7, characterized in that, The regulation decision-making generation module includes: Match the preset regulation rule library: According to the mycelial growth inhibition index and the probability of environmental imbalance , the received evaluation result, match the coping strategies in the preset regulation rule library. When and , the matching rule is forced intervention, triggering enhanced ventilation, increased humidity or cooling, and homogenized lighting. When or , the matching rule is moderate regulation, turning on the medium-speed fan, humidifying or dehumidifying, and fine-tuning the distribution of the LED supplementary light array. When and , the matching rule is normal maintenance, without performing regulation, maintaining the existing parameter monitoring, and recording the status data. In addition, when any environmental characteristic parameter exceeds its preset safety threshold, even if and are not exceeded, the corresponding matching rules are triggered respectively; Generating a regulation instruction sequence: For the matched response rules, extract the corresponding control actions to form a structured regulation instruction sequence; Instruction priority sorting: Sort the generated regulation instruction sequence according to the degree of influence on mycelium growth and response speed. The sorting rules include that the parameter with the greatest contribution to the inhibition index is prioritized, the measures for quickly restoring environmental balance are prioritized, and the multi-parameter linkage scenarios are sorted according to the dependency order.

9. The multi-dimensional monitoring system for the edible mushroom cultivation environment according to claim 1, characterized in that The actuator control module includes: Analyzing regulation targets and parameters: Analyze the regulation instruction sequence, and extract the control object, specific action, and target set value in each instruction; Converting to drive control signals: According to the analysis results, call the preset control mapping logic to convert the regulation instructions into low-level control signals, including PWM pulse width modulation, relay triggering, and current control; Actuator collaborative control: According to the instruction priority and response dependency relationship, control the collaborative actions of the ventilation unit, humidification device, LED supplementary lighting array, and substrate spraying system.

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