An AI optimization-based intelligent monitoring system for a fermentation environment of a tremella fuciformis

Abstract

The application discloses a kind of based on AI optimization's Fuzhuan tea fermentation environment intelligent monitoring system, it is related to rice processing technical field, including real-time monitoring fermentation environment, according to the time interval of preset, from each for obtaining environmental monitoring data in sensor;The application is by monitoring fermentation environment, based on AI AI analysis module, constructs environmental fluctuation prediction model and fermentation state evaluation model, realizes the prediction of fermentation environment change trend and the accurate evaluation of fermentation state, and according to environmental data prediction value, stage change rate and real-time environmental monitoring data are combined, judge the stage fermentation state where Fuzhuan tea fermentation is located, carry out fermentation state overall analysis, generate the fermentation stage state instruction of environment, according to state instruction automatic dynamic adjustment equipment's operating basic parameter, realize the intelligentization and automation of fermentation environment control, can real-time, accurately monitor and control Fuzhuan tea fermentation environment, ensure that fermentation is carried out under optimum environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an AI-optimized intelligent monitoring system for the fermentation environment of Fuzhuan tea. BACKGROUND

[0002] In recent years, artificial intelligence (AI) technology has developed rapidly and has been widely applied in various fields. In the field of environmental monitoring, AI technology has shown great advantages due to its powerful data processing capabilities, pattern recognition capabilities, and intelligent decision-making capabilities. AI has the ability to learn and optimize itself. When AI is integrated into the intelligent monitoring of the fermentation environment of Fuzhuan tea, Fuzhuan tea, as a specialty variety of dark tea, has unique processing techniques and quality characteristics. Its fermentation process is a complex and critical process that involves the growth and metabolism of various microorganisms, which plays a decisive role in the formation of tea quality. The fermentation process of Fuzhuan tea is highly sensitive to environmental factors such as temperature, humidity, and oxygen content, which can significantly affect the growth and reproduction of microorganisms and the activity of enzymes, thereby affecting the progress and final quality of Fuzhuan tea fermentation.

[0003] In the traditional fermentation process of Fuzhuan tea, the monitoring of environmental parameters mainly relies on traditional sensors. Sensors can provide basic environmental information, but there are many shortcomings. The monitoring data is single, relying only on limited parameters such as temperature, humidity, and oxygen concentration, which cannot fully reflect the complex process of Fuzhuan tea fermentation. The traditional monitoring method cannot respond to environmental changes in a timely and effective manner. Artificial judgment and control based on limited monitoring data are not only inefficient, but also difficult to achieve precise environmental control due to subjective factors and judgment errors.

[0004] In view of the above technical defects, a solution is proposed. SUMMARY

[0005] The purpose of the present application is to use artificial intelligence algorithms to analyze and process multi-source data, construct an environmental fluctuation prediction model and a fermentation state evaluation model, and predict environmental parameter fluctuations in advance to accurately determine the fermentation stage and state.

[0006] To achieve the above purpose, the present application adopts the following technical scheme: an AI-optimized intelligent monitoring system for the fermentation environment of Fuzhuan tea, comprising a data monitoring module, an AI analysis module, a fluctuation analysis module, and an intelligent control module.

[0007] The data monitoring module is used to monitor the fermentation environment in real time. According to the preset time interval, environmental monitoring data is obtained from each sensor. The environmental monitoring data is divided into external and internal data sets.

[0008] The AI analysis module comprises a data analysis unit and a fusion analysis unit, the data analysis unit is used to acquire environmental monitoring data in the fermentation environment monitoring process, divide the data into four categories according to the quarterly boundary, analyze the influence coefficient between the fermentation environment and the external environment of each category, generate the influence proportion value of the fermentation environment, and obtain different influence proportion sets;

[0009] The fusion analysis unit acquires the environmental monitoring data, intercepts real-time image data when the abnormality occurs according to the time point when the fermentation of the Fuzhuan tea produces the abnormality, divides three stages into a normal stage state, a pre-abnormal state and an abnormal state according to the abnormal time point, analyzes the fermentation process time change rate of each stage, and obtains stage change rate data.

[0010] The fluctuation analysis module is used to acquire environmental monitoring data, construct an environmental fluctuation model based on the environmental monitoring data, output environmental data prediction values of a future time period, and combine the environmental data prediction values, the stage change rate and real-time environmental monitoring data to judge the fermentation state of the Fuzhuan tea fermentation stage, perform overall analysis on the fermentation state, and generate a fermentation stage state instruction of the environment.

[0011] The intelligent control module is used to control the equipment in the fermentation environment, acquire the fermentation stage state instruction, and control the equipment to perform operation parameter regulation of the fermentation environment in real time according to the fermentation stage state instruction of the environment.

[0012] Further, the data monitoring module constructs a sensor implementation map according to the fermentation environment layout when monitoring the fermentation environment, and specifically comprises the following steps:

[0013] Step one: acquire the overall shape, size, door and window position and equipment distribution point of the fermentation workshop, combine the construction map of the workshop, and generate an initial implementation map;

[0014] Step two: according to the sensors selected according to the Fuzhuan tea fermentation process requirements, perform high-density coverage in the monitoring area, set the specific distance between each corresponding sensor, set different distances according to different sensor types, set different installation distances according to the space layout of the workshop and the properties of the sensors;

[0015] Step three: mark the installation position of each sensor in the initial implementation map, use different types of mark symbols for different sensors, take the center point of the workshop as the origin, generate a sensor link map, set it as a dynamic monitoring node, and obtain the constructed sensor implementation map.

[0016] Further, the AI analysis module further comprises a data processing step:

[0017] According to the different types of sensors, databases of different environments are established, and each database is automatically coded and sorted. When data transmission is performed, AI is used to identify data characteristics and automatically classify them.

[0018] The database judges and identifies the transmitted data, automatically identifies the data, removes noise, error data and abnormal value processing steps, and automatically adjusts the cleaning method step according to the transmitted data.

[0019] According to the preset time interval, data from each sensor is obtained and stored. When the sensor is connected to the network, the same gateway communication state is set.

[0020] Further, the different influence ratio sets obtained include the following:

[0021] S100, obtain the environmental monitoring data in the fermentation environment monitoring process, divide the data into four categories according to the quarterly boundary, generate the external environment and each environment data set of each type through the environmental monitoring data of each category, and perform classification labeling;

[0022] S101, obtain the fermentation environment and external environment data set of each category, analyze the influence coefficient between the fermentation environment and external environment data set in each category, generate the influence ratio value of the fermentation environment, and calculate the influence ratio value as follows:

[0023] Internal environment influence coefficient: ,

[0024] External environment influence coefficient: ,

[0025] Internal environment influence ratio: External environment influence ratio: ,

[0026] In the formula, n is the number of internal environment factors, M is the number of external environment factors, is the set influence weight, is the data of the Jth internal environment factor in the internal environment data set, is the set influence weight, is the data of the Kth external environment factor in the external environment data set;

[0027] S102, substitute the environmental monitoring data of each category into the above formula to obtain different influence ratio values under four categories, arrange to obtain different influence ratio sets, and set the data mean value of each category as the early warning threshold.

[0028] Further, the stage change rate data is obtained, which includes the following:

[0029] S200, obtain image information of the Fuzhuan tea during fermentation by the monitoring device, according to the time point when the Fuzhuan tea fermentation produces an anomaly, intercept the real-time image data before and after the anomaly occurs, and divide three stages according to the anomaly time point as a normal stage state, a pre-anomaly state, and an anomaly state, the normal stage state is the stage when no anomaly occurs, the pre-anomaly state is the near-time state before the anomaly occurs, and the anomaly state is the state of the anomaly;

[0030] S201, obtain the time of each stage of the normal stage state, the pre-anomaly state, and the anomaly state, analyze the change time between each stage, perform image fermentation process time change rate analysis according to the change time, obtain stage change rate data, and the calculation process is as follows:

[0031] Normal stage state: the image taken in the normal stage, denoted as , and the shooting time is denoted as ;

[0032] Pre-anomaly state: image before the anomaly occurs, denoted as , and the shooting time is denoted as ;

[0033] Anomaly state: image when the anomaly occurs, denoted as , and the shooting time is denoted as ;

[0034] Similar target function M:

[0035] M= ,

[0036] The change rate from the normal stage state to the pre-anomaly state is R1: R1= ,

[0037] The change rate from the pre-anomaly state to the anomaly state is R2: ;

[0038] S202, according to the change rates R1 and R2, perform a preset stage change rate R.

[0039] Further, an environmental fluctuation model is constructed based on the environmental monitoring data, specifically including the following:

[0040] S300, obtain the environmental monitoring data, divide the environmental monitoring data into a training set, a test set, and a validation set, obtain related feature data after preprocessing the environmental monitoring data, take the feature data of the environment as sample data as the input of the input layer, analyze the feature data in different time periods as the processing layer, and take the predicted value of the environmental data in the future time period as the output;

[0041] S301, the processing layer analyzes the input features when performing the analysis:

[0042] ,

[0043] In the formula, is the data value of the current time N, is the average change rate of the data in a period of time, is the data value of the predicted future time, is the fluctuation factor, and T is the time from the current time to the predicted future time.

[0044] Further, the fermentation state of the Fuzhuan tea is judged, which specifically includes the following:

[0045] S400, the real-time environmental data prediction value is obtained, the data value of the environmental data prediction value exceeding the standard environmental data prediction value is identified, the real-time image data is identified according to the preset stage change rate, the change rate of the real-time image data is obtained, and the real-time environmental monitoring data is combined to calculate the real-time fermentation evaluation state of the Fuzhuan tea, and the calculation process is as follows:

[0046] S= ,

[0047] In the formula, S is the evaluation value, all are weight coefficients, E is the actual prediction value, D is the real-time environmental data value, M is the similarity value of the previous image and the standard image, m is the similarity value of the current image and the standard image, S>F, then it is three categories, F>S>f, then it is two categories, and S<f, then it is three categories;

[0048] S401, the evaluation value is obtained to judge the fermentation stage state of the Fuzhuan tea, when the evaluation value is one category, the Fuzhuan tea is in the normal fermentation stage, when the evaluation value is two categories, the Fuzhuan tea is in the abnormal fermentation middle stage, and when the evaluation value is three categories, the Fuzhuan tea is in the abnormal fermentation state stage, different fermentation stage state instructions are generated according to different stages.

[0049] Further, it further includes a device control unit for dynamically adjusting the internal environment of the fermentation site, combining the fermentation stage with the early warning threshold, dynamically adjusting the environmental basic parameters of the fermentation site, when the fermentation state evaluation judges that the fermentation is in a certain specific stage and the environmental parameters need to be adjusted, the device can work cooperatively, simultaneously adjust the running state, and judge the environmental basic parameters according to the early warning threshold.

[0050] As described above, due to the adoption of the above technical scheme, the present application has the following beneficial effects:

[0051] This AI-optimized intelligent monitoring system for the fermentation environment of Fu brick tea monitors the fermentation environment and, based on the AI ​​analysis module, constructs an environmental fluctuation prediction model and a fermentation state assessment model. This enables the prediction of changes in the fermentation environment and accurate assessment of the fermentation state. By combining predicted environmental data, stage change rates, and real-time environmental monitoring data, the system determines the current stage of the Fu brick tea fermentation, performs an overall analysis of the fermentation state, generates fermentation stage status commands, and automatically and dynamically adjusts the basic operating parameters of the equipment according to these commands. This achieves intelligent and automated control of the fermentation environment, enabling real-time and precise monitoring and control of the Fu brick tea fermentation environment to ensure fermentation takes place in the optimal environment, thereby improving the stability of Fu brick tea quality. Attached Figure Description

[0052] Figure 1 A schematic diagram of the overall system structure of the present invention is shown. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0054] Example 1:

[0055] like Figure 1 As shown, the AI-optimized intelligent monitoring system for the fermentation environment of Fu brick tea includes a data monitoring module, an AI analysis module, a fluctuation analysis module, and an intelligent control module.

[0056] The data monitoring module is used to monitor the fermentation environment in real time. It acquires environmental monitoring data from various sensors at preset time intervals and divides the data into external and internal datasets.

[0057] The AI ​​analysis module includes a data analysis unit and a fusion analysis unit. The data analysis unit is used to acquire environmental monitoring data during the fermentation environment monitoring process, divide the data into four categories according to the quarterly boundary, and analyze the influence coefficient between the fermentation environment and the external environment for each category to generate the influence ratio value of the fermentation environment, so as to obtain different influence ratio sets.

[0058] The fusion analysis unit acquires environmental monitoring data, extracts real-time image data at the time when the fermentation of Fu tea becomes abnormal, and divides the abnormal time point into three stages: normal stage, pre-abnormal stage, and abnormal stage. It then performs fermentation process time change rate analysis for each stage to obtain stage change rate data.

[0059] The fluctuation analysis module is used to obtain environmental monitoring data, construct an environmental fluctuation model based on the environmental monitoring data, output an environmental data prediction value of a future time period, and determine the fermentation stage state of the Fuzhuan tea fermentation based on the environmental data prediction value, the stage change rate, and real-time environmental monitoring data, perform overall analysis of the fermentation state, and generate a fermentation stage state instruction of the environment;

[0060] The intelligent control module is used to control the equipment in the fermentation environment, obtain the fermentation stage state instruction, and control the equipment in real time based on the fermentation stage state instruction of the environment to regulate the operating parameters of the fermentation environment.

[0061] The data monitoring module constructs a sensor implementation map based on the fermentation environment layout when monitoring the fermentation environment, and specifically includes the following steps:

[0062] Step one: Obtain the overall shape, size, door and window position, and equipment distribution point of the fermentation workshop, combine the construction map of the workshop, and generate an initial implementation map;

[0063] Step two: Cover the monitoring area with high density according to the selected sensors based on the Fuzhuan tea fermentation process requirements, set the specific distance between each corresponding sensor, set different distances according to different sensor types, and set different installation distances according to the space layout of the workshop and the properties of the sensors;

[0064] Step three: Label the installation position of each sensor in the initial implementation map, use different types of marker symbols for different sensors, take the center point of the workshop as the origin, generate a sensor link map, set it as a dynamic monitoring node, and obtain the constructed sensor implementation map.

[0065] The AI analysis module further includes a data processing step:

[0066] According to the different types of sensors, a database for collecting different environments is established, and each database is automatically coded and sorted. When data is transmitted, the data is automatically classified based on AI recognition of data characteristics;

[0067] The database judges and identifies the transmitted data, automatically identifies the data, removes noise, error data, and abnormal value processing steps, and automatically adjusts the cleaning method step according to the transmitted data.

[0068] According to the preset time interval, data is obtained from each sensor for storage. When the sensor is connected to the network, the same gateway communication state is set.

[0069] To obtain different influence ratio sets, specifically including the following:

[0070] S100, obtain the environmental monitoring data in the fermentation environment monitoring process, divide the data into four categories according to the quarterly boundary, generate the external environment and each environment data set of each type through the environmental monitoring data of each category, and perform classification labeling;

[0071] S101, obtain the fermentation environment and external environment data set of each category, and analyze the influence coefficient between the fermentation environment and the external environment data set in each category, generate the influence proportion value of the fermentation environment, and calculate the influence proportion value as follows:

[0072] Internal environment influence coefficient: ,

[0073] External environment influence coefficient: ,

[0074] Internal environment influence proportion: External environment influence proportion: ,

[0075] In the formula, n is the number of internal environment factors, M is the number of external environment factors, is the set influence weight, is the data of the Jth internal environment factor in the internal environment data set, is the set influence weight, is the data of the Kth external environment factor in the external environment data set;

[0076] S102, substitute the environmental monitoring data of each category into the above formula to obtain different influence proportion values under four categories, arrange to obtain different influence proportion sets, and set the data mean value of each category as the early warning threshold.

[0077] Obtain the stage change rate data, which specifically includes the following:

[0078] S200, obtain image information of Fuzhuan tea during fermentation through a monitoring device, intercept real-time image data before and after the occurrence of an anomaly according to the time point when the Fuzhuan tea fermentation produces an anomaly, and divide three stages as normal stage state, pre-anomaly state, and anomaly state according to the anomaly time point, the normal stage state is the stage when no anomaly occurs, the pre-anomaly state is the near-time state before the anomaly occurs, and the anomaly state is the state of the anomaly;

[0079] S201, obtain the time of each stage of the normal stage state, the pre-anomaly state, and the anomaly state, analyze the change time between each stage, perform image fermentation process time change rate analysis according to the change time, obtain stage change rate data, and the calculation process is as follows:

[0080] Normal stage state: the image taken in the normal stage, denoted as , the shooting time is recorded as ;

[0081] Pre-Abnormal State: the image before the abnormality occurs, recorded as , the shooting time is recorded as ;

[0082] Abnormal State: the image when the abnormality occurs, recorded as , the shooting time is recorded as ;

[0083] Similar Target Function M:

[0084] M= ,

[0085] Normal Stage State to Pre-Abnormal State Change Rate R1: R1= ,

[0086] Pre-Abnormal State to Abnormal State Change Rate R2: ;

[0087] S202, according to the change rates R1 and R2, a preset stage change rate R is performed.

[0088] An environmental fluctuation model is constructed based on environmental monitoring data, specifically including the following:

[0089] S300, environmental monitoring data is obtained, the environmental monitoring data is divided into a training set, a test set, and a validation set, the environmental monitoring data is preprocessed to obtain relevant feature data, the feature data of the environment is taken as sample data as input of an input layer, and different time periods of the feature data are analyzed as a processing layer, and the predicted future time period of the environmental data prediction value is taken as output;

[0090] S301, when the processing layer analyzes the input features:

[0091] ,

[0092] In the formula, is the data value of the current time N, is the average change rate of the data in a period of time, is the data value of the predicted future time, is a fluctuation factor, and T is the time from the current time to the predicted future time.

[0093] The stage of the fermentation state of the Fuzhuan tea is determined, specifically including the following:

[0094] S400, obtain the real-time environmental data prediction value, identify the data value of the environmental data prediction value exceeding the standard environmental data prediction value, identify the real-time image data according to the preset stage change rate, obtain the change rate of the real-time image data, and combine the real-time environmental monitoring data to calculate the real-time fermentation evaluation state of the Fuzhuan tea, and the calculation process is as follows:

[0095] S= ,

[0096] In the formula, S is the evaluation value, All are weight coefficients, E is the actual prediction value, D is the real-time environmental data value, M is the similarity value of the previous image and the standard image, m is the similarity value of the current image and the standard image, S>F, then it is three categories, F>S>f, then it is two categories, and S<f, then it is three categories.

[0097] S401, obtain the evaluation value to judge the fermentation stage state of the Fuzhuan tea, when the evaluation value is one category, the Fuzhuan tea is in the normal fermentation stage, when the evaluation value is two categories, the Fuzhuan tea is in the abnormal fermentation middle stage, and when the evaluation value is three categories, the Fuzhuan tea is in the abnormal fermentation state stage, and different fermentation stage state instructions are generated according to different stages.

[0098] It also includes a device control unit for dynamically adjusting the internal environment of the fermentation site, dynamically adjusting the environmental basic parameters of the fermentation site according to the fermentation stage and the early warning threshold, when the fermentation state evaluation judges that the fermentation is in a certain specific stage and the environmental parameters need to be adjusted, the device can work cooperatively and adjust the running state, and the environmental basic parameters are judged according to the early warning threshold.

[0099] In the scheme, the data monitoring module can monitor the fermentation environment in real time, obtain data from various sensors at preset time intervals, ensure timely capture of changes in the fermentation environment, and provide accurate and up-to-date data support for subsequent analysis, divide the external and internal data sets according to the environmental monitoring data, and help to analyze the different effects of external and internal environmental factors on Fuzhuan tea fermentation, making subsequent analysis more targeted;

[0100] Influence coefficient and proportion analysis: the data analysis unit of the AI analysis module classifies the data according to the quarterly boundary, analyzes the influence coefficient of the fermentation environment and the external environment, and generates an influence proportion value, which helps to understand the degree of influence of environmental factors on fermentation in different seasons;

[0101] The fusion analysis unit intercepts real-time image data when the Fuzhuan tea fermentation is abnormal, and divides different stages to analyze the fermentation process time change rate;

[0102] Environmental fluctuation modeling prediction: the fluctuation analysis module constructs an environmental fluctuation model based on environmental monitoring data, outputs future environmental data prediction values, and can predict environmental change trends in advance to prepare for adjusting the fermentation environment;

[0103] Combined with the environmental data prediction values, the phase change rate, and the real-time environmental monitoring data, the fermentation stage and state of the Fuzhuan tea can be comprehensively judged, and the fermentation state can be more accurately and reliably evaluated, avoiding the one-sidedness of single data judgment;

[0104] The intelligent control module controls the equipment in the fermentation environment in real time according to the fermentation stage state instruction, automatically adjusts the operating parameters, realizes intelligent and automatic control of the fermentation environment, and does not need frequent manual intervention, thereby improving the production efficiency and the stability of the product quality, and reducing fermentation problems caused by untimely human operation;

[0105] Through the cooperative work of the above modules, the system can deeply understand the influence of environmental factors on the fermentation process of Fuzhuan tea, timely find abnormalities and adjust the fermentation environment, thereby optimizing the entire fermentation process, improving the quality and production efficiency of Fuzhuan tea, and reducing production costs.

[0106] The setting of the size of the interval and the threshold value is for easy comparison, and the size of the threshold value depends on the number of sample data and the base number set by the person skilled in the art for each group of sample data; as long as the proportional relationship between the parameters and the quantized values is not affected.

[0107] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation, and the preset parameters in the formula are set by the person skilled in the art according to the actual situation;

[0108] In the two embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways; for example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed; another point, the coupling or direct coupling or communication connection between the displayed or discussed ones can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms;

[0109] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can make equivalent replacement or change according to the technical solution and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. An AI optimization-based intelligent monitoring system for a fermentation environment of a Fuzhuan tea, characterized in that, Comprise data monitoring module, AI analysis module, fluctuation analysis module, intelligent control module; The data monitoring module is used for monitoring the fermentation environment in real time, acquiring environment monitoring data from each sensor according to a preset time interval, and dividing external and internal data sets according to the environment monitoring data; The AI analysis module comprises a data analysis unit and a fusion analysis unit, the data analysis unit is used for acquiring environment monitoring data in the fermentation environment monitoring process, dividing the data into four categories according to the quarterly boundary, analyzing the influence coefficient between the fermentation environment and the external environment of each category, and generating the influence proportion value of the fermentation environment to obtain different influence proportion sets; The fusion analysis unit acquires environment monitoring data, intercepts real-time image data at the time when the abnormality occurs during the fermentation of the Fuzhuan tea, divides three stages as normal stage state, abnormal state before the abnormality and abnormal state according to the abnormal time point, analyzes the fermentation process time change rate of each stage, and obtains stage change rate data; The fluctuation analysis module is used for acquiring environment monitoring data, constructing an environment fluctuation model based on the environment monitoring data, outputting environment data prediction values of future time periods, and combining the environment data prediction values, the stage change rate and real-time environment monitoring data to judge the fermentation state of the Fuzhuan tea fermentation stage and perform overall analysis of the fermentation state to generate a fermentation stage state instruction of the environment; The intelligent control module is used for controlling the equipment in the fermentation environment, acquiring the fermentation stage state instruction, and controlling the equipment to perform operation parameter regulation of the fermentation environment in real time according to the fermentation stage state instruction of the environment. 2.The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, When the data monitoring module monitors the fermentation environment, a sensor implementation diagram is constructed according to the fermentation environment layout, and the specific steps are as follows: Step one: acquire the overall shape, size, door and window position and distribution point of the equipment of the fermentation workshop, combine the construction diagram of the workshop to generate an initial implementation diagram; Step two: according to the sensors selected according to the Fuzhuan tea fermentation process requirements, high-density coverage is performed in the monitoring area, the specific distance between each sensor is set, the distance is set according to the different types of sensors, and the installation distance of different heights is set according to the space layout of the workshop and the properties of the sensors; Step three: mark the installation position of each sensor in the initial implementation diagram, different sensors adopt different types of mark symbols, take the center point of the workshop as the origin, generate a link diagram of the sensors, set it as a dynamic monitoring node, and obtain the constructed sensor implementation diagram. 3.The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, The AI analysis module further comprises a data processing step: According to the different types of sensors, databases for collecting different environments are established, each database is automatically coded and sorted, and when data is transmitted, the data is automatically classified based on AI recognition of data characteristics; The database judges and identifies the transmitted data, automatically identifies the data, removes noise, error data and abnormal value processing steps, and automatically adjusts the cleaning method step according to the transmitted data; According to the preset time interval, the data from each sensor is obtained and stored, and the same gateway communication state is set when the sensor is connected to the network.

4. The AI optimization-based intelligence monitoring system for the fermentation environment of Fuzhuan tea according to claim 1, characterized in that, The different influence ratio sets obtained specifically include the following: S100, obtain the environmental monitoring data in the fermentation environment monitoring process, divide the data into four categories according to the quarterly boundaries, generate the external environment and each environment data set of each type through the environmental monitoring data of each category, and perform classification labeling; S101, obtain the fermentation environment and external environment data set of each category, analyze the influence coefficient between the fermentation environment and the external environment data set in each category, generate the influence ratio value of the fermentation environment, and calculate the influence ratio value as follows: Internal environmental impact coefficient: , External environmental influence coefficient: , Internal environment impact ratio: External environment impact ratio: , wherein n is the number of internal environmental factors, M is the number of external environmental factors, is a set influence weight, is data of the Jth internal environmental factor in the internal environmental data set, is a set influence weight, is data of the Kth external environmental factor in the external environmental data set; S102, substitute the environmental monitoring data of each category into the above formula to obtain different influence ratio values under four categories, and arrange to obtain different influence ratio sets, and set the data mean value of each category as the early warning threshold. 5.The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, Obtain the stage change rate data, specifically including the following: S200, obtain image information of Fuzhuan tea during fermentation through a monitoring device, divide the real-time image data before and after the abnormal time into three stages of normal stage state, abnormal state before abnormality, and abnormal state according to the time point when Fuzhuan tea fermentation produces an anomaly, and the normal stage state is the stage when no anomaly occurs, the abnormal state before abnormality is the state close to the time before the anomaly occurs, and the abnormal state is the state of anomaly; S201, obtain the time of each stage of the normal stage state, the abnormal state before abnormality, and the abnormal state, analyze the change time between each stage, and perform image fermentation process time change rate analysis according to the change time to obtain stage change rate data, and the calculation process is as follows: Normal stage state: the image taken in the normal stage is recorded as , and the time of taking the image is recorded as ; Abnormal pre-state: image before abnormality, denoted as , and the shooting time is denoted as ; Abnormal state generation: image at the time of abnormality occurrence, denoted as , and the photographing time is denoted as ; Similar objective function M: M= ， Normal stage state to pre-exception state change rate is R1: R1= , The abnormal pre-state to the abnormal state change rate is: R2= ; S202, according to the change rates R1 and R2, a preset stage change rate R is performed. 6.The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, Construct an environmental fluctuation model based on environmental monitoring data, specifically including the following: S300, obtain the environmental monitoring data, divide the environmental monitoring data into a training set, a test set, and a validation set, obtain relevant feature data after preprocessing the environmental monitoring data, take the feature data of the environment as sample data as the input of the input layer, analyze the feature data in different time periods as the processing layer, and take the predicted future time period environmental data prediction value as the output; S301, when the processing layer performs feature analysis: ， wherein, is the data value at the current time N, is the average rate of change of the data over a period of time, is the data value at a future time, is the volatility factor and T is the time into the future from the current time.

7. The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, Determine the fermentation state of the stage where Fuzhuan tea is fermented, specifically including the following: S400, obtain the real-time environmental data prediction value, identify the data value that exceeds the standard environmental data prediction value, identify the real-time image data according to the preset stage change rate, obtain the change rate of the real-time image data, and combine the real-time environmental monitoring data to calculate the real-time fermentation evaluation state of Fuzhuan tea, and the calculation process is as follows: S= ， In the formula, S is an evaluation value, are weight coefficients, E is an actual prediction value, D is a real-time environment data value, M is a similarity value of a previous image and a standard image, m is a similarity value of a current image and a standard image, S>F, then it is a three-class, F>S>f, then it is a two-class, and S<f, then it is a three-class. S401, an evaluation value is acquired to judge the fermentation stage state of the Fuzhuan tea, when the evaluation value is a first type, the Fuzhuan tea is in a normal fermentation stage, when the evaluation value is a second type, the Fuzhuan tea is in an abnormal fermentation middle stage, when the evaluation value is a third type, the Fuzhuan tea is in an abnormal fermentation state stage, and different fermentation stage state instructions are generated according to different stages. 8.The AI optimization-based intelligence monitoring system for a tremella fuciformis fermentation environment according to claim 1, characterized in that, The device control unit is further included for dynamically adjusting the internal environment of the fermentation site, dynamically adjusting the environmental basic parameters of the fermentation site according to the fermentation stage and the early warning threshold, when the fermentation state evaluation judges that the fermentation is in a certain specific stage and the environmental parameters need to be adjusted, the device can work cooperatively to adjust the operating state, and the environmental basic parameters are judged according to the early warning threshold.

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