A mushroom automated cultivation system and method based on big data

Abstract

The present invention relates to the field of mushroom cultivation technology, specifically to a mushroom automated cultivation system and method based on big data, the system comprising a risk analysis and optimization module, the risk analysis and optimization module combines the acquired near-state environmental information and the temperature adjustment fluctuation deviation time interval under different environmental information to analyze the temperature adjustment risk intervention value corresponding to the mushroom cultivation cabin at the current time; through the database preset form to query the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value, the temperature control parameters in the mushroom cultivation cabin at the current time are optimized and updated. The present invention uses a temperature sensor to monitor the temperature in the cabin, and takes into account that the temperature change rate in the cabin is constantly changing due to the difference in environmental information, and combines the environmental information to realize the dynamic evaluation of the temperature adjustment risk corresponding to the mushroom cultivation cabin, thereby ensuring the dynamic adjustment of the temperature sensor acquisition interval time, and realizing the effective control of the temperature control and adjustment operation state in the mushroom cultivation cabin.

Description

Technical Field

[0001] The present invention relates to the technical field of fungus cultivation, and in particular to an automated fungus cultivation system and method based on big data. Background Art

[0002] Morels are a product of the environment. To achieve high yields, bionic cultivation or high-precision environmental control is required. Morels are produced in greenhouses, usually sown in winter and harvested in spring, and can only be planted once a year. Compared with traditional greenhouse production, the production of morels in cubicles uses intelligent cultivation programmed equipment, which can better meet the strict control of the environmental needs of morels. It can break seasonal restrictions and sow at any time, realizing the year-round production of morels, increasing annual output and off-season income, saving a lot of labor costs, and also bringing considerable economic benefits to morel growers.

[0003] However, the existing cabin temperature control system usually uses a temperature sensor to monitor the temperature inside the cabin. During the monitoring process, the temperature sensor's temperature collection interval is usually a preset fixed constant. However, in the actual production process, the temperature change rate in the cabin is constantly changing due to differences in environmental information, and thus the fixed collection interval of the temperature sensor cannot meet the needs of actual production. A short collection interval will increase the sensor's data collection frequency, which not only increases the sensor's energy consumption rate, but also affects the service life of the corresponding sensor. If the temperature sensor collection interval is long, it will affect the cabin's regulation of the cabin's temperature. Therefore, how to regulate the temperature sensor collection interval in the cabin has become an urgent problem to be solved in the current industry. Summary of the invention

[0004] The purpose of the present invention is to provide a fungus automated cultivation system and method based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solution: a method for automatic cultivation of fungi based on big data, the method comprising the following steps:

[0006] S100, collecting environmental information corresponding to the mushroom culture cabin through a sensor, and generating near-state environmental information according to the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time;

[0007] S200, obtaining the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom of the cabin, and constructing a first temperature sensing position distribution map; analyzing the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzing the temperature adjustment fluctuation deviation time interval under different environmental information;

[0008] S300, combining the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information, analyzing the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; querying the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through the preset table in the database, and optimizing and updating the temperature control parameters in the mushroom cultivation cabin at the current time;

[0009] S400, recording the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judging the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generating early warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeding back to the administrator corresponding to the mushroom cultivation cabin, so as to assist the administrator in making decisions on temperature control state adjustment.

[0010] Furthermore, the environmental information includes an average value of the temperature outside the cabin, a monitoring temperature of each sensor inside the cabin, and an average value of the humidity inside the cabin;

[0011] The temperature control parameters include the interval time of temperature sensor data collection, constant temperature parameters and fluctuation threshold during temperature adjustment.

[0012] The interval duration of the temperature sensor data collection indicates the interval duration between two consecutive temperature data collections by the same temperature sensor;

[0013] The constant temperature parameter represents the minimum value of the cabin temperature at the end of each temperature adjustment process;

[0014] The fluctuation threshold value during temperature adjustment represents the minimum value of the temperature difference between the cabin temperature and the corresponding constant temperature parameter when the temperature adjustment starts.

[0015] Furthermore, when analyzing the temperature change of each temperature sensor monitoring position point in the mushroom culture cabin in S200, the historical monitoring data collected in the mushroom culture cabin except for the time period corresponding to the temperature adjustment process is obtained, and the relationship function between the temperature change rate corresponding to each temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin remains unchanged is obtained, and the relationship function between the temperature change rate corresponding to the i-th temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin is k is recorded as Hik;

[0016] The temperature difference between the inside and outside of the cabin corresponding to Hik is the temperature difference between the monitoring temperature of the i-th temperature sensor and the average value of the outside temperature corresponding to the corresponding time point;

[0017] The temperature change rate corresponding to Hik represents the inverse of the quotient of the difference between the monitored temperature of the ith temperature sensor and the subsequent nth monitored temperature of the corresponding temperature sensor divided by the corresponding acquisition interval, and n is a constant preset in the database;

[0018] In the process of obtaining Hik, the temperature difference between the monitored temperature of the ith temperature sensor and the average value of the outside temperature corresponding to the corresponding time point when the average value of the humidity in the cabin is k and the corresponding temperature change rate are obtained in the historical monitoring data, and the temperature difference correlation data pairs are constructed. The corresponding coordinate points of each obtained temperature difference correlation data pair in the plane coordinate system with the temperature difference as the x-axis and the temperature change rate as the y-axis are marked, and the adjacent marked points are connected in the order of the x-axis coordinate value from small to large. The function corresponding to the obtained broken line is recorded as Hik;

[0019] When analyzing the temperature adjustment fluctuation deviation time interval under different environments in S200, the interval between the time point when each temperature adjustment starts and the minimum time point when the signs of the temperature change rates corresponding to the temperature sensors in the cabin are opposite to those before the temperature adjustment is obtained from the historical monitoring data when the average value of the humidity in the cabin is k, is recorded as the temperature fluctuation deviation time interval; the interval consisting of the minimum and maximum values ​​of the temperature fluctuation deviation time intervals corresponding to the average value of the humidity in the cabin k in the historical data is recorded as the temperature adjustment fluctuation deviation time interval.

[0020] The present invention analyzes the relationship function between the temperature change rate corresponding to each temperature sensor and the temperature difference between the inside and outside of the cabin. This is because when the temperature difference between the inside and outside of the cabin is different, the temperature change rate in the cabin is different. The relationship between the two is quantified in order to provide data support for the effective temperature adjustment fluctuation deviation duration Bm when the humidity is calculated in the subsequent steps and the average value of the humidity in the cabin in the near-state environmental information is the same; at the same time, in the process of obtaining the relationship function, the humidity in the cabin is limited. This is because the air humidity in the cabin affects the change of the temperature in the cabin. Limiting the humidity in the cabin serves the purpose of dimensionality reduction in the process of obtaining the functional relationship (originally the humidity in the cabin and the temperature difference between the inside and outside of the cabin are both related to the rate of change of the temperature in the cabin, but after limiting the humidity in the cabin, it is only necessary to analyze the relationship between the temperature difference between the inside and outside of the cabin and the rate of change of the temperature in the cabin).

[0021] Furthermore, the method of analyzing the temperature adjustment risk intervention value corresponding to the current bacterial culture cabin in S300 includes the following steps:

[0022] S301, obtaining near-state environmental information and temperature adjustment fluctuation deviation duration intervals under different environmental information;

[0023] S302, obtaining a temperature adjustment fluctuation deviation time interval corresponding to the time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, and recording it as a first temperature adjustment fluctuation deviation time interval; and recording the maximum value in the first temperature adjustment fluctuation deviation time interval as B;

[0024] S303, obtaining the effective temperature adjustment fluctuation deviation time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, recorded as Bm, wherein Bm=max{B1, B2-t1-W / (t1·H{t1}+W)·B1-t2},

[0025] In the present invention, t1·H{t1} represents the multi-adjusted temperature due to the monitoring delay of the temperature sensor; W / (t1·H{t1}+W)·B1 represents the adjustment time prediction value required in the process of multi-adjusting the temperature;

[0026] Wherein, B1 represents the minimum value in the first temperature adjustment fluctuation deviation time interval; B2 represents the maximum value in the first temperature adjustment fluctuation deviation time interval; max{} represents the operation of finding the maximum value; t1 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment start in each temperature adjustment process when the temperature fluctuation deviation time interval is B2; t2 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment end in each temperature adjustment process when the temperature fluctuation deviation time interval is B2;

[0027] H{t1} represents the relationship function between the temperature change rate of each temperature adjustment process and the temperature difference between the inside and outside of the cabin when the temperature fluctuation deviation duration is B2, and the temperature difference between the inside and outside of the cabin is the difference between the temperature of the corresponding sensor and the indoor temperature sensor when the temperature adjustment starts, and the average value of each function value obtained;

[0028] W represents the minimum value of the temperature difference before and after each temperature adjustment in each temperature adjustment process corresponding to the temperature fluctuation deviation duration B2;

[0029] S304, obtain the temperature adjustment risk intervention value corresponding to the current time of the bacterial culture cabin, recorded as FXD,

[0030] ,

[0031] Among them, e1 and e2 both represent risk assessment coefficients, and both e1 and e2 are constants preset in the database;

[0032] BM1 represents the minimum interval between the start and end of temperature adjustment in the previous temperature adjustment process based on the current time;

[0033] Wmax represents the maximum temperature difference between the temperature monitored by the cabin sensor and the temperature outside the cabin during the last temperature adjustment process based on the current time;

[0034] WT represents the fluctuation threshold of the temperature control parameters in the culture cabin during the previous temperature control process based on the current time.

[0035] The purpose of obtaining the temperature regulation risk intervention value corresponding to the mushroom culture cabin at the current time in the present invention is to evaluate the risk impact of the temperature sensor's method of collecting the cabin temperature on the cabin temperature regulation under the current situation. The larger the obtained temperature regulation risk intervention value, the more serious the risk impact of the temperature sensor's method of collecting the cabin temperature (temperature control parameters) on the cabin temperature regulation under the current situation, the more it is necessary to optimize and update the temperature control parameters of the temperature sensor, and the greater the change range of the temperature control parameters after optimization and update.

[0036] Furthermore, when the temperature control parameters in the current mushroom cultivation cabin are optimized and updated in S300, the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried through the preset form in the database, and the product of the obtained risk adjustment coefficient and the interval duration of the temperature sensor data collection in the temperature control parameters in the current mushroom cultivation cabin is used as the interval duration of the new temperature sensor data collection in the temperature control parameters optimized and updated in the current mushroom cultivation cabin; the constant temperature parameters and the fluctuation threshold value during temperature adjustment in the temperature control parameters optimized and updated in the current mushroom cultivation cabin remain unchanged.

[0037] Further, the time of each optimization and update of the temperature control parameters in the mushroom culture cabin is obtained, and the obtained optimization and update times are sequentially summarized into a blank set in order to obtain an update time statistical set, which is recorded as HG;

[0038] Calculate the time interval between the optimization update times corresponding to any two adjacent elements in HG, denoted as R; the time interval between the two optimization update times corresponding to R is denoted as DR;

[0039] If R is greater than the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is normal;

[0040] If R is less than or equal to the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is abnormal;

[0041] When generating the warning information in combination with the first temperature sensing position distribution map, the warning information includes the time interval of the abnormal temperature control operation state and the visual monitoring temperature distribution map in the corresponding time interval;

[0042] The temperature data collected by each temperature sensor in the mushroom culture cabin during the time interval when the temperature control adjustment operation status is abnormal are obtained; and a feedback array is generated by the temperature data collected by each temperature sensor in the mushroom culture cabin at the same time point, and each temperature data in the obtained feedback array is bound to the position of the corresponding temperature sensor in the first temperature sensing position distribution map, and the corresponding temperature data is visualized at the bound position to obtain a visualized monitoring temperature distribution map corresponding to the time of the feedback array.

[0043] A mushroom automatic cultivation system based on big data, the system comprises the following modules:

[0044] A near-state environment acquisition module, which collects environmental information corresponding to the mushroom culture cabin through sensors, and generates near-state environment information based on the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time;

[0045] A temperature adjustment fluctuation deviation analysis module, wherein the temperature adjustment fluctuation deviation analysis module obtains the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom surface of the cabin, and constructs a first temperature sensing position distribution map; analyzes the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzes the temperature adjustment fluctuation deviation time interval under different environmental information;

[0046] A risk analysis and optimization module, which combines the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information to analyze the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; through the database preset form, the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried, and the temperature control parameters in the current time of the mushroom cultivation cabin are optimized and updated;

[0047] A status warning management module records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judges the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generates warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeds back to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control status adjustment.

[0048] Furthermore, the risk analysis and optimization module includes a temperature control risk analysis unit and a control parameter optimization unit.

[0049] The temperature adjustment risk analysis unit analyzes the temperature adjustment risk intervention value corresponding to the current time of the bacterial culture cabin by combining the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information;

[0050] The control parameter optimization unit queries the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through a preset table in the database, and optimizes and updates the temperature control parameters in the mushroom culture cabin at the current time.

[0051] Furthermore, the state warning management module includes a temperature adjustment state determination unit and a warning feedback unit.

[0052] The temperature control state determination unit records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and determines the temperature control operation state in the mushroom cultivation cabin according to the interval between each adjacent time in each optimization and update time obtained;

[0053] When the temperature control operation state is abnormal, the early warning feedback unit generates early warning information in combination with the first temperature sensing position distribution map, and feeds back the information to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control state adjustment.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: during the process of automated mushroom cultivation, the present invention uses a temperature sensor to monitor the temperature inside the cabin, and takes into account that the temperature change rate inside the cabin is constantly changing due to differences in environmental information, and combines environmental information to achieve a dynamic assessment of the temperature adjustment risk corresponding to the mushroom cultivation cabin, thereby ensuring dynamic adjustment of the temperature sensor collection interval, and achieving effective management and control of the temperature control and adjustment operating status in the mushroom cultivation cabin. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 It is a structural schematic diagram of a mushroom automatic cultivation system based on big data of the present invention;

[0057] Figure 2 It is a schematic diagram of the process of an automated fungus cultivation method based on big data of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] See also Figure 1 The present invention provides a technical solution: a mushroom automatic cultivation system based on big data, the system includes the following modules:

[0060] A near-state environment acquisition module, which collects environmental information corresponding to the mushroom culture cabin through sensors, and generates near-state environment information based on the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time;

[0061] A temperature adjustment fluctuation deviation analysis module, wherein the temperature adjustment fluctuation deviation analysis module obtains the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom surface of the cabin, and constructs a first temperature sensing position distribution map; analyzes the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzes the temperature adjustment fluctuation deviation time interval under different environmental information;

[0062] A risk analysis and optimization module, which combines the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information to analyze the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; through the database preset form, the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried, and the temperature control parameters in the current time of the mushroom cultivation cabin are optimized and updated;

[0063] A status warning management module records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judges the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generates warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeds back to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control status adjustment.

[0064] In the present invention, the mushroom culture cabin refers to a cabin for cultivating mushrooms;

[0065] The risk analysis and optimization module includes a temperature control risk analysis unit and a control parameter optimization unit.

[0066] The temperature adjustment risk analysis unit combines the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information to analyze the temperature adjustment risk intervention value corresponding to the mushroom culture cabin at the current time;

[0067] The control parameter optimization unit queries the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through a preset table in the database, and optimizes and updates the temperature control parameters in the mushroom culture cabin at the current time.

[0068] The state warning management module includes a temperature adjustment state determination unit and a warning feedback unit.

[0069] The temperature control state determination unit records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and determines the temperature control operation state in the mushroom cultivation cabin according to the interval between each adjacent time in each optimization and update time obtained;

[0070] When the temperature control operation state is abnormal, the early warning feedback unit generates early warning information in combination with the first temperature sensing position distribution map, and feeds back the information to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control state adjustment.

[0071] The mushroom cultivation square cabin in this embodiment is a special cabin for cultivating morels, including a cultivation cabin body and a cultivation rack. The cabin body is 4.5 meters * 8 meters. A three-dimensional cultivation rack is installed inside the cultivation cabin to improve the utilization rate within a limited unit area.

[0072] The cultivation cabin body is equipped with a cooling plate and a humidifying curtain for air cooling. Morchella is a low-temperature mushroom. Generally, the temperature can be adjusted between 10-18°C during the growth period. In addition to a lower temperature, a larger temperature difference is required to stimulate the differentiation of Morchella mycelium during the growth period. This equipment will automatically heat up or cool down according to the temperature requirements at different stages.

[0073] The cultivation cabin body is equipped with a 10 kg ultrasonic atomizer. Morchella is a moisture-loving mushroom, and the humidity in the space should be around 85%. The fruiting body primordium and young mushrooms are very sensitive to open water. If water is directly sprayed on the primordium and young mushrooms with a height of less than 2 cm, they will die directly. Therefore, we use an ultrasonic atomizer to spray water into the air, and do not spray water directly on the surface fruiting bodies. The droplets are fine and have good dispersion, which meets the humidity requirements of Morchella.

[0074] The cultivation cabin body adopts an integrated fill light with adjustable light intensity. Although the morel mycelium does not require light during the growth stage, the fruiting body grows in the dark and is white, grayish white, or light white. If you want it to turn into a dark color, you must use a fill light to provide sufficient light.

[0075] The cultivation cabin body is equipped with a drip irrigation device. The growth process of morels requires two large waterings. Watering is required during sowing. Although watering is not required before budding, the soil moisture must be maintained at around 25%. The drip irrigation device will automatically open when the soil moisture is lower than the set value to ensure soil moisture. A small amount of primordium will leak on the surface during the mature stage of mycelium. At this time, large watering will inevitably damage the formed primordium. The drip irrigation equipment can well avoid this situation.

[0076] The cultivation cabin body is equipped with non-sensing ventilation equipment. Morchella is an aerobic mushroom and can withstand high CO2 concentration during the mycelium growth period. However, good ventilation can make the mycelium grow faster. The fruiting body has a strong demand for oxygen. Sufficient oxygen is very important for the development and growth of Morchella. Ordinary ventilation equipment will cause a sharp temperature difference reaction during ventilation. Our non-sensing ventilation equipment will not cause any harm to the young mushrooms while ensuring sufficient oxygen.

[0077] During the primordium differentiation and young mushroom stage, the oxygen concentration is forced to increase to between 22% and 25% to enable the primordium to differentiate quickly and improve the survival rate of young mushrooms.

[0078] The control system used in the cultivation cabin is an expert program module control, that is, different parameters are set for the required temperature, light, water, air humidity, nutrients, oxygen, etc. at different stages of morel growth, and are written into the module in the form of a program. Different stages automatically execute parameter controls at different stages, so that the morels can maximize their growth at each stage.

[0079] like Figure 2 As shown, a method for automatic cultivation of fungi based on big data, the method comprises the following steps:

[0080] S100, collecting environmental information corresponding to the mushroom culture cabin through a sensor, and generating near-state environmental information according to the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time;

[0081] The environmental information includes the average value of the temperature outside the cabin, the monitored temperature of each sensor inside the cabin, and the average value of the humidity inside the cabin;

[0082] The temperature control parameters include the interval time of temperature sensor data collection, constant temperature parameters and fluctuation threshold during temperature adjustment.

[0083] The interval duration of the temperature sensor data collection indicates the interval duration between two consecutive temperature data collections by the same temperature sensor;

[0084] The constant temperature parameter represents the minimum value of the cabin temperature at the end of each temperature adjustment process;

[0085] The fluctuation threshold value during temperature adjustment represents the minimum value of the temperature difference between the cabin temperature and the corresponding constant temperature parameter when the temperature adjustment starts.

[0086] In this embodiment, the magnitude relationship between the constant temperature parameter and the fluctuation threshold value during temperature adjustment is not fixed. The constant temperature parameter may be larger than the fluctuation threshold value during temperature adjustment, or may be smaller than the fluctuation threshold value during temperature adjustment.

[0087] For example, when the temperature outside the cabin is higher than the temperature inside the cabin, the corresponding constant temperature parameter may be smaller than the fluctuation threshold during temperature adjustment; when the temperature outside the cabin is lower than the temperature inside the cabin, the corresponding constant temperature parameter may be larger than the fluctuation threshold during temperature adjustment.

[0088] S200, obtaining the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom of the cabin, and constructing a first temperature sensing position distribution map; analyzing the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzing the temperature adjustment fluctuation deviation time interval under different environmental information;

[0089] When analyzing the temperature change of each temperature sensor monitoring position point in the mushroom cultivation square cabin in S200, the historical monitoring data collected in the mushroom cultivation square cabin except for the time period corresponding to the temperature adjustment process is obtained, and the relationship function between the temperature change rate corresponding to each temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin remains unchanged is obtained, and the relationship function between the temperature change rate corresponding to the i-th temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin is k is recorded as Hik;

[0090] The temperature difference between the inside and outside of the cabin corresponding to Hik is the temperature difference between the monitoring temperature of the i-th temperature sensor and the average value of the outside temperature corresponding to the corresponding time point;

[0091] The temperature change rate corresponding to Hik represents the inverse of the quotient of the difference between the monitored temperature of the ith temperature sensor and the subsequent nth monitored temperature of the corresponding temperature sensor divided by the corresponding acquisition interval, and n is a constant preset in the database;

[0092] In the process of obtaining Hik, the temperature difference between the monitored temperature of the ith temperature sensor and the average value of the outside temperature corresponding to the corresponding time point when the average value of the humidity in the cabin is k and the corresponding temperature change rate are obtained in the historical monitoring data, and the temperature difference correlation data pairs are constructed. The corresponding coordinate points of each obtained temperature difference correlation data pair in the plane coordinate system with the temperature difference as the x-axis and the temperature change rate as the y-axis are marked, and the adjacent marked points are connected in the order of the x-axis coordinate value from small to large. The function corresponding to the obtained broken line is recorded as Hik;

[0093] When analyzing the temperature adjustment fluctuation deviation time interval under different environments in S200, the interval between the time point when each temperature adjustment starts and the minimum time point when the signs of the temperature change rates corresponding to the temperature sensors in the cabin are opposite to those before the temperature adjustment is obtained from the historical monitoring data when the average value of the humidity in the cabin is k, is recorded as the temperature fluctuation deviation time interval; the interval consisting of the minimum and maximum values ​​of the temperature fluctuation deviation time intervals corresponding to the average value of the humidity in the cabin k in the historical data is recorded as the temperature adjustment fluctuation deviation time interval.

[0094] S300, combining the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information, analyzing the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; querying the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through the preset table in the database, and optimizing and updating the temperature control parameters in the mushroom cultivation cabin at the current time;

[0095] The method for analyzing the temperature adjustment risk intervention value corresponding to the current bacterial culture cabin in S300 includes the following steps:

[0096] S301, obtaining near-state environmental information and temperature adjustment fluctuation deviation duration intervals under different environmental information;

[0097] S302, obtaining a temperature adjustment fluctuation deviation time interval corresponding to the time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, and recording it as a first temperature adjustment fluctuation deviation time interval; and recording the maximum value in the first temperature adjustment fluctuation deviation time interval as B;

[0098] S303, obtaining the effective temperature adjustment fluctuation deviation time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, recorded as Bm, wherein Bm=max{B1, B2-t1-W / (t1·H{t1}+W)·B1-t2},

[0099] t1·H{t1} represents the multiple-adjustment temperature due to the monitoring delay of the temperature sensor; W / (t1·H{t1}+W)·B1 represents the adjustment time prediction value required in the process of multiple temperature adjustment;

[0100] Wherein, B1 represents the minimum value in the first temperature adjustment fluctuation deviation time interval; B2 represents the maximum value in the first temperature adjustment fluctuation deviation time interval; max{} represents the operation of finding the maximum value; t1 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment start in each temperature adjustment process when the temperature fluctuation deviation time interval is B2; t2 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment end in each temperature adjustment process when the temperature fluctuation deviation time interval is B2;

[0101] H{t1} represents the relationship function between the temperature change rate of each temperature adjustment process and the temperature difference between the inside and outside of the cabin when the temperature fluctuation deviation duration is B2, and the temperature difference between the inside and outside of the cabin is the difference between the temperature of the corresponding sensor and the indoor temperature sensor when the temperature adjustment starts, and the average value of each function value obtained;

[0102] W represents the minimum value of the temperature difference before and after each temperature adjustment in each temperature adjustment process corresponding to the temperature fluctuation deviation duration B2;

[0103] S304, obtain the temperature adjustment risk intervention value corresponding to the current time of the bacterial culture cabin, recorded as FXD,

[0104] ,

[0105] Among them, e1 and e2 both represent risk assessment coefficients, and both e1 and e2 are constants preset in the database;

[0106] BM1 represents the minimum interval between the start and end of temperature adjustment in the previous temperature adjustment process based on the current time;

[0107] Wmax represents the maximum temperature difference between the temperature monitored by the cabin sensor and the temperature outside the cabin during the last temperature adjustment process based on the current time;

[0108] WT represents the fluctuation threshold of the temperature control parameters in the culture cabin during the previous temperature control process based on the current time.

[0109] When the temperature control parameters in the current mushroom cultivation cabin are optimized and updated in S300; the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried through the preset form in the database, and the product of the obtained risk adjustment coefficient and the interval duration of the temperature sensor data collection in the temperature control parameters in the current mushroom cultivation cabin is used as the new interval duration of the temperature sensor data collection in the temperature control parameters optimized and updated in the current mushroom cultivation cabin; the constant temperature parameters and the fluctuation threshold value during temperature adjustment in the temperature control parameters optimized and updated in the current mushroom cultivation cabin remain unchanged.

[0110] S400, recording the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judging the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generating early warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeding back to the administrator corresponding to the mushroom cultivation cabin, so as to assist the administrator in making decisions on temperature control state adjustment.

[0111] Obtain the time of each optimization update of the temperature control parameters in the mushroom culture cabin, and summarize the obtained optimization update times in sequence into a blank set to obtain an update time statistical set, recorded as HG;

[0112] Calculate the time interval between the optimization update times corresponding to any two adjacent elements in HG, denoted as R; the time interval between the two optimization update times corresponding to R is denoted as DR;

[0113] If R is greater than the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is normal;

[0114] If R is less than or equal to the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is abnormal;

[0115] When generating the warning information in combination with the first temperature sensing position distribution map, the warning information includes the time interval of the abnormal temperature control operation state and the visual monitoring temperature distribution map in the corresponding time interval;

[0116] The temperature data collected by each temperature sensor in the mushroom culture cabin during the time interval when the temperature control adjustment operation status is abnormal are obtained; and a feedback array is generated by the temperature data collected by each temperature sensor in the mushroom culture cabin at the same time point, and each temperature data in the obtained feedback array is bound to the position of the corresponding temperature sensor in the first temperature sensing position distribution map, and the corresponding temperature data is visualized at the bound position to obtain a visualized monitoring temperature distribution map corresponding to the time of the feedback array.

[0117] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0118] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for automated cultivation of fungi based on big data, characterized in that: The method comprises the following steps: S100, collecting environmental information corresponding to the mushroom culture cabin through a sensor, and generating near-state environmental information according to the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time; S200, obtaining the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom of the cabin, and constructing a first temperature sensing position distribution map; analyzing the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzing the temperature adjustment fluctuation deviation time interval under different environmental information; S300, combining the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information, analyzing the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; querying the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through the preset table in the database, and optimizing and updating the temperature control parameters in the mushroom cultivation cabin at the current time; S400, recording the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judging the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generating early warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeding back to the administrator corresponding to the mushroom cultivation cabin, so as to assist the administrator in making decisions on temperature control state adjustment.

2. The method for automated cultivation of fungi based on big data according to claim 1, characterized in that: The environmental information includes the average value of the temperature outside the cabin, the monitored temperature of each sensor inside the cabin, and the average value of the humidity inside the cabin; The temperature control parameters include the interval time of temperature sensor data collection, constant temperature parameters and fluctuation threshold during temperature adjustment. The interval duration of the temperature sensor data collection indicates the interval duration between two consecutive temperature data collections by the same temperature sensor; The constant temperature parameter represents the minimum value of the cabin temperature at the end of each temperature adjustment process; The fluctuation threshold value during temperature adjustment represents the minimum value of the temperature difference between the cabin temperature and the corresponding constant temperature parameter when the temperature adjustment starts.

3. The method for automated cultivation of fungi based on big data according to claim 2, characterized in that: When analyzing the temperature change of each temperature sensor monitoring position point in the mushroom cultivation square cabin in S200, the historical monitoring data collected in the mushroom cultivation square cabin except for the time period corresponding to the temperature adjustment process is obtained, and the relationship function between the temperature change rate corresponding to each temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin remains unchanged is obtained, and the relationship function between the temperature change rate corresponding to the i-th temperature sensor and the temperature difference between the inside and outside of the cabin when the average value of the humidity in the cabin is k is recorded as Hik; The temperature difference between the inside and outside of the cabin corresponding to Hik is the temperature difference between the monitoring temperature of the i-th temperature sensor and the average value of the outside temperature corresponding to the corresponding time point; The temperature change rate corresponding to Hik represents the inverse of the quotient of the difference between the monitored temperature of the ith temperature sensor and the subsequent nth monitored temperature of the corresponding temperature sensor divided by the corresponding acquisition interval, and n is a constant preset in the database; In the process of obtaining Hik, the temperature difference between the monitored temperature of the ith temperature sensor and the average value of the outside temperature corresponding to the corresponding time point when the average value of the humidity in the cabin is k and the corresponding temperature change rate are obtained in the historical monitoring data, and the temperature difference correlation data pairs are constructed. The corresponding coordinate points of each obtained temperature difference correlation data pair in the plane coordinate system with the temperature difference as the x-axis and the temperature change rate as the y-axis are marked, and the adjacent marked points are connected in the order of the x-axis coordinate value from small to large. The function corresponding to the obtained broken line is recorded as Hik; When analyzing the temperature adjustment fluctuation deviation time interval under different environments in S200, the interval between the time point when each temperature adjustment starts and the minimum time point when the signs of the temperature change rates corresponding to the temperature sensors in the cabin are opposite to those before the temperature adjustment is obtained from the historical monitoring data when the average value of the humidity in the cabin is k, is recorded as the temperature fluctuation deviation time interval; the interval consisting of the minimum and maximum values ​​of the temperature fluctuation deviation time intervals corresponding to the average value of the humidity in the cabin k in the historical data is recorded as the temperature adjustment fluctuation deviation time interval.

4. The method for automated cultivation of fungi based on big data according to claim 3, characterized in that: The method for analyzing the temperature adjustment risk intervention value corresponding to the current bacterial culture cabin in S300 includes the following steps: S301, obtaining near-state environmental information and temperature adjustment fluctuation deviation duration intervals under different environmental information; S302, obtaining a temperature adjustment fluctuation deviation time interval corresponding to the time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, and recording it as a first temperature adjustment fluctuation deviation time interval; and recording the maximum value in the first temperature adjustment fluctuation deviation time interval as B; S303, obtaining the effective temperature adjustment fluctuation deviation time when the humidity is the same as the average value of the humidity in the cabin in the near-state environment information, recorded as Bm, wherein Bm=max{B1, B2-t1-W / (t1·H{t1}+W)·B1-t2}, Wherein, B1 represents the minimum value in the first temperature adjustment fluctuation deviation time interval; B2 represents the maximum value in the first temperature adjustment fluctuation deviation time interval; max{} represents the operation of finding the maximum value; t1 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment start in each temperature adjustment process when the temperature fluctuation deviation time interval is B2; t2 represents the average value of the time intervals of the adjacent temperature data collected by the sensor corresponding to the time point of each temperature adjustment end in each temperature adjustment process when the temperature fluctuation deviation time interval is B2; H{t1} represents the relationship function between the temperature change rate of each temperature adjustment process and the temperature difference between the inside and outside of the cabin when the temperature fluctuation deviation duration is B2, and the temperature difference between the inside and outside of the cabin is the difference between the temperature of the corresponding sensor and the indoor temperature sensor when the temperature adjustment starts, and the average value of each function value obtained; W represents the minimum value of the temperature difference before and after each temperature adjustment in each temperature adjustment process corresponding to the temperature fluctuation deviation duration B2; S304, obtain the temperature adjustment risk intervention value corresponding to the current time of the bacterial culture cabin, recorded as FXD, ， Among them, e1 and e2 both represent risk assessment coefficients, and both e1 and e2 are constants preset in the database; BM1 represents the minimum interval between the start and end of temperature adjustment in the previous temperature adjustment process based on the current time; Wmax represents the maximum temperature difference between the temperature monitored by the cabin sensor and the temperature outside the cabin during the last temperature adjustment process based on the current time; WT represents the fluctuation threshold of the temperature control parameters in the culture cabin during the previous temperature control process based on the current time.

5. The method for automated cultivation of fungi based on big data according to claim 1, characterized in that: When the temperature control parameters in the current mushroom cultivation cabin are optimized and updated in S300; the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried through the preset form in the database, and the product of the obtained risk adjustment coefficient and the interval duration of the temperature sensor data collection in the temperature control parameters in the current mushroom cultivation cabin is used as the new interval duration of the temperature sensor data collection in the temperature control parameters optimized and updated in the current mushroom cultivation cabin; the constant temperature parameters and the fluctuation threshold value during temperature adjustment in the temperature control parameters optimized and updated in the current mushroom cultivation cabin remain unchanged.

6. The method for automated cultivation of fungi based on big data according to claim 5, characterized in that: Obtain the time of each optimization update of the temperature control parameters in the mushroom culture cabin, and summarize the obtained optimization update times in sequence into a blank set to obtain an update time statistical set, recorded as HG; Calculate the time interval between the optimization update times corresponding to any two adjacent elements in HG, denoted as R; the time interval between the two optimization update times corresponding to R is denoted as DR; If R is greater than the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is normal; If R is less than or equal to the first preset value, it is determined that the temperature control operation state in the time interval corresponding to DR is abnormal; When generating the warning information in combination with the first temperature sensing position distribution map, the warning information includes the time interval of the abnormal temperature control operation state and the visual monitoring temperature distribution map in the corresponding time interval; The temperature data collected by each temperature sensor in the mushroom culture cabin during the time interval when the temperature control adjustment operation status is abnormal are obtained; and a feedback array is generated by the temperature data collected by each temperature sensor in the mushroom culture cabin at the same time point, and each temperature data in the obtained feedback array is bound to the position of the corresponding temperature sensor in the first temperature sensing position distribution map, and the corresponding temperature data is visualized at the bound position to obtain a visualized monitoring temperature distribution map corresponding to the time of the feedback array.

7. A mushroom automated cultivation system based on big data, characterized in that: The system includes the following modules: A near-state environment acquisition module, which collects environmental information corresponding to the mushroom culture cabin through sensors, and generates near-state environment information based on the environmental information most recently collected by the sensor and the temperature control parameters in the mushroom culture cabin at the current time; A temperature adjustment fluctuation deviation analysis module, wherein the temperature adjustment fluctuation deviation analysis module obtains the projection position of the temperature sensor in the mushroom cultivation cabin on the bottom surface of the cabin, and constructs a first temperature sensing position distribution map; analyzes the temperature change of each temperature sensor monitoring position point in the mushroom cultivation cabin according to the historical monitoring data in the cabin, and analyzes the temperature adjustment fluctuation deviation time interval under different environmental information; A risk analysis and optimization module, which combines the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information to analyze the temperature adjustment risk intervention value corresponding to the current time of the mushroom cultivation cabin; through the database preset form, the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value is queried, and the temperature control parameters in the current time of the mushroom cultivation cabin are optimized and updated; A status warning management module records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and judges the temperature control and adjustment operation status in the mushroom cultivation cabin according to the interval length between each adjacent time in each optimization and update time obtained, and generates warning information in combination with the first temperature sensing position distribution map when the temperature control and adjustment operation status is abnormal, and feeds back to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control status adjustment.

8. The automatic mushroom cultivation system based on big data according to claim 7 is characterized by: The risk analysis and optimization module includes a temperature control risk analysis unit and a control parameter optimization unit. The temperature adjustment risk analysis unit analyzes the temperature adjustment risk intervention value corresponding to the current time of the bacterial culture cabin by combining the acquired near-state environmental information and the temperature adjustment fluctuation deviation duration interval under different environmental information; The control parameter optimization unit queries the risk adjustment coefficient corresponding to each temperature adjustment risk intervention value through a preset table in the database, and optimizes and updates the temperature control parameters in the mushroom culture cabin at the current time.

9. The automatic mushroom cultivation system based on big data according to claim 7, characterized in that: The state warning management module includes a temperature adjustment state determination unit and a warning feedback unit. The temperature control state determination unit records the time of each optimization and update of the temperature control parameters in the mushroom cultivation cabin, and determines the temperature control operation state in the mushroom cultivation cabin according to the interval between each adjacent time in each optimization and update time obtained; When the temperature control operation state is abnormal, the early warning feedback unit generates early warning information in combination with the first temperature sensing position distribution map, and feeds back the information to the administrator corresponding to the mushroom cultivation cabin to assist the administrator in making decisions on temperature control state adjustment.

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