Intelligent regulation and control method and system for tobacco leaf storage environment

Abstract

The invention relates to the technical field of tobacco leaf storage, in particular to an intelligent regulation and control method and system for a tobacco leaf storage environment, and the method comprises the following steps: based on a tobacco leaf storage bin, fusing temperature, humidity and oxygen concentration sensor data, obtaining environment characteristics, analyzing airflow velocity and stacking density, identifying abnormal regions, counting variety deviation, and carrying out the control grading of associated equipment. And environment regulation and control are optimized. According to the method, multiple types of sensor data are integrated, the environment states of different storage partitions are reflected through space correlation and weighting operation, the sensitive partitions with local abnormity are effectively locked by means of correlation analysis of airflow and stacking density, tobacco variety characteristics and storage stage parameters are combined, grading dynamic recognition of environment sensitive points is achieved, and the method is suitable for large-scale popularization and application. The equipment response sequence is configured in a layered manner, and the regulated and controlled parameter trend adjustment operation is combined, so that the long-term quality stability of various tobacco leaves in the storage space is enhanced, and the quality deviation risk caused by environmental disturbance is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco storage, and in particular to a method and system for intelligently controlling a tobacco storage environment. Background Art

[0002] Tobacco storage technology involves environmental management, quality maintenance, and prevention of mold and pest infestation during the storage process. This primarily involves the regulation and monitoring of parameters such as temperature, humidity, and gas composition, as well as the maintenance and management of related storage facilities. The goal is to ensure the stability of the color, aroma, structure, and composition of tobacco leaves during long-term storage, and to prevent quality issues caused by environmental changes. Traditional intelligent control methods for tobacco storage environments utilize temperature and humidity sensors to collect real-time environmental data within the warehouse. These methods then automatically adjust the storage environment through hardware such as electric ventilation devices, dehumidification equipment, and humidification equipment, ultimately completing the regulation and management of the tobacco storage environment.

[0003] In warehouse operations, existing technologies routinely rely on fixed-point monitoring of a single parameter and mechanical equipment start-stop instructions. Spatial heterogeneity and time-varying environmental characteristics are difficult to perceive dynamically. There is a lack of joint identification of stacking density, local ventilation status, and variety stage parameters, resulting in a slow response to imbalances in the zoned microenvironment. Tobacco leaves in some areas are prone to quality problems such as color changes, mold, and flavor loss due to delayed regulation, making it difficult for environmental control measures to meet the precise management needs under conditions of variety diversity and dynamic changes. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for intelligently controlling the tobacco storage environment.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a method for intelligently controlling the tobacco storage environment, comprising the following steps: S1: Based on the tobacco storage warehouse, the temperature collected by the temperature sensor is analyzed. Combined with the parameters of the humidity sensor and the oxygen concentration sensor, the spatial distance between the measurement point and the stack center is calculated. The data in the same area is then integrated to obtain the storage space environmental characteristics. S2: Based on the environmental characteristics of the storage space, screen the airflow data from the wind speed sensors in each area, compare the regional airflow speed with the stacking density, identify the intersection area of low wind speed and high density, analyze the standard deviation of temperature and humidity changes, and obtain the abnormal characteristics of the airflow structure; S3: Based on the abnormal characteristics of the airflow structure, the relationship between the variety classification file parameters of each stacking area and the target environmental range is determined, and the temperature, humidity and oxygen concentration of the intersection area are analyzed to see whether they exceed the standards. The data are grouped and counted to obtain the variety partition sensitivity deviation; S4: Based on the sensitive deviation of the variety partition, compare the high-sensitivity parameter area with the distribution of the control equipment, determine the intersection of the air supply, humidification, exhaust equipment and the abnormal number, assign equipment operation priority, and obtain the equipment hierarchical control configuration.

[0006] The improvements of the present invention are that the storage space environmental characteristics include temperature and humidity environmental status, oxygen concentration distribution, and parameter correlation indicators; the abnormal characteristics of the airflow structure include airflow distribution status, ventilation resistance performance, and abnormal fluctuation characteristics; the variety partition sensitive deviation includes variety response level, environmental adaptation difference, and deviation distribution characteristics; the equipment hierarchical control configuration includes equipment hierarchical sequence, space control mapping relationship, and operation priority.

[0007] The present invention is improved in that the step of acquiring the storage space environment characteristics is specifically as follows: S111: Based on the tobacco storage bin, analyze the data collected by the temperature, humidity, and oxygen sensors, calculate the spatial distance between each sensor measurement point and the center point of the tobacco stack, determine the distribution relationship between the measurement points and each center point, and obtain a sensor distance data set; S112: Calculating the spatial correlation between the temperature, humidity, and oxygen parameters in the same area and the ranging data group based on the sensor ranging data group, analyzing the variation characteristics of the parameters with spatial distribution, optimizing the performance of similar parameters in each area, and obtaining a regional environmental fusion index; S113: Based on the partition environment fusion index, the temperature, humidity and oxygen parameters of each area are screened, the corresponding relationship between the internal parameter characteristics of each partition is analyzed, the parameter performance under the same partition is judged and summarized, and the storage space environment characteristics are obtained.

[0008] The present invention is improved in that the steps of obtaining the abnormal characteristics of the airflow structure are specifically as follows: S211: Based on the environmental characteristics of the storage space, comparing the airflow speed collected by the wind speed sensor in each area with the tobacco leaf stacking density, selecting areas with low airflow speed and high stacking density, determining the corresponding relationship between the area numbers, and obtaining the wind-tight interaction area; S212: Based on the wind-tight interaction area, analyze the changes in temperature and humidity in each numbered area, calculate the amplitude of temperature and humidity fluctuations in the same area, integrate the fluctuation amplitude data, optimize the environmental fluctuation conditions of each area, and obtain the environmental fluctuation integration amplitude; S213: Based on the environmental fluctuation fusion amplitude, the ventilation path length of each area, the number of equipment air outlets and the sensor density are integrated, the data fusion method is adjusted, the regional abnormal structure performance is optimized, and the airflow structure abnormal characteristics are obtained.

[0009] The present invention is improved in that the step of obtaining the sensitive deviation of the variety partition is specifically as follows: S311: Based on the abnormal characteristics of the airflow structure, the temperature, humidity, and oxygen concentration monitored in the intersection area are analyzed with the environmental range set in the variety grading file to determine whether any data deviates, and the data that meets the deviation conditions is selected to generate a parameter deviation item. S312: Based on the parameter deviation item, compare the spatial region numbers corresponding to each type of data, count the occurrence frequencies of each type of data deviation in the spatial region, optimize the spatial distribution performance of similar parameters, and obtain the distribution quantity of category parameters; S313: Based on the number of category parameter distributions, combined with the response levels and environmental adaptability differences of varieties in each region, the relationship between the deviation performance and response characteristics of each region under the difference data category is analyzed, and the performance of parameter distribution and response differences in each region is screened to obtain the variety zoning sensitive deviation amount.

[0010] The present invention is improved in that the steps of obtaining the device hierarchical control configuration are specifically as follows: S411: Based on the product-zone sensitive deviation, extract the abnormal area identifiers of the temperature, humidity, and oxygen parameters, call the device identifier of each control device and the space identifier it covers, compare the space identifier covered by the device with the abnormal area identifier, filter out devices with overlapping space identifiers, and generate abnormal device association information; S412: Based on the abnormal device association information, the devices are grouped by parameter type, the number of overlapping areas of each group of devices on the spatial identifier is counted as the distribution overlap number, the device impact offset amplitude is calculated, the device groups are sorted according to the offset amplitude, and the device identifiers of the top-ranked groups are extracted to obtain the abnormal control distribution gradient; S413: Based on the abnormal control distribution gradient, collect the current control logic priority and associated parameter type of each device, classify the devices and their corresponding environmental parameter types, detect the control status and spatial distribution of each group of devices, assign device operation priority, and obtain the device hierarchical control configuration.

[0011] The present invention is improved in that the steps further include: S5: Based on the device hierarchical control configuration, the device number and area are called, the temperature, humidity, and oxygen concentration time series collected before and after the device operation are analyzed, the parameter trend changes are calculated, the device operation amplitude is compared, the cycle priority is corrected, and the control parameter correction result is obtained; The control parameter correction result includes the control amplitude adjustment basis, periodic action optimization information, and parameter adjustment level.

[0012] The present invention is improved in that the step of obtaining the control parameter correction result is specifically as follows: S511: Based on the device hierarchical control configuration, analyze the device number and the collected data of temperature, humidity, and oxygen concentration in the corresponding area, determine the change direction and trend of the parameters before and after the device is activated, compare the correlation of the parameter trends under the action of different devices, and obtain a trend classification structure; S512: Based on the trend classification structure, optimizing the matching relationship between the device motion amplitude and the environmental parameter trend, screening the regional combinations where the motion effect and the environmental change are not synchronized, classifying and sorting the inconsistent response parts, and obtaining the response difference combination; S513: Based on the response difference combination, adjust the correspondence between the device operation priority and the device number, analyze the feedback performance of the previous action, judge the rationality of the device sequence setting and the actual effect, optimize the operation sequence of the next cycle, and obtain the control parameter correction result.

[0013] A tobacco storage environment intelligent control system, the system comprising: The environmental parameter fusion module, based on the tobacco storage bin, analyzes the temperature collected by the temperature sensor, combines it with the humidity data from the humidity sensor, and then calls the oxygen parameters from the oxygen concentration sensor to calculate the spatial distance between the sensor measurement point and the center of the tobacco stack. It then takes a weighted average of the parameters in the same area based on the distance to obtain the storage space environmental characteristics. Based on the environmental characteristics of the storage space, the airflow analysis module screens the airflow data monitored by the wind speed sensors in each area, compares the airflow speed in each area with the tobacco leaf stacking density parameters, sorts the wind speed to identify the end area of the sequence, sorts the stacking density to identify the front area of the sequence, extracts the intersection area, and analyzes the standard deviation of the temperature and humidity changes in the intersection space to obtain the abnormal characteristics of the airflow structure; Based on the abnormal characteristics of the airflow structure, the variety adaptability discrimination module determines the relationship between the variety grading file parameters of each tobacco leaf stacking area and the target environmental range of the current storage stage. It analyzes whether the temperature, humidity, and oxygen concentration in the intersection area exceed the corresponding variety standards. It groups the parameters one by one, collects statistics on the areas that exceed the standards, and obtains the variety zone sensitivity deviation. The equipment configuration module compares the distribution of highly sensitive parameter areas with each control device based on the sensitive deviation of the product partition, determines the air supply equipment, humidification equipment, and exhaust equipment associated with temperature, humidity, and oxygen anomalies, identifies the intersection of the equipment control number and the anomaly number, assigns equipment operation priority, and obtains the equipment hierarchical control configuration; The control parameter correction module calls the device number and area based on the device hierarchical control configuration, analyzes the temperature, humidity and oxygen concentration time series collected before and after the operation of each device, calculates the trend change of each group of parameters, compares it with the current device action amplitude and duration, and corrects the device operation priority in the next period according to the trend amplitude difference to obtain the control parameter correction result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, multi-type sensor data are integrated to reflect the environmental status of different storage partitions through spatial correlation and weighted operations. With the help of correlation analysis between airflow and stacking density, sensitive partitions with local abnormalities are effectively locked. The characteristics of tobacco varieties are combined with storage stage parameters to achieve hierarchical dynamic identification of environmental sensitive points, layered configuration of equipment response sequence, and combined with the post-control parameter trend adjustment operation to form a three-dimensional, hierarchical, dynamic feedback environmental control closed loop, enhance the long-term quality stability of various varieties of tobacco in the storage space, reduce the risk of quality deviation caused by environmental disturbances, and promote the adaptability of tobacco environmental control. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the main steps of the present invention; Figure 2 This is a flow chart for obtaining storage space environment characteristics in the present invention; Figure 3 A flow chart for obtaining abnormal characteristics of airflow structure in the present invention; Figure 4 This is a flow chart for obtaining the sensitive deviation amount of the variety partition in the present invention; Figure 5 A flowchart for obtaining the hierarchical control configuration of the device in the present invention; Figure 6 This is a flow chart for obtaining the results of regulating parameter correction in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0018] Example: See also Figure 1 The present invention provides a technical solution: a method for intelligently controlling a tobacco storage environment, comprising the following steps: S1: Based on the tobacco storage warehouse, the temperature collected by the temperature sensor is analyzed, combined with the humidity data of the humidity sensor, and the oxygen parameters of the oxygen concentration sensor are called. The spatial distance between the sensor measurement point and the center point of the tobacco stack is calculated. The parameters in the same area are weighted averaged based on the distance, and all parameters in the same area are integrated to obtain the storage space environmental characteristics; S2: Based on the environmental characteristics of the storage space, the airflow data monitored by the wind speed sensors in each area are screened and compared with the airflow speed and tobacco stacking density parameters in each area. The end of the sequence is identified by sorting the wind speed, and the front of the sequence is identified by sorting the stacking density. The intersection area is extracted and the standard deviation of the temperature and humidity changes in the intersection space is analyzed to obtain the abnormal characteristics of the airflow structure. S3: Based on the abnormal characteristics of the airflow structure, the relationship between the variety grading file parameters of each tobacco leaf stacking area and the target environmental range of the current storage stage is determined. The temperature, humidity, and oxygen concentration in the intersection area are analyzed to see if they exceed the corresponding variety standards. The parameters are grouped one by one, and the areas exceeding the standards are statistically analyzed to obtain the variety zoning sensitive deviation; S4: Based on the sensitivity deviation of each product zone, the distribution of highly sensitive parameter areas and each control device is compared to determine the air supply equipment, humidification equipment, and exhaust equipment associated with temperature, humidity, and oxygen anomalies. The intersection of the equipment control number and the anomaly number is identified, and the equipment operation priority is assigned to obtain the equipment hierarchical control configuration. S5: Based on the equipment hierarchical control configuration, call the equipment number and area, analyze the temperature, humidity, and oxygen concentration time series collected before and after the operation of each device, calculate the trend change of each group of parameters, and compare them with the current equipment operation amplitude and duration. According to the difference in trend amplitude, the operation priority of the next period equipment is corrected to obtain the correction result of the control parameters.

[0019] The environmental characteristics of the storage space include the temperature and humidity environment status, oxygen concentration distribution, and parameter correlation indicators. The abnormal characteristics of the airflow structure include the airflow distribution status, ventilation resistance performance, and abnormal fluctuation characteristics. The variety zoning sensitive deviation includes the variety response level, environmental adaptation difference, and deviation distribution characteristics. The equipment hierarchical control configuration includes the equipment hierarchical sequence, space control mapping relationship, and operation priority. The control parameter correction results include the basis for adjusting the control amplitude, periodic action optimization information, and parameter adjustment level.

[0020] In S1, the tobacco leaf stacking center point refers to the physical center of each tobacco leaf stacking point in the storage space, which is used to establish a spatial correspondence with various sensor measurement points and serves as the reference point for data weighting and spatial operations; the spatial distance refers to the physical distance between the installation location of the temperature, humidity, and oxygen sensors and the tobacco leaf stacking center point, which is used as the basis for parameter weighting operations; the weighted average of parameters in the same area refers to the same type of parameters collected by multiple sensors in the same area, which are weighted according to their respective distances from the stacking center to obtain a weighted result representing the environmental characteristics of the area.

[0021] In S2, the regional airflow velocity refers to the airflow velocity measured by the wind speed sensors in each area, which is used to analyze the ventilation conditions of different partitions; the tobacco leaf stacking density parameter refers to the density of tobacco leaf stacking in different areas, reflecting whether the stacking is compact; the end area of the sequence refers to the area at the lowest end after the wind speed parameters are arranged in ascending order, that is, the space with the weakest wind speed; the front area of the sequence refers to the area at the highest end after the stacking density parameters are arranged in descending order, that is, the space with the highest density; the intersection area refers to the spatial area where both the weak wind speed and the high stacking density conditions are met at the same time; the intersection space is the same as the intersection area, and refers to the specific space referred to after the above conditions overlap.

[0022] In S3, the variety grading file parameters refer to the set values of temperature, humidity, oxygen and other environmental data related to the storage requirements of each variety of tobacco leaves in the file established when each batch of tobacco leaves is put into storage; the relationship between the target environmental ranges refers to the comparison between the actual monitoring environmental parameters of tobacco leaf storage and the target parameters in the variety file; the variety standard refers to the allowable or recommended range of storage temperature, humidity, oxygen, etc. formulated for different varieties and stages; grouping by parameters refers to classifying the items of monitoring parameters in each area that exceed the standards according to temperature, humidity and oxygen categories; statistics of areas exceeding the standards refer to counting and recording the spaces and parameter categories that exceed the standards item by item.

[0023] In S4, the highly sensitive parameter area refers to the varieties or partitions that are sensitive to environmental changes such as temperature, humidity, and oxygen and have already shown abnormalities; the distribution of control equipment refers to the distribution and coverage of various types of air supply, humidification, exhaust and other control equipment in various areas of the storage space; the equipment control number refers to the number used to uniquely identify the control equipment to facilitate the issuance of operation instructions and management; the abnormality number refers to the area number where the environmental parameters have been determined to be abnormal in the previous steps; the equipment operation priority refers to the execution order or importance level of each control equipment based on the degree of regional abnormality and the equipment control range.

[0024] In S5, time series refers to the collected values of parameters such as temperature, humidity, and oxygen at multiple consecutive time points before and after the device is operated; trend change refers to the changing trajectory of the above time series parameters over time, such as rising, falling, or remaining stable; the amplitude and duration of the device operation refer to the output adjustment capacity of the device during a single operation and the duration of continuous operation; the trend amplitude difference refers to the difference in change amplitude between the actual environmental parameter change trend after the device is operated and the amplitude and duration of the device operation, which is used for the next control adjustment.

[0025] See also Figure 2 ,The steps for obtaining the storage space environment features are as follows: S111: Based on the tobacco storage bin, analyze the data collected by the temperature, humidity, and oxygen sensors, calculate the spatial distance between each sensor measurement point and the center point of the tobacco stack, determine the distribution relationship between the measurement points and each center point, and obtain a sensor distance data set; Read the environmental data collected by the temperature, humidity and oxygen sensors, and mark the spatial installation coordinates of each sensor. Relying on the tobacco stacking structure drawing or sensor deployment diagram, determine the physical center coordinates of each tobacco stacking point. For each sensor data point, combine its spatial position coordinates with the corresponding stacking center point position to calculate its spatial straight-line distance and mark it as the ranging value. The ranging value is used to reflect the strength of the sensor's representativeness of the stacking environment. The shorter the distance, the higher the correlation between its collection parameters and the center point. For example, a temperature sensor is arranged about 1 meter away from the tobacco stack, and the other is arranged 3 meters away. After the distances of multiple sensors in the same area and a center point are calculated, a sensor ranging data group is formed. The data set not only records the physical distance between each sensor position and the center point, but also establishes a spatial correspondence between sensor parameters and the environmental characteristics of the stacking point. In practical applications, for example, five sensors are deployed in a warehouse area, at distances of 0.8 meters, 1.1 meters, 1.6 meters, 2.3 meters, and 2.8 meters from the target stacking center. The ranging results can identify three sensors within a range of 1.5 meters as high-weight points, whose data has a greater impact on the target stacking point. A complete spatial ranging matrix is thus constructed, and the ranging calculation is cyclically executed in each area of the storage warehouse. Based on the density distribution of tobacco leaf stacking, the distribution mapping of all measuring points to the center point is completed, thus obtaining a sensor ranging data set with a complete structure and comprehensive coverage.

[0026] S112: Based on the sensor ranging data set, calculate the spatial correlation between the temperature, humidity, and oxygen parameters in the same area and the ranging data set, analyze the variation characteristics of the parameters with spatial distribution, optimize the performance of similar parameters in each area, and obtain the regional environment fusion index; The temperature, humidity and oxygen parameters collected by each sensor in the same area are processed by spatial distance weighting. For each type of parameter, the weight is set according to the distance value from the collection point to the target stack center. The weight decreases as the distance increases. The original parameter value of each sensor is multiplied by the corresponding distance weight, and then the weighted parameters of this type are summed and divided by the weight sum to obtain the representative weighted temperature, humidity and oxygen concentration values of the area, forming a preliminary fusion parameter set. For example, the five temperature points in a certain area correspond to distances of 0.5 meters, 0.8 meters, 1.5 meters, 2.2 meters and 3.0 meters respectively. The weights are assigned according to the distance. After recalculating and weighting, the regional temperature fusion value is 25.6°C. A similar method is used for humidity and oxygen. Furthermore, based on the fusion results, the parameter differences between adjacent sensors are statistically analyzed by analyzing the spatial distribution characteristics of each parameter value. For example, if there is a 1.8% difference in the humidity fusion value in a row of stacks, it is considered that the humidity changes greatly. After smoothing the fusion results of the region, a partitioned environmental fusion index is constructed. This fusion index not only includes the fusion value of the parameter itself, but also records its spatial consistency, fluctuation intensity and distribution trend, etc., to achieve a centralized expression of the internal environmental characteristics of the storage area.

[0027] S113: Based on the partition environment fusion index, the temperature, humidity, and oxygen parameters of each area are screened, the corresponding relationship between the internal parameter characteristics of each partition is analyzed, and the parameter performance of the same partition is determined and summarized to obtain the storage space environment characteristics; For each storage area, the three parameters of temperature, humidity, and oxygen are extracted and fused. A set structure of the three parameters within the partition is established for each category. This structure then determines pairwise associations between each pair of parameters. For example, it determines whether a temperature change is accompanied by a humidity decrease, or whether an oxygen concentration increase is accompanied by a temperature change. By comparing the three parameter pairs recorded for the partition over multiple consecutive time periods, it is determined whether the change directions of each pair of parameters are consistent. If the proportion of simultaneous temperature increases and humidity decreases in multiple time periods exceeds 70%, the pair of parameters is considered to have a stable trend relationship within the partition. Statistical features of each parameter category are extracted, including maximum, minimum, and median values, and their dispersion within the partition is further calculated. For example, if the maximum fused temperature value in a certain area is 27.5°C, the minimum is 25.1°C, and the median is 26.0°C, then the temperature distribution in this area is relatively concentrated and can be classified as a temperature-stable area. After processing the humidity and oxygen data using the same method, the corresponding performance of the three parameters is summarized to form an overall environmental characteristic label for the area. This is used to subsequently identify storage climate types and identify areas with abnormal distribution, thereby obtaining storage space environmental characteristics.

[0028] See also Figure 3 , the specific steps for obtaining the abnormal characteristics of airflow structure are: S211: Based on the environmental characteristics of the storage space, the airflow velocity collected by the wind speed sensor in each area is compared with the tobacco leaf stacking density, the area with low airflow velocity and high stacking density is selected, the corresponding relationship between the area numbers is determined, and the wind-tight interaction area is obtained; Read the real-time data of the wind speed sensor in the storage space, record the wind speed value of each area and the corresponding area number information, and extract the tobacco stacking density data in the area. The density data is obtained by converting the weight of tobacco leaves per unit volume. For example, if the stacking volume of a certain area is 2 cubic meters and the mass of tobacco leaves is 160 kilograms, then its stacking density is 80 kilograms / cubic meter. After establishing a one-to-one correspondence between the wind speed and stacking density in each area, the preset low-value threshold of wind speed is set to 0.25 meters / second, and the high-value threshold of stacking density is set to 70 kilograms / cubic meter. Perform conditional screening on each numbered area, and only retain regional data points that meet the wind speed of less than or equal to 0.25 meters / second and the stacking density of greater than or equal to 70 kilograms / cubic meter. During the execution process, all area numbers are compared in turn to see if the two parameters are met at the same time, and a list of area numbers that meet both conditions is screened out. For example, numbers A03, B07, and D12 meet this condition, that is, the wind speeds are only 0.18, 0.22, and 0.19 m / s, and the corresponding stacking densities are 75, 78, and 83 kg / m3, respectively. A03, B07, and D12 are marked as areas that meet the screening criteria. Their spatial distribution positions are further located by area numbers, and their correspondence with environmental characteristics is recorded and organized into a numbered area table. The table records the wind speed value, density value, and location coordinate information corresponding to each interactive area, and obtains the intersection area where the wind speed is lower than the threshold and the density is greater than the set threshold.

[0029] S212: Based on the wind-tight interaction area, analyze the changes in temperature and humidity in each numbered area, calculate the amplitude of temperature and humidity fluctuations in the same area, integrate the fluctuation amplitude data, optimize the environmental fluctuation conditions of each area, and obtain the integrated environmental fluctuation amplitude; Extract the temperature and humidity data within a 24-hour period in each numbered area, and form two independent time series data groups with the temperature and humidity readings at each time point. Take the absolute values of the temperature difference and humidity difference between two adjacent moments in each time series and summarize them to obtain a set of hourly fluctuation values. For example, the temperature changes in area A03 from 8:00 to 20:00 are 0.3, 0.1, 0.4, 0.2, etc., and the hourly temperature fluctuation amplitude is no more than 0.5 degrees Celsius. The humidity changes are 1.2%, 0.8%, 1.5%, 1.1%, etc., and the humidity fluctuation is less than 2%. Then record the temperature and humidity fluctuations of A03 as the respective parameter fluctuation value sets. By performing the same processing on all wind-tight interaction areas, the respective temperature and humidity fluctuation amplitude data are obtained, and then the temperature fluctuation and humidity fluctuation are summed up and averaged in units of area. The average value is used to obtain the environmental fluctuation performance value of a single area. For example, the total temperature fluctuation of area B07 throughout the day is 4.5 degrees Celsius, and the total humidity fluctuation is 19%. The average values are 0.375 degrees Celsius and 1.58% humidity, respectively. The average temperature and humidity fluctuation data of all wind-dense interaction areas are then merged into a set of environmental fluctuation performance sets. The set is classified by area number, and its temperature fluctuation mean, humidity fluctuation mean and combined fluctuation amplitude are sorted out. The combined fluctuation amplitude is defined as the sum of the temperature fluctuation mean and the humidity fluctuation mean, and the units are unified. For example, if the temperature fluctuation mean is 0.4 degrees Celsius and the humidity is 1.2%, the combined fluctuation amplitude is defined as 1.6 units. By comparing the combined fluctuation amplitudes of each area, the environmental stability performance of the area under the conditions of insufficient wind speed and concentrated density is judged, and the environmental fluctuation fusion amplitude of each numbered area is obtained.

[0030] S213: Based on the fusion amplitude of environmental fluctuations, the ventilation path length of each area, the number of equipment air outlets, and the sensor density are integrated, using the formula: ; Adjust the data fusion method, optimize the regional abnormal structure performance, and obtain the abnormal characteristics of the airflow structure ,in, Indicates the total number of overlapping area numbers, Indicates the The temperature fluctuation range of the region, Indicates the The humidity fluctuation range of the region, Indicates the The ventilation path length of the area, Indicates the The number of equipment air outlets in the area, Indicates the The sensor density of the area.

[0031] The abnormal characteristics of airflow structure refer to indicators that reflect whether the airflow distribution in each area of the tobacco storage warehouse is uneven, abnormal, or inconsistent with the expected ventilation structure, based on monitoring and structural information such as wind speed, stacking density, temperature and humidity fluctuations, ventilation paths, equipment distribution, and sensor density. After analysis and calculation, it is used to comprehensively measure and identify the severity and spatial distribution of areas with abnormal airflow in the ventilation system within the storage space, providing data support for subsequent zoning control and equipment priority adjustment.

[0032] The structural parameters such as the ventilation path length, the number of equipment air outlets, and the sensor density of each wind-tight interaction area are integrated. After dimensionality unification of each participating item, the data are normalized into dimensionless data for calculation. In the calculation process, it is assumed that: Temperature fluctuation range in area 1 The humidity fluctuation range is 2.1℃. 5.0%RH, ventilation path length 12.0m, number of air outlets of the equipment 2, sensor density 0.4 pieces / m 2 , after normalization, they are set to , , , , ; Area 2 , , , , ; Region 3 , , , , ; Substitute the above normalization parameters into the formula: In region 1, the numerator is , the denominator is ,have to ; In region 2, the numerator is , the denominator is ,have to ; In region 3, the numerator is , the denominator is ,have to ; Sum the results of all area calculations ; The results show that the comprehensive assessment value of the degree of airflow anomaly in the overlapping area structure is 11.697, representing the combined anomaly intensity of this group of areas in the current storage space in terms of temperature and humidity fluctuations, ventilation structure complexity, and sensor distribution characteristics. This value can provide a decision-making basis for the next stage of zoning control priority. The formula achieves full-parameter mapping of structural anomaly performance by multiplying the fluctuation amplitude and path complexity, and then compressing and transforming them through hardware distribution constraints.

[0033] See also Figure 4 The specific steps for obtaining the sensitive deviation of the variety partition are as follows: S311: Based on the abnormal characteristics of the airflow structure, analyze the temperature, humidity, and oxygen concentration monitored in the intersection area with the environmental range set in the variety grading file, determine whether there is any deviation in the data, select the data that meets the deviation conditions, and generate parameter deviation items; Select the numbered list of identified wind-tight interaction areas, extract the real-time monitoring data of temperature, humidity and oxygen concentration in the area corresponding to each number in turn, and retrieve the grading file parameters of the tobacco variety corresponding to the area. The file parameters include the target storage temperature range, humidity range and oxygen concentration range. For example, the storage parameter range set for a batch of K326 tobacco is temperature 22~26℃, humidity 55%~65%, and oxygen concentration 17%~20%. Compare the monitoring data with the above range values item by item to determine whether it exceeds the upper limit or is lower than the lower limit. For example, if the current temperature in a certain area is 27.4℃, the humidity is 62%, and the oxygen concentration is 18.5%, then the temperature item is higher than the upper limit and is judged to be deviated, and the humidity item and oxygen item are within the set range and are judged to be not deviated. The above judgment results are summarized to generate a preliminary A judgment list is created, which records the area number, the exceeded parameter category, the actual parameter value, and the corresponding target interval range. After executing the same process in all intersection areas, the judgment list is further screened, and only the recorded data with deviations are retained to form a parameter deviation item list. In this list, the spatial area number, deviation parameter type, and numerical deviation direction of each deviation record are marked. For example, the temperature in area B07 is 27.9°C, which is 2.9°C higher than the specified value; the oxygen concentration in area C11 is 15.6%, which is 1.4% lower than the specified value; and the humidity in area D04 is 67.2%, which is 2.2% higher than the specified value. All deviation information is uniformly converted into deviation amounts expressed as positive values, and combined with the parameter type and area number to form a three-field set to construct a complete parameter deviation item data group, thereby filtering out various parameter abnormality record data that meet the deviation conditions.

[0034] S312: Based on the parameter deviation item, compare the spatial region numbers corresponding to each type of data, count the occurrence frequencies of each type of data deviation in the spatial region, optimize the spatial distribution performance of similar parameters, and obtain the distribution quantity of category parameters; Classify the temperature, humidity, and oxygen parameters involved in each deviation record, extract the area numbers corresponding to all temperature deviation items into a group, and group the humidity deviation items and oxygen deviation items similarly. Then count the frequency of occurrence of the area number corresponding to each type of data. For example, if the temperature deviation items involve areas A03, B07, A03, D04, and A03, then the temperature deviation occurs 3 times in the A03 area, 1 time each in B07 and D04. Establish a frequency statistics table, and each row records the parameter type, area number, and number of deviation occurrences. At the same time, set the baseline value of the total number of sampling times in the spatial area. For example, a total of 24 hours × 1 hour / time = 24 times of data are collected during the statistical period, then the frequency of A03 temperature deviation is 3 / 24 = 1 2.5%, and set the significant deviation threshold to 10%. The temperature deviation of A03 is judged to be a frequent deviation area. This type of statistical logic is used to process the deviation items of all parameter types and complete the spatial frequency judgment. All records with frequencies higher than the set deviation threshold are summarized. Then, spatial number clustering statistics are performed on similar parameters. The total number of concentrated area numbers of temperature deviation items, humidity deviation items, and oxygen deviation items are recorded respectively. For example, the statistical results show that temperature deviation items appear in 7 numbered areas, humidity in 4, and oxygen in 5. This is used as the basic data for the distribution quantity of the category parameters. The deviation quantity of the three types of parameters in the spatial area is summarized to obtain the distribution quantity of the category parameters.

[0035] S313: Based on the number of category parameter distributions, combined with the response levels and environmental adaptability differences of varieties in each region, the relationship between the deviation performance and response characteristics of each region under the difference data category is analyzed using the formula: ; The parameter distribution and response differences in each region were screened to obtain the sensitive deviation of the variety partition, where: Representative Variety partition sensitivity deviation of spatial regions, Representative The spatial region in The frequency of deviations from monitoring under category parameters (e.g. temperature, humidity, oxygen), Representative The spatial region The category parameter corresponds to the response level parameter of the variety. Representative The spatial region Category parameters correspond to the environmental adaptation difference parameters of varieties, Indicates the total number of category parameters involved in the calculation (temperature, humidity, and oxygen are three categories, and the actual number is determined by the set parameter category).

[0036] Variety zoning sensitivity deviation refers to the quantitative results of the overall sensitivity and deviation range of the region-variety to storage environment anomalies for different spatial areas and different tobacco varieties in the tobacco storage warehouse, based on the target environmental requirements of each variety classification file (such as temperature, humidity, and oxygen concentration). The deviation is analyzed. Then, combined with the response level and adaptability of the variety to environmental changes, it comprehensively reflects the safety risk or regulation urgency of each area and each variety of tobacco in the current environment, which is convenient for the subsequent intelligent control system to configure the equipment action priority and make dynamic adjustments.

[0037] The deviation frequencies of the three parameters of temperature, humidity, and oxygen obtained in the previous step are called, and the response level parameters and environmental adaptation difference parameters set in the archive of each variety are matched respectively. A one-to-one correspondence is maintained according to the parameter category. By analyzing the offset direction and degree between the response level and the adaptation difference, and superimposing the deviation frequency of the parameters in the actual space, a comprehensive expression is formed to reflect the response characteristics of the variety in each area to the current environmental state. The total number of parameter categories involved in the calculation is set to 3. The response levels and adaptation differences of different indicator types are normalized into dimensionless standardized values. The following calculation is based on the spatial area number A1. The deviation frequencies of the three types of parameters in area A1 are: temperature 3 times, humidity 4 times, and oxygen 2 times. The corresponding normalized response levels are: temperature 2.0, humidity 2.5, oxygen 1.8, and the normalized adaptation differences are: temperature 1.0, humidity 1.3, oxygen 1.1. Substitute them into the formula for calculation as follows: The first item is temperature: , , , calculated ; Item 2 is humidity: , , , calculated ; Item 3 is oxygen: , , , calculated ; Adding the above three results, we get: ; The results show that in area A1, based on the current environmental parameter deviation performance, combined with the response level and adaptability of the variety, the variety zoning sensitive deviation in this area is 3.428, which can be used as a basis for priority division and zoning identification in subsequent regulatory links. By combining the spatial deviation performance with the difference in variety environmental adaptation, the formula can collaboratively quantify regional anomalies and variety characteristics, and construct an environment and variety coupling response system.

[0038] See also Figure 5 , the specific steps for obtaining the device hierarchical control configuration are: S411: Based on the sensitive deviation of the product partition, the abnormal area identification of the temperature, humidity, and oxygen parameters is extracted. The device identification of each control device and the space identification it covers are called. The space identification covered by the device is compared with the abnormal area identification. Devices with overlapping space identifications are screened out to generate abnormal device association information. Extract the abnormal area identification of the three identified parameters of temperature, humidity and oxygen in the spatial distribution. The abnormal area identification is a set of area numbers with significant deviation values and a frequency exceeding the set threshold. Establish a list of abnormal areas for the three types of parameters, such as temperature abnormal areas A01, A03, and B02, humidity abnormal areas B02 and C01, and oxygen abnormal areas A03, C01, and D04. Then retrieve the information of all control devices in the control device management library. This information must include the device identification code of each device and the spatial number identification of the device's action area to clarify the device control coverage. For example, device FT-01 acts on A01 and A02, device FT-02 covers A03 and B02, device HT-03 covers C01 and C02, and device EX-05 covers B03, C01, and D04. For each type of abnormal parameter item, call its area number list and compare it with the space covered by each device. The system compares the space number list with the area number list and determines whether there are overlapping area numbers through set intersection. If at least one number in the device control range is consistent with the abnormal area number, the device is considered to be associated with the abnormal parameter. All devices that meet the space number intersection condition are screened out, and a three-field record table of "abnormal parameter type-area number-device number" is constructed. For example, in the case of temperature parameter abnormality, A01 is covered by FT-01, A03 is covered by FT-02, and B02 is also covered by FT-02. In this case, FT-01 and FT-02 are recorded as temperature abnormality-associated devices. In the case of humidity abnormality, C01 is covered by both HT-03 and EX-05. In this case, both are recorded as humidity abnormality-associated devices. In the case of oxygen abnormality, D04 matches EX-05, and A03 matches FT-02. This forms a list of associations between this type of abnormal devices and the corresponding parameters, and summarizes all matching items to generate abnormal device association information.

[0039] S412: Based on the abnormal device association information, the devices are grouped by parameter type, and the number of overlapping areas of each group of devices on the spatial identifier is counted as the distribution overlap number, using the formula: ; Calculate the impact offset of the equipment, sort the equipment groups according to the offset, extract the equipment identifiers in the top-ranked groups, and obtain the abnormal control distribution gradient, where: Indicates the The device impact offset of the group, Indicates the The number of group devices, Indicates the No. The number of spatially overlapping areas of the devices, Indicates the The frequency of abnormal areas appearing in the working space of the device, Indicates the The monitoring deviation degree of the control parameters of the equipment in the covered space, Indicates the The total number of abnormal spaces associated with the group devices, Indicates the The total number of all space identifiers in the group; The equipment impact offset amplitude refers to the quantified result of the comprehensive offset between the overall control effect of a group of control equipment (such as air supply, humidification, exhaust, etc.) and the abnormal environmental distribution under the background of spatial distribution and parameter abnormalities. It reflects the degree of matching and response deviation between this group of equipment and the abnormal area of the partitioned environment in terms of space and parameter control intensity, that is, the distance or degree of inconsistency between the actual control capability of the equipment and the requirements of the abnormal area in the space. It is a comprehensive measure of the degree of deviation of the equipment group from the degree of control fit of the current abnormal area in the space. The smaller the value, the more reasonable the equipment distribution and control status, while the larger the value, the need to optimize the equipment control configuration.

[0040] Each device is grouped according to the type of parameter it controls. For example, air supply devices are classified according to temperature control, humidification devices are classified according to humidity control, and exhaust devices are classified according to oxygen control. For each type of device, the number of overlapping areas between its coverage space and the space covered by other devices in the group is counted, and this is defined as the spatial distribution overlap of the device. ,Three devices are selected from the temperature equipment group for calculation. The original spatial overlap numbers are 12, 9, and 11 respectively. After dimension normalization, they are 0.80, 0.60, and 0.73 respectively. At the same time, the frequency of abnormality in the space covered by the corresponding equipment in environmental monitoring is extracted and recorded as The original frequencies are 5, 3, and 6, which are normalized to 0.71, 0.43, and 0.86. At the same time, the monitoring deviation amplitude of the equipment control parameters in its coverage space is called, which is recorded as The original deviations are 2.5, 1.8, and 2.1, which are 0.83, 0.60, and 0.70 after normalization. , the number of devices is , Substitute the above normalized parameters into the formula, and the calculation process is as follows: 1st device: ; Second device: ; 3rd device: ; The summation result is: ; The first mean is: ; Then set the number of spaces associated with this group of devices in all abnormal spaces , total number of space identifiers , after normalization, set to 0.78 and 0.86, substituting into the second term: ; The impact offset of this group of devices is obtained as follows: ; The results show that the temperature parameter equipment group has a spatial control matching deviation of 0.4762 for the current abnormal area, which provides a sorting basis for subsequent equipment sorting and priority control, thereby forming an abnormal control distribution gradient. The formula introduces the degree of deviation of the control parameters. and abnormal frequency The product of square root type regulation demand benchmark is constructed and compared with the spatial coverage behavior of the equipment Perform difference calculations to effectively identify the offset structure of the equipment control structure, and reflect the basis for control accuracy in the subsequent sorting and grading of control equipment.

[0041] S413: Based on the abnormal control distribution gradient, the current control logic priority and associated parameter type of each device are collected, the devices and their corresponding environmental parameter types are classified, the control status and spatial distribution of each group of devices are detected, the device operation priority is assigned, and the device hierarchical control configuration is obtained; First, the control logic priority of each control device and its associated environmental parameter type information are extracted. The device number, parameter category, and current priority value are recorded. For example, FT-01 corresponds to temperature and priority 2, HT-03 corresponds to humidity and priority 4, and EX-05 corresponds to oxygen and humidity and priority 3. Devices are categorized according to their associated parameter types, and lists of temperature, humidity, and oxygen devices are constructed. Within each list, the devices are sorted from lowest to highest priority. The spatial area number controlled by each device is then retrieved. For example, FT-01 affects A01 and A02, and FT-02 affects A02 and B01. The correspondence between the control areas and numbers of all devices is recorded. Areas with overlapping control ranges are identified, and the priority values of the corresponding devices in these areas are compared with the current control status. If two devices are found to be activated simultaneously in the same area and the priority difference is less than or equal to 1, they are recorded as a conflicting device group. The higher priority device is retained, and the lower priority device is downgraded to avoid interference. A complete device control configuration table is generated, which records each device number, parameter type, control area number, and adjusted priority value, forming a hierarchical device control configuration.

[0042] See also Figure 6 , the specific steps for obtaining the control parameter correction results are: S511: Based on the equipment hierarchical control configuration, analyze the equipment number and the collected data of temperature, humidity, and oxygen concentration in the corresponding area, determine the change direction and trend of the parameters before and after the equipment is operated, compare the correlation of the parameter trends under the action of different equipment, and obtain the trend classification structure; Extract the number of each device and the number of the space area it controls. Read the monitoring data of the three parameters of temperature, humidity, and oxygen concentration in the area at each collection time before and after the device is activated. Construct a time series data group, set the device activation time point as the dividing line, and analyze the parameter change direction of the four groups of data before and after the action in groups to determine whether the parameter is rising, falling, or stable. For example, if the device FT-01 acts on area A03, the temperature before the action is 25.3, 25.4, 25.4, 25.5, and after the action is 25.2, 25.1, 24.9, 24.8, it is judged to be a downward trend. If there is no significant change in humidity, it is considered stable. If the oxygen concentration rises from 18.2 to 18.8, it is considered an upward trend. After performing the same action trend analysis on the corresponding areas of all devices, the trend direction of each device and parameter type is recorded. Then, the trends of the same parameters under different devices are compared. For example, FT-01 and FT-02 both act on temperature, but FT-01 causes the temperature to drop, while FT-02 has no obvious change. It is determined that there is a response difference between the two devices in temperature control. The trend direction is further classified into rising, falling, and stable categories. Whether there is a trend reversal or the difference exceeds 0.5 units is compared. Such trend differences are marked as trend conflicts. A trend classification structure list is established, recording the device number, area number, parameter type, trend direction, and whether it is consistent with the trend of similar devices for subsequent classification analysis.

[0043] S512: Based on the trend classification structure, the matching relationship between the device motion amplitude and the environmental parameter trend is optimized, and the regional combinations where the motion effect and environmental changes are not synchronized are screened. The inconsistent response parts are classified and sorted to obtain the response difference combination; The parameter trend and amplitude of each device are compared item by item. The device amplitude is extracted as the output intensity or operating power level in the control settings. The amplitude and the direction of parameter change should correspond. For example, when the device FT-01 is in the maximum air supply mode (output level 5), the regional temperature only drops by 0.2 units, which is considered insufficient response. When the device HT-03 is in the minimum humidification mode, the regional humidity increases by 1.9 units, which is considered excessive response. The response difference judgment thresholds are set to ±0.5 units for temperature, ±2 units for humidity, and ±1 unit for oxygen. The difference between the amplitude of each device and the environmental response is calculated and categorized, and the logical consistency of the response direction and amplitude is recorded. If the actual trend direction is inconsistent with the expected device movement direction or the change value does not reach 80% of the control range, it is recorded as inconsistent response. The device numbers and area numbers with inconsistent responses are classified and aggregated based on the trend classification list. This forms a combination structure of abnormal responses of each parameter type in different areas. This combination information is organized into response difference combinations.

[0044] S513: Based on the response difference combination, adjust the device operation priority and the corresponding relationship between the device number, analyze the feedback performance of the previous action, judge the rationality of the device sequence setting and the actual effect, optimize the operation sequence of the next cycle, and obtain the control parameter correction result; Retrieve the operation priority setting value of the device in the current control logic, and compare the rationality between its response performance and the priority level. For example, the current priority of EX-02 is 2, but there are inconsistent responses in multiple areas. It is determined that the response capability of the device does not match the existing ranking, so its priority is raised to 4. At the same time, check the action sequence position of the device in the control task. If it is the first execution device of a certain type of parameter but the feedback effect is insufficient, it is recorded as an unreasonable sequence setting. Then check the control effect of the preceding device on the same parameter. If the preceding device has a more obvious control effect, adjust the order relationship between the two, move the device with better effect to the front, and the low-response device to the back, and rebuild the operation sequence of all devices in the parameter category, update the correspondence between the number and priority, form the device execution sequence and priority structure configuration for the next cycle, and obtain the control parameter correction result.

[0045] An intelligent control system for tobacco storage environment, the system comprising: The environmental parameter fusion module, based on the tobacco storage bin, analyzes the temperature collected by the temperature sensor, combines it with the humidity data from the humidity sensor, and then calls the oxygen parameters from the oxygen concentration sensor to calculate the spatial distance between the sensor measurement point and the center of the tobacco stack. It then takes a weighted average of the parameters in the same area based on the distance to obtain the storage space environmental characteristics. Based on the environmental characteristics of the storage space, the airflow analysis module screens the airflow data monitored by wind speed sensors in each area, compares the airflow speed in each area with the tobacco stacking density parameters, identifies the end of the sequence based on wind speed, and identifies the front of the sequence based on stacking density, extracts the intersection area, and analyzes the standard deviation of temperature and humidity changes in the intersection space to obtain abnormal characteristics of the airflow structure; The variety adaptability discrimination module, based on the abnormal characteristics of airflow structure, determines the relationship between the variety grading file parameters of each tobacco leaf stacking area and the target environmental range of the current storage stage. It analyzes whether the temperature, humidity, and oxygen concentration in the intersection area exceed the corresponding variety standards. It then groups each parameter item by item, collects statistics on the areas that exceed the standards, and obtains the variety zone sensitivity deviation. The equipment configuration module compares the distribution of highly sensitive parameter areas with each control device based on the sensitivity deviation of each product zone. It then determines the air supply, humidification, and exhaust devices associated with temperature, humidity, and oxygen anomalies. It then identifies the intersection of device control numbers and anomaly numbers, assigns device operation priorities, and obtains hierarchical control configuration for the equipment. The control parameter correction module is based on the equipment hierarchical control configuration, calls the equipment number and area, analyzes the temperature, humidity and oxygen concentration time series collected before and after the operation of each device, calculates the trend change of each group of parameters, and compares it with the current equipment operation amplitude and duration. According to the difference in trend amplitude, the operation priority of the next period equipment is corrected to obtain the control parameter correction result.

[0046] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for intelligently controlling tobacco storage environment, characterized in that: The following steps are involved: S1: Based on the tobacco storage warehouse, the temperature collected by the temperature sensor is analyzed. Combined with the parameters of the humidity sensor and the oxygen concentration sensor, the spatial distance between the measurement point and the stack center is calculated. The data in the same area is then integrated to obtain the storage space environmental characteristics. S2: Based on the environmental characteristics of the storage space, screen the airflow data from the wind speed sensors in each area, compare the regional airflow speed with the stacking density, identify the intersection area of low wind speed and high density, analyze the standard deviation of temperature and humidity changes, and obtain the abnormal characteristics of the airflow structure; S3: Based on the abnormal characteristics of the airflow structure, the relationship between the variety classification file parameters of each stacking area and the target environmental range is determined, and the temperature, humidity and oxygen concentration of the intersection area are analyzed to see whether they exceed the standards. The data are grouped and counted to obtain the variety partition sensitivity deviation; S4: Based on the sensitive deviation of the variety partition, compare the high-sensitivity parameter area with the distribution of the control equipment, determine the intersection of the air supply, humidification, exhaust equipment and the abnormal number, assign equipment operation priority, and obtain the equipment hierarchical control configuration.

2. The method for intelligently controlling tobacco storage environment according to claim 1, characterized in that: The storage space environmental characteristics include temperature and humidity environmental status, oxygen concentration distribution, and parameter correlation indicators; the abnormal characteristics of the airflow structure include airflow distribution status, ventilation resistance performance, and abnormal fluctuation characteristics; the variety partition sensitive deviation includes variety response level, environmental adaptation difference, and deviation distribution characteristics; the equipment hierarchical control configuration includes equipment hierarchical sequence, space control mapping relationship, and operation priority.

3. The intelligent control method for tobacco storage environment according to claim 1, characterized in that: The steps for acquiring the storage space environment characteristics are specifically as follows: S111: Based on the tobacco storage bin, analyze the data collected by the temperature, humidity, and oxygen sensors, calculate the spatial distance between each sensor measurement point and the center point of the tobacco stack, determine the distribution relationship between the measurement points and each center point, and obtain a sensor distance data set; S112: Calculating the spatial correlation between the temperature, humidity, and oxygen parameters in the same area and the ranging data group based on the sensor ranging data group, analyzing the variation characteristics of the parameters with spatial distribution, optimizing the performance of similar parameters in each area, and obtaining a regional environmental fusion index; S113: Based on the partition environment fusion index, the temperature, humidity and oxygen parameters of each area are screened, the corresponding relationship between the internal parameter characteristics of each partition is analyzed, the parameter performance under the same partition is judged and summarized, and the storage space environment characteristics are obtained.

4. The intelligent control method for tobacco storage environment according to claim 1, characterized in that: The steps for obtaining the abnormal characteristics of the airflow structure are specifically as follows: S211: Based on the environmental characteristics of the storage space, comparing the airflow speed collected by the wind speed sensor in each area with the tobacco leaf stacking density, selecting areas with low airflow speed and high stacking density, determining the corresponding relationship between the area numbers, and obtaining the wind-tight interaction area; S212: Based on the wind-tight interaction area, analyze the changes in temperature and humidity in each numbered area, calculate the amplitude of temperature and humidity fluctuations in the same area, integrate the fluctuation amplitude data, optimize the environmental fluctuation conditions of each area, and obtain the environmental fluctuation integration amplitude; S213: Based on the environmental fluctuation fusion amplitude, the ventilation path length of each area, the number of equipment air outlets and the sensor density are integrated, the data fusion method is adjusted, the regional abnormal structure performance is optimized, and the airflow structure abnormal characteristics are obtained.

5. The intelligent control method for tobacco storage environment according to claim 1, characterized in that: The specific steps for obtaining the sensitive deviation of the variety partition are as follows: S311: Based on the abnormal characteristics of the airflow structure, the temperature, humidity, and oxygen concentration monitored in the intersection area are analyzed with the environmental range set in the variety grading file to determine whether any data deviates, and the data that meets the deviation conditions is selected to generate a parameter deviation item. S312: Based on the parameter deviation item, compare the spatial region numbers corresponding to each type of data, count the occurrence frequencies of each type of data deviation in the spatial region, optimize the spatial distribution performance of similar parameters, and obtain the distribution quantity of category parameters; S313: Based on the number of category parameter distributions, combined with the response levels and environmental adaptability differences of varieties in each region, the relationship between the deviation performance and response characteristics of each region under the difference data category is analyzed, and the performance of parameter distribution and response differences in each region is screened to obtain the variety zoning sensitive deviation amount.

6. The method for intelligently controlling tobacco storage environment according to claim 1, characterized in that: The steps for obtaining the device hierarchical control configuration are specifically as follows: S411: Based on the product-zone sensitive deviation, extract the abnormal area identifiers of the temperature, humidity, and oxygen parameters, call the device identifier of each control device and the space identifier it covers, compare the space identifier covered by the device with the abnormal area identifier, filter out devices with overlapping space identifiers, and generate abnormal device association information; S412: Based on the abnormal device association information, the devices are grouped by parameter type, the number of overlapping areas of each group of devices on the spatial identifier is counted as the distribution overlap number, the device impact offset amplitude is calculated, the device groups are sorted according to the offset amplitude, and the device identifiers of the top-ranked groups are extracted to obtain the abnormal control distribution gradient; S413: Based on the abnormal control distribution gradient, collect the current control logic priority and associated parameter type of each device, classify the devices and their corresponding environmental parameter types, detect the control status and spatial distribution of each group of devices, assign device operation priority, and obtain the device hierarchical control configuration.

7. The method for intelligently controlling tobacco storage environment according to claim 1, characterized in that: The steps also include: S5: Based on the device hierarchical control configuration, the device number and area are called, the temperature, humidity, and oxygen concentration time series collected before and after the device operation are analyzed, the parameter trend changes are calculated, the device operation amplitude is compared, the cycle priority is corrected, and the control parameter correction result is obtained; The control parameter correction result includes the control amplitude adjustment basis, periodic action optimization information, and parameter adjustment level.

8. The method for intelligently controlling tobacco storage environment according to claim 7, characterized in that: The steps for obtaining the control parameter correction result are specifically as follows: S511: Based on the device hierarchical control configuration, analyze the device number and the collected data of temperature, humidity, and oxygen concentration in the corresponding area, determine the change direction and trend of the parameters before and after the device is activated, compare the correlation of the parameter trends under the action of different devices, and obtain a trend classification structure; S512: Based on the trend classification structure, optimizing the matching relationship between the device motion amplitude and the environmental parameter trend, screening the regional combinations where the motion effect and the environmental change are not synchronized, classifying and sorting the inconsistent response parts, and obtaining the response difference combination; S513: Based on the response difference combination, adjust the correspondence between the device operation priority and the device number, analyze the feedback performance of the previous action, judge the rationality of the device sequence setting and the actual effect, optimize the operation sequence of the next cycle, and obtain the control parameter correction result.

9. An intelligent control system for tobacco storage environment, characterized in that: The system is used to implement the tobacco storage environment intelligent control method according to any one of claims 1 to 8, and the system includes: The environmental parameter fusion module, based on the tobacco storage bin, analyzes the temperature collected by the temperature sensor, combines it with the humidity data from the humidity sensor, and then calls the oxygen parameters from the oxygen concentration sensor to calculate the spatial distance between the sensor measurement point and the center of the tobacco stack. It then takes a weighted average of the parameters in the same area based on the distance to obtain the storage space environmental characteristics. Based on the environmental characteristics of the storage space, the airflow analysis module screens the airflow data monitored by the wind speed sensors in each area, compares the airflow speed in each area with the tobacco leaf stacking density parameters, sorts the wind speed to identify the end area of the sequence, sorts the stacking density to identify the front area of the sequence, extracts the intersection area, and analyzes the standard deviation of the temperature and humidity changes in the intersection space to obtain the abnormal characteristics of the airflow structure; Based on the abnormal characteristics of the airflow structure, the variety adaptability discrimination module determines the relationship between the variety grading file parameters of each tobacco leaf stacking area and the target environmental range of the current storage stage. It analyzes whether the temperature, humidity, and oxygen concentration in the intersection area exceed the corresponding variety standards. It groups the parameters one by one, collects statistics on the areas that exceed the standards, and obtains the variety zone sensitivity deviation. The equipment configuration module compares the distribution of highly sensitive parameter areas with each control device based on the sensitive deviation of the product partition, determines the air supply equipment, humidification equipment, and exhaust equipment associated with temperature, humidity, and oxygen anomalies, identifies the intersection of the equipment control number and the anomaly number, assigns equipment operation priority, and obtains the equipment hierarchical control configuration; The control parameter correction module calls the device number and area based on the device hierarchical control configuration, analyzes the temperature, humidity and oxygen concentration time series collected before and after the operation of each device, calculates the trend change of each group of parameters, compares it with the current device action amplitude and duration, and corrects the device operation priority in the next period according to the trend amplitude difference to obtain the control parameter correction result.

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