Confiscated Tobacco Warehouse Based on Multi-Position Triggered Alarm and Its Control Method

Abstract

The present invention discloses a confiscated tobacco warehouse based on multi-position trigger alarm and its control method, which relates to the technical field of tobacco storage. By collecting the environmental parameters, position parameters, characteristic parameters of confiscated tobacco in different storage areas of the warehouse and the mildew rate at the end of the storage cycle, calculating the correlation coefficients between each parameter and the mildew rate respectively, screening out the mildew influence parameters, constructing a prediction model to perform data fitting on the obtained mildew influence parameters and the corresponding mildew rate values at the end of the storage cycle, predicting the mildew rate of confiscated tobacco in different storage areas of the warehouse, calculating the number of tobaccos that have mildew accidents within a fixed range centered on any storage area in a storage cycle, calculating the mildew warning LLR of all storage areas according to the quantity result and the mildew rate prediction value, setting the safety threshold level of the mildew warning LLR, and alarming the risk of mildew occurrence in the storage areas exceeding the set safety threshold of the mildew warning LLR.

Description

Technical Field

[0001] The present invention relates to the technical field of tobacco storage, and particularly to a confiscated tobacco warehouse based on multi-position trigger alarm and its control method. Background Technique

[0002] Confiscated tobacco refers to cigarette products legally purchased or confiscated by tobacco management departments. According to relevant management measures, confiscated tobacco needs to be centrally stored in designated warehouses. Generally, during the tobacco storage process, the warehouse needs to maintain appropriate temperature, humidity and environmental parameters to prevent tobacco from mildewing and causing losses. To ensure that the environment in the warehouse is maintained within a suitable range, monitoring devices for real-time detection of temperature and humidity are usually set in the warehouse. By setting temperature and humidity alarm thresholds, when the set value is exceeded, the alarm mechanism of the monitoring device is triggered.

[0003] The operation state of the early warning mechanism using the threshold method completely depends on the setting of the alarm threshold, with low intelligence. Moreover, due to the diversification of tobacco types and the differentiation of storage environments in the warehouse, it is difficult for this early warning mechanism to meet the actual situation needs, resulting in an increased risk of mildew rate of tobacco in the warehouse. Therefore, we propose a confiscated tobacco warehouse based on multi-position trigger alarm and its control method. Summary of the Invention

[0004] The main purpose of the present invention is to provide a confiscated tobacco warehouse based on multi-position trigger alarm and its control method, which can effectively solve the problems in the background technique.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0006] A control method for a confiscated tobacco warehouse based on multi-position trigger alarm includes:

[0007] Collecting the environmental parameters, position parameters, characteristic parameters of confiscated tobacco in different storage areas of the warehouse and the mildew rate at the end of the storage cycle;

[0008] Calculating the correlation coefficient between each parameter and the mildew rate respectively, and screening out the parameters that are positively correlated with the mildew rate as mildew influence parameters according to the calculation results. Among them, the calculation formula of the correlation coefficient is:

[0009]

[0010] In the formula, ρ it is the correlation coefficient between the i-th parameter and the mildew rate at the end of the t-th storage cycle; P it is the value of the i-th parameter during the t-th storage cycle; is the average value of the i-th parameter in all storage areas at the end of the t-th storage cycle; Mr it is the mildew rate at the end of the t-th storage cycle; is the average mildew rate in all storage areas at the end of the t-th storage cycle; n is the number of the i-th parameter values in the t-th storage cycle;

[0011] Construct a prediction model to perform data fitting on the obtained mildew influence parameters and the mildew rate values at the end of the corresponding storage cycle, and predict the mildew rate at the end of the (t + 1)-th storage cycle according to the prediction model, where the expression of the prediction model is:

[0012]

[0013] In the formula, Mr i(t+1) is the mildew rate at the end of the (t + 1)-th storage cycle; is the coefficient of the i-th mildew influence parameter;

[0014] Randomly select any storage area in the warehouse, calculate the number of tobacco products that have mildew accidents within a fixed range centered on this storage area in T storage cycles, and calculate the mildew warning LLR according to the calculation result and the obtained predicted mildew rate value. The calculation formula is:

[0015]

[0016] In the formula, Mr LLR is the mildew warning LLR; R is the number of all tobacco products that have mildew accidents within the fixed range centered on this storage area; R A is the number of tobacco products that have mildew accidents within the fixed range centered on this storage area in T storage cycles; where c is the total amount of stored tobacco products, Mr is the mildew rate, represents within the range of all storage cycles, represents within the range of T storage cycles, represents within the Z area range;

[0017] Set the safety threshold level of the mildew warning LLR, calculate the mildew warning LLR of the remaining storage areas in the warehouse in turn, and alarm the mildew occurrence risk for the storage areas that exceed the set safety threshold of the mildew warning LLR.

[0018] The confiscated tobacco warehouse based on multi-position trigger alarm includes:

[0019] A data acquisition module, which is evenly distributed in a matrix form in each storage area of the warehouse, and is used to collect the environmental parameters, position parameters, characteristic parameters of the confiscated tobacco products in the storage area and the mildew rate at the end of the storage cycle;

[0020] A data processing module, which is connected to the data acquisition module and is used for preprocessing the collected data, including data cleaning and filling in missing values;

[0021] A data analysis module, which is connected to the data processing module and is used for calculating the correlation coefficients between each parameter and the mildew rate respectively according to the preprocessed data, and screening out the parameters that are positively correlated with the mildew rate as mildew influence parameters according to the calculation results;

[0022] A data fitting module, which is used for constructing a prediction model, fitting the obtained mildew influence parameters and the mildew rate values at the end of the corresponding storage period through the prediction model, and predicting the tobacco mildew rate at the end of the next storage period for each storage area according to the prediction model;

[0023] An early warning control module, which is used for constructing an early warning model, calculating the number of tobaccos with mildew accidents within a fixed range centered on any storage area in the warehouse within T storage periods through the early warning model, calculating the mildew early warning LLR according to the calculation results and the obtained mildew rate prediction values, setting the safety threshold level of the mildew early warning LLR, and calculating the mildew early warning LLR for the remaining storage areas in the warehouse in turn, and generating an early warning signal for the storage areas that exceed the set safety threshold of the mildew early warning LLR;

[0024] An alarm module, which is connected to the early warning control module and, in response to the early warning signal generated by the early warning control module, alarms the risk of mildew occurrence for the storage areas that exceed the set safety threshold of the mildew early warning LLR.

[0025] The warehouse further includes:

[0026] A storage module, which is connected to the data acquisition module and is used for storing the obtained environmental parameters, position parameters, characteristic parameters and the mildew rate at the end of the storage period.

[0027] The warehouse further includes:

[0028] A visualization module, which is connected to the alarm module and is used for visually displaying the level of the risk of mildew occurrence, where the level of the risk of mildew occurrence includes low risk, medium risk and high risk.

[0029] The warehouse further includes a memory, a processor and an electronic program stored in the memory and capable of running on the processor.

[0030] Further, the fixed range includes the area of a region with a radius of d centered on the storage area, where the value of d is determined according to the shelf structure and space parameters of the warehouse, and the determination principle is:

[0031] When the shelf structure of the warehouse is a through-aisle shelf,

[0032] When the shelf structure of the warehouse is a shuttle shelf,

[0033] where S is the floor area value of the warehouse shelves, is the average distance between adjacent shelves in the warehouse.

[0034] Furthermore, the storage cycle is determined according to the inbound and outbound time of tobacco in the warehouse, and the determination formula is: where Mc is the duration of the storage cycle, in hours, t m is the residence time of the m-th type of tobacco in the warehouse, and q is the number of tobacco types in the warehouse.

[0035] Furthermore, the value range of T is determined according to the storage cycle duration and the empirical formula, and the formula is: where is the floor function operation, is a constant in the range of [0.5, 1].

[0036] Furthermore, the method for setting the mildew warning LLR safety threshold level includes:

[0037] Obtain the historical data of the mildew warning LLR values within k storage cycles, and create a sample set using the historical data of the mildew warning LLR values, denoted as where k is the total number of samples of the historical data obtained;

[0038] Obtain the mean and standard deviation in the sample set, and standardize the data using the mean and standard deviation. The standardization formula is In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0039] After completing the standardization, use the standard parameter Adjust the numerical range to between [0, 1], and classify the mildew warning LLR values using the function value of f(k). The classification mechanism is:

[0040] When f(k) min ≤ f(k) < f(k) 1 the mildew warning LLR value is classified as level one;

[0041] When f(k) 1 ≤ f(k) < f(k) 2 the mildew warning LLR value is classified as level two;

[0042] When f(k)2 ≤ f(k) < f(k) max When this occurs, the mildew warning LLR value is classified as level three;

[0043] Among them, f(k) min < f(k) 1 < f(k) 2 < f(k) max , f(k) min , f(k) max are respectively the minimum and maximum values of the function value of f(k), and f(k) 1 , f(k) 2 are respectively the intermediate values of the function value of f(k).

[0044] Furthermore, the principle for determining the risk of mildew occurrence is as follows:

[0045] When the mildew warning LLR value is classified as level one, the mildew occurrence level is low risk;

[0046] When the mildew warning LLR value is classified as level two, the mildew occurrence level is medium risk;

[0047] When the mildew warning LLR value is classified as level three, the mildew occurrence level is high risk.

[0048] The present invention has the following beneficial effects

[0049] Compared with the prior art, the technical solution of the present invention collects the environmental parameters, location parameters, characteristic parameters of the confiscated tobacco in different storage areas of the warehouse and the mildew rate at the end of the storage cycle, calculates the correlation coefficients between each parameter and the mildew rate respectively, screens out the parameters that are positively correlated with the mildew rate as the mildew influencing parameters according to the calculation results, constructs a prediction model to perform data fitting on the obtained mildew influencing parameters and the mildew rate values at the end of the corresponding storage cycle, predicts the mildew rate of the confiscated tobacco in different storage areas of the warehouse at the end of the (t + 1)-th storage cycle according to the prediction model, randomly selects any storage area in the warehouse, calculates the number of tobaccos that have mildew accidents within a fixed range centered on this storage area within T storage cycles, calculates the mildew warning LLR according to the calculation results and the obtained mildew rate prediction values, sets the safety threshold level of the mildew warning LLR, calculates the mildew warning LLR of the remaining storage areas in the warehouse in turn, and alarms the risk of mildew occurrence for the storage areas that exceed the set safety threshold of the mildew warning LLR. Different from the threshold method alarm mode of the existing warehouse warning system, by adopting a multi-point trigger alarm mechanism based on time series data analysis, it can improve the accuracy and intelligence of the warning results, realize the refined and intelligent management of the confiscated tobacco warehouse, and reduce the risk of tobacco mildew rate. Description of the Drawings

[0050] Figure 1 This is a flowchart of the control method for a confiscated tobacco warehouse based on multi - location triggered alarms in the present invention;

[0051] Figure 2 This is a structural block diagram of a confiscated tobacco warehouse based on multi - location triggered alarms in the present invention. Specific embodiments

[0052] The following further explains the present invention in combination with specific embodiments. Among them, the attached drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention. To better illustrate the specific embodiments of the present invention, some components in the attached drawings will be omitted, enlarged, or reduced, which does not represent the size of the actual product.

[0053] Embodiment 1

[0054] As Figure 1 shown, the flowchart of the control method for a confiscated tobacco warehouse based on multi - location triggered alarms in the present invention and Figure 2 shown, the structural block diagram of a confiscated tobacco warehouse based on multi - location triggered alarms in the present invention.

[0055] The implementation process of the technical solution of the present invention includes the following steps:

[0056] Step 1: The data acquisition module evenly distributed in a matrix in each storage area of the warehouse collects the environmental parameters, position parameters, characteristic parameters of the confiscated tobacco in the storage area, and the mildew rate at the end of the storage cycle. Among them, the environmental parameters include the temperature value, humidity value, air pressure value, ventilation volume inside the warehouse, and the temperature value, humidity value, air pressure value outside the warehouse, etc.; the position parameters include the storage position of the tobacco in the warehouse, the distance between the shelves, the distance between the storage position and the ventilation opening, etc.; the characteristic parameters include the type of tobacco, the storage quantity, the storage density, and other potential factors that may affect the mildew rate.

[0057] Step 2: The data processing module pre - processes the collected data, including data cleaning and filling in missing values, and sends the processed data to the data analysis module.

[0058] Step 3: After obtaining the data through the data analysis module, calculate the correlation coefficient between each parameter and the mildew rate respectively according to the pre - processed data, and screen out the parameters that are positively correlated with the mildew rate as mildew - affecting parameters. It should be noted that when it is positively correlated with the mildew rate, it means that an increase in the value of this type of parameter will lead to an increase in the mildew rate of the tobacco. Among them, the calculation formula of the correlation coefficient is:

[0059]

[0060] In the formula, ρ itis the correlation coefficient between the i-th parameter and the mildew rate at the end of the t-th storage cycle; P it is the value of the i-th parameter during the t-th storage cycle; is the average value of the i-th parameter in all storage areas at the end of the t-th storage cycle; Mr it is the mildew rate at the end of the t-th storage cycle; is the average mildew rate in all storage areas at the end of the t-th storage cycle; n is the number of values of the i-th parameter during the t-th storage cycle, where the storage cycle is determined according to the inbound and outbound time of tobacco in the warehouse, and the determination formula is: In the formula, Mc is the duration of the storage cycle, in hours, t m is the residence time of the m-th type of tobacco in the warehouse, and q is the number of tobacco types in the warehouse.

[0061] Step 4, use the data fitting module to construct a prediction model to perform data fitting on the obtained mildew influence parameters and the mildew rate values at the end of the corresponding storage cycle, and predict the mildew rate at the end of the (t + 1)-th storage cycle according to the prediction model. Among them, the expression of the prediction model is:

[0062]

[0063] In the formula, Mr i(t+1) is the mildew rate at the end of the (t + 1)-th storage cycle; is the coefficient of the i-th mildew influence parameter.

[0064] Step 5, use the early warning control module to construct an early warning model, calculate the number of tobaccos with mildew accidents in a fixed range centered on any storage area in the warehouse within T storage cycles through the early warning model, calculate the mildew early warning LLR according to the calculation result and the obtained predicted mildew rate value, set the safety threshold level of the mildew early warning LLR, and calculate the mildew early warning LLR of the remaining storage areas in the warehouse in turn, and generate an early warning signal for the storage areas that exceed the set safety threshold of the mildew early warning LLR;

[0065] Among them, the calculation formula of the mildew early warning LLR is:

[0066]

[0067] In the formula, Mr LLR is the mildew early warning LLR; R is the number of tobaccos with mildew accidents in the fixed range centered on this storage area; R A is the number of tobaccos with mildew accidents in the fixed range centered on this storage area within T storage cycles; among them, c is the total amount of stored tobacco, Mr is the mildew rate, represents within the range of all storage cycles, Expressed within T storage cycles, Expressed within the range of Z the area range of;

[0068] Among them, the fixed range includes the area of a region with a radius of d centered on the storage area, and the value of d is determined according to the shelf structure and space parameters of the warehouse. The determination principle is:

[0069] When the shelf structure of the warehouse is a through-aisle shelf,

[0070] When the shelf structure of the warehouse is a shuttle shelf,

[0071] In the formula, S is the floor area value of the warehouse shelves, is the average distance between adjacent shelves in the warehouse;

[0072] Among them, the value range of T is determined according to the storage cycle duration and the empirical formula. The formula is: Among them, is the floor function operation, is a constant in [0.5, 1];

[0073] Among them, the method for setting the mildew warning LLR safety threshold level includes;

[0074] Obtain the historical data of k mildew warning LLR values within T storage cycles, and use the historical data of the mildew warning LLR values to create a sample set, denoted as Among them, k is the total number of samples of the historical data obtained;

[0075] Obtain the mean and standard deviation in the sample set, and standardize the data using the mean and standard deviation. The standardization formula is In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0076] After completing the standardization, use the standard parameter Adjust the numerical interval to between [0, 1], and classify the mildew warning LLR value using the function value of f(k). The classification mechanism is:

[0077] When f(k) min ≤ f(k) < f(k) 1 , the mildew warning LLR value is classified as level one;

[0078] When f(k) 1 ≤ f(k) < f(k) 2 , the mildew warning LLR value is classified as level two;

[0079] When f(k) 2 ≤ f(k) < f(k) max , the mildew warning LLR value is classified as level three;

[0080] Among them, f(k) min < f(k) 1 < f(k) 2 < f(k) max , f(k) min , f(k) max are respectively the minimum value and the maximum value of the function value of f(k), and f(k) 1 , f(k) 2 are respectively the intermediate values of the function value of f(k);

[0081] Among them, the principle for determining the mildew occurrence risk is as follows:

[0082] When the mildew warning LLR value is classified as level one, the mildew occurrence level is low risk;

[0083] When the mildew warning LLR value is classified as level two, the mildew occurrence level is medium risk;

[0084] When the mildew warning LLR value is classified as level three, the mildew occurrence level is high risk.

[0085] Step 6, receive and respond to the warning signal generated by the warning control module through the alarm module to generate an alarm message, alarm for the mildew occurrence risk in the storage area exceeding the set mildew warning LLR safety threshold, and the visualization module visually displays the level of the mildew occurrence risk.

[0086] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling a confiscated tobacco warehouse based on multi-location trigger alarms, characterized in that: include, Collect environmental parameters, location parameters, characteristic parameters and mildew rate of confiscated tobacco at the end of storage period in different storage areas of the warehouse; The correlation coefficient between each parameter and the mildew rate is calculated respectively, and the parameters that are positively correlated with the mildew rate are selected as mildew influencing parameters according to the calculation results, wherein the calculation formula of the correlation coefficient is: In the formula, ρ it is the correlation coefficient between the ith parameter and the mildew rate at the end of the tth storage cycle; P it is the value of the i-th parameter in the t-th storage cycle; is the mean value of the i-th parameter in all storage areas at the end of the t-th storage cycle; Mr it is the mildew rate at the end of the tth storage cycle; is the mean mildew rate of all storage areas at the end of the t-th storage cycle; n is the number of parameter values ​​of the i-th item in the t-th storage cycle; A prediction model is constructed to perform data fitting on the obtained mildew influencing parameters and the mildew rate value at the end of the corresponding storage period, and the mildew rate at the end of the t+1th storage period is predicted according to the prediction model, wherein the expression of the prediction model is: In the formula, Mr i(t+1) is the mildew rate at the end of the t+1th storage cycle; is the coefficient of the i-th mildew influencing parameter; Randomly select any storage area in the warehouse, calculate the number of tobacco that has mold accidents within a fixed range centered on the storage area within T storage cycles, and calculate the mold warning LLR based on the calculation results and the obtained mold rate prediction value. The calculation formula is: In the formula, Mr LLR is the mildew warning LLR; R is the number of tobacco with mildew accidents within a fixed range centered on the storage area; R A The number of tobaccos that have mildewed within a fixed range centered on the storage area within T storage cycles; c is the total amount of tobacco stored, Mr is the mildew rate, Expressed as the range of all storage cycles, Expressed as T storage cycles, Expressed as Z within the region; Set the mildew warning LLR safety threshold level, calculate the mildew warning LLR of the remaining storage areas in the warehouse in turn, and issue a mildew risk alarm for the storage areas that exceed the set mildew warning LLR safety threshold; The fixed range includes an area with a radius of d centered on the storage area, wherein the value of d is determined according to the shelf structure and space parameters of the warehouse, and the determination principle is: When the warehouse shelf structure is a corridor shelf, When the warehouse shelf structure is a shuttle shelf, In the formula, S is the floor space of the warehouse shelf. is the average distance between adjacent shelves in the warehouse.

2. The method for controlling confiscated tobacco warehouses based on multi-location trigger alarms according to claim 1, characterized in that: The storage period is determined according to the time when tobacco enters and leaves the warehouse, and the determination formula is: Where Mc is the storage cycle duration, in h, t m is the length of time the mth type of tobacco stays in the warehouse, and q is the number of tobacco types in the warehouse.

3. The method for controlling confiscated tobacco warehouses based on multi-location trigger alarms according to claim 1, characterized in that: The value range of T is determined according to the storage cycle duration and the empirical formula, the formula is: in, To round down, is a constant in the range [0.5,1]; Mc is the storage cycle duration, in h.

4. The method for controlling confiscated tobacco warehouses based on multi-location trigger alarms according to claim 1, characterized in that: The method for setting the LLR safety threshold level for mildew early warning includes: Get k historical data of mildew warning LLR values ​​within T storage cycles, and use the historical data of mildew warning LLR values ​​to create a sample set, recorded as Among them, k is the total number of samples of historical data obtained; Get the mean and standard deviation of the sample set, and use the mean and standard deviation to standardize the data. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data; After standardization is completed, the standard parameters are used The numerical interval is adjusted to [0,1], and the function value of f(k) is used to classify the mold warning LLR value. The classification mechanism is: When f(k) min When ≤f(k)<f(k)1, the mold warning LLR value is classified as level one; When f(k)1≤f(k)<f(k)2, the mold warning LLR value is classified as level 2; When f(k)2≤f(k)<f(k) max When the mildew warning LLR value is classified into three levels; Among them, f(k) min <f(k)1<f(k)2<f(k) max ,f(k) min 、f(k) max are the minimum and maximum values ​​of the function value of f(k), and f(k)1 and f(k)2 are the intermediate values ​​of the function value of f(k).

5. The method for controlling confiscated tobacco warehouses based on multi-location trigger alarms according to claim 1, characterized in that: The principles for determining the risk of mildew occurrence are as follows: When the mold warning LLR value is classified as level one, the mold occurrence level is low risk; When the mold warning LLR value is classified as level 2, the mold occurrence level is medium risk; When the mold warning LLR value is classified as level three, the mold occurrence level is high risk.

6. A confiscated tobacco warehouse based on multi-location trigger alarm, characterized in that: include: A data collection module, which is evenly distributed in each storage area of ​​the warehouse in a matrix form and is used to collect environmental parameters, position parameters, characteristic parameters and mildew rate of confiscated tobacco in the storage area at the end of the storage period; A data processing module, which is connected to the data acquisition module and is used to perform preprocessing on the acquired data, including data cleaning and supplementation of missing values; A data analysis module, which is connected to the data processing module and is used to calculate the correlation coefficient between each parameter and the mildew rate according to the preprocessed data, and select the parameters that are positively correlated with the mildew rate as mildew influencing parameters according to the calculation results; A data fitting module, the data fitting module is used to construct a prediction model, and the obtained mildew influencing parameters are fitted with the mildew rate value at the end of the corresponding storage cycle through the prediction model, and the tobacco mildew rate of each storage area at the end of the next storage cycle is predicted according to the prediction model; The early warning control module is used to construct an early warning model, and the early warning model is used to calculate the number of tobaccos that have mold accidents in a fixed range centered on any storage area in the warehouse within T storage cycles, and the mold early warning LLR is calculated according to the calculation result and the obtained mold rate prediction value, and the mold early warning LLR safety threshold level is set, and the mold early warning LLR of the remaining storage areas in the warehouse is calculated in turn, and a warning signal is generated for the storage area that exceeds the set mold early warning LLR safety threshold. The calculation formula of the mold early warning LLR is: In the formula, Mr LLR is the mildew warning LLR; R is the number of tobacco with mildew accidents within a fixed range centered on the storage area; R A The number of tobaccos that have mildewed within a fixed range centered on the storage area within T storage cycles; c is the total amount of tobacco stored, Mr is the mildew rate, Expressed as the range of all storage cycles, Expressed as T storage cycles, Expressed as Z within the region; An alarm module, the alarm module is connected to the early warning control module, and in response to the early warning signal generated by the early warning control module, the alarm module issues a risk alarm for mold occurrence for a storage area that exceeds a set mold early warning LLR safety threshold; The fixed range includes an area with a radius of d centered on the storage area, wherein the value of d is determined according to the shelf structure and space parameters of the warehouse, and the determination principle is: When the warehouse shelf structure is a corridor shelf, When the warehouse shelf structure is a shuttle shelf, In the formula, S is the floor space of the warehouse shelf. is the average distance between adjacent shelves in the warehouse.

7. The confiscated tobacco warehouse based on multi-location trigger alarm according to claim 6, characterized in that: The warehouse also includes: A storage module is connected to the data acquisition module and is used to store the acquired environmental parameters, location parameters, characteristic parameters and the mildew rate at the end of the storage period.

8. The confiscated tobacco warehouse based on multi-location trigger alarm according to claim 6, characterized in that: The warehouse also includes: A visualization module is connected to the alarm module and is used to visualize the storage area with the risk of mildew occurrence and the risk level information, wherein the risk level of mildew occurrence risk includes low risk, medium risk and high risk.

9. The confiscated tobacco warehouse based on multi-location trigger alarm according to claim 6, characterized in that: The warehouse also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor can implement the steps of the method described in any one of claims 1-5 when running the electronic program.

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