An animal ecological monitoring and management system based on remote monitoring

Abstract

The invention relates to the field of ecological monitoring, and discloses an animal ecological monitoring and management system based on remote monitoring, which first collects animal activity data, and then collects temperature, air humidity and physiological parameters of animals; inputs the physiological parameters of animals into a physiological parameter analysis model to obtain the physiological health index of animals; obtains an activity evaluation index according to the physiological health index and animal activity data, obtains a minimum activity coefficient according to the activity evaluation index and temperature and air humidity, and performs behavior analysis on animals according to the minimum activity coefficient; obtains an animal abnormal warning coefficient and predicts the risk of abnormal behavior according to the deviation between animal activity data and historical reference data combined with the physiological parameters of animals; then analyzes the changes in temperature and air humidity to obtain an environmental fluctuation index and compares it with the warning value to determine whether it is necessary to adjust the animal abnormal warning coefficient; finally, determines whether the animal has a risk of abnormal behavior according to the adjusted animal abnormal warning coefficient, and issues a warning.

Description

Technical Field

[0001] The present invention relates to the field of ecological monitoring, and in particular to an animal ecological monitoring and management system based on remote monitoring. Background Art

[0002] In the fields of biology, psychology, neuroscience, etc., monitoring animal behavior can help researchers understand the animal's nervous system, behavior patterns, learning and memory abilities, emotional states, etc., thereby providing a theoretical basis and experimental model for the study and treatment of human diseases. As mice are the most commonly used experimental animals, it is essential to monitor the growth of bred mice. Mice are generally placed in cages, and staff record data to reflect their growth.

[0003] Since the staff cannot conduct uninterrupted supervision, surveillance cameras are introduced to achieve the purpose of remote monitoring. The surveillance cameras can record the movement trajectory of animals and analyze their behavior, but multiple animals may exist in different states such as activity and sleep. If the behavior of the animal is analyzed based only on the activity state of individual animals in a certain period of time, it is obviously not true to represent the overall behavior state of the animals in the cage, which may cause certain errors in subsequent experiments. Summary of the invention

[0004] The purpose of the present invention is to provide an animal ecological monitoring and management system based on remote monitoring to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An animal ecological monitoring and management system based on remote monitoring, comprising:

[0007] A remote monitoring module divides each mouse cage into a target area, and a remote monitoring module is installed on one side of each target area to collect animal activity data in each target area;

[0008] Environmental data collection module, used to collect temperature and air humidity in each target area;

[0009] Animal data collection module, used to collect physiological parameters of animals in each target area;

[0010] A behavior analysis module is used to input the physiological parameters of the animals in each target area into a physiological parameter analysis model to obtain the physiological health index of the animals in each target area;

[0011] The activity evaluation index of the animals in the target area is calculated based on the physiological health index and the animal activity data, the minimum activity coefficient of the target area is calculated based on the activity evaluation index in the target area and the temperature and air humidity in the target area, and the behavior analysis of each animal is performed based on the minimum activity coefficient;

[0012] In a further embodiment, the process of obtaining the minimum activity coefficient includes:

[0013] According to formula (1):

[0014]

[0015] Calculate the minimum activity coefficient of the i-th target area

[0016] According to formula (2):

[0017]

[0018] Calculate the activity evaluation index K in the i-th target area i ;

[0019] In the formula, ω(T i , P i ) is the influence function of temperature and humidity in the target area on the animal activity coefficient, T i is the temperature of the target area, P i is the air humidity in the target area, H j is the physiological health index of the jth animal in the current target area, H j0 is the standard physiological health index, j∈[1,n], n is the number of animals in the current target area, L j is the movement length of the jth animal in a unit time, Tim j is the moving time of the jth animal in one unit time, V j is the average moving speed of the jth animal in a unit time, ΔTim is the unit time, σ l , σ t , σ v is the preset proportional coefficient, θ j is the preset weight coefficient, n is the number of animals in the target area, and n≥1.

[0020] Furthermore, the process of behavioral analysis for each animal according to the minimum activity coefficient is:

[0021] The minimum activity coefficient for each target area The activity interval preset by the system contrast;

[0022] like It is judged that the animals in the current target area are less active and are in the sleeping type;

[0023] like It is judged that the animals in the current target area are highly active and are of the athletic type;

[0024] like It is judged that the animals in the current target area are active and normal, and are of active type.

[0025] Among them, the influence function of temperature and humidity in the target area on the animal activity coefficient is:

[0026]

[0027] Where, T imin , T imax are the lower temperature limit and the upper temperature limit, respectively, imin , P imax are the lower and upper limits of air humidity, respectively. It is a preset fixed coefficient.

[0028] In a further embodiment, the system further comprises:

[0029] The abnormality analysis module calculates the animal abnormality warning coefficient based on the deviation between the animal activity data and the historical reference data and the physiological parameters of the animal. It determines whether the animals in each target area have the risk of abnormal behavior based on the abnormality warning coefficient.

[0030] The abnormality elimination module is used to analyze the changes in temperature and air humidity and calculate the environmental fluctuation index, compare the environmental fluctuation index with the warning value, and determine whether the animal abnormality warning coefficient needs to be adjusted based on the comparison result;

[0031] The early warning module determines whether there is a risk of abnormal behavior of animals in each target area based on the adjusted animal abnormality early warning coefficient, and issues an early warning based on the judgment result.

[0032] The working process of the abnormal analysis module is as follows:

[0033] According to formula (3):

[0034]

[0035] Calculate the animal abnormality warning coefficient

[0036] In the formula, is the mean physiological health index of the jth animal in the current target area, Y is the total number of detection time points, H rjis the physiological health index obtained at the rth detection time point, ΔM is the reference value obtained based on historical data, is the reference coefficient, is the time-varying curve of the minimum activity coefficient in the kth unit time of the current target area, is the reference curve of the minimum activity coefficient of the current target area over time, t a ,t b are the starting time point and the ending time point of the unit duration respectively, N is the total number of unit durations, k∈[1,N].

[0037] A further solution is to determine whether animals in each target area have a risk of abnormal behavior based on the abnormal warning coefficient:

[0038] The obtained animal abnormal warning coefficient and warning threshold interval To compare:

[0039] like It is judged that the animal does not have abnormal behavior;

[0040] like It is judged that the animal has abnormal behavior.

[0041] In a further solution, the working process of the abnormality elimination module is as follows:

[0042] According to formula (4):

[0043]

[0044] Calculate the environmental fluctuation index of the i-th target area Where λ1 and λ1 are preset weight coefficients: is the temperature fluctuation coefficient, is the humidity fluctuation coefficient;

[0045] Will With warning value Make comparisons;

[0046] like It is determined that there are abnormal fluctuations in the environment of the current target area, and the animal abnormality warning coefficient needs to be adjusted; and Make adjustments; where q is the preset proportional coefficient;

[0047] Otherwise, it is determined that the environmental fluctuations in the current target area are normal.

[0048] According to formula (5):

[0049]

[0050] Calculate the temperature fluctuation coefficient

[0051] Through formula (6):

[0052]

[0053] Calculate the humidity fluctuation coefficient

[0054] Where T i (t), P i (t) are the temperature and air humidity variation curves over time, respectively. ith (t), P ith (t) is the reference curve of temperature changing with time and the reference curve of air humidity changing with time, and θ is the dimensionless coefficient.

[0055] Beneficial effects of the present invention:

[0056] (1) The present invention collects animal activity data first, and then collects temperature, air humidity and physiological parameters of the animals; inputs the physiological parameters of the animals into a physiological parameter analysis model to obtain the physiological health index of the animals; obtains an activity evaluation index based on the physiological health index and the animal activity data; obtains a minimum activity coefficient based on the activity evaluation index and temperature and air humidity; performs behavioral analysis on the animals based on the minimum activity coefficient; after comparing the minimum activity coefficient of each target area with the activity interval preset by the system, the animals in each target area can be divided into different types, thereby achieving the purpose of accurately analyzing animal habits;

[0057] (2) In order to be able to predict abnormal animal behavior in advance, the present invention calculates the animal abnormality warning coefficient based on the deviation between the animal activity data and the historical reference data combined with the animal's physiological parameters. The risk of abnormal behavior can be predicted based on the size of the animal abnormality warning coefficient, thereby making advance predictions on the illness or death of the animal, timely discovering potential risks, improving the animal's life cycle, and providing sufficient response time for subsequent animal treatment; then, the environmental fluctuation index is obtained by analyzing the changes in temperature and air humidity and then compared with the warning value. According to the above process, it is possible to determine whether the cause of the animal abnormality is based on the changes in environmental data, thereby determining in advance whether the abnormality is due to environmental factors or animal factors. If it is due to environmental factors, the animal abnormality warning coefficient is dynamically adjusted to truly reflect the actual situation of the animal; finally, it is determined whether the animal has a risk of abnormal behavior based on the adjusted animal abnormality warning coefficient, and a warning is issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 A logical schematic diagram of the animal behavior analysis in the present invention;

[0060] Figure 2 It is a logical schematic diagram of animal abnormality analysis in the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] See also Figure 1-Figure 2 As shown, the present invention is an animal ecological monitoring and management system based on remote monitoring, comprising:

[0063] A remote monitoring module is installed on one side of each target area to collect animal activity data in each target area; the target areas in this embodiment are preferably mouse cages, each target area is a mouse cage, and the animals are mice in the mouse cages;

[0064] Environmental data acquisition module, used to collect the temperature and air humidity in each target area; the temperature and air humidity are detected and acquired by temperature and humidity sensors;

[0065] The animal data collection module is used to collect the physiological parameters of the animals in each target area; the physiological parameters include but are not limited to body temperature, heart rate and other physiological parameters, and can be detected by installing a dedicated sensor or detection module on the animal, which will not be described in detail here;

[0066] The behavior analysis module is used to input the physiological parameters of the animals in each target area into the physiological parameter analysis model to obtain the physiological health index of the animals in each target area; the physiological parameter analysis model is a pre-trained neural network model;

[0067] The activity evaluation index of the animals in the target area is calculated based on the physiological health index and the animal activity data, the minimum activity coefficient of the target area is calculated based on the activity evaluation index in the target area and the temperature and air humidity in the target area, and the behavior analysis of each animal is performed based on the minimum activity coefficient;

[0068] The abnormality analysis module calculates the animal abnormality warning coefficient based on the deviation between the animal activity data and the historical reference data and the physiological parameters of the animal. It determines whether the animals in each target area have the risk of abnormal behavior based on the abnormality warning coefficient.

[0069] The abnormality elimination module is used to analyze the changes in temperature and air humidity and calculate the environmental fluctuation index, compare the environmental fluctuation index with the warning value, and determine whether the animal abnormality warning coefficient needs to be adjusted based on the comparison result;

[0070] The early warning module determines whether there is a risk of abnormal behavior of animals in each target area based on the adjusted animal abnormality early warning coefficient, and issues an early warning based on the judgment result.

[0071] In this embodiment, the animal activity data is first collected, and then the temperature, air humidity and physiological parameters of the animal are collected; the physiological parameters of the animal are input into the physiological parameter analysis model to obtain the physiological health index of the animal; the activity evaluation index is obtained according to the physiological health index and the animal activity data, and the minimum activity coefficient is obtained according to the activity evaluation index and the temperature and air humidity. The behavior of the animal is analyzed according to the minimum activity coefficient. After comparing the minimum activity coefficient of each target area with the activity interval preset by the system, the animals in each target area can be divided into different types, thereby achieving the purpose of accurately analyzing the habits of the animals;

[0072] At the same time, in order to be able to predict abnormal animal behavior in advance, the animal abnormality warning coefficient is calculated based on the deviation between the animal activity data and the historical reference data combined with the animal's physiological parameters. The risk of abnormal behavior can be predicted based on the size of the animal abnormality warning coefficient, so as to make advance predictions for the illness or death of animals, timely discover potential risks, improve the animal life cycle, and provide sufficient response time for subsequent animal treatment; then the environmental fluctuation index is obtained by analyzing the changes in temperature and air humidity and compared with the warning value. According to the above process, it can be determined whether the cause of the animal abnormality is based on the changes in environmental data, so as to divide in advance whether the abnormality is due to environmental factors or animal factors. If it is an environmental factor, the animal abnormality warning coefficient is dynamically adjusted to truly reflect the actual situation of the animal; finally, it is determined whether the animal has a risk of abnormal behavior based on the adjusted animal abnormality warning coefficient, and a warning is issued.

[0073] The process of obtaining the minimum activity coefficient includes:

[0074] According to formula (1):

[0075]

[0076] Calculate the minimum activity coefficient of the i-th target area

[0077] According to formula (2):

[0078]

[0079] Calculate the activity evaluation index K in the i-th target area i ;

[0080] In the formula, ω(T i , P i ) is the influence function of temperature and humidity in the target area on the animal activity coefficient, T i is the temperature of the target area, P i is the air humidity in the target area, H j is the physiological health index of the jth animal in the current target area, H j0 is the standard physiological health index, j∈[1,n], n is the number of animals in the current target area, L j is the movement length of the jth animal in a unit time, Tim j is the moving time of the jth animal in one unit time, V j is the average moving speed of the jth animal in a unit time, ΔTim is the unit time; σ l , σ t , σ v is the preset proportional coefficient, θ j It is a preset weight coefficient, which is formulated based on historical data and experimental data.

[0081] The procedure for behavioral analysis of each animal based on the minimum activity coefficient was:

[0082] The minimum activity coefficient for each target area The activity interval preset by the system contrast;

[0083] like It is judged that the animals in the current target area are less active and are in the sleeping type;

[0084] like It is judged that the animals in the current target area are highly active and are of the athletic type;

[0085] like It is judged that the animals in the current target area are active and normal, and are active. The influence function of temperature and humidity in the target area on the animal activity coefficient is:

[0086]

[0087] Where, T imin , T imax are the lower temperature limit and the upper temperature limit, respectively, imin , P imax are the lower and upper limits of air humidity, respectively. The lower and upper temperature limits, the lower and upper humidity limits, and the upper and lower humidity limits are obtained through statistical analysis of multiple sets of experimental data and historical data, and will not be described in detail.

[0088] In the present invention, the activity tracks and movement speeds of animals in the target area are obtained through the remote monitoring module. Obviously, the more activity tracks of animals per unit time, the longer the movement length, indicating that the animal is more active; the faster the movement speed, the faster the average movement speed, indicating that the animal is more active; and the longer the time in the active state per unit time, the more active the animal is. The formula Calculate the activity evaluation index K in the i-th target area i , and then substitute into The minimum activity coefficient of the i-th target area can be calculated By comprehensively analyzing the animals in each target area, we can accurately analyze the activity status of the animals in the target area per unit time. Obviously, The larger the proportion of moving time to unit time, the higher the activity evaluation index K i The larger the value, the lower the overall minimum activity coefficient. The larger the |H j -H j0 |Indicates that the smaller the difference between the animal's real-time physiological health index and the standard physiological health index, the higher the activity evaluation index K i The smaller, The larger the number, n is the number of animals in the target area, and n≥1; in the present invention, the activity data and physiological health index of the animals are integrated to establish the minimum activity coefficient of each target area. To reflect the real status of animals in each target area, provide accurate data support for subsequent analysis of animal behavior and reduce the error rate;

[0089] Then the minimum activity coefficient of each target area The activity interval preset by the system Contrast; if It is judged that the animals in the current target area are less active and are mostly sleeping or inactive, so they are sleeping type; if It is judged that the animals in the current target area are highly active and are mostly in motion or exploration, so they are athletic; if It is judged that the animals in the current target area are active and normal, and most of them are in a state of eating and daily life, so they are active.

[0090] The working process of the abnormal analysis module is as follows:

[0091] According to formula (3):

[0092]

[0093] Calculate the animal abnormality warning coefficient

[0094] In the formula, is the mean physiological health index of the jth animal in the current target area, Y is the total number of detection time points, H rj is the physiological health index obtained at the rth detection time point, ΔM is the reference value obtained based on historical data, is the reference coefficient, is the time-varying curve of the minimum activity coefficient in the kth unit time of the current target area, is the reference curve of the minimum activity coefficient of the current target area over time, t a ,t b are the starting time point and the ending time point of the unit duration respectively, N is the total number of unit durations, k∈[1,N].

[0095] In the present invention, the formula Calculate the animal abnormality warning coefficient Obviously, The greater the difference, the greater the volatility of the current animal's physiological health index. The greater the volatility, the higher the animal abnormal warning coefficient. The bigger, The greater the deviation, the greater the deviation of the minimum activity coefficient in the current target area, which means that the state stability of the animals in the target area is worse, that is, the higher the probability of abnormalities in the animals in the target area. Combined with the classification of the current target area, it can be seen that if the animals in the current target area are sleep-type, and the physiological health index fluctuates greatly and the state stability of the animals in the target area is poor, the more abnormal the animal is. The above scheme can accurately predict the abnormal situation of animals, providing basic conditions for taking relevant measures in a timely manner.

[0096] The process of judging whether animals in each target area have abnormal behavior risks based on the abnormal warning coefficient is as follows:

[0097] The obtained animal abnormal warning coefficient and warning threshold interval To compare:

[0098] like It is judged that the animal does not have abnormal behavior;

[0099] like It is judged that the animal has abnormal behavior.

[0100] In the present invention, the obtained animal abnormality warning coefficient and warning threshold interval For comparison, it is obvious that if The animal is judged to have abnormal behavior, because the higher and lower the abnormal warning coefficient at this stage, the more it exceeds the safe warning threshold range. It is determined through statistical analysis of historical data and combined with experimental data, which will not be elaborated here.

[0101] The working process of the exception elimination module is as follows:

[0102] According to formula (4):

[0103]

[0104] Calculate the environmental fluctuation index of the i-th target area Where λ1 and λ1 are preset weight coefficients; is the temperature fluctuation coefficient, is the humidity fluctuation coefficient;

[0105] Will With warning value Make comparisons;

[0106] like It is determined that there are abnormal fluctuations in the environment of the current target area, and the animal abnormality warning coefficient needs to be adjusted; and Make adjustments; where q is the preset proportional coefficient;

[0107] Otherwise, it is determined that the environmental fluctuations in the current target area are normal.

[0108] According to formula (5):

[0109]

[0110] Calculate the temperature fluctuation coefficient

[0111] Through formula (6):

[0112]

[0113] Calculate the humidity fluctuation coefficient Where T i (t), P i (t) are the temperature and air humidity variation curves over time, respectively. ith (t), P ith (t) is the reference curve of temperature and air humidity over time, θ is the dimensionless coefficient, max{T i}、max{P i} are ta -t b Maximum internal temperature and maximum air humidity, min{T i}、min{P i} are t a -t b Maximum temperature and minimum air humidity.

[0114] The present invention provides a working method of an abnormality elimination module, firstly through the formula Calculate the environmental fluctuation index of the i-th target area Then With warning value For comparison; if It is judged that there are abnormal fluctuations in the environment of the current target area, and the animal abnormal warning coefficient needs to be adjusted. Adjustment is made; where q is the preset proportional coefficient; otherwise, the environmental fluctuation of the current target area is judged to be normal; through the above process, it is possible to quickly identify whether the animal abnormality is caused by the environment. If not, timely adjustment is made to ensure the accuracy of the animal abnormality warning coefficient; the temperature fluctuation coefficient and the humidity fluctuation coefficient are respectively Obtain, calculate comprehensively the stability and extreme value conditions to obtain more accurate data.

[0115] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An animal ecological monitoring and management system based on remote monitoring, characterized in that: include: A remote monitoring module divides each mouse cage into a target area, and a remote monitoring module is installed on one side of each target area to collect animal activity data in each target area; Environmental data collection module, used to collect temperature and air humidity in each target area; Animal data collection module, used to collect physiological parameters of animals in each target area; A behavior analysis module is used to input the physiological parameters of the animals in each target area into a physiological parameter analysis model to obtain the physiological health index of the animals in each target area; The activity evaluation index of the animals in the target area is calculated based on the physiological health index and the animal activity data, the minimum activity coefficient of the target area is calculated based on the activity evaluation index in the target area and the temperature and air humidity in the target area, and the behavior analysis of each animal is performed based on the minimum activity coefficient; The process of obtaining the minimum activity coefficient includes: According to formula (1): Calculate the minimum activity coefficient of the i-th target area According to formula (2): Calculate the activity evaluation index K in the i-th target area i ; In the formula, ω(T i , P i ) is the influence function of temperature and humidity in the target area on the animal activity coefficient, T i is the temperature of the target area, P i is the air humidity in the target area, H j is the physiological health index of the jth animal in the current target area, H j0 is the standard physiological health index, j∈[1,n], n is the number of animals in the current target area, L j is the movement length of the jth animal in a unit time, Tim j is the moving time of the jth animal in one unit time, V j is the average moving speed of the jth animal in a unit time, ΔTim is the unit time, σ l , σ t , σ v is the preset proportional coefficient, θ j is the preset weight coefficient, n is the number of animals in the target area, and n≥1; The procedure for behavioral analysis of each animal based on the minimum activity coefficient was: The minimum activity coefficient for each target area The activity interval preset by the system contrast; like It is judged that the animals in the current target area are less active and are in the sleeping type; like It is judged that the animals in the current target area are highly active and are of the athletic type; like It is judged that the animals in the current target area are active and normal, and are active; The influence function of temperature and humidity in the target area on the animal activity coefficient is: Where, T imin , T imax are the lower temperature limit and the upper temperature limit, respectively, imin , P imax are the lower and upper limits of air humidity, respectively. It is a preset fixed coefficient.

2. The animal ecological monitoring and management system based on remote monitoring according to claim 1 is characterized in that: The system further comprises: The abnormality analysis module calculates the animal abnormality warning coefficient based on the deviation between the animal activity data and the historical reference data and the physiological parameters of the animal. It determines whether the animals in each target area have the risk of abnormal behavior based on the abnormality warning coefficient. The abnormality elimination module is used to analyze the changes in temperature and air humidity and calculate the environmental fluctuation index, compare the environmental fluctuation index with the warning value, and determine whether the animal abnormality warning coefficient needs to be adjusted based on the comparison result; The early warning module determines whether there is a risk of abnormal behavior of animals in each target area based on the adjusted animal abnormality early warning coefficient, and issues an early warning based on the judgment result.

3. The animal ecological monitoring and management system based on remote monitoring according to claim 2 is characterized in that: The working process of the abnormal analysis module is as follows: According to formula (3): Calculate the animal abnormal warning coefficient In the formula, is the mean physiological health index of the jth animal in the current target area, Y is the total number of detection time points, H rj is the physiological health index obtained at the rth detection time point, ΔM is the reference value obtained based on historical data, is the reference coefficient, is the time-varying curve of the minimum activity coefficient in the kth unit time of the current target area, is the reference curve of the minimum activity coefficient of the current target area over time, t a ,t b are the starting time point and the ending time point of the unit duration respectively, N is the total number of unit durations, k∈[1,N].

4. The animal ecological monitoring and management system based on remote monitoring according to claim 3 is characterized in that: The process of judging whether animals in each target area have abnormal behavior risks based on the abnormal warning coefficient is as follows: The obtained animal abnormal warning coefficient and warning threshold interval To compare: like It is judged that the animal does not have abnormal behavior; like It is judged that the animal has abnormal behavior.

5. The animal ecological monitoring and management system based on remote monitoring according to claim 4 is characterized in that: The working process of the exception elimination module is as follows: According to formula (4): Calculate the environmental fluctuation index of the i-th target area Where λ1 and λ1 are preset weight coefficients; is the temperature fluctuation coefficient, is the humidity fluctuation coefficient; Will With warning value Make comparisons; like It is determined that there are abnormal fluctuations in the environment of the current target area, and the animal abnormality warning coefficient needs to be adjusted; and Make adjustments; where q is the preset proportional coefficient; Otherwise, it is determined that the environmental fluctuations in the current target area are normal.

6. The animal ecological monitoring and management system based on remote monitoring according to claim 5, characterized in that: According to formula (5): Calculate the temperature fluctuation coefficient According to formula (6): Calculate the humidity fluctuation coefficient Where T i (t), P i (t) are the temperature and air humidity variation curves over time, respectively. ith (t), P ith (t) is the reference curve of temperature and air humidity over time, θ is the dimensionless coefficient, max{T i }、max{P i } are t a -t b Maximum internal temperature and maximum air humidity, min{T i }、min{P i } are t a -t b Maximum temperature and minimum air humidity.

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