CN108334975B - Oxygen content dynamic prediction method and device for anhydrous storage environment - Google Patents
Oxygen content dynamic prediction method and device for anhydrous storage environment Download PDFInfo
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Abstract
The invention provides a method and a device for dynamically predicting oxygen content in an anhydrous storage environment, wherein the method for dynamically predicting the oxygen content comprises the following steps: calculating a correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment; acquiring the correlation degrees of carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient; and establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment. The dynamic prediction method for the oxygen content can accurately acquire the oxygen content in the anhydrous storage environment, can effectively avoid accidental errors caused by a single oxygen acquisition method, and can effectively improve the economic loss of organisms caused by oxygen deficiency during storage.
Description
Technical Field
The invention relates to the technical field of detection, in particular to a method and a device for dynamically predicting oxygen content in an anhydrous storage environment.
Background
With the improvement of living standard of people, the demand of people for various agricultural products and seafood products is increased, which requires the market to ensure sufficient supply, but different kinds of organisms have certain requirements on the storage environment, and the main supply sources are coastal cities and other farmers, so that the on-site sale and on-site fishing are difficult to achieve in most areas, so that various agricultural products and seafood products need to be transported in large quantities and temporarily stored at the point of sale, but the death of organisms caused by body hypoxia due to the problems of storage space size, storage density and the like is frequent in transportation and temporary culture, so that the oxygen content of the storage space of the organisms needs to be monitored and predicted so as to be processed in time, the economic cost is reduced, and the nutritional value and the transportation efficiency are improved.
At present, the prediction of the oxygen content in the organism anhydrous storage environment is less, and a plurality of prediction methods are provided for a certain variable, such as a time series prediction method, a neural network, a fuzzy control method, a gray scale prediction and the like, but the methods concentrate on a method for training from the variable change angle or from a plurality of variables, wherein the former depends on the accuracy of acquired data, if a sensor or other acquisition devices have faults, data of important parameters cannot be obtained, the prediction method has no effect, the change of a single factor is easily influenced by other factors, and the prediction cannot be excluded; the latter utilizes the training method, has no specific formula, realizes high hardware cost, can not meet the public demand, and has the defects of complex operation process and long operation time even if the specific formula exists.
Disclosure of Invention
In order to at least partially overcome the problems in the prior art, the invention provides a method and a device for dynamically predicting oxygen content in a water-free storage environment.
According to one aspect of the invention, the invention provides a dynamic prediction method for oxygen content in a waterless storage environment, which comprises the following steps:
calculating a correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment;
acquiring the correlation degrees of carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
and establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment.
Wherein, still include: grouping samples by using a fuzzy control model based on physical characteristic parameters of each organism in the samples;
time series of carbon dioxide, temperature, humidity and oxygen in a non-aqueous storage environment were obtained for each set of samples.
Wherein, still include: for any sample set, the time series of carbon dioxide, temperature, humidity and oxygen are cumulatively recombined to obtain a new time series of carbon dioxide, temperature, humidity and oxygen.
Wherein, time nodes are set according to the storage time and storage density of organisms in each group of samples.
Wherein the parameter is carbon dioxide, temperature or humidity.
According to another aspect of the present invention, the present invention provides an apparatus for dynamically predicting oxygen content in a non-water storage environment, comprising:
the correlation coefficient calculation module is used for calculating the correlation coefficient between the time sequence of carbon dioxide, temperature and humidity and the time sequence of oxygen in the anhydrous storage environment of each group of samples;
the correlation degree acquisition module is used for acquiring the correlation degrees between carbon dioxide, temperature, humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
and the oxygen content dynamic prediction module is used for establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment.
Wherein, still include: the data acquisition module is used for grouping the samples by utilizing a fuzzy control model based on the body characteristic parameters of each organism in the samples;
time series of carbon dioxide, temperature, humidity and oxygen in a non-aqueous storage environment were obtained for each set of samples.
Wherein, still include: and the data accumulation recombination unit is used for respectively carrying out accumulation recombination on the time sequences of the carbon dioxide, the temperature, the humidity and the oxygen for any sample group so as to obtain a new time sequence of the carbon dioxide, the temperature, the humidity and the oxygen.
Wherein, still include: and the time node setting unit is used for setting time nodes according to the storage time length and the storage density of the organisms in each group of samples.
Wherein, still include: an alarm module; the alarm module is used for giving an alarm when the oxygen content predicted value of the oxygen content dynamic prediction module exceeds a preset threshold value.
In summary, the present invention provides a dynamic prediction method and device for oxygen content in an anhydrous storage environment, wherein the dynamic prediction method for oxygen content comprises: calculating a correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment; acquiring the correlation degrees of carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient; and establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment. The dynamic prediction method for the oxygen content can accurately acquire the oxygen content in the anhydrous storage environment, can effectively avoid accidental errors caused by a single oxygen acquisition method, and can effectively improve the economic loss of organisms caused by oxygen deficiency during storage.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for dynamically predicting oxygen content in an anhydrous storage environment according to an embodiment of the present invention;
fig. 2 is a block diagram of a dynamic oxygen content prediction apparatus for a non-water storage environment according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flowchart of a dynamic oxygen content prediction method for a non-water storage environment according to an embodiment of the present invention, as shown in fig. 1, including:
s1, calculating the correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment;
specifically, the carbon dioxide X, the temperature Y, the humidity Z and the oxygen L in the environment of each group of samples are subjected to data acquisition to respectively form a carbon dioxide time sequence Xi(i ═ 1, 2, 3, … n, …), temperature time series Yi(i ═ 1, 2, 3, … n, …), humidity time series Zi(i ═ 1, 2, 3, … n, …), oxygen time series Li(i＝1，2，3，…n，…)。
And calculating the correlation coefficient between the carbon dioxide, the temperature and the humidity and the oxygen at the same time aiming at the acquired time sequence of the carbon dioxide, the temperature and the humidity, and obtaining the correlation relation between the change of the carbon dioxide, the temperature and the humidity and the change of the oxygen, so as to be used as the selection standard of the optimal reference variable. Specifically, the correlation coefficient is calculated using the following formula:
where Δ is the absolute difference between an environmental parameter and oxygen at a time; min Δ is the minimum two step difference; max Δ is the largest two-step difference; ρ is the resolution, 0 < ρ < 1, usually 0.5.
S2, acquiring the correlation degree between carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
specifically, the magnitude of the degree of association is calculated by using the following formula:
wherein n represents the number of correlation coefficients obtained by the carbon dioxide, the temperature and the humidity and the oxygen between two adjacent nodes.
And S3, establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment.
Preferably, the parameter is carbon dioxide, temperature or humidity.
In this step, the original data time series of the parameter corresponding to the maximum degree of association is obtained by using the degree of association obtained in step S2, so as to obtain the main parameters in the dynamic prediction model of oxygen content, and the dynamic prediction model of oxygen content is established by combining the oxygen time series. Specifically, an oxygen dynamic prediction model is established by using the following formula:
wherein mu and alpha are related to the parameter sequence corresponding to the maximum association degree, and the sequence is used for calculation:
wherein A is a data matrix formed by parameter sequences corresponding to the maximum correlation degree; and B is the parameter sequence corresponding to the maximum relevance.
In the embodiment of the invention, the correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment is calculated; acquiring the correlation degrees of carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient; and establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment. The dynamic prediction method for the oxygen content can accurately acquire the oxygen content in the anhydrous storage environment, can effectively avoid accidental errors caused by a single oxygen acquisition method, and can effectively improve the economic loss of organisms caused by oxygen deficiency during storage.
On the basis of the above embodiment, the method further includes: grouping samples by using a fuzzy control model based on physical characteristic parameters of each organism in the samples;
time series of carbon dioxide, temperature, humidity and oxygen in a non-aqueous storage environment were obtained for each set of samples.
Specifically, because the metabolism conditions of organisms with different lengths and weights are different, and the oxygen consumption is different, the samples are grouped by utilizing the relationship between the lengths and the weights of the samples, the minimum oxygen consumption is ensured, and the samples with large oxygen consumption are prevented from dying first.
Specifically, the length L and the weight W of each sample are recorded and input into the fuzzy control model as two parameters; grouping samples by using a fuzzy control model, displaying grouped sample information, and performing anhydrous storage on the groups to complete the grouping;
on the basis of the above embodiment, the method further includes: for any sample set, the time series of carbon dioxide, temperature, humidity and oxygen are cumulatively recombined to obtain a new time series of carbon dioxide, temperature, humidity and oxygen.
Specifically, the obtained time series of carbon dioxide, temperature, humidity and oxygen are recombined in an accumulation mode respectively, the influence of individual factors is eliminated, and the accumulation effect of the whole change process is calculated and is closer to the effect in actual storage.
Specifically, the recombination is carried out by using the following formula:
wherein X is a time series corresponding to any one of the variables; m is the number of times of the accumulation,
is the accumulation of the first k data of the X variable.
On the basis of the above-described embodiment, time nodes are set according to the storage time and storage density of the living organisms in each group of samples.
Specifically, the time nodes are used to meet different monitoring requirements, and are related to factors such as storage density, storage space size and storage time.
Fig. 2 is a block diagram of an apparatus for dynamically predicting oxygen content in a non-water storage environment according to an embodiment of the present invention, as shown in fig. 2, including: a correlation coefficient calculation module 601, a correlation degree acquisition module 602 and an oxygen content dynamic prediction module 603; wherein the content of the first and second substances,
the correlation coefficient calculation module 601 is configured to calculate a correlation coefficient between a time series of carbon dioxide, temperature, and humidity and a time series of oxygen in the anhydrous storage environment for each group of samples;
the correlation degree obtaining module 602 is configured to obtain correlation degrees between carbon dioxide, temperature, humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
the dynamic oxygen content prediction module 603 is configured to establish a dynamic oxygen content prediction model based on the time series of oxygen and the time series of the parameter corresponding to the maximum correlation degree in the correlation degrees, so as to achieve dynamic oxygen content prediction in the anhydrous storage environment.
Wherein the environmental parameters comprise carbon dioxide, temperature, humidity and oxygen, and correspondingly the time series of environmental parameters comprises a time series of carbon dioxide, a time series of temperature, a time series of humidity and a time series of oxygen.
The correlation coefficient calculation module 601 is configured to calculate a correlation coefficient between carbon dioxide, temperature, humidity and oxygen at the same time for the obtained time sequence of carbon dioxide, temperature and humidity, and obtain a correlation relationship between changes in carbon dioxide, temperature and humidity and changes in oxygen, so as to serve as a criterion for selecting an optimal reference variable. Specifically, the correlation coefficient is calculated using the following formula:
where Δ is the absolute difference between an environmental parameter and oxygen at a time; min Δ is the minimum two step difference; max Δ is the largest two-step difference; ρ is the resolution, 0 < ρ < 1, usually 0.5.
The association degree obtaining module 602 specifically calculates the association degree according to the following formula:
wherein n represents the number of correlation coefficients obtained by the carbon dioxide, the temperature and the humidity and the oxygen between two adjacent nodes.
Preferably, the parameter is carbon dioxide, temperature or humidity.
In this step, the relevance obtained by the relevance obtaining module 602 is used to arrange the relevance from large to small, the original data time series of the parameters corresponding to the maximum relevance and the maximum relevance is selected to obtain the main parameters in the dynamic prediction model of oxygen content, and the dynamic prediction model of oxygen content is established by combining the oxygen time series. Specifically, an oxygen dynamic prediction model is established by using the following formula:
wherein mu and alpha are related to the parameter sequence corresponding to the maximum association degree, and the sequence is used for calculation:
wherein A is a data matrix formed by parameter sequences corresponding to the maximum correlation degree; and B is the parameter sequence corresponding to the maximum relevance.
In the embodiment of the invention, the correlation coefficient calculation module is used for calculating the correlation coefficient between the time sequence of carbon dioxide, temperature and humidity and the time sequence of oxygen of each group of samples in the anhydrous storage environment; the correlation degree acquisition module is used for acquiring the correlation degrees between carbon dioxide, temperature, humidity and oxygen between two adjacent time nodes based on the correlation coefficient; the oxygen content dynamic prediction module is used for establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment. The dynamic prediction device for oxygen content provided by the invention can accurately acquire the oxygen content in the anhydrous storage environment, can effectively avoid accidental errors caused by a single oxygen acquisition method, and can effectively improve the economic loss of organisms caused by oxygen deficiency during storage.
On the basis of the above embodiment, the method further includes: the data acquisition module is used for grouping the samples by utilizing a fuzzy control model based on the body characteristic parameters of each organism in the samples; time series of carbon dioxide, temperature, humidity and oxygen in a non-aqueous storage environment were obtained for each set of samples.
Specifically, because the metabolism conditions of organisms with different lengths and weights are different, and the oxygen consumption is different, the samples are grouped by utilizing the relationship between the lengths and the weights of the samples, the minimum oxygen consumption is ensured, and the samples with large oxygen consumption are prevented from dying first.
Specifically, the length L and the weight W of each sample are recorded and input into the fuzzy control model as two parameters; grouping samples by using a fuzzy control model, displaying grouped sample information, and performing anhydrous storage on the groups to complete the grouping;
specifically, the data acquisition module is used for acquiring data of carbon dioxide X, temperature Y, humidity Z and oxygen L in each group of sample environments to respectively form a carbon dioxide time sequence Xi(i ═ 1, 2, 3, … n, …), temperature time series Yi(i ═ 1, 2, 3, … n, …), humidity time series Zi(i ═ 1, 2, 3, … n, …), oxygen time series Li(i＝1，2，3，…n，…)。
On the basis of the above embodiment, the method further includes: and the data accumulation recombination unit is used for respectively carrying out accumulation recombination on the time sequences of the carbon dioxide, the temperature, the humidity and the oxygen for any sample group so as to obtain a new time sequence of the carbon dioxide, the temperature, the humidity and the oxygen.
Specifically, the obtained time series of carbon dioxide, temperature, humidity and oxygen are recombined in an accumulation mode respectively, the influence of individual factors is eliminated, and the accumulation effect of the whole change process is calculated and is closer to the effect in actual storage.
The data accumulation recombination unit is specifically recombined by using the following formula:
wherein X is a time series corresponding to any one of the variables; m is the number of times of the accumulation,
is the accumulation of the first k data of the X variable.
On the basis of the above embodiment, the method further includes: and the time node setting unit is used for setting time nodes according to the storage time length and the storage density of the organisms in each group of samples.
Specifically, the time nodes are used to meet different monitoring requirements, and are related to factors such as storage density, storage space size and storage time.
On the basis of the above embodiment, the method further includes: an alarm module; wherein the content of the first and second substances,
and the alarm module is used for giving an alarm when the oxygen content predicted value of the oxygen content dynamic prediction module exceeds a preset threshold value.
Preferably, the system further comprises a human-computer interaction module, wherein the human-computer interaction module is used for displaying the data obtained by the oxygen content dynamic prediction module and the data acquisition module, and providing time node values, oxygen content early warning threshold values and other conventional auxiliary functions for a user.
And the alarm module compares the oxygen content early warning threshold value set in the man-machine interaction module with the predicted value obtained in the oxygen content dynamic prediction module, and sends out an alarm by using a buzzer or an early warning short message and the like when the oxygen content early warning threshold value is greater than the set threshold value.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (4)
1. A method for dynamically predicting oxygen content in an anhydrous storage environment is characterized by comprising the following steps:
calculating a correlation coefficient between the time series of carbon dioxide, temperature and humidity and the time series of oxygen of each group of samples in the anhydrous storage environment;
acquiring the correlation degrees of carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment;
further comprising: grouping samples by using a fuzzy control model based on physical characteristic parameters of each organism in the samples;
setting time nodes according to the storage time and storage density of organisms in each group of samples;
grouping by using the relation between the length and the weight of each organism in the sample, inputting the length and the weight of each organism in the sample into a fuzzy control model as two parameters, grouping the samples by using the fuzzy control model, displaying the grouped sample information, and storing the grouped samples in a waterless manner;
for any sample set, the time series of carbon dioxide, temperature, humidity and oxygen are cumulatively recombined to obtain a new time series of carbon dioxide, temperature, humidity and oxygen.
2. The method of claim 1, wherein the parameter is carbon dioxide, temperature or humidity.
3. An apparatus for dynamically predicting oxygen content in an anhydrous storage environment, comprising:
the correlation coefficient calculation module is used for calculating the correlation coefficient between the time sequence of carbon dioxide, temperature and humidity and the time sequence of oxygen in the anhydrous storage environment of each group of samples;
the correlation degree acquisition module is used for acquiring the correlation degrees between carbon dioxide, temperature, humidity and oxygen between two adjacent time nodes based on the correlation coefficient;
the oxygen content dynamic prediction module is used for establishing an oxygen content dynamic prediction model based on the time sequence of the oxygen and the time sequence of the parameter corresponding to the maximum correlation degree in the correlation degrees so as to realize the oxygen content dynamic prediction in the anhydrous storage environment;
the data acquisition module is used for grouping the samples by using the fuzzy control model based on the body characteristic parameters of each organism in the samples, grouping by using the relationship between the length and the weight of each organism in the samples, inputting the length and the weight of each organism in the samples into the fuzzy control model as two parameters, grouping the samples by using the fuzzy control model, displaying the grouped sample information, and storing the grouped sample information in a water-free mode;
further comprising: the time node setting unit is used for setting time nodes according to the storage time length and the storage density of the organisms in each group of samples;
further comprising: and the data accumulation recombination unit is used for respectively carrying out accumulation recombination on the time sequences of the carbon dioxide, the temperature, the humidity and the oxygen for any sample group so as to obtain a new time sequence of the carbon dioxide, the temperature, the humidity and the oxygen.
4. The dynamic oxygen content prediction device of claim 3, further comprising: an alarm module; wherein the content of the first and second substances,
and the alarm module is used for giving an alarm when the oxygen content predicted value of the oxygen content dynamic prediction module exceeds a preset threshold value.
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