CN108334975A - Towards the oxygen content dynamic prediction method and device under anhydrous storage environment - Google Patents
Towards the oxygen content dynamic prediction method and device under anhydrous storage environment Download PDFInfo
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Abstract
The present invention provides a kind of oxygen content dynamic prediction method and device towards under anhydrous storage environment, and the oxygen content dynamic prediction method includes：Calculate incidence coefficient of the every group of sample between the time series and the time series of oxygen of anhydrous storage environment carbon dioxide, temperature and humidity；Based on the incidence coefficient, carbon dioxide between two adjacent time nodes, the degree of association between temperature and humidity and oxygen are obtained；The time series of the parameter corresponding to most relevance degree in time series and the above-mentioned degree of association based on oxygen, establishes oxygen content dynamic prediction model, to realize the oxygen content dynamic prediction under anhydrous storage environment.Oxygen content dynamic prediction method provided by the invention can accurately obtain the oxygen content in anhydrous storage environment, it is possible to prevente effectively from accidental error caused by single oxygen acquisition methods, the economic loss that organism brings by anoxic when can effectively improve storage.
Description
Technical field
The present invention relates to detection technique fields, more particularly, to a kind of oxygen content towards under anhydrous storage environment
Dynamic prediction method and device.
Background technology
As the improvement of people's living standards, people increase the demand of each agricultural products, seafood products, this requirement
Market ensures sufficient supply, but different types of organism has living environment certain requirement, and main supply
Source is coastal cities and other raisers, is difficult in most area and now sells existing fishing, this is just needed all kinds of agricultural productions
Product, seafood products carry out amount transport, and in the temporary of point of sale, but either in transit, or in temporarily supporting, it is raw
Object is needed because of the death that the problems such as storage space size, storage density causes body anoxic and occurs is commonplace
The oxygen content of its storage space is monitored and is predicted, so as to timely processing, economic cost is reduced, improves nutrition
Value and conevying efficiency.
Currently, the prediction about oxygen content in the anhydrous storage environment of organism is less, for the prediction side of some variable
There are many method, such as Time Series Forecasting Methods, neural network, fuzzy control method and gray scale prediction, but these methods are concentrated
In from variable Self-variation angle or from the method that multiple variables set ins are trained, the former depends on gathered data
Accuracy just cannot obtain the data of important parameter, prediction technique if sensor or other harvesters break down
Just there is no an effect, and the variation of single factors is also easy affected by other factors, when prediction can not rule out outside；The latter
Using training method, without specific formula, the hardware cost of realization is larger, can not meet public demand, even if having specific
There is also calculating process complexity, the disadvantages of operation time length for formula.
Invention content
In order to overcome the above-mentioned problems in the prior art, the present invention to provide one kind towards anhydrous storage at least partly
Hide the oxygen content dynamic prediction method and device under environment.
According to an aspect of the present invention, the present invention provides a kind of oxygen content dynamic towards under anhydrous storage environment
Prediction technique, including：
Calculate every group of sample anhydrous storage environment carbon dioxide, temperature and humidity time series and oxygen
Incidence coefficient between time series；
Based on the incidence coefficient, obtain between two adjacent time nodes carbon dioxide, temperature and humidity and oxygen it
Between the degree of association；
The time series of the parameter corresponding to most relevance degree in time series and the above-mentioned degree of association based on oxygen,
Oxygen content dynamic prediction model is established, to realize the oxygen content dynamic prediction under anhydrous storage environment.
Wherein, further include：Based on the physical trait parameter of each organism in sample, using fuzzy control model to institute
Sample is stated to be grouped；
Obtain every group of sample in anhydrous storage environment carbon dioxide, temperature, humidity and oxygen time series.
Wherein, further include：For any sample group, respectively to the time sequence of carbon dioxide, temperature, humidity and oxygen
Row carry out cumulative recombination, to obtain new carbon dioxide, temperature, humidity and the time series of oxygen.
Wherein, according to organism storage time length in every group of sample and storage density timing node is arranged.
Wherein, the parameter is carbon dioxide, temperature or humidity.
According to another aspect of the present invention, the present invention provides a kind of dynamic towards the oxygen content under anhydrous storage environment
State prediction meanss, including：
Incidence coefficient computing module, for calculating every group of sample in anhydrous storage environment carbon dioxide, temperature and wet
Incidence coefficient between the time series of degree and the time series of oxygen；
Degree of association acquisition module, for be based on the incidence coefficient, obtain two adjacent time nodes between carbon dioxide,
The degree of association between temperature and humidity and oxygen；
Oxygen content dynamic prediction module, for the most relevance in time series and the above-mentioned degree of association based on oxygen
The time series of the corresponding parameter of degree, establishes oxygen content dynamic prediction model, to realize the oxygen under anhydrous storage environment
Content dynamic prediction.
Wherein, further include：Data acquisition module is used for the physical trait parameter based on each organism in sample, utilizes
Fuzzy control model is grouped the sample；
Obtain every group of sample in anhydrous storage environment carbon dioxide, temperature, humidity and oxygen time series.
Wherein, further include：Data accumulation recomposition unit, for for any sample group, respectively to carbon dioxide, temperature,
Humidity and the time series of oxygen carry out cumulative recombination, with obtain new carbon dioxide, temperature, humidity and oxygen when
Between sequence.
Wherein, further include：Timing node setting unit, for according to organism storage time length in every group of sample and
Density is stored so that timing node is arranged.
Wherein, further include：Alarm module；Wherein, the alarm module, for working as the oxygen content dynamic prediction mould
When the oxygen content predicted value of block is more than pre-set threshold value, the alarm module is alarmed.
To sum up, the present invention provides a kind of oxygen content dynamic prediction method and device towards under anhydrous storage environment, institute
Stating oxygen content dynamic prediction method is：Every group of sample is calculated in anhydrous storage environment carbon dioxide, temperature and humidity
Incidence coefficient between time series and the time series of oxygen；Based on the incidence coefficient, two adjacent time nodes are obtained
Between the degree of association between carbon dioxide, temperature and humidity and oxygen；In time series and the above-mentioned degree of association based on oxygen
Most relevance degree corresponding to parameter time series, oxygen content dynamic prediction model is established, to realize anhydrous storage ring
Oxygen content dynamic prediction under border.Oxygen content dynamic prediction method provided by the invention can accurately obtain anhydrous storage
Oxygen content in environment, it is possible to prevente effectively from accidental error caused by single oxygen acquisition methods, can effectively improve storage
When the economic loss brought by anoxic of organism.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will to embodiment or
Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Some bright embodiments for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is a kind of oxygen content dynamic prediction method towards under anhydrous storage environment according to the embodiment of the present invention
Flow diagram；
Fig. 2 is a kind of oxygen content dynamic prediction device towards under anhydrous storage environment according to the embodiment of the present invention
Structure diagram.
Specific implementation mode
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing, the present invention is implemented
Technical solution in example is explicitly described, it is clear that and described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, shall fall within the protection scope of the present invention.
Fig. 1 is a kind of oxygen content dynamic prediction method towards under anhydrous storage environment according to the embodiment of the present invention
Flow diagram, as shown in Figure 1, including：
S1, calculate every group of sample anhydrous storage environment carbon dioxide, temperature and humidity time series and oxygen
Time series between incidence coefficient；
Specifically, data acquisition is carried out to carbon dioxide X, temperature Y, humidity Z, the oxygen L in every group of sample environment, point
It Gou Cheng not carbon dioxide time series Xi(i=1,2,3 ... n ...), temperature-time sequence Yi(i=1,2,3 ... n ...), it is wet
Spend time series Zi(i=1,2,3 ... n ...), oxygen time series Li(i=1,2,3 ... n ...).
For the time series of the carbon dioxide, temperature and the humidity that get, carbon dioxide, temperature in the same time are calculated
And the incidence coefficient between humidity and oxygen, obtain carbon dioxide, the variation of temperature and humidity change to oxygen it is related
Relationship, the standard chosen in this, as optimal reference variable.Specifically utilize following formula calculate correlation coefficient：
Wherein Δ is the absolute difference between certain moment environmental parameter and oxygen；Min Δs are minimum two differential；max
It is differential that Δ is the largest two；ρ is resolution ratio, and 0 ＜ ρ ＜ 1 usually take 0.5.
S2 is based on the incidence coefficient, obtains carbon dioxide, temperature and humidity and oxygen between two adjacent time nodes
The degree of association between gas；
Specifically utilize following formula calculating correlation size：
Wherein n indicates the incidence coefficient of required carbon dioxide, temperature and humidity and oxygen acquisition between two adjacent nodes
Number.
S3, the time sequence of the parameter corresponding to most relevance degree in time series and the above-mentioned degree of association based on oxygen
Row, establish oxygen content dynamic prediction model, to realize the oxygen content dynamic prediction under anhydrous storage environment.
Preferably, parameter is carbon dioxide, temperature or humidity.
In this step, using the degree of association size obtained in step S2, the original of the corresponding parameter of most relevance degree is obtained
Beginning data time series obtain the major parameter in oxygen content dynamic prediction model, in conjunction with oxygen time series, establish oxygen
The dynamic prediction model of Gas content.Specifically oxygen dynamic prediction model is established using following formula：
Wherein μ, α are that argument sequence corresponding to most relevance degree is related, are calculated using its sequence：
Wherein A is the data matrix that the corresponding argument sequence of most relevance degree is constituted；B is the corresponding ginseng of most relevance degree
Number Sequence.
In embodiments of the present invention, every group of sample is calculated in anhydrous storage environment carbon dioxide, temperature and humidity
Incidence coefficient between time series and the time series of oxygen；Based on the incidence coefficient, two adjacent time nodes are obtained
Between the degree of association between carbon dioxide, temperature and humidity and oxygen；In time series and the above-mentioned degree of association based on oxygen
The time series of parameter corresponding to most relevance degree establishes oxygen content dynamic prediction model, to realize anhydrous storage ring
Oxygen content dynamic prediction under border.Oxygen content dynamic prediction method provided by the invention can accurately obtain anhydrous storage
Oxygen content in environment, it is possible to prevente effectively from accidental error caused by single oxygen acquisition methods, can effectively improve storage
When the economic loss brought by anoxic of organism.
On the basis of the above embodiments, further include：Based on the physical trait parameter of each organism in sample, utilize
Fuzzy control model is grouped the sample；
Obtain every group of sample in anhydrous storage environment carbon dioxide, temperature, humidity and oxygen time series.
Specifically, since the situation of the organism of different length and weight metabolism has differences, while oxygen demand
It is different, it is grouped using the relationship between the length and weight of sample, ensures the oxygen demand of bottom line, avoid consuming
The big sample of oxygen amount is first dead.
Specifically, its length L and weight W are recorded for each sample, fuzzy control is inputted as two parameters
In model；Sample packet is carried out using fuzzy control model, and the sample information after grouping is shown, grouping carries out nothing
Water is stored, and grouping is completed；
On the basis of the above embodiments, further include：For any sample group, respectively to carbon dioxide, temperature, humidity
And the time series of oxygen carries out cumulative recombination, to obtain the time sequence of new carbon dioxide, temperature, humidity and oxygen
Row.
Specifically, be utilized respectively cumulative mode to the carbon dioxide of above-mentioned acquisition, temperature, humidity and oxygen when
Between sequence recombinated, exclude the influence of individual factor, calculate the cumulative effect of entire change procedure, closer to practical storage
When effect.
Specifically recombinated using following formula：
Wherein, X is the corresponding time series of any variable in above-mentioned variable；M is cumulative number,It is that X becomes
K number evidence is cumulative before amount.
On the basis of the above embodiments, according to organism storage time length in every group of sample and storage density to set
Set timing node.
Specifically, timing node is used for meeting different monitoring requirements, with storage density and storage space size and storage
The factors such as time length are related.
Fig. 2 is a kind of oxygen content dynamic prediction device towards under anhydrous storage environment according to the embodiment of the present invention
Structure diagram, as shown in Fig. 2, including：Incidence coefficient computing module 601, degree of association acquisition module 602 and oxygen content
Dynamic prediction module 603；Wherein,
Incidence coefficient computing module 601 for calculate every group of sample anhydrous storage environment carbon dioxide, temperature and
Incidence coefficient between the time series of humidity and the time series of oxygen；
Degree of association acquisition module 602 is used to be based on the incidence coefficient, obtains titanium dioxide between two adjacent time nodes
The degree of association between carbon, temperature and humidity and oxygen；
Oxygen content dynamic prediction module 603 is used for the most high point in time series and the above-mentioned degree of association based on oxygen
The time series of parameter corresponding to connection degree establishes oxygen content dynamic prediction model, to realize the oxygen under anhydrous storage environment
Gas content dynamic prediction.
Wherein, the environmental parameter includes carbon dioxide, temperature, humidity and oxygen, correspondingly the environmental parameter
Time series includes the time sequence of the time series of carbon dioxide, the time series of temperature, the time series of humidity and oxygen
Row.
Incidence coefficient computing module 601 is used for the time series for carbon dioxide, temperature and the humidity got,
The incidence coefficient between carbon dioxide, temperature and humidity and oxygen in the same time is calculated, carbon dioxide, temperature and wet are obtained
The correlativity of the variation of degree and oxygen variation, the standard chosen in this, as optimal reference variable.Specifically calculated using following formula
Incidence coefficient：
Wherein Δ is the absolute difference between certain moment environmental parameter and oxygen；Min Δs are minimum two differential；max
It is differential that Δ is the largest two；ρ is resolution ratio, and 0 ＜ ρ ＜ 1 usually take 0.5.
Degree of association acquisition module 602 specifically utilizes following formula calculating correlation size：
Wherein n indicates the incidence coefficient of required carbon dioxide, temperature and humidity and oxygen acquisition between two adjacent nodes
Number.
Preferably, parameter is carbon dioxide, temperature or humidity.
In this step, using degree of association acquisition module 602 obtain degree of association size, to the above-mentioned degree of association installation from
Arrive greatly it is small arranged, choose most relevance degree therein, the initial data time series of the corresponding parameter of most relevance degree,
The major parameter in oxygen content dynamic prediction model is obtained, in conjunction with oxygen time series, establishes the dynamic prediction of oxygen content
Model.Specifically oxygen dynamic prediction model is established using following formula：
Wherein μ, α are that argument sequence corresponding to most relevance degree is related, are calculated using its sequence：
Wherein A is the data matrix that the corresponding argument sequence of most relevance degree is constituted；B is the corresponding ginseng of most relevance degree
Number Sequence.
In embodiments of the present invention, incidence coefficient computing module is for calculating every group of sample two under anhydrous storage environment
Incidence coefficient between carbonoxide, the time series of temperature and humidity and the time series of oxygen；Degree of association acquisition module is used
Pass between based on the incidence coefficient, obtaining two adjacent time nodes between carbon dioxide, temperature and humidity and oxygen
Connection degree；Oxygen content dynamic prediction module is used for the most relevance degree institute in time series and the above-mentioned degree of association based on oxygen
The time series of corresponding parameter establishes oxygen content dynamic prediction model, to realize the oxygen content under anhydrous storage environment
Dynamic prediction.The oxygen that oxygen content dynamic prediction device provided by the invention can accurately obtain in anhydrous storage environment contains
Amount, it is possible to prevente effectively from accidental error caused by single oxygen acquisition methods, organism is because of anoxic when can effectively improve storage
The economic loss brought.
On the basis of the above embodiments, further include：Data acquisition module, for based on each organism in sample
Physical trait parameter is grouped the sample using fuzzy control model；Every group of sample is obtained in anhydrous storage environment
The time series of carbon dioxide, temperature, humidity and oxygen.
Specifically, since the situation of the organism of different length and weight metabolism has differences, while oxygen demand
It is different, it is grouped using the relationship between the length and weight of sample, ensures the oxygen demand of bottom line, avoid consuming
The big sample of oxygen amount is first dead.
Specifically, its length L and weight W are recorded for each sample, fuzzy control is inputted as two parameters
In model；Sample packet is carried out using fuzzy control model, and the sample information after grouping is shown, grouping carries out nothing
Water is stored, and grouping is completed；
Specifically, data acquisition module is used for carbon dioxide X, temperature Y, humidity Z, the oxygen L in every group of sample environment
Data acquisition is carried out, carbon dioxide time series X is respectively constitutedi(i=1,2,3 ... n ...), temperature-time sequence 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 embodiments, further include：Data accumulation recomposition unit, for for any sample group, difference
Cumulative recombination is carried out to the time series of carbon dioxide, temperature, humidity and oxygen, with obtain new carbon dioxide, temperature,
The time series of humidity and oxygen.
Specifically, be utilized respectively cumulative mode to the carbon dioxide of above-mentioned acquisition, temperature, humidity and oxygen when
Between sequence recombinated, exclude the influence of individual factor, calculate the cumulative effect of entire change procedure, closer to practical storage
When effect.
Data accumulation recomposition unit is specifically recombinated using following formula：
Wherein, X is the corresponding time series of any variable in above-mentioned variable；M is cumulative number,It is that X becomes
K number evidence is cumulative before amount.
On the basis of the above embodiments, further include：Timing node setting unit, for according to biological in every group of sample
Body storage time length and storage density are to be arranged timing node.
Specifically, timing node is used for meeting different monitoring requirements, with storage density and storage space size and storage
The factors such as time length are related.
On the basis of the above embodiments, further include：Alarm module；Wherein,
The alarm module, for being more than default when the oxygen content predicted value of the oxygen content dynamic prediction module
When threshold values, the alarm module is alarmed.
Preferably, further include human-computer interaction module, human-computer interaction module is for oxygen content dynamic prediction module sum number
According to acquisition module obtain data shown, while provide user setting timing node numerical value, oxygen content early warning threshold values with
And other conventional miscellaneous functions.
The alarm module, according to the oxygen content early warning threshold values being arranged in human-computer interaction module and oxygen content dynamic
The predicted value obtained in prediction module is compared, and when more than the threshold values of setting, utilizes the side such as buzzer or early warning short message
Formula sends out alarm.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：It is still
Can be with technical scheme described in the above embodiments is modified, or which part technical characteristic is equally replaced
It changes；And these modifications or replacements, various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
Spirit and scope.
Claims (10)
1. a kind of oxygen content dynamic prediction method towards under anhydrous storage environment, which is characterized in that including：
Every group of sample is calculated in the time series of anhydrous storage environment carbon dioxide, temperature and humidity and the time sequence of oxygen
Incidence coefficient between row；
Based on the incidence coefficient, carbon dioxide between two adjacent time nodes is obtained, between temperature and humidity and oxygen
The degree of association；
The time series of the parameter corresponding to most relevance degree in time series and the above-mentioned degree of association based on oxygen, establishes oxygen
Gas content dynamic prediction model, to realize the oxygen content dynamic prediction under anhydrous storage environment.
2. oxygen content dynamic prediction method according to claim 1, which is characterized in that further include：
Based on the physical trait parameter of each organism in sample, the sample is grouped using fuzzy control model；
Obtain every group of sample in anhydrous storage environment carbon dioxide, temperature, humidity and oxygen time series.
3. oxygen content dynamic prediction method according to claim 2, which is characterized in that further include：
For any sample group, cumulative recombination is carried out to the time series of carbon dioxide, temperature, humidity and oxygen respectively, with
Obtain the time series of new carbon dioxide, temperature, humidity and oxygen.
4. oxygen content dynamic prediction method according to claim 1, which is characterized in that according to organism in every group of sample
Storage time length and storage density are to be arranged timing node.
5. oxygen content dynamic prediction method according to claim 1, which is characterized in that the parameter be carbon dioxide,
Temperature or humidity.
6. a kind of oxygen content dynamic prediction device towards under anhydrous storage environment, which is characterized in that including：
Incidence coefficient computing module, for calculating every group of sample in anhydrous storage environment carbon dioxide, temperature and humidity
Incidence coefficient between time series and the time series of oxygen；
Degree of association acquisition module, for be based on the incidence coefficient, obtain two adjacent time nodes between carbon dioxide, temperature with
And the degree of association between humidity and oxygen；
Oxygen content dynamic prediction module is right for the most relevance degree institute in time series and the above-mentioned degree of association based on oxygen
The time series for the parameter answered establishes oxygen content dynamic prediction model, to realize that the oxygen content under anhydrous storage environment is dynamic
State is predicted.
7. oxygen content dynamic prediction device according to claim 6, which is characterized in that further include：
Data acquisition module is used for the physical trait parameter based on each organism in sample, using fuzzy control model to institute
Sample is stated to be grouped；
Obtain every group of sample in anhydrous storage environment carbon dioxide, temperature, humidity and oxygen time series.
8. oxygen content dynamic prediction device according to claim 6, which is characterized in that further include：
Data accumulation recomposition unit, for for any sample group, respectively to carbon dioxide, temperature, humidity and oxygen when
Between sequence carry out cumulative recombination, to obtain new carbon dioxide, temperature, humidity and the time series of oxygen.
9. oxygen content dynamic prediction device according to claim 6, which is characterized in that further include：
Timing node setting unit, for according to organism storage time length in every group of sample and storage density the time is arranged
Node.
10. oxygen content dynamic prediction device according to claim 6, which is characterized in that further include：Alarm module；Its
In,
The alarm module, for being more than pre-set threshold value when the oxygen content predicted value of the oxygen content dynamic prediction module
When, the alarm module is alarmed.
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