CN117348635B - Intelligent wine cabinet temperature control system and control method thereof - Google Patents
Intelligent wine cabinet temperature control system and control method thereof Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
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Abstract
The application provides an intelligent wine cabinet temperature control system and a control method thereof, wherein the temperature in an intelligent wine cabinet is collected, so that a redundant temperature data set in the cabinet is obtained; converting the temperature redundancy elimination data set in the cabinet into a plurality of temperature redundancy elimination polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence, and further determining the thermal state screen convergence amount by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences; extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain temperature instability characteristics of the cabinet, determining characteristic turbulence typical bases of the temperature instability characteristics of the cabinet, and further determining redundancy removal characteristic entropy elimination degree of the temperature instability characteristics of the cabinet according to the characteristic turbulence typical bases; determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state screening trend same quantity and redundancy characteristic entropy eliminating degree; the temperature control is carried out on the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature, so that the accuracy of the temperature data in the cabinet predicted in the intelligent wine cabinet can be effectively improved.
Description
Technical Field
The application relates to the technical field of intelligent temperature control, in particular to an intelligent wine cabinet temperature control system and a control method thereof.
Background
The intelligent temperature control is an intelligent scheme based on an advanced sensing technology and an automatic regulating system, environmental temperature data is monitored and analyzed in real time through a sensor and an algorithm, and the technology of accurately regulating indoor temperature is realized by using the algorithm and control logic so as to automatically regulate air conditioners, central heating or other temperature control intelligent equipment to maintain an ideal temperature range of a specific area.
The intelligent wine cabinet is a device for managing and keeping wine products by utilizing advanced technologies and intelligent technologies, combines the functions of an intelligent sensor, an internet of things technology, data analysis, artificial intelligence and the like, is generally provided with a temperature control system, is one of key functions of the intelligent wine cabinet, can adjust and keep proper temperature according to different types of wine products, is capable of performing temperature control through remote control, can be used by a user to monitor and adjust the temperature of the wine cabinet by using a smart phone, can realize remote control wherever the user is, and can be influenced by factors such as environment, temperature sensor and the like in actual life, so that temperature fluctuation is large, the temperature fluctuation can be caused, the wine temperature in a wine bottle is unstable, the quality and the taste of the wine are influenced, and a certain temperature deviation exists in the temperature control system, namely, the temperature in the wine bottle is possibly deviated from the ideal storage temperature due to difference between the set temperature and the actual temperature, and the quality of the wine is influenced. In the prior art, temperature data in the intelligent cabinet obtained in real time through a temperature sensor has the problem that the temperature data in the cabinet is complicated and huge, and the temperature data in the cabinet cannot be efficiently extracted and utilized, so that the problem that the accuracy of the temperature value in the intelligent cabinet obtained by prediction is low when the temperature data in the intelligent cabinet is predicted is caused.
Disclosure of Invention
The application provides an intelligent wine cabinet temperature control system and a control method thereof, which are used for solving the technical problem that the accuracy of temperature data in a cabinet obtained by prediction in an intelligent wine cabinet is low.
In order to solve the technical problems, the application adopts the following technical scheme:
in a first aspect, the present application provides a control method of an intelligent wine cabinet temperature control system, including:
acquiring the temperature in the intelligent wine cabinet, and further obtaining a temperature redundancy data set in the cabinet;
converting the temperature redundancy elimination data set in the cabinet into a plurality of temperature redundancy elimination polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence, and further determining the thermal state screen convergence amount by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences;
extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability characteristics, determining characteristic turbulence typical bases of the wine cabinet temperature instability characteristics, and further determining redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics according to the characteristic turbulence typical bases;
determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state filter screen trend quantity and redundancy characteristic entropy eliminating degree of the wine cabinet temperature instability characteristic;
and controlling the temperature of the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature.
In some embodiments, collecting the temperature inside the intelligent wine cabinet, and further obtaining the temperature redundancy data set inside the cabinet specifically includes:
presetting a temperature redundancy factor in a cabinet;
determining redundancy removal values corresponding to the temperatures in each cabinet according to the redundancy removal factors of the temperatures in the cabinets;
and taking a set formed by redundancy removal values corresponding to all the temperatures in the cabinet as a cabinet temperature redundancy removal data set.
In some embodiments, converting the intra-cabinet temperature redundancy elimination data set into a plurality of temperature redundancy elimination aggregate sequences is performing data cluster conversion on the intra-cabinet temperature redundancy elimination data set, thereby obtaining a plurality of temperature redundancy elimination aggregate sequences.
In some embodiments, determining the thermal screen convergence from the temperature pulsation convergence factors of all temperature redundancy polymerization sequences specifically includes:
obtaining a total number of temperature redundancy-removing polymeric sequences；
According to the total number of the temperature redundancy polymerization sequencesAnd the temperature pulsation convergence factors of all temperature redundancy polymerization sequences to determine the thermal state screen convergence amount +.>Wherein the thermal state screen is in the same amount +.>According to the following formula:
wherein,represents the nth temperature redundancy polymerization sequence,/->Representing a temperature redundancy polymerization sequence->Temperature pulsation convergence factor of (c).
In some embodiments, determining the predicted value of the temperature in the intelligent wine cabinet according to the trend amount of the thermal state filter screen and the redundancy eliminating feature entropy eliminating degree of the temperature instability feature of the wine cabinet specifically includes:
obtaining characteristic thermal homogeneity representative coefficients of the wine cabinet temperature instability characteristics according to the thermal state filter screen trend and redundancy removal characteristic entropy removal degree of the wine cabinet temperature instability characteristics, and further determining the characteristic thermal homogeneity representative coefficients of all the wine cabinet temperature instability characteristics;
taking the temperature abnormal stability characteristic of the wine cabinet corresponding to the maximum characteristic thermal homogeneity representative coefficient as the temperature prediction characteristic in the cabinet;
and obtaining the predicted value of the temperature in the intelligent wine cabinet through the predicted characteristic of the temperature in the cabinet.
In some embodiments, the characteristic thermal homogeneity representative coefficient of the temperature instability characteristic of the wine cabinet is determined according to the following formula:
wherein,indicating the temperature instability characteristic of the wine cabinet>Characteristic thermal homogeneity representative coefficient of>Indicating the temperature instability characteristic of the wine cabinet>Redundancy characteristic entropy elimination degree, +.>Indicating the approach of the thermal state screen.
In some embodiments, the temperature control of the intelligent wine cabinet by the predicted value of the temperature in the cabinet and the preset wine storage temperature specifically includes:
when the predicted value of the temperature in the cabinet is higher than the preset wine storage temperature, the refrigerating capacity in the intelligent wine cabinet is increased;
and when the predicted value of the temperature in the cabinet is lower than the preset wine storage temperature, reducing the refrigerating capacity in the cabinet of the intelligent wine cabinet.
In a second aspect, the present application provides an intelligent wine cabinet temperature control system, including control unit, control unit includes:
the in-cabinet temperature redundancy removing data set determining module is used for collecting the in-cabinet temperature of the intelligent wine cabinet, and further obtaining an in-cabinet temperature redundancy removing data set;
the thermal state screen convergence determining module is used for converting the temperature redundancy removing data set in the cabinet into a plurality of temperature redundancy removing polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy removing polymerization sequence, and further determining the thermal state screen convergence by the temperature pulsation convergence factors of all the temperature redundancy removing polymerization sequences;
the redundancy removal feature entropy removal degree determination module is used for extracting temperature instability features of the temperature redundancy removal data set in the cabinet to obtain wine cabinet temperature instability features, determining feature turbulence typical bases of the wine cabinet temperature instability features, and further determining redundancy removal feature entropy removal degree of the wine cabinet temperature instability features according to the feature turbulence typical bases;
the in-cabinet temperature predicted value acquisition module is used for determining an in-cabinet temperature predicted value of the intelligent wine cabinet according to the trend amount of the thermal state screen and redundancy characteristic entropy elimination degree of the wine cabinet temperature instability characteristic;
and the temperature control module is used for controlling the temperature of the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature.
In a third aspect, the application provides a computer device, the computer device including a memory and a processor, the memory being configured to store a computer program, and the processor being configured to invoke and run the computer program from the memory, so that the computer device performs the control method of the intelligent wine cabinet temperature control system described above.
In a fourth aspect, the present application provides a computer readable storage medium, where instructions or codes are stored, where the instructions or codes, when executed on a computer, cause the computer to implement a control method of the intelligent wine cabinet temperature control system described above when executed.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
in the intelligent wine cabinet temperature control system and the intelligent wine cabinet temperature control method, the temperature in the intelligent wine cabinet is collected, and then a cabinet temperature redundancy data set is obtained; converting the temperature redundancy elimination data set in the cabinet into a plurality of temperature redundancy elimination polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence, and further determining the thermal state screen convergence amount by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences; extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability characteristics, determining characteristic turbulence typical bases of the wine cabinet temperature instability characteristics, and further determining redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics according to the characteristic turbulence typical bases; determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state filter screen trend quantity and redundancy characteristic entropy eliminating degree of the wine cabinet temperature instability characteristic; and controlling the temperature of the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature.
In this application, at first, through predetermining the interior temperature redundant factor and carrying out redundant processing to interior temperature dataset, can be in removing noise and redundant information, make the interior temperature redundant data that obtains more steady and reliable, help improving the prediction accuracy of follow-up interior temperature, secondly, can measure the importance that temperature redundant polymerization sequence was concentrated at temperature redundant polymerization sequence through temperature pulsation convergence factor, through the thermal state filter screen trend volume of confirming interior temperature redundant dataset, can quantify out the information content of interior temperature redundant dataset, and then can provide valuable information for wine article storage and temperature control's decision, then, through the redundant characteristic desuperheating degree of confirm characteristic typical base and gradevin temperature abnormal characteristic, can measure the importance of gradevin temperature abnormal characteristic to interior temperature prediction, help improving the accuracy of interior temperature prediction of intelligent cabinet, and then, through confirming the characteristic thermal homogeneity representative coefficient, help discerning the importance of temperature abnormal characteristic and the importance of gradevin temperature abnormal characteristic and the thermal state filter screen trend volume of confirming interior temperature redundant dataset, can quantify out the information content of interior temperature redundant dataset, and then can provide valuable information for wine article storage and temperature control's decision, then, through confirming characteristic typical base and the redundant characteristic of gradevin temperature abnormal characteristic, can measure the importance of interior temperature prediction of interior temperature, and intelligent temperature characteristic of wine cabinet temperature abnormal characteristic, and temperature characteristic can be compared with the intelligent temperature control in the intelligent temperature cabinet, and the aspect of the interior temperature has been adjusted.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is an exemplary flow chart of a control method of an intelligent wine cabinet temperature control system according to some embodiments of the present application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software of a control unit shown in accordance with some embodiments of the present application;
fig. 3 is a schematic structural diagram of a computer device implementing a control method of an intelligent wine cabinet temperature control system according to some embodiments of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The embodiment of the application provides an intelligent wine cabinet temperature control system and a control method thereof, wherein the intelligent wine cabinet temperature control system is characterized in that the temperature in an intelligent wine cabinet is collected, and then a cabinet temperature redundancy data set is obtained; converting the temperature redundancy elimination data set in the cabinet into a plurality of temperature redundancy elimination polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence, and further determining the thermal state screen convergence amount by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences; extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability characteristics, determining characteristic turbulence typical bases of the wine cabinet temperature instability characteristics, and further determining redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics according to the characteristic turbulence typical bases; determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state filter screen trend quantity and redundancy characteristic entropy eliminating degree of the wine cabinet temperature instability characteristic; and the temperature control is carried out on the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature, so that the accuracy of the temperature data in the cabinet predicted in the intelligent wine cabinet is effectively improved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart illustrating a control method of an intelligent wine cabinet temperature control system according to some embodiments of the present application, the control method 100 of the intelligent wine cabinet temperature control system mainly includes the following steps:
in step 101, the temperature inside the intelligent wine cabinet is collected, and then the temperature redundancy data set inside the cabinet is obtained.
In specific implementation, the temperature sensor arranged in the intelligent wine cabinet collects the internal temperature data of the intelligent wine cabinet according to the equal time intervals, so that the temperature in the intelligent wine cabinet is obtained.
In some embodiments, the method for acquiring the temperature in the intelligent wine cabinet and further obtaining the temperature redundancy data set in the cabinet specifically may be as follows:
presetting a temperature redundancy factor in a cabinet;
determining redundancy removal values corresponding to the temperatures in each cabinet according to the redundancy removal factors of the temperatures in the cabinets;
and taking a set formed by redundancy removal values corresponding to all the temperatures in the cabinet as a cabinet temperature redundancy removal data set.
In the above embodiment, the redundancy value corresponding to each temperature in the cabinet is determined according to the redundancy factor of the temperature in the cabinet, and in a specific implementation, the redundancy value corresponding to the temperature in the cabinet may be determined according to the following formula:
wherein,indicating the temperature in the cabinet>Redundancy value of->Indicating the temperature redundancy factor in the cabinet +.>Represent the firsti temperatures in the cabinet,/, and>the redundancy removal value representing the i-1 th internal temperature of the cabinet can be obtained through the above method, and then a set formed by redundancy removal values corresponding to all internal temperatures is used as an internal temperature redundancy removal data set, and it is to be noted that in the application, the internal temperature redundancy removal factor is an index for balancing redundancy removal precision in the redundancy removal process, the value of the internal temperature redundancy removal factor can be obtained through historical experience or experimental data, the limitation is not made here, and when the redundancy removal value corresponding to each internal temperature is obtained, the value of each internal temperature is replaced by the redundancy removal value obtained through calculation, and then the internal temperature redundancy removal data set is obtained, and at the moment, interference noise and redundancy data in the internal temperature redundancy removal data set are removed.
It should be noted that the temperature in the cabinet may be affected by various factors, such as environmental noise, sensor errors, etc., resulting in larger temperature fluctuation in the cabinet, and the redundancy removal process is performed on the temperature data in the cabinet by presetting the redundancy removal factor in the cabinet, so that the noise can be removed, the obtained redundancy removal data in the cabinet is more stable and reliable, and the subsequent prediction accuracy of the temperature in the cabinet is improved.
In step 102, the temperature redundancy elimination dataset in the cabinet is converted into a plurality of temperature redundancy elimination polymerization sequences, the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence is determined, and then the thermal state screen convergence quantity is determined by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences.
In some embodiments, the converting the intra-cabinet temperature redundancy removing dataset into the plurality of temperature redundancy removing aggregate sequences is performing data cluster conversion on the intra-cabinet temperature redundancy removing dataset, so as to obtain the plurality of temperature redundancy removing aggregate sequences, and when the method is specifically implemented, the intra-cabinet temperature redundancy removing dataset is clustered, that is, the intra-cabinet temperature redundancy removing dataset is divided into different temperature redundancy removing datasets, a clustering algorithm used in the method is K-means clustering, the intra-cabinet temperature redundancy removing dataset is divided into different temperature redundancy removing datasets through a K-means clustering method, so that a plurality of temperature redundancy removing datasets can be obtained, and other clustering algorithms can be used when the method is actually implemented, without limitation, each temperature redundancy removing dataset is converted into a corresponding temperature redundancy removing aggregate sequence, so that a plurality of temperature redundancy removing aggregate sequences can be obtained, and each temperature redundancy removing aggregate sequence is a time sequence containing the intra-cabinet temperature redundancy removing dataset.
In some embodiments, when determining the temperature pulsation convergence factor of each temperature redundancy removing aggregation sequence, in specific implementation, first, the total number of temperature redundancy removing data in the cabinet in the temperature redundancy removing data set can be obtained, then the total number of temperature redundancy removing data in the cabinet in each temperature redundancy removing aggregation sequence is determined, for each temperature redundancy removing aggregation sequence, the ratio of the total number of temperature redundancy removing data in the cabinet in the temperature redundancy removing aggregation sequence to the total number of temperature redundancy removing data in the cabinet in the temperature redundancy removing aggregation sequence is taken as the temperature pulsation convergence factor of the temperature redundancy removing aggregation sequence, and it is required to be noted that in the application, the temperature pulsation convergence factor represents the degree that the value of all the temperature redundancy removing data in the cabinet in the temperature redundancy removing aggregation sequence tends to be of the same category, and the larger the temperature pulsation convergence factor is, the stronger the representativeness of the temperature redundancy removing aggregation sequence is represented, and the temperature redundancy removing aggregation sequence is more important in the temperature redundancy removing data set.
Preferably, in some embodiments, the thermal state screen trend is determined from the temperature pulsation convergence factors of all temperature redundancy polymerization sequences in the following manner, namely:
obtaining a total number of temperature redundancy-removing polymeric sequences；
According to the total number of the temperature redundancy polymerization sequencesAnd the temperature pulsation convergence factors of all temperature redundancy polymerization sequences to determine the thermal state screen convergence amount +.>In particular, theThermal state screen approach equivalent +.>The determination may be made according to the following equation:
wherein,represents the nth temperature redundancy polymerization sequence,/->Representing a temperature redundancy polymerization sequence->It should be noted that, in the present application, the thermal state screen convergence amount is an index for measuring the accuracy of all the temperature redundancy data in the cabinet in the temperature redundancy data set, and the thermal state screen convergence amount represents that the temperature redundancy data set in the cabinet contains more available temperature information, and useless temperature information is screened and filtered, so that the accuracy of temperature prediction in the intelligent cabinet is improved.
The importance of the temperature redundancy removing aggregation sequence in the temperature redundancy removing aggregation sequence set can be measured through the temperature pulsation convergence factor, the information content of the temperature redundancy removing data set in the cabinet can be quantified through determining the thermal state screen trend of the temperature redundancy removing data set in the cabinet, and valuable information can be provided for decision making of wine storage and temperature control.
And 103, extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability characteristics, determining characteristic turbulence typical bases of the wine cabinet temperature instability characteristics, and further determining redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics according to the characteristic turbulence typical bases.
In some embodiments, the temperature instability feature extraction is performed on the temperature redundancy elimination dataset in the cabinet to obtain a temperature instability feature of the wine cabinet, and when the temperature instability feature extraction is performed on the temperature redundancy elimination dataset in the cabinet through a distribution statistics mode to obtain a temperature instability feature of the wine cabinet, it is to be noted that in the application, the temperature instability feature of the wine cabinet represents the temperature feature of the wine cabinet with stability excluding the abnormality, the temperature instability feature of the wine cabinet can be a temperature distribution statistic, such as an intra-cabinet temperature average value, a maximum intra-cabinet temperature value, a minimum intra-cabinet temperature value, and the like, and the extracted temperature instability feature of the wine cabinet should cover main information of the intra-cabinet temperature redundancy elimination data so as to represent characteristics of the intra-cabinet temperature redundancy elimination data.
In some embodiments, when determining the characteristic turbulence typical base of the wine cabinet temperature instability characteristic, in specific implementation, firstly, determining a characteristic value of the wine cabinet temperature instability characteristic, further determining the occurrence number of the characteristic value in the temperature redundancy data set in the wine cabinet, taking the ratio of the occurrence number to the total number of the temperature redundancy data in the temperature redundancy data set in the wine cabinet as the characteristic turbulence typical base of the wine cabinet temperature instability characteristic, and it is required to explain that in the application, the characteristic turbulence typical base is used for measuring the degree of trend of the value of the temperature redundancy data in the wine cabinet towards the temperature instability characteristic, the larger the characteristic turbulence typical base is, the more concentrated and typical the value of the temperature redundancy data in the wine cabinet is, and the smaller the characteristic turbulence typical base is, and the more turbulent and dispersed the value of the temperature redundancy data in the wine cabinet is represented.
Preferably, in some embodiments, the redundancy removal feature entropy removal degree of the wine cabinet temperature instability feature determined by the feature turbulence representative base specifically may be the following manner, that is:
obtaining a total number of temperature redundancy-removing polymeric sequences；
Acquiring temperature abnormal stability characteristics of wine cabinetIs characterized by turbulence of the group>；
When the temperature redundancy data set in the cabinet contains the temperature instability characteristic of the wine cabinet, respectively determining the probability that the temperature instability characteristic of the wine cabinet belongs to each temperature redundancy aggregation sequence;
based on the total number of temperature redundancy-eliminating polymeric sequencesTemperature instability characteristic of wine cabinet>Is characterized by turbulence of the group>Determining the temperature instability characteristic of the wine cabinet according to the probability that the temperature instability characteristic of the wine cabinet belongs to each temperature redundancy polymerization sequence>Redundancy characteristic entropy degree->In specific implementation, redundancy feature entropy elimination degree +.>The determination may be made according to the following equation:
wherein,represents the nth temperature redundancy polymerization sequence,/->Representative of characteristic turbulence>Is used as a base for the reaction of the amino acids,the temperature redundancy data set in the display cabinet contains the temperature instability characteristic of the wine cabinet>And the temperature of the wine cabinet is different and stable>Belonging to the temperature redundancy polymerization sequence->Probability of->Representation->The opposite value of (1), it is stated that +.>Andthe method is characterized in that the redundancy removal feature entropy removal degree represents information accuracy of the wine cabinet temperature instability feature, the redundancy removal feature entropy removal degree is high in representing that the wine cabinet temperature instability feature contains more available temperature information, accuracy of the wine cabinet temperature instability feature is high, useless information in the wine cabinet temperature instability feature is eliminated, information confusion in the wine cabinet temperature instability feature is restrained, and accuracy of temperature prediction in the wine cabinet is improved.
In the embodiment, the characteristic turbulence typical base of the temperature instability characteristic of the wine cabinet can be obtainedThe difference from the absolute value of 1 is taken as a characteristic turbulence representative basis +.>Opposite radicals>When the temperature redundancy data set in the cabinet contains the temperature instability characteristic of the wine cabinet, for each temperature redundancy polymerization sequence, the temperature instability characteristic of the wine cabinet needs to be calculatedThe method comprises the steps that the occurrence probability of the characteristics in the temperature redundancy removing and polymerizing sequence can be calculated, the number of times that the temperature redundancy removing and polymerizing sequence contains the value of the temperature instability characteristic of the wine cabinet or the number of temperature redundancy removing and polymerizing data in the wine cabinet can be calculated, then the number of times is divided by the total number of the temperature redundancy removing and polymerizing data in the wine cabinet in the temperature redundancy removing and polymerizing sequence, the probability that the temperature instability characteristic of the wine cabinet belongs to the temperature redundancy removing and polymerizing sequence is obtained, and when the temperature redundancy removing and polymerizing data set in the wine cabinet does not contain the temperature instability characteristic of the wine cabinet, the probability that the temperature instability characteristic of the wine cabinet belongs to each temperature redundancy removing and polymerizing sequence can be determined through the probability that the temperature instability characteristic of the wine cabinet belongs to the temperature redundancy removing and polymerizing sequence.
The importance of the temperature instability characteristic of the wine cabinet to the temperature prediction in the cabinet can be measured by determining the characteristic turbulence typical basis and the redundancy characteristic entropy eliminating degree, and the accuracy of the temperature prediction in the intelligent wine cabinet can be improved.
And 104, determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the trend amount of the thermal state filter screen and the redundancy characteristic entropy eliminating degree of the temperature instability characteristic of the wine cabinet.
In some embodiments, the determining the predicted value of the temperature in the intelligent wine cabinet according to the thermal state filter approach amount and the redundancy feature entropy eliminating degree may specifically adopt the following manner, that is:
obtaining characteristic thermal homogeneity representative coefficients of the temperature instability characteristics of the wine cabinet according to the thermal state screen trend same quantity and the redundancy removal characteristic entropy removal degree, and further determining the characteristic thermal homogeneity representative coefficients of the temperature instability characteristics of all the wine cabinet;
taking the temperature abnormal stability characteristic of the wine cabinet corresponding to the maximum characteristic thermal homogeneity representative coefficient as the temperature prediction characteristic in the cabinet;
and obtaining the predicted value of the temperature in the intelligent wine cabinet through the predicted characteristic of the temperature in the cabinet.
In the above embodiment, the characteristic thermal homogeneity representative coefficient of the temperature instability characteristic of the wine cabinet is determined according to the following formula:
wherein,indicating the temperature instability characteristic of the wine cabinet>Characteristic thermal homogeneity representative coefficient of>Indicating the temperature instability characteristic of the wine cabinet>Redundancy characteristic entropy elimination degree, +.>The characteristic thermal homogeneity representative coefficient is an index for measuring the importance and representativeness of the temperature instability characteristic of the wine cabinet, the higher the characteristic thermal homogeneity representative coefficient is, the higher the accuracy of the temperature prediction in the wine cabinet is when the temperature instability characteristic of the wine cabinet is used, the lower the characteristic thermal homogeneity representative coefficient is, the accuracy of the temperature prediction in the wine cabinet is when the temperature instability characteristic of the wine cabinet is used, and the importance and stability of each characteristic in the whole temperature redundancy data set and each temperature redundancy polymerization sequence in the whole wine cabinet can be comprehensively considered through determining the characteristic thermal homogeneity representative coefficient, so that the importance and the key effect of the temperature instability characteristic of the wine cabinet can be recognized, and the temperature instability characteristic of the wine cabinet with the higher characteristic thermal homogeneity representative coefficient can play a more important role in the aspects of intelligent temperature control in the wine cabinet and wine storage.
In the above embodiment, the temperature abnormal characteristic corresponding to the maximum characteristic thermal homogeneity representative coefficient is used as the temperature abnormal characteristic in the cabinet, the temperature predicted value in the intelligent cabinet is obtained through the temperature predicted characteristic in the cabinet, when the method is specifically implemented, firstly, the characteristic thermal homogeneity representative coefficient of each temperature abnormal characteristic is determined according to the method in step 103, then, the temperature abnormal characteristic with the maximum characteristic thermal homogeneity representative coefficient is selected from all the temperature abnormal characteristics to be used as the temperature predicted characteristic in the cabinet, the temperature abnormal characteristic with the maximum characteristic thermal homogeneity representative coefficient means that in the whole cabinet temperature redundancy data set, the accuracy is highest when the temperature predicted in the cabinet is carried out through the temperature abnormal characteristic in the cabinet, the temperature predicted characteristic in the cabinet is used as the temperature predicted model in the cabinet, the temperature predicted characteristic in the cabinet is used as the input of the autoregressive integral sliding average model, the temperature predicted value in the cabinet is predicted, and when the method is actually implemented, other predicted models can be used to predict and obtain the temperature predicted value in the cabinet, and the temperature predicted value is not limited.
And 105, controlling the temperature of the intelligent wine cabinet through the predicted value of the temperature in the cabinet and the preset wine storage temperature.
In some embodiments, the temperature control of the intelligent wine cabinet by the predicted value of the temperature in the cabinet and the preset wine temperature may specifically be performed by the following manner:
when the predicted value of the temperature in the cabinet is higher than the preset wine storage temperature, the refrigerating capacity in the intelligent wine cabinet is increased;
and when the predicted value of the temperature in the cabinet is lower than the preset wine storage temperature, reducing the refrigerating capacity in the cabinet of the intelligent wine cabinet.
When the temperature predicted value in the cabinet is lower than the preset wine storage temperature, the temperature predicted value in the cabinet is not lower than the preset wine storage temperature, the refrigerating capacity of the intelligent wine cabinet is reduced, and the temperature falling rate is reduced.
It should be noted that, by adopting corresponding adjustment measures according to the comparison result of the predicted value of the temperature in the cabinet and the preset wine storage temperature, the temperature control and adjustment of the intelligent wine cabinet can be realized, the temperature in the cabinet is always maintained within the preset wine storage temperature range, the quality and the storage time of wine products can be effectively protected by the temperature adjustment mode, meanwhile, the temperature control precision of the intelligent wine cabinet is improved, and better use experience is provided for users.
In this application, at first, through predetermining the interior temperature redundant factor and carrying out redundant processing to the interior temperature dataset of cabinet, can be in getting rid of noise and redundant information, make the interior temperature redundant data of obtaining more steady and reliable, help improving the follow-up prediction accuracy to interior temperature of cabinet, secondly, can measure the importance in the interior temperature redundant polymerization sequence of temperature redundant polymerization sequence through the temperature pulsation convergence factor, through the thermal state filter screen trend volume of confirming interior temperature redundant dataset, can quantify out the information content of interior temperature redundant dataset, and then can provide valuable information for wine article storage and temperature control's decision, then, through confirming characteristic typical basis and redundant characteristic degree of eliminating entropy, can measure the importance of temperature abnormal characteristic to interior temperature prediction of cabinet, help improving the accuracy to interior temperature prediction of intelligent cabinet, and then, through confirming characteristic thermal homogeneity representative coefficient, help discernment cabinet temperature abnormal characteristic's importance and key effect, have higher characteristic thermal state filter screen trend volume of heat redundant dataset, can quantify out the information content of interior temperature redundant dataset, and then can provide valuable information for wine article storage and temperature control's decision, then, through confirming characteristic typical basis of characteristic and redundant characteristic degree of redundancy characteristic degree of eliminating entropy, can measure the importance of interior temperature abnormal characteristic to interior temperature prediction of cabinet, and the quality of wine cabinet, and the quality of temperature abnormal characteristic of temperature of wine cabinet, and the temperature average characteristic can be adjusted in the aspect of the interior temperature cabinet.
In addition, in another aspect of the present application, in some embodiments, the present application provides an intelligent wine cabinet temperature control system, the system further including a control unit, referring to fig. 2, which is a schematic diagram of exemplary hardware and/or software of the control unit according to some embodiments of the present application, the control unit 200 includes: the in-cabinet temperature redundancy data set determining module 201, the thermal state screen convergence determining module 202, the redundancy feature entropy eliminating degree determining module 203, the in-cabinet temperature predicted value obtaining module 204 and the temperature control module 205 are respectively described as follows:
the in-cabinet temperature redundancy data set determining module 201 is mainly used for collecting the in-cabinet temperature of the intelligent wine cabinet, so as to obtain an in-cabinet temperature redundancy data set;
the thermal state screen convergence determining module 202 is mainly used for converting the temperature redundancy removing dataset in the cabinet into a plurality of temperature redundancy removing polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy removing polymerization sequence, and further determining the thermal state screen convergence by the temperature pulsation convergence factors of all the temperature redundancy removing polymerization sequences;
the redundancy removal feature entropy removal degree determination module 203 is mainly used for extracting temperature instability features of the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability features, determining feature turbulence typical bases of the wine cabinet temperature instability features, and further determining redundancy removal feature entropy removal degree of the wine cabinet temperature instability features according to the feature turbulence typical bases;
the intra-cabinet temperature predicted value obtaining module 204, where the intra-cabinet temperature predicted value obtaining module 204 is mainly configured to determine an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state screen trend amount and the redundancy characteristic entropy degree of the wine cabinet temperature abnormal stability characteristic;
the temperature control module 205, in this application, the temperature control module 205 is mainly used for controlling the temperature of the intelligent wine cabinet through the predicted value of the temperature in the cabinet and the preset wine temperature.
The foregoing describes in detail examples of the intelligent wine cabinet temperature control system and the control method thereof provided in the embodiments of the present application, and it may be understood that, in order to implement the foregoing functions, the corresponding devices include corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In some embodiments, the present application further provides a computer device, where the computer device includes a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computer device performs the control method of the intelligent wine cabinet temperature control system described above.
In some embodiments, reference is made to fig. 3, in which a dashed line indicates that the unit or the module is optional, which is a schematic structural diagram of a computer device for a control method of an intelligent wine cabinet temperature control system according to an embodiment of the present application. The control method of the intelligent cabinet temperature control system in the above embodiment may be implemented by a computer device shown in fig. 3, where the computer device 300 includes at least one processor 301, a memory 302, and at least one communication unit 305, and the computer device 300 may be a terminal device or a server or a chip.
Processor 301 may be a general purpose processor or a special purpose processor. For example, the processor 301 may be a central processing unit (central processing unit, CPU) which may be used to control the computer device 300, execute software programs, process data of the software programs, and the computer device 300 may further comprise a communication unit 305 for enabling input (receiving) and output (transmitting) of signals.
For example, the computer device 300 may be a chip, the communication unit 305 may be an input and/or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, which may be an integral part of a terminal device or a network device or other devices.
For another example, the computer device 300 may be a terminal device or a server, the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.
The computer device 300 may include one or more memories 302 having a program 304 stored thereon, the program 304 being executable by the processor 301 to generate instructions 303 such that the processor 301 performs the methods described in the method embodiments above in accordance with the instructions 303. Optionally, data (e.g., a goal audit model) may also be stored in memory 302. Alternatively, the processor 301 may also read data stored in the memory 302, which may be stored at the same memory address as the program 304, or which may be stored at a different memory address than the program 304.
The processor 301 and the memory 302 may be provided separately or may be integrated together, for example, on a System On Chip (SOC) of the terminal device.
It should be appreciated that the steps of the above-described method embodiments may be accomplished by logic circuitry in the form of hardware or instructions in the form of software in the processor 301, and the processor 301 may be a central processing unit, a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), a field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, such as discrete gates, transistor logic, or discrete hardware components.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
For example, in some embodiments, the present application further provides a computer readable storage medium having instructions or codes stored therein, which when executed on a computer, cause the computer to implement the control method of the intelligent wine cabinet temperature control system described above.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (6)
1. The control method of the intelligent wine cabinet temperature control system is characterized by comprising the following steps:
acquiring the temperature in the intelligent wine cabinet, and further obtaining a temperature redundancy data set in the cabinet;
converting the temperature redundancy elimination data set in the cabinet into a plurality of temperature redundancy elimination polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy elimination polymerization sequence, and further determining the thermal state screen convergence amount by the temperature pulsation convergence factors of all the temperature redundancy elimination polymerization sequences;
extracting temperature instability characteristics from the temperature redundancy removal dataset in the cabinet to obtain wine cabinet temperature instability characteristics, determining characteristic turbulence typical bases of the wine cabinet temperature instability characteristics, and further determining redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics according to the characteristic turbulence typical bases;
determining an intra-cabinet temperature predicted value of the intelligent wine cabinet according to the thermal state filter screen trend quantity and redundancy characteristic entropy eliminating degree of the wine cabinet temperature instability characteristic;
the temperature control is carried out on the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature;
wherein, gather intelligent wine cabinet's interior temperature, and then obtain the interior temperature redundant data set specifically include:
presetting a temperature redundancy factor in a cabinet;
determining redundancy removal values corresponding to the temperatures in each cabinet according to the redundancy removal factors of the temperatures in the cabinets;
taking a set formed by redundancy removal values corresponding to all temperatures in the cabinet as a cabinet temperature redundancy removal data set;
for each temperature redundancy elimination and aggregation sequence, taking the ratio of the total number of the temperature redundancy elimination data in the cabinet to the total number of the temperature redundancy elimination data in the cabinet in the temperature redundancy elimination data set as a temperature pulsation convergence factor of the temperature redundancy elimination and aggregation sequence;
wherein determining the thermal state screen convergence gauge from the temperature pulsation convergence factors of all temperature redundancy polymerization sequences comprises:
obtaining a total number of temperature redundancy-removing polymeric sequences；
According to the total number of the temperature redundancy polymerization sequencesAnd the temperature pulsation convergence factors of all temperature redundancy polymerization sequences to determine the thermal state screen convergence amount +.>Wherein the thermal state screen is in the same amount +.>According to the following formula:
；
wherein,represents the nth temperature redundancy polymerization sequence,/->Indicating temperature redundancy polymerization sequenceColumn->Temperature pulsation convergence factor of (2);
wherein, the characteristic turbulence typical base for determining the temperature instability characteristic of the wine cabinet specifically comprises;
determining a characteristic value of the temperature instability characteristic of the wine cabinet, and further determining the occurrence times of the characteristic value in the temperature redundancy data set in the cabinet;
taking the ratio of the occurrence times to the total number of the temperature redundancy data in the cabinet in the temperature redundancy data set as a characteristic turbulence typical base of the temperature instability characteristic of the wine cabinet;
the redundancy removal feature entropy eliminating degree for determining the temperature instability feature of the wine cabinet by the feature turbulence typical base specifically comprises the following steps:
obtaining a total number of temperature redundancy-removing polymeric sequences；
Acquiring temperature abnormal stability characteristics of wine cabinetIs characterized by turbulence of the group>；
When the temperature redundancy data set in the cabinet contains the temperature instability characteristic of the wine cabinet, respectively determining the probability that the temperature instability characteristic of the wine cabinet belongs to each temperature redundancy aggregation sequence;
based on the total number of temperature redundancy-eliminating polymeric sequencesTemperature instability characteristic of wine cabinet>Is characterized by turbulence of the group>Temperature of wine cabinetThe probability that the abnormal stability characteristic belongs to each temperature redundancy polymerization sequence determines the temperature abnormal stability characteristic of the wine cabinet>Redundancy characteristic entropy degree->In specific implementation, redundancy feature entropy elimination degree +.>According to the following formula:
；
wherein,represents the nth temperature redundancy polymerization sequence,/->Representative of characteristic turbulence>Is used as a base for the reaction of the amino acids,the temperature redundancy data set in the display cabinet contains the temperature instability characteristic of the wine cabinet>And the temperature of the wine cabinet is different and stable>Belonging to the temperature redundancy polymerization sequence->Probability of->Representation->Is the opposite of the value of (2);
wherein, according to the thermal state filter screen trend and the redundancy characteristic entropy eliminating degree of the wine cabinet temperature different stability characteristic, determining the cabinet internal temperature predicted value of the intelligent wine cabinet specifically comprises:
obtaining characteristic thermal homogeneity representative coefficients of the wine cabinet temperature instability characteristics according to the thermal state filter screen trend and redundancy removal characteristic entropy elimination degree of the wine cabinet temperature instability characteristics, and further determining the characteristic thermal homogeneity representative coefficients of all the wine cabinet temperature instability characteristics, wherein the characteristic thermal homogeneity representative coefficients of the wine cabinet temperature instability characteristics are determined according to the following formula:
；
wherein,indicating the temperature instability characteristic of the wine cabinet>Characteristic thermal homogeneity representative coefficient of>Indicating the temperature differential stability of the wine cabinetRedundancy characteristic entropy elimination degree, +.>Representing the trend of the thermal state screen;
taking the temperature abnormal stability characteristic of the wine cabinet corresponding to the maximum characteristic thermal homogeneity representative coefficient as the temperature prediction characteristic in the cabinet;
and obtaining the predicted value of the temperature in the intelligent wine cabinet through the predicted characteristic of the temperature in the cabinet.
2. The method of claim 1, wherein converting the in-cabinet temperature redundancy elimination data set into a plurality of temperature redundancy elimination aggregate sequences is performing data cluster conversion on the in-cabinet temperature redundancy elimination data set, thereby obtaining a plurality of temperature redundancy elimination aggregate sequences.
3. The method of claim 1, wherein the temperature control of the intelligent wine cabinet by the predicted value of the temperature in the cabinet and the preset wine temperature specifically comprises:
when the predicted value of the temperature in the cabinet is higher than the preset wine storage temperature, the refrigerating capacity in the intelligent wine cabinet is increased;
and when the predicted value of the temperature in the cabinet is lower than the preset wine storage temperature, reducing the refrigerating capacity in the cabinet of the intelligent wine cabinet.
4. An intelligent wine cabinet temperature control system controlled by the method of claim 1, wherein the intelligent wine cabinet temperature control system comprises a control unit, the control unit comprises:
the in-cabinet temperature redundancy removing data set determining module is used for collecting the in-cabinet temperature of the intelligent wine cabinet, and further obtaining an in-cabinet temperature redundancy removing data set;
the thermal state screen convergence determining module is used for converting the temperature redundancy removing data set in the cabinet into a plurality of temperature redundancy removing polymerization sequences, determining the temperature pulsation convergence factor of each temperature redundancy removing polymerization sequence, and further determining the thermal state screen convergence by the temperature pulsation convergence factors of all the temperature redundancy removing polymerization sequences;
the redundancy removal feature entropy removal degree determination module is used for extracting temperature instability features of the temperature redundancy removal data set in the cabinet to obtain wine cabinet temperature instability features, determining feature turbulence typical bases of the wine cabinet temperature instability features, and further determining redundancy removal feature entropy removal degree of the wine cabinet temperature instability features according to the feature turbulence typical bases;
the in-cabinet temperature predicted value acquisition module is used for determining an in-cabinet temperature predicted value of the intelligent wine cabinet according to the trend amount of the thermal state screen and redundancy characteristic entropy elimination degree of the wine cabinet temperature instability characteristic;
and the temperature control module is used for controlling the temperature of the intelligent wine cabinet through the temperature predicted value in the cabinet and the preset wine storage temperature.
5. A computer device comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, such that the computer device performs the control method of the intelligent cabinet temperature control system of any one of claims 1 to 3.
6. A computer readable storage medium having instructions or code stored therein which, when run on a computer, cause the computer to perform a control method of the intelligent wine cabinet temperature control system of any one of claims 1 to 3.
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| CN103156412A (en) * | 2011-12-16 | 2013-06-19 | 上海逸酒轩投资有限公司 | Wine cabinet and operation method thereof |
| CN104122909A (en) * | 2013-04-28 | 2014-10-29 | 海尔集团公司 | Wine cabinet temperature remote control method and control system and cloud-side server |
| CN113779107A (en) * | 2021-08-26 | 2021-12-10 | 北京理工大学 | Computer room temperature prediction method based on deep parallel network |
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