CN111338400B - Beverage vending machine temperature control method based on cloud platform - Google Patents

Beverage vending machine temperature control method based on cloud platform Download PDF

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CN111338400B
CN111338400B CN202010161733.9A CN202010161733A CN111338400B CN 111338400 B CN111338400 B CN 111338400B CN 202010161733 A CN202010161733 A CN 202010161733A CN 111338400 B CN111338400 B CN 111338400B
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temperature
vending machine
reference temperature
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optimal reference
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CN111338400A (en
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张文聪
何德峰
余世明
姚奕
潘佳怡
赵舒磊
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Zhejiang University of Technology ZJUT
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D23/00—Control of temperature
    • G05D23/19—Control of temperature characterised by the use of electric means
    • G05D23/1927—Control of temperature characterised by the use of electric means using a plurality of sensors
    • G05D23/193—Control of temperature characterised by the use of electric means using a plurality of sensors sensing the temperaure in different places in thermal relationship with one or more spaces
    • G05D23/1931—Control of temperature characterised by the use of electric means using a plurality of sensors sensing the temperaure in different places in thermal relationship with one or more spaces to control the temperature of one space

Abstract

The invention discloses a beverage vending machine temperature control method based on a cloud platform, which comprises the following steps: step S10, constructing an optimal reference temperature model; step S20, determining the optimum reference temperature; step S30, controlling the temperature of the vending machine according to the optimal reference temperature; the steps S10 and S20 are realized through cloud computing; the vending machine transmits the environmental temperature, sales income and power consumption of the vending machine to a control center through a wireless network according to the sampling period, the control center calls cloud computing according to energy consumption constraint, preservation temperature constraint and human body adaptive temperature constraint, and the optimal reference temperature is determined through repeated tests and statistical analysis; and the control center transmits the optimal reference temperature to the vending machine through a wireless network.

Description

Beverage vending machine temperature control method based on cloud platform
Technical Field
The invention relates to the technical field of temperature control of vending machines, in particular to a beverage vending machine temperature control method based on a cloud platform.
Background
The drink vending machine of the Japanese Fuji iceberg is popular among operators due to the characteristics of high reliability, convenience in adding goods, good sealing performance, remarkable energy saving and the like. However, the beverage vending machine of the Fushi iceberg can only sell canned or bottled beverages due to the structural characteristics of the beverage vending machine. Vending machines with auger delivery mechanisms have been developed for the sale of other packages, such as boxed or bagged beverages.
With the increasing requirements on environment protection, energy conservation and emission reduction, corresponding emission standards are developed for household appliances, beverage vending machines and other civil products, and energy consumption indexes, beverage preservation requirements and human body adaptability are taken as constraint conditions. In order to keep the beverage fresh and ensure the taste of the beverage, the beverage vending machine needs to control the beverage within a certain temperature range, however, in the existing control, the temperature is usually set to be a fixed value and kept constant for a long time, and even set to be a minimum temperature value and kept constant for a long time. The temperature is too low, which not only affects the digestive system and health of people, but also increases the energy consumption of the refrigeration of the vending machine; the temperature is too high, which is not favorable for keeping the beverage fresh, and the taste is also deteriorated, which affects the sale.
Disclosure of Invention
In order to solve the defects of the prior art, realize reasonable control of temperature, achieve the purposes of reducing energy consumption, saving energy, reducing emission, simultaneously keeping the taste of the beverage, keeping the freshness of the beverage and increasing the sales volume, the invention adopts the following technical scheme:
the method comprises the following steps:
a beverage vending machine temperature control method based on a cloud platform comprises the following steps:
step S10, constructing an optimal reference temperature model;
Figure GDA0002905209850000011
Figure GDA0002905209850000012
when T (k) ≦ TL[Ta,Tb]And when the temperature is higher than the preset temperature, the refrigeration of the vending machine is stopped.
The V isdayRepresenting sales revenue for the entire day, N representing dividing a day into N sampling periods, Vin(k) Representing sales revenue for the kth sampling period; said s.t. represents a restricted condition, said TRefDenotes a reference temperature, said TWLRepresents an upper reference temperature limit, T (k) represents an ambient temperature, TCRepresents normal temperature, said TL,TURespectively representing the lower limit temperature and the upper limit temperature of the human body adaptive temperature determined according to the environment temperature T (k), wherein TrIndicating an upper reference temperature limit determined according to the freshness requirement of the goods in the vending machine.
Step S20, determining the optimum reference temperature;
setting the interval of the environmental temperature as [ Ta,Tb]Dividing said interval equally into m zones of ambient temperatureWhen the environment temperature falls into the environment temperature cell, representing the environment temperature by using a midpoint value between the environment temperature cells, and obtaining m discrete midpoint values which are sequentially T1,T2,…,Tm；
The T obtained according to the optimal reference temperature modelRefIs set as [ T ]ar,Tbr]Equally dividing the interval into n reference temperature cells, representing the reference temperature by the midpoint value of the reference temperature cells, and sequentially obtaining n discrete midpoint values which are TR1,TR2,…,TRn；
The total number of the vending machines is L, and the environment temperature corresponding to the ith station is TMi∈{T1,T2,…,TmV, reference temperature TVi∈{TR1,TR2,…,TRnIs the TM according to the Monte Carlo principleiSelecting the corresponding TVi；
The V of the L vending machinesdayAnd sequencing, and taking the reference temperature corresponding to the vending machine with the largest sales volume as the optimal reference temperature.
And taking the optimal reference temperature with the highest occurrence frequency as the optimal reference temperature to eliminate the influence of randomness.
And step S30, controlling the temperature of the vending machine through the optimal reference temperature.
The step S10 includes the following steps:
step S101, setting the capacity of the vending machine as M drinks, and the power consumption of the vending machine in the Kth sampling period as W (K) kilowatt hours, adopting the power consumption W of a single commodityM(k) To express the energy consumption, i.e. the power consumption of a single commodity in the Kth sampling period is WM(k) W (k)/M, wherein W isM(k)＝W0+SW·KWTRef(k) W is as described0For the power consumption when the refrigeration compressor is closed, when the refrigeration compressor is opened, SW1, when the refrigeration compressor is turned off, the SW0, said KWTo be related to a reference temperatureThe energy consumption coefficient of (2);
s102, setting the environmental temperature of the Kth sampling period as T (K), and using TCDenotes normal temperature, TCL、TCURespectively represents the lower limit and the upper limit, k, of the human body suitable temperature at normal temperatureaFor the human body adaptive temperature coefficient, the lower limit and the upper limit of the human body adaptive temperature at different environmental temperatures are respectively as follows:
Figure GDA0002905209850000021
when T (k) > TC
And TL(k)≤TRef(k)≤TU(k) When T (k) ≦ TLIf so, closing the refrigeration compressor and stopping the refrigeration of the vending machine;
step S103, setting the upper limit of the fresh-keeping temperature of the beverage as TrThen T isRef(k)≤Tr；
Step S104, setting the upper limit of the energy consumption index to KWLT (K), the KWLIs the upper limit coefficient of energy consumption, WM(k)≤KWL·T(k)；
Step S105, converting W of the step S101M(k)＝W0+SW·KWTRef(k) W substituted into the step S104M(k)≤KWLT (k), to obtain W0+SW·KWTRef(k)≤KWLT (k) is TRef(k)≤(WML-W0)/KWAnd SW1, said WMLRepresents the upper limit of energy consumption of the commodity, let TWL≤[KWL·T(k)-W0]/KWThen T isRef(k)≤TWL；
Step S106, setting the newly added sales income of the Kth sampling period as VIn(k) Let N equal to 24/T, then the sales income of the day is
Figure GDA0002905209850000031
Step S107, the TRef(k) Under the condition of satisfying certain constraint conditions, the change track of the pin enables the pin to be pinned every daySales revenue VdayTo a maximum, i.e.
Figure GDA0002905209850000032
Figure GDA0002905209850000033
When T (k) ≦ TLAnd when the automatic vending machine is started, the refrigeration compressor is closed, and the automatic vending machine stops refrigerating.
The steps S10 and S20 are realized through cloud computing; the vending machine transmits the environmental temperature, sales income and power consumption of the vending machine to a control center through a wireless network according to the sampling period, the control center calls cloud computing according to energy consumption constraint, preservation temperature constraint and human body adaptive temperature constraint, and the optimal reference temperature is determined through repeated tests and statistical analysis; and the control center transmits the optimal reference temperature to the vending machine through a wireless network. The optimal reference temperature is a dynamically changing trajectory that maximizes sales revenue.
The invention has the advantages and beneficial effects that:
the reasonable control of the refrigeration temperature of the vending machine is realized, the purposes of reducing energy consumption, saving energy, reducing emission, keeping the taste of the beverage, keeping the freshness of the beverage and increasing the sales volume are achieved, and the reference temperature of refrigeration dynamically changes along with the environmental temperature, time and place, so that the purposes of saving energy, reducing emission and improving the operation income are achieved.
Drawings
FIG. 1 is a schematic diagram of a control loop for the vending machine temperature of the present invention.
Fig. 2 is a schematic diagram of a reference temperature optimization process based on cloud computing in the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
A beverage vending machine temperature control method based on a cloud platform comprises the following steps:
step S10, constructing an optimal reference temperature model;
Figure GDA0002905209850000041
Figure GDA0002905209850000042
when T (k) ≦ TL[Ta,Tb]And when the temperature is higher than the preset temperature, closing the compressor and stopping refrigerating the vending machine.
The V isdayRepresents sales revenue for the entire day, N-24, representing a division of 24 hours a day into N sampling periods, Vin(k) Representing sales revenue for the kth sampling period; said s.t. represents a restricted condition, said TRefDenotes a reference temperature, said TWLRepresenting the upper limit of the reference temperature determined according to the energy consumption index requirement, wherein T (k) represents the ambient temperature, and TCRepresents normal temperature, normal temperature is about 25 ℃, and the T isL,TURespectively representing the lower limit temperature and the upper limit temperature of the human body adaptive temperature determined according to the environment temperature T (k), wherein TrIndicating an upper reference temperature limit determined according to the freshness requirement of the goods in the vending machine.
Step S20, determining the optimum reference temperature;
setting the interval of the environmental temperature as [ Ta,Tb]Equally dividing the interval into m environment temperature cells, when the environment temperature falls into the environment temperature cells, representing the environment temperature by the midpoint value among the environment temperature cells, and obtaining m discrete midpoint values which are sequentially T1,T2,…,Tm；
The T obtained according to the optimal reference temperature modelRefIs set as [ T ]ar,Tbr]Dividing said interval equally into n reference temperature cells, and usingThe midpoint value among the reference temperature cells represents the reference temperature, and the obtained n discrete midpoint values are sequentially TR1,TR2,…,TRn；
The total number of the vending machines is L, and the environment temperature corresponding to the ith station is TMi∈{T1,T2,…,TmV, reference temperature TVi∈{TR1,TR2,…,TRnIs the TM according to the Monte Carlo principleiSelecting the corresponding TVi；
The V of the L vending machinesdayAnd sequencing, and taking the reference temperature corresponding to the vending machines with the largest sales volume as the optimal reference temperature.
And (3) taking the optimal reference temperature with the highest occurrence frequency as the final optimal reference temperature for each optimal reference temperature through a large number of experiments to eliminate the influence of randomness.
And step S30, controlling the temperature of the vending machine through the optimal reference temperature.
The step S10 includes the following steps:
step S101, comprehensively considering the refrigerating speed and the ambient temperature change speed, setting a cloud computing sampling period, and assuming that T iss(hour), the capacity of the vending machine is set as M (can, bottle, bag, box, etc.) drinks, when the power consumption of the vending machine in the Kth sampling period is W (K) kilowatt, the capacity of the vending machine is large or small, and the power consumption of the whole vending machine is not proper as an energy consumption index in a general way, so that the power consumption W of a single commodity is adoptedM(k) To express the energy consumption, i.e. the power consumption of a single commodity in the Kth sampling period is WM(k) W (k)/M, wherein W isM(k) In relation to the temperature set point, TRef(k) The smaller the WM(k) The larger the size and vice versa, let W beM(k)＝W0+SW·KWTRef(k) W is as described0For the power consumption when the refrigeration compressor is closed, when the refrigeration compressor is opened, SW1, when the refrigeration compressor is turned off, the refrigerant compressor is turned offSW0, said KWThe coefficient of energy consumption, which is related to the reference temperature, depends on the structural characteristics of the vending machine;
s102, setting the environmental temperature of the Kth sampling period as T (K), and using TCDenotes normal temperature, TCL、TCURespectively represents the lower limit and the upper limit, k, of the human body suitable temperature at normal temperatureaFor the human body adaptive temperature coefficient, the lower limit and the upper limit of the human body adaptive temperature at different environmental temperatures are respectively as follows:
Figure GDA0002905209850000051
and TL(k)≤TRef(k)≤TU(k) When T (k) ≦ TLIf so, closing the refrigeration compressor and stopping the refrigeration of the vending machine;
step S103, setting the upper limit of the fresh-keeping temperature of the beverage as TrThen T isRef(k)≤Tr；
Step S104, setting the upper limit of the energy consumption index to KWLT (K), the KWLIs the upper limit coefficient of energy consumption, WM(k)≤KWL·T(k)；
Step S105, converting W of the step S101M(k)＝W0+SW·KWTRef(k) W substituted into the step S104M(k)≤KWLT (k), to obtain W0+SW·KWTRef(k)≤KWLT (k) is TRef(k)≤(WML-W0)/KWAnd SW1, said WMLRepresents the upper limit of energy consumption of the commodity, let TWL≤[KWL·T(k)-W0]/KWThen T isRef(k)≤TWL；
Step S106, setting the newly added sales income of the Kth sampling period as VIn(k) Let N equal to 24/T, then the sales income of the day is
Figure GDA0002905209850000061
Step S107, said TRef(k) The change track of (2) enables the sales income V of each day to be increased under the condition of meeting certain constraint conditionsdayTo a maximum, i.e.
Figure GDA0002905209850000062
Figure GDA0002905209850000063
When T (k) ≦ TLAnd when the automatic vending machine is started, the refrigeration compressor is closed, and the automatic vending machine stops refrigerating.
As shown in fig. 1, the temperature control is realized by a frequency conversion technology, and comprises a sensorless vector control loop of a three-phase alternating current asynchronous motor in a dashed line frame and a temperature control loop in the vending machine outside the dashed line frame;
the vector control mainly carries out real-time frequency conversion and energy saving control on the rotating speed for driving the refrigeration compressor, and comprises an internal current loop and an external rotating speed loop; the current loop comprises a torque current loop and an excitation current loop, and the current loop is controlled by the following steps:
step S301, detecting three-phase alternating current, carrying out coordinate transformation, and estimating values of exciting current and torque current;
step S302, respectively connecting the values of the estimated exciting current and the torque current with corresponding reference value I of the direct-current component of the weak exciting currentdRefReference value I of DC component of torque currentqRefComparing to obtain two error values;
step S303, obtaining two direct current voltage components through a PI (proportional integral) regulation algorithm according to the two error values;
step S304, performing inverse transformation on the two direct current voltage components to obtain two alternating current voltage components;
step S305, according to the two alternating-current voltage components, controlling the switch of the IGBT through Space Vector Modulation (SVM) (space Vector modulation), namely, through a space Vector principle, inverting the direct-current bus voltage into three-phase sinusoidal alternating current with adjustable frequency, and implementing variable-frequency control on the rotating speed of the three-phase alternating-current asynchronous motor;
the control steps of the rotating speed ring are as follows:
step S311, detecting the three-phase alternating current and carrying out coordinate transformation, and estimating the rotating speed of the motor by using Sliding Mode Control (SMC) on the basis of the transformation of current detection and the inverse transformation;
step S312, the rotating speed of the motor is compared with a reference rotating speed omegaRefComparing and determining the reference value I of the DC component of the torque currentqRefDetermining the reference value I of the direct-current component of the weak excitation current within the range of the rated rotating speeddRefAt this time, the IdRefApproximately zero, and in engineering applications, a very small value close to zero is taken.
The temperature control controls the working frequency of the refrigeration compressor by controlling the rotating speed of the three-phase alternating current asynchronous motor, so as to control the temperature of the VDM of the vending machine, the internal temperature of the vending machine and the reference temperature TRefComparing, and determining the reference rotation speed omega of the three-phase alternating current asynchronous motor through a PI algorithm according to the comparison resultRef。
Taking the optimal reference temperature as a reference temperature TRefAnd effectively controlling the temperature of the vending machine through a vector control technology.
As shown in fig. 2, the steps S10, S20 are implemented by cloud computing; the vending machine transmits the environmental temperature, sales income and power consumption of the vending machine location to a control center through a 4G/5G wireless network according to the sampling period, the control center calls cloud computing according to energy consumption constraint, preservation temperature constraint and human body adaptive temperature constraint, and determines an optimal reference temperature through repeated tests and statistical analysis; and the control center transmits the optimal reference temperature to the vending machine through a 4G/5G wireless network, and the set value is tracked through vector control. The optimal reference temperature is a dynamically changing trajectory that maximizes sales revenue.

Claims (3)

1. A beverage vending machine temperature control method based on a cloud platform is characterized by comprising the following steps:
step S10, constructing an optimal reference temperature model;
Figure FDA0002905209840000011
Figure FDA0002905209840000012
when T (k) ≦ TL[Ta,Tb]When the automatic vending machine is started, the automatic vending machine is stopped to refrigerate;
the V isdayRepresenting sales revenue for the entire day, N representing dividing a day into N sampling periods, Vin(k) Representing sales revenue for the kth sampling period; said s.t. represents a restricted condition, said TRefDenotes a reference temperature, said TWLRepresents an upper reference temperature limit, T (k) represents an ambient temperature, TCRepresents normal temperature, said TL、TURespectively representing the lower limit temperature and the upper limit temperature of the human body adaptive temperature determined according to the environment temperature T (k), wherein TrRepresenting a reference upper temperature limit determined according to the freshness keeping requirement of the goods in the vending machine;
step S20, determining the optimum reference temperature;
setting the interval of the environmental temperature as [ Ta,Tb]Equally dividing the interval into m environment temperature cells, when the environment temperature falls into the environment temperature cells, representing the environment temperature by the midpoint value among the environment temperature cells, and obtaining m discrete midpoint values which are sequentially T1,T2,…,Tm；
The T obtained according to the optimal reference temperature modelRefIs set as [ T ]ar,Tbr]Equally dividing said interval into n reference temperature cells, using said reference temperaturesThe midpoint value between the cells represents the reference temperature, and the obtained n discrete midpoint values are TR1,TR2,…,TRn；
The total number of the vending machines is L, and the environment temperature corresponding to the ith station is TMi∈{T1,T2,…,TmV, reference temperature TVi∈{TR1,TR2,…,TRnIs the TM according to the Monte Carlo principleiSelecting the corresponding TVi；
The V of the L vending machinesdaySequencing, wherein the reference temperature corresponding to the vending machine with the largest sales volume is used as the optimal reference temperature;
taking the optimal reference temperature with the highest occurrence frequency as an optimal reference temperature;
and step S30, controlling the temperature of the vending machine through the optimal reference temperature.
2. The beverage vending machine temperature control method based on the cloud platform as claimed in claim 1, wherein the step S10 includes the following steps:
step S101, setting the capacity of the vending machine as M drinks, and the power consumption of the vending machine in the Kth sampling period as W (K) kilowatt hours, adopting the power consumption W of a single commodityM(k) To express the energy consumption, i.e. the power consumption of a single commodity in the Kth sampling period is WM(k) W (k)/M, wherein W isM(k)＝W0+SW·KWTRef(k) W is as described0For the power consumption when the refrigeration compressor is closed, when the refrigeration compressor is opened, SW1, when the refrigeration compressor is turned off, the SW0, said KWIs the coefficient of energy consumption related to the reference temperature;
s102, setting the environmental temperature of the Kth sampling period as T (K), and using TCDenotes normal temperature, TCL、TCURespectively represents the lower limit and the upper limit, k, of the human body suitable temperature at normal temperatureaFor the human body to adapt to the temperature coefficient, under different environmental temperatures,the lower limit and the upper limit of the suitable temperature of the human body are respectively as follows:
Figure FDA0002905209840000021
when T (k) > TC
And TL(k)≤TRef(k)≤TU(k) When T (k) ≦ TLIf so, closing the refrigeration compressor and stopping the refrigeration of the vending machine;
step S103, setting the upper limit of the fresh-keeping temperature of the beverage as TrThen T isRef(k)≤Tr；
Step S104, setting the upper limit of the energy consumption index to KWLT (K), the KWLIs the upper limit coefficient of energy consumption, WM(k)≤KWL·T(k)；
Step S105, converting W of the step S101M(k)＝W0+SW·KWTRef(k) W substituted into the step S104M(k)≤KWLT (k), to obtain W0+SW·KWTRef(k)≤KWL·T(k)，
Namely TRef(k)≤(WML-W0)/KWAnd SW1, said WMLRepresents the upper limit of energy consumption of the commodity, let TWL≤[KWL·T(k)-W0]/KWThen T isRef(k)≤TWL；
Step S106, setting the newly added sales income of the Kth sampling period as VIn(k) Let N equal to 24/T, then the sales income of the day is
Figure FDA0002905209840000022
Step S107, the TRef(k) The change track of (2) enables the sales income V of each day to be increased under the condition of meeting certain constraint conditionsdayTo a maximum, i.e.
Figure FDA0002905209840000023
Figure FDA0002905209840000031
When T (k) ≦ TLAnd when the automatic vending machine is started, the refrigeration compressor is closed, and the automatic vending machine stops refrigerating.
3. The cloud platform based beverage vending machine temperature control method according to claim 1, wherein the steps S10 and S20 are implemented by cloud computing; the vending machine transmits the environmental temperature, sales income and power consumption of the vending machine to a control center through a wireless network according to the sampling period, the control center calls cloud computing according to energy consumption constraint, preservation temperature constraint and human body adaptive temperature constraint, and the optimal reference temperature is determined through repeated tests and statistical analysis; and the control center transmits the optimal reference temperature to the vending machine through a wireless network.
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Application publication date: 20200626

Assignee: HANGZHOU YONGGUAN NETWORK TECHNOLOGY CO.,LTD.

Assignor: JIANG University OF TECHNOLOGY

Contract record no.: X2024980000361

Denomination of invention: A temperature control method for beverage vending machines based on cloud platform

Granted publication date: 20210316

License type: Common License

Record date: 20240109