CN113325896A - Multi-target temperature optimization control method of intelligent retail machine - Google Patents
Multi-target temperature optimization control method of intelligent retail machine Download PDFInfo
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Abstract
A multi-target temperature optimization control method for an intelligent retail machine comprises the following steps: (1) constructing a multi-target appropriate temperature model under the condition of meeting the constraint condition according to two decision variables of the external environment temperature and the set temperature inside the equipment, and acquiring an appropriate temperature solution set; (2) determining a multi-target optimal temperature solution aiming at commodity fresh-keeping time, equipment energy consumption and sales; and extracting the optimal internal set temperature values at different environmental temperatures in the optimal temperature solution, and performing closed-loop stable control by using a compressor. The intelligent retail machine compatibility control method carries out compatibility control on three targets of food preservation, equipment energy conservation and economic benefit required to be met in the operation process of the intelligent retail machine, reduces energy consumption, improves economic benefit and achieves the final target of optimal comprehensive benefit while meeting the food preservation requirement.
Description
Technical Field
The invention belongs to the field of unmanned retail, and particularly relates to a multi-target temperature optimization control method of an intelligent retail machine.
Technical Field
The intelligent retail machine is an unsupervised machine for self-service commodity purchase, commodities are provided by a supplier and are temporarily stored in the intelligent retail machine, the intelligent retail machine needs to provide a proper environment for the commodities so as to meet the storage requirement, particularly for food commodities, the freshness retaining requirement is high, a merchant needs to prolong the freshness retaining time as long as possible, and hopes to save energy consumption of the retail machine and reduce operation and maintenance cost as much as possible so as to achieve the aim of improving economic benefits, however, the existing temperature control method of the intelligent retail machine is difficult to meet the multi-target requirement.
Since the introduction of the concept of "new retail" in 2016, various forms of unmanned retail were emerging as spring shoots. Although the display forms of various intelligent retail machines are different, the research aims of the intelligent retail machines are that the storage time of commodities is increased, the cost is reduced and the distribution degree is improved. The intelligent retail machine is taken as typical modern intelligent equipment, is particularly important in the development of new retail in the future, and is expected to lead the third new retail revolution. The existing intelligent retail machine can only adopt a extensive control method for controlling the internal temperature due to the imperfection of a data management system and the deficiency of a control method, and can not carry out compatibility control on the targets of food preservation, equipment energy saving and the like, namely under the condition of better preservation condition, the equipment energy consumption can be inevitably greatly improved, and the preservation effect can be inevitably reduced when the energy consumption is reduced, and the two indirectly influence the sales volume of commodities, so that the overall target optimization can not be achieved.
Disclosure of Invention
In order to overcome the defects of the prior art and solve the problem that the three targets of food preservation, energy conservation and economic benefit cannot be considered in the operation process of the intelligent retail machine, a multi-target temperature optimization control method of intelligent retail equipment is provided.
The invention provides the following technical scheme for solving the technical problems:
a multi-target temperature optimization control method for an intelligent retail machine, the method comprises the following steps:
(1) according to two decision variables of external environment temperature and equipment internal set temperature, a multi-target appropriate temperature model is constructed under the condition of meeting the constraint condition, and an appropriate temperature solution set is obtained, wherein the method comprises the following steps:
the multi-target suitable temperature model:
n in the formula refers to the subscript of dividing the day from 9 to 20 points into n identical time blocks, wherein different k values respectively represent different time blocks; wherein tin (k) is the equipment internal temperature of the time block, Tmin is the food preservation temperature lower limit constraint, Tmax is the food preservation temperature upper limit constraint, En (k) is the intelligent retail machine energy consumption of the time block, Emax is the energy consumption upper limit constraint in unit time, Sal (k) is the single intelligent retail machine sales of the time block, and Smin is the sales lower limit constraint in unit time;
expressing the external environment temperature by x and the internal set temperature of the equipment by y, changing the internal set temperature of the equipment under the condition that the external environment temperature is kept unchanged, simultaneously satisfying the constraint of food preservation temperature, forming a plurality of groups (x, y) of elements, respectively collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under different groups (x, y), substituting the energy consumption and sales volume into a multi-target appropriate temperature model, if the constraint condition of the model is satisfied, classifying the group (x, y) into an appropriate temperature solution set, and if the constraint condition of the model is not satisfied, discarding the group (x, y);
under the condition that the internal set temperature is unchanged, measuring the external environment temperature in different time periods, thereby forming a plurality of groups of (x, y) elements, also collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under the condition of different groups of (x, y), substituting the energy consumption and sales volume into a multi-target suitable temperature model, if the model constraint condition is met, classifying the group of (x, y) into a suitable temperature solution set, and if the model constraint condition is not met, discarding the group of (x, y);
(2) determining a multi-target optimal temperature solution aiming at commodity fresh-keeping time, equipment energy consumption and sales, wherein the process is as follows:
2.1, sorting m groups of (x, y) according to a non-dominant relationship, and artificially calibrating a fitness value at each layer;
2.2, generating a next generation set W through polymerization, crossing and recombination, wherein the size of the set is L;
2.3, set W of nth generationnMerging the first generation set P to generate a set R, and carrying out non-dominated sorting on the set R to generate a series of optimized solutions Fi(i ═ 0,1, …), and calculates the congestion degree;
2.4, solving optimization FiPut into the n +1 generation set Pn+1Performing the following steps;
2.5, judging: if Pn+1If the number of the middle element groups is equal to L, adding 1 to n to further judge whether the temperature solution is the optimal temperature solution, if so, outputting the group of solutions, otherwise, entering the step 1; if Pn+1If the number of the middle element groups is less than L, adding 1 to i, and entering 2.4; if Pn+1If the number of the middle element groups is more than L, the pair FiThe crowdedness of the element groups in (1) is sorted, and a better temperature solution is reserved, so that P isn+1The number of the middle element groups is equal to L, then n is added with 1, and whether the temperature solution is the optimal temperature solution or not is judged, if the temperature solution is the optimal temperature solution, the group of solutions is output, and if not, the step 2.1 is carried out;
and outputting the obtained multi-target optimal temperature solution so as to obtain the set temperature values inside the equipment under different external environment temperatures, and regulating the internal temperature to be the set value by using a compressor.
The invention has the following beneficial effects: compatibility control is carried out on three targets of food preservation, equipment energy saving and economic benefit required to be met in the operation process of the intelligent retail machine, the food preservation requirement is met, meanwhile, energy consumption is reduced, the economic benefit is improved, and the final target of optimal comprehensive benefit is achieved.
Drawings
FIG. 1 is a flow chart of a multi-objective temperature optimization algorithm of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
Referring to fig. 1, a multi-objective temperature optimization control method for an intelligent retail machine, the method comprising the steps of:
(1) constructing a multi-target appropriate temperature model under the condition of meeting the constraint condition according to two decision variables of the external environment temperature and the set temperature inside the equipment, and acquiring an appropriate temperature solution set;
the multi-target suitable temperature model:
n in the formula refers to the subscript of dividing the day from 9 to 20 points into n identical time blocks, wherein different k values respectively represent different time blocks; wherein tin (k) is the equipment internal temperature of the time block, Tmin is the food preservation temperature lower limit constraint, Tmax is the food preservation temperature upper limit constraint, En (k) is the intelligent retail machine energy consumption of the time block, Emax is the energy consumption upper limit constraint in unit time, Sal (k) is the single intelligent retail machine sales of the time block, and Smin is the sales lower limit constraint in unit time;
expressing the external environment temperature by x and the internal set temperature of the equipment by y, changing the internal set temperature of the equipment under the condition that the external environment temperature is kept unchanged, simultaneously satisfying the constraint of food preservation temperature, forming a plurality of groups (x, y) of elements, respectively collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under different groups (x, y), substituting the energy consumption and sales volume into a multi-target appropriate temperature model, if the constraint condition of the model is satisfied, classifying the group (x, y) into an appropriate temperature solution set, and if the constraint condition of the model is not satisfied, discarding the group (x, y);
under the condition that the internal set temperature is unchanged, measuring the external environment temperature in different time periods, thereby forming a plurality of groups of (x, y) elements, also collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under the condition of different groups of (x, y), substituting the energy consumption and sales volume into a multi-target suitable temperature model, if the model constraint condition is met, classifying the group of (x, y) into a suitable temperature solution set, and if the model constraint condition is not met, discarding the group of (x, y);
stopping collecting when the (x, y) element group in the proper temperature solution set reaches m, thereby obtaining the proper temperature solution set;
(2) determining a multi-target optimal temperature solution aiming at the commodity fresh-keeping time, equipment energy consumption and sales volume, wherein the process is as follows:
2.1, sorting m groups of (x, y) according to a non-dominant relationship, and artificially calibrating a fitness value at each layer;
2.2, generating a next generation set W through polymerization, crossing and recombination, wherein the size of the set is L;
2.3, set W of nth generationnMerging the first generation set P to generate a set R, and carrying out non-dominated sorting on the set R to generate a series of optimized solutions Fi(i ═ 0,1, …), and calculates the congestion degree;
2.4, solving optimization FiPut into the n +1 generation set Pn+1Performing the following steps;
2.5, judging: if Pn+1If the number of the middle element groups is equal to L, adding 1 to n to further judge whether the temperature solution is the optimal temperature solution, if so, outputting the group of solutions, otherwise, entering the step 1; if Pn+1If the number of the middle element groups is less than L, adding 1 to i, and entering 2.4; if Pn+1If the number of the middle element groups is more than L, the pair FiThe crowdedness of the element groups in (1) is sorted, and a better temperature solution is reserved, so that P isn+1The number of the middle element groups is equal to L, then n is added with 1, and whether the temperature solution is the optimal temperature solution or not is judged, if the temperature solution is the optimal temperature solution, the group of solutions is output, and if not, the temperature solution enters 2.1;
and outputting the obtained multi-target optimal temperature solution so as to obtain the set temperature values inside the equipment under different external environment temperatures, and regulating the internal temperature to be the set value by using a compressor.
And then the corresponding optimal equipment internal set temperature values under different external environments can be obtained, and in the process of actual operation, the equipment internal temperature is stabilized above and below the optimal equipment internal set temperature value by means of the compressor through a closed-loop system, so that the optimal comprehensive target of prolonging the commodity fresh-keeping time, reducing the energy consumption of the intelligent retail machine and improving the sales volume is realized.
Claims (1)
1. A multi-target temperature optimization control method of an intelligent retail machine is characterized by comprising the following steps:
(1) according to two decision variables of external environment temperature and equipment internal set temperature, a multi-target appropriate temperature model is constructed under the condition of meeting the constraint condition, and an appropriate temperature solution set is obtained, wherein the method comprises the following steps:
the multi-target suitable temperature model:
n in the formula refers to the subscript of dividing the day from 9 to 20 points into n identical time blocks, wherein different k values respectively represent different time blocks; wherein tin (k) is the equipment internal temperature of the time block, Tmin is the food preservation temperature lower limit constraint, Tmax is the food preservation temperature upper limit constraint, En (k) is the intelligent retail machine energy consumption of the time block, Emax is the energy consumption upper limit constraint in unit time, Sal (k) is the single intelligent retail machine sales of the time block, and Smin is the sales lower limit constraint in unit time;
expressing the external environment temperature by x and the internal set temperature of the equipment by y, changing the internal set temperature of the equipment under the condition that the external environment temperature is kept unchanged, simultaneously satisfying the constraint of food preservation temperature, forming a plurality of groups (x, y) of elements, respectively collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under different groups (x, y), substituting the energy consumption and sales volume into a multi-target appropriate temperature model, if the constraint condition of the model is satisfied, classifying the group (x, y) into an appropriate temperature solution set, and if the constraint condition of the model is not satisfied, discarding the group (x, y);
under the condition that the internal set temperature is unchanged, measuring the external environment temperature in different time periods, thereby forming a plurality of groups of (x, y) elements, also collecting the energy consumption and sales volume of the intelligent retail machine in different time blocks under the condition of different groups of (x, y), substituting the energy consumption and sales volume into a multi-target suitable temperature model, if the model constraint condition is met, classifying the group of (x, y) into a suitable temperature solution set, and if the model constraint condition is not met, discarding the group of (x, y);
(2) determining a multi-target optimal temperature solution aiming at commodity fresh-keeping time, equipment energy consumption and sales, wherein the process is as follows:
2.1, sorting m groups of (x, y) according to a non-dominant relationship, and artificially calibrating a fitness value at each layer;
2.2, generating a next generation set W through polymerization, crossing and recombination, wherein the size of the set is L;
2.3, set W of nth generationnMerging the first generation set P to generate a set R, and carrying out non-dominated sorting on the set R to generate a series of optimized solutions Fi(i ═ 0,1, …), and calculates the congestion degree;
2.4, solving optimization FiPut into the n +1 generation set Pn+1Performing the following steps;
2.5, judging: if Pn+1If the number of the middle element groups is equal to L, adding 1 to n to further judge whether the temperature solution is the optimal temperature solution, if so, outputting the group of solutions, otherwise, entering the step 1; if Pn+1If the number of the middle element groups is less than L, adding 1 to i, and entering 2.4; if Pn+1If the number of the middle element groups is more than L, the pair FiThe crowdedness of the element groups in (1) is sorted, and a better temperature solution is reserved, so that P isn+1The number of the middle element groups is equal to L, then n is added with 1, and whether the temperature solution is the optimal temperature solution or not is judged, if the temperature solution is the optimal temperature solution, the group of solutions is output, and if not, the step 2.1 is carried out;
and outputting the obtained multi-target optimal temperature solution so as to obtain the set temperature values inside the equipment under different external environment temperatures, and regulating the internal temperature to be the set value by using a compressor.
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