CN117057453A - Energy-saving optimization method and system based on actually measured operation data of refrigerating unit - Google Patents
Energy-saving optimization method and system based on actually measured operation data of refrigerating unit Download PDFInfo
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Abstract
The invention discloses an energy-saving optimization method and a system based on measured operation data of a refrigerating unit, wherein the method comprises the following steps: calculating performance parameters of each refrigerating unit according to the actually measured operation data, calculating system cold load of the refrigerating system in a first period, judging and obtaining an on-off operation strategy according to the system cold load, executing the on-off operation strategy, obtaining input variable parameter array data influencing the performance of each refrigerating unit in the first period, forming a to-be-processed performance data set with the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit, and processing the to-be-processed performance data set to obtain optimized input variable parameter array data of the refrigerating system in a second period; the invention solves the problems that operation staff cannot realize the fine control of the refrigerating unit from the system angle based on actual operation data, and further causes the poor operation strategy, low energy efficiency, relatively high operation cost and the like of the refrigerating system.
Description
Technical Field
The invention belongs to the technical field of energy digitizing, and particularly relates to an energy-saving optimization method and system based on measured operation data of a refrigerating unit.
Background
Along with the development of artificial intelligence, big data and Internet of things, the intelligent energy management and control system is more and more applied to an actual energy system, so that the energy running cost is reduced for users, the energy system running efficiency is improved, and energy conservation and carbon reduction are realized. However, the existing energy digitizing technology lacks an effective data processing method, so that the value of the actually measured operation data is not fully exerted.
Refrigeration systems, which are one of the typical energy subsystems, generally consume a significant amount of energy. Through research, the actual operation data is only used for keeping records in the operation and maintenance management process of the actual refrigeration station room. Because of the lack of effective means for processing and analyzing the operation data, the lack of effective energy efficiency evaluation indexes, the real operation condition of the refrigeration system cannot be displayed, and the energy saving potential of the system cannot be quantified. The reasons cause that operation and maintenance personnel cannot realize fine control over the refrigerating unit from the angle of the system, and the operation energy efficiency of the refrigerating system needs to be further improved. For the optimal control of the refrigerating system, the actual dynamic performance curves of different refrigerating units are different due to different operation years, performance parameters and the like, and how to obtain a data model representing the actual operation curves of the refrigerating units is also one of the key problems of optimization as the dynamic constraint of system optimization.
Disclosure of Invention
The invention aims to provide an energy-saving optimization method and system based on actual measurement operation data of a refrigerating unit, so as to solve the problems that operation staff cannot realize fine control of the refrigerating unit from the system angle based on the actual operation data, and further the refrigerating system has poor operation strategy, low energy efficiency and high operation cost.
The invention adopts the following technical scheme: an energy-saving optimization method based on measured operation data of a refrigerating unit comprises the following steps:
step 1: obtaining actual measurement operation data of each refrigerating unit of the refrigerating system in a first period, and calculating performance parameters of each refrigerating unit according to the actual measurement operation data, wherein the performance parameters are a cooling capacity ratio, a power ratio and a refrigerating efficiency ratio when the refrigerating unit is a screw unit, and the performance parameters are a cooling capacity ratio, a heat ratio and a refrigerating efficiency ratio when the refrigerating unit is a lithium bromide unit;
step 2: calculating the system cooling load of the refrigeration system in the first period according to the refrigeration capacity and the refrigeration capacity ratio of the outlet nameplate of each refrigeration unit,
step 3: judging and obtaining an on-off operation strategy according to the system cold load and executing the on-off operation strategy,
step 4: obtaining input variable parameter series data affecting the performance of each refrigerating unit in a first period, and forming a performance data set to be processed with the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit,
step 5: and processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
Further, the formula for calculating the system cooling load of the refrigeration system in the step 2 is as follows:
in the method, in the process of the invention,for the cold ratio of the screw unit, +.>The cooling capacity ratio of the lithium bromide unit; q'. ch The rated working condition refrigerating capacity of the screw unit is Q' LiBr Is the rated working condition refrigerating capacity of the lithium bromide unit, Q co.sys And m is the number of lithium bromide units and n is the number of screw units.
Further, the inputting variable parameter column data in step 4 includes: any three of current, voltage, evaporator water inlet temperature, evaporator water outlet temperature and condenser water inlet temperature.
Further, the processing method of step 5 is composed of the following steps:
step 501: eliminating all data of a time point corresponding to any parameter value of 0 in the performance data set to be processed;
step 502: sorting the columns of the residual performance data sets, and removing all data of time points corresponding to a plurality of maximum values and a plurality of minimum values, wherein the number of the removed maximum values and the removed minimum values is 3% of the number of the residual performance data sets;
step 503: and rounding the removed performance data set according to the numerical value in the range of the row interval, and sequencing different row intervals to obtain optimized input variable parameter column data.
Further, the judging method in the step 3 comprises the following steps:
when the system cooling load is not less than the rated refrigerating capacity of the screw machine group, the lithium bromide machine group and the screw machine group are simultaneously started, and when the system cooling load is not more than the rated refrigerating capacity of the screw machine group, only the screw machine group is started.
A system for energy conservation optimization based on measured operational data of a refrigeration unit, comprising:
the acquisition module is used for acquiring the actually measured operation data of each refrigerating unit of the refrigerating system in the first period and also used for acquiring the input variable parameter series data affecting the performance of each refrigerating unit in the first period,
the processing module is used for calculating the cold energy ratio, the power ratio or the heat energy ratio and the refrigeration efficiency ratio of each refrigerating unit according to the actually measured operation data, and calculating the system cold load of the refrigerating system in the first period according to the refrigeration capacity and the cold energy ratio of the outlet nameplate of each refrigerating unit,
a judging module for judging the on-off operation strategy according to the system cold load and executing,
an output module for composing the input variable parameter series data affecting the performance of each refrigerating unit in the first period with the power ratio or the heat ratio of each refrigerating unit and the refrigerating efficiency ratio into a performance data set to be processed,
and the optimizing module is used for processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
The beneficial effects of the invention are as follows:
1. the invention solves the problems that the actual running condition of the existing refrigeration station house is unquantifiable, so that diagnosis is difficult and a large amount of energy is wasted;
2. in the optimization process of the refrigeration system in the prior art, the actual dynamic performance operation curve of the refrigeration unit is difficult to acquire, so that the actually measured operation data is preprocessed through the energy efficiency model to obtain the cold energy ratio, the power ratio or the heat energy ratio and the refrigeration efficiency ratio, then the performance data set is obtained by combining the data with the input variable parameter array data influencing the performance of each refrigeration unit, the performance data set is processed based on logic, and then the optimized input variable parameter array data is obtained, so that dynamic constraint conditions are provided for the control and optimization of the refrigeration system;
3. the intelligent comprehensive energy management and control platform solves the problems that the data volume, the data structure and the data quality of different refrigeration systems acquired by the intelligent comprehensive energy management and control platform are large, namely, the operation data of different refrigeration units are effectively processed by adopting a mechanism model and a dimensionless method, so that the data dimension is simplified, the data structure is unified, and the data quality is improved;
4. according to the invention, the actually measured operation data is preprocessed through the energy efficiency model to obtain the cold energy ratio, the power ratio or the heat ratio and the refrigeration efficiency ratio, the data quantity and the data structure are simplified, the data are cleaned by utilizing the correlation function, and the bad data are removed to improve the data quality; quantifying the actual running condition of a refrigerating unit, and quantifying the energy-saving potential and the optimization direction of a refrigerating system;
5. the optimized input variable parameter array data obtained by the invention has a uniform data structure, can provide dynamic constraint for the optimization of the cold system, and predicts the running power consumption and the energy efficiency value of the refrigerating unit through a data model, so that the whole optimization process has interpretability and descriptability;
6. the invention solves the problems that operation staff cannot finely control the refrigerating unit based on actual operation data from the system perspective, namely from the perspective of quantifying energy saving potential and defining the operation performance of the refrigerating unit, thereby causing poor operation strategy, lower energy efficiency, relatively higher operation cost and the like of the refrigerating system.
Drawings
FIG. 1 is the measured operation data in example 1 of the present invention;
FIG. 2 is a diagnostic analysis chart of the refrigeration system according to embodiment 1 of the present invention;
FIG. 3 is a diagnostic analysis chart of the screw machine in example 1 of the present invention;
FIG. 4 is a graph showing the performance of the screw machine according to example 1 of the present invention, wherein the horizontal axis isThe vertical axis is the number of hours of the running data;
FIG. 5 is a chart of the optimized input variable parameter array data of the refrigeration system in the second period of time according to embodiment 1 of the present invention;
FIG. 6 is a comparison of the predicted and actual effects of the screw machine data model in example 1 of the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that it can be practiced, since modifications, changes in the proportions, or adjustments of the sizes, which are otherwise, used in the practice of the invention, are included in the spirit and scope of the invention which is otherwise, without departing from the spirit or scope thereof.
The invention discloses an energy-saving optimization method based on measured operation data of a refrigerating unit, which comprises the following steps:
step 1: and obtaining the actually measured operation data of each refrigerating unit of the refrigerating system in the first period, and calculating the performance parameters of each refrigerating unit according to the actually measured operation data, wherein the performance parameters are the cold energy ratio, the power ratio and the refrigerating efficiency ratio when the refrigerating unit is a screw unit, and the performance parameters are the cold energy ratio, the heat energy ratio and the refrigerating efficiency ratio when the refrigerating unit is a lithium bromide unit.
The refrigerating system consists of a screw machine group and a lithium bromide machine group. When data are acquired, original data are read, and the actual power consumption, the energy consumption and the COP of the screw unit are calculated through an energy efficiency mechanism model of the screw unit; the actual power consumption, the energy consumption and the COP of the lithium bromide unit are calculated through the lithium bromide unit energy efficiency mechanism model, and then the actual data are subjected to dimensionless processing by utilizing the unit nameplate working condition data to respectively obtain the power ratio, the cold energy ratio and the refrigeration efficiency ratio of the screw unit, and the heat ratio, the cold energy ratio and the refrigeration efficiency ratio of the lithium bromide unit.
Step 2: and calculating the system cooling load of the refrigerating system in the first period according to the refrigeration capacity and the refrigeration capacity ratio of the outlet nameplate of each refrigerating unit.
The formula for calculating the system cooling load of the refrigeration system in the step 2 is as follows:
in the method, in the process of the invention,for the cold ratio of the screw unit, +.>The cooling capacity ratio of the lithium bromide unit; q (Q) c ′ h The rated working condition refrigerating capacity of the screw unit is Q' LiBr Is the rated working condition refrigerating capacity of the lithium bromide unit, Q co.sys And m is the number of lithium bromide units and n is the number of screw units.
Step 3: the on-off operation strategy is obtained and executed according to the system cold load judgment, and the judgment method of the step 3 comprises the following steps:
when the system cooling load is not less than the rated refrigerating capacity of the screw machine group, the lithium bromide machine group and the screw machine group are simultaneously started, and when the system cooling load is not more than the rated refrigerating capacity of the screw machine group, only the screw machine group is started.
Step 4: and acquiring input variable parameter series data affecting the performance of each refrigerating unit in a first period, and forming a performance data set to be processed together with the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit.
The variable parameter column data input in step 4 includes: any three of current, voltage, evaporator water inlet temperature, evaporator water outlet temperature and condenser water inlet temperature.
Step 5: and processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
The processing method of the step 5 comprises the following steps:
step 501: all data of the time point corresponding to the arbitrary parameter value of the performance data set being 0 are removed,
step 502: and sorting the columns of the residual performance data sets, and removing all data of time points corresponding to the maximum values and the minimum values, wherein the number of the removed maximum values and the removed minimum values is 3% of the number of the residual performance data sets.
The specific operation is as follows: and cleaning the residual performance data set by using a minmax interval function, filtering data outside the interval, traversing all the row intervals in sequence to find out that the values of all the output result columns are in the range of the designated row interval, averaging the values of the output result columns in the designated row interval, and reserving 4-bit effective numbers.
Step 503: and rounding the removed performance data set according to the numerical value in the range of the row interval, and sequencing different row intervals to obtain optimized input variable parameter column data.
The invention also discloses an energy-saving optimization system based on the actually measured operation data of the refrigerating unit, which comprises the following steps:
the acquisition module is used for acquiring the actually measured operation data of each refrigerating unit of the refrigerating system in the first period and also used for acquiring the input variable parameter series data affecting the performance of each refrigerating unit in the first period,
the processing module is used for calculating the cold energy ratio, the power ratio or the heat energy ratio and the refrigeration efficiency ratio of each refrigerating unit according to the actually measured operation data, and calculating the system cold load of the refrigerating system in the first period according to the refrigeration capacity and the cold energy ratio of the outlet nameplate of each refrigerating unit,
a judging module for judging the on-off operation strategy according to the system cold load and executing,
an output module for composing the input variable parameter series data affecting the performance of each refrigerating unit in the first period with the power ratio or the heat ratio of each refrigerating unit and the refrigerating efficiency ratio into a performance data set to be processed,
and the optimizing module is used for processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
According to the invention, actually measured operation data are imported into the edge side controller and processed through the unit energy efficiency model, and the processed data are respectively used for diagnosis and analysis of a refrigerating system and standardization processing of a unit dynamic characteristic curve. And obtaining a quantification result of the running condition of the refrigerating system through diagnostic analysis. And generating a data model representing the actual running performance of the unit by processing, and optimizing the refrigerating system based on the diagnosis analysis result and the data model representing the actual performance of the unit.
Example 1
Taking an actual refrigerating station as an example, the station is provided with 1 screw unit and 2 lithium bromide units, and the actual measured operation data of the refrigerating station rooms of 7 months and 8 months are shown in figure 1 through an energy efficiency model; processing the data in fig. 1 to obtain a daily start-up and shut-down operation strategy and total cold load change condition of the refrigerating station room unit, wherein the daily start-up and shut-down operation strategy and the total cold load change condition are shown in fig. 2; and the occupation of the actual operation energy efficiency of the unit in different intervals is obtained, as shown in fig. 3 and 4. And the actual running condition of the refrigerating system is quantified by carrying out data analysis based on the model, so that the energy saving potential and the optimization direction of the refrigerating system are obtained.
Step 1: and obtaining the actual measurement operation data of each refrigerating unit of the refrigerating system in 7 months and 8 months, and calculating the cold energy ratio, the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit according to the actual measurement operation data.
Wherein, screw rod unit energy efficiency model is:
P ch ＝1.732*(I 1 +I 2 +···I n )*U*0.8/1000
wherein P is ch And P' ch The actual power consumption of the screw unit and the power consumption of the outgoing nameplate are respectively; i 1 、I 2 、I n The running currents of different compressors are provided, and U is the running voltage of the unit; η (eta) p Is the ratio of the actual power to the nameplate power.
Q ch ＝c p m c*hw (T in -T out )
Q′ ch ＝c p m′ chw (T′ in -T′ out )
In which Q ch And Q' ch The actual consumption refrigeration capacity of the screw unit and the refrigeration capacity of the out-of-field nameplate are respectively; c p Is the specific heat capacity of water; m is m chw And m' chw The mass flow of the chilled water under actual and design working conditions are respectively; t (T) in And T out 、T′ in And T' out The temperatures of the inlet and outlet of chilled water in actual and design working conditions of the unit are respectively,the cold energy ratio of the screw machine, namely the load rate PLR of the unit.
COP＝Q ch /P ch
COP′＝Q′ ch /P′ ch
In the formula, COP and COP' are the actual refrigeration efficiency and the rated working condition refrigeration efficiency of the screw unit respectively;the refrigerating efficiency ratio of the actual screw machine to the rated working condition is i.
Wherein, lithium bromide unit energy efficiency model is:
Q z ＝θ z m i (h i -h o )/c i
Q′ z ＝m′ i (h′ i -h′ o )/c′ i
η z ＝Q z /Q′ z ＝θ z c′ i /c i
in which Q z And Q' z The steam heat supply quantity under the actual working condition and the standard working condition respectively; θ z Is the opening of a steam inlet valve; m is m i And m' i The inlet steam volume flow under the actual pressure and the standard working condition pressure respectively; c i And c' i The specific volume of saturated steam under the actual pressure and the standard working condition pressure is respectively; h is a i And h o 、h′ i And h' o The enthalpy value of the steam inlet and the enthalpy value of the condensed water outlet under the actual pressure and the standard working condition pressure are respectively, eta z The heat ratio of driving energy for the input end of the lithium bromide unit.
Q LiBr ＝c p m LiBr (T in -T out )
Q′ LiBr ＝c p m′ LiBr (T′ in -T′ out )
Wherein, c p Is the specific heat capacity of water; m is m LiBr And m' LiBr The mass flow of the chilled water under actual and design working conditions are respectively; t (T) in And T out 、T′ in And T' out The temperatures of the inlet and outlet of chilled water in actual and design working conditions of the unit are respectively,the cooling capacity ratio of the lithium bromide unit i is the load factor PLR of the lithium bromide unit.
COP＝Q LiBr /Q z
COP′＝Q′ LiBr /Q z ′
In the formula, COP and COP' are respectively the actual refrigeration efficiency and the rated working condition refrigeration efficiency of the lithium bromide unit;the refrigerating efficiency ratio of the lithium bromide unit under actual and rated working conditions is obtained.
Step 2: calculating the system cooling load of the refrigerating system in 7 months and 8 months according to the refrigeration capacity and the refrigeration capacity ratio of the outlet nameplate of each refrigerating unit, wherein the system cooling load calculation model is as follows
In the method, in the process of the invention,for the cold ratio of the screw unit, +.>The cooling capacity ratio of the lithium bromide unit; q (Q) c ′ h The rated working condition refrigerating capacity of the screw unit is Q' LiBr Is the rated working condition refrigerating capacity of the lithium bromide unit, Q co.sys And m is the number of lithium bromide units and n is the number of screw units.
Step 3: judging and obtaining an on-off operation strategy according to the system cold load and executing the on-off operation strategy,
step 4: and acquiring input variable parameter series data which influences the performance of each refrigerating unit for 7 months and 8 months, and forming a performance data set to be processed together with the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit.
Taking a screw refrigerator as an example, the specific steps are as follows:
and acquiring input variable parameter column data affecting the performance of each refrigerating unit, wherein the input variable parameter column data comprises 5 columns of data of a load rate PLR, a chilled water outlet temperature, a cooling water inlet temperature, a power ratio and a COP ratio, and the data, the power ratio and the refrigerating efficiency ratio form a performance data set to be processed.
Step 5: and processing the performance array to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
Step 501: deleting the row with null value;
step 502: numerical sequencing is carried out on three rows of data of the load factor PLR, the chilled water outlet temperature and the cooling water inlet temperature, the determined interval range and interval number are [0.4,1.0,0.1], [6,10,0.5], [26,32,0.5], the data are cleaned by utilizing a minmax interval function, and the data outside the interval are filtered; and traversing all row intervals, namely intervals comprising a load rate plr, a chilled water outlet temperature and a cooling water inlet temperature, so as to find rows with values (power ratio and cop ratio) of all columns in a specified interval, averaging the values of the columns in the specified row interval, and reserving 4-bit valid numbers.
Step 503: rounding the removed performance data set according to the numerical value in the interval range to realize interval processing of the variable original data line, and meanwhile, sequencing different line intervals and outputting a dynamic characteristic curve data model of the screw machine, as shown in fig. 5.
Verifying a screw machine data model through actually measured operation data, wherein the effect of comparing the predicted result of the energy efficiency ratio and the power ratio with the actual value under the data model is shown in fig. 6; η (eta) p Andthe root mean square error RMSE of (2.2) and 2.4%, respectively, the fitness R 2 90% and 94%, respectively.
Finally, static optimization is carried out on the on-off operation strategies of the 3 units based on the diagnosis result and the dynamic performance curve data model of the units, and the adjustable parameters of the refrigerating units, such as the chilled water outlet temperature set value, the chilled water flow and the like, are dynamically optimized, and the optimized unit operation energy efficiency can be directly calculated through the model, so that quantitative evaluation of the optimizing effect is realized.
The foregoing is only illustrative of the present invention and is not to be construed as limiting thereof, but rather as various modifications, equivalent arrangements, improvements, etc., within the spirit and principles of the present invention.
Claims (6)
1. An energy-saving optimization method based on measured operation data of a refrigerating unit is characterized by comprising the following steps:
step 1: obtaining actual measurement operation data of each refrigerating unit of the refrigerating system in a first period, and calculating performance parameters of each refrigerating unit according to the actual measurement operation data, wherein the performance parameters are a cooling capacity ratio, a power ratio and a refrigerating efficiency ratio when the refrigerating unit is a screw unit, and the performance parameters are a cooling capacity ratio, a heat ratio and a refrigerating efficiency ratio when the refrigerating unit is a lithium bromide unit;
step 2: calculating the system cooling load of the refrigeration system in the first period according to the refrigeration capacity and the refrigeration capacity ratio of the outlet nameplate of each refrigeration unit,
step 3: judging and obtaining an on-off operation strategy according to the system cold load and executing the on-off operation strategy,
step 4: obtaining input variable parameter series data affecting the performance of each refrigerating unit in a first period, and forming a performance data set to be processed with the power ratio or the heat ratio and the refrigerating efficiency ratio of each refrigerating unit,
step 5: and processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
2. The energy-saving optimization method based on measured operation data of a refrigerating unit according to claim 1, wherein the formula for calculating the system cooling load of the refrigerating system in step 2 is:
in the method, in the process of the invention,for the cold ratio of the screw unit, +.>The cooling capacity ratio of the lithium bromide unit; q (Q) c ′ h The rated working condition refrigeration capacity of the screw unit is Q ′ LiBr Is the rated working condition refrigerating capacity of the lithium bromide unit, Q co.sys And m is the number of lithium bromide units and n is the number of screw units.
3. The energy-saving optimization method based on measured operation data of a refrigerating unit according to claim 1, wherein the inputting variable parameter column data in step 4 comprises: any three of current, voltage, evaporator water inlet temperature, evaporator water outlet temperature and condenser water inlet temperature.
4. The energy-saving optimization method based on measured operation data of a refrigerating unit according to claim 1, wherein the processing method of step 5 comprises the following steps:
step 501: eliminating all data of a time point corresponding to any parameter value of 0 in the performance data set to be processed;
step 502: sorting the columns of the residual performance data sets, and removing all data of time points corresponding to a plurality of maximum values and a plurality of minimum values, wherein the number of the removed maximum values and the removed minimum values is 3% of the number of the residual performance data sets;
step 503: and rounding the removed performance data set according to the numerical value in the range of the row interval, and sequencing different row intervals to obtain optimized input variable parameter column data.
5. The energy-saving optimization method based on measured operation data of a refrigerating unit according to claim 1, wherein the judging method of step 3 comprises the following steps:
when the system cooling load is not less than the rated refrigerating capacity of the screw machine group, the lithium bromide machine group and the screw machine group are simultaneously started, and when the system cooling load is not more than the rated refrigerating capacity of the screw machine group, only the screw machine group is started.
6. A system for energy conservation optimization based on measured operational data of a refrigeration unit, comprising:
the acquisition module is used for acquiring the actually measured operation data of each refrigerating unit of the refrigerating system in the first period and also used for acquiring the input variable parameter series data affecting the performance of each refrigerating unit in the first period,
the processing module is used for calculating the cold energy ratio, the power ratio or the heat energy ratio and the refrigeration efficiency ratio of each refrigerating unit according to the actually measured operation data, and calculating the system cold load of the refrigerating system in the first period according to the refrigeration capacity and the cold energy ratio of the outlet nameplate of each refrigerating unit,
a judging module for judging the on-off operation strategy according to the system cold load and executing,
an output module for composing the input variable parameter series data affecting the performance of each refrigerating unit in the first period with the power ratio or the heat ratio of each refrigerating unit and the refrigerating efficiency ratio into a performance data set to be processed,
and the optimizing module is used for processing the performance data set to be processed to obtain optimized input variable parameter array data of the refrigerating system in the second period.
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