Energy-saving optimization method and device of multi-host central air conditioning system, terminal and medium

Abstract

The application relates to the energy-saving technical field of a multi-host central air conditioning system. The application discloses an energy-saving optimization method and device of a multi-host central air conditioning system, a terminal and a medium, which can improve the accuracy of measuring the minimum energy consumption of the multi-host central air conditioning system and realize energy-saving optimization of the multi-host central air conditioning system. The method comprises the following steps: a first energy consumption algorithm is used to calculate and process a first load rate and a first energy efficiency coefficient of a first host in the multi-host central air conditioning system and a second load rate and a second energy efficiency coefficient of a second host in the multi-host central air conditioning system, a plurality of first energy consumptions and a plurality of second energy consumptions are obtained; a second energy consumption algorithm is used to calculate and process each first energy consumption and a second energy consumption at the same time as each first energy consumption, a plurality of target energy consumptions are obtained, the minimum value in all the target energy consumptions is selected as the minimum energy consumption, and the minimum energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy saving of multi-host central air conditioning systems. More specifically, the present application relates to an energy saving optimization method, device, terminal and medium for a multi-host central air conditioning system. BACKGROUND

[0002] The energy saving optimization method of the traditional central air conditioning system is to obtain target data of the central air conditioning system, the target data including device parameters, operation data and environment data, to pre-process the target data and calculate device energy efficiency parameters; to construct a knowledge graph, to evaluate the device energy efficiency of the central air conditioning system based on the knowledge graph, and to identify devices in the central air conditioning system that do not meet the requirements and the causes based on the device energy efficiency; to establish simulation models of each device in the central air conditioning system based on the device parameters, operation data and environment data, and to build a simulation model of the entire central air conditioning system; for the devices that do not meet the energy efficiency requirements, to establish and verify a simulation model of a new device in combination with the causes, technical data of the new device and actual operation conditions; to update the simulation model of the entire central air conditioning system using the simulation model of the new device; to select k typical days, to use a model predictive control method to optimize the set point parameters of the updated simulation model of the entire central air conditioning system, to calculate the average energy saving amount, and to determine an energy saving interval by a Monte Carlo simulation method, which can be understood as a low energy consumption interval, the low energy consumption interval including the lowest energy consumption. This method measures the lowest energy consumption by using at least five technical means, such as the construction of the knowledge graph, the simulation models of each device in the central air conditioning system, the simulation model of the new device, the typical days and the Monte Carlo simulation method, so that the calculation process of the method is too complex, and the use of each technical means may produce errors, and the more technical means are used, the larger the errors will be, thereby causing a large deviation in the measurement of the lowest energy consumption and reducing the accuracy of measuring the lowest energy consumption. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide an energy saving optimization method, device, terminal and medium for a multi-host central air conditioning system, which can simplify the calculation process of the lowest energy consumption, improve the accuracy of measuring the lowest energy consumption of the multi-host central air conditioning system, and realize energy saving optimization of the multi-host central air conditioning system. The embodiments of the present application mainly achieve the purpose by the following technical solutions:

[0004] In a first aspect, the embodiments of the present application provide an energy saving optimization method for a multi-host central air conditioning system, comprising:

[0005] Within a preset time period, multiple first load rates of the first unit in the multi-unit central air conditioning system and the first energy efficiency coefficient corresponding to each first load rate are obtained. At the same time, multiple second load rates of the second unit in the multi-unit central air conditioning system and the second energy efficiency coefficient corresponding to each second load rate are obtained. Each first load rate corresponds to the same moment in the preset time period with one of the multiple second load rates.

[0006] The first energy consumption algorithm is used to calculate the energy consumption of all first load rates, all first energy efficiency coefficients, all second load rates and all second energy efficiency coefficients to obtain the first energy consumption corresponding to each first load rate and the second energy consumption corresponding to each second load rate.

[0007] The second energy consumption algorithm is used to calculate the energy consumption of each first energy consumption and the second energy consumption at the same time as each first energy consumption to obtain the target energy consumption corresponding to each first energy consumption. The minimum value is selected as the lowest energy consumption among all target energy consumptions. The lowest energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state.

[0008] According to one embodiment of this application, the step of using a first energy consumption algorithm to perform energy consumption calculation processing on all first load rates, all first energy efficiency coefficients, all second load rates, and all second energy efficiency coefficients to obtain the first energy consumption corresponding to each first load rate and the second energy consumption corresponding to each second load rate includes:

[0009] The first sub-algorithm of the first energy consumption algorithm is used to calculate and process each first load rate and the first energy efficiency coefficient corresponding to each first load rate to obtain the first energy consumption corresponding to each first load rate.

[0010] The second sub-algorithm of the first energy consumption algorithm is used to calculate and process each second load rate and the second energy efficiency coefficient corresponding to each second load rate to obtain the second energy consumption corresponding to each second load rate.

[0011] According to one embodiment of this application, the calculation formula for the step of calculating the first energy consumption corresponding to each first load rate and the first energy efficiency coefficient corresponding to each first load rate using the first sub-algorithm of the first energy consumption algorithm is as follows:

[0012] ;

[0013] in, It is the first The first energy consumption corresponding to the first load rate; It is the first The first energy efficiency coefficient corresponding to the first load rate; It is the first The first load rate; is the rated refrigerating capacity of the first host.

[0014] According to an embodiment of the present application, the rated refrigerating capacity of the first host and the rated refrigerating capacity of the second host satisfy a first preset condition, and a calculation formula of the first preset condition is:

[0015] ;

[0016] wherein, is the total refrigerating capacity of the multi-host central air conditioning system corresponding to the i th first load rate; is the i th second load rate; is the total refrigerating capacity of the multi-host central air conditioning system corresponding to the i th first load rate; is the i th second load rate; is the rated refrigerating capacity of the second host.

[0017] According to an embodiment of the present application, the total refrigerating capacity of each multi-host central air conditioning system needs to satisfy a second preset condition.

[0018] According to an embodiment of the present application, the step of obtaining a plurality of first load rates of a first host in a multi-host central air conditioning system and a first energy efficiency coefficient corresponding to each first load rate comprises:

[0019] obtaining a plurality of first load rates;

[0020] performing energy efficiency coefficient calculation and processing on each first load rate by using a first numerical model to obtain a first energy efficiency coefficient corresponding to each first load rate.

[0021] According to an embodiment of the present application, a calculation formula of the step of performing energy efficiency coefficient calculation and processing on each first load rate by using a first numerical model to obtain a first energy efficiency coefficient corresponding to each first load rate is:

[0022] ;

[0023] wherein, is a fourth segmented value; is a third segmented value; is a second segmented value; is a first segmented value.

[0024] The second aspect of the embodiment of the present application provides a multi-host central air conditioning system energy-saving optimization device, comprising:

[0025] The acquisition module is configured to acquire a plurality of first load rates of a first host in a multi-host central air conditioning system and a first energy efficiency coefficient corresponding to each first load rate, and simultaneously acquire a plurality of second load rates of a second host in the multi-host central air conditioning system and a second energy efficiency coefficient corresponding to each second load rate, each first load rate corresponding to a same time as one of the plurality of second load rates in the preset time period;

[0026] The energy consumption calculation module is configured to perform energy consumption calculation processing on all the first load rates, all the first energy efficiency coefficients, all the second load rates and all the second energy efficiency coefficients by using a first energy consumption algorithm, to obtain a first energy consumption corresponding to each first load rate and a second energy consumption corresponding to each second load rate.

[0027] The selection module is configured to perform energy consumption calculation processing on each first energy consumption and a second energy consumption at a same time as each first energy consumption by using a second energy consumption algorithm, to obtain a target energy consumption corresponding to each first energy consumption, and to select a minimum value as a lowest energy consumption from all the target energy consumptions, the lowest energy consumption being used to reflect that the multi-host central air conditioning system is in an energy-saving state.

[0028] In a third aspect, a terminal device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, and the processor is configured to invoke and run the computer program stored in the memory to execute the steps of the energy-saving optimization method of the multi-host central air conditioning system provided in the first aspect.

[0029] In a fourth aspect, a computer readable storage medium is provided, which is configured to store a computer program, and the computer program causes a computer to execute the steps of the energy-saving optimization method of the multi-host central air conditioning system provided in the first aspect.

[0030] The beneficial effects of the embodiments of the present application include:

[0031] The embodiment of the present application obtains a plurality of first load rates of a first host in a multi-host central air conditioning system and a first energy efficiency coefficient corresponding to each first load rate within a preset time period, simultaneously, obtains a plurality of second load rates of a second host in the multi-host central air conditioning system and a second energy efficiency coefficient corresponding to each second load rate, each first load rate corresponds to a same time of the preset time period with one of the plurality of second load rates; performs energy consumption calculation processing on all first load rates, all first energy efficiency coefficients, all second load rates and all second energy efficiency coefficients by using a first energy consumption algorithm, obtains a first energy consumption corresponding to each first load rate and a second energy consumption corresponding to each second load rate; performs energy consumption calculation processing on each first energy consumption and a second energy consumption at a same time of each first energy consumption by using a second energy consumption algorithm, obtains a target energy consumption corresponding to each first energy consumption, and selects a minimum value as a lowest energy consumption in all target energy consumptions, the lowest energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state. Compared with the prior art which uses at least five technical means to measure the lowest energy consumption, the embodiment of the present application only uses two energy consumption algorithms, i.e., the first energy consumption algorithm and the second energy consumption algorithm, to calculate the lowest energy consumption. Therefore, the embodiment of the present application can simplify the calculation process of the lowest energy consumption, reduce errors generated in the calculation process, improve the accuracy of measuring the lowest energy consumption of the multi-host central air conditioning system, and thus realize energy-saving optimization of the multi-host central air conditioning system. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0033] Figure 1 Flowchart of the energy-saving optimization method of the multi-host central air conditioning system of the present application in some embodiments;

[0034] Figure 2 Characteristic curve diagram of energy efficiency corresponding to all first load rates and all first load rates in the present application;

[0035] Figure 3 Characteristic curve diagram of energy efficiency corresponding to all second load rates and all second load rates of the present application;

[0036] Figure 4 Principle block diagram of the energy-saving optimization device of the multi-host central air conditioning system of the present application in some embodiments;

[0037] Figure 5 Principle block diagram of the terminal device of the present application in some embodiments. Detailed Implementation

[0038] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0039] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0040] The terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0041] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or that are inherent to such process, method, product, or apparatus.

[0042] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items.

[0043] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0044] refer to Figure 1 The diagram shown is a flowchart of an energy-saving optimization method for a multi-host central air conditioning system provided in the first aspect of an embodiment of this application. Figure 1 The energy-saving optimization method for the multi-host central air conditioning system includes:

[0045] S1, in a preset time period, a plurality of first load rates of a first host in a multi-host central air conditioning system and a first energy efficiency coefficient corresponding to each first load rate are acquired, and a plurality of second load rates of a second host in the multi-host central air conditioning system and a second energy efficiency coefficient corresponding to each second load rate are acquired, each first load rate corresponds to a same time of the preset time period with one of the plurality of second load rates.

[0046] Further, the step of acquiring the plurality of first load rates of the first host in the multi-host central air conditioning system and the first energy efficiency coefficient corresponding to each first load rate comprises: acquiring the plurality of first load rates; and performing energy efficiency coefficient calculation processing on each first load rate by using a first numerical model to obtain the first energy efficiency coefficient corresponding to each first load rate.

[0047] Further, a calculation formula of each first load rate is:

[0048] ;

[0049] wherein, is an actual running power of the first host acquired for the first time in the preset time period; is an actual running power of the first host acquired for the first time in the preset time period; is a rated power of the first host.

[0050] Further, a calculation formula of the step of performing energy efficiency coefficient calculation processing on each first load rate by using the first numerical model to obtain the first energy efficiency coefficient corresponding to each first load rate is:

[0051] ;

[0052] wherein, is a fourth segmented numerical value; is a third segmented numerical value; is a second segmented numerical value; is a first segmented numerical value.

[0053] Further, the first segment value, the second segment value, the third segment value and the fourth segment value can be obtained by a polynomial segment approximation manner. More specifically, a plurality of first historical load rates of the first host and a first historical energy efficiency coefficient corresponding to each first historical load rate in the multi-host central air conditioning system are obtained within a preset historical time; the plurality of first historical load rates are segmented (which can also be understood as division processing), to obtain a first segment first historical load rate, a second segment first historical load rate, a third segment first historical load rate and a fourth segment first historical load rate; a first target historical load rate is selected from the first segment first historical load rate, a second target historical load rate is selected from the second segment first historical load rate, a third target historical load rate is selected from the third segment first historical load rate, a fourth target historical load rate is selected from the fourth segment first historical load rate, a first historical energy efficiency coefficient corresponding to the first target historical load rate is selected from all the first historical energy efficiency coefficients as a first target historical energy efficiency coefficient, a first historical energy efficiency coefficient corresponding to the second target historical load rate is selected from all the first historical energy efficiency coefficients as a second target historical energy efficiency coefficient, a first historical energy efficiency coefficient corresponding to the third target historical load rate is selected from all the first historical energy efficiency coefficients as a third target historical energy efficiency coefficient, a first historical energy efficiency coefficient corresponding to the fourth target historical load rate is selected from all the first historical energy efficiency coefficients as a fourth target historical energy efficiency coefficient, and the first target historical load rate, the second target historical load rate, the third target historical load rate, the fourth target historical load rate, the first target historical energy efficiency coefficient, the second target historical energy efficiency coefficient, the third target historical energy efficiency coefficient and the fourth target historical energy efficiency coefficient are calculated and processed by a first historical value model to obtain the first segment value, the second segment value, the third segment value and the fourth segment value.

[0054] The segment can be determined according to the accuracy of the sensor. The error of the polynomial segment approximation manner can be determined according to the accuracy of the sensor. In the above segment processing, the plurality of first historical load rates are divided into four segments, and in other embodiments, they can be divided into other numbers of segments, which can be set by those skilled in the art according to actual needs.

[0055] Further, the first historical numerical model is used to calculate and process the first target historical load rate, the second target historical load rate, the third target historical load rate, the fourth target historical load rate, the first target historical energy efficiency coefficient, the second target historical energy efficiency coefficient, the third target historical energy efficiency coefficient and the fourth target historical energy efficiency coefficient to obtain the calculation formula of the first segment value, the second segment value, the third segment value and the fourth segment value.

[0056] ;

[0057] ;

[0058] ;

[0059] ;

[0060] wherein, is the first target historical load rate; is the first target historical energy efficiency coefficient; is the second target historical load rate; is the second target historical energy efficiency coefficient; is the third target historical load rate; is the third target historical energy efficiency coefficient; is the fourth target historical load rate; is the fourth target historical energy efficiency coefficient.

[0061] Further, the step of obtaining the plurality of second load rates of the second host in the multi-host central air conditioning system and the second energy efficiency coefficient corresponding to each second load rate comprises: obtaining a plurality of second load rates; and using a second numerical model to calculate and process the energy efficiency coefficient of each second load rate to obtain the second energy efficiency coefficient corresponding to each second load rate.

[0062] Further, the calculation formula of each second load rate is as follows:

[0063] ;

[0064] wherein, is the actual running power of the second host obtained for the first time in the preset time period; is the actual running power of the second host obtained for the second time in the preset time period; is the rated power of the second host.

[0065] Further, the second numerical model is used to calculate the energy efficiency coefficient of each second load rate, and the calculation formula of the step of obtaining the second energy efficiency coefficient corresponding to each second load rate is:

[0066] ;

[0067] wherein, is the eighth segment value; is the seventh segment value; is the sixth segment value; is the fifth segment value.

[0068] Further, the fifth segment value, the sixth segment value, the seventh segment value and the eighth segment value can be obtained by polynomial segment approximation. More specifically, a plurality of second historical load rates of the second host in the multi-host central air conditioning system and the second historical energy efficiency coefficients corresponding to each second historical load rate are obtained simultaneously in the preset historical time; the plurality of second historical load rates are segmented (which can also be understood as division processing), to obtain a first segment of second historical load rates, a second segment of second historical load rates, a third segment of second historical load rates and a fourth segment of second historical load rates; a first second target historical load rate is selected from the first segment of second historical load rates, a second second target historical load rate is selected from the second segment of second historical load rates, a third second target historical load rate is selected from the third segment of second historical load rates, a fourth second target historical load rate is selected from the fourth segment of second historical load rates, a second historical energy efficiency coefficient corresponding to the first second target historical load rate is selected from all second historical energy efficiency coefficients as a first second target historical energy efficiency coefficient, a second historical energy efficiency coefficient corresponding to the second second target historical load rate is selected from all second historical energy efficiency coefficients as a second second target historical energy efficiency coefficient, a second historical energy efficiency coefficient corresponding to the third second target historical load rate is selected from all second historical energy efficiency coefficients as a third second target historical energy efficiency coefficient, and a second historical energy efficiency coefficient corresponding to the fourth second target historical load rate is selected from all second historical energy efficiency coefficients as a fourth second target historical energy efficiency coefficient; the first second target historical load rate, the second second target historical load rate, the third second target historical load rate, the fourth second target historical load rate, the first second target historical energy efficiency coefficient, the second second target historical energy efficiency coefficient, the third second target historical energy efficiency coefficient and the fourth second target historical energy efficiency coefficient are calculated by using the second historical numerical model to obtain the fifth segment value, the sixth segment value, the seventh segment value and the eighth segment value.

[0069] Further, the second historical numerical model is used to calculate and process the first second target historical load rate, the second second target historical load rate, the third second target historical load rate, the fourth second target historical load rate, the first second target historical energy efficiency coefficient, the second second target historical energy efficiency coefficient, the third second target historical energy efficiency coefficient, and the fourth second target historical energy efficiency coefficient, to obtain the calculation formula of the steps of obtaining the fifth segmented numerical value, the sixth segmented numerical value, the seventh segmented numerical value, and the eighth segmented numerical value:

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] wherein, is the first second target historical load rate; is the first second target historical energy efficiency coefficient; is the second second target historical load rate; is the second second target historical energy efficiency coefficient; is the third second target historical load rate; is the third second target historical energy efficiency coefficient; is the fourth second target historical load rate; is the fourth second target historical energy efficiency coefficient.

[0075] The first segmented numerical value, the second segmented numerical value, the third segmented numerical value, the fourth segmented numerical value, the fifth segmented numerical value, the sixth segmented numerical value, the seventh segmented numerical value, and the eighth segmented numerical value are determined through data learning.

[0076] S2, using the first energy consumption algorithm to calculate and process all first load rates, all first energy efficiency coefficients, all second load rates, and all second energy efficiency coefficients, to obtain the first energy consumption corresponding to each first load rate and the second energy consumption corresponding to each second load rate.

[0077] Further, the S2 step includes: using a first sub-algorithm of the first energy consumption algorithm to calculate and process each first load rate and the first energy efficiency coefficient corresponding to each first load rate, to obtain the first energy consumption corresponding to each first load rate; using a second sub-algorithm of the first energy consumption algorithm to calculate and process each second load rate and the second energy efficiency coefficient corresponding to each second load rate, to obtain the second energy consumption corresponding to each second load rate.

[0078] Furthermore, the calculation formula for the step of using the first sub-algorithm of the first energy consumption algorithm to calculate each first load rate and the first energy efficiency coefficient corresponding to each first load rate to obtain the first energy consumption corresponding to each first load rate is as follows:

[0079] ;

[0080] in, It is the first The first energy consumption corresponding to the first load rate; It is the first The first energy efficiency coefficient corresponding to the first load rate; It is the first The first load rate; This is the rated cooling capacity of the first host unit. The rated cooling capacity of the first host unit is a constant.

[0081] Furthermore, the calculation formula for the step of using the second sub-algorithm of the first energy consumption algorithm to calculate each second load rate and the second energy efficiency coefficient corresponding to each second load rate to obtain the second energy consumption corresponding to each second load rate is as follows:

[0082] ;

[0083] in, It is the first The second energy consumption corresponding to the second load rate; It is the first The second energy efficiency coefficient corresponding to the second load rate; It is the first A second load rate; This is the rated cooling capacity of the second main unit. The rated cooling capacity of the second main unit is a constant.

[0084] Furthermore, the rated cooling capacity of the first host and the rated cooling capacity of the second host meet a first preset condition, the calculation formula for which the first preset condition is:

[0085] ;

[0086] in, Is with the first The total cooling capacity of the multi-host central air conditioning system corresponding to the first load rate. These are values ​​determined under specific circumstances.

[0087] Determining the total cooling capacity of each of the aforementioned multi-host central air conditioning systems is a gradual learning and accumulation process.

[0088] Further, the total refrigerating capacity of each of the multi-host central air conditioning system needs to satisfy a second preset condition.

[0089] Specifically, the calculation formula of the second preset condition is:

[0090] ;

[0091] wherein, is the energy efficiency corresponding to the i th first load rate; is the energy efficiency corresponding to the i th second load rate. Further, the energy efficiency corresponding to all the first load rates and the characteristic curve diagram of all the first load rates can refer to FIG. 1. The energy efficiency corresponding to all the second load rates and the characteristic curve diagram of all the second load rates can refer to FIG. 2.

[0092] Figure 2 Figure 3

[0093] The second host can be a screw machine. In other embodiments, the second host can also be other hosts, which can be set by those skilled in the art according to actual needs. The adjustable range of the second load rate of the screw machine is relatively large, which can be from 1% to 100%, but generally the second load rate will not be lower than 20%.

[0094] Further, ; is the refrigerating capacity corresponding to the i th first load rate. The refrigerating capacity corresponding to the i th first load rate can be understood as the refrigerating capacity generated per unit of electricity. The higher the value of the refrigerating capacity per unit of electricity, the higher the efficiency of the use of electricity, and the more energy-saving the first host is. The first host can be a centrifugal machine, and in other embodiments, the first host can be set by those skilled in the art according to actual needs. It should also be understood that among all the first load rates of the centrifugal machine, the interval below 50% is an unstable running interval, and thus the first host will automatically shut down in this interval, and therefore the first load rate below 50% has little reference value.

[0095] Further,

[0096] ; is the refrigerating capacity corresponding to the i th second load rate.

[0097] ​​​​​S3, performing energy consumption calculation processing on each first energy consumption and second energy consumption at the same time as each first energy consumption by using a second energy consumption algorithm to obtain a target energy consumption corresponding to each first energy consumption, and selecting a minimum value among all target energy consumptions as a lowest energy consumption, wherein the lowest energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state.

[0098] The lowest energy consumption corresponds to the configuration with the lowest energy consumption.

[0099] Further, the calculation formula of the step of performing energy consumption calculation processing on each first energy consumption and second energy consumption at the same time as each first energy consumption by using a second energy consumption algorithm to obtain a target energy consumption corresponding to each first energy consumption is as follows:

[0100] ;

[0101] wherein, is the target energy consumption corresponding to each first energy consumption.

[0102] Through the above embodiments, the embodiments of the present application only use two energy consumption algorithms, i.e., a first energy consumption algorithm and a second energy consumption algorithm, to calculate the lowest energy consumption. Therefore, the embodiments of the present application can simplify the calculation process of the lowest energy consumption, reduce errors generated in the calculation process, improve the accuracy of measuring the lowest energy consumption of the multi-host central air conditioning system, and thus realize energy-saving optimization of the multi-host central air conditioning system.

[0103] The embodiments of the present application have wide adaptability and can be suitable for host devices under various working conditions and various types of hosts.

[0104] In some embodiments, the energy-saving optimization method of the multi-host central air conditioning system further includes performing detection processing on the first segmented value, the second segmented value, the third segmented value, and the fourth segmented value by using a verification algorithm to obtain a first predicted segmented value; performing comparison processing on the first predicted segmented value and a first target predicted segmented value to obtain a first comparison result; and if the first comparison result is matched, the first segmented value, the second segmented value, the third segmented value, and the fourth segmented value are correct values.

[0105] The first target predicted segmented value can be set by a person skilled in the art according to actual needs.

[0106] Further, the step of obtaining the first predicted segmented value by detecting the first segmented value, the second segmented value, the third segmented value and the fourth segmented value using the verification algorithm comprises: performing feature extraction processing on the first segmented value, the second segmented value, the third segmented value and the fourth segmented value using a first hidden layer of the verification algorithm to obtain a first feature vector; performing nonlinear transformation processing on the first feature vector using a target activation function of the verification algorithm to obtain a second feature vector; performing feature extraction processing on the second feature vector using a second hidden layer of the verification algorithm to obtain a third feature vector; performing nonlinear transformation processing on the third feature vector using the target activation function to obtain a fourth feature vector; performing feature extraction processing on the fourth feature vector using a third hidden layer of the verification algorithm to obtain a fifth feature vector; and performing nonlinear transformation processing on the fifth feature vector using the target activation function to obtain the first predicted segmented value.

[0107] The neurons of the first hidden layer are set to 64.

[0108] The first hidden layer performs linear transformation processing on the first segmented value, the second segmented value, the third segmented value and the fourth segmented value using a first weight matrix and a first bias, thereby obtaining the first feature vector.

[0109] Specifically, the calculation formula of the step of performing feature extraction processing on the first segmented value, the second segmented value, the third segmented value and the fourth segmented value using the first hidden layer of the verification algorithm to obtain a first feature vector can be: ; is the first feature vector; ; is the first weight matrix; is the first bias.

[0110] The target activation function is a ReLU activation function. In other embodiments, the target activation function is not limited to the ReLU activation function, and can be set by a person skilled in the art according to actual needs.

[0111] Further, the calculation formula of the step of performing nonlinear transformation processing on the first feature vector using the target activation function of the verification algorithm to obtain a second feature vector is: ; is the second feature vector.

[0112] The neurons of the second hidden layer are set to 32.

[0113] The second hidden layer adopts a second weight matrix and a second bias to perform linear transformation processing on the second feature vector, so as to obtain a third feature vector.

[0114] Further, a calculation formula of the step of performing feature extraction processing on the second feature vector by using the second hidden layer of the verification algorithm to obtain a third feature vector can be: ; is the third feature vector; is the second weight matrix; is the second bias.

[0115] Further, a calculation formula of the step of performing non-linear transformation processing on the third feature vector by using the target activation function to obtain a fourth feature vector can be: ; is the fourth feature vector.

[0116] The neurons of the third hidden layer are set to 16.

[0117] The third hidden layer adopts a third weight matrix and a third bias to perform linear transformation processing on the fourth feature vector, so as to obtain a fifth feature vector.

[0118] A calculation formula of the step of performing feature extraction processing on the fourth feature vector by using the third hidden layer of the verification algorithm to obtain a fifth feature vector can be: ; is the fifth feature vector; is the third weight matrix; is the third bias.

[0119] Further, a calculation formula of the step of performing non-linear transformation processing on the fifth feature vector by using the target activation function to obtain the first predicted segmentation value can be: ; is the first predicted segmentation value.

[0120] Further, the step of performing comparison processing on the first predicted segmentation value and a first target predicted segmentation value to obtain a first comparison result comprises: judging whether the first predicted segmentation value is equal to the first target predicted segmentation value, if yes, the first comparison result is matching, and if no, the first comparison result is not matching.

[0121] In some embodiments, the energy-saving optimization method of the multi-host central air conditioning system further comprises: detecting the fifth segmented value, the sixth segmented value, the seventh segmented value and the eighth segmented value by using a verification algorithm to obtain a second predicted segmented value; comparing the second predicted segmented value with a second target predicted segmented value to obtain a second comparison result; if the second comparison result is matched, the fifth segmented value, the sixth segmented value, the seventh segmented value and the eighth segmented value are correct values.

[0122] The second target predicted segmented value can be set by a person skilled in the art according to actual needs.

[0123] Further, the specific implementation process of the step of "detecting the fifth segmented value, the sixth segmented value, the seventh segmented value and the eighth segmented value by using a verification algorithm to obtain a second predicted segmented value" is the same as that of the step of "detecting the first segmented value, the second segmented value, the third segmented value and the fourth segmented value by using a verification algorithm to obtain a first predicted segmented value", which will not be repeated here.

[0124] Further, the step of comparing the second predicted segmented value with a second target predicted segmented value to obtain a second comparison result comprises: judging whether the second predicted segmented value is equal to the second target predicted segmented value, if yes, the second comparison result is matched, and if not, the second comparison result is not matched.

[0125] Reference Figure 4 Fig. 2 shows a principle block diagram of an energy-saving optimization device of a multi-host central air conditioning system provided by the second aspect of the embodiments of the present application. In Figure 4 the energy-saving optimization device 100 of the multi-host central air conditioning system comprises:

[0126] The acquisition module 101 is configured to acquire a plurality of first load rates of a first host in a multi-host central air conditioning system and a first energy efficiency coefficient corresponding to each first load rate, and simultaneously acquire a plurality of second load rates of a second host in the multi-host central air conditioning system and a second energy efficiency coefficient corresponding to each second load rate, each first load rate corresponding to a same time in the preset time period as one of the plurality of second load rates.

[0127] The energy consumption calculation module 102 is configured to perform energy consumption calculation processing on all first load rates, all first energy efficiency coefficients, all second load rates and all second energy efficiency coefficients by using a first energy consumption algorithm to obtain a first energy consumption corresponding to each first load rate and a second energy consumption corresponding to each second load rate.

[0128] The selecting module is configured to perform energy consumption calculation on each first energy consumption and a second energy consumption at the same time as each first energy consumption by using a second energy consumption algorithm, to obtain a target energy consumption corresponding to each first energy consumption, and to select a minimum value from all target energy consumptions as a lowest energy consumption, which is used to reflect that the multi-host central air conditioning system is in an energy-saving state.

[0129] A third aspect of the embodiment of the present application provides a terminal device, a principle block diagram of which can be as shown in Figure 5 The terminal device includes a processor, a memory, a network interface, a display screen and a temperature sensor connected through a system bus. The processor is configured to provide calculation and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the terminal device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the energy-saving optimization method of the multi-host central air conditioning system. The display screen can be a liquid crystal display screen or an electronic ink display screen. The temperature sensor is pre-set in the terminal device to detect the running temperature of the internal device.

[0130] Those skilled in the art can understand that Figure 5 The principle block diagram shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0131] In some embodiments, the embodiment of the present application provides a terminal device including a processor and a memory for storing a computer program. The processor is configured to call and run the computer program stored in the memory to perform the steps of the energy-saving optimization method of the multi-host central air conditioning system provided in the first aspect of the embodiment of the present application.

[0132] The fourth aspect of the embodiment of the present application provides a computer readable storage medium for storing a computer program. The computer program enables a computer to perform the steps of the energy-saving optimization method of the multi-host central air conditioning system provided in the first aspect of the embodiment of the present application.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] The technical features of the above embodiments can be combined without changing the basic principles of the present application. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0135] The above embodiments only express several implementation manners of the present application, and the description is specific and detailed, but it should not be understood as a limitation on the patent scope of the application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the patent protection scope of the present application should be subject to the appended claims.

Claims

1. An energy-saving optimization method for a multi-host central air conditioning system, characterized in that, include: Within a preset time period, multiple first load rates of the first unit in the multi-unit central air conditioning system and the first energy efficiency coefficient corresponding to each first load rate are obtained. At the same time, multiple second load rates of the second unit in the multi-unit central air conditioning system and the second energy efficiency coefficient corresponding to each second load rate are obtained. Each first load rate corresponds to the same moment in the preset time period with one of the multiple second load rates. The first energy consumption algorithm is used to calculate the energy consumption of all first load rates, all first energy efficiency coefficients, all second load rates and all second energy efficiency coefficients to obtain the first energy consumption corresponding to each first load rate and the second energy consumption corresponding to each second load rate. The second energy consumption algorithm is used to calculate the energy consumption of each first energy consumption and the second energy consumption at the same time as each first energy consumption to obtain the target energy consumption corresponding to each first energy consumption. The minimum value among all target energy consumptions is selected as the minimum energy consumption. The minimum energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state. The steps of using a first energy consumption algorithm to calculate energy consumption for all first load rates, all first energy efficiency coefficients, all second load rates, and all second energy efficiency coefficients to obtain the first energy consumption for each first load rate and the second energy consumption for each second load rate include: using a first sub-algorithm of the first energy consumption algorithm to calculate energy consumption for each first load rate and the first energy efficiency coefficient corresponding to each first load rate to obtain the first energy consumption for each first load rate; and using a second sub-algorithm of the first energy consumption algorithm to calculate energy consumption for each second load rate and the second energy efficiency coefficient corresponding to each second load rate to obtain the second energy consumption for each second load rate. The calculation formula for the first energy consumption step, which uses the first sub-algorithm of the first energy consumption algorithm to calculate each first load rate and the first energy efficiency coefficient corresponding to each first load rate, is as follows: ; The calculation formula for the step of using the second sub-algorithm of the first energy consumption algorithm to calculate each second load rate and the second energy efficiency coefficient corresponding to each second load rate to obtain the second energy consumption corresponding to each second load rate is as follows: ; The calculation formula for obtaining the target energy consumption corresponding to each first energy consumption by using the second energy consumption algorithm to calculate the energy consumption of each first energy consumption and the second energy consumption at the same time as each first energy consumption is as follows: ; in, It is the first The first energy consumption corresponding to the first load rate; It is the first The first energy efficiency coefficient corresponding to the first load rate; It is the first The first load rate; This is the rated cooling capacity of the first host unit; It is the first The second energy consumption corresponding to the second load rate; It is the first The second energy efficiency coefficient corresponding to the second load rate; It is the first The second load rate; This is the rated cooling capacity of the second main unit; It is the target energy consumption corresponding to each first energy consumption.

2. The energy-saving optimization method for a multi-host central air conditioning system according to claim 1, characterized in that, The rated cooling capacity of the first host and the rated cooling capacity of the second host meet the first preset condition, and the calculation formula for the first preset condition is: ； in, Is with the first The total cooling capacity of the multi-host central air conditioning system corresponding to each first load rate; It is the first The second load rate; This is the rated cooling capacity of the second main unit.

3. The energy-saving optimization method for a multi-host central air conditioning system according to claim 2, characterized in that, The total cooling capacity of each of the multi-host central air conditioning systems must meet the second preset condition.

4. The energy-saving optimization method for a multi-host central air conditioning system according to claim 1, characterized in that, The steps for obtaining multiple first load rates of the first unit in a multi-unit central air conditioning system and the first energy efficiency coefficient corresponding to each first load rate include: Obtain multiple first load rates; The first numerical model is used to calculate the energy efficiency coefficient for each first load rate, and the first energy efficiency coefficient corresponding to each first load rate is obtained.

5. The energy-saving optimization method for a multi-host central air conditioning system according to claim 4, characterized in that, The formula for calculating the energy efficiency coefficient for each first load rate using the first numerical model is as follows: ； in, It is the value of the fourth segment; It is the value of the third segment; It is the value of the second segment; This is the value of the first segment.

6. An energy-saving optimization device for a multi-host central air conditioning system, characterized in that, include: The acquisition module is used to acquire multiple first load rates of the first unit in the multi-unit central air conditioning system and the first energy efficiency coefficient corresponding to each first load rate within a preset time period, and simultaneously acquire multiple second load rates of the second unit in the multi-unit central air conditioning system and the second energy efficiency coefficient corresponding to each second load rate, wherein each first load rate and one of the multiple second load rates correspond to the same moment in the preset time period. The energy consumption calculation module is used to perform energy consumption calculation on all first load rates, all first energy efficiency coefficients, all second load rates and all second energy efficiency coefficients using a first energy consumption algorithm, so as to obtain the first energy consumption corresponding to each first load rate and the second energy consumption corresponding to each second load rate. The selection module is used to perform energy consumption calculation processing on each first energy consumption and the second energy consumption at the same time as each first energy consumption using the second energy consumption algorithm, to obtain the target energy consumption corresponding to each first energy consumption, and to select the minimum value among all target energy consumptions as the minimum energy consumption. The minimum energy consumption is used to reflect that the multi-host central air conditioning system is in an energy-saving state. The energy consumption calculation module is also used to calculate and process each first load rate and the first energy efficiency coefficient corresponding to each first load rate using the first sub-algorithm of the first energy consumption algorithm to obtain the first energy consumption corresponding to each first load rate; and to calculate and process each second load rate and the second energy efficiency coefficient corresponding to each second load rate using the second sub-algorithm of the first energy consumption algorithm to obtain the second energy consumption corresponding to each second load rate. The calculation formula for the first energy consumption step, which uses the first sub-algorithm of the first energy consumption algorithm to calculate each first load rate and the first energy efficiency coefficient corresponding to each first load rate, is as follows: ; The calculation formula for the step of using the second sub-algorithm of the first energy consumption algorithm to calculate each second load rate and the second energy efficiency coefficient corresponding to each second load rate to obtain the second energy consumption corresponding to each second load rate is as follows: ; The calculation formula for obtaining the target energy consumption corresponding to each first energy consumption by using the second energy consumption algorithm to calculate the energy consumption of each first energy consumption and the second energy consumption at the same time as each first energy consumption is as follows: ; in, It is the first The first energy consumption corresponding to the first load rate; It is the first The first energy efficiency coefficient corresponding to the first load rate; It is the first The first load rate; This is the rated cooling capacity of the first host unit; It is the first The second energy consumption corresponding to the second load rate; It is the first The second energy efficiency coefficient corresponding to the second load rate; It is the first The second load rate; This is the rated cooling capacity of the second main unit; It is the target energy consumption corresponding to each first energy consumption.

7. A terminal device, characterized in that, include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory to perform the steps of the energy-saving optimization method for a multi-host central air conditioning system as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the steps of the energy-saving optimization method for a multi-host central air conditioning system as described in any one of claims 1 to 5.

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