Air conditioner energy-saving control method and system of digital twin model

Abstract

The invention relates to an air conditioner energy-saving control method and system of a digital twinborn model, and belongs to the field of air conditioning. The method comprises the steps that the temperature adjusting range of a target refrigeration air conditioner is evenly divided to obtain a plurality of reference set temperatures, inputting each reference set temperature into a digital twin model to obtain predicted consumption power consumption and predicted consumption duration corresponding to the reference set temperature; the reference set temperature corresponding to the predicted power consumption with the minimum value serves as the preferential energy-saving set temperature; the technical problem that in the prior art, an air conditioner is difficult to obtain the optimal initial refrigeration temperature is solved. A traversal type processing mechanism is used, and a digital twinborn model designed by a customized structure is adopted to intelligently predict a plurality of consumed powers and a plurality of continuous durations when the refrigeration air conditioner reaches the same expected refrigeration temperature at different initial set temperatures, so that the technical problem is solved.

Description

Technical Field

[0001] The present invention relates to the field of air conditioning, and in particular to an air conditioning energy-saving control method and system based on a digital twin model. Background Art

[0002] As global warming continues, energy issues are becoming increasingly serious, and energy conservation and environmental protection have become an indispensable part of our daily lives. As one of the main devices for electricity consumption in fixed places such as homes and offices, how to save electricity and reduce energy consumption during use is an issue that people need to pay attention to. For example, when a refrigeration air conditioner is in operation, after reaching the preset refrigeration temperature, the outdoor unit of the air conditioner (i.e., compressor, fan) will instruct the outdoor unit to stop operating through the thermostat sensing signal obtained by the air conditioner computer motherboard. Of course, when the indoor temperature exceeds the preset temperature, the outdoor unit of the air conditioner will also instruct the outdoor compressor to operate through the thermostat sensing signal obtained by the air conditioner computer motherboard.

[0003] Therefore, when starting to turn on the refrigeration air conditioner, people habitually preset the temperature to the lowest, which actually prolongs the working time of the air conditioner compressor. The longer the air conditioner compressor works, the more electricity it consumes. Therefore, experienced regulators choose to preset a higher temperature when turning on the machine, such as 28°C. When the indoor temperature reaches 28°C and the air conditioner runs a working cycle, people slowly reduce the temperature according to their needs. It can be seen from this that even if the same desired refrigeration temperature needs to be reached, using the same refrigeration air conditioner in the same set closed space, different set temperatures are initially set, and the power consumed and duration of the same refrigeration air conditioner during the refrigeration process to reach the desired refrigeration temperature are different.

[0004] For example, the Chinese invention patent publication CN107990487A proposes an air conditioner and a prediction method and device for air conditioner power consumption, wherein the prediction method for air conditioner power consumption includes the following steps: using the least squares support vector machine LSSVM algorithm to establish a prediction model for air conditioner power consumption; obtaining the current outdoor dry-bulb and wet-bulb temperature, the current indoor dry-bulb and wet-bulb temperature, and the current indoor ambient temperature; predicting the power consumption of the air conditioner according to the prediction model for air conditioner power consumption, the current outdoor dry-bulb and wet-bulb temperature, the current indoor dry-bulb and wet-bulb temperature, and the current indoor ambient temperature to obtain the power consumption value of the air conditioner. The prediction method establishes a prediction model for air conditioner power consumption through the LSSVM algorithm, eliminating complex intermediate variables and without assumptions. The prediction model established is simple and has good applicability and extensibility.

[0005] For example, the Chinese invention patent publication CN109959122A proposes an air conditioning energy consumption prediction method based on a long short-term memory recurrent neural network, comprising the following steps: Step 1, collecting data, collecting water-cooled central air conditioning data and corresponding environmental data of the normal operation of the water-cooled central air conditioning project; Step 2, preprocessing the water-cooled central air conditioning data and corresponding environmental data obtained for normal operation; Step 3, training the data set to achieve air conditioning energy consumption prediction, using the LSTM-RNN long short-term memory recurrent neural network, using the preprocessed data set and the corresponding power consumption as the input of the LSTM-RNN long short-term memory recurrent neural network, and obtaining the final prediction model after network training; Step 4, inputting the test data into the prediction model to obtain the energy consumption value of the air conditioner under the current working condition. The present invention simplifies the model training process and improves the prediction accuracy.

[0006] However, the above-mentioned prior art is only limited to the prediction of power consumption in a single working scenario of the air-conditioning equipment, for example, the prediction of power consumption under a single set temperature, and it is impossible to exhaustively predict the power consumption of each working scenario corresponding to all set temperatures as much as possible, not to mention the automatic setting of the priority set temperature based on the prediction results. At the same time, the basic data used to execute the prediction mechanism based on the digital twin model is not comprehensive and sufficient, resulting in the predicted power consumption data being not stable and reliable enough. In addition, the above-mentioned prior art is only limited to the prediction of power consumption and cannot predict other functional parameters of the air-conditioning equipment. Therefore, there is a need for a prediction mechanism based on a digital twin model that traverses the multifunctional parameters of each working scenario corresponding to all set temperatures, so as to provide more effective basic data for the energy-saving control and functional control of the air-conditioning equipment. Summary of the invention

[0007] In order to solve the technical problems in the prior art, the present invention provides an air conditioning energy-saving control method and system based on a digital twin model. By using a traversal processing mechanism and a digital twin model with a customized structure design, based on a comprehensive and sufficient basic data screened by multiple targeted methods, the method and system intelligently predict the multiple powers consumed and the multiple durations of a refrigeration air conditioner to control the same set closed space to reach the same expected refrigeration temperature at different initial setting temperatures. Based on the intelligently predicted multiple power consumptions and multiple durations, the method and system automatically set the refrigeration air conditioner by selecting the optimal initial setting temperature, thereby taking into account the energy saving needs and refrigeration efficiency needs of the air conditioner, and improving the energy saving level and automation control level of the air conditioner.

[0008] According to a first aspect of the present invention, a digital twin model air conditioning energy-saving control method is provided, the method comprising:

[0009] Acquire various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0010] Acquire multiple internal environmental parameters of a set closed space and multiple external environmental parameters of the set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0011] Performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0012] Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0013] The reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions is sent as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

[0014] According to a second aspect of the present invention, there is provided an air conditioning energy-saving control system of a digital twin model, the system comprising a memory and one or more processors, the memory storing a computer program, the computer program being configured to be executed by the one or more processors to complete the following steps:

[0015] Acquire various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0016] Acquire multiple internal environmental parameters of a set closed space and multiple external environmental parameters of the set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0017] Performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0018] Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0019] The reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions is sent as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

[0020] According to a third aspect of the present invention, there is provided an air conditioning energy-saving control system based on a digital twin model, the system comprising:

[0021] The first acquisition device is used to obtain various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0022] The second acquisition device is used to obtain a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0023] A multi-layer construction device, used to perform a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and output it as a digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0024] A traversal analysis device is respectively connected to the first acquisition device, the second acquisition device and the multi-layer construction device, and is used to evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, and input each reference set temperature into the digital twin model to obtain a predicted power consumption and a predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0025] The energy-saving control device is respectively connected to the traversal analysis device and the main controller of the target refrigeration air conditioner, and is used to send the reference set temperature corresponding to the predicted power consumption with the minimum value among multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

[0026] Compared with the prior art, the present invention has at least the following five key inventive features:

[0027] Invention point A: For the set closed space refrigerated by the target refrigeration air conditioner, each set refrigeration temperature is traversed to respectively predict the predicted power consumption and predicted consumption time of the target refrigeration air conditioner when the target refrigeration air conditioner reaches the user's expected refrigeration temperature at each set refrigeration temperature, thereby providing a prediction data basis for automatically setting the screening of the set refrigeration temperature with the best energy-saving effect;

[0028] Invention point B: The set refrigeration temperature corresponding to the predicted power consumption with the minimum numerical value is sent as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner to automatically set the set refrigeration temperature with the best energy-saving effect. It is particularly critical that when there are two sets of equal minimum predicted power consumptions, the set refrigeration temperature with the shortest predicted consumption time corresponding to the two sets of equal minimum predicted power consumptions is used as the priority energy-saving set temperature, thereby taking into account both the energy saving of the air conditioner and the refrigeration efficiency of the air conditioner;

[0029] Invention point C: A digital twin model is introduced to simulate, analyze and optimize the operation process of the air-conditioning entity based on the collected data and environmental data of the air-conditioning entity, and to complete the intelligent prediction of the predicted power consumption and predicted consumption time of the target refrigeration air-conditioning when the target refrigeration air-conditioning reaches the user's expected refrigeration temperature at each set refrigeration temperature. Specifically, the digital twin model is a deep neural network that has been trained multiple times, and the number of training times is positively correlated with the compression ratio of the compressor of the target refrigeration air-conditioning, so as to design digital twin models with different structures for different refrigeration air-conditioning;

[0030] Invention point D: Using the working voltage and working frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environments of the set closed space as the basic information for the digital twin model to perform intelligent prediction, the comprehensive and targeted screening of the above basic information ensures the stability and effectiveness of the intelligent prediction results of the digital twin model. Specifically, the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioning number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner, the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space, and the set closed space is the space where the target refrigeration air conditioner performs temperature control;

[0031] Inventive point E: In each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as the two output contents of the deep neural network, and the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input content of the deep neural network item by item to complete this training, thereby ensuring the training effect of each training of the deep neural network, and further ensuring the stability and effectiveness of the intelligent prediction results of the digital twin model. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0033] Figure 1 It is a technical flow chart of the air conditioning energy-saving control method and system according to the digital twin model of the present invention.

[0034] Figure 2This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 1 of the present invention.

[0035] Figure 3 This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 2 of the present invention.

[0036] Figure 4 This is a step flow chart of an air conditioning energy-saving control method based on a digital twin model according to Example 3 of the present invention.

[0037] Figure 5 This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 4 of the present invention.

[0038] Figure 6 This is a structural schematic diagram of an air conditioning energy-saving control system based on a digital twin model according to Example 5 of the present invention.

[0039] Figure 7 This is a structural schematic diagram of an air-conditioning energy-saving control system based on a digital twin model according to Example 6 of the present invention. DETAILED DESCRIPTION

[0040] like Figure 1 As shown, a technical flow chart of the air conditioning energy-saving control method and system based on the digital twin model shown in the present invention is given.

[0041] like Figure 1 As shown, the specific technical process of the present invention is as follows:

[0042] Technical process 1: Design a customized digital twin model for the target refrigeration air conditioner to provide a solution mechanism for intelligent prediction of the power consumption and consumption time required by the target refrigeration air conditioner when each current set refrigeration temperature reaches the user's expected refrigeration temperature;

[0043] Specifically, the structural customization of the digital twin model lies in the following aspects:

[0044] In the first aspect, the digital twin model is a deep neural network after multiple trainings. For example, the deep neural network includes multiple hidden layers, a single input layer and a single output layer, and the multiple hidden layers are arranged between the single input layer and the single output layer.

[0045] Second aspect: the number of training times of the deep neural network is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, so as to design digital twin models with different structures for different refrigeration air conditioners;

[0046] Third aspect: In each training of the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as the two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input content of the deep neural network item by item to complete this training, thereby ensuring the training effect of each training of the deep neural network and ensuring the stability and effectiveness of the intelligent prediction results of the digital twin model;

[0047] Technical process 2: Targeted screening of multiple basic information for the digital twin model. The comprehensive and targeted screening of the multiple basic information further ensures the stability and effectiveness of the intelligent prediction results of the digital twin model.

[0048] Specifically, the above-mentioned multiple basic information includes the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environment parameters of the set closed space, and multiple external environments of the set closed space;

[0049] More specifically, the various equipment data of the target refrigeration and air conditioning are the energy efficiency ratio, applicable area, air conditioning number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration and air conditioning; the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space; the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space, wherein the set closed space is the space where the target refrigeration and air conditioning performs temperature control;

[0050] Technical process three: for the set closed space refrigerated by the target refrigeration air conditioner, a digital twin model with customized structure design is used to traverse each set refrigeration temperature based on multiple basic information selected in a targeted manner to predict the predicted power consumption and predicted consumption time of the target refrigeration air conditioner when the target refrigeration air conditioner reaches the user's expected refrigeration temperature at each set refrigeration temperature;

[0051] The predicted power consumption and predicted consumption duration corresponding to each set refrigeration temperature obtained by traversing each set refrigeration temperature respectively provide a prediction data basis for automatically setting the set refrigeration temperature with the best energy saving effect;

[0052] Technical process 4: obtaining the predicted power consumption and predicted consumption duration corresponding to each set refrigeration temperature outputted in technical process 3, and sending the set refrigeration temperature corresponding to the predicted power consumption with the smallest value as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner to automatically set the set refrigeration temperature with the best energy-saving effect;

[0053] Specifically, when two sets of minimum predicted power consumption with equal values ​​appear, the set refrigeration temperature with the shortest predicted consumption time corresponding to the two sets of set refrigeration temperatures with equal values ​​is used as the priority energy-saving set temperature, thereby taking into account both air conditioning energy saving and air conditioning refrigeration efficiency, and improving the intelligent level of refrigeration and air conditioning;

[0054] It is particularly critical that the temperature adjustment range of the target refrigeration air conditioner is evenly divided to obtain a plurality of reference set temperatures, and the number of the plurality of reference set temperatures exceeds a set number threshold. Through the above settings, each selectable set temperature value of the temperature adjustment range of the target refrigeration air conditioner can be exhausted as much as possible and traversed intelligent prediction can be performed, thereby providing data guarantee for the accuracy of the priority energy-saving set temperature selection;

[0055] It can be seen that through the design of the above-mentioned technical processes, it is possible to intelligently predict the multiple powers consumed and the multiple durations of time for the same refrigeration air conditioner to control the same set closed space to achieve the same expected refrigeration temperature at different initial setting temperatures, and select the optimal initial setting temperature based on the intelligently predicted multiple power consumption and multiple durations to automatically set the refrigeration air conditioner, thereby taking into account the air conditioning energy saving needs and refrigeration efficiency needs, and improving the energy saving level and automation control level of the air conditioner.

[0056] The key point of the present invention is: Figure 1 As shown, the digital twin model is introduced to simulate and analyze the operation process of the air-conditioning entity based on the collected data and environmental data of the air-conditioning entity, provide a data basis for subsequent optimization, complete the intelligent prediction of the predicted power consumption and predicted consumption time of the target refrigeration air-conditioning when the target refrigeration air-conditioning reaches the user's expected refrigeration temperature at each set refrigeration temperature, and automatically select and set the optimal set refrigeration temperature that takes into account both power consumption and consumption time.

[0057] Below, the air conditioning energy-saving control method and system of the digital twin model of the present invention will be specifically described in the form of an embodiment.

[0058] Example 1

[0059] Figure 2 This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 1 of the present invention.

[0060] like Figure 2 As shown, the air conditioning energy-saving control method of the digital twin model includes the following specific steps:

[0061] Step S11: acquiring various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner include energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0062] For example, obtaining various equipment data of a target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner, including: a plurality of different Internet of Things acquisition components can be used to respectively collect the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner;

[0063] Step S12: acquiring a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0064] Specifically, obtaining a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space, including: storing the plurality of internal environmental parameters of the set closed space and the plurality of external environmental parameters of the set closed space in different physical storage spaces respectively;

[0065] Step S13: performing a set number of trainings on the deep neural network multiple times to obtain a deep neural network after performing the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0066] For example, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: a numerical mapping function can be used to represent the numerical mapping relationship of the positive correlation between the value of the set number and the compression ratio of the compressor of the target refrigeration air conditioner;

[0067] Specifically, in the numerical mapping function, the compression ratio of the compressor of the target refrigeration air conditioner is the input data of the numerical mapping function, and the value of the set quantity is the output data of the numerical mapping function;

[0068] And by way of example, a set number of multiple trainings are performed on the deep neural network to obtain a deep neural network after multiple trainings and output it as the digital twin model of the target refrigeration air conditioner, and the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, and further includes: when the compression ratio of the compressor of the target refrigeration air conditioner is 3, the value of the set number is 100, when the compression ratio of the compressor of the target refrigeration air conditioner is 5, the value of the set number is 150, when the compression ratio of the compressor of the target refrigeration air conditioner is 7, the value of the set number is 180, and when the compression ratio of the compressor of the target refrigeration air conditioner is 9, the value of the set number is 200, and so on;

[0069] Step S14: Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain a predicted power consumption and a predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0070] Specifically, the MATLAB toolbox may be selected to implement the simulation and testing of the numerical processing process of inputting each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature;

[0071] Step S15: sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner;

[0072] For example, sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner includes: the main controller of the target refrigeration air conditioner is an MCU controller, such as a 51 single-chip microcomputer series or an ARM series;

[0073] The step of sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner comprises: after receiving the priority energy-saving set temperature, the main controller of the target refrigeration air conditioner automatically sets the priority energy-saving set temperature as the refrigeration set temperature of the target refrigeration air conditioner;

[0074] Among them, sending the reference set temperature corresponding to the predicted power consumption with the smallest value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner also includes: when two predicted power consumptions with the smallest values ​​being equal in the multiple predicted power consumptions, using the two reference set temperatures corresponding to the two predicted power consumptions with the smallest values ​​being equal in value as the first energy-saving set temperature and the second energy-saving set temperature, using the predicted consumption time corresponding to the first energy-saving set temperature as the first predicted consumption time, using the predicted consumption time corresponding to the second energy-saving set temperature as the second predicted consumption time, and using the energy-saving set temperature corresponding to the predicted consumption time with the smallest value between the first predicted consumption time and the second predicted consumption time as the priority energy-saving set temperature;

[0075] Among them, each reference set temperature is input into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature, and obtaining multiple sets of predicted power consumption and multiple sets of predicted consumption time corresponding to multiple reference set temperatures respectively includes: using the digital twin model to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the use time of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature;

[0076] wherein, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: in each training performed on the deep neural network, using the predicted power consumption and predicted consumption time corresponding to a known past set temperature as two output contents of the deep neural network, and using the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature as input contents of the deep neural network item by item to complete this training;

[0077] Specifically, in each training of the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the working voltage and working frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item. The training includes: FPGA chips can be selected to realize the simulation and test of each training of the deep neural network;

[0078] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item, and completing this training includes: the predicted power consumption and predicted consumption time corresponding to the past set temperature are the actual power consumption and actual usage time of the target cooling air conditioner from the time the target cooling air conditioner is set to the past set temperature to the time the set enclosed space reaches the user's expected cooling temperature;

[0079] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input content of the deep neural network item by item, and completing this training also includes: the multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple internal environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature, and the multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple external environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature.

[0080] Example 2

[0081] Figure 3 This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 2 of the present invention.

[0082] like Figure 3 As shown, Figure 2 Different from the embodiments in, in the air conditioning energy-saving control method of the digital twin model, after performing a set number of multiple trainings on the deep neural network to obtain the deep neural network after the multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, that is, after step S13, the method further includes:

[0083] Step S16: using a cloud computing storage node to store various model parameters of the digital twin model to complete the model storage of the digital twin model;

[0084] By way of example, using a cloud computing storage node to store various model parameters of the digital twin model to complete the model storage of the digital twin model includes: the cloud computing storage node includes various cloud computing storage network elements, which are used to respectively store various model parameters of the digital twin model.

[0085] Example 3

[0086] Figure 4 This is a step flow chart of an air conditioning energy-saving control method based on a digital twin model according to Example 3 of the present invention.

[0087] like Figure 4 As shown, Figure 2 Different from the embodiments in the embodiment, in the air conditioning energy-saving control method of the digital twin model, after sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner, that is, after step S15, the method further includes:

[0088] Step S17: using an LED display array or a liquid crystal display screen to receive and display the priority energy-saving set temperature;

[0089] Alternatively, an LCD display array may be selected to replace the LED display array or the liquid crystal display screen to receive and display the priority energy-saving set temperature.

[0090] Example 4

[0091] Figure 5 This is a flowchart of the steps of the air conditioning energy-saving control method of the digital twin model according to Example 4 of the present invention.

[0092] like Figure 5 As shown, Figure 2 Different from the embodiments in the embodiment, in the air conditioning energy-saving control method of the digital twin model, after sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner, that is, after step S15, the method further includes:

[0093] Step S18: wirelessly sending the received priority energy-saving setting temperature to a remote energy-saving management server using a frequency division duplex communication link or a time division duplex communication link;

[0094] For example, a frequency division duplex communication link or a time division duplex communication link is used to wirelessly send the received priority energy-saving set temperature to a remote energy-saving management server, including: the remote energy-saving management server is a big data service network element or a blockchain service network element, which is used to perform energy-saving management of air-conditioning equipment at various locations.

[0095] Next, various method embodiments of the present invention are described in detail.

[0096] In the air conditioning energy-saving control method of the digital twin model according to each method embodiment of the present invention:

[0097] Acquire various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner, including: when the frequency conversion mark of the target refrigeration air conditioner is 0B01, it indicates that the target refrigeration air conditioner is a variable frequency air conditioner, and when the frequency conversion mark of the target refrigeration air conditioner is 0B00, it indicates that the target refrigeration air conditioner is a fixed frequency air conditioner;

[0098] Specifically, various equipment data of the target refrigeration air conditioner are obtained, and the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioning horsepower, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner, and also include: multiple different local storage devices can be used to respectively store the energy efficiency ratio, applicable area, air conditioning horsepower, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner.

[0099] And in the air conditioning energy-saving control method of the digital twin model according to each method embodiment of the present invention:

[0100] The digital twin model is used to intelligently predict the power consumed by the target refrigeration air conditioner when the set enclosed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space, and multiple external environmental parameters of the set enclosed space as the predicted power consumption corresponding to the reference set temperature, and the use time of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature, including: the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space, and multiple external environmental parameters of the set enclosed space are input into the digital twin model in parallel;

[0101] Among them, the digital twin model is used to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the duration of use of the target refrigeration air conditioner as the predicted consumption duration corresponding to the reference set temperature. It also includes: running the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature output by the digital twin model.

[0102] Example 5

[0103] Figure 6 This is a structural schematic diagram of an air conditioning energy-saving control system based on a digital twin model according to Example 5 of the present invention.

[0104] like Figure 6 As shown, the air conditioning energy-saving control system of the digital twin model includes a memory and one or more processors, the memory stores a computer program, and the computer program is configured to be executed by the one or more processors to complete the following steps:

[0105] Step S11: acquiring various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner include energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0106] For example, obtaining various equipment data of a target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner, including: a plurality of different Internet of Things acquisition components can be used to respectively collect the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner;

[0107] Step S12: acquiring a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0108] Specifically, obtaining a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space, including: storing the plurality of internal environmental parameters of the set closed space and the plurality of external environmental parameters of the set closed space in different physical storage spaces respectively;

[0109] Step S13: performing a set number of trainings on the deep neural network multiple times to obtain a deep neural network after performing the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0110] For example, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: a numerical mapping function can be used to represent the numerical mapping relationship of the positive correlation between the value of the set number and the compression ratio of the compressor of the target refrigeration air conditioner;

[0111] Specifically, in the numerical mapping function, the compression ratio of the compressor of the target refrigeration air conditioner is the input data of the numerical mapping function, and the value of the set quantity is the output data of the numerical mapping function;

[0112] And by way of example, a set number of multiple trainings are performed on the deep neural network to obtain a deep neural network after multiple trainings and output it as the digital twin model of the target refrigeration air conditioner, and the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, and further includes: when the compression ratio of the compressor of the target refrigeration air conditioner is 3, the value of the set number is 100, when the compression ratio of the compressor of the target refrigeration air conditioner is 5, the value of the set number is 150, when the compression ratio of the compressor of the target refrigeration air conditioner is 7, the value of the set number is 180, and when the compression ratio of the compressor of the target refrigeration air conditioner is 9, the value of the set number is 200, and so on;

[0113] Step S14: Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain a predicted power consumption and a predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0114] Specifically, the MATLAB toolbox may be selected to implement the simulation and testing of the numerical processing process of inputting each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature;

[0115] Step S15: sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner;

[0116] For example, sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner includes: the main controller of the target refrigeration air conditioner is an MCU controller, such as a 51 single-chip microcomputer series or an ARM series;

[0117] The step of sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner comprises: after receiving the priority energy-saving set temperature, the main controller of the target refrigeration air conditioner automatically sets the priority energy-saving set temperature as the refrigeration set temperature of the target refrigeration air conditioner;

[0118] Among them, sending the reference set temperature corresponding to the predicted power consumption with the smallest value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner also includes: when two predicted power consumptions with the smallest values ​​being equal in the multiple predicted power consumptions, using the two reference set temperatures corresponding to the two predicted power consumptions with the smallest values ​​being equal in value as the first energy-saving set temperature and the second energy-saving set temperature, using the predicted consumption time corresponding to the first energy-saving set temperature as the first predicted consumption time, using the predicted consumption time corresponding to the second energy-saving set temperature as the second predicted consumption time, and using the energy-saving set temperature corresponding to the predicted consumption time with the smallest value between the first predicted consumption time and the second predicted consumption time as the priority energy-saving set temperature;

[0119] Among them, each reference set temperature is input into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature, and obtaining multiple sets of predicted power consumption and multiple sets of predicted consumption time corresponding to multiple reference set temperatures respectively includes: using the digital twin model to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the use time of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature;

[0120] wherein, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: in each training performed on the deep neural network, using the predicted power consumption and predicted consumption time corresponding to a known past set temperature as two output contents of the deep neural network, and using the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature as input contents of the deep neural network item by item to complete this training;

[0121] Specifically, in each training of the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the working voltage and working frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item. The training includes: FPGA chips can be selected to realize the simulation and test of each training of the deep neural network;

[0122] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item, and completing this training includes: the predicted power consumption and predicted consumption time corresponding to the past set temperature are the actual power consumption and actual usage time of the target cooling air conditioner from the time the target cooling air conditioner is set to the past set temperature to the time the set enclosed space reaches the user's expected cooling temperature;

[0123] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item, and completing this training also includes: the multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple internal environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature, and the multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple external environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature;

[0124] like Figure 6 As shown, illustratively, P processors are provided, where P is a natural number greater than or equal to 1.

[0125] Example 6

[0126] Figure 7 This is a structural schematic diagram of an air-conditioning energy-saving control system based on a digital twin model according to Example 6 of the present invention.

[0127] like Figure 7 As shown, the air conditioning energy-saving control system of the digital twin model includes the following components:

[0128] The first acquisition device is used to obtain various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner;

[0129] For example, obtaining various equipment data of a target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner, including: a plurality of different Internet of Things acquisition components can be used to respectively collect the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume, and frequency conversion mark of the target refrigeration air conditioner;

[0130] The second acquisition device is used to obtain a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space;

[0131] Specifically, obtaining a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space, including: storing the plurality of internal environmental parameters of the set closed space and the plurality of external environmental parameters of the set closed space in different physical storage spaces respectively;

[0132] A multi-layer construction device, used to perform a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and output it as a digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner;

[0133] For example, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: a numerical mapping function can be used to represent the numerical mapping relationship of the positive correlation between the value of the set number and the compression ratio of the compressor of the target refrigeration air conditioner;

[0134] Specifically, in the numerical mapping function, the compression ratio of the compressor of the target refrigeration air conditioner is the input data of the numerical mapping function, and the value of the set quantity is the output data of the numerical mapping function;

[0135] And by way of example, a set number of multiple trainings are performed on the deep neural network to obtain a deep neural network after multiple trainings and output it as the digital twin model of the target refrigeration air conditioner, and the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, and further includes: when the compression ratio of the compressor of the target refrigeration air conditioner is 3, the value of the set number is 100, when the compression ratio of the compressor of the target refrigeration air conditioner is 5, the value of the set number is 150, when the compression ratio of the compressor of the target refrigeration air conditioner is 7, the value of the set number is 180, and when the compression ratio of the compressor of the target refrigeration air conditioner is 9, the value of the set number is 200, and so on;

[0136] A traversal analysis device is used to evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain a predicted power consumption and a predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumptions and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold;

[0137] Specifically, the MATLAB toolbox may be selected to implement the simulation and testing of the numerical processing process of inputting each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature;

[0138] An energy-saving control device, used for sending a reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as a priority energy-saving set temperature to a main controller of the target refrigeration air conditioner;

[0139] For example, sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner includes: the main controller of the target refrigeration air conditioner is an MCU controller, such as a 51 single-chip microcomputer series or an ARM series;

[0140] The step of sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner comprises: after receiving the priority energy-saving set temperature, the main controller of the target refrigeration air conditioner automatically sets the priority energy-saving set temperature as the refrigeration set temperature of the target refrigeration air conditioner;

[0141] Among them, sending the reference set temperature corresponding to the predicted power consumption with the smallest value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner also includes: when two predicted power consumptions with the smallest values ​​being equal in the multiple predicted power consumptions, using the two reference set temperatures corresponding to the two predicted power consumptions with the smallest values ​​being equal in value as the first energy-saving set temperature and the second energy-saving set temperature, using the predicted consumption time corresponding to the first energy-saving set temperature as the first predicted consumption time, using the predicted consumption time corresponding to the second energy-saving set temperature as the second predicted consumption time, and using the energy-saving set temperature corresponding to the predicted consumption time with the smallest value between the first predicted consumption time and the second predicted consumption time as the priority energy-saving set temperature;

[0142] Among them, each reference set temperature is input into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature, and obtaining multiple sets of predicted power consumption and multiple sets of predicted consumption time corresponding to multiple reference set temperatures respectively includes: using the digital twin model to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the duration of use of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature;

[0143] wherein, performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: in each training performed on the deep neural network, using the predicted power consumption and predicted consumption time corresponding to a known past set temperature as two output contents of the deep neural network, and using the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature as input contents of the deep neural network item by item to complete this training;

[0144] Specifically, in each training of the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the working voltage and working frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item. The training includes: FPGA chips can be selected to realize the simulation and test of each training of the deep neural network;

[0145] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input contents of the deep neural network item by item, and completing this training includes: the predicted power consumption and predicted consumption time corresponding to the past set temperature are the actual power consumption and actual usage time of the target cooling air conditioner from the time the target cooling air conditioner is set to the past set temperature to the time the set enclosed space reaches the user's expected cooling temperature;

[0146] And wherein, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input content of the deep neural network item by item, and completing this training also includes: the multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple internal environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature, and the multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple external environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature.

[0147] In addition, the present invention may also cite the following technical contents to highlight the significant technical progress of the present invention:

[0148] Inputting the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space in parallel into the digital twin model includes: before inputting the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space in parallel into the digital twin model, respectively performing numerical normalization processing on the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space;

[0149] For example, before the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space are input into the digital twin model in parallel, the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space are respectively subjected to numerical normalization processing, including: before the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space are input into the digital twin model in parallel, the user's expected cooling temperature, the reference set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space are respectively subjected to octal numerical conversion processing;

[0150] And wherein, running the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature output by the digital twin model includes: the predicted power consumption and predicted consumption time corresponding to the reference set temperature output by the digital twin model are both in the form of numerical representation after numerical normalization processing;

[0151] For example, the predicted power consumption and predicted consumption time corresponding to the reference set temperature output by the digital twin model are both in the form of numerical representation after numerical normalization processing, including: the predicted power consumption and predicted consumption time corresponding to the reference set temperature output by the digital twin model are both in the form of numerical representation after octal value conversion processing.

[0152] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A digital twin model air conditioning energy-saving control method, characterized in that: The method comprises: Acquire various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner; Acquire multiple internal environmental parameters of a set closed space and multiple external environmental parameters of the set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space; Performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner; Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold; The reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions is sent as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

2. The air conditioning energy-saving control method of the digital twin model according to claim 1, characterized in that: The method includes sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner, wherein the main controller of the target refrigeration air conditioner automatically sets the priority energy-saving set temperature as the refrigeration set temperature of the target refrigeration air conditioner after receiving the priority energy-saving set temperature; Among them, sending the reference set temperature corresponding to the predicted power consumption with the smallest value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner also includes: when two predicted power consumptions with the smallest values ​​being equal in the multiple predicted power consumptions, using the two reference set temperatures corresponding to the two predicted power consumptions with the smallest values ​​being equal in value as the first energy-saving set temperature and the second energy-saving set temperature, using the predicted consumption time corresponding to the first energy-saving set temperature as the first predicted consumption time, using the predicted consumption time corresponding to the second energy-saving set temperature as the second predicted consumption time, and using the energy-saving set temperature corresponding to the predicted consumption time with the smallest value between the first predicted consumption time and the second predicted consumption time as the priority energy-saving set temperature; Among them, each reference set temperature is input into the digital twin model to obtain the predicted power consumption and predicted consumption time corresponding to the reference set temperature, and obtaining multiple predicted power consumption and multiple predicted consumption time corresponding to multiple reference set temperatures respectively includes: using the digital twin model to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the usage time of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature.

3. The air conditioning energy-saving control method of the digital twin model according to claim 2, characterized in that: Performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, including: in each training performed on the deep neural network, using the predicted power consumption and the predicted consumption time corresponding to a known past set temperature as two output contents of the deep neural network, and using the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature as input contents of the deep neural network item by item to complete this training; Among them, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected cooling temperature, the past set temperature, the operating voltage and operating frequency of the target cooling air conditioner, various equipment data of the target cooling air conditioner, multiple internal environmental parameters of the set closed space corresponding to the past set temperature, and multiple external environmental parameters of the set closed space corresponding to the past set temperature are used as the input content of the deep neural network item by item, and completing this training includes: the predicted power consumption and predicted consumption time corresponding to the past set temperature are the actual power consumption and actual usage time of the target cooling air conditioner from the time when the target cooling air conditioner is set to the past set temperature to the time when the set closed space reaches the user's expected cooling temperature; Among them, in each training performed on the deep neural network, the predicted power consumption and predicted consumption time corresponding to a known past set temperature are used as two output contents of the deep neural network, and the user's expected refrigeration temperature, the past set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature, and multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are used as the input content of the deep neural network item by item. Completing this training also includes: the multiple internal environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple internal environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature, and the multiple external environmental parameters of the set enclosed space corresponding to the past set temperature are the multiple external environmental parameters of the set enclosed space when the target refrigeration air conditioner is set to the past set temperature.

4. The air conditioning energy-saving control method of the digital twin model according to claim 3, characterized in that: After performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting it as the digital twin model of the target refrigeration air conditioner, and the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner, the method further includes: A cloud computing storage node is used to store various model parameters of the digital twin model to complete the model storage of the digital twin model.

5. The air conditioning energy-saving control method of the digital twin model according to claim 3, characterized in that: After sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner, the method further includes: An LED display array or a liquid crystal display screen is used to receive and display the priority energy-saving set temperature.

6. The air conditioning energy-saving control method of the digital twin model according to claim 3, characterized in that: After sending the reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner, the method further includes: The received priority energy-saving set temperature is wirelessly sent to a remote energy-saving management server using a frequency division duplex communication link or a time division duplex communication link.

7. The air conditioning energy-saving control method of the digital twin model according to claim 6, characterized in that: Obtain various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioning number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner, including: when the frequency conversion mark of the target refrigeration air conditioner is 0B01, it indicates that the target refrigeration air conditioner is a variable frequency air conditioner; when the frequency conversion mark of the target refrigeration air conditioner is 0B00, it indicates that the target refrigeration air conditioner is a fixed frequency air conditioner.

8. The air conditioning energy-saving control method based on the digital twin model according to claim 6, characterized in that: The digital twin model is used to intelligently predict the power consumed by the target refrigeration air conditioner when the set enclosed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space, and multiple external environmental parameters of the set enclosed space as the predicted power consumption corresponding to the reference set temperature, and the use time of the target refrigeration air conditioner as the predicted consumption time corresponding to the reference set temperature, including: the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set enclosed space, and multiple external environmental parameters of the set enclosed space are input into the digital twin model in parallel; Among them, the digital twin model is used to intelligently predict the power consumed by the target refrigeration air conditioner when the set closed space reaches the user's expected refrigeration temperature after the reference set temperature is the refrigeration set temperature of the target refrigeration air conditioner based on the user's expected refrigeration temperature, the reference set temperature, the operating voltage and operating frequency of the target refrigeration air conditioner, various equipment data of the target refrigeration air conditioner, multiple internal environmental parameters of the set closed space, and multiple external environmental parameters of the set closed space as the predicted power consumption corresponding to the reference set temperature, and the duration of use of the target refrigeration air conditioner as the predicted consumption duration corresponding to the reference set temperature. It also includes: running the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature output by the digital twin model.

9. An air conditioning energy-saving control system based on a digital twin model, characterized in that: The system comprises a memory and one or more processors, the memory storing a computer program, the computer program being configured to be executed by the one or more processors to perform the following steps: Acquire various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner; Acquire multiple internal environmental parameters of a set closed space and multiple external environmental parameters of the set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the multiple internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the multiple external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space; Performing a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and outputting the deep neural network as the digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner; Evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, input each reference set temperature into the digital twin model to obtain the predicted power consumption and predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold; The reference set temperature corresponding to the predicted power consumption with the minimum value among the multiple predicted power consumptions is sent as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

10. An air conditioning energy-saving control system based on a digital twin model, characterized in that: The system comprises: The first acquisition device is used to obtain various equipment data of the target refrigeration air conditioner, wherein the various equipment data of the target refrigeration air conditioner are the energy efficiency ratio, applicable area, air conditioner number, cooling power, heating power, circulating air volume and frequency conversion mark of the target refrigeration air conditioner; The second acquisition device is used to obtain a plurality of internal environmental parameters of a set closed space and a plurality of external environmental parameters of a set closed space, wherein the set closed space is a space in which the target refrigeration air conditioner performs temperature control, the plurality of internal environmental parameters of the set closed space are the temperature, humidity, air pressure and space volume inside the set closed space, and the plurality of external environmental parameters of the set closed space are the temperature, humidity, wind direction, wind speed and sunlight angle outside the set closed space; A multi-layer construction device, used to perform a set number of multiple trainings on the deep neural network to obtain a deep neural network after the multiple trainings and output it as a digital twin model of the target refrigeration air conditioner, wherein the value of the set number is positively correlated with the compression ratio of the compressor of the target refrigeration air conditioner; A traversal analysis device is respectively connected to the first acquisition device, the second acquisition device and the multi-layer construction device, and is used to evenly divide the temperature adjustment range of the target refrigeration air conditioner to obtain a plurality of reference set temperatures, and input each reference set temperature into the digital twin model to obtain a predicted power consumption and a predicted consumption duration corresponding to the reference set temperature, and obtain a plurality of predicted power consumption and a plurality of predicted consumption durations corresponding to the plurality of reference set temperatures, wherein the number of the plurality of reference set temperatures exceeds a set number threshold; The energy-saving control device is respectively connected to the traversal analysis device and the main controller of the target refrigeration air conditioner, and is used to send the reference set temperature corresponding to the predicted power consumption with the minimum value among multiple predicted power consumptions as the priority energy-saving set temperature to the main controller of the target refrigeration air conditioner.

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