Air conditioner, server and energy-saving optimization method of air conditioner

Abstract

The invention relates to an air conditioner, a server and an energy-saving optimization method of the air conditioner. The air conditioner comprises a target sensor which is configured to collect operation related parameters of the air conditioner; the controller is connected with the target sensor, and is configured to respond to energy-saving control operation for the air conditioner, and obtain operation related parameters and historical load data of the air conditioner in at least one target time period; according to the historical load data of the at least one target time period, load orientation parameters of the target environment where the air conditioner is located are determined; inputting the associated data into the optimization control model to obtain optimization control parameters; the associated data comprises operation associated parameters and load azimuth parameters; and the air conditioner is controlled to operate according to the optimized control parameters. By adopting the air conditioner, the energy-saving effect of the air conditioner can be improved, the energy consumption of the air conditioner is reduced, and the air conditioner can adapt to different application scenes.

Description

Technical Field

[0001] This application relates to the field of air conditioner control technology, and in particular to an air conditioner, a server, and an energy-saving optimization method for air conditioners. Background Technology

[0002] With the development of air conditioner control technology, energy-saving function has become one of the core indicators of air conditioners. Energy-saving control can reduce the energy consumption of air conditioners and avoid energy waste.

[0003] However, traditional energy-saving methods typically control energy consumption based on set temperature and indoor temperature. The energy-saving strategy is relatively simple, making it difficult to adapt to different application environments and resulting in unsatisfactory energy-saving effects. Summary of the Invention

[0004] Therefore, it is necessary to provide an energy-saving optimization method for air conditioners, servers, and air conditioners to address the aforementioned technical problems and improve the energy-saving effect of air conditioners.

[0005] In a first aspect, some embodiments provide an air conditioner, including:

[0006] The target sensor is configured to collect operating parameters related to the air conditioner;

[0007] The controller, connected to the target sensor, is configured as follows:

[0008] In response to energy-saving control operations for the air conditioner, acquire operational parameters and historical load data of the air conditioner for at least one target time period;

[0009] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0010] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0011] Control the air conditioner to operate according to optimized control parameters.

[0012] In the above embodiments, by responding to energy-saving control operations on the air conditioner, operational correlation parameters and historical load data of the air conditioner during at least one target time period are acquired. This provides a data foundation for dynamic adjustment of the air conditioner's energy-saving control based on both operational correlation and historical load dimensions. By determining the load orientation parameters of the target environment in which the air conditioner is located based on the historical load data of at least one target time period, the impact of the target environment on the load can be characterized, enabling the subsequent dynamic adjustment process to adapt specifically to environmental characteristics. By using the correlation data, including operational correlation parameters and load orientation parameters, as input to the optimized control model, optimized control parameters that fit the current target environment and the current operational correlation scenario can be obtained. By controlling the air conditioner to operate according to the optimized control parameters, the energy-saving effect of the air conditioner is improved, its energy consumption is reduced, and it can be adapted to different application scenarios.

[0013] Secondly, some embodiments provide a server, including:

[0014] A communication device is configured to communicate with an air conditioner;

[0015] and at least one processor, connected to the communication device, and configured to:

[0016] Obtain the energy-saving control request sent by the air conditioner. The energy-saving control request includes operating parameters.

[0017] In response to an energy-saving control request, acquire historical load data of the air conditioner for at least one target time period;

[0018] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0019] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0020] Send optimized control parameters to the air conditioner so that it operates according to the optimized control parameters.

[0021] In the above embodiments, by acquiring the energy-saving control request sent by the air conditioner, which includes operational correlation parameters, and in response to the energy-saving control request, historical load data of the air conditioner during at least one target time period is acquired. This provides a data foundation for dynamic adjustment of the air conditioner's energy-saving control based on both operational correlation and historical load dimensions. By determining the load orientation parameters of the target environment where the air conditioner is located based on the historical load data of at least one target time period, the impact of the target environment on the load can be characterized, enabling the subsequent dynamic adjustment process to adapt specifically to environmental characteristics. By using the correlation data, including operational correlation parameters and load orientation parameters, as input to the optimized control model, optimized control parameters that fit the current target environment and relevant operating scenarios can be obtained. By sending optimized control parameters to the air conditioner, the air conditioner operates according to the optimized control parameters, thereby improving the energy-saving effect of the air conditioner, reducing its energy consumption, and adapting to different application scenarios.

[0022] Thirdly, some embodiments provide an energy-saving optimization method for an air conditioner, including:

[0023] In response to energy-saving control operations on the air conditioner, acquire the air conditioner's operating parameters and historical load data for at least one target time period;

[0024] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0025] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0026] Control the air conditioner to operate according to optimized control parameters.

[0027] In the above embodiments, by responding to energy-saving control operations on the air conditioner, operational correlation parameters and historical load data of the air conditioner during at least one target time period are acquired. This provides a data foundation for dynamic adjustment of the air conditioner's energy-saving control based on both operational correlation and historical load dimensions. By determining the load orientation parameters of the target environment in which the air conditioner is located based on the historical load data of at least one target time period, the impact of the target environment on the load can be characterized, enabling the subsequent dynamic adjustment process to adapt specifically to environmental characteristics. By using the correlation data, including operational correlation parameters and load orientation parameters, as input to the optimized control model, optimized control parameters that fit the current target environment and the current operational correlation scenario can be obtained. By controlling the air conditioner to operate according to the optimized control parameters, the energy-saving effect of the air conditioner is improved, its energy consumption is reduced, and it can be adapted to different application scenarios.

[0028] Fourthly, this application also provides an energy-saving optimization device for an air conditioner, comprising:

[0029] The first acquisition module is used to acquire the operating parameters of the air conditioner and the historical load data of the air conditioner during at least one target time period in response to the energy-saving control operation of the air conditioner.

[0030] The first determining module is used to determine the load orientation parameters of the target environment where the air conditioner is located based on historical load data for at least one target time period.

[0031] The first input module is used to input the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0032] The first control module is used to control the air conditioner to operate according to optimized control parameters.

[0033] In the above embodiments, by responding to energy-saving control operations on the air conditioner, operational correlation parameters and historical load data of the air conditioner during at least one target time period are acquired. This provides a data foundation for dynamic adjustment of the air conditioner's energy-saving control based on both operational correlation and historical load dimensions. By determining the load orientation parameters of the target environment in which the air conditioner is located based on the historical load data of at least one target time period, the impact of the target environment on the load can be characterized, enabling the subsequent dynamic adjustment process to adapt specifically to environmental characteristics. By using the correlation data, including operational correlation parameters and load orientation parameters, as input to the optimized control model, optimized control parameters that fit the current target environment and the current operational correlation scenario can be obtained. By controlling the air conditioner to operate according to the optimized control parameters, the energy-saving effect of the air conditioner is improved, its energy consumption is reduced, and it can be adapted to different application scenarios.

[0034] Fifthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] In response to energy-saving control operations on the air conditioner, acquire the air conditioner's operating parameters and historical load data for at least one target time period;

[0036] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0037] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0038] Control the air conditioner to operate according to optimized control parameters.

[0039] Sixthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0040] In response to energy-saving control operations on the air conditioner, acquire the air conditioner's operating parameters and historical load data for at least one target time period;

[0041] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0042] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0043] Control the air conditioner to operate according to optimized control parameters.

[0044] In a seventh aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0045] In response to energy-saving control operations on the air conditioner, acquire the air conditioner's operating parameters and historical load data for at least one target time period;

[0046] Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located;

[0047] The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0048] Control the air conditioner to operate according to optimized control parameters. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 Perspective views of the air conditioner provided for some embodiments;

[0051] Figure 2 Structural diagrams of an air conditioner provided for some embodiments;

[0052] Figure 3A schematic diagram of the refrigerant circulation loop of an air conditioner provided for some embodiments;

[0053] Figure 4 A flowchart illustrating a control method for an air conditioner provided in some embodiments;

[0054] Figure 5 A flowchart illustrating the steps for determining meteorological impact parameters provided in some embodiments;

[0055] Figure 6 A flowchart illustrating the steps for determining load association parameters provided in some embodiments;

[0056] Figure 7 A flowchart illustrating the steps for determining dynamic average load parameters provided in some embodiments;

[0057] Figure 8 A flowchart illustrating a control method for an air conditioner provided for other embodiments;

[0058] Figure 9 Timing diagrams of air conditioner control methods provided for some embodiments;

[0059] Figure 10 Structural block diagram of the control device for an air conditioner provided in some embodiments;

[0060] Figure 11 Structural block diagram of the control device for an air conditioner provided in other embodiments;

[0061] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0062] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0063] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0064] The terms "first," "second," "third," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar or related objects or entities, and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms are interchangeable where appropriate.

[0065] The terms “include” and “have”, and any variations thereof, are intended to cover but not exclusively include, for example, a product or device that includes a range of components is not necessarily limited to all of the components that are clearly listed, but may include other components that are not clearly listed or that are inherent to such product or device.

[0066] The term "module" refers to any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code that is capable of performing the functions associated with that element.

[0067] Please see Figures 1 to 2 , Figure 1 This is a perspective view of an air conditioner according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an air conditioner according to an embodiment of the present invention. The air conditioner 1 provided in this embodiment includes:

[0068] Indoor unit 2, which contains indoor heat exchanger 21 and indoor fan 22;

[0069] The outdoor unit 3 is equipped with an outdoor heat exchanger 31, an outdoor fan 32, a compressor 33, a throttling component 34, and a four-way valve 35. The compressor 33, the throttling component 34, the four-way valve 35, the outdoor heat exchanger 31, and the indoor heat exchanger 21 are connected by pipelines to form a refrigerant circulation loop.

[0070] The indoor heat exchanger 21 is configured to act as an evaporator or condenser depending on the operating state of the indoor unit, so that the refrigerant flowing in the heat transfer tube exchanges heat with the air passing through the indoor heat exchanger.

[0071] The outdoor heat exchanger 31 is configured to act as a condenser or evaporator depending on the operating state of the outdoor unit, so that the refrigerant flowing in the heat transfer tube exchanges heat with the air passing through the outdoor heat exchanger.

[0072] The compressor 33 is configured to compress the refrigerant into a high-temperature, high-pressure gas;

[0073] The throttling component 34 is configured to convert the medium-temperature, high-pressure liquid after the outdoor heat exchanger absorbs and releases heat into a low-temperature, low-pressure liquid.

[0074] The four-way valve 35 is configured to switch between cooling and heating by changing the direction of refrigerant flow in the circulation loop.

[0075] Specifically, in some embodiments, the air conditioner 1 includes an indoor unit 2. Taking a wall-mounted indoor unit (shown in the figure) as an example, the indoor unit is usually installed on an indoor wall. Another example is a floor-standing indoor unit (not shown in the figure), which is also a type of indoor unit. The outdoor unit 3 is usually located outdoors and is used for heat exchange in the indoor environment. Additionally, in... Figure 1 In the diagram, outdoor unit 3, located on the opposite side of indoor unit 2, is represented by a dashed line. Indoor unit 2 and outdoor unit 3 are connected by connecting pipes 4. Indoor unit 2 contains an indoor heat exchanger 21 and an indoor fan 22. When the air conditioner is in cooling mode, the indoor heat exchanger 21 functions as an evaporator. Depending on the operating state of the indoor unit, the indoor heat exchanger 21 functions as either an evaporator or a radiator, facilitating heat exchange between the refrigerant flowing in the heat transfer tubes and the air passing through the indoor heat exchanger. The indoor fan 22 generates airflow through the indoor heat exchanger 21 to promote heat exchange between the refrigerant flowing in the heat transfer tubes of the indoor heat exchanger 21 and the indoor air. Outdoor unit 3 contains an outdoor heat exchanger 31, an outdoor fan 32, a compressor 33, a throttling component (i.e., a flow control valve) 34, and a four-way valve 35. When the air conditioner is in cooling mode, the outdoor heat exchanger 31 functions as a condenser. The outdoor fan 32 generates an airflow of outdoor air through the outdoor heat exchanger 31 to promote heat exchange between the refrigerant flowing in the heat transfer tubes of the outdoor heat exchanger 31 and the outdoor air.

[0076] Please see Figure 3 , Figure 3This is a schematic diagram of a refrigerant circulation loop for an air conditioner according to some embodiments of the present invention. The compressor 33, throttling component 34, four-way valve 35, outdoor heat exchanger 31, and indoor heat exchanger 21 are connected by pipelines to form the refrigerant circulation loop. When the air conditioner is in cooling mode, the indoor heat exchanger 21 and outdoor heat exchanger 31 function as the evaporator and condenser, respectively. The refrigerant is compressed by the compressor into a high-temperature, high-pressure gas, which enters the outdoor heat exchanger of the outdoor unit through the four-way valve. After absorbing cold and releasing heat in the outdoor heat exchanger, it becomes a medium-temperature, high-pressure liquid. After passing through the flow regulating valve, it becomes a low-temperature, low-pressure liquid. After absorbing heat and releasing cold in the indoor heat exchanger of the indoor unit, it becomes a low-temperature, low-pressure gas, returning to the compressor through the four-way valve, and then continuing the cycle. By circulating the refrigerant in the refrigerant loop, a vapor compression refrigeration cycle can be executed. The flow regulating valve can change its opening degree; decreasing the opening degree increases the flow resistance of the refrigerant passing through the flow regulating valve, while increasing the opening degree decreases the flow resistance of the refrigerant passing through the flow regulating valve. Such a flow control valve causes the refrigerant flowing from the indoor heat exchanger to the outdoor heat exchanger to expand and depressurize during refrigeration operation. Furthermore, even if the states of other components installed in the refrigerant circuit remain unchanged, the flow rate of the refrigerant flowing in the refrigerant circuit will change when the opening of the flow control valve changes.

[0077] In some embodiments, the operating parameters of the air conditioner can be collected based on the Internet of Things (IoT) data collection method.

[0078] In some embodiments, the air conditioner may include a target sensor configured to collect operating parameters associated with the air conditioner. These operating parameters may include at least one of the following: indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, target fan speed, compressor frequency, expansion valve opening, outdoor fan speed, device ID, and set temperature. This embodiment does not limit the specific type or number of these operating parameters.

[0079] Optionally, the target sensor may include at least one of a temperature sensor, humidity sensor, wind speed sensor, and rotational speed sensor. For example, the target sensor may be a comprehensive sensor used to simultaneously collect operational correlation parameters of different operational correlation types. It should be noted that this embodiment does not limit the specific type of the target sensor.

[0080] In some embodiments, the air conditioner may also integrate a meteorological data acquisition module for collecting meteorological element data and target weather type. In other embodiments, the air conditioner may communicate with a meteorological system via a network to obtain meteorological element data and target weather type. In still other embodiments, the server may also communicate with a meteorological system via a network to obtain meteorological element data and target weather type.

[0081] In some embodiments, the server may store at least one of the following: historical operating information of the air conditioner and different reference air conditioners, historical operating parameters, and historical load data. Optionally, the air conditioner may send temperature-reaching data to the server when the temperature-reaching conditions are met. The temperature-reaching data may include at least one of the following: temperature-reaching duration, current target time period, operating parameters, and air conditioner load. This embodiment does not impose any limitations on this.

[0082] With the development of air conditioner control technology, energy-saving function has become one of the core indicators of air conditioners. Energy-saving control can reduce the energy consumption of air conditioners and avoid energy waste. However, traditional energy-saving methods usually control energy saving based on the set temperature and indoor temperature. The energy-saving strategy is relatively simple and difficult to adapt to different application environments, resulting in unsatisfactory energy-saving effects.

[0083] Based on this, an energy-saving optimization method for air conditioners is proposed to improve their energy efficiency. It should be noted that the above-mentioned energy-saving optimization method can be implemented by the air conditioner itself, or by interaction between the air conditioner and a server; no limitation is imposed in this regard.

[0084] The server can communicate with the air conditioner via a network; correspondingly, both the air conditioner and the server can include communication devices for network communication. For example, the server may include a communication device and at least one processor. The communication device is configured to communicate with the air conditioner.

[0085] For example, the server can be a standalone server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services; there is no limitation in this regard. For example, the data storage system can store the data that the server needs to process, such as the historical load of different air conditioners. The data storage system can be integrated on the server or located in the cloud or on other network servers.

[0086] In some alternative embodiments, see Figure 4 A control method for an air conditioner is provided, applied to the controller of the air conditioner, comprising:

[0087] S410, in response to an energy-saving control operation for the air conditioner, acquires the operating parameters associated with the air conditioner, as well as historical load data of the air conditioner for at least one target time period.

[0088] Among them, energy-saving control operation can be understood as the operation of energy-saving control for air conditioners.

[0089] For example, the energy-saving control operation for an air conditioner may include at least one of the following: triggering the energy-saving mode option in the air conditioner remote control; triggering the energy-saving mode option in the operation interface of the air conditioner display screen; triggering the energy-saving mode option on the air conditioner; and gesture interaction operation corresponding to the energy-saving mode.

[0090] Among them, operation-related parameters can be understood as parameters associated with the operating status or operation process of the air conditioner. For example, operation-related parameters can be used to characterize at least one of the following: the current operating status of the air conditioner, user setting preferences, and the performance characteristics of the air conditioner, and can serve as the basis for load forecasting and optimized control.

[0091] In some embodiments, the running correlation parameters correspond to at least one target parameter type. Optionally, at least one target parameter type related to the load can be selected from different candidate parameter types by performing data analysis on sample running correlation parameters of the air conditioner.

[0092] Optionally, the operation-related parameters may include at least one of the following: the air conditioner's operating parameters, the air conditioner's device identification parameters, and operating environment parameters and setting parameters associated with the air conditioner's operation. For example, the operation-related parameters may include at least one of the following: indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, the air conditioner's target fan speed, compressor frequency, expansion valve opening, outdoor unit speed, device ID (identifier), and set temperature. This embodiment does not limit the specific type or quantity of the operation-related parameters.

[0093] The target time period can be understood as at least one time period selected within a day. It should be noted that this embodiment does not impose any limitations on the specific length of the target time period, the specific method of selecting the time period, or the specific number of target time periods.

[0094] For example, a day can be divided into 9 target time periods T. i Let i = 9. The time periods are: T1 (8:00-10:00), T2 (10:00-14:00), T3 (14:00-16:00), T4 (16:00-18:00), T5 (18:00-20:00), T6 (20:00-22:00), T7 (22:00-0:00), T8 (0:00-6:00), and T9 (6:00-8:00). Optionally, at least one time period from the above can be selected as the target time period.

[0095] For example, the target time period can be a time period with time attribute characteristics, such as at least one of the morning period, noon period, afternoon period and evening period.

[0096] Historical load data can be understood as data used to characterize the air conditioning cooling / heating load demand for a target period under historical time.

[0097] In some embodiments, for each historical sample day, historical operating frequency data of the air conditioner during different target time periods of the historical sample day can be obtained; for each target time period of the historical sample day, the historical operating frequency data of the target time period is input into the load determination model to obtain the historical load data of the target time period.

[0098] The load determination model can be a trained machine learning model or a neural network model; this embodiment does not limit the specific model type of the load determination model.

[0099] The load determination model can be deployed locally on the air conditioner or on a server; this embodiment does not impose any limitations on this.

[0100] Optionally, a remote service interface can be invoked to obtain historical load data of the air conditioner for at least one target time period. For example, the server can obtain historical operating frequency data of the air conditioner for different target time periods within each historical sample day; for each target time period of the historical sample day, the historical operating frequency data for that target time period is input into the load determination model to obtain the historical load data for that target time period; and the historical load data of the air conditioner for at least one target time period is sent to the air conditioner. Furthermore, the above-mentioned steps for determining historical load data can also be implemented by the air conditioner's controller itself, which will not be elaborated further here.

[0101] Optionally, for each historical sample day, the historical expansion valve opening and historical fan speed of the air conditioner during different target time periods of the historical sample day can be obtained; for each target time period of the historical sample day, at least one of the historical operating frequency data, historical expansion valve opening and historical fan speed of the target time period can be input into the load determination model to obtain the historical load data of the target time period.

[0102] In other embodiments, for each historical sample day, historical operating frequency data of the air conditioner during different target time periods of the historical sample day can be obtained; for each target time period of the historical sample day, historical load data corresponding to the historical operating frequency data of the target time period can be determined according to a preset energy efficiency relationship.

[0103] The preset energy efficiency relationship can be understood as a relationship between the air conditioner's operating data and the corresponding load. For example, the preset energy efficiency relationship can be determined by fitting sample data or by means of experiments and simulations; this embodiment does not impose any limitations on this.

[0104] Similarly, a remote service interface can be invoked to obtain historical load data of the air conditioner for at least one target time period. That is, the server can obtain historical operating frequency data of the air conditioner for different target time periods within each historical sample day; for each target time period of the historical sample day, based on a preset energy efficiency relationship, determine the historical load data corresponding to the historical operating frequency data for that target time period; and send the historical load data of the air conditioner for at least one target time period to the air conditioner. Furthermore, the above steps for determining the historical load data can also be implemented by the air conditioner's controller itself, which will not be elaborated upon here.

[0105] Similarly, for each historical sample day, the historical expansion valve opening and historical fan speed of the air conditioner during different target periods of the historical sample day can be obtained; based on the preset energy efficiency relationship, and according to at least one of the historical operating frequency data, historical expansion valve opening and historical fan speed of the target period, the historical load data of the target period can be determined.

[0106] S420. Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located.

[0107] Among them, the load orientation parameter can be understood as a parameter used to quantify the heat load characteristics of the target environment due to its orientation.

[0108] In some embodiments, for each target time period, the average load data of the historical load data corresponding to the target time period can be determined; and the load orientation parameters of the target environment where the air conditioner is located can be determined based on the average load data of each target time period.

[0109] Optionally, the target time period includes a first reference time period, a second reference time period, and a base time period; correspondingly, the first load orientation parameter can be determined based on the proportion of the difference between the average load data of the first reference time period and the base time period in the average load data of the base time period; the second load orientation parameter can be determined based on the proportion of the difference between the average load data of the second reference time period and the base time period in the average load data of the base time period; and the load orientation parameter can be determined based on the difference between the first load orientation parameter and the second load orientation parameter.

[0110] The first reference period, the second reference period, and the baseline period can be used to characterize different environmental characteristics.

[0111] For example, for each target time period, the average of the historical load data corresponding to the target time period can be taken to obtain the average load data corresponding to the target time period; the unit of the average load data can be kW.

[0112] For example, the load orientation parameter R can be determined according to the following formula. orient :

[0113]

[0114] Among them, R orient Indicates the load orientation parameter; P a This represents the average load data for the first reference period; P b This represents the average load data for the baseline period; P b This represents the average load data for the second reference period.

[0115] Among them, in the load orientation parameter R orient When the value is positive, it can be used to reflect the intensity of the western sun exposure effect; in the load orientation parameter R orient When the value is positive, it can be used to characterize the heating characteristics in the morning.

[0116] For example, the first reference time period may include the evening period, the second reference time period may include the afternoon period, and the base time period may include the morning period.

[0117] To facilitate understanding, the following example illustrates the time period divisions described above. It should be noted that this should not be construed as a limitation on the specific division method of the target time period or the specific steps for determining the load orientation parameters. For example, the first reference time period can be T5 (6 PM - 8 PM); the base time period can be T3 (2 PM - 4 PM); the second reference time period can be T1 (8 AM - 10 AM), and the load orientation parameter R... orient It can be represented as:

[0118]

[0119] Among them, R orient Indicates load orientation parameters; This represents the average load data for the T5 time period; This represents the average load data for the T3 period; This represents the average load data for the T2 period.

[0120] In some embodiments, the target constraint interval to which the load orientation parameter belongs can be determined; and the target orientation that matches the target constraint interval can be determined from a preset matching table.

[0121] Table 1 Preset Matching Table

[0122] Constraint interval Orientation determination <![CDATA[R orient >0.4]]> West <![CDATA[0.2<R orient ≤0.4]]> West by South <![CDATA[-0.3≤R orient ≤0.2]]> South / North <![CDATA[R orient <-0.3]]> East

[0123] For example, Table-1 shows a preset matching table. The preset matching table displays the orientations corresponding to different constraint intervals. Optionally, the orientation can be determined as west if the load orientation parameter is greater than the first parameter threshold; the orientation can be determined as west-southwest if the load orientation parameter is greater than the second parameter threshold but not greater than the first parameter threshold; the orientation can be determined as south or north if the load orientation parameter is not less than the third parameter threshold but not greater than the second parameter threshold; and the orientation can be determined as east if the load orientation parameter is less than the third parameter threshold. The first parameter threshold is greater than the second parameter threshold, and the second parameter threshold is greater than the third parameter threshold. The first, second, and third parameter thresholds can be set by a technician according to needs or experience, or determined through extensive experimentation; this embodiment does not impose any limitations on this. Optionally, the first, second, and third parameter thresholds can be 0.4, 0.2, and -0.3, respectively.

[0124] S430. Input the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes the operating associated parameters and the load orientation parameters.

[0125] Among them, associated data can be understood as data related to the optimized control parameters.

[0126] For example, the associated data may also include at least one of the following: current time period, target orientation, heat attenuation parameter, meteorological impact parameter, load association parameter, and dynamic average load parameter. The heat attenuation parameter can be understood as a parameter characterizing heat dissipation (e.g., heat dissipation rate, in kW·h / ℃·h) within the current time period; the meteorological impact parameter can be understood as a parameter characterizing the impact of current weather on the load; the load association parameter can be understood as the associated load performance combining the current air conditioner and different reference air conditioners; and the dynamic average load parameter characterizes the dynamic load performance of the current air conditioner under the time attenuation mechanism.

[0127] In some embodiments, the heat attenuation parameters can be determined based on the target temperature difference between indoor and outdoor temperatures, radiation intensity, target orientation, and the cumulative power consumption of the air conditioner. The outdoor temperature can be obtained from a target sensor in the air conditioner or from a meteorological system.

[0128] Optionally, an orientation correction factor can be determined based on the target orientation; the radiation intensity can be corrected based on the orientation correction factor to obtain a first attenuation parameter; the unit power consumption can be determined based on the cumulative power consumption and cumulative time; the second attenuation parameter can be determined based on the unit power consumption and the target temperature difference; and the thermal attenuation parameter can be determined based on the first attenuation parameter and the second attenuation parameter.

[0129] For example, the length of the accumulated time can be set by technicians according to their needs or experience, or determined through extensive experimentation; this embodiment does not impose any limitations in this regard. Referring to the foregoing, a day can be divided into 9 target time periods T. i The cumulative time can be the length of the target period, and the cumulative power consumption can be the cumulative power consumption of the target period.

[0130] For example, the thermal decay parameter k can be determined using the following formula. t :

[0131]

[0132] Where k represents the thermal decay parameter; Q represents the cumulative power consumption in kWh; Δt represents the cumulative time in hours; ΔT represents the target temperature difference; μ represents the orientation correction factor; and S... rad This represents radiation intensity, measured in W / m². 2 .

[0133] For example, the orientation correction factor corresponding to the target orientation can be queried from the correction factor matching table. For example, if the target orientation is south, the orientation correction factor can be 0.8; if the target orientation is west, the orientation correction factor can be 1.2.

[0134] Optionally, data processing and feature extraction can be performed on the associated data, and the extracted associated feature data can be input into the optimization control model to obtain the optimization control parameters. For example, the target time period or weather category can be converted into a unique thermal code or embedding vector, thereby enhancing the optimization control model's ability to express discrete variables. Furthermore, for continuous variables (such as temperature, humidity, and radiation intensity), normalization processing can be performed to eliminate the influence of dimensions.

[0135] It is understandable that by incorporating the current time period, the optimization control model can distinguish the impact of different time periods on the optimization control parameters during the determination process. For example, during periods of strong sunlight, the cooling capacity can be appropriately increased or pre-cooling can be initiated to prevent peak loads. By incorporating the target orientation, the optimization control model can distinguish the impact of different building orientation scenarios on the optimization control parameters. For example, based on the building orientation, operating parameters can be adjusted specifically for different time periods to optimize energy efficiency. By incorporating meteorological influence parameters, the optimization control model can acquire the ability to optimize control parameters for different meteorological scenarios. For example, in the case of cloudy or rainy weather at night, energy consumption can be reduced by decreasing the cooling frequency.

[0136] In some embodiments, associated data can be spatiotemporally aligned and feature-fused to form unified input data.

[0137] Optionally, before performing spatiotemporal alignment and feature fusion, the associated data can be preprocessed to improve data quality and provide a basis for determining subsequent optimization control parameters. Preprocessing may include at least one of the following: data cleaning, anomaly detection, and missing value imputation.

[0138] In some embodiments, the optimization control model can be a trained machine learning model or a neural network model. This embodiment does not limit the specific model type of the optimization control model.

[0139] In some embodiments, the optimization control model may include a load forecasting sub-model and a target optimization sub-model; correspondingly, the associated data can be input into the forecasting sub-model to obtain the temperature prediction type, load forecast value, and temperature duration forecast value; when the temperature prediction type is the temperature reachable type, the load forecast value and temperature duration forecast value are input into the target optimization sub-model to obtain the optimization control parameters.

[0140] Optionally, the load prediction sub-model includes a multi-layer neural network to capture the complex nonlinear relationship between multi-dimensional input features and load. For example, the load prediction sub-model may include an input layer, several hidden layers (which may include fully connected, convolutional, and attention mechanisms, etc.), and an output layer. The input layer receives associated data; the hidden layers can model high-order interactions between features using nonlinear activation functions; and the output layer outputs the temperature prediction type, load prediction value, and temperature duration prediction value. The temperature prediction type may include an attainable temperature type and an unattainable temperature type.

[0141] For example, the load prediction sub-model can employ a multi-task learning framework to jointly optimize three tasks: temperature classification, load regression, and time to temperature regression. The loss function can be a weighted combination of cross-entropy loss (for temperature classification) and mean squared error loss (for regression), improving overall performance through end-to-end training.

[0142] For example, the load prediction sub-model can be deployed on a server or in an air conditioner; this embodiment does not impose any limitations on this. Optionally, the load prediction sub-model can be trained offline using massive amounts of historical user data in the cloud, and the model parameters can be continuously iterated using optimization algorithms such as batch gradient descent. Optionally, the load prediction sub-model can support online fine-tuning and adaptively adjust based on the latest collected user data to improve its adaptability to environmental changes. To balance the model's generalization ability with users' personalized needs, personalized features such as target encoding of device IDs and target orientation can be introduced during training, and overfitting can be prevented through regularization and other means to ensure that the load prediction sub-model can work stably under different users and in different environments.

[0143] Optionally, if the temperature prediction type is "unreachable temperature", the current control parameters of the air conditioner can be maintained to avoid excessive energy saving leading to a decrease in comfort.

[0144] Optionally, the objective optimization sub-model can be based on multiple objective constraints, combined with an optimization algorithm, to output optimal control parameters, achieving a balance between energy saving and comfort. For example, objective constraints may include minimizing energy consumption, maximizing cooling rate, and optimizing user comfort. For example, the optimization algorithm may include at least one of genetic algorithms, particle swarm optimization algorithms, and gradient descent algorithms.

[0145] S440: Control the air conditioner to operate according to optimized control parameters.

[0146] Optionally, optimized control parameters may include at least one of the following: optimized compressor frequency, optimized expansion valve opening, and optimized outdoor unit speed.

[0147] By responding to energy-saving control operations on the air conditioner, operational correlation parameters and historical load data of the air conditioner during at least one target time period are acquired. This provides a data foundation for dynamic adjustment of the air conditioner's energy-saving control based on both operational correlation and historical load dimensions. By determining the load orientation parameters of the target environment in which the air conditioner is located based on the historical load data of at least one target time period, the impact of the target environment on the load can be characterized, enabling the subsequent dynamic adjustment process to adapt to environmental characteristics. By using the correlation data, including operational correlation parameters and load orientation parameters, as input to the optimized control model, optimized control parameters that fit the current target environment and current operational correlation scenario can be obtained. By controlling the air conditioner to operate according to the optimized control parameters, energy-saving effects can be improved, air conditioner energy consumption can be reduced, and the system can adapt to different application scenarios.

[0148] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which a step of determining meteorological impact parameters is added.

[0149] See Figure 5 The steps for determining the meteorological impact parameters shown include:

[0150] S510. Obtain current meteorological element data and target weather type; the meteorological element data corresponds to at least one element type.

[0151] For example, meteorological element data may include at least one of the following: weather temperature, weather humidity, radiation intensity, air quality index, weather wind speed, and weather wind direction. Optionally, current meteorological element data and target weather type can be obtained from a meteorological system.

[0152] For example, the target weather type may include at least one of the following: sunny, cloudy, rainy, foggy, and hazy.

[0153] S520. Based on the preset matching relationship, determine the target fusion weights of different element types that match the target weather type.

[0154] For example, the preset matching relationship can be a fusion weight matching table. Accordingly, the target fusion weights of different element types that match the target weather type can be determined from the fusion weight matching table.

[0155] Table 2 Fusion Weight Matching Table

[0156] Weather type Temperature weight α Humidity weight β Radiation weight γ sunny 0.3 0.1 0.6 cloudy day 0.5 0.3 0.2 rainstorm 0.4 0.5 0.1 ... ... ... ......

[0157] Table 2 shows the fusion weight matching table. The table indicates that the feature types can include temperature, humidity, and radiation types, and the target weather type can include sunny, cloudy, and heavy rain types. Table 2 also shows the target fusion weights for each feature type under different target weather types. For example, when the target weather type is sunny, the temperature weight is 0.3, the humidity weight is 0.1, and the radiation weight is 0.6.

[0158] It should be noted that this embodiment does not impose any limitations on the specific number and classification method of the target weather type and element type, nor does it impose any limitations on the specific weight value of the target fusion weight.

[0159] S530. Determine meteorological impact parameters based on meteorological element data of different element types and corresponding target fusion weights.

[0160] In some embodiments, meteorological element data can be weighted and summed according to the target fusion weights to obtain meteorological impact parameters.

[0161] In other embodiments, environmental data within the target environment can be acquired; meteorological impact parameters can be determined based on the environmental data, meteorological element data of different element types, and corresponding fusion weights.

[0162] For example, environmental data may include indoor temperature and indoor humidity; meteorological element data may include weather temperature, weather humidity, and radiation intensity. Accordingly, a target temperature difference between indoor temperature and weather temperature can be determined; a target humidity difference between indoor humidity and weather humidity can be determined; a first meteorological impact parameter can be determined based on the fusion weights corresponding to the target temperature difference and temperature type; a second meteorological impact parameter can be determined based on the fusion weights corresponding to the target humidity difference and humidity type; a third meteorological impact parameter can be determined based on the fusion weights corresponding to radiation intensity and radiation type; and the first, second, and third meteorological impact parameters can be fused to obtain the meteorological impact parameter.

[0163] Optionally, the meteorological impact parameter W can be determined using the following formula. total :

[0164] W total =α*ΔT+β*ΔH+γ*S rad

[0165] Among them, W total Represents meteorological influence parameters; α represents temperature weight; ΔT represents target temperature difference; β represents humidity weight; ΔH represents target humidity difference; γ represents radiation weight; S rad Indicates radiation intensity.

[0166] The above steps provide a data foundation for determining subsequent meteorological impact parameters by acquiring current meteorological element data and target weather type. Based on preset matching relationships, target fusion weights for different element types matching the target weather type are determined. This ensures that the fusion weights of meteorological element data are determined based on two dimensions: element type and target weather type. The resulting fusion weights reflect the comprehensive impact of different meteorological element data. By weighting the corresponding meteorological element data using these target fusion weights, the final meteorological impact parameters can be obtained.

[0167] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which a step of determining load association parameters is added. For example, a remote service interface can be invoked to determine the load association parameters of the air conditioner.

[0168] See Figure 6 The steps for determining the load association parameters shown include:

[0169] S610: Obtain the historical frequency of temperature reaching the air conditioner within a historical time period and the historical average load under preset operating conditions.

[0170] In some embodiments, the device ID of the air conditioner can be obtained, the historical temperature frequency corresponding to the device ID can be obtained from the historical database, and the historical average load under preset operating conditions can be determined.

[0171] Optionally, the preset operating conditions may include operating conditions that have a greater similarity to the current operating conditions than a preset similarity. For example, the current operating conditions may include at least one of the current indoor temperature and the current indoor humidity.

[0172] Optionally, priority can be given to statistically analyzing equipment performance during peak load periods (such as summer afternoons) to improve the sensitivity of forecasts to extreme operating conditions.

[0173] Optionally, at least one historical load of the air conditioner under preset operating conditions can be obtained; the average of each historical load can be used as the historical average load.

[0174] S620, Obtain the preset reference frequency and reference average load; the reference average load is determined based on the historical load of different reference air conditioners.

[0175] In some embodiments, the historical load of different reference air conditioners can be obtained, and the average of the historical loads of different reference air conditioners can be used as the reference average load.

[0176] S630. Determine the target historical load based on the historical frequency of temperature rise and the historical average load.

[0177] Optionally, the target historical load can be obtained by multiplying the historical temperature frequency by the historical average load.

[0178] S640. Determine the target reference load based on the preset reference frequency and reference average load.

[0179] Optionally, the target reference load can be obtained by multiplying the preset reference frequency and the reference average load.

[0180] S650. Determine the load correlation parameters based on the ratio of the sum of the target historical load and the target reference load to the target frequency; the target frequency is the sum of the historical temperature reaching frequency and the preset reference frequency.

[0181] The load association parameter is essentially a dynamic encoding of the device ID. Compared to directly using the device ID as a discrete label, introducing the load association parameter allows for the differentiation of individual differences between different air conditioners.

[0182] For example, the load association parameter M can be determined according to the following formula:

[0183]

[0184] Where M represents the load-related parameter; n1 represents the historical temperature frequency; Pc n represents the reference average load; n2 represents the preset reference frequency; P d This represents the reference average load.

[0185] For example, the preset reference frequency can be adjusted based on data sparsity to enhance the personalized characteristics of high-frequency devices, while low-frequency devices rely on global commonalities. Optionally, a preset reference frequency that matches the historical temperature-reaching frequency can be queried from a reference frequency matching table.

[0186] In the above steps, the target historical load is determined based on the historical temperature reach frequency and historical average load, thereby determining the current historical load of the air conditioner; the target reference load is determined based on the preset reference frequency and reference average load, thereby determining the global load of different reference air conditioners; and the load correlation parameters are determined by the ratio of the sum of the target historical load and the target reference load to the target frequency, thereby effectively alleviating the problem of the historical load of the air conditioner deviating from the actual situation due to the small historical sample size, which is conducive to improving the reliability and stability of the historical load assessment of the air conditioner.

[0187] Based on the technical solutions of the above embodiments, this application also provides an optional embodiment, in which a step of determining dynamic average load parameters is added.

[0188] See Figure 7 The steps for determining the dynamic average load parameters shown include:

[0189] S710: Obtain the target load of the air conditioner at different historical moments.

[0190] S720. For each historical moment, determine the time decay weight of the historical moment.

[0191] Optionally, the closer a historical moment is to the current moment, the greater the corresponding time decay weight; conversely, the farther a historical moment is from the current moment, the smaller the corresponding time decay weight.

[0192] For example, the time decay weight w can be determined according to the following formula. t :

[0193]

[0194] Among them, w t λ represents the time decay weight; λ represents the decay coefficient; T represents the current time; t represents the historical time.

[0195] For example, the attenuation coefficient λ can dynamically adjust the weight distribution of historical data to respond to load changes caused by seasonal migrations, such as summer cooling or winter heating.

[0196] For example, when the amount of data on the target load of the air conditioner at a historical time is small (e.g., the number of samples is less than a preset threshold), the user characteristics of the air conditioner can be obtained; based on the user characteristics, a preset number of target users can be selected from each candidate user; and the target load of the reference air conditioner of the target user at different historical times can be used as the target load of the current air conditioner at different historical times.

[0197] For example, user characteristics may include at least one of the following: regional characteristics of the user's location, cooling habits, heating habits, and usage time.

[0198] Optionally, the similarity of user features between the current user and each candidate user can be determined; according to the preset screening number, the target user with the highest similarity of user features is selected from each candidate user. The preset screening number can be set by technicians according to their needs or experience, or determined through a large number of experiments. This embodiment does not impose any limitations on this.

[0199] In some embodiments, if the similarity of user features between the current user and each candidate user is lower than a preset similarity threshold, a regional benchmark model can be invoked to generate a benchmark load, which can then be used as the target load.

[0200] S730. Determine the dynamic average load parameters based on the target load at different historical moments and the corresponding time decay weights.

[0201] For example, the dynamic average load parameter P can be determined according to the following formula. w :

[0202]

[0203] Among them, P w Represents the dynamic average load parameter; T represents the current time; t represents the historical time; w t P represents the time decay weight corresponding to historical time t; t This represents the target load corresponding to historical time t.

[0204] In the above steps, by introducing time decay weights, more recent historical moments can be given higher weights and more distant historical moments can be given lower weights, thereby making the dynamic average load parameters more consistent with the recent trend changes of the air conditioner.

[0205] Based on the above embodiments, see Figure 8 This provides an alternative method for controlling air conditioners, applied to server processors, including:

[0206] S810: Obtain the energy-saving control request sent by the air conditioner. The energy-saving control request includes operating parameters.

[0207] S820, in response to an energy-saving control request, acquires historical load data of the air conditioner for at least one target time period.

[0208] S830. Based on historical load data for at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located.

[0209] S840. Input the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes the operating associated parameters and the load orientation parameters.

[0210] S850: Send optimized control parameters to the air conditioner so that the air conditioner operates according to the optimized control parameters.

[0211] In some embodiments, for each target time period, the average load data of the historical load data corresponding to the target time period can be determined; and the load orientation parameters of the target environment where the air conditioner is located can be determined based on the average load data of each target time period.

[0212] In some embodiments, the target time period may include a first reference time period, a second reference time period, and a base time period; correspondingly, a first load orientation parameter may be determined based on the proportion of the difference between the average load data of the first reference time period and the base time period in the average load data of the base time period; a second load orientation parameter may be determined based on the proportion of the difference between the average load data of the second reference time period and the base time period in the average load data of the base time period; and a load orientation parameter may be determined based on the difference between the first load orientation parameter and the second load orientation parameter.

[0213] In some embodiments, for each historical sample day, historical operating frequency data of the air conditioner during different target time periods of the historical sample day can be obtained; for each target time period of the historical sample day, the historical operating frequency data of the target time period can be input into the load determination model to obtain the historical load data of the target time period; and / or, for each target time period of the historical sample day, the historical load data corresponding to the historical operating frequency data of the target time period can be determined according to a preset energy efficiency relationship.

[0214] In some embodiments, the associated data further includes meteorological impact parameters; correspondingly, the above method may also include: acquiring current meteorological element data and target weather type; the meteorological element data corresponds to at least one element type; determining the target fusion weights of different element types that match the target weather type according to a preset matching relationship; and determining meteorological impact parameters according to the meteorological element data of different element types and the corresponding target fusion weights.

[0215] In some embodiments, the associated data further includes load association parameters; correspondingly, the above method may further include: acquiring the historical temperature reach frequency and the historical average load under preset operating conditions of the air conditioner within a historical time period; and acquiring a preset reference frequency and a reference average load; the reference average load is determined based on the historical load of different reference air conditioners; a target historical load is determined based on the historical temperature reach frequency and the historical average load; a target reference load is determined based on the preset reference frequency and the reference average load; and a load association parameter is determined based on the ratio of the sum of the target historical load and the target reference load to the target frequency; the target frequency is the sum of the historical temperature reach frequency and the preset reference frequency.

[0216] In some embodiments, the associated data further includes dynamic average load parameters; correspondingly, the above method may also include: obtaining the target load of the air conditioner at different historical times; determining the time decay weight for each historical time; and determining the dynamic average load parameters based on the target load and corresponding time decay weight for different historical times.

[0217] In some embodiments, the optimization control model may include a load forecasting sub-model and a target optimization sub-model; correspondingly, the associated data can be input into the forecasting sub-model to obtain the temperature prediction type, load forecast value, and temperature duration forecast value; when the temperature prediction type is the temperature reachable type, the load forecast value and temperature duration forecast value are input into the target optimization sub-model to obtain the optimization control parameters.

[0218] Based on the above embodiments, an energy-saving optimization method for air conditioners can be implemented through interaction between the air conditioner and the server.

[0219] refer to Figure 9 The diagram shown is a timing diagram of an energy-saving optimization method for an air conditioner in one embodiment, including:

[0220] S901. In response to the energy-saving control operation, the air conditioner sends an energy-saving control request to the server; the energy-saving control request includes the air conditioner's operating parameters.

[0221] S902. In response to the energy-saving control request, the server obtains historical load data of the air conditioner for at least one target time period.

[0222] S903. For each target time period, the server determines the average load data of the historical load data corresponding to the target time period.

[0223] S904. The server determines the load orientation parameters of the target environment where the air conditioner is located based on the average load data for each target time period.

[0224] S905. The server obtains the current meteorological element data and the target weather type; the meteorological element data corresponds to at least one element type.

[0225] S906. The server determines the target fusion weights of different element types that match the target weather type based on the preset matching relationship.

[0226] S907. The server determines meteorological impact parameters based on meteorological element data of different element types and corresponding target fusion weights.

[0227] S908 The server determines the target historical load based on the historical frequency of the air conditioner reaching the desired temperature within a historical period and the historical average load under preset operating conditions.

[0228] S909. The server determines the target reference load based on the preset reference frequency and reference average load.

[0229] S910. The server determines the load association parameters based on the ratio of the sum of the target historical load and the target reference load to the target frequency.

[0230] The target frequency is the sum of the historical temperature reaching frequency and the preset reference frequency.

[0231] S911. The server determines the dynamic average load parameters based on the target load of the air conditioner at different historical times and the corresponding time attenuation weight.

[0232] S912. The server inputs the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters, load orientation parameters, meteorological influence parameters, load associated parameters, and dynamic average load parameters.

[0233] S913, The server sends optimized control parameters to the air conditioner.

[0234] S914. The air conditioner operates according to optimized control parameters.

[0235] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0236] Based on the same inventive concept, this application also provides an energy-saving optimization device for an air conditioner to implement the energy-saving optimization method for the air conditioner described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the energy-saving optimization device for air conditioners provided below can be found in the limitations of the energy-saving optimization method for air conditioners described above, and will not be repeated here.

[0237] In one exemplary embodiment, such as Figure 10 As shown, an energy-saving optimization device for an air conditioner is provided, comprising: a first acquisition module 1010, a first determination module 1020, a first input module 1030, and a first control module 1040, wherein:

[0238] The first acquisition module 1010 is used to acquire the operating parameters of the air conditioner and the historical load data of the air conditioner during at least one target time period in response to the energy-saving control operation of the air conditioner.

[0239] The first determining module 1020 is used to determine the load orientation parameters of the target environment where the air conditioner is located based on historical load data for at least one target time period.

[0240] The first input module 1030 is used to input the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes the operation associated parameters and the load orientation parameters.

[0241] The first control module 1040 is used to control the air conditioner to operate according to optimized control parameters.

[0242] In some embodiments, the first determining module 1020 includes: a first determining unit, configured to determine the average load data of historical load data corresponding to each target time period; and a second determining unit, configured to determine the load orientation parameters of the target environment where the air conditioner is located based on the average load data of each target time period.

[0243] In some embodiments, the target time period includes a first reference time period, a second reference time period, and a base time period; correspondingly, the second determining unit includes: a first determining subunit, configured to determine a first load orientation parameter based on the proportion of the difference between the average load data of the first reference time period and the base time period in the average load data of the base time period; a second determining subunit, configured to determine a second load orientation parameter based on the proportion of the difference between the average load data of the second reference time period and the base time period in the average load data of the base time period; and a third determining subunit, configured to determine a load orientation parameter based on the difference between the first load orientation parameter and the second load orientation parameter.

[0244] In some embodiments, the first acquisition module 1010 includes: a first acquisition unit, configured to acquire historical operating frequency data of the air conditioner during different target time periods of the historical sample day for each historical sample day; a first input unit, configured to input the historical operating frequency data of the target time period into the load determination model for each target time period of the historical sample day to obtain the historical load data of the target time period; and / or, a third determination unit, configured to determine the historical load data corresponding to the historical operating frequency data of the target time period according to a preset energy efficiency relationship for each target time period of the historical sample day.

[0245] In some embodiments, the associated data further includes meteorological impact parameters; correspondingly, it further includes: a second acquisition module, used to acquire current meteorological element data and target weather type; the meteorological element data corresponds to at least one element type; a second determination module, used to determine the target fusion weight of different element types that match the target weather type according to a preset matching relationship; and a third determination module, used to determine the meteorological impact parameters according to the meteorological element data of different element types and the corresponding target fusion weights.

[0246] In some embodiments, the associated data further includes load association parameters; correspondingly, it also includes: a calling module for calling a remote service interface to determine the load association parameters of the air conditioner; wherein the load association parameters are determined through the following steps: obtaining the historical temperature reach frequency and the historical average load under preset operating conditions of the air conditioner in a historical period; and obtaining a preset reference frequency and a reference average load; the reference average load is determined based on the historical load of different reference air conditioners; a target historical load is determined based on the historical temperature reach frequency and the historical average load; a target reference load is determined based on the preset reference frequency and the reference average load; and the load association parameters are determined based on the ratio of the sum of the target historical load and the target reference load to the target frequency; the target frequency is the sum of the historical temperature reach frequency and the preset reference frequency.

[0247] In some embodiments, the associated data further includes: dynamic average load parameters; correspondingly, it also includes: a third acquisition module for acquiring the target load of the air conditioner at different historical times; a fourth determination module for determining the time decay weight of each historical time; and a fifth determination module for determining the dynamic average load parameters based on the target load and corresponding time decay weight of different historical times.

[0248] In some embodiments, the optimization control model includes a load prediction sub-model and a target optimization sub-model; correspondingly, the first input module 1030 includes: a second input unit, used to input associated data into the prediction sub-model to obtain the temperature prediction type, load prediction value, and temperature duration prediction value; and a third input unit, used to input the load prediction value and temperature duration prediction value into the target optimization sub-model when the temperature prediction type is the temperature-reaching type, to obtain the optimization control parameters.

[0249] In one exemplary embodiment, such as Figure 11 As shown, an energy-saving optimization device for an air conditioner is provided, comprising: a fourth acquisition module 1110, a fifth acquisition module 1120, a sixth determination module 1130, a second input module 1140, and a second control module 1150, wherein:

[0250] The fourth acquisition module 1110 is used to acquire the energy-saving control request sent by the air conditioner. The energy-saving control request includes operating related parameters.

[0251] The fifth acquisition module 1120 is used to acquire historical load data of the air conditioner during at least one target time period in response to an energy-saving control request;

[0252] The sixth determining module 1130 is used to determine the load orientation parameters of the target environment where the air conditioner is located based on historical load data for at least one target time period.

[0253] The second input module 1140 is used to input the associated data into the optimization control model to obtain the optimization control parameters; the associated data includes operating associated parameters and load orientation parameters;

[0254] The second control module 1150 is used to send optimized control parameters to the air conditioner so that the air conditioner operates according to the optimized control parameters.

[0255] The various modules in the energy-saving optimization device of the aforementioned air conditioner can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0256] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an energy-saving optimization device for an air conditioner. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0257] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0258] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0259] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0260] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0261] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0262] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0263] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0264] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An air conditioner, characterized in that, include: The target sensor is configured to collect operating parameters related to the air conditioner; The controller, connected to the target sensor, is configured to: In response to an energy-saving control operation for an air conditioner, the operating correlation parameters and historical load data of the air conditioner for at least one target time period are acquired; Based on the historical load data of the at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located; The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes the operating associated parameters and the load orientation parameters. Control the air conditioner to operate according to the optimized control parameters.

2. The air conditioner according to claim 1, characterized in that, When the controller performs the task of determining the load orientation parameters of the target environment where the air conditioner is located based on historical load data of at least one target time period, it is configured to: For each target time period, determine the average load data of the historical load data corresponding to the target time period; Based on the average load data for each target time period, the load orientation parameters of the target environment where the air conditioner is located are determined.

3. The air conditioner according to claim 2, characterized in that, The target time period includes a first reference time period, a second reference time period, and a baseline time period; correspondingly, when the controller performs the step of determining the load orientation parameters of the target environment where the air conditioner is located based on the average load data of each of the target time periods, it is configured to: The first load orientation parameter is determined based on the difference between the average load data of the first reference period and the base period, and its proportion in the average load data of the base period. The second load orientation parameter is determined based on the difference between the average load data of the second reference period and the base period, and its proportion in the average load data of the base period. The load orientation parameter is determined based on the difference between the first load orientation parameter and the second load orientation parameter.

4. The air conditioner according to claim 1, characterized in that, When the controller acquires historical load data of the air conditioner during at least one target time period, it is configured to: For each historical sample day, acquire the historical operating frequency data of the air conditioner during different target time periods within that historical sample day; For each target time period of the historical sample day, the historical operating frequency data of the target time period is input into the load determination model to obtain the historical load data of the target time period; And / or, For each target time period of the historical sample day, the historical load data corresponding to the historical operating frequency data of the target time period is determined according to the preset energy efficiency relationship.

5. The air conditioner according to any one of claims 1-4, characterized in that, The associated data also includes meteorological impact parameters; correspondingly, the controller is further configured to: Acquire current meteorological element data and target weather type; the meteorological element data corresponds to at least one element type; determine the target fusion weights of different element types that match the target weather type according to a preset matching relationship; The meteorological impact parameters are determined based on meteorological element data of different element types and corresponding target fusion weights.

6. The air conditioner according to any one of claims 1-4, characterized in that, The associated data also includes load association parameters; correspondingly, the controller is further configured to: Call the remote service interface to determine the load association parameters of the air conditioner; The load correlation parameters are determined through the following steps: Obtain the historical frequency of temperature reaching the air conditioner within a historical time period and the historical average load under preset operating conditions; and, Obtain a preset reference frequency and a reference average load; the reference average load is determined based on the historical load of different reference air conditioners. The target historical load is determined based on the historical temperature frequency and the historical average load. The target reference load is determined based on the preset reference frequency and the reference average load. The load correlation parameters are determined based on the ratio of the sum of the target historical load and the target reference load to the target frequency; the target frequency is the sum of the historical temperature reaching frequency and the preset reference frequency.

7. The air conditioner according to any one of claims 1-4, characterized in that, The associated data also includes dynamic average load parameters; correspondingly, the controller is further configured to: Obtain the target load of the air conditioner at different historical moments; For each historical moment, determine the time decay weight of that historical moment; The dynamic average load parameters are determined based on the target load at different historical moments and the corresponding time decay weights.

8. The air conditioner according to any one of claims 1-4, characterized in that, The optimized control model includes a load prediction sub-model and a target optimization sub-model; correspondingly, the controller, when executing the process of inputting associated data into the optimized control model, obtains optimized control parameters including: The associated data is input into the prediction sub-model to obtain the temperature prediction type, load prediction value, and temperature duration prediction value; if the temperature prediction type is the temperature reachable type, the load prediction value and temperature duration prediction value are input into the target optimization sub-model to obtain the optimization control parameters.

9. A server, characterized in that, include: A communication device is configured to communicate with an air conditioner; and at least one processor, connected to the communication device, and configured to: Obtain the energy-saving control request sent by the air conditioner. The energy-saving control request includes operating parameters. In response to the energy-saving control request, historical load data of the air conditioner during at least one target time period is obtained; Based on the historical load data of the at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located; The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes the operating associated parameters and the load orientation parameters. The optimized control parameters are sent to the air conditioner so that the air conditioner operates according to the optimized control parameters.

10. An energy-saving optimization method for an air conditioner, characterized in that, include: In response to an energy-saving control operation for an air conditioner, the system acquires the operating parameters associated with the air conditioner, as well as the historical load data of the air conditioner for at least one target time period. Based on the historical load data of the at least one target time period, determine the load orientation parameters of the target environment where the air conditioner is located; The associated data is input into the optimization control model to obtain the optimization control parameters; the associated data includes the operating associated parameters and the load orientation parameters. Control the air conditioner to operate according to the optimized control parameters.

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