Air conditioner adjusting method and air conditioner

Abstract

The invention provides an air conditioner adjusting method and an air conditioner. According to the highest simulation temperature and the lowest design temperature of a room where the air conditioner is installed and the highest simulation temperature and the lowest design temperature of a target room with the highest simulation temperature in a building, a first numerical value for quantifying the temperature fluctuation range of the room relative to the target room is determined; according to the highest control temperature and the lowest control temperature of the air conditioner, the highest outdoor temperature in the target time period and the lowest design temperature of the target room, a second numerical value for evaluating the matching degree of the air conditioner temperature control capacity and the climate extreme condition is determined; according to the temperature difference between the current outdoor temperature and the lowest design temperature of the target room, the first numerical value and the second numerical value, a temperature compensation value is determined; the reference temperature is adjusted through the temperature compensation value to obtain the target temperature, the air conditioner temperature is adjusted, the air conditioner temperature is automatically adjusted by sensing the environment temperature change in real time and combining the air conditioner temperature control capacity and the room building characteristics, manual intervention is not needed, and intelligent adjustment of the air conditioner is achieved.

Description

Technical Field

[0001] This application relates to the field of multi-split air conditioning energy-saving technology, and in particular to an air conditioning regulation method and an air conditioner. Background Technology

[0002] To conserve resources, energy-saving measures in related technologies mainly fall into two categories: equipment-level energy saving and time-based energy saving. Equipment-level energy saving typically involves improving the energy efficiency of individual components such as compressors and heat exchangers, but its widespread adoption is limited by the high cost and long development cycle of hardware modifications. Time-based energy saving generally employs a fixed timetable strategy, executing equipment control according to configured fixed parameters at set intervals. This approach cannot dynamically adapt to changes in building load, such as fluctuations in pedestrian traffic or weather conditions, leading to over-cooling / over-heating. Most solutions lack artificial intelligence (AI) learning capabilities, requiring frequent manual adjustments by users, making the operation cumbersome and reliant on experience.

[0003] Therefore, how to intelligently adjust the air conditioner according to the actual situation has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides an air conditioning regulation method and an air conditioner to solve the problem of energy supply deviation and energy waste caused by the reliance on manual experience or fixed strategies in energy-saving control in the prior art.

[0005] In a first aspect, this application provides an air conditioning adjustment method, the method comprising:

[0006] A first value is determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and the lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located.

[0007] A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions.

[0008] The temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value.

[0009] The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

[0010] Secondly, this application provides an intelligent air conditioning control device, the device comprising:

[0011] The module determines a first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room. This first value quantifies the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located. A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value evaluates the matching degree between the air conditioner's temperature control capability and extreme climatic conditions. Finally, a temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the lowest design temperature of the target room, as well as the first and second values.

[0012] The control module is used to adjust the preset reference temperature using the temperature compensation value to obtain the target temperature, and to adjust the set temperature of the air conditioner to the target temperature.

[0013] Thirdly, embodiments of this application also provide an air conditioner, the air conditioner including a processor, the processor being configured to execute a computer program stored in a memory to implement the steps of any of the air conditioner adjustment methods described above.

[0014] Fourthly, embodiments of this application also provide an electronic device, the electronic device including a processor, the processor being configured to execute a computer program stored in a memory to implement the steps of any of the above-described air conditioning adjustment methods.

[0015] Fifthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the air conditioning adjustment methods described above.

[0016] In this embodiment, a first value is determined to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room, based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room in the building where the room is located. A second value is determined to evaluate the matching degree between the air conditioner's temperature control capability and extreme climatic conditions, based on the air conditioner's highest control temperature and lowest control temperature, the highest outdoor temperature during the simulated target time period, and the target room's lowest design temperature. Then, a temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the target room's lowest design temperature, as well as the determined first and second values. The temperature compensation value is then used to adjust the preset reference temperature to obtain the target temperature, and the air conditioner's set temperature is adjusted to the target temperature. By sensing changes in ambient temperature in real time and combining the air conditioner's temperature control capability with the characteristics of the building, the set temperature of the air conditioner is automatically adjusted without manual intervention, achieving intelligent air conditioning control. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an intelligent air conditioning adjustment process provided in this application embodiment;

[0019] Figure 2 A flowchart illustrating an intelligent air conditioning adjustment process provided in this application embodiment;

[0020] Figure 3 This is a schematic diagram of the structure of an intelligent air conditioning control device provided in an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art are within the scope of protection of this application.

[0023] This application provides an air conditioning adjustment method and an air conditioner. In this method, a first value is determined based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room. This first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is installed. A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the matching degree between the air conditioner's temperature control capability and extreme climatic conditions. A temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the lowest design temperature of the target room, as well as the first and second values. The temperature compensation value is used to adjust a preset reference temperature to obtain the target temperature, and the air conditioner's set temperature is adjusted to the target temperature.

[0024] Figure 1 This application provides a flowchart illustrating an intelligent air conditioning adjustment process, as shown in the embodiments below. Figure 1 As shown, the process includes the following steps:

[0025] S101: Based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, a first value is determined. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the room is located.

[0026] The air conditioning regulation method provided in this application is applied to temperature control equipment, such as air conditioners.

[0027] Since different rooms have different building structures, their heat preservation and insulation capabilities also differ. Therefore, in this embodiment of the application, the temperature fluctuation range of the room where the air conditioner is installed relative to the room with the highest simulated temperature in the building can be quantified.

[0028] In this embodiment of the application, a first value can be determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, as well as the highest simulated temperature and the lowest design temperature of the target room. The target room is the room in the building where the air conditioner is installed located that has the highest simulated temperature.

[0029] Specifically, the first value can be determined based on the following formula:

[0030] First value = (highest simulated temperature of the room where the air conditioner is installed - lowest design temperature of the room where the air conditioner is installed) / (highest simulated temperature of the target room - lowest design temperature of the target room).

[0031] S102: Determine a second value based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. The second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions.

[0032] Since air conditioners from different manufacturers and models have varying temperature control capabilities, this embodiment of the application can determine a second value for evaluating the matching degree between the air conditioner's temperature control capability and extreme climatic conditions. In this embodiment, the second value can be determined based on the air conditioner's highest and lowest control temperatures, the highest outdoor temperature during a simulated target time period, and the lowest design temperature of the target room. The target time period can be the highest outdoor temperature during a simulated cooling season.

[0033] Specifically, the second value can be determined based on the following formula:

[0034] The second value = (highest control temperature - lowest control temperature) / (highest outdoor temperature during the simulated target time period - lowest design temperature of the target room).

[0035] In the embodiments of this application, the smaller the second value, the more limited the temperature control capability of the air conditioner under extreme climate conditions, and the more careful compensation is required.

[0036] S103: Determine a temperature compensation value based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value.

[0037] After determining the first and second values, the temperature compensation value can be determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first and second values.

[0038] In this embodiment, the product of the first and second values ​​can be determined as the correction coefficient. For ease of understanding, the correction coefficient can be expressed using the following formula:

[0039]

[0040] Wherein, factor represents the correction factor; T_max_sim represents the highest simulated temperature of the room where the air conditioner is installed; T_min_design represents the lowest design temperature of the room where the air conditioner is installed; T_max_sim_worst represents the highest simulated temperature of the target room; T_min_design_worst represents the lowest design temperature of the target room; T_max_ctrl represents the highest control temperature of the air conditioner; T_min_ctrl represents the lowest control temperature of the air conditioner; and T_max_outdoor represents the highest outdoor temperature during the target time period of the simulation.

[0041] As shown in the formula above, once the air conditioner and the room where it is installed are determined, all parameter values ​​in the formula are fixed. The highest and lowest control temperatures of the air conditioner are configurable constants, while the values ​​of other parameters can be derived from the building mechanism model. In this embodiment, the Swell building energy consumption simulation software can be used to draw each floor and room according to the building drawings, setting parameters such as materials, doors and windows, climate, and equipment, considering the thermal inertia of the building envelope and the room's purpose, to establish a building mechanism model. The model is then imported into Swell's thermal comfort module for simulation calculations, exporting the hourly indoor temperature of all rooms throughout the year, as well as other parameters. In other words, in this embodiment, the temperature compensation value is dynamically adjusted by comparing the building thermal environment simulation results with the equipment control capabilities.

[0042] For example, the correction factor = (33-26) / (43-24)*(25-20) / (38-24) = 0.13.

[0043] Once the correction factor is determined, the temperature compensation value can be determined based on the following formula:

[0044] ΔT_weather=(-1)*factor*(T_out-T_min_design)

[0045] Where ΔT_weather represents the temperature compensation value; factor represents the correction coefficient; T_out represents the current outdoor temperature; and T_min_design represents the minimum design temperature of the target room.

[0046] The reason for multiplying by -1 in the above formula is that when using the temperature compensation value to compensate for the reference temperature, it is necessary to subtract it from the reference temperature.

[0047] In this embodiment of the application, when the outdoor temperature rises, the temperature compensation value increases proportionally, but linear growth is suppressed by a correction coefficient.

[0048] For example, ΔT_weather = -1 * 0.13 * (32 - 24) = -1.04.

[0049] S104: The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

[0050] After obtaining the temperature compensation value, the preset reference temperature can be adjusted using this value to obtain the target temperature. In other words, the preset reference temperature can be increased or decreased to achieve the most comfortable temperature for the user. Once the target temperature is obtained, the air conditioner's set temperature can be adjusted to that target temperature.

[0051] Specifically, the target temperature can be determined based on the following formula:

[0052] T_set = T_base + ΔT_weather

[0053] Where T_set represents the target temperature; T_base represents the reference temperature; and ΔT_weather represents the temperature compensation value.

[0054] It should be noted that, since indoor and outdoor temperatures change in real time, an adjustment frequency can be configured in this embodiment. For example, it can be dynamically adjusted every 30 minutes. Those skilled in the art can configure this adjustment frequency as needed.

[0055] In one possible implementation, different control frequencies can be set for different time periods. For example, the control frequency is 30 minutes during working hours, and no intelligent control is performed during non-working hours.

[0056] In this embodiment, a first value is determined to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room, based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room in the building where the room is located. A second value is determined to evaluate the matching degree between the air conditioner's temperature control capability and extreme climatic conditions, based on the air conditioner's highest control temperature and lowest control temperature, the highest outdoor temperature during the simulated target time period, and the target room's lowest design temperature. Then, a temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the target room's lowest design temperature, as well as the determined first and second values. The temperature compensation value is then used to adjust the preset reference temperature to obtain the target temperature, and the air conditioner's set temperature is adjusted to the target temperature. By sensing changes in ambient temperature in real time and combining the air conditioner's temperature control capability with the characteristics of the building, the set temperature of the air conditioner is automatically adjusted without manual intervention, achieving intelligent air conditioning control.

[0057] To further improve the accuracy of air conditioner temperature control, based on the above embodiments, in this embodiment, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0058] Determine the orientation of the room where the air conditioner will be installed;

[0059] Based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods, the target orientation compensation value corresponding to the current orientation is determined;

[0060] The target temperature is adjusted using the target orientation compensation value.

[0061] Since indoor temperature is closely related to the room's orientation—for example, the temperature of a room facing south will be higher than that of a room facing north during the same period—in this embodiment of the application, the orientation of the room where the air conditioner is installed can be obtained before adjusting the air conditioner's set temperature to the target temperature. This orientation can be pre-configured by relevant personnel.

[0062] To facilitate determining the target orientation compensation value corresponding to the orientation of the room where the air conditioner is installed, in this embodiment, a pre-configured correspondence between different orientations and orientation compensation values ​​for different time periods can be established. When adjusting the air conditioner temperature subsequently, the target orientation compensation value for the current time period can be determined based on this pre-configured correspondence between different orientations and orientation compensation values ​​for different time periods.

[0063] Specifically, the space can be divided into five orientations: east, west, south, north, and center. Time can be divided into six time periods: morning (6:00-8:00), forenoon (8:00-12:00), noon (12:00-14:00), afternoon (14:00-16:00), evening (16:00-19:00), and night (19:00-6:00 the next day). The pre-configured correspondence between different orientations and orientation compensation values ​​for different time periods is shown in Table 1 below.

[0064] Table 1

[0065] Orientation time Compensation value East morning -0.1℃ East morning -0.75℃ East noon 0℃ East afternoon +0.25℃ East evening +0.5℃ East night +1℃ West morning +0.5℃ West morning 0℃ West noon -0.75℃ West afternoon -1℃ West evening -1℃ West night +1℃ South morning -0.5℃ South morning -0.75℃ South noon -1℃ South afternoon -0.75℃ South evening -0.5℃ South night +1℃ north morning +1℃ north morning +0.75℃ north noon 0℃ north afternoon +0.5℃ north evening +0.75℃ north night +1℃

[0066] After determining the target orientation compensation value, the target temperature can be adjusted using this value before setting the air conditioner to the target temperature.

[0067] Specifically, the final target temperature can be obtained based on the following formula:

[0068] T_set=T_base+ΔT_weather+ΔT_orientation

[0069] Where T_set represents the target temperature; T_base represents the reference temperature; ΔT_weather represents the temperature compensation value; and ΔT_orientation represents the target orientation compensation value.

[0070] In one possible implementation, when configuring the correspondence between different orientations and orientation compensation values ​​for different time periods, the orientation compensation value can be a coefficient that the target temperature needs to be multiplied by. When it is necessary to increase the target temperature, the orientation compensation value can be any value greater than 1; when it is necessary to decrease the target temperature, the orientation compensation value can be any value less than 1. The specific values ​​of the orientation compensation values ​​corresponding to rooms with different orientations at different time periods can be configured as needed by those skilled in the art.

[0071] To further improve the accuracy of air conditioner temperature control, based on the above embodiments, in this embodiment, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0072] Get the floor number of the room where the air conditioner is installed;

[0073] Based on the preset correspondence between different floors and floor compensation values, the target floor compensation value corresponding to the floor is determined;

[0074] The target temperature is adjusted using the target floor compensation value.

[0075] Since the temperature of different rooms is also affected by the floor height, for example, the indoor temperature on the first floor is lower than that on the 33rd floor during the same period, different correspondences between floors and floor compensation values ​​can be pre-configured in this embodiment of the application.

[0076] Specifically, the following division can be made, with a compensation value of 0.2℃ set for every 10 floors of building height:

[0077] Low zones (floors 1-10): Reference temperature +0℃;

[0078] Central Zone (Floors 11-20): -0.2℃ (compensation for hot air accumulation at higher floors);

[0079] High-rise area (above the 21st floor): -0.4℃.

[0080] In this embodiment of the application, before adjusting the set temperature of the air conditioner to the target temperature, the floor of the room where the air conditioner is installed can be obtained, and the target floor compensation value corresponding to the floor where the room is located can be determined according to the preset correspondence between different floors and floor compensation values, and the target temperature can be adjusted using the target floor compensation value.

[0081] Specifically, the final target temperature can be determined based on the following formula:

[0082] Target temperature = T_set = T_base + ΔT_weather + ΔT_floor

[0083] Where T_set represents the target temperature; T_base represents the base temperature; ΔT_weather represents the temperature compensation value; and ΔT_floor represents the target floor compensation value.

[0084] In one possible implementation, when configuring the correspondence between different floors and floor compensation values, the floor compensation value can be a coefficient that the target temperature needs to be multiplied by. When the target temperature needs to be increased, the floor compensation value can be any value greater than 1; when the target temperature needs to be decreased, the floor compensation value can be any value less than 1. The specific values ​​of the different floors and floor compensation values ​​can be configured as needed by those skilled in the art.

[0085] To further improve the accuracy of air conditioner temperature control, based on the above embodiments, in this embodiment, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0086] Acquire the pedestrian flow data collected by the pedestrian flow sensor installed in the room;

[0087] Based on the flow of people, determine the number of people currently in the room;

[0088] The target temperature is adjusted based on the number of people, and the target temperature is negatively correlated with the number of people.

[0089] Since room temperature is closely related to the number of people in the room, in this embodiment, after determining the target temperature, before adjusting the air conditioner's set temperature to the target temperature, it is possible to acquire the flow data collected by the room's occupancy sensor, determine the current number of people in the room based on this flow data, and adjust the target temperature accordingly. In this embodiment, the more people currently in the room, the lower the target temperature. That is, the target temperature is negatively correlated with the number of people currently in the room. For example, the target temperature can be adjusted by decreasing it by 0.1°C for every 10 additional people in the room.

[0090] To further conserve resources, based on the above embodiments, in this embodiment of the application, before determining the first value according to the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0091] If the room where the air conditioner is installed is a public area, then continue with the next step of determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room.

[0092] If the room is a non-public area, the outdoor temperature, the current season, and the current time are input into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and the set temperature of the air conditioner is adjusted to the target temperature.

[0093] To further conserve resources, in this embodiment, space can be divided into two main categories according to its use: public areas and private areas. For example, public areas may include corridors, lobbies, etc., while private areas may include offices, meeting rooms, etc. These private areas can be understood as non-public areas. In this embodiment, the air conditioning in the public areas can be entirely controlled by the calculation method described in the above-described format example.

[0094] In one possible implementation, the air conditioner can be turned on and off according to preset rules in public areas, while in non-public areas, since get off work hours are not fixed, the air conditioner can be manually turned off by staff after get off work. To avoid wasting resources, the shutdown status can be checked after a set time. For example, after 9:00 PM, a scheduled task can be used to perform a shutdown check every hour to prevent situations where the air conditioner is forgotten to be turned off.

[0095] In this embodiment of the application, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, it can be determined whether the room where the air conditioner is currently installed belongs to a public area.

[0096] If it is determined that the room where the air conditioner is installed is a public area, then the subsequent steps of determining the first value can be performed based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, as well as the highest simulated temperature and lowest design temperature of the target room.

[0097] If the room where the air conditioner is installed is determined to be a non-public area, the outdoor temperature, current season, and current time can be input into a pre-trained temperature prediction model to obtain the target temperature output by the model, and the air conditioner's set temperature can be adjusted to the target temperature. This temperature prediction model can be trained using training data determined based on user habits.

[0098] Since different users have different behavioral habits at different times, in this embodiment of the application, the trained temperature prediction model can be retrained at preset time intervals.

[0099] In this embodiment, during any training of the temperature model, it can be determined whether the user has adjusted the set target temperature between the current time and the last model training. If so, it indicates that the user believes the target temperature predicted by the current temperature prediction model is not their preferred temperature, and the current temperature prediction model needs to be retrained.

[0100] In this embodiment of the application, if it is determined that the user adjusted the set target temperature between the current time and the last model training period, the adjusted corrected temperature and the target time period when the user adjusted the temperature can be obtained. The corrected temperature and the target time period can be the temperature and time period most recently adjusted by the user. For example, the target time period can be morning, forenoon, noon, afternoon, evening, etc.

[0101] Since adjusting the temperature manually by the user would result in a very limited adjustable range if only the sample data corresponding to the current outdoor temperature were adjusted based on the temperature difference between the current corrected temperature and the target temperature, multiple training iterations might be needed to meet the user's needs, this embodiment of the application, after obtaining the corrected temperature, determines the difference between the corrected temperature and the target temperature at the time of the corresponding adjustment. This difference can be used as the user's temperature preference. Then, based on this difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted.

[0102] Specifically, suppose a user recently adjusted their target temperature from 25℃ at 12:00 on the 25th to 23℃, confirming the corrected temperature as 23℃ and the target time period as noon. Then, the standard temperatures of all sample data corresponding to this noon time period in the training set can be adjusted. Assume the training data corresponding to the target time period in the training set includes the following sample data:

[0103] Sample data 1: Summer - noon - outdoor temperature 32℃ - standard indoor temperature 25℃;

[0104] Sample data 2: Summer - noon - outdoor temperature 33℃ - standard indoor temperature 24.5℃;

[0105] Sample data 3: Summer - noon - outdoor temperature 30℃ - indoor standard temperature 26℃.

[0106] If the difference between the corrected temperature and the target temperature is -2℃, then the sample data adjusted based on this difference is as follows:

[0107] Sample data 1: Summer - noon - outdoor temperature 32℃ - standard indoor temperature 23℃;

[0108] Sample data 2: Summer - noon - outdoor temperature 33℃ - indoor standard temperature 22.5℃;

[0109] Sample data 3: Summer - noon - outdoor temperature 30℃ - indoor standard temperature 24℃.

[0110] After obtaining the adjusted sample data for the target time period, the temperature prediction model can be trained using this adjusted sample data to obtain a retrained temperature prediction model. How to train the model based on the training data is existing technology, and this application will not elaborate on this process in its embodiments.

[0111] In this embodiment of the application, the deep integration of building physics model and machine learning technology has achieved a technological leap from "experience-based control" to "predictive control", maximizing energy efficiency while ensuring comfort.

[0112] To further conserve resources, based on the above embodiments, the method in this application embodiment further includes:

[0113] The target temperature, the current indoor temperature, and the current time are input into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and the set wind speed of the air conditioner is adjusted to the target wind speed.

[0114] Under normal circumstances, the air conditioner will continue to run until the target temperature is reached. If the current indoor temperature differs significantly from the target temperature, and the current air conditioner fan speed is too low, the air conditioner will run for an extended period. Therefore, in this embodiment, the target fan speed can be predicted based on the deviation between the current indoor temperature and the target temperature, and then the air conditioner's set fan speed can be adjusted to the target fan speed.

[0115] In this embodiment, a wind speed prediction model can be used to predict the target wind speed. The target temperature, the current indoor temperature, and the current time can be input into a pre-trained wind speed prediction model to obtain the target wind speed output by the model.

[0116] In this embodiment, the training process of the wind speed prediction model is similar to that of the temperature prediction model. The wind speed prediction model can be trained at preset time intervals, and the following steps are performed each time the wind speed prediction model is trained:

[0117] If the user adjusted the target wind speed during the period from the current time to the last model training, then obtain the adjusted corrected wind speed and the target time period when the user adjusted the wind speed.

[0118] Determine the difference between the corrected wind speed and the target wind speed;

[0119] Based on this difference, the standard wind speed of the sample data corresponding to the target time period in the training set is adjusted. The sample data stored in the training set was determined during the last model training. The training set stores the correspondence between different target temperatures, different time periods, different outdoor temperatures and standard wind speeds.

[0120] The wind speed prediction model was trained using sample data adjusted for the target time period, resulting in a retrained wind speed prediction model.

[0121] The specific training process is similar to that of the temperature prediction model described above. The training process of the wind speed prediction model will not be repeated in this embodiment.

[0122] In one possible implementation, wind speed levels can be divided into three categories: low, medium, and high. The target wind speed is determined based on the temperature difference between the indoor temperature and the target temperature. For example, a temperature difference within 2°C indicates a low target wind speed, a difference between 2°C and 4°C indicates a medium target wind speed, and a difference greater than 4°C indicates a high target wind speed. In this embodiment, the correspondence between temperature difference and wind speed can be mapped through configuration.

[0123] To further conserve resources, based on the above embodiments, in this embodiment of the application, before determining the first value according to the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0124] Determine the first difference between the current outdoor temperature and the indoor design temperature;

[0125] Determine the second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature;

[0126] Determine a first ratio between the first difference and the second difference;

[0127] The first product of the first ratio and the first benchmark value of the preset early power-on time is determined as the candidate early power-on time;

[0128] The minimum value between the candidate early power-on time and the first benchmark value is determined as the target early power-on time;

[0129] Based on the preset start-up time and the target advance start-up time, the current start-up time is determined, and the air conditioner is turned on at the current start-up time.

[0130] To ensure users experience a comfortable temperature upon entering a room, this embodiment of the application can control the air conditioner to turn on earlier. However, turning it on too early wastes resources, while turning it on too late means the room temperature hasn't reached a comfortable level by the time the user enters. Therefore, this embodiment of the application provides a scheme for determining the earlier start-up time. In this embodiment, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the earlier start-up time is calculated based on the current actual situation.

[0131] In this embodiment, a first difference between the current outdoor temperature and the indoor design temperature can be determined, as well as a second difference between the predicted highest outdoor temperature and the indoor design temperature at the preset start-up time. A first ratio between the first difference and the second difference is determined, and then the first product of the first ratio and a first reference value of the preset early start-up time is determined as the candidate early start-up time.

[0132] For ease of understanding, the candidate early power-on time can be determined based on the following formula:

[0133]

[0134] Where T_out represents the current outdoor temperature; T_design represents the indoor design temperature; T_max represents the predicted maximum outdoor temperature at the preset power-on time; and Baseline represents the first baseline value for the preset early power-on time.

[0135] After determining the candidate early power-on times, the minimum value between the candidate early power-on times and the first benchmark value can be determined as the target early power-on time. Specifically, the determination process of the target early power-on time can be represented by the following formula:

[0136]

[0137] Where preOpen represents the target early power-on time; T_out represents the current outdoor temperature; T_design represents the indoor design temperature; T_max represents the highest outdoor temperature at the predicted preset power-on time; Baseline represents the first baseline value of the preset early power-on time; and min() represents the minimum value between the candidate early power-on time and the first baseline value.

[0138] In this embodiment, the first baseline value for the preset advance start-up time is a fixed value, such as 10 minutes, which can be configured as needed by those skilled in the art. The indoor design temperature is also a fixed value based on the building mechanism model, and the predicted highest outdoor temperature at the preset start-up time is also a fixed value; only the outdoor temperature is a variable. That is to say, in this embodiment, the advance start-up time of the air conditioner is dynamically adjusted according to the deviation ratio between the real-time outdoor temperature and the design conditions.

[0139] After determining the target advance start time, the current start time can be determined based on the preset start time and the target advance start time, and the air conditioner can be turned on at that current start time. After the air conditioner is turned on, the step of determining the first value is performed based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, as well as the highest simulated temperature and the lowest design temperature of the target room.

[0140] Specifically, assuming (33-26) / (38-26)*10 = 5.8 minutes, the device needs to be powered on 5.8 minutes before the preset power-on time. In this embodiment, the higher the outdoor temperature, the greater the number of minutes required to power on in advance.

[0141] In one possible implementation, the system can be configured to allow for earlier activation based on the room's orientation. For example, during the cooling season, on sunny days, air conditioners in east-facing rooms may need to be activated earlier.

[0142] To further conserve resources, based on the above embodiments, the method in this application embodiment further includes:

[0143] Determine the third difference between the outdoor temperature and the indoor design temperature;

[0144] The fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature;

[0145] Determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value of a preset early shutdown time;

[0146] The fifth difference between the second benchmark value and the second product value is determined as the candidate early shutdown time;

[0147] The minimum value between the candidate early shutdown time and the second benchmark value is determined as the target early shutdown time;

[0148] Based on the preset shutdown time and the target early shutdown time, the current shutdown time is determined, and the air conditioner is turned off at the current shutdown time.

[0149] If the air conditioner is only turned off when no one is in the room, the indoor temperature will remain at a comfortable level even after everyone has left, resulting in wasted resources. Therefore, in this embodiment, an early shutdown time can be set to minimize resource waste. However, turning off the air conditioner too early may result in the room temperature rising before everyone has left, while turning it off too late may also lead to resource waste. To conserve resources, in this embodiment, the early shutdown time can be determined by considering the current indoor temperature and the characteristics of the room's building structure.

[0150] In this embodiment, a third difference between the outdoor temperature and the indoor design temperature, and a fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature, can be determined. After determining the third and fourth differences, a second ratio between the third and fourth differences, and a second product of the second ratio and a second reference value for the preset early shutdown time, are determined. After determining the second product, the minimum value between the candidate early shutdown time and the second reference value is determined as the target early shutdown time.

[0151] For ease of understanding, the target early shutdown time can be determined based on the following formula:

[0152]

[0153] Where preClose represents the target early shutdown time; Baseline2 represents the second baseline value for the preset early shutdown time; T out Indicates the current outdoor temperature; T design T_max represents the indoor design temperature; T_max represents the predicted maximum outdoor temperature at the preset shutdown time.

[0154] In the above formula This represents the candidate early shutdown time. min() represents the minimum value between the candidate early shutdown time and the second baseline.

[0155] In this embodiment, the baseline value for the early shutdown time is a fixed value, which can be set by those skilled in the art as needed, such as 10 minutes. The indoor design temperature, obtained from the building mechanism model, is also a fixed value, as is the predicted highest outdoor temperature at the preset shutdown time. Only the outdoor temperature is a variable. That is to say, in this embodiment, the early shutdown time of the equipment is dynamically adjusted based on the difference between the current outdoor temperature and the design conditions.

[0156] After determining the target early shutdown time, the current shutdown time can be determined based on the preset shutdown time and the target early shutdown time, and the air conditioner can be turned off at the determined current shutdown time.

[0157] Specifically, assuming the determined early shutdown time is: 10 - (33 - 26) / (38 - 26) * 10 = 4.2 minutes, that is, the device needs to be shut down 4.2 minutes before the end of the workday. In this embodiment, the higher the outdoor temperature, the smaller the number of minutes required for early shutdown.

[0158] In one possible implementation, if the indoor and outdoor temperatures are both at a preset comfortable human temperature during the spring-autumn transition season or during rainy summer weather, the air conditioner can be prevented from turning on.

[0159] In one possible implementation, since most people are not working on weekends or holidays in the office building, a target date can be set in this embodiment. This target date can be understood as a working day. On the corresponding target date, all air conditioners in the office building are turned on, and the intelligent air conditioning adjustment process described in the above embodiments is executed. On non-target dates, only the air conditioners in some designated rooms are turned on, and the intelligent air conditioning adjustment process described in the above embodiments is executed.

[0160] The process of intelligent air conditioning adjustment is explained below with reference to a specific embodiment. Figure 2 This application provides a flowchart illustrating an intelligent air conditioning adjustment process, as shown in the embodiments below. Figure 2 As shown: During device initialization, the algorithms described in the above embodiments are activated. Subsequently, each day it is determined whether it is a workday. If not, some public area air conditioners are turned on. If so, the air conditioners in the east-facing public and private areas are turned on in advance, and the remaining public and private area air conditioners are turned on at the preset start-up time. The public areas are public spaces, and the private areas are non-public spaces. The activated air conditioners are then adjusted every half hour.

[0161] In this embodiment, a public area control method is used for the public area. This public area control method is the prediction method described in the above embodiments that does not involve a pre-trained model. Public area control parameters, such as temperature, wind speed, and on / off times (e.g., turning off when a comfortable temperature is reached, or turning off at the end of the workday), can be determined based on the methods described in the above real-time methods. Furthermore, due to the diverse number of people in the public area, upper and lower temperature limits can be set and controlled.

[0162] In this embodiment, a private zone control method is used for the private zone. This private zone control method is the prediction method described in the above embodiments, which involves designing and pre-training a model. Private zone control parameters can be determined according to the methods described in the above real-time methods, such as temperature (determined through AI self-learning model training), wind speed (determined through AI self-learning model training), and power-on / off times (e.g., powering off when a comfortable temperature is reached, and checking for forgotten power-off after 9 PM). In this embodiment, upper and lower temperature limits can also be set for the private zone and controlled and locked. In this embodiment, the temperature prediction model and wind speed prediction model are trained at preset time intervals, and the data used during training is determined based on user control operations. Since the above embodiments have already provided detailed descriptions, this embodiment will not repeat this process.

[0163] In addition, for ease of management, in this embodiment of the application, the corresponding adjustment information can be recorded in the algorithm control log each time the air conditioner is intelligently adjusted, and the relevant information of each user adjustment can be recorded in the user adjustment log.

[0164] It should be noted that both the stage of controlling the public area and the stage of determining the training data for the model are based on the building mechanism model, meteorological data, pedestrian flow, floor and orientation data. The specific determination process has been described in detail in the above embodiments.

[0165] In this embodiment, the control parameters of the multi-split air conditioning system are dynamically calculated, taking into account variables such as building orientation, floor height, area usage, pedestrian traffic, time period, and meteorological data to calculate the optimal control parameters in real time. Traditional energy-saving control relies on manual experience or fixed strategies, resulting in low energy efficiency, slow response, and extensive management, leading to energy supply deviations and energy waste. In this embodiment, by sensing environmental changes in real time and combining them with a building mechanism model, the system automatically adjusts equipment operating parameters and continuously iterates algorithms based on historical data and usage habits to automatically adapt to seasonal and regional functional changes without manual intervention. This better meets user needs and habits, ultimately achieving precise "on-demand energy supply" control and reducing overall energy consumption by 15%-30%.

[0166] Based on the same inventive concept, embodiments of this application provide an intelligent air conditioning control device. Figure 3 Please refer to the schematic diagram of an intelligent air conditioning control device provided in this application embodiment. Figure 3 The device includes:

[0167] The determining module 301 is used to determine a first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located. The determining module 301 is used to determine a second value based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. The second value is used to evaluate the matching degree between the air conditioner's temperature control capability and extreme climate conditions. The determining module 301 is used to determine a temperature compensation value based on the temperature difference between the current outdoor temperature and the lowest design temperature of the target room, as well as the first value and the second value.

[0168] The control module 302 is used to adjust the preset reference temperature using the temperature compensation value to obtain the target temperature, and to adjust the set temperature of the air conditioner to the target temperature.

[0169] In one possible implementation, the device further includes:

[0170] Module 303 is used to obtain the orientation of the room where the air conditioner is installed;

[0171] The determining module 301 is further configured to determine the target orientation compensation value corresponding to the current orientation based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods;

[0172] The control module 302 is also used to adjust the target temperature using the target orientation compensation value.

[0173] In one possible implementation, the acquisition module 303 is also used to acquire the floor of the room where the air conditioner is installed;

[0174] The determining module 301 is further configured to determine the target floor compensation value corresponding to the floor based on the preset correspondence between different floors and floor compensation values;

[0175] The control module 302 is also used to adjust the target temperature using the target floor compensation value.

[0176] In one possible implementation, the acquisition module 303 is further configured to acquire the people flow data collected by the people flow sensor installed in the room;

[0177] The determining module 301 is further configured to determine the number of people currently present in the room based on the people flow data;

[0178] The control module 302 is also used to adjust the target temperature according to the number of people, wherein the target temperature is negatively correlated with the number of people.

[0179] In one possible implementation, the control module 302 is further configured to, if the room where the air conditioner is installed is a public area, continue to execute the subsequent step of determining a first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room; if the room is a non-public area, input the outdoor temperature, the current season, and the current time into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and adjust the set temperature of the air conditioner to the target temperature.

[0180] In one possible implementation, the control module 302 is further configured to input the target temperature, the current indoor temperature, and the current time into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and adjust the set wind speed of the air conditioner to the target wind speed.

[0181] In one possible implementation, the device further includes:

[0182] The training module 304 is used to train the temperature prediction model at preset time intervals. Each time the temperature prediction model is trained, the following steps are performed: if the user adjusted the set target temperature during the period from the current time to the last model training, the adjusted corrected temperature and the target time period when the user adjusted the temperature are obtained; the difference between the corrected temperature and the target temperature is determined; based on the difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted, wherein the sample data stored in the training set is determined during the last model training, and the training set stores the correspondence between different seasons, different time periods, different outdoor temperatures and standard temperatures; the temperature prediction model is trained using the sample data adjusted for the target time period to obtain a retrained temperature prediction model.

[0183] In one possible implementation, the determining module 301 is further configured to: determine a first difference between the current outdoor temperature and the indoor design temperature; determine a second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature; determine a first ratio between the first difference and the second difference; determine a first product of the first ratio and a first reference value of the preset early start-up time as a candidate early start-up time; and determine the minimum value between the candidate early start-up time and the first reference value as the target early start-up time.

[0184] The control module 302 is also used to determine the current start time based on the preset start time and the target advance start time, and to turn on the air conditioner at the current start time.

[0185] In one possible implementation, the determining module 301 is further configured to: determine a third difference between the outdoor temperature and the indoor design temperature; determine a fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature; determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value for the preset early shutdown time; determine a fifth difference between the second reference value and the second product value as a candidate early shutdown time; and determine the minimum value between the candidate early shutdown time and the second reference value as the target early shutdown time.

[0186] The control module 302 is further configured to determine the current shutdown time based on the preset shutdown time and the target early shutdown time, and to turn off the air conditioner at the current shutdown time.

[0187] Based on the same inventive concept, embodiments of this application provide an electronic device that can implement the steps of the vehicle trajectory prediction method described above. Figure 4 This application provides a schematic diagram of an electronic device structure, such as... Figure 4 As shown, it includes: processor 401, communication interface 402, memory 403 and communication bus 404, wherein processor 401, communication interface 402 and memory 403 communicate with each other through communication bus 404.

[0188] The memory 403 stores a computer program. When the program is executed by the processor 401, the processor 401 performs the following steps:

[0189] A first value is determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and the lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located.

[0190] A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions.

[0191] The temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value.

[0192] The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

[0193] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0194] Determine the orientation of the room where the air conditioner will be installed;

[0195] Based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods, the target orientation compensation value corresponding to the current orientation is determined;

[0196] The target temperature is adjusted using the target orientation compensation value.

[0197] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0198] Get the floor number of the room where the air conditioner is installed;

[0199] Based on the preset correspondence between different floors and floor compensation values, the target floor compensation value corresponding to the floor is determined;

[0200] The target temperature is adjusted using the target floor compensation value.

[0201] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0202] Acquire the pedestrian flow data collected by the pedestrian flow sensor installed in the room;

[0203] Based on the flow of people, determine the number of people currently in the room;

[0204] The target temperature is adjusted based on the number of people, and the target temperature is negatively correlated with the number of people.

[0205] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0206] If the room where the air conditioner is installed is a public area, then continue with the next step of determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room.

[0207] If the room is a non-public area, the outdoor temperature, the current season, and the current time are input into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and the set temperature of the air conditioner is adjusted to the target temperature.

[0208] In one possible implementation, the method further includes:

[0209] The target temperature, the current indoor temperature, and the current time are input into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and the set wind speed of the air conditioner is adjusted to the target wind speed.

[0210] In one possible implementation, the method further includes:

[0211] The temperature prediction model is trained at preset time intervals, and the following steps are performed each time the temperature prediction model is trained:

[0212] If the user adjusted the target temperature during the period from the current time to the last model training, then obtain the adjusted corrected temperature and the target time period when the user adjusted the temperature.

[0213] Determine the difference between the corrected temperature and the target temperature;

[0214] Based on the difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted. The sample data stored in the training set is determined during the last model training. The training set stores the correspondence between different seasons, different time periods, different outdoor temperatures and standard temperatures.

[0215] The temperature prediction model is trained using the sample data adjusted for the target time period to obtain a retrained temperature prediction model.

[0216] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0217] Determine the first difference between the current outdoor temperature and the indoor design temperature;

[0218] Determine the second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature;

[0219] Determine a first ratio between the first difference and the second difference;

[0220] The first product of the first ratio and the first benchmark value of the preset early power-on time is determined as the candidate early power-on time;

[0221] The minimum value between the candidate early power-on time and the first benchmark value is determined as the target early power-on time;

[0222] Based on the preset start-up time and the target advance start-up time, the current start-up time is determined, and the air conditioner is turned on at the current start-up time.

[0223] In one possible implementation, the method further includes:

[0224] Determine the third difference between the outdoor temperature and the indoor design temperature;

[0225] The fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature;

[0226] Determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value of a preset early shutdown time;

[0227] The fifth difference between the second benchmark value and the second product value is determined as the candidate early shutdown time;

[0228] The minimum value between the candidate early shutdown time and the second benchmark value is determined as the target early shutdown time;

[0229] Based on the preset shutdown time and the target early shutdown time, the current shutdown time is determined, and the air conditioner is turned off at the current shutdown time.

[0230] Since the principle of the above-mentioned electronic device in solving the problem is similar to that of the air conditioning adjustment method, the implementation of the above-mentioned electronic device can be found in the embodiments of the method, and repeated parts will not be described again.

[0231] The communication bus mentioned in the above-mentioned electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used in the figure, but this does not indicate that there is only one bus or one type of bus. Communication interface 402 is used for communication between the above-mentioned electronic device and other devices. The memory can include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0232] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital instruction processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0233] Based on the same inventive concept, embodiments of this application provide an air conditioner, which includes a processor. When the processor executes a computer program stored in a memory, it performs the following steps:

[0234] A first value is determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and the lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located.

[0235] A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions.

[0236] The temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value.

[0237] The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

[0238] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0239] Determine the orientation of the room where the air conditioner will be installed;

[0240] Based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods, the target orientation compensation value corresponding to the current orientation is determined;

[0241] The target temperature is adjusted using the target orientation compensation value.

[0242] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0243] Get the floor number of the room where the air conditioner is installed;

[0244] Based on the preset correspondence between different floors and floor compensation values, the target floor compensation value corresponding to the floor is determined;

[0245] The target temperature is adjusted using the target floor compensation value.

[0246] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0247] Acquire the pedestrian flow data collected by the pedestrian flow sensor installed in the room;

[0248] Based on the flow of people, determine the number of people currently in the room;

[0249] The target temperature is adjusted based on the number of people, and the target temperature is negatively correlated with the number of people.

[0250] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0251] If the room where the air conditioner is installed is a public area, then continue with the next step of determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room.

[0252] If the room is a non-public area, the outdoor temperature, the current season, and the current time are input into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and the set temperature of the air conditioner is adjusted to the target temperature.

[0253] In one possible implementation, the method further includes:

[0254] The target temperature, the current indoor temperature, and the current time are input into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and the set wind speed of the air conditioner is adjusted to the target wind speed.

[0255] In one possible implementation, the method further includes:

[0256] The temperature prediction model is trained at preset time intervals, and the following steps are performed each time the temperature prediction model is trained:

[0257] If the user adjusted the target temperature during the period from the current time to the last model training, then obtain the adjusted corrected temperature and the target time period when the user adjusted the temperature.

[0258] Determine the difference between the corrected temperature and the target temperature;

[0259] Based on the difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted. The sample data stored in the training set is determined during the last model training. The training set stores the correspondence between different seasons, different time periods, different outdoor temperatures and standard temperatures.

[0260] The temperature prediction model is trained using the sample data adjusted for the target time period to obtain a retrained temperature prediction model.

[0261] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0262] Determine the first difference between the current outdoor temperature and the indoor design temperature;

[0263] Determine the second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature;

[0264] Determine a first ratio between the first difference and the second difference;

[0265] The first product of the first ratio and the first benchmark value of the preset early power-on time is determined as the candidate early power-on time;

[0266] The minimum value between the candidate early power-on time and the first benchmark value is determined as the target early power-on time;

[0267] Based on the preset start-up time and the target advance start-up time, the current start-up time is determined, and the air conditioner is turned on at the current start-up time.

[0268] In one possible implementation, the method further includes:

[0269] Determine the third difference between the outdoor temperature and the indoor design temperature;

[0270] The fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature;

[0271] Determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value of a preset early shutdown time;

[0272] The fifth difference between the second benchmark value and the second product value is determined as the candidate early shutdown time;

[0273] The minimum value between the candidate early shutdown time and the second benchmark value is determined as the target early shutdown time;

[0274] Based on the preset shutdown time and the target early shutdown time, the current shutdown time is determined, and the air conditioner is turned off at the current shutdown time.

[0275] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program executable by a processor. When the program runs on the processor, it causes the processor to perform the following steps:

[0276] A first value is determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and the lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located.

[0277] A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions.

[0278] The temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value.

[0279] The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

[0280] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0281] Determine the orientation of the room where the air conditioner will be installed;

[0282] Based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods, the target orientation compensation value corresponding to the current orientation is determined;

[0283] The target temperature is adjusted using the target orientation compensation value.

[0284] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0285] Get the floor number of the room where the air conditioner is installed;

[0286] Based on the preset correspondence between different floors and floor compensation values, the target floor compensation value corresponding to the floor is determined;

[0287] The target temperature is adjusted using the target floor compensation value.

[0288] In one possible implementation, after obtaining the target temperature and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes:

[0289] Acquire the pedestrian flow data collected by the pedestrian flow sensor installed in the room;

[0290] Based on the flow of people, determine the number of people currently in the room;

[0291] The target temperature is adjusted based on the number of people, and the target temperature is negatively correlated with the number of people.

[0292] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0293] If the room where the air conditioner is installed is a public area, then continue with the next step of determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room.

[0294] If the room is a non-public area, the outdoor temperature, the current season, and the current time are input into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and the set temperature of the air conditioner is adjusted to the target temperature.

[0295] In one possible implementation, the method further includes:

[0296] The target temperature, the current indoor temperature, and the current time are input into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and the set wind speed of the air conditioner is adjusted to the target wind speed.

[0297] In one possible implementation, the method further includes:

[0298] The temperature prediction model is trained at preset time intervals, and the following steps are performed each time the temperature prediction model is trained:

[0299] If the user adjusted the target temperature during the period from the current time to the last model training, then obtain the adjusted corrected temperature and the target time period when the user adjusted the temperature.

[0300] Determine the difference between the corrected temperature and the target temperature;

[0301] Based on the difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted. The sample data stored in the training set is determined during the last model training. The training set stores the correspondence between different seasons, different time periods, different outdoor temperatures and standard temperatures.

[0302] The temperature prediction model is trained using the sample data adjusted for the target time period to obtain a retrained temperature prediction model.

[0303] In one possible implementation, before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes:

[0304] Determine the first difference between the current outdoor temperature and the indoor design temperature;

[0305] Determine the second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature;

[0306] Determine a first ratio between the first difference and the second difference;

[0307] The first product of the first ratio and the first benchmark value of the preset early power-on time is determined as the candidate early power-on time;

[0308] The minimum value between the candidate early power-on time and the first benchmark value is determined as the target early power-on time;

[0309] Based on the preset start-up time and the target advance start-up time, the current start-up time is determined, and the air conditioner is turned on at the current start-up time.

[0310] In one possible implementation, the method further includes:

[0311] Determine the third difference between the outdoor temperature and the indoor design temperature;

[0312] The fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature;

[0313] Determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value of a preset early shutdown time;

[0314] The fifth difference between the second benchmark value and the second product value is determined as the candidate early shutdown time;

[0315] The minimum value between the candidate early shutdown time and the second benchmark value is determined as the target early shutdown time;

[0316] Based on the preset shutdown time and the target early shutdown time, the current shutdown time is determined, and the air conditioner is turned off at the current shutdown time.

[0317] Since the principle of the computer-readable storage medium in solving the problem is similar to that of the air conditioning adjustment method, the implementation of the computer-readable storage medium can be found in the implementation of the method, and the repeated parts will not be described again.

[0318] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0319] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0320] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0321] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of user-operated steps to be executed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0322] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An air conditioning regulation method, characterized in that, The method includes: A first value is determined based on the highest simulated temperature and the lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and the lowest design temperature of the target room. The first value is used to quantify the temperature fluctuation range of the room where the air conditioner is installed relative to the target room. The target room is the room with the highest simulated temperature in the building where the air conditioner is located. A second value is determined based on the highest and lowest control temperatures of the air conditioner, the highest outdoor temperature during the simulated target time period, and the lowest design temperature of the target room. This second value is used to evaluate the degree of matching between the air conditioner's temperature control capability and extreme climatic conditions. The temperature compensation value is determined based on the temperature difference between the current outdoor temperature and the minimum design temperature of the target room, as well as the first value and the second value. The preset reference temperature is adjusted using the temperature compensation value to obtain the target temperature, and the set temperature of the air conditioner is adjusted to the target temperature.

2. The method according to claim 1, characterized in that, After obtaining the target temperature, and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes: Determine the orientation of the room where the air conditioner will be installed; Based on the preset correspondence between different orientations and orientation compensation values ​​for different time periods, the target orientation compensation value corresponding to the current orientation is determined; The target temperature is adjusted using the target orientation compensation value.

3. The method according to claim 1, characterized in that, After obtaining the target temperature, and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes: Get the floor number of the room where the air conditioner is installed; Based on the preset correspondence between different floors and floor compensation values, the target floor compensation value corresponding to the floor is determined; The target temperature is adjusted using the target floor compensation value.

4. The method according to claim 1, characterized in that, After obtaining the target temperature, and before adjusting the set temperature of the air conditioner to the target temperature, the method further includes: Acquire the pedestrian flow data collected by the pedestrian flow sensor installed in the room; Based on the flow of people, determine the number of people currently in the room; The target temperature is adjusted based on the number of people, and the target temperature is negatively correlated with the number of people.

5. The method according to claim 1, characterized in that, Before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes: If the room where the air conditioner is installed is a public area, then continue with the next step of determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room. If the room is a non-public area, the outdoor temperature, the current season, and the current time are input into the pre-trained temperature prediction model to obtain the target temperature output by the temperature prediction model, and the set temperature of the air conditioner is adjusted to the target temperature.

6. The method according to claim 5, characterized in that, The method further includes: The target temperature, the current indoor temperature, and the current time are input into a pre-trained wind speed prediction model to obtain the target wind speed output by the wind speed prediction model, and the set wind speed of the air conditioner is adjusted to the target wind speed.

7. The method according to claim 5, characterized in that, The method further includes: The temperature prediction model is trained at preset time intervals, and the following steps are performed each time the temperature prediction model is trained: If the user adjusted the target temperature during the period from the current time to the last model training, then obtain the adjusted corrected temperature and the target time period when the user adjusted the temperature. Determine the difference between the corrected temperature and the target temperature; Based on the difference, the standard temperature of the sample data corresponding to the target time period in the training set is adjusted. The sample data stored in the training set is determined during the last model training. The training set stores the correspondence between different seasons, different time periods, different outdoor temperatures and standard temperatures. The temperature prediction model is trained using the sample data adjusted for the target time period to obtain a retrained temperature prediction model.

8. The method according to claim 1, characterized in that, Before determining the first value based on the highest simulated temperature and lowest design temperature of the room where the air conditioner is installed, and the highest simulated temperature and lowest design temperature of the target room, the method further includes: Determine the first difference between the current outdoor temperature and the indoor design temperature; Determine the second difference between the highest outdoor temperature at the predicted preset start-up time and the indoor design temperature; Determine a first ratio between the first difference and the second difference; The first product of the first ratio and the first benchmark value of the preset early power-on time is determined as the candidate early power-on time; The minimum value between the candidate early power-on time and the first benchmark value is determined as the target early power-on time; Based on the preset start-up time and the target advance start-up time, the current start-up time is determined, and the air conditioner is turned on at the current start-up time.

9. The method according to claim 1, characterized in that, The method further includes: Determine the third difference between the outdoor temperature and the indoor design temperature; The fourth difference between the highest outdoor temperature at the predicted preset shutdown time and the indoor design temperature; Determine a second ratio between the third difference and the fourth difference, and a second product of the second ratio and a second reference value of a preset early shutdown time; The fifth difference between the second benchmark value and the second product value is determined as the candidate early shutdown time; The minimum value between the candidate early shutdown time and the second benchmark value is determined as the target early shutdown time; Based on the preset shutdown time and the target early shutdown time, the current shutdown time is determined, and the air conditioner is turned off at the current shutdown time.

10. An air conditioner, characterized in that, The air conditioner includes a processor, which executes a computer program stored in a memory to implement the steps of the air conditioner adjustment method as described in any one of claims 1-9.

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