Temperature control method and device of air conditioner and air conditioner

Abstract

Provided are a temperature control method and device of an air conditioner and the air conditioner, the method comprising: obtaining actual operation data of different air conditioners through a big data platform, and establishing an actual temperature curve evaluation function of the air conditioner according to different operation conditions and set states; evaluating different actual temperature curves of the air conditioner through the evaluation function, and obtaining an optimal indoor temperature curve; fitting the actual temperature curve of the air conditioner into the optimal indoor temperature curve according to time changes, and controlling the corresponding air conditioner according to the optimal indoor temperature curve. According to the actual operation data fed back by the air conditioner big data platform, the actual temperature change curve evaluation function is established according to different operation conditions and set states, and is used for evaluating the rapidity, stability and accuracy of air conditioner control; and by taking the optimal temperature control curve as a target control curve, the high-efficiency energy saving of air conditioner operation is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and more specifically to a temperature control method, apparatus and air conditioner for an air conditioner. Background Technology

[0002] Currently, heat pump air conditioners control the temperature based on the user's set temperature. However, due to differences in building envelope, usage habits, room size, and other factors, the actual temperature drop effect varies greatly between different rooms, resulting in different cooling curves: some cool down quickly, some cool down slowly, some overshoot, some fluctuate, and some fail to reach the set temperature. Due to the diversity and complexity of the load, it is very difficult to determine the actual optimal temperature control curve for different operating conditions through experimental testing, and the amount of testing required is enormous.

[0003] Therefore, a method is needed that can accurately determine the temperature curve based on big data and control the temperature according to the optimal temperature curve.

[0004] The information disclosed in the background section above is only used to further understand the background of the present invention, and therefore may include information known to those skilled in the art that does not constitute prior art. Summary of the Invention

[0005] This invention relates to a temperature control method, apparatus, and air conditioner for an air conditioner. The solution of this invention can accurately determine the optimal temperature curve based on air conditioner operation data and user behavior data obtained from big data analytics, and perform precise temperature control on the air conditioner, achieving high-efficiency and energy-saving operation.

[0006] The first aspect of the present invention provides a temperature control method for an air conditioner, comprising: S1: acquiring actual operating data of different air conditioners through a big data platform, and establishing an evaluation function for the actual temperature curve of the air conditioner based on different operating conditions and setting states; S2: evaluating different actual temperature curves of the air conditioner through the evaluation function to obtain the optimal indoor temperature curve; S3: fitting the actual temperature curve of the air conditioner to the optimal indoor temperature curve based on time changes, and controlling the temperature of the corresponding air conditioner based on the optimal indoor temperature curve.

[0007] A second aspect of the present invention provides a temperature control device for an air conditioner, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the temperature control method of the air conditioner of the present invention when the computer program is executed.

[0008] A third aspect of the present invention provides one or more non-transitory storage media, characterized in that the storage media stores a computer program that, when executed by a processor, implements the temperature control method of the air conditioner of the present invention.

[0009] A fourth aspect of the present invention provides an air conditioner that uses the temperature control method of the air conditioner of the present invention, or includes the temperature control device of the air conditioner of the present invention, or includes one or more non-transitory storage media of the present invention.

[0010] This invention utilizes actual operating data fed back from an air conditioning big data platform to establish an evaluation function for actual room temperature change curves based on different operating conditions and settings. This function is used to assess the speed, stability, and accuracy of air conditioning control. By using the optimal temperature control curve as the target control curve, high-efficiency energy saving in air conditioning operation is achieved. Attached Figure Description

[0011] To more clearly illustrate the technical solution of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0013] Figure 2 This is a flowchart illustrating the determination of an evaluation function for an actual temperature curve according to an exemplary embodiment of the present invention.

[0014] Figure 3 This is a flowchart illustrating the implementation of a temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0015] Figure 4 This is another implementation flowchart of the temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0016] Figure 5 This is a schematic diagram of the optimal temperature curve for another implementation of the temperature control method for an air conditioner according to an exemplary embodiment of the present invention. Specific Implementation

[0017] As used herein, the terms "first," "second," etc., can be used to describe elements in exemplary embodiments of the present invention. These terms are used only to distinguish one element from another, and the inherent features or order of the corresponding elements are not limited by the term. Unless otherwise defined, all terms used herein (including technical or scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in common dictionaries are to be interpreted as having the same meaning as in the context of the relevant technical field, and not as having an ideal or overly formal meaning, unless explicitly defined as having such a meaning in this invention.

[0018] Those skilled in the art will understand that the apparatus and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments, and the scope of the invention is defined only by the claims. Features illustrated or described in conjunction with an exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are included within the scope of the invention.

[0019] In the following description, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, detailed descriptions of known functions or configurations are omitted to avoid unnecessarily obscuring the key technical aspects of the invention. Furthermore, throughout the description, the same reference numerals always refer to the same circuits, modules, or units, and for the sake of brevity, repeated descriptions of the same circuits, modules, or units are omitted.

[0020] Furthermore, it should be understood that one or more of the following methods or aspects can be performed by at least one control unit or controller. The terms "control unit," "controller," "control module," or "main control module" can refer to a hardware device including a memory and a processor. The memory or computer-readable storage medium is configured to store program instructions, and the processor is specifically configured to execute the program instructions to perform one or more processes, which will be further described below. Moreover, it should be understood that, as those skilled in the art will recognize, the following methods can be performed by including a processor in conjunction with one or more other components.

[0021] The method for determining the target temperature control curve of this invention includes air conditioning load classification, temperature curve characteristic parameter calculation, characteristic parameter sorting, evaluation function processing optimization, optimal temperature control curve fitting and push.

[0022] First, this invention calculates the characteristic parameters of the temperature curve: based on user comfort and energy-saving requirements, the actual temperature curve is decomposed into five characteristic state parameters, including temperature drop rate (A), overshoot (B), fluctuation time (C), fluctuation frequency (D), and stable temperature range (E).

[0023] T 内环i ={A i B i C i D i E i}

[0024] Among them, T 内环 It is a set of five characteristic state parameters: A. temperature drop rate, B. overshoot, C. fluctuation time, D. number of fluctuations, and E. stable temperature range, so it is represented as T. 内环i ={A i B i C i D iE i}, because T 内环 The database contains a very large amount of data, where i represents the i-th data set, i.e., T. 内环i A represents the indoor temperature value of the i-th group; i B represents the rate of temperature drop corresponding to the i-th group of indoor temperature values; i C represents the overshoot corresponding to the i-th group of indoor temperature values; i D represents the fluctuation time corresponding to the i-th group of indoor temperature values; i E represents the number of fluctuations corresponding to the i-th group of indoor temperature values; i This represents the stable temperature range corresponding to the i-th group of indoor temperature values.

[0025] Secondly, the feature parameters are sorted: the five feature parameters of the temperature curve are compared separately, then sorted in ascending order, and the corresponding rank percentage is output. The actual temperature curve is represented by the rank percentage of the feature parameters.

[0026] T 内环i ={S Ai S Bi S Ci S Di S Ei}

[0027] Among them, T 内环i S represents the indoor temperature value of the i-th group; Ai S represents the rate of temperature drop of the i-th group of indoor temperature values, and the corresponding percentage of the values ​​in ascending order; Bi S represents the overshoot of the i-th group of indoor temperature values, and the percentage of the corresponding rank in ascending order; Ci S represents the percentage of time the indoor temperature value fluctuates in the i-th group, sorted in ascending order; Di S represents the number of fluctuations in the i-th group of indoor temperature values, and the percentage of the corresponding rank in ascending order; Ei This represents the stable temperature range of the i-th group of indoor temperature values, and the corresponding percentage of the ranking in ascending order.

[0028] In addition, the evaluation function is optimized: an evaluation function based on feature parameters is established, the evaluation function values ​​of each temperature curve on the big data platform are calculated, the function values ​​are compared, the corresponding maximum or minimum values ​​are found, and the temperature curves that meet the objective function are found and marked.

[0029] Figure 1 This is a flowchart of a temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0030] like Figure 1As shown, in step S1, the actual operating data of different air conditioners are obtained through the big data platform, and an evaluation function for the actual temperature curve of the air conditioner is established based on different operating conditions and setting states.

[0031] In step S2, the evaluation function is used to evaluate different actual temperature curves of the air conditioner to obtain the optimal indoor temperature curve.

[0032] In step S3, the actual temperature curve of the air conditioner is fitted to the optimal indoor temperature curve based on the time change, and the corresponding air conditioner is temperature controlled according to the optimal indoor temperature curve.

[0033] Figure 2 This is a flowchart illustrating the determination of an evaluation function for an actual temperature curve according to an exemplary embodiment of the present invention.

[0034] like Figure 2 As shown, at S21, the data in the air conditioner operation data that are consistent with the set state and operating condition are classified to obtain the temperature data corresponding to different loads of the air conditioner, and the actual temperature curve of the air conditioner is obtained based on the temperature data.

[0035] At point S22, the characteristic parameters of the actual operating temperature curve of the air conditioner are decomposed according to the user's needs.

[0036] S23: Process the temperature data of the air conditioner according to the characteristic parameters, associate the actual temperature curve with the characteristic parameters, and construct the evaluation function of the actual temperature curve through the characteristic parameters.

[0037] Figure 3 This is a flowchart illustrating the implementation of a temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0038] like Figure 3 As shown, the parameters collected by the air conditioning big data platform include user behavior data such as set temperature, fan speed, and air guide plate position; air conditioning operation data such as indoor temperature, relative humidity, outdoor temperature, compressor frequency, electronic expansion valve opening, exhaust, external pipe, and internal pipe; and air conditioning fault data such as various protection or fault shutdown data.

[0039] exist Figure 3 The temperature control method for air conditioners includes the following steps:

[0040] Step 1:

[0041] By combining user behavior data and air conditioner operation data, operating conditions with consistent set states (including set temperature, fan speed, airflow direction, etc.) and consistent indoor and outdoor temperature and humidity are grouped into one category. Data related to fault protection or shutdown in this category is then cleared. Furthermore, the system is judged based on the air conditioner's collected parameters, with identical parameters grouped together. Typically, air conditioner temperature and humidity values ​​are displayed as integers, but there is actually a certain temperature range. Moreover, the big data platform contains tens of millions of sample units, and the temperature ranges within different temperature zones in my country are relatively small, with significant overlap between temperature zones, resulting in a large amount of data on the actual operating conditions.

[0042] Step 2:

[0043] Different temperature data correspond to different loads on the air conditioner. The actual temperature curve is represented by the temperature points at different sampling times, as shown below.

[0044] T 内环i ={T i-0 T i-1 T i-2 …………T i-关机}

[0045] Among them, T 内环i T represents the indoor temperature value of the i-th group; i-0 The inner ring temperature at the moment of power-on;

[0046] T i-1 The sampling period is the inner ring temperature at the first sampling time, and can be set according to requirements, such as 1 second (s) or 1 minute (min); T i-2 The inner ring temperature at the second sampling time; T i-关机 This is the inner ring temperature at the moment of shutdown.

[0047] Step 3:

[0048] Based on user experience comfort and energy-saving requirements, the actual temperature curve is decomposed into 5 characteristic state parameters:

[0049] T 内环i ={A i B i C i D i E i}

[0050] ① The cooling rate A represents the time required to reach the set / stable temperature, as shown below.

[0051] A=(t 首次降到设定 / 稳定温度 -t 开机 )

[0052] Among them, t 首次降到设定 / 稳定温度Indicates the time it takes for the indoor temperature to first drop to the set temperature or stabilized temperature; t 开机 Indicates the boot time.

[0053] ②Overshoot B represents the difference between the room's lowest temperature and the set / stable temperature, as shown below.

[0054] B = (T) 设定 / 稳定温度 -T 最低 )

[0055] Among them, T 设定 / 稳定温度 Indicates the indoor set temperature or stable temperature; T 最低 This indicates the lowest temperature reached indoors.

[0056] ③ The fluctuation time C represents the time from below the set / stable temperature to rising to the set / stable temperature, as shown below.

[0057] C=(t 恢复设定 / 稳定温度 -t 首次降到设定 / 稳定温度 )

[0058] Among them, t 恢复设定 / 稳定温度 This indicates the time it takes for the indoor temperature to eventually return to the set or stable temperature; t 首次降到设定 / 稳定温度 This indicates the time it takes for the indoor temperature to first drop to the set temperature or a stable temperature.

[0059] Among them, parameters A and C have different meanings. A is the time when the set / stable temperature is first reached after power-on, and C is the time when the temperature stabilizes again after overshooting or fluctuations occur after reaching the set temperature.

[0060] ④ The number of fluctuations, D, represents the number of times the temperature deviates from the set / stable temperature, as shown below.

[0061] D = N 偏离稳定温度 / 设定温度

[0062] Where, N 偏离稳定温度 / 设定温度 This indicates the number of times the indoor temperature deviates from the stable or set temperature.

[0063] The counting condition for determining the number of fluctuations is: after the first stable temperature / set temperature is reached, |T 采样 -T 设定 / 稳定温度 |>△T 波动 , △T 波动 ∈[0, 5℃], can be taken as 1℃;

[0064] T 采样 Indicates the real-time indoor sampling temperature; T 设定 / 稳定温度 Indicates the indoor set temperature or stable temperature; △T 波动 This indicates the range by which the indoor temperature deviates from the stable or set temperature.

[0065] ⑤ The stable temperature range E represents the size of the deviation from the set temperature, as shown below.

[0066] E = (T 稳定温度 -T 设定温度 )

[0067] Among them, T 稳定温度 Indicates the stable indoor temperature; T 设定温度 This indicates the user-set temperature.

[0068] Step 4: After preliminary processing of the indoor temperature data based on the feature parameters, the five feature parameters are compared separately, then sorted in ascending order, and the corresponding rank percentage is output. The actual temperature curve is represented by the rank percentage of the feature parameters.

[0069] T 内环i ={S Ai S Bi S Ci S Di S Ei}

[0070] in:

[0071] ① The percentage of the cooling rate A in terms of its ranking: S Ai =N Ai-升序位次 / N 总数据量

[0072] ② The positional percentage of overshoot B: S Bi =N Bi-升序位次 / N 总数据量

[0073] ③ The proportion of the position of fluctuation time C: S Ci =N Ci-升序位次 / N 总数据量

[0074] ④ The rank percentage of fluctuation number D: S Di =N Di-升序位次 / N 总数据量

[0075] ⑤ Percentage of the order of stable temperature range E: S Ei =N Ei-升序位次 / N 总数据量

[0076] in,

[0077] N Ai-升序位次 This represents the rate of temperature drop of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0078] N Bi-升序位次 This represents the overshoot of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0079] N Ci-升序位次 This represents the fluctuation time of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0080] N Di-升序位次 This represents the number of fluctuations in the i-th group of indoor temperature values, corresponding to the digit count sorted in ascending order.

[0081] N Ei-升序位次 This represents the stable temperature range of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0082] N 总数据量 This indicates the total number of indoor temperature groups.

[0083] According to one or more embodiments of the present invention, the descending order is arranged from largest to smallest, such as 10, 9, 8, 7, 6; the descending position is such that the position corresponding to 10 is 1, the position corresponding to 9 is 2, the position corresponding to 8 is 3, the position corresponding to 7 is 4, and the position corresponding to 6 is 5.

[0084] According to one or more embodiments of the present invention, the stable temperature range is: E = (T 稳定温度 -T 设定温度 The calculated values ​​can be positive or negative. When sorting, they are arranged in ascending order of absolute value: if the absolute values ​​are the same, the values ​​of the actual values ​​in the cooling mode that are less than 0 are placed before the values ​​of the actual values ​​that are greater than 0, and the values ​​of the actual values ​​in the heating mode that are greater than 0 are placed before the values ​​of the actual values ​​that are less than 0.

[0085] Step 5:

[0086] The temperature curve is evaluated using an evaluation function, which is shown below:

[0087] The evaluation function can be T. 内环i ={S Ai S Bi S Ci S Di S Ei The evaluation function can be a linear combination of the parameters in the given evaluation function. For example, it can be a linear combination of the rank proportions of the feature parameters, where the sum of the coefficients in the linear combination is 1. Alternatively, the evaluation function can be a linear combination of the rank proportions of some feature parameters, where the coefficients of other feature parameters are related to the coefficients of those other feature parameters (as shown in evaluation function 2 below), where the sum of the coefficients in the linear combination is 1. Examples of these two evaluation functions are given below.

[0088] ① Evaluation function scheme 1:

[0089] Ψ=K1×S Ai +K2×S Bi +K3×S Ci +K4×SDi +K5×S Ei

[0090] K1+K2+K3+K4+K5=1

[0091] K1, K2, K3, K4, and K5 all belong to [0,1], with K1 taking 0.4, K2 taking 0.1, K3 taking 0.1, K4 taking 0.1, and K5 taking 0.3.

[0092] ② Evaluation function scheme 2:

[0093] Ψ=K1×S Ai +K2×S Bi +K3×S Ci +K4×S Di

[0094] K1, K2, K3, and K4 all belong to [0,1].

[0095] K1 is related to the stable range of the room temperature curve. To ensure the accuracy and efficiency of room temperature curve selection, the stable temperature range of the room temperature curve is first determined. If ΔT i If ∈ [-0.5℃, 0.5℃], then it is calibrated as the optimal temperature range, and K1 is set to 0.6; if ΔT i If the temperature is less than -0.5℃, the cooling output is considered too high, and K1 should be set to 0.3; if ΔT i If the temperature is greater than 0.5℃, the calibrated cooling output is too low. K1 should be 0.2; K2 should be 0.15; K3 should be 0.1; and K4 should be 0.15.

[0096] According to one or more embodiments of the present invention, in evaluation function scheme 2, the evaluation function is directly based on the E value, i.e., ΔT. i The range of values ​​determines the value of K1, that is, the value of E determines S. Ai The value of coefficient K1.

[0097] Step 6:

[0098] By comparing the evaluation function values ​​of different temperature curves, the temperature curve corresponding to the maximum target evaluation function value is identified as the optimal temperature curve.

[0099] Max{Ψ i}

[0100] Where, when Max{Ψ i When there are multiple values, take Min{P}. i-耗电量}

[0101] Step 7: After obtaining the optimal temperature curve, the actual temperature curve data needs to be fitted and pushed to air conditioners with the same settings and operating conditions over time, serving as the target temperature control curve.

[0102]

[0103] Where a, b, c, d, e, f, and g are constants, belonging to [-10]. 4 10 4 Of course, in engineering practice, the inner loop curve can be fitted according to specific design requirements, involving various fitting methods, and is not limited to the above formula.

[0104] Figure 4 This is another implementation flowchart of the temperature control method for an air conditioner according to an exemplary embodiment of the present invention.

[0105] exist Figure 4 In the implementation process, in the data processing section and Figure 3 The implementation process shown is the same, except for the differences starting from step 5. Figure 4 The corresponding implementation process steps are as follows.

[0106] Step 5':

[0107] The temperature curve is evaluated using two evaluation functions, as shown below:

[0108] Ψ1=S Ai

[0109] Ψ2=K1×S Ai +K2×S Bi +K3×S Ci +K4×S Di

[0110] Where K1, K2, K3, and K4 all belong to [0,1]; among them, K1 is related to the stable range of the room temperature curve. To ensure the accuracy and efficiency of room temperature curve selection, the stable temperature range of the room temperature curve is first determined. If ΔT i If ∈ [-0.5℃, 0.5℃], then it is calibrated as the optimal temperature range, and K1 is set to 0.6; if ΔT i If the temperature is less than -0.5℃, the cooling output is considered too high, and K1 should be set to 0.3; if ΔT i If the temperature is greater than 0.5℃, the calibrated cooling output is too low. K1 should be 0.2; K2 should be 0.15; K3 should be 0.1; and K4 should be 0.15.

[0111] Step 6':

[0112] By comparing the evaluation function values ​​of different temperature curves, we can find and mark the temperature curves that satisfy the following objective evaluation function:

[0113] Min{Ψ 1i}and Max{Ψ 2i}

[0114] Max{Ψ 2i Replace the characteristic parameter cooling rate A of the corresponding temperature curve with Min{Ψ}. 1i The corresponding cooling rate A of the temperature curve, and the optimal temperature curve at this time are shown in the figure and formula below:

[0115]

[0116] Among them, T 最佳曲线 This represents the target curve for the optimal indoor temperature.

[0117] This represents the rate of temperature drop in the room when the evaluation function Ψ1 is at its minimum.

[0118] This represents the overshoot of the indoor temperature when the evaluation function Ψ2 value is at its maximum.

[0119] This indicates the time of indoor temperature fluctuation when the evaluation function Ψ2 value is at its maximum.

[0120] This indicates the number of fluctuations in indoor temperature when the evaluation function Ψ² value is at its maximum.

[0121] This represents the stable temperature range of the indoor temperature corresponding to the maximum value of the evaluation function Ψ². According to one or more embodiments of the present invention, in... Figure 4 In another implementation process shown, the value of K1 is determined directly based on the range of E value, i.e., ΔTi value, that is, the value of E value determines the value of SAi coefficient K1.

[0122] Figure 5 This is a schematic diagram of the optimal temperature curve for another implementation of the temperature control method for an air conditioner according to an exemplary embodiment of the present invention. The curve shown in the figure is as defined above.

[0123] According to one or more embodiments of the present invention, the present invention also provides an air conditioner temperature control device, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the above-described air conditioner temperature control method when the computer program is executed.

[0124] According to one or more embodiments of the present invention, the present invention also provides one or more non-transitory storage media, characterized in that the storage media stores a computer program, which, when executed by a processor, implements the above-described air conditioner temperature control method.

[0125] According to one or more embodiments of the present invention, the present invention also provides an air conditioner that uses the air conditioner temperature control method according to the above, or includes the air conditioner temperature control device according to the above, or includes one or more non-temporary storage media as described above.

[0126] According to one or more embodiments of the present invention, the control logic of the present invention can implement the processing of the processes in the system described above using encoded instructions (e.g., computer and / or machine-readable instructions) stored on a non-transitory computer and / or machine-readable medium (e.g., hard disk drive, flash memory, read-only memory, optical disk, digital multifunction disk, cache, random access memory, and / or any other storage device or storage disk), storing information for any time period (e.g., extended time periods, permanent, transient instances, temporary caches, and / or information caches) in the non-transitory computer and / or machine-readable medium. As used herein, the term "non-transitory computer-readable medium" is explicitly defined to include any type of computer-readable storage device and / or storage disk, excluding propagated signals and transmission media.

[0127] According to one or more embodiments of the present invention, the logic in the system of the present invention can be implemented using control circuitry (control logic, main control system, or control module), which may include one or more processors and may also internally include non-transitory computer-readable media. Specifically, the main control system or control module may include a microcontroller (MCU). The processor used to implement the processing of the logic in the system of the present invention may be, for example, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). The processor may be coupled thereto and / or may include memory / storage devices and may be configured to execute instructions stored in the memory / storage devices to implement various applications and / or operating systems running on the controller in the present invention.

[0128] The accompanying drawings and detailed description of the invention, cited above as examples, serve to explain the invention but do not limit its meaning or scope as described in the claims. Therefore, those skilled in the art can readily make modifications from the above description. Furthermore, those skilled in the art can remove some of the components described herein without degrading performance, or add other components to improve performance. Additionally, those skilled in the art can change the order of steps in the method described herein depending on the process or equipment environment. Therefore, the scope of the invention should not be determined by the embodiments described above, but rather by the claims and their equivalents.

[0129] Although the invention has been described in conjunction with embodiments now considered to be achievable, it should be understood that the invention is not limited to the disclosed embodiments, but rather is intended to cover various modifications and equivalent configurations included within the spirit and scope of the appended claims.

[0130] The following are further examples of the present invention:

[0131] Example 1. A temperature control method for an air conditioner, comprising:

[0132] S1: Obtain actual operating data of different air conditioners through a big data platform, and establish an evaluation function for the actual temperature curve of the air conditioner based on different operating conditions and settings.

[0133] S2: The evaluation function is used to evaluate different actual temperature curves of the air conditioner to obtain the optimal indoor temperature curve;

[0134] S3: Fit the actual temperature curve of the air conditioner to the optimal indoor temperature curve based on the time change, and control the temperature of the corresponding air conditioner according to the optimal indoor temperature curve.

[0135] Example 2. The method according to Example 1, wherein step S1 further includes:

[0136] S21: Based on user behavior data and air conditioner operation data obtained from the big data platform, classify the data that are consistent with the set state and operating conditions to obtain temperature data corresponding to different loads of the air conditioner, and represent the indoor temperature data as the actual temperature curve.

[0137] S21: Classify the data in the air conditioner operation data that are consistent with the set state and operating condition to obtain the temperature data corresponding to different loads of the air conditioner, and obtain the actual temperature curve of the air conditioner based on the temperature data.

[0138] S22: Based on the user's needs, decompose the actual operating temperature curve of the air conditioner into characteristic parameters;

[0139] S23: Process the temperature data of the air conditioner according to the characteristic parameters, associate the actual temperature curve with the characteristic parameters, and construct the evaluation function of the actual temperature curve through the characteristic parameters.

[0140] Example 3. According to the method described in Example 1, wherein in step S1, the actual operating data of different air conditioners are obtained through a big data platform:

[0141] The system acquires user behavior data and air conditioner operation data through a big data platform, and removes data related to fault protection or shutdown.

[0142] Example 4. According to the method described in Example 2, wherein the actual temperature curve of the air conditioner is represented as the temperature points at different sampling times of the air conditioner.

[0143] Example 5. According to the method described in Example 2, wherein in step S22, the actual temperature curve is decomposed into characteristic parameters related to the cooling rate, temperature overshoot, temperature fluctuation time, number of temperature fluctuations, and stable temperature range of the air conditioner.

[0144] Example 6. According to the method described in Example 5, wherein,

[0145] The cooling rate A represents the time required to reduce the temperature to the set or stable temperature.

[0146] The temperature overshoot B represents the difference between the lowest room temperature and the set or stable temperature.

[0147] The fluctuation time C represents the time from below the set or stable temperature to rise to the set or stable temperature.

[0148] The fluctuation number D represents the number of times the temperature deviates from the set / stable temperature;

[0149] The stable temperature range E represents the range of deviation from the set temperature.

[0150] Example 7. According to the method described in Example 6, the counting condition for determining the number of fluctuations D is:

[0151] After reaching the stable temperature or set temperature for the first time, |T 采样 -T 设定 / 稳定温度 |>△T 波动 , △T 波动 The value range of T is [0, 5℃], where T 采样 Indicates the real-time indoor sampling temperature; T 设定 / 稳定温度 Indicates the indoor set temperature or stable temperature; △T 波动 This indicates the range by which the indoor temperature deviates from the stable or set temperature.

[0152] Example 8. The method according to any one of Examples 2-7, wherein, in step S23, processing the temperature data of the air conditioner according to the characteristic parameter includes:

[0153] The characteristic parameters of the air conditioner are compared and sorted, and the actual temperature curve is represented as the rank proportion of the characteristic parameters.

[0154] Example 9. According to the method described in Example 8, wherein: the five characteristic parameters A to E of the air conditioner are compared respectively, then sorted in ascending order, and the rank percentage of the corresponding characteristic parameters is output. The actual temperature curve is represented by the rank percentage of the characteristic parameters as follows:

[0155] T 内环i ={S Ai S Bi S Ci S Di S Ei}

[0156] T 内环i S represents the indoor temperature value of the i-th group; Ai S represents the rate of temperature drop of the i-th group of indoor temperature values, and the corresponding percentage of the values ​​in ascending order; Bi S represents the overshoot of the i-th group of indoor temperature values, and the percentage of the corresponding rank in ascending order; Ci S represents the percentage of time the indoor temperature value fluctuates in the i-th group, sorted in ascending order; Di S represents the number of fluctuations in the i-th group of indoor temperature values, and the percentage of the corresponding rank in ascending order; Ei Represents the stable temperature range of the i-th group of indoor temperature values, and the corresponding percentage of the positions sorted in ascending order; {} represents: a set, where the indoor temperature is represented as a set of the above parameters.

[0157] Example 10. According to the method described in Example 8, wherein,

[0158] The rank of cooling rate A: S Ai =N Ai-降序位次 / N 总数据量 ;

[0159] The positional percentage of overshoot B: S Bi =N Bi-降序位次 / N 总数据量 ;

[0160] Percentage of the position of fluctuation time C: S Ci =N Ci-降序位次 / N 总数据量 ;

[0161] Percentage of the number of fluctuations D: S Di =N Di-降序位次 / N 总数据量 ;

[0162] Percentage of the order of stable temperature range E: S Ei =N Ei-降序位次 / N 总数据量 ;

[0163] in,

[0164] N Ai-升序位次 This represents the rate of temperature drop of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0165] NBi-升序位次 This represents the overshoot of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0166] N Ci-升序位次 This represents the fluctuation time of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0167] N Di-升序位次 This represents the number of fluctuations in the i-th group of indoor temperature values, corresponding to the digit count sorted in ascending order.

[0168] N Ei-升序位次 This represents the stable temperature range of the i-th group of indoor temperature values, and the corresponding digit number sorted in ascending order.

[0169] N 总数据量 This indicates the total number of indoor temperature groups.

[0170] Example 11. According to the method described in Example 6, wherein the evaluation function is a linear combination of the rank proportions of the feature parameters, and the sum of the coefficients in the linear combination is 1.

[0171] Example 12. According to the method described in Example 11, the evaluation function is:

[0172] Ψ=K1×S Ai +K2×S Bi +K3×S Ci +K4×S Di +K5×S Ei ;

[0173] Where K1+K2+K3+K4+K5=1; K1, K2, K3, K4, and K5 all belong to [0,1].

[0174] Example 13. The method described in Example 12, wherein K1 is 0.4, K2 is 0.1, K3 is 0.1, K4 is 0.1, and K5 is 0.3.

[0175] Example 14. According to the method described in Example 6, wherein the evaluation function is a linear combination of the rank proportions of some feature parameters, wherein the sum of the coefficients in the linear combination is 1.

[0176] Example 15. The method according to Example 14, wherein the evaluation function is:

[0177] Ψ=K1×S Ai +K2×S Bi +K3×S Ci +K4×S Di ;

[0178] K1, K2, K3, and K4 all belong to [0,1].

[0179] Example 16. Following the method described in Example 15, where K1 is related to the stable range of the room temperature curve, first determine the stable temperature range of the room temperature curve. If ΔT i If the temperature range is ∈ [-0.5℃, 0.5℃], then it is defined as the optimal temperature range, and K1 takes a value of 0.6; if ΔT i If the temperature is less than -0.5℃, the cooling output is considered too high, and K1 should be set to 0.3; if ΔT i If the temperature is greater than 0.5℃, the cooling output is too low, and K1 should be set to 0.2.

[0180] Example 17. The method described in Example 16, wherein K2 is 0.15, K3 is 0.1, and K4 is 0.15.

[0181] Example 18. According to the method described in Example 1, wherein, in step S2, obtaining the optimal indoor temperature curve includes: by comparing the evaluation function values ​​of different actual temperature curves, the temperature curve corresponding to the maximum evaluation function value is obtained as the optimal temperature curve.

[0182] Example 19. According to the method described in Example 1, when there are multiple optimal temperature curves, the one with the lowest power consumption of the air conditioner is selected from the multiple optimal temperature curves as the final optimal temperature curve.

[0183] Example 20. According to the method described in Example 1, wherein in step S3, the fitted formula is:

[0184] in,

[0185] T 内环 Indicates: Indoor ambient temperature;

[0186] T 稳定 Indicates: Stable indoor ambient temperature;

[0187] t represents: boot time;

[0188] Where a, b, c, d, e, f, and g are constants, and their values ​​range from [-10]. 4 10 4 ].

[0189] Example 21. According to the method described in Example 1, wherein in step S2, a first evaluation function and a second evaluation function are set to evaluate different actual temperature curves of the air conditioner in order to obtain the optimal indoor temperature curve.

[0190] The optimal temperature curve is related to the minimum value of different actual temperature curves under the first evaluation function, the maximum value of different actual temperature curves under the second evaluation function, and the characteristic parameters associated with the actual temperature curves.

[0191] Example 22. The method according to any one of Examples 1-21, wherein the first evaluation function is related to characteristic parameters related to the cooling rate of the air conditioner, and the second evaluation function is related to characteristic parameters related to temperature overshoot, temperature fluctuation time, number of temperature fluctuations, and stable temperature range.

[0192] Example 23. The method described in Example 22, wherein,

[0193] The first evaluation function is: Ψ1 = S Ai ;

[0194] The second evaluation function is: Ψ2=K1×S Ai +K2×S Bi +K3×S Ci +K4×S Di ;

[0195] The values ​​of K1, K2, K3, and K4 are in the range of [0, 1].

[0196] Example 24. Following the method described in Example 23, where K1 is related to the stable range of the room temperature curve, first determine the stable temperature range of the room temperature curve. If ΔT i If the temperature range is ∈ [-0.5℃, 0.5℃], then it is defined as the optimal temperature range, and K1 takes a value of 0.6; if ΔT i If the temperature is less than -0.5℃, the cooling output is considered too high, and K1 should be set to 0.3; if ΔT i If the temperature is greater than 0.5℃, the cooling output is too low, and K1 should be set to 0.2.

[0197] Example 25. The method described in Example 24, wherein K2 is 0.15, K3 is 0.1, and K4 is 0.15.

[0198] Example 26. The method described in Example 23, wherein:

[0199] Find the evaluation function Min{Ψ 1i}and Max{Ψ 2i} represents the temperature curve corresponding to the objective evaluation function given the condition; and

[0200] Max{Ψ 2i Replace the characteristic parameter cooling rate A of the corresponding temperature curve with Min{Ψ}. 1iThe corresponding cooling rate A of the temperature curve is used to obtain the optimal temperature curve, which is expressed as:

[0201]

[0202] in,

[0203] Min{Ψ 1i} represents the minimum value corresponding to the data set of the objective function Ψ1.

[0204] Max{Ψ 2i} represents the maximum value corresponding to the data set for the objective function Ψ2;

[0205] T 最佳曲线 This represents the target curve for the optimal indoor temperature.

[0206] This represents the rate of temperature drop in the room when the evaluation function Ψ1 is at its minimum.

[0207] This represents the overshoot of the indoor temperature when the evaluation function Ψ2 value is at its maximum.

[0208] This indicates the time of indoor temperature fluctuation when the evaluation function Ψ2 value is at its maximum.

[0209] This indicates the number of fluctuations in indoor temperature when the evaluation function Ψ² value is at its maximum.

[0210] This indicates the stable temperature range of the indoor temperature when the evaluation function Ψ2 value is at its maximum.

[0211] Example 27. A temperature control device for an air conditioner, comprising a memory and a processor; the memory for storing a computer program; the processor for implementing, when executing the computer program, the temperature control method for the air conditioner as described in any one of Examples 1 to 26.

[0212] Example 28. One or more non-transitory storage media, characterized in that the storage media stores a computer program that, when executed by a processor, implements the temperature control method of an air conditioner as described in any one of Examples 1 to 26.

[0213] Example 29. An air conditioner that uses the temperature control method of an air conditioner according to any one of Examples 1-26, or includes the temperature control device of an air conditioner according to Example 27, or includes one or more non-transitory storage media according to Example 28.

Claims

1. A method for controlling the temperature of an air conditioner, comprising: S1: Obtain actual operating data of different air conditioners through a big data platform, and establish an evaluation function for the actual temperature curve of the air conditioner based on different operating conditions and settings. S2: The evaluation function is used to evaluate different actual temperature curves of the air conditioner to obtain the optimal indoor temperature curve; S3: Fit the actual temperature curve of the air conditioner to the optimal indoor temperature curve based on the time change, and control the temperature of the corresponding air conditioner according to the optimal indoor temperature curve.

2. The method according to claim 1, wherein step S1 further comprises: S21: Classify the data in the air conditioner operation data that are consistent with the set state and operating condition to obtain the temperature data corresponding to different loads of the air conditioner, and obtain the actual temperature curve of the air conditioner based on the temperature data. S22: Based on the user's needs, decompose the actual operating temperature curve of the air conditioner into characteristic parameters; S23: Process the temperature data of the air conditioner according to the characteristic parameters, associate the actual temperature curve with the characteristic parameters, and construct the evaluation function of the actual temperature curve through the characteristic parameters.

3. The method according to claim 1, wherein, In step S1, which involves obtaining actual operating data of different air conditioners through a big data platform: The system acquires user behavior data and air conditioner operation data through a big data platform, and removes data related to fault protection or shutdown.

4. The method according to claim 2, wherein, The actual temperature curve of the air conditioner is represented by the temperature points at different sampling times of the air conditioner.

5. The method according to claim 2, wherein, In step S22, the actual temperature curve is decomposed into characteristic parameters related to the air conditioner's cooling rate, temperature overshoot, temperature fluctuation time, number of temperature fluctuations, and stable temperature range.

6. The method according to claim 2, wherein, In step S23, processing the temperature data of the air conditioner according to the characteristic parameters includes: The characteristic parameters of the air conditioner are compared and sorted, and the actual temperature curve is represented as the rank proportion of the characteristic parameters.

7. The method according to claim 6, wherein, The evaluation function is a linear combination of the rank proportions of the feature parameters, wherein the sum of the coefficients in the linear combination is 1.

8. The method according to claim 6, wherein, The evaluation function is a linear combination of the rank proportions of some feature parameters, wherein the sum of the coefficients in the linear combination is 1.

9. The method according to claim 1, wherein, In step S2, obtaining the optimal indoor temperature curve includes: by comparing the evaluation function values ​​of different actual temperature curves, the temperature curve corresponding to the maximum evaluation function value is obtained as the optimal temperature curve.

10. The method according to claim 9, wherein, When there are multiple optimal temperature curves, the one with the lowest power consumption of the air conditioner is selected as the final optimal temperature curve.

11. The method according to claim 9, wherein, In step S2, a first evaluation function and a second evaluation function are set to evaluate different actual temperature curves of the air conditioner in order to obtain the optimal indoor temperature curve. The optimal temperature curve is related to the minimum value of different actual temperature curves under the first evaluation function, the maximum value of different actual temperature curves under the second evaluation function, and the characteristic parameters associated with the actual temperature curves.

12. The method according to claim 11, wherein, The first evaluation function is related to the characteristic parameters of the air conditioner's cooling rate, while the second evaluation function is related to the characteristic parameters of temperature overshoot, temperature fluctuation time, number of temperature fluctuations, and stable temperature range.

13. A temperature control device for an air conditioner, comprising a memory and a processor; the memory being used to store a computer program; the processor being used to implement the method according to any one of claims 1 to 12 when the computer program is executed.

14. An air conditioner that uses the temperature control method of an air conditioner according to any one of claims 1-12, or includes the temperature control device of an air conditioner according to claim 13.

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