Operation control method, device and equipment of air conditioner and storage medium

Abstract

The invention relates to the technical field of air conditioners, in particular to an air conditioner operation control method, device and equipment and a storage medium. According to the air conditioner operation control method and device, operation data of an air conditioner during operation are obtained, and comfort level evaluation is conducted through a trained comfort level evaluation model according to the operation data of the air conditioner; the initial comfort degree and the initial comfort curve of the air conditioner at the current moment are obtained, then air conditioner operation data at the next moment are predicted based on the initial comfort curve and a target comfort curve corresponding to the initial comfort degree, and finally the operation state of the air conditioner at the next moment is controlled according to the predicted operation data. The technical problem that in the prior art, in the operation control process of the air conditioner, the comfort degree is low is solved, and the use experience of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioners, and in particular to an operation control method, device, equipment and storage medium of an air conditioner. Background Art

[0002] In traditional technology, there are two major defects in the comfort control technology during the operation of the air conditioner: biased control and control solidification. The main reasons for the above two defects are the lack of quantitative standards for comfort control and the air conditioner control logic is obtained from expert experience and experimental simulation without considering the user's usage habits. As a result, when controlling the operation of the air conditioner, the comfort level is low and cannot meet the user's usage needs.

[0003] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present invention is to provide an air conditioner operation control method, device, equipment and storage medium, aiming to solve the technical problem of low comfort level in the air conditioner operation control process in the prior art.

[0005] To achieve the above object, the present invention provides an operation control method of an air conditioner, the method comprising the following steps:

[0006] When the air conditioner is running, obtaining operation data of the air conditioner;

[0007] Determine the initial comfort level and initial comfort curve of the air conditioner through a trained comfort level evaluation model based on the operating data;

[0008] Perform prediction based on the initial comfort curve and a set of target comfort curves corresponding to the initial comfort level to obtain predicted operating data of the air conditioner;

[0009] The operating state of the air conditioner at the next moment is controlled according to the predicted operating data.

[0010] Optionally, before determining the initial comfort level and initial comfort curve of the air conditioner through a trained comfort level evaluation model based on the operating data, the method further includes:

[0011] Generate a training sample set according to the operation data samples of the air conditioner and the comfort data samples of the comfort equipment;

[0012] Model training is performed based on the training sample set using an initial comfort prediction model to obtain a trained comfort assessment model.

[0013] Optionally, performing model training based on the training sample set through an initial comfort prediction model to obtain a trained comfort assessment model includes:

[0014] Performing model training based on the training sample set through an initial regression model to obtain a first prediction model;

[0015] Predicting a comfort level prediction value corresponding to the operation data sample according to the first prediction model;

[0016] Calculating an error value between the comfort prediction value and the actual comfort value in the comfort data sample;

[0017] Model training is performed using an initial comfort prediction model according to the operating data samples and the error value to obtain a trained comfort assessment model.

[0018] Optionally, before performing prediction according to the initial comfort curve and the target comfort curve set corresponding to the initial comfort level to obtain the predicted operation data of the air conditioner, the method further includes:

[0019] Acquire historical reported data of the air conditioner;

[0020] Performing comfort evaluation based on the historical reported data through a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve;

[0021] The historical comfort curves are classified to obtain a set of comfort curves.

[0022] Optionally, performing comfort evaluation through a trained comfort evaluation model according to the historical reported data to obtain a historical initial comfort and a corresponding historical comfort curve includes:

[0023] Cleaning and sampling the historical reported data to obtain target historical reported data;

[0024] The comfort level is evaluated by using a trained comfort level evaluation model according to the historical reported data of the target, and a historical initial comfort level and a corresponding historical comfort level curve are obtained.

[0025] Optionally, the classifying the historical comfort curves to obtain a set of comfort curves includes:

[0026] Obtaining the extreme value, initial comfort and curve similarity of the historical comfort curve;

[0027] Classifying the historical comfort curves based on the extreme values, the initial comfort levels, and the curve similarities to obtain a set of comfort curves;

[0028] The comfort curve set is preprocessed so that each comfort curve in the comfort curve set corresponds to a different initial comfort value.

[0029] Optionally, the predicting according to the initial comfort curve and the target comfort curve set corresponding to the initial comfort level to obtain the predicted operating data of the air conditioner includes:

[0030] Calculating the similarity between the initial comfort curve and the target comfort curve set;

[0031] Determining a target predicted comfort curve in the target comfort curve set according to the similarity;

[0032] The predicted comfort level at the next moment and the predicted operation data of the air conditioner corresponding to the predicted comfort level are determined based on the target predicted comfort curve.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides an operation control device for an air conditioner, the operation control device for the air conditioner comprising:

[0034] An acquisition module, used for acquiring operation data of the air conditioner when the air conditioner is running;

[0035] A calculation module, used for determining the initial comfort and initial comfort curve of the air conditioner through a trained comfort evaluation model based on the operating data;

[0036] A prediction module, used for making predictions based on the initial comfort curve and a target comfort curve set corresponding to the initial comfort level, to obtain predicted operation data of the air conditioner;

[0037] A control module is used to control the operating state of the air conditioner at the next moment according to the predicted operating data.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also proposes an operation control device of an air conditioner, which includes: a memory, a processor, and an operation control program of the air conditioner stored in the memory and executable on the processor, and the operation control program of the air conditioner is configured to implement the steps of the operation control method of the air conditioner as described above.

[0039] In addition, to achieve the above objectives, the present invention also proposes a storage medium, on which an operation control program of an air conditioner is stored. When the operation control program of the air conditioner is executed by a processor, the steps of the operation control method of the air conditioner described above are implemented.

[0040] The present invention discloses an operation control method for an air conditioner, the operation control method for the air conditioner comprising: obtaining operation data of the air conditioner when the air conditioner is operating; determining the initial comfort and initial comfort curve of the air conditioner through a trained comfort evaluation model based on the operation data; making predictions based on the initial comfort curve and a set of target comfort curves corresponding to the initial comfort to obtain predicted operation data of the air conditioner; and controlling the operation state of the air conditioner according to the predicted operation data of the air conditioner. Compared with the prior art, the present invention obtains the operation data of the air conditioner when it is operating, and performs comfort evaluation based on the operation data of the air conditioner through a trained comfort evaluation model to obtain the initial comfort and initial comfort curve of the air conditioner at the current moment, and then predicts the operation data of the air conditioner at the next moment based on the initial comfort curve and the target comfort curve corresponding to the initial comfort, and finally controls the operation state of the air conditioner at the next moment according to the predicted operation data, thereby avoiding the technical problem of low comfort during the operation control process of the air conditioner in the prior art and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a structural diagram of an operation control device of an air conditioner in a hardware operation environment involved in an embodiment of the present invention;

[0042] Figure 2 A schematic flow chart of a first embodiment of an operation control method for an air conditioner according to the present invention;

[0043] Figure 3 A schematic flow chart of a second embodiment of an operation control method for an air conditioner according to the present invention;

[0044] Figure 4 A schematic flow chart of a third embodiment of an operation control method for an air conditioner according to the present invention;

[0045] Figure 5 FIG. 1 is a structural block diagram of a first embodiment of an operation control device for an air conditioner according to the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0048] Reference Figure 1 , Figure 1 The schematic diagram of the structure of the operation control device of the air conditioner in the hardware operation environment involved in the embodiment of the present invention is shown in FIG.

[0049] like Figure 1As shown, the operation control device of the air conditioner may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the operation control device of the air conditioner, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0051] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and an operation control program of the air conditioner.

[0052] exist Figure 1 In the operation control device of the air conditioner shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the operation control device of the air conditioner of the present invention can be set in the operation control device of the air conditioner, and the operation control device of the air conditioner calls the operation control program of the air conditioner stored in the memory 1005 through the processor 1001, and executes the operation control method of the air conditioner provided in the embodiment of the present invention.

[0053] The embodiment of the present invention provides an operation control method of an air conditioner, referring to Figure 2 , Figure 2 The figure is a flow chart of a first embodiment of an operation control method for an air conditioner according to the present invention.

[0054] In this embodiment, the operation control method of the air conditioner includes the following steps:

[0055] Step S10: When the air conditioner is running, obtaining the operating data of the air conditioner.

[0056] It should be noted that the execution subject of the method of this embodiment can be the air conditioner device, which has functions such as data processing, data communication and program running, and can also be other devices with the same or similar functions, and this implementation condition does not limit this. For the convenience of explanation, this implementation is described by taking the controller of the air conditioner as an example.

[0057] It is worth noting that in order to achieve comfort control of air conditioners in traditional technologies, the operation of air conditioners is generally controlled through: temperature control technology, humidity control technology, air flow rate control technology and air quality control technology, so as to improve the user's comfort. Among them, temperature control technology refers to controlling the cooling or heating capacity of the air conditioner so that the indoor temperature reaches the temperature value set by the user within the time range expected by the user, and can maintain a stable temperature without overcooling or overheating; humidity control technology refers to controlling the humidification or dehumidification capacity of the air conditioner so that the indoor humidity is kept at the user's desired temperature. Within the set comfort range, in the actual control of air conditioning, humidity control is generally coupled with temperature control to reduce humidity by cooling; air flow rate control technology refers to adjusting the indoor air flow rate by controlling parameters such as the opening and closing degree of the air supply vents of the air conditioner and the air supply direction to achieve the comfort level expected by the user, such as: windless technology, etc.; air quality control technology refers to improving indoor air quality and reducing harmful substances in the air through technical means such as air filtration, negative ion generation or UV sterilization, such as natural wind or air conditioning purification functions, etc. This embodiment does not make specific restrictions on this.

[0058] Regardless of the control technology of the above-mentioned air conditioners, they all face two major defects: biased control and control solidification. Among them, in biased control: temperature control technology mainly affects the comfortable temperature level, humidity control technology mainly acts on the comfortable humidity level, as for wind control technology and air quality control, they also act on comfortable wind and health dimensions respectively. Even if some combined technologies combine the centralized control technologies to perform multi-dimensional joint control, there is still a problem of control priority, and it cannot be truly comprehensively controlled according to the complete comfort standard; control solidification means that the air conditioner uses the same set of control logic regardless of equipment, location, user, or time.

[0059] The main reasons for the above two defects are the lack of practical quantitative standards for air-conditioning comfort measurement (for example, traditional comfort measurement indicators such as PMV or SET require expensive experimental equipment or professional instruments to achieve); or because the current air-conditioning control logic is mostly derived from expert experience + experimental simulation, and the exclusive information such as the behavioral habits of air-conditioning users, the environmental conditions of air-conditioning operation, and the reported data during the operation of air-conditioning is not fully utilized.

[0060] In this embodiment, the operating data of the air conditioner includes but is not limited to: environmental information, operating condition information and user setting information, wherein the environmental information includes indoor ambient temperature, indoor ambient humidity and outdoor thermal environment information, the operating condition information includes fan speed, throttling element opening and windshield opening, etc., and the user setting information includes user-set temperature and air conditioner operating mode, etc. This embodiment does not make specific restrictions on this.

[0061] In a specific implementation, when the air conditioner is running, the operating data of the air conditioner can be collected at preset intervals, and periodic features in the operating data can be extracted: including but not limited to environmental information, operating condition information, and user setting information, etc., wherein the preset period can be 30s or 1min, and can also be set by the user. In this embodiment and the following embodiments, 30s is taken as an example for explanation.

[0062] Step S20: determining the initial comfort and initial comfort curve of the air conditioner through a trained comfort evaluation model based on the operating data.

[0063] It should be noted that the trained comfort evaluation model is used to calculate the comfort corresponding to the operating data uploaded by the air conditioner. In this embodiment, the trained comfort evaluation model can be based on the improved human comfort intelligent control model (Predicted Mean Vote, PMV), which is mainly composed of the Lasso model and the integrated learning model (eXtremeGradient Boosting, XGB) to jointly achieve the purpose of calculating the comfort corresponding to the operating data uploaded by the air conditioner.

[0064] In a specific implementation, the initial comfort level is calculated by reporting the operating data of the air conditioner every 30 seconds. The initial comfort level of the air conditioner in a second consecutive preset period is counted, and an initial comfort curve is generated based on the initial comfort level in the second period. The second preset period is greater than the preset period. For example, if the preset period is 30 seconds, the second preset period can be 5 minutes. This embodiment does not impose any specific restrictions on this.

[0065] It can be understood that the initial comfort level corresponds to the operating data of the air conditioner at the current moment, and the initial comfort curve corresponds to the operating data of the air conditioner 5 minutes before the current moment.

[0066] Step S30: performing prediction based on the initial comfort curve and a target comfort curve set corresponding to the initial comfort level to obtain predicted operating data of the air conditioner.

[0067] It should be noted that the predicted operation data refers to the operation data that achieves the best comfort level during the operation of the air conditioner at the next moment.

[0068] Further, the prediction is performed based on the initial comfort curve and the target comfort curve set corresponding to the initial comfort level to obtain the predicted operation data of the air conditioner, including:

[0069] Calculating the similarity between the initial comfort curve and the target comfort curve set;

[0070] Determining a target predicted comfort curve in the target comfort curve set according to the similarity;

[0071] The predicted comfort level at the next moment and the predicted operation data of the air conditioner corresponding to the predicted comfort level are determined based on the target predicted comfort curve.

[0072] In a specific implementation, due to changes in the operating parameters of the air conditioner, the corresponding comfort level will also change, and there will be differences in the comfort curve over a period of time. This embodiment can first extract the comfort curve corresponding to the initial comfort level from the database as the basis for the air conditioner operating parameters at the next moment.

[0073] This embodiment is based on the initial comfort value and the comfort curve of the previous five minutes. By calculating the similarity in the comfort curve set with the same initial comfort, the curve with the highest similarity is taken as the expected comfort curve (i.e., the target predicted comfort curve); the similarity is calculated based on the current operating parameters of the air conditioner and the feasible parameter set, and the feasible parameters with the highest similarity are taken as the operating parameters of the air conditioner at the next moment; the similarity is calculated based on the current operating parameters of the air conditioner and the feasible parameter set, and the feasible parameters with the highest similarity are taken as the operating parameters of the air conditioner at the next moment.

[0074] Step S40: controlling the operating state of the air conditioner at the next moment according to the predicted operating data.

[0075] This embodiment obtains the operating data of the air conditioner during operation, and performs comfort evaluation by training a comfort evaluation model based on the operating data of the air conditioner, so as to obtain the initial comfort and initial comfort curve of the air conditioner at the current moment, and then predicts the operating data of the air conditioner at the next moment based on the initial comfort curve and the target comfort curve corresponding to the initial comfort, and finally controls the operating state of the air conditioner at the next moment according to the predicted operating data, thereby avoiding the technical problem of low comfort in the operation control process of the air conditioner in the prior art and improving the user experience.

[0076] refer to Figure 3 , Figure 3 It is a flow chart of a second embodiment of an operation control method of an air conditioner according to the present invention.

[0077] Based on the first embodiment above, in this embodiment, before step S20, the following steps are further included:

[0078] Step S110: generating a training sample set according to the operation data samples of the air conditioner and the comfort data samples of the comfort equipment.

[0079] It should be noted that the comfort data samples of comfort equipment include but are not limited to six indicators: air temperature, radiant temperature, air speed, relative humidity, metabolic rate and clothing resistance.

[0080] Step S120: performing model training based on the training sample set through an initial comfort prediction model to obtain a trained comfort assessment model.

[0081] It should be noted that the initial comfort prediction model in this embodiment can be a model established based on the Lasso+XGB hybrid regression algorithm, which uses Lasso to learn the basic characteristics of the data, uses XGB to learn the residual, and constructs the final prediction model by integrating the model training results.

[0082] Furthermore, the model training is performed based on the training sample set through the initial comfort prediction model to obtain a trained comfort assessment model, including:

[0083] Performing model training based on the training sample set through an initial regression model to obtain a first prediction model;

[0084] Predicting a comfort level prediction value corresponding to the operation data sample according to the first prediction model;

[0085] Calculating an error value between the comfort prediction value and the actual comfort value in the comfort data sample;

[0086] Model training is performed using an initial comfort prediction model according to the operating data samples and the error value to obtain a trained comfort assessment model.

[0087] In the specific implementation, the air conditioner and the PMV comfort measurement device are connected to collect the air conditioner operation report data and the comfort data reported by the comfort device in real time. The air conditioner working condition information, environmental conditions and user operation data are used as feature variables, and the PMV data reported by the comfort device at the corresponding moment is used as the dependent variable. Through multiple groups of experimental operations, the corresponding training data set is constructed; based on the constructed training data set, Lasso regression is used to fit the feature variables and PMV in the training data set to obtain the corresponding Lasso model; the PMV predicted value lasso_pmv_pred of the training data is predicted by the Lasso model, and the actual PMV value is subtracted from the predicted value to obtain P MV prediction error lasso_pmv_error=pmv-lasso_pmv_pred; build an XGB model to learn the model representation between the feature variables in the training data set and the lasso prediction error, and obtain the corresponding XGB model; predict the training data set based on the XGB model to obtain the prediction xgb_pred; add up the XGB prediction and the Lasso prediction value to obtain the final PMV prediction value pmv_pred=lasso_pmv_pred+xgb_pred, and obtain new prediction values ​​through continuous iteration until the trained model loss value is less than a certain threshold, then the trained comfort assessment model can be output.

[0088] This embodiment trains the model established by the Lasso+XGB hybrid regression algorithm, continuously iterating the Lasso prediction value and the XGB prediction value, so as to train a model that can accurately adjust the operating state of the air conditioner and improve the user's comfort in using the air conditioner.

[0089] refer to Figure 4 , Figure 4 It is a flow chart of a third embodiment of an operation control method for an air conditioner according to the present invention.

[0090] Based on the above second embodiment, in this embodiment, before step S30, the following is further included:

[0091] Step S210: Acquire historical reported data of the air conditioner.

[0092] It should be noted that the historical reported data of the air conditioner includes but is not limited to historical environmental information, historical operating condition information, and historical user setting information within a period of time.

[0093] Step S220: Performing comfort evaluation based on the historical reported data through a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve.

[0094] Since the air conditioner does not consider which data is effective for improving comfort when reporting data, there is a large amount of invalid data when reporting data. In order to reduce the amount of erroneous data and improve the efficiency of air conditioner control, the comfort evaluation is performed based on the historical reported data through the trained comfort evaluation model to obtain the historical initial comfort and the corresponding historical comfort curve, including:

[0095] Cleaning and sampling the historical reported data to obtain target historical reported data;

[0096] The comfort level is evaluated by using a trained comfort level evaluation model according to the historical reported data of the target, and a historical initial comfort level and a corresponding historical comfort level curve are obtained.

[0097] It is understandable that data cleaning includes but is not limited to missing value processing, outlier processing, data deduplication, data format conversion, dedimensionalization, data unification and error correction, etc. This embodiment does not make specific restrictions on this.

[0098] In the specific implementation, the historical reported data of the air conditioner is cleaned to obtain neat and clean air conditioning data. Then, based on random sampling, 2,000 sets of equipment are randomly selected as the construction set for generating the comfort curve. Then, based on the trained comfort evaluation model, the comfort curve of each set of equipment in each operating range (i.e., the historical comfort curve) is obtained.

[0099] Step S230: classify the historical comfort curves to obtain a comfort curve set.

[0100] It should be noted that the basis for classifying the historical comfort curves may be different heating and cooling demands, different environmental conditions, and different comfort change demands. In specific performance, the extreme range (peak or valley) of the comfort curve may represent the heating and cooling demands, the initial comfort of the curve may represent the environmental conditions, and the similarity of the curve may represent the comfort change demands. This embodiment does not impose any specific restrictions on this.

[0101] Furthermore, the historical comfort curves are classified to obtain a set of comfort curves, including:

[0102] Obtaining the extreme value, initial comfort and curve similarity of the historical comfort curve;

[0103] Classifying the historical comfort curves based on the extreme values, the initial comfort levels, and the curve similarities to obtain a set of comfort curves;

[0104] The comfort curve set is preprocessed so that each comfort curve in the comfort curve set corresponds to a different initial comfort value.

[0105] In the specific implementation, based on three criteria, namely, the peak or valley value of the comfort curve, the initial comfort of the curve, and the similarity of the curve, the historical comfort curves are classified into different clusters, and the curves in each cluster are smoothed and truncated and replaced to obtain a set of comfort curves corresponding to different initial comfort values.

[0106] This embodiment obtains multiple historical comfort curves by evaluating the historical reported data of the air conditioner through a trained comfort evaluation model, and then classifies the historical comfort curves into different clusters according to three criteria, namely, the peak or valley value of the comfort curve, the initial comfort of the curve, and the similarity of the curve, to obtain a set of comfort curves corresponding to different initial comfort values, which facilitates the subsequent curve similarity calculation and improves the accuracy of the air conditioner control at the next moment.

[0107] In addition, an embodiment of the present invention further provides a storage medium on which an operation control program of an air conditioner is stored. When the operation control program of the air conditioner is executed by a processor, the steps of the operation control method of the air conditioner described above are implemented.

[0108] Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0109] Reference Figure 5 , Figure 5 FIG. 1 is a structural block diagram of a first embodiment of an operation control device for an air conditioner according to the present invention.

[0110] like Figure 5 As shown, the operation control device of the air conditioner provided in the embodiment of the present invention comprises:

[0111] The acquisition module 10 is used to acquire the operation data of the air conditioner when the air conditioner is running.

[0112] The calculation module 20 is used to determine the initial comfort and initial comfort curve of the air conditioner based on the operating data through a trained comfort evaluation model.

[0113] The prediction module 30 is used to make predictions based on the initial comfort curve and a target comfort curve set corresponding to the initial comfort level to obtain predicted operating data of the air conditioner.

[0114] The control module 40 is used to control the operating state of the air conditioner at the next moment according to the predicted operating data.

[0115] In one embodiment, the calculation module 20 is further used to generate a training sample set based on the operating data samples of the air conditioner and the comfort data samples of the comfort equipment; and perform model training based on the training sample set through an initial comfort prediction model to obtain a trained comfort evaluation model.

[0116] In one embodiment, the calculation module 20 is also used to perform model training through an initial regression model based on the training sample set to obtain a first prediction model; predict a comfort prediction value corresponding to the operating data sample according to the first prediction model; calculate an error value between the comfort prediction value and the actual comfort value in the comfort data sample; perform model training through an initial comfort prediction model according to the operating data sample and the error value to obtain a trained comfort assessment model.

[0117] In one embodiment, the prediction module 30 is also used to obtain historical reported data of the air conditioner; perform comfort evaluation based on the historical reported data through a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve; and classify the historical comfort curves to obtain a comfort curve set.

[0118] In one embodiment, the prediction module 30 is further used to clean and sample the historical reported data to obtain target historical reported data; perform comfort evaluation based on the target historical reported data using a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve.

[0119] In one embodiment, the prediction module 30 is also used to obtain the extreme value, initial comfort and curve similarity of the historical comfort curve; classify the historical comfort curve based on the extreme value, the initial comfort and the curve similarity to obtain a comfort curve set; and preprocess the comfort curve set so that each comfort curve in the comfort curve set corresponds to a different initial comfort value.

[0120] In one embodiment, the prediction module 30 is also used to calculate the similarity between the initial comfort curve and the target comfort curve set; determine the target predicted comfort curve in the target comfort curve set based on the similarity; and determine the predicted comfort at the next moment and the predicted operating data of the air conditioner corresponding to the predicted comfort based on the target predicted comfort curve.

[0121] This embodiment obtains the operating data of the air conditioner during operation, and performs comfort evaluation by training a comfort evaluation model based on the operating data of the air conditioner, so as to obtain the initial comfort and initial comfort curve of the air conditioner at the current moment, and then predicts the operating data of the air conditioner at the next moment based on the initial comfort curve and the target comfort curve corresponding to the initial comfort, and finally controls the operating state of the air conditioner at the next moment according to the predicted operating data, thereby avoiding the technical problem of low comfort in the operation control process of the air conditioner in the prior art and improving the user experience.

[0122] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0123] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0124] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0125] In addition, for technical details that are not described in detail in this embodiment, reference can be made to the operation control method of the air conditioner provided in any embodiment of the present invention, and they will not be described in detail here.

[0126] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0127] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0129] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An operation control method for an air conditioner, characterized in that: The operation control method of the air conditioner comprises: When the air conditioner is running, obtaining operation data of the air conditioner; Determine the initial comfort level and initial comfort curve of the air conditioner through a trained comfort level evaluation model based on the operating data; Perform prediction based on the initial comfort curve and a set of target comfort curves corresponding to the initial comfort level to obtain predicted operating data of the air conditioner; The operating state of the air conditioner at the next moment is controlled according to the predicted operating data.

2. The operation control method of the air conditioner according to claim 1, characterized in that: Before determining the initial comfort level and initial comfort curve of the air conditioner through the trained comfort level evaluation model based on the operating data, the method further includes: Generate a training sample set according to the operation data samples of the air conditioner and the comfort data samples of the comfort equipment; Model training is performed based on the training sample set using an initial comfort prediction model to obtain a trained comfort assessment model.

3. The operation control method of the air conditioner according to claim 2, characterized in that: The model training is performed based on the training sample set through the initial comfort prediction model to obtain a trained comfort evaluation model, including: Performing model training based on the training sample set through an initial regression model to obtain a first prediction model; Predicting a comfort level prediction value corresponding to the operation data sample according to the first prediction model; Calculating an error value between the comfort prediction value and the actual comfort value in the comfort data sample; Model training is performed using an initial comfort prediction model according to the operating data samples and the error value to obtain a trained comfort assessment model.

4. The operation control method of the air conditioner according to any one of claims 1 to 3, characterized in that: Before performing prediction according to the initial comfort curve and the target comfort curve set corresponding to the initial comfort level to obtain the predicted operation data of the air conditioner, the method further includes: Acquire historical reported data of the air conditioner; Performing comfort evaluation based on the historical reported data through a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve; The historical comfort curves are classified to obtain a set of comfort curves.

5. The operation control method of the air conditioner according to claim 4, characterized in that: The comfort evaluation is performed according to the historical reported data through a trained comfort evaluation model to obtain a historical initial comfort and a corresponding historical comfort curve, including: Cleaning and sampling the historical reported data to obtain target historical reported data; The comfort level is evaluated by using a trained comfort level evaluation model according to the historical reported data of the target, and a historical initial comfort level and a corresponding historical comfort level curve are obtained.

6. The operation control method of the air conditioner according to claim 4, characterized in that: The classifying of the historical comfort curves to obtain a set of comfort curves includes: Obtaining the extreme value, initial comfort and curve similarity of the historical comfort curve; Classifying the historical comfort curves based on the extreme values, the initial comfort levels, and the curve similarities to obtain a set of comfort curves; The comfort curve set is preprocessed so that each comfort curve in the comfort curve set corresponds to a different initial comfort value.

7. The operation control method of the air conditioner according to claim 1, characterized in that: The step of performing prediction according to the initial comfort curve and a target comfort curve set corresponding to the initial comfort level to obtain predicted operation data of the air conditioner includes: Calculating the similarity between the initial comfort curve and the target comfort curve set; Determining a target predicted comfort curve in the target comfort curve set according to the similarity; The predicted comfort level at the next moment and the predicted operation data of the air conditioner corresponding to the predicted comfort level are determined based on the target predicted comfort curve.

8. An operation control device for an air conditioner, characterized in that: The operation control device of the air conditioner comprises: An acquisition module, used for acquiring operation data of the air conditioner when the air conditioner is running; A calculation module, used for determining the initial comfort and initial comfort curve of the air conditioner through a trained comfort evaluation model based on the operating data; A prediction module, used for making predictions based on the initial comfort curve and a target comfort curve set corresponding to the initial comfort level, to obtain predicted operation data of the air conditioner; A control module is used to control the operating state of the air conditioner at the next moment according to the predicted operating data.

9. An operation control device for an air conditioner, characterized in that: The operation control device of the air conditioner includes: a memory, a processor, and an operation control program of the air conditioner stored in the memory and executable on the processor, wherein the operation control program of the air conditioner is configured to implement the operation control method of the air conditioner as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores an operation control program of the air conditioner, and when the operation control program of the air conditioner is executed by the processor, the operation control method of the air conditioner according to any one of claims 1 to 7 is implemented.

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