Data processing method, data processing apparatus, electronic device, and storage medium

Abstract

The application provides a data processing method, a data processing device, an electronic equipment and a storage medium. The data processing method comprises the following steps: acquiring real-time temperature data of an indoor environment at a current time, and generating predicted temperature data at the current time by using a temperature prediction model configured in the air conditioner; if a temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, optimizing the temperature prediction model according to the real-time temperature data to obtain an optimized temperature prediction model and model optimization data; and transmitting the model optimization data to a cloud server. The data processing method provided by the application filters the data to be uploaded by setting the preset condition, thereby saving the corresponding data transmission cost, avoiding the real-time transmission of data from the air conditioner to the cloud server, effectively reducing the parameter reporting amount, alleviating the network congestion problem when the indoor temperature fluctuates, and effectively reducing the communication cost.

Description

Technical Field

[0001] This application belongs to the field of air conditioning technology, and in particular relates to a data processing method, a data processing device, an electronic device, and a storage medium. Background Technology

[0002] With the development and popularization of technology, the Internet of Things (IoT) is gradually integrating into people's lives. The connection rate of existing home appliances is gradually increasing, and users can monitor and manage home appliances through the cloud. For example, air conditioners can now send local data to a server, and users can access the server via their mobile phones to know the current operating status of the air conditioner.

[0003] In existing technologies, air conditioners typically upload collected indoor temperature data to a server in real time so that users can monitor the indoor temperature. When the air conditioner uses a mobile data network for communication, each data transmission has its own cost. Therefore, this method of real-time data upload consumes a large amount of data traffic, resulting in high communication costs and increasing the cost of data interaction between the air conditioner and the server. Summary of the Invention

[0004] This application provides a data processing method, data processing device, electronic device, and storage medium to solve the problem that existing air conditioners typically upload collected indoor temperature data to a server in real time, which increases the cost of data interaction between the air conditioning unit and the server.

[0005] This application provides a data processing method applied to an air conditioner, comprising the following steps:

[0006] The system acquires real-time indoor temperature data at the current moment and generates predicted temperature data for the current moment using the temperature prediction model configured in the air conditioner.

[0007] If the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, then the temperature prediction model is optimized based on the real-time temperature data to obtain the optimized temperature prediction model and the model optimization data.

[0008] The optimized model data is transmitted to a cloud server, which is also configured with the temperature prediction model. The cloud server optimizes the temperature prediction model configured in the cloud server based on the optimized model data.

[0009] Optionally, obtaining the model optimization data includes:

[0010] Set the real-time temperature data as the model optimization data;

[0011] Alternatively, the model parameters of the optimized temperature prediction model can be extracted and set as model optimization data.

[0012] Optionally, before optimizing the temperature prediction model based on the real-time temperature data to obtain the optimized temperature prediction model and obtaining the model optimization data if the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, the method further includes:

[0013] Obtain environmental information when using the air conditioner;

[0014] The usage scenario information of the air conditioner is determined based on the environmental information;

[0015] Set the temperature difference threshold corresponding to the usage scenario information as the preset condition.

[0016] Optionally, the preset condition includes exceeding a temperature difference threshold. After optimizing the temperature prediction model based on the real-time temperature data, the method further includes:

[0017] The optimization frequency of the temperature prediction model is statistically analyzed.

[0018] The preset conditions are adjusted according to the optimized frequency to obtain the adjusted preset conditions.

[0019] Optionally, after receiving a temperature query request, the cloud server generates predicted temperature data corresponding to the temperature query request based on the optimized temperature prediction model within the cloud server, and outputs the predicted temperature data.

[0020] This application also provides a data processing method applied to a cloud server, comprising the following steps:

[0021] If model optimization data is received from the air conditioner, the temperature prediction model configured in the cloud server is optimized according to the model optimization data to obtain the optimized temperature prediction model.

[0022] If a temperature query request is received, the optimized temperature prediction model is used to generate the predicted temperature data corresponding to the temperature query request, and the predicted temperature data is output.

[0023] The model optimization data is obtained by optimizing the temperature prediction model inside the air conditioner based on the acquired real-time temperature data when the temperature difference meets the preset conditions. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner.

[0024] This application embodiment also provides a data processing device applied to an air conditioner, the device comprising:

[0025] The temperature acquisition module is configured to acquire the real-time indoor temperature data at the current moment and generate the predicted temperature data at the current moment through the temperature prediction model configured in the air conditioner.

[0026] The generation module is configured to optimize the temperature prediction model based on the real-time temperature data if the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, thereby obtaining the optimized temperature prediction model and model optimization data.

[0027] The transmission module transmits the model optimization data to the cloud server, wherein the cloud server is also configured with the temperature prediction model, and the cloud server optimizes the temperature prediction model configured in the cloud server based on the model optimization data.

[0028] This application embodiment also provides a data processing apparatus applied to a cloud server, the apparatus comprising:

[0029] The optimization module is configured to optimize the temperature prediction model configured in the cloud server based on the model optimization data sent by the air conditioner if it receives model optimization data, so as to obtain an optimized temperature prediction model.

[0030] The output module is configured to, if a temperature query request is received, generate the predicted temperature data corresponding to the temperature query request through the optimized temperature prediction model, and output the predicted temperature data.

[0031] The model optimization data is obtained by optimizing the temperature prediction model inside the air conditioner based on the acquired real-time temperature data when the temperature difference meets the preset conditions. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner.

[0032] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data processing method described above.

[0033] This application embodiment also provides a storage medium storing control instructions, which, when executed by a processor, implement the data processing method described above.

[0034] The data processing method provided in this application embodiment optimizes the temperature prediction model within the air conditioner if the temperature difference between the real-time indoor temperature data and the predicted temperature data generated by the temperature prediction model reaches a preset condition. This improves the prediction accuracy of the optimized temperature prediction model within the air conditioner, and the optimized model data is uploaded to the cloud server. If the temperature difference between the real-time indoor temperature data and the predicted temperature data generated by the temperature prediction model does not reach the preset condition, the temperature prediction model within the air conditioner is not optimized, and the air conditioner does not upload the optimized model data to the cloud server. In other words, by setting preset conditions to filter the data that needs to be uploaded, the corresponding data transmission cost is saved, and the real-time data transmission from the air conditioner to the cloud server is avoided. This effectively reduces the amount of parameter reporting, alleviates network congestion when indoor temperature fluctuates, and effectively reduces communication costs.

[0035] Meanwhile, the cloud server optimizes its temperature prediction model based on the model optimization data uploaded by the air conditioner, thereby improving the prediction accuracy of the model. When a user accesses the cloud server to query indoor temperature data, the cloud server generates predicted temperature data using the configured model and outputs it to the user. By ensuring the prediction accuracy of the temperature prediction model within the cloud server, the accuracy of the data received by the user is guaranteed. This data processing method reduces data transmission between the air conditioner and the cloud server to a certain extent while ensuring the accuracy of the data obtained by the user. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings. In the following description, the same reference numerals denote the same parts.

[0038] Figure 1 This is a diagram illustrating a use case of the data processing method provided in an embodiment of this application.

[0039] Figure 2 This is a first control flowchart of the data processing method provided in the embodiments of this application.

[0040] Figure 3 This is a second control flowchart of the data processing method provided in the embodiments of this application.

[0041] Figure 4 This is a main structural block diagram of a data processing device for air conditioning provided in an embodiment of this application.

[0042] Figure 5 This is a main structural block diagram of a data processing device applied to a cloud server, provided in an embodiment of this application.

[0043] Figure 6 This is a main structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0045] In the description of the embodiments of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, and memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, a microprocessor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc.

[0046] This application provides a data processing method, data processing device, electronic device, and storage medium to solve the problem that existing air conditioners typically upload collected indoor temperature data to a server in real time, which increases the cost of data interaction between the air conditioning unit and the server. The following description is in conjunction with the accompanying drawings.

[0047] The data processing method provided in this application is applied to air conditioning. Please refer to [link / reference]. Figure 1 and Figure 2 , Figure 1 This is a diagram illustrating a use case of the data processing method provided in the embodiments of this application. Figure 2 A first control flowchart of the data processing method provided in this application embodiment, the method includes the following steps:

[0048] Step S101: Obtain the real-time indoor temperature data at the current moment, and generate the predicted temperature data at the current moment through the temperature prediction model configured in the air conditioner;

[0049] Step S102: If the temperature difference between the real-time temperature data and the predicted temperature data meets the preset conditions, then optimize the temperature prediction model based on the real-time temperature data to obtain the optimized temperature prediction model and obtain the model optimization data.

[0050] Step S103: Transmit the model optimization data to the cloud server. The cloud server is also configured with a temperature prediction model. The cloud server optimizes the temperature prediction model configured in the cloud server based on the model optimization data.

[0051] The data processing method provided in this application embodiment optimizes the temperature prediction model within the air conditioner if the temperature difference between the real-time indoor temperature data and the predicted temperature data generated by the temperature prediction model reaches a preset condition. This improves the prediction accuracy of the optimized temperature prediction model within the air conditioner, and the optimized model data is uploaded to the cloud server. If the temperature difference between the real-time indoor temperature data and the predicted temperature data generated by the temperature prediction model does not reach the preset condition, the temperature prediction model within the air conditioner is not optimized, and the air conditioner does not upload the optimized model data to the cloud server. In other words, by setting preset conditions to filter the data that needs to be uploaded, the corresponding data transmission cost is saved, and the real-time data transmission from the air conditioner to the cloud server is avoided. This effectively reduces the amount of parameter reporting, alleviates network congestion when indoor temperature fluctuates, and effectively reduces communication costs.

[0052] Meanwhile, the cloud server optimizes its temperature prediction model based on the model optimization data uploaded by the air conditioner, thereby improving the prediction accuracy of the model. When a user accesses the cloud server to query indoor temperature data, the cloud server generates predicted temperature data using the configured model and outputs it to the user. By ensuring the prediction accuracy of the temperature prediction model within the cloud server, the accuracy of the data received by the user is guaranteed. This data processing method reduces data transmission between the air conditioner and the cloud server to a certain extent while ensuring the accuracy of the data obtained by the user.

[0053] Optionally, the model optimization data obtained in step S102 includes:

[0054] Step S1021: Set the real-time temperature data as model optimization data. That is, the temperature prediction model within the air conditioner optimizes itself using the current time and the real-time temperature data as parameters, resulting in an optimized temperature prediction model. Simultaneously, the air conditioner uploads the current time and the real-time temperature data as model optimization data to the cloud server, allowing the cloud server to optimize its own temperature prediction model using these two parameters, thus obtaining an optimized temperature prediction model within the cloud server.

[0055] Alternatively, step S1022: Extract the model parameters of the optimized temperature prediction model and set the model parameters as the model optimization data. That is, the temperature prediction model in the air conditioner optimizes the current temperature prediction model using two parameters: the current time and the real-time temperature data, thus obtaining an optimized temperature prediction model. The air conditioner then extracts the model parameters of the optimized temperature prediction model and uploads the extracted model parameters to the cloud server. The cloud server directly optimizes the temperature prediction model within the cloud server using the model parameters, thus obtaining an optimized temperature prediction model within the cloud server.

[0056] By setting two different types of parameters in steps S1021 and S1022, the temperature prediction model inside the air conditioner can be optimized, and further selection can be made according to the specific usage.

[0057] Optionally, please refer to Figure 3 , Figure 3 The second control flowchart of the data processing method provided in this application embodiment, before executing step S102: if the temperature difference between the real-time temperature data and the predicted temperature data meets the preset condition, then optimizing the temperature prediction model based on the real-time temperature data to obtain the optimized temperature prediction model, and obtaining the model optimization data, the method further includes:

[0058] Step S1011: Obtain environmental information when using air conditioning;

[0059] Step S1012: Determine the usage scenario information of the air conditioner based on the environmental information;

[0060] Step S1013: Set the temperature difference threshold corresponding to the usage scenario information as a preset condition.

[0061] Among them, the environmental information when using the air conditioner can be information collected by the sensors pre-installed in the air conditioner, such as indoor humidity, indoor space size or indoor temperature, etc. The usage scenario information of the air conditioner can be different pre-installed usage scenarios, such as spring, summer or winter, or morning, noon or evening, or spring morning, winter evening or summer noon, etc.

[0062] This involves using environmental information obtained when the air conditioner is in use to classify the air conditioner's usage scenario, and then setting the corresponding temperature difference threshold as the current preset condition. For example, if the current indoor humidity is 50% and the indoor space volume is approximately 112.5 m³, this would be a suitable setting. 3The indoor temperature is 22℃, so the usage scenario of the air conditioner is determined to be midday in spring. Therefore, the temperature difference threshold corresponding to midday in spring, such as 3℃, is set as a preset condition. When the temperature difference between the real-time temperature data and the predicted temperature data is greater than 3℃, the temperature prediction model is optimized based on the real-time temperature data to obtain the optimized temperature prediction model and the model optimization data. When the temperature difference between the real-time temperature data and the predicted temperature data is less than or equal to 3℃, the temperature prediction model is not optimized, and the air conditioner does not upload the real-time temperature data at the current moment. The air conditioner only continues to collect real-time temperature data.

[0063] In step S102, if the temperature difference between the real-time temperature data and the predicted temperature data meets the preset condition, that is, the temperature difference between the real-time temperature data and the predicted temperature data is greater than the temperature difference threshold.

[0064] Optionally, the preset conditions include exceeding a temperature difference threshold. After optimizing the temperature prediction model based on real-time temperature data in step S102, the method further includes:

[0065] Step S1023: Optimization frequency of the statistical temperature prediction model;

[0066] Step S1024: Adjust the preset conditions according to the optimization frequency and obtain the adjusted preset conditions.

[0067] The optimization frequency of the temperature prediction model refers to the number of times the model is optimized. This optimization frequency can be monitored by executing steps S1023 and S1024. If the optimization frequency is too high, it indicates that the current preset condition (temperature difference threshold) is too low, and the threshold needs to be appropriately increased. If the optimization frequency is too low, it indicates that the current preset condition (temperature difference threshold) is too high, and the threshold needs to be appropriately decreased. If the optimization frequency is within the expected range, it indicates that the current preset condition (temperature difference threshold) is suitable, and no change to the threshold is necessary. The expected range of the optimization frequency is not further limited here; specific execution strategies can be formulated based on specific usage scenarios or experimental data.

[0068] Optionally, before obtaining the real-time indoor temperature data at the current moment in step S101, the method further includes:

[0069] Step S100: Obtain sample data and train the preset prediction model based on the sample data until the preset stopping condition is met to obtain the temperature prediction model.

[0070] The sample data can be multiple sets of real-time indoor temperature data collected within a preset period and the time parameters corresponding to each set of temperature data. Multiple sets of real-time temperature data are used as outputs, and multiple time parameters are used as inputs. The preset prediction model is trained using the above multiple sets of data. The preset stopping condition here can be to minimize the loss of the preset prediction model, thereby obtaining the target temperature prediction model.

[0071] In some examples, the preset prediction model can be a regression-based prediction model, specifically a mathematical model for the air temperature in a refrigerator, with the following formula:

[0072]

[0073] Where C is the indoor object volume coefficient, k1 is the wall equivalent heat transfer coefficient, A1 is the wall heat transfer area, k2 is the evaporator wall equivalent heat transfer coefficient, A2 is the evaporator heat transfer area, and θ is the real-time indoor temperature data at the current moment. B Let C be the outdoor air temperature and θ2 be the evaporator temperature of the refrigerator. The model parameters mentioned in this paper are C, k1, A1, k2, A2, and θ. B The above model parameters are obtained by training the preset prediction model based on the sample data, and any one or a combination of θ2.

[0074] Optionally, after receiving a temperature query request, the cloud server generates the predicted temperature data corresponding to the temperature query request based on the optimized temperature prediction model within the cloud server, and outputs the predicted temperature data. There are no further restrictions on the output end of the data, such as it can be the user end.

[0075] The temperature query request is not further limited here. For example, it can be a query for indoor temperature data at a past time, or a query for indoor temperature data at a current time, or an indoor temperature data at a future time.

[0076] As an alternative implementation, the temperature prediction model in this application embodiment can also be a prediction model with other parameters, such as a humidity prediction model. In this case, a preset prediction model for predicting indoor humidity values ​​needs to be configured in the air conditioner and server, and the preset prediction model is trained based on sample data to obtain the target humidity prediction model. The prediction method will not be described in detail here, but you can refer to the execution method of the indoor temperature prediction model in this application.

[0077] This application also provides a data processing method applied to a cloud server, the method comprising the following steps:

[0078] Step S104: If model optimization data is received from the air conditioner, the temperature prediction model configured in the cloud server is optimized according to the model optimization data to obtain the optimized temperature prediction model.

[0079] Step S105: If a temperature query request is received, the predicted temperature data corresponding to the temperature query request is generated through the optimized temperature prediction model, and the predicted temperature data is output.

[0080] The model optimization data is obtained by optimizing the temperature prediction model inside the air conditioner based on the acquired real-time temperature data when the temperature difference meets the preset conditions. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner.

[0081] When the cloud server receives a temperature query request, it can directly generate and output predicted temperature data through its built-in temperature prediction model, eliminating the need for the air conditioner to upload data to the cloud server in real time, thus saving transmission time. At the same time, since the temperature prediction model can be updated under preset conditions, the accuracy of the output predicted temperature data is guaranteed, improving the accuracy and timeliness of data queries.

[0082] This application also provides a data processing device applied to an air conditioner; please refer to [link / reference]. Figure 4 , Figure 4 The main structural block diagram of the data processing device for air conditioning provided in this application embodiment is shown. The data processing device includes a temperature acquisition module 1, a generation module 2, and a transmission module 3. The temperature acquisition module 1 is configured to control the air conditioner to collect real-time indoor temperature data at the current moment, and generate predicted temperature data at the current moment through a temperature prediction model configured in the air conditioner. The generation module 2 is configured to optimize the temperature prediction model based on the real-time temperature data if the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, thereby obtaining an optimized temperature prediction model and model optimization data. The transmission module 3 transmits the model optimization data to a cloud server, wherein the cloud server is also configured with a temperature prediction model, and the cloud server optimizes the temperature prediction model configured in the cloud server based on the model optimization data.

[0083] This application also provides a data processing apparatus applied to a cloud server. Please refer to [link / reference]. Figure 5 , Figure 5The main structural block diagram of the data processing device applied to a cloud server provided in this application embodiment is shown. The data processing device includes an optimization module 4 and an output module 5. The optimization module 4 is configured to optimize the temperature prediction model configured in the cloud server according to the model optimization data sent by the air conditioner if it receives model optimization data, thereby obtaining an optimized temperature prediction model. The output module 5 is configured to generate the predicted temperature data corresponding to the temperature query request through the optimized temperature prediction model and output the predicted temperature data if it receives a temperature query request. The model optimization data is obtained by the air conditioner optimizing the temperature prediction model in the air conditioner based on the acquired real-time temperature data when the temperature difference meets a preset condition. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner.

[0084] This application also provides an electronic device 6, please refer to... Figure 6 , Figure 6 The main structural block diagram of the electronic device provided in the embodiments of this application is shown. The electronic device 6 includes a memory 61, a processor 62, and a computer program 611 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 611, it implements the data processing method as described above.

[0085] For example, the computer program 611 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 62 to complete the present invention. The one or more modules / units may be a series of computer program 611 instruction segments capable of performing a specific function, which describe the execution process of the computer program 611 in the electronic device 6.

[0086] Electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device 6. Electronic device 6 may include, but is not limited to, a processor 62 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 6 may also include input / output devices, network access devices, buses, etc.

[0087] The processor 62 can be a central processing unit (CPU), or other general-purpose processor 62, digital signal processor 62 (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor 62 can be a microprocessor 62, or any conventional processor 62, etc.

[0088] In the embodiments provided by this invention, it should be understood that the disclosed device / electronic device 6 and method can be implemented in other ways. For example, the embodiments of device / electronic device 6 described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0090] This application embodiment also provides a storage medium that stores control instructions, which, when executed by processor 62, implement the data processing method described above.

[0091] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program 611 instructing related hardware. The computer program 611 can be stored in a computer-readable storage medium, and when executed by the processor 62, it can implement the steps of the various method embodiments described above. The computer program 611 may include computer program 611 code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program 611 code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory 61, read-only memory 61 (ROM), random access memory 61 (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in computer-readable media may be appropriately added to or subtracted from the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, computer-readable media may not include electrical carrier signals and telecommunication signals, in accordance with legislation and patent practice.

[0092] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0093] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features.

[0094] The data processing method, data processing device, electronic device, and storage medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data processing method, characterized in that, When applied to air conditioners, the following steps are included: The system acquires real-time indoor temperature data at the current moment and generates predicted temperature data for the current moment using the temperature prediction model configured in the air conditioner. If the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, then the temperature prediction model is optimized based on the real-time temperature data to obtain the optimized temperature prediction model and the model optimization data. The optimized model data is transmitted to a cloud server, wherein the cloud server is also configured with the temperature prediction model, and the cloud server optimizes the temperature prediction model configured in the cloud server based on the optimized model data. If the temperature difference between the real-time temperature data and the predicted temperature data meets a preset condition, then before optimizing the temperature prediction model based on the real-time temperature data to obtain the optimized temperature prediction model and obtaining the model optimization data, the method further includes: Obtain environmental information when using the air conditioner; The usage scenario information of the air conditioner is determined based on the environmental information; Set the temperature difference threshold corresponding to the usage scenario information as the preset condition.

2. The data processing method according to claim 1, characterized in that, The obtained model optimization data includes: Set the real-time temperature data as the model optimization data; Alternatively, the model parameters of the optimized temperature prediction model can be extracted and set as model optimization data.

3. The data processing method according to claim 1, characterized in that, The preset conditions include exceeding a temperature difference threshold. After optimizing the temperature prediction model based on the real-time temperature data, the method further includes: The optimization frequency of the temperature prediction model is statistically analyzed. The preset conditions are adjusted according to the optimized frequency to obtain the adjusted preset conditions.

4. The data processing method according to claim 1, characterized in that, After receiving a temperature query request, the cloud server generates predicted temperature data corresponding to the temperature query request based on the optimized temperature prediction model within the cloud server, and outputs the predicted temperature data.

5. A data processing method, characterized in that, When applied to cloud servers, the following steps are included: If model optimization data is received from the air conditioner, the temperature prediction model configured in the cloud server is optimized according to the model optimization data to obtain the optimized temperature prediction model. If a temperature query request is received, the optimized temperature prediction model is used to generate the predicted temperature data corresponding to the temperature query request, and the predicted temperature data is output. The model optimization data is obtained by optimizing the temperature prediction model inside the air conditioner based on the acquired real-time temperature data when the temperature difference meets the preset conditions. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner. The preset conditions include a temperature difference threshold corresponding to the usage scenario information of the air conditioner, and the usage scenario information is determined by the air conditioner based on the environmental information during use.

6. A data processing apparatus, characterized in that, The device, used in air conditioning, includes: The temperature acquisition module is configured to acquire the real-time indoor temperature data at the current moment and generate the predicted temperature data at the current moment through the temperature prediction model configured in the air conditioner. The generation module is configured to acquire environmental information when the air conditioner is used; determine the usage scenario information of the air conditioner based on the environmental information; set the temperature difference threshold corresponding to the usage scenario information as a preset condition; if the temperature difference between the real-time temperature data and the predicted temperature data meets the preset condition, then optimize the temperature prediction model based on the real-time temperature data to obtain the optimized temperature prediction model and obtain the model optimization data. The transmission module transmits the model optimization data to the cloud server, wherein the cloud server is also configured with the temperature prediction model, and the cloud server optimizes the temperature prediction model configured in the cloud server based on the model optimization data.

7. A data processing apparatus, characterized in that, The device, applied to a cloud server, includes: The optimization module is configured to optimize the temperature prediction model configured in the cloud server based on the model optimization data sent by the air conditioner if it receives model optimization data, so as to obtain an optimized temperature prediction model. The output module is configured to, if a temperature query request is received, generate the predicted temperature data corresponding to the temperature query request through the optimized temperature prediction model, and output the predicted temperature data. The model optimization data is obtained by optimizing the temperature prediction model inside the air conditioner based on the acquired real-time temperature data when the temperature difference meets the preset conditions. The temperature difference is the difference between the real-time temperature data and the predicted temperature data. The predicted temperature data is predicted by the temperature difference prediction model in the air conditioner. The preset conditions include a temperature difference threshold corresponding to the usage scenario information of the air conditioner, and the usage scenario information is determined by the air conditioner based on the environmental information during use.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the data processing method as described in any one of claims 1 to 4, or the data processing method as described in claim 5.

9. A storage medium, characterized in that, The storage medium stores control instructions, which, when executed by a processor, implement the data processing method as described in any one of claims 1 to 5.

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