Smart home data analysis method, device, terminal equipment and storage medium

Abstract

The embodiment of the present invention discloses a smart home data analysis method, device, terminal device and storage medium. The method includes: obtaining the operation data of each smart home device and storing it in a historical database; obtaining the target operation data of each smart home device from the historical database, inputting the target operation data into the data analysis engine, so that the data analysis engine calls the corresponding data analysis model to analyze the target operation data, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule; obtains the historical operation data of each smart home device from the historical database, and generates the user's device usage preference information according to the historical operation data. The embodiment of the present invention solves the technical problem of low management efficiency of smart home devices in the prior art.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart home devices, and in particular, to a smart home data analysis method, apparatus, terminal device, and storage medium. Background Art

[0002] At present, with the continuous development of Internet of Things technology, smart home devices have gradually replaced traditional homes, bringing a lot of convenience to people's lives. However, in the existing technology, the control center can only control smart home devices and detect the status of smart home devices, and cannot analyze the data of smart home devices. Users cannot grasp the overall operating status of smart home devices, resulting in low management efficiency of smart home devices. Summary of the invention

[0003] The embodiments of the present invention provide a smart home data analysis method, apparatus, terminal device and storage medium, which solve the technical problem of low management efficiency of smart home devices in the prior art.

[0004] In a first aspect, an embodiment of the present invention provides a smart home data analysis method, comprising the following steps:

[0005] Acquire the operation data of each smart home device, and store the operation data of each smart home device in a historical database;

[0006] Acquire target operation data of each of the smart home devices from the historical database;

[0007] Inputting the target operation data of each smart home device into a data analysis engine, so that the data analysis engine calls a data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains a data analysis result of each smart home device, calls a distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to a target terminal for visualization according to the distribution rule;

[0008] The historical operation data of each of the smart home devices is obtained from the historical database, and the device usage preference information of the user is generated according to the historical operation data.

[0009] Preferably, before obtaining the operating data of each smart home device, the following steps are also included:

[0010] The configuration information of each of the smart home devices is set, and the configuration information is sent to the data analysis engine, so that the data analysis engine generates a distribution rule for each of the smart home devices according to the configuration information.

[0011] Preferably, after acquiring the operating data of each smart home device and before storing the operating data of each smart home device in the historical database, the following steps are also included:

[0012] The operation data of each of the smart home devices is preprocessed.

[0013] Preferably, the specific process of preprocessing the operating data of each smart home device is:

[0014] The operating data of each of the smart home devices is converted into operating data in a preset format.

[0015] Preferably, the data analysis model includes a power consumption calculation model, a failure probability calculation model and a loss degree calculation model.

[0016] Preferably, the specific process of acquiring the historical operation data of each smart home device from the historical database and generating the device usage preference information of the user according to the historical operation data is:

[0017] Acquire historical operation data of each of the smart home devices from the historical database, perform status analysis on the historical operation data of each of the smart home devices, and obtain the historical operation status of each of the smart home devices;

[0018] The user's device usage preference information is generated according to the historical operating status of each of the smart home devices.

[0019] Preferably, the specific process of generating the user's device usage preference information according to the historical operating status of each of the smart home devices is:

[0020] According to the historical operating status of each of the smart home devices, the operating status change trend of each of the smart home devices is determined; according to the operating status change trend of each of the smart home devices, the user's usage preference for each of the smart home devices is determined; and device usage preference information is generated according to the usage preference.

[0021] In a second aspect, an embodiment of the present invention provides a smart home data analysis device, including an operation data acquisition module, a target data acquisition module, a data analysis module, and a preference information generation module;

[0022] The operation data acquisition module is used to acquire the operation data of each smart home device and store the operation data of each smart home device in a history database;

[0023] The target data acquisition module is used to acquire the target operation data of each smart home device from the historical database;

[0024] The data analysis module is used to input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule;

[0025] The preference information generating module is used to obtain the historical operation data of each of the smart home devices from the historical database, and generate the device usage preference information of the user according to the historical operation data.

[0026] In a third aspect, an embodiment of the present invention provides a terminal device, wherein the terminal device includes a processor and a memory;

[0027] The memory is used to store a computer program and transmit the computer program to the processor;

[0028] The processor is used to execute a smart home data analysis method as described in the first aspect according to the instructions in the computer program.

[0029] In a fourth aspect, an embodiment of the present invention provides a storage medium storing computer executable instructions, which, when executed by a computer processor, are used to execute the smart home data analysis method as described in the first aspect.

[0030] As described above, the embodiment of the present invention obtains the operating data of each smart home device and stores the operating data of each smart home device in a historical database; obtains the target operating data of each smart home device from the historical database; inputs the target operating data of each smart home device into a data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operating data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule; obtains the historical operating data of each smart home device from the historical database, and generates the user's device usage preference information according to the historical operating data.

[0031] The embodiment of the present invention uses a data analysis engine to analyze the target operation data, thereby obtaining the data analysis results of the smart home devices, so that the user can grasp the overall operation status of the smart home devices, and generate the user's device usage preference information according to the historical operation data of each smart home device, so as to know the user's historical usage of the smart home devices, improve the management efficiency of the smart home devices, and solve the technical problem of low management efficiency of the smart home devices in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A method flow chart of a smart home data analysis method provided by an embodiment of the present invention.

[0033] Figure 2 A method flow chart of another smart home data analysis method provided by an embodiment of the present invention.

[0034] Figure 3 A schematic diagram of the structure of a smart home data analysis device provided by an embodiment of the present invention.

[0035] Figure 4 A schematic diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following description and accompanying drawings fully illustrate the specific embodiments of the present application so that those skilled in the art can practice them. The examples represent possible variations only. Unless explicitly required, separate components and functions are optional, and the order of operation can vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. The scope of the embodiments of the present application includes the entire scope of the claims, and all available equivalents of the claims. In this article, each embodiment may be represented individually or generally by the term "invention", which is only for convenience, and if more than one invention is disclosed in fact, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, without requiring or implying any actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed. The various embodiments are described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. As for the structures, products, etc. disclosed in the embodiments, since they correspond to the parts disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0037] Embodiment 1

[0038] like Figure 1 As shown, Figure 1 The flowchart of a smart home data analysis method provided by an embodiment of the present invention. The smart home data analysis method provided by an embodiment of the present invention can be executed by a smart home data analysis device, which can be implemented by software and / or hardware. The smart home data analysis device can be composed of two or more physical entities or one physical entity. For example, the smart home data analysis device can be a computer, a host computer, a server, a tablet, and other devices.

[0039] The method comprises the following steps:

[0040] Step 101: Obtain the operating data of each smart home device, and store the operating data of each smart home device in a historical database.

[0041] In this embodiment, it is first necessary to obtain the operating data of each smart home device. After obtaining the operating data of each smart home, the operating data of the smart home device is stored in the historical database. It should be further explained that the operating data includes the action data of the smart home device and the status data of the smart home device; the action data is the current action behavior of the smart home device, such as the rotation angle and rotation speed of the fan motor of the smart fan device, the rotation angle and rotation speed of the angle conversion motor, etc. The status data is the current working voltage data, working current data, and heating data of the smart home device, and the status data can be obtained through sensor detection.

[0042] Step 102: Obtain target operation data of each smart home device from the historical database.

[0043] After the operation data of the smart home devices are stored in the historical database, the target operation data of each smart home device can be obtained from the historical database. In one embodiment, the user can pre-set data acquisition rules as needed, filter the operation data according to the data acquisition rules, and thus obtain the target data. Exemplarily, the user sets a target time range in the data acquisition rules, and then, according to the target time range set by the user, the operation data of each smart home within the target time range can be obtained from the historical database as the target operation data.

[0044] Step 103: input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule.

[0045] After obtaining the target operation data of each smart home device, the target operation data of each smart home device is input into the data analysis engine. After obtaining the target operation data, the data analysis engine calls the data analysis model corresponding to each smart home device. The data analysis model analyzes the target operation data of each smart home device, thereby obtaining the data analysis results of each smart home device. Exemplarily, in one embodiment, the data analysis model analyzes and processes the target operation data to obtain the power consumption, predicted faults, and loss degree of the smart home device, thereby obtaining the data analysis results of the smart home device.

[0046] After obtaining the data analysis results, the distribution rules corresponding to each smart home device are further called, and according to the distribution rules, the data analysis results of the smart home devices are sent to the corresponding target terminals for visual display, so that users can know the overall operating status of each smart home device. It should be noted that the distribution rules are provided with target terminals corresponding to each smart home device, and the number of target terminals can be set according to actual needs. In this embodiment, the number of target terminals is not specifically limited.

[0047] Step 104: Obtain historical operation data of each smart home device from the historical database, and generate device usage preference information of the user based on the historical operation data.

[0048] In this embodiment, after the data analysis results of each smart home device are sent to the target terminal, the historical operation data of each smart home device is obtained from the historical database, and the user's device usage preference information is generated based on the historical operation data of each smart home device. Exemplarily, in one embodiment, the state data and action data in the target operation data are analyzed to determine the historical operation state of each smart home device, and the user's device usage preference information is generated based on the historical operation state. It should be further explained that the device usage preference information includes the user's usage preference for each smart home device.

[0049] As described above, the embodiment of the present invention uses a data analysis engine to analyze the target operation data, thereby obtaining the data analysis results of the smart home devices, so that the user can grasp the overall operation status of the smart home devices, and generate the user's device usage preference information according to the historical operation data of each smart home device, so as to know the user's historical usage of the smart home devices, improve the management efficiency of the smart home devices, and solve the technical problem of low management efficiency of smart home devices in the prior art.

[0050] Embodiment 2

[0051] like Figure 2 As shown, Figure 2 A method flow chart of another smart home data analysis method provided by an embodiment of the present invention includes the following steps:

[0052] Step 201: Set configuration information for each smart home device, and send the configuration information to a data analysis engine, so that the data analysis engine generates distribution rules for each smart home device based on the configuration information.

[0053] In this embodiment, it is first necessary to set the configuration information of each smart home device, that is, to set the target terminal corresponding to each smart home. It can be understood that the target terminal corresponding to each smart home can be set according to actual needs, and the number of target terminals can also be flexibly selected. In this embodiment, no specific limit is imposed on the target terminal. After the setting is completed, the configuration information of each smart home device is sent to the data analysis engine. After receiving the configuration information, the data analysis engine can generate the distribution rules for each smart home device according to the configuration information for subsequent calls.

[0054] Step 202: Obtain the operating data of each smart home device, and store the operating data of each smart home device in a historical database.

[0055] On the basis of the above embodiment, after the operation data of each smart home device is acquired, before the operation data of each smart home device is stored in the history database, the following steps are also included:

[0056] Preprocess the operating data of each smart home device.

[0057] In this embodiment, after the operation data of each smart home device is acquired, the operation data of each smart home device needs to be preprocessed, and then the preprocessed operation data is stored in a history database.

[0058] In one embodiment, the specific process of preprocessing the operation data of each smart home device is as follows:

[0059] Convert the operating data of each smart home device into operating data in a preset format.

[0060] By converting the operating data of each smart home device into operating data in a preset format, the format of the operating data is unified to facilitate subsequent analysis and processing of the operating data.

[0061] Step 203: Obtain target operation data of each smart home device from the historical database.

[0062] Step 204: input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule.

[0063] Based on the above embodiment, the data analysis model includes a power consumption calculation model, a fault prediction model and a loss calculation model.

[0064] It should be further explained that, in this embodiment, the data analysis model includes a power consumption calculation model, a fault prediction model and a loss calculation model, and each type of smart home device corresponds to a power consumption calculation model, a fault prediction model and a loss calculation model. In one embodiment, the operation data of each smart home device includes the type information of the smart home device, and each power consumption calculation model, each fault prediction model and each loss calculation model includes the type information of different smart home devices. After receiving the target operation data, the data analysis engine extracts the type information from the target operation data, and calls the data analysis model with the same type information according to the type information, and inputs the target operation data into the corresponding data analysis model.

[0065] In this embodiment, the operation data includes the status data of the smart home device and the action data of the smart home device. After obtaining the target operation data of each smart home device, the data analysis engine inputs the status data in the target operation data into the corresponding power consumption calculation model and the corresponding fault prediction model, and inputs the action data in the target operation data into the corresponding loss calculation model. The power consumption calculation model outputs the power consumption of the smart home device, the fault prediction model outputs the predicted fault of the smart home device, and the loss calculation model outputs the loss of the smart home device, thereby obtaining the data analysis results of each smart home device. It can be understood that if the target operation data is the operation data within a certain time period, the power consumption calculation model can only output the power consumption of the smart home device within the time period, the fault prediction model can only output the predicted fault of the smart home device within the time period, and the loss calculation model can only output the loss of the smart home device within the time period.

[0066] It should be further explained that, in this embodiment, the power consumption calculation model and the fault prediction model are obtained by pre-training the neural network model. Exemplarily, in one embodiment, the historical status data of different types of smart home devices are obtained as a training set, and the historical power consumption is marked in the training set. The training sets of different types of smart home devices are input into different neural network models for training until the error between the power consumption output by the neural network model and the marked historical power consumption is within a preset threshold, thereby obtaining a trained power consumption calculation model, and using different power consumption calculation models, the power consumption of corresponding types of smart home devices can be calculated.

[0067] For the fault prediction model, the historical status data of different types of smart home devices when faults occur are obtained as training sets, and the fault types are marked in the training sets. The training sets of different types of smart home devices are input into different neural network models for training until the output error of the neural network model is within the preset threshold, thereby obtaining a trained fault prediction model. Different fault prediction models can be used to predict the faults of corresponding types of smart home devices.

[0068] For the loss calculation model, different loss calculation models are provided with different loss calculation rules. In the loss calculation rules, each action behavior of the smart home device has a different wear score. The loss of the smart home device is determined by calculating the wear score. The loss calculation rules can be set according to different types of smart home devices. Exemplarily, in one embodiment, in the loss calculation model corresponding to the smart fan, the loss calculation rules stipulate that the smart fan blades rotate once corresponding to the first loss score, and the smart fan head rotates left and right once corresponding to the second loss score. The loss calculation model counts the number of left and right rotations and the number of blade rotations of the smart fan based on the action data in the target operation data, and multiplies the number of blade rotations by the first loss score, and the number of left and right rotations of the fan head by the second loss score, and adds the two multiplication results to obtain the loss of the smart fan.

[0069] After obtaining the data analysis results of each smart home device, the data analysis engine calls the distribution rules corresponding to each smart home device, and sends the corresponding data analysis results to the corresponding target terminal for visualization according to the distribution rules. In one embodiment, the operation data includes a device identifier, and the data analysis results obtained by the data analysis engine from analyzing the target operation data also include the device identifier, and the distribution rules also include the device identifier. The data analysis engine calls the distribution rules with the same device identifier according to the device identifier in the data analysis results.

[0070] Step 205: Obtain historical operation data of each smart home device from the historical database, perform status analysis on the historical operation data of each smart home device, and obtain the historical operation status of each smart home device.

[0071] After the data analysis results are sent to the target terminal, the historical operation data of each smart home device is obtained from the historical database, and then the status analysis is performed on the historical operation data of each smart home device to obtain the historical operation status of each smart home device. Exemplarily, in one embodiment, the working mode of the smart home device in history is determined based on the status data in the historical operation data. For example, the working power of the smart home device is calculated based on the working voltage data and the working current data in the status data, and the working mode of the smart home device in history is determined based on the working power. The action behavior of the smart home device in history is determined based on the action data in the historical operation data, so as to obtain the historical operation status of each smart home device.

[0072] Step 206: Generate device usage preference information of the user according to the historical operating status of each smart home device.

[0073] After obtaining the historical operating status of each smart home device, the user's usage preference for each smart home device in history can be determined based on the historical operating status, thereby generating the user's device usage preference information.

[0074] On the basis of the above embodiment, the specific process of generating the user's device usage preference information according to the historical operation status of each smart home device is as follows:

[0075] According to the historical operating status of each smart home device, the operating status change trend of each smart home device is determined. According to the operating status change trend of each smart home device, the user's usage preference for each smart home device is determined, and device usage preference information is generated according to the usage preference.

[0076] After obtaining the historical operating status of each smart home device, the operating status change trend of each smart home device is determined, including the working mode change trend and the action behavior change trend, and from the operating status change trend, the first working mode with the longest running time and the first action behavior with the longest running time of the smart home device are determined, and based on the first working model and the first working behavior, the user's usage preference for each smart home device is determined, and the device usage preference information is generated according to the usage preference.

[0077] As described above, the embodiment of the present invention uses a data analysis engine to analyze the target operation data, thereby obtaining the data analysis results of the smart home devices, so that the user can grasp the overall operation status of the smart home devices, and generate the user's device usage preference information according to the historical operation data of each smart home device, so as to know the user's historical usage of the smart home devices, improve the management efficiency of the smart home devices, and solve the technical problem of low management efficiency of smart home devices in the prior art.

[0078] Embodiment 3

[0079] like Figure 3 As shown, Figure 3 A schematic diagram of the structure of a smart home data analysis device provided by an embodiment of the present invention, comprising an operation data acquisition module 301, a target data acquisition module 302, a data analysis module 303 and a preference information generation module 304;

[0080] The operation data acquisition module 301 is used to acquire the operation data of each smart home device and store the operation data of each smart home device in a historical database;

[0081] The target data acquisition module 302 is used to acquire the target operation data of each smart home device from the historical database;

[0082] The data analysis module 303 is used to input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule;

[0083] The preference information generating module 304 is used to obtain the historical operation data of each smart home device from the historical database, and generate the user's device usage preference information according to the historical operation data.

[0084] Based on the above embodiment, it also includes a configuration information setting module, which is used to set the configuration information of each smart home device before obtaining the operating data of each smart home device, and send the configuration information to the data analysis engine so that the data analysis engine generates distribution rules for each smart home device based on the configuration information.

[0085] Based on the above embodiment, a preprocessing module is also included. The preprocessing module is used to preprocess the operating data of each smart home device before storing the operating data of each smart home device in a historical database after the operating data of each smart home device is acquired.

[0086] Based on the above embodiment, the specific process of the preprocessing module for preprocessing the operation data of each smart home device is as follows:

[0087] Used to convert the operating data of each smart home device into operating data in a preset format.

[0088] Based on the above embodiment, the data analysis model includes a power consumption calculation model, a fault probability calculation model and a loss degree calculation model.

[0089] Based on the above embodiment, the data analysis module 303 includes a state analysis submodule and a preference information submodule;

[0090] The status analysis submodule is used to obtain the historical operation data of each smart home device from the historical database, perform status analysis on the historical operation data of each smart home device, and obtain the historical operation status of each smart home device;

[0091] The preference information submodule is used to generate the user's device usage preference information based on the historical operating status of each smart home device.

[0092] Based on the above embodiment, the specific process of the preference information submodule for generating the user's device usage preference information according to the historical operation status of each smart home device is as follows:

[0093] Used to determine the operating status change trend of each smart home device based on the historical operating status of each smart home device, determine the user's usage preference for each smart home device based on the operating status change trend of each smart home device, and generate device usage preference information based on the usage preference.

[0094] Embodiment 4

[0095] This embodiment also provides a terminal device, such as Figure 4 As shown, a terminal device 40 includes a processor 400 and a memory 401;

[0096] The memory 401 is used to store a computer program 402 and transmit the computer program 402 to the processor;

[0097] The processor 400 is used to execute the steps in the above-mentioned embodiment of the smart home data analysis method according to the instructions in the computer program 402.

[0098] Exemplarily, the computer program 402 may be divided into one or more modules / units, which are stored in the memory 401 and executed by the processor 400 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 402 in the terminal device 40.

[0099] The terminal device 40 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device 40 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4 It is only an example of the terminal device 40 and does not constitute a limitation of the terminal device 40. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device 40 may also include input and output devices, network access devices, buses, etc.

[0100] The processor 400 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0101] The memory 401 may be an internal storage unit of the terminal device 40, such as a hard disk or memory of the terminal device 40. The memory 401 may also be an external storage terminal device of the terminal device 40, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 40. Further, the memory 401 may also include both an internal storage unit of the terminal device 40 and an external storage device. The memory 401 is used to store the computer program and other programs and data required by the terminal device 40. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0102] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0103] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0104] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store computer programs.

[0107] Embodiment 5

[0108] An embodiment of the present invention further provides a storage medium containing a computer-controllable request instruction, wherein the computer-controllable request instruction is used to execute a smart home data analysis method when executed by a computer processor, the method comprising the following steps:

[0109] Obtaining the operating data of each smart home device, and storing the operating data of each smart home device in a historical database;

[0110] Obtain target operation data of each smart home device from the historical database;

[0111] Input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, obtains the data analysis result of each smart home device, calls the distribution rule corresponding to each smart home device, and sends the corresponding data analysis result to the target terminal for visualization according to the distribution rule;

[0112] The historical operation data of each smart home device is obtained from the historical database, and the user's device usage preference information is generated based on the historical operation data.

[0113] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the embodiments of the present invention are not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the embodiments of the present invention. Therefore, although the embodiments of the present invention are described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the embodiments of the present invention, and the scope of the embodiments of the present invention is determined by the scope of the appended claims.

Claims

1. A smart home data analysis method, characterized in that: The following steps are involved: Setting configuration information of each smart home device, that is, setting a target terminal corresponding to each smart home, and sending the configuration information to a data analysis engine, so that the data analysis engine generates a distribution rule for each smart home device according to the configuration information, wherein the distribution rule is provided with a target terminal corresponding to each smart home device, and the number of target terminals can be set according to actual needs; Acquire the operation data of each smart home device, and store the operation data of each smart home device in a historical database; Acquire target operation data of each of the smart home devices from the historical database; Input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, and obtains the data analysis result of each smart home device, which includes: after the data analysis engine obtains the target operation data of each smart home device, inputs the status data in the target operation data into the corresponding power consumption calculation model and the corresponding fault prediction model, inputs the action data in the target operation data into the corresponding loss calculation model, outputs the power consumption of the smart home device based on the power calculation model, outputs the predicted fault of the smart home device by the fault prediction model, and outputs the loss of the smart home device by the loss calculation model, thereby obtaining the data analysis result of each smart home device, calling the distribution rule corresponding to each smart home device, and sending the corresponding data analysis result to the target terminal for visualization according to the distribution rule; The historical operation data of each of the smart home devices is obtained from the historical database, and the device usage preference information of the user is generated according to the historical operation data.

2. A smart home data analysis method according to claim 1, characterized in that: After the operation data of each smart home device is acquired, before the operation data of each smart home device is stored in the history database, the following steps are also included: The operation data of each of the smart home devices is preprocessed.

3. A smart home data analysis method according to claim 2, characterized in that: The specific process of preprocessing the operation data of each smart home device is as follows: The operating data of each of the smart home devices is converted into operating data in a preset format.

4. A smart home data analysis method according to claim 1, characterized in that: The specific process of obtaining the historical operation data of each smart home device from the historical database and generating the device usage preference information of the user according to the historical operation data is as follows: Acquire historical operation data of each of the smart home devices from the historical database, perform status analysis on the historical operation data of each of the smart home devices, and obtain the historical operation status of each of the smart home devices; The user's device usage preference information is generated according to the historical operating status of each of the smart home devices.

5. A smart home data analysis method according to claim 4, characterized in that: The specific process of generating the user's device usage preference information according to the historical operating status of each smart home device is as follows: According to the historical operating status of each of the smart home devices, the operating status change trend of each of the smart home devices is determined; according to the operating status change trend of each of the smart home devices, the user's usage preference for each of the smart home devices is determined; and device usage preference information is generated according to the usage preference.

6. A smart home data analysis device, characterized in that: It includes a configuration information setting module, an operation data acquisition module, a target data acquisition module, a data analysis module and a preference information generation module; The configuration information setting module is used to set the configuration information of each smart home device, that is, to set the target terminal corresponding to each smart home, and send the configuration information to the data analysis engine so that the data analysis engine generates a distribution rule for each smart home device according to the configuration information. The distribution rule is provided with a target terminal corresponding to each smart home device, and the number of target terminals can be set according to actual needs; The operation data acquisition module is used to acquire the operation data of each smart home device and store the operation data of each smart home device in a history database; The target data acquisition module is used to acquire the target operation data of each smart home device from the historical database; The data analysis module is used to input the target operation data of each smart home device into the data analysis engine, so that the data analysis engine calls the data analysis model corresponding to each smart home device, analyzes the target operation data of each smart home device according to the data analysis model, and obtains the data analysis result of each smart home device, which includes: after the data analysis engine obtains the target operation data of each smart home device, inputs the state data in the target operation data into the corresponding power consumption calculation model and the corresponding fault prediction model, inputs the action data in the target operation data into the corresponding loss calculation model, outputs the power consumption of the smart home device based on the power calculation model, outputs the predicted fault of the smart home device by the fault prediction model, and outputs the loss of the smart home device by the loss calculation model, thereby obtaining the data analysis result of each smart home device, calling the distribution rule corresponding to each smart home device, and sending the corresponding data analysis result to the target terminal for visualization according to the distribution rule; The preference information generating module is used to obtain the historical operation data of each of the smart home devices from the historical database, and generate the device usage preference information of the user according to the historical operation data.

7. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store a computer program and transmit the computer program to the processor; The processor is used to execute a smart home data analysis method as described in any one of claims 1-5 according to instructions in the computer program.

8. A storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the smart home data analysis method according to any one of claims 1 to 5.

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