Equipment control method and device, computer equipment and storage medium
By introducing data analysis models and device standard material models into AI models, the inaccuracy and standard differences in AI models when controlling networked devices are solved, and precise control of devices in complex environments is achieved.
Patent Information
- Application Number
- CN202411872948.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-18
- Publication Date
- 2025-04-25
AI Technical Summary
Existing AI models are prone to fantasies when controlling networked devices, resulting in inaccurate control instructions and lack of unified device control standards, making it difficult to meet the intelligent needs in complex environments.
The voice data is analyzed through the data analysis model to generate accurate device control instructions. This data analysis model is trained and optimized according to the device standard model, allowing more accurately understanding and execution of control instructions for specific devices.
It realizes precise control of networked devices in complex environments, gets rid of the limitations of hardware computing power and voice comprehension capabilities, and meets the needs of intelligence.
Smart Images
Figure CN119889293A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technology, and in particular to a device control method, apparatus, computer equipment and storage medium. Background Art
[0002] With the rapid development of artificial intelligence technology, AI big models are playing an increasingly important role in smart homes, industrial automation and other fields due to their excellent data processing and learning adaptability.
[0003] However, there are two major challenges in controlling networked devices with current AI models: first, the model is prone to illusions, resulting in inaccurate control instructions; second, the control instructions for devices of different brands and types vary greatly and lack a unified standard. Existing solutions such as smart speakers and mobile phone APP control can achieve basic operations, but are limited by hardware computing power and voice comprehension capabilities, and are difficult to meet the needs of intelligence in complex environments. Summary of the invention
[0004] In view of this, the present disclosure proposes a device control method, apparatus, computer equipment and storage medium to solve the problem that the existing device control technology is limited by hardware computing power and voice comprehension ability and is difficult to meet the intelligent needs in complex environments.
[0005] The first aspect of the present disclosure provides a device control method, the method comprising:
[0006] Receive voice data from the client;
[0007] The voice data is analyzed by a data analysis model to obtain a data analysis result; the data analysis model is obtained by training and optimizing a device standard object model, and the device standard object model is used to characterize functions and attributes of the same type of devices in multiple spaces;
[0008] A device control instruction corresponding to the data analysis result is sent to a device control system, so that the device control system performs device control on a corresponding device in a corresponding space according to the device control instruction.
[0009] The disclosed embodiment analyzes voice data through a data analysis model to obtain data analysis results, and sends device control instructions corresponding to the data analysis results to the device control system, so that the device control system controls the corresponding devices in the corresponding space according to the device control instructions. The control method is no longer limited by hardware computing power and voice comprehension capabilities, and can meet the intelligent needs in complex environments.
[0010] In the embodiment of the present disclosure, the initial model is trained in the following manner to obtain a data analysis model:
[0011] Collecting the same type of device data from multiple spaces, wherein the same type of device data includes device attributes and device behaviors;
[0012] Associating the collected data of the same type of devices with the corresponding voice commands to obtain a label for each voice command; the label is used to characterize the specific device control command corresponding to the voice command;
[0013] The voice command is used as a model input, the label of the voice command is used as a model output, and the initial model is trained to obtain the data analysis model; wherein the goal of the model training is to map the voice command to the correct label.
[0014] The disclosed embodiment obtains a data analysis model by fine-tuning the initial model through the equipment standard object model. The data analysis model obtained after fine-tuning can more accurately understand and execute control instructions for specific equipment, especially when controlling the same type of equipment in multiple spaces. It can generate more precise control instructions based on the functions and properties represented by the equipment standard object model.
[0015] In the embodiment of the present disclosure, the voice data is analyzed by a data analysis model to obtain a data analysis result, including:
[0016] Inputting the speech data into a data analysis model, and outputting a target label corresponding to the speech data;
[0017] Determine identification information, attribute information and behavior information of the device to be controlled through the target tag;
[0018] The identification information, attribute information and control information are used as the data analysis result.
[0019] In an embodiment of the present disclosure, before receiving voice data from a client, the method includes:
[0020] The equipment standard object model is configured according to the equipment in the multiple spaces; wherein the configuration includes the following steps:
[0021] Define common properties that apply to all devices of the same type, as well as specific properties for specific devices;
[0022] Define common behaviors that apply to all devices of the same type, as well as define specific behaviors for specific devices;
[0023] Define common events that apply to all devices of the same type, as well as specific events for specific devices;
[0024] Define common services that apply to all devices of the same type, as well as specific services for specific devices;
[0025] Add a corresponding identifier and corresponding location information for each device.
[0026] In an embodiment of the present disclosure, the method further includes:
[0027] Receiving feedback data from a device control system; the feedback data is sent to the device control system by a corresponding device located in a corresponding space when a preset trigger condition is met;
[0028] According to a preset adjustment strategy, an adjustment instruction corresponding to the feedback data is issued to the device control system, and the feedback data and adjustment status data are sent to the client.
[0029] An embodiment of a second aspect of the present disclosure provides a device control method, including:
[0030] Receiving a device control instruction from a data analysis model; the device control instruction includes identification information, attribute information and control information;
[0031] Filtering a target device corresponding to the identification information from a plurality of devices in a plurality of spaces;
[0032] Generate a specific control instruction for the target device according to the attribute information and the control information;
[0033] The specific control instruction is sent to the target device, so that the target device performs device regulation according to the specific control instruction.
[0034] An embodiment of a third aspect of the present disclosure provides a device control apparatus, including:
[0035] A voice data receiving module, used for receiving voice data from a client;
[0036] A data analysis module, used to perform data analysis on the voice data through a data analysis model to obtain a data analysis result; the data analysis model is obtained by training and optimizing a device standard object model, and the device standard object model is used to characterize the functions and properties of the same type of devices in multiple spaces;
[0037] The device control module is used to send a device control instruction corresponding to the data analysis result to the device control system, so that the device control system controls the corresponding device in the corresponding space according to the device control instruction.
[0038] An embodiment of a fourth aspect of the present disclosure provides a device control apparatus, including:
[0039] A device control instruction receiving module, used to receive a device control instruction from a data analysis model; the device control instruction includes number information, attribute information and control information;
[0040] A target device determination module, used for selecting a target device corresponding to the number information from a plurality of devices in a plurality of spaces;
[0041] A specific control instruction generating module, used to generate a specific control instruction of the target device according to the attribute information and the control information;
[0042] The device control module is used to send the specific control instruction to the target device, so that the target device performs device control according to the specific control instruction.
[0043] An embodiment of the fourth aspect of the present disclosure provides a computer device, which includes a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the device control method described in the first aspect above by executing the computer instructions.
[0044] An embodiment of the fifth aspect of the present disclosure provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the device control method described in the first aspect.
[0045] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those of ordinary skill in the art. The accompanying drawings are only used for the purpose of illustrating the preferred embodiment and are not considered to be limitations of the present disclosure. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings.
[0047] In the attached picture:
[0048] Figure 1 A schematic diagram of a flow chart of a device control method provided by an embodiment of the present disclosure is shown;
[0049] Figure 2 A schematic diagram showing a flow chart of another device control method provided by an embodiment of the present disclosure;
[0050] Figure 3 A schematic diagram showing a flow chart of another device control method provided by an embodiment of the present disclosure;
[0051] Figure 4 A schematic diagram showing a flow chart of another device control method provided by an embodiment of the present disclosure;
[0052] Figure 5 A schematic diagram of the structure of a device control device provided by an embodiment of the present disclosure is shown;
[0053] Figure 6 A schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure is shown;
[0054] Figure 7 A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0055] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0056] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the present disclosure should have the common meanings understood by those skilled in the art to which the present disclosure belongs.
[0057] The following describes the technical scenarios involved in the embodiments of the present disclosure.
[0058] With the rapid development of AI, there are various solutions to realize the intelligence of environment and space through smart devices, and the functions and implementation principles are also different. Common practices are as follows: 1. Use the terminal algorithm to identify the surrounding environment information and then control the device; 2. Collect the surrounding environment information through the terminal, send the information to the cloud, and then use various algorithms in the cloud to identify the information, and finally send the control instructions of the device through the network; 3. Install a smart speaker in the room, connect all the devices in the room to the smart speaker, and use the smart speaker to communicate with people and control other devices; 4. Install customized mobile phone software, connect various devices in the mobile phone software, and control various devices through mobile phone software interaction;
[0059] Each of the above methods has its own advantages and disadvantages. The advantage of the first solution is that it is simple. It can complete the control only through the device's own recognition of environmental information, and no other additional software and hardware support is required. The disadvantage is that the function is single, it can only control the device itself, and cannot link other devices; the advantage of the second solution is that it only requires the device to have the ability to report data and receive commands through the network, and realize the recognition of environmental information through the algorithm in the cloud. The disadvantage is that different algorithms need to be developed for different scenarios; the advantage of the third solution is that it can have voice dialogue with smart speakers, and complete the interaction with other devices through the understanding of voice by smart speakers. The disadvantage is that it is limited by the hardware computing power of smart speakers (computing power refers to the computing power of CPU or GPU, and strong computing power is required to support the completion of some complex tasks), and the understanding of language is not high, and only simple instructions can be understood. The advantage of the fourth solution is that it is lightweight and does not require the support of hardware computing power. After connecting the device to the mobile phone APP, the device can be controlled by mobile phone operation. The disadvantage is that it cannot interact with people by voice, and can only realize automatic control of the device by presetting some rules.
[0060] Based on this, the present disclosure proposes a set of equipment intelligent control methods and devices based on large models and equipment control systems. This method can realize intelligent upgrades for various physical spaces with networked devices installed, and control of the equipment can be completed through voice dialogue. And through the analysis of data collected by related equipment and the understanding of user feelings, the equipment control can be completed autonomously without clear control instructions.
[0061] According to an embodiment of the present disclosure, an embodiment of a device control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.
[0062] In this embodiment, a device control method is provided, which can be used in an application program; Figure 1 is a flow chart of a device control method according to an embodiment of the present disclosure, such as Figure 1 As shown, the process includes the following steps:
[0063] Step S101, receiving voice data from a client.
[0064] Step S102, performing data analysis on the voice data through a data analysis model to obtain a data analysis result.
[0065] In the embodiment of the present disclosure, the data analysis model is obtained by training and optimization based on the equipment standard model, and the equipment standard model is used to characterize the functions and properties of the same type of equipment in multiple spaces. For example: the properties of the air conditioner include the current cooling or heating mode, the current temperature, the current wind speed, the current collected outlet temperature, etc.; the functions of the air conditioner include turning on the air conditioner, setting the mode, setting the temperature, setting the wind speed, etc.
[0066] In some specific embodiments, the following steps S201 to S203 are performed to train the initial model using the equipment standard model to obtain a data analysis model:
[0067] Step S201 : collecting device data of the same type from multiple spaces, wherein the device data of the same type includes device attributes and device behaviors.
[0068] Specifically, space refers to physical space, e.g. Figure 2 Space 1 and space N in the figure, each space contains one or more devices. Spaces include but are not limited to offices, conference rooms, shopping malls, parks, bedrooms, and living rooms. Devices refer to devices that support networking in physical spaces. If a device does not have networking capabilities, it is necessary to first connect the device to the network module, which is responsible for reporting data or issuing remote commands.
[0069] More specifically, the same type of device data also includes device events and device services, among which device attributes include but are not limited to temperature, humidity and brightness; device behaviors include but are not limited to turning on and off lights and adjusting temperature; device events include but are not limited to opening and closing doors and temperature changes; device services include but are not limited to obtaining current status and setting target values.
[0070] Step S202: Associating the collected data of the same type of devices with the corresponding voice commands to obtain a label for each voice command. The label is used to represent the specific device control command corresponding to the voice command.
[0071] In the disclosed embodiment, in order to train the data analysis model, the collected device data needs to be labeled. The labeling process is to associate the standard behavior, attribute change or event of the device with the corresponding voice command. For example, if the user says "turn on the light in the living room", the corresponding label is "the smart light bulb in the living room performs the light-on action". In this way, a label can be generated for each voice command, indicating the specific device control instruction corresponding to the command.
[0072] In some specific embodiments, the above step S102 includes steps S1021 to S1023:
[0073] Step S1021, input the voice data into a data analysis model, and output a target label corresponding to the voice data.
[0074] Step S1022: determining identification information, attribute information, and behavior information of the device to be controlled through the target tag.
[0075] Step S1023: taking the identification information, attribute information and control information as the data analysis result.
[0076] Take steps S1021 to S1023 as an example to illustrate: when the voice data is "turn on the light in the living room", the corresponding target tag is "light bulb 101 performs the light-on action"; the identification information refers to "101", which is used to indicate which light bulb in the space is specifically referred to; the attribute information refers to "the switch status of the light"; and the control information refers to "turn on". After obtaining the identification information, attribute information and behavior information from the target tag, it can be used as the data analysis result.
[0077] Step S203: taking the voice command as a model input, taking the label of the voice command as a model output, performing model training on the initial model, and obtaining the data analysis model.
[0078] In the disclosed embodiment, the data analysis model can be trained by supervised learning. During the training process, the model receives the labeled voice data and the corresponding device control instructions as input and output pairs. The goal of the model is to learn how to map voice commands to correct device control instructions. Among them, supervised learning methods include but are not limited to neural networks (such as convolutional neural networks CNN, recurrent neural networks RNN), support vector machines (SVM), etc.
[0079] In the embodiments of the present disclosure, the data analysis model includes but is not limited to a large model, which is also called a basic model, and refers to a machine learning model with large-scale parameters and complex computing structure, which can process massive data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc. These models are usually built with deep neural networks and have billions or even hundreds of billions of parameters. The purpose of designing a large model is to improve the expressive power and predictive performance of the model, so that it can handle more complex tasks and data. Large models are widely used in various fields, including natural language processing, computer vision, speech recognition, and recommendation systems. Large models learn complex patterns and features by training massive data, have more powerful generalization capabilities, and can make accurate predictions on unseen data.
[0080] In some specific embodiments, the model will perform device control based on the user's voice commands and adjust its strategy based on the device's feedback (such as whether the command was successfully executed, user satisfaction, etc.). The device states and events in the device standard object model can be used as reward signals in reinforcement learning to help the model learn the optimal control strategy.
[0081] Step S103, sending a device control instruction corresponding to the data analysis result to a device control system, so that the device control system controls the corresponding device in the corresponding space according to the device control instruction.
[0082] In some specific embodiments, before step S101, the method includes:
[0083] A device standard object model is configured according to devices in multiple spaces; wherein the configuration includes the following steps: defining common properties applicable to all devices of the same type, and defining specific properties for specific devices; defining common behaviors applicable to all devices of the same type, and defining specific behaviors for specific devices; defining common events applicable to all devices of the same type, and defining specific events for specific devices; defining common services applicable to all devices of the same type, and defining specific services for specific devices; adding a corresponding identifier and corresponding location information to each device.
[0084] In the embodiments of the present disclosure, common attributes are attributes that all devices of the same type have, for example, all temperature sensors should have attributes such as "current temperature", "unit of measurement", and "sampling frequency"; specific attributes refer to attributes that are specific to a certain device. Common behaviors are operations that can be performed by all devices of the same type, for example, all smart light bulbs should support behaviors such as "turn on the light", "turn off the light", and "adjust the brightness". Common events refer to changes in device status or the occurrence of specific conditions, for example, all door sensors should support "open door" and "close door" events. Common services refer to interfaces for devices to interact with other systems or applications, for example, all smart thermostats should provide services such as "get current temperature" and "set target temperature". Identifiers refer to assigning a unique identifier to each device, which helps to distinguish different devices. Location information refers to assigning a unique location identifier to each device, such as "living room", "bedroom", "kitchen", etc., which helps to distinguish devices in different spaces.
[0085] In some specific embodiments, the configuration further comprises the following steps:
[0086] Define the device topology in the space and describe the relationship between devices. For example, in a smart home system, a smart socket may be associated with a smart bulb to form a "lighting system". By defining the relationship between devices, collaborative control of multiple devices can be achieved.
[0087] Group devices in a space for batch control. For example, users can turn off lights in all rooms or adjust the temperature of all air conditioners with one command. Device grouping can be achieved through the "Device Group" field in the device standard object model.
[0088] Define a rule engine for each space to trigger automated actions based on the status or events of the device. For example, when the door sensor detects the "door open" event, the lights in the living room are automatically turned on; when the temperature sensor detects that the temperature exceeds the set value, the air conditioner is automatically started. These rules can be implemented through the "rule" field in the device standard object model.
[0089] Define automation scenarios for each space, describing a combination of device operations. For example, a user can start "home mode" with a single command, automatically turning on the lights, adjusting the temperature, and playing music. These scenarios can be implemented through the "scene" field in the device standard object model.
[0090] In some specific embodiments, the method further comprises:
[0091] Step S301, receiving feedback data from a device control system.
[0092] Specifically, the feedback data is sent to the device control system by the corresponding device in the corresponding space when the preset trigger condition is met. The preset trigger condition can be set according to actual conditions and is not specifically limited here, for example: when the indoor temperature is greater than the preset temperature threshold, or the humidity is less than the preset humidity threshold.
[0093] Step S302: issuing an adjustment instruction corresponding to the feedback data to the device control system according to a preset adjustment strategy, and sending the feedback data and adjustment status data to the client.
[0094] Specifically, the preset adjustment strategy can be set according to actual conditions, and is not specifically limited here. For example, when the indoor temperature is greater than the preset temperature threshold, the air conditioner is turned on for cooling, and the air conditioner is turned off when the indoor temperature is less than or equal to the preset temperature threshold. Another example: when the humidity is less than the preset humidity threshold, the humidifier is turned on, and the humidifier is turned off when the indoor humidity is greater than or equal to the preset humidity threshold.
[0095] The present disclosure also provides a device control method, which is applied to a device control system; the method includes:
[0096] Step S401, receiving device control instructions from a data analysis model.
[0097] The device control instruction includes identification information, attribute information and control information.
[0098] Step S402: Filter a target device corresponding to the identification information from a plurality of devices in a plurality of spaces.
[0099] Step S403: Generate a specific control instruction for the target device according to the attribute information and the control information.
[0100] Step S404: sending the specific control instruction to the target device, so that the target device performs device regulation according to the specific control instruction.
[0101] The above steps S401 to S404 are described according to the following specific examples:
[0102] The user says: "Turn on the lights in the living room and dim them to 80%." The voice data is sent to a data analysis model (such as a natural language processing (NLP) model), which parses the user's voice command and extracts the following information: Identification information: smart light bulb in the living room; Attribute information: brightness; Control information: turn on the lights and set the brightness to 80%; The device control system can determine that the device to be controlled is the smart light bulb in the living room based on the identification information, and can determine the specific control instruction to be "turn on the lights and set the brightness to 80%" based on the attribute information and the control information, and send its specific control instruction to the smart light bulb in the living room to turn on the smart light bulb and set its brightness to 80%.
[0103] In some specific embodiments, a corresponding device control system can be constructed according to the device control method described in the above embodiment, and the process of device control in the space is as follows: Figure 3 As shown in the figure: First, develop and run the device driver to connect the device to the device control system; second, configure the device standard object model according to the devices in multiple spaces, and use the configured device standard object model to fine-tune the large model; finally, load and run the fine-tuned large model. By using the configured device standard object model to fine-tune the large model, the performance of the model in processing the task of controlling devices in different spaces can be greatly optimized, thereby improving the task processing efficiency of the large model.
[0104] In some specific embodiments, the present disclosure also provides a user usage process of the device control method described in the above embodiments, such as Figure 4As shown: the user performs voice interaction through the APP, and the APP sends the voice data to the big model. Through the reasoning and analysis of the big model, the corresponding device control instructions are obtained and sent to the device control system so that the device control system can control the corresponding device; after the device is controlled, the device can also actively report its data to the device control system in real time. The device control system will convert its data into the data structure of the standard object model and report the converted data to the big model so that the big model can provide result feedback to the user through the APP.
[0105] Corresponding to the implementation of the above device control method, the present disclosure also provides a device control device for executing the above Figures 1 to 4 The device control method described in any of the embodiments shown in the figure. Figure 5 As shown, the device control device includes:
[0106] A voice data receiving module, used for receiving voice data from a client;
[0107] A data analysis module, used to perform data analysis on the voice data through a data analysis model to obtain a data analysis result; the data analysis model is obtained by training and optimizing a device standard object model, and the device standard object model is used to characterize the functions and properties of the same type of devices in multiple spaces;
[0108] The device control module is used to send a device control instruction corresponding to the data analysis result to the device control system, so that the device control system controls the corresponding device in the corresponding space according to the device control instruction.
[0109] Optionally, the device also includes: a model training module, used to collect the same type of device data from multiple spaces, the same type of device data including device attributes and device behaviors; associating the collected same type of device data with the corresponding voice instructions to obtain a label for each voice instruction; the label is used to characterize the specific device control instruction corresponding to the voice instruction; using the voice instruction as the model input, using the label of the voice instruction as the model output, performing model training on the initial model, and obtaining the data analysis model; wherein the goal of the model training is to map the voice command to the correct label.
[0110] Optionally, the data analysis module is also used to input the voice data into a data analysis model, and output a target tag corresponding to the voice data; determine the identification information, attribute information and behavior information of the device to be controlled through the target tag; and use the identification information, attribute information and control information as the data analysis result.
[0111] Optionally, a device standard object model configuration module is used to configure a device standard object model according to devices in multiple spaces before receiving voice data from the client; wherein the configuration includes the following steps:
[0112] Define common properties that apply to all devices of the same type, as well as specific properties for specific devices;
[0113] Define common behaviors that apply to all devices of the same type, as well as define specific behaviors for specific devices;
[0114] Define common events that apply to all devices of the same type, as well as specific events for specific devices;
[0115] Define common services that apply to all devices of the same type, as well as specific services for specific devices;
[0116] Add a corresponding identifier and corresponding location information for each device.
[0117] Optionally, the device also includes: a feedback adjustment module, used to receive feedback data from a device control system; the feedback data is sent to the device control system by a corresponding device located in a corresponding space when a preset trigger condition is met; an adjustment instruction corresponding to the feedback data is issued to the device control system according to a preset adjustment strategy, and the feedback data and adjustment status data are sent to the client.
[0118] Corresponding to the implementation of the above device control method, the present disclosure also provides a device control device for executing the above Figures 1 to 4 The device control method described in any of the illustrated embodiments. The device control device comprises:
[0119] A device control instruction receiving module, used to receive a device control instruction from a data analysis model; the device control instruction includes number information, attribute information and control information;
[0120] A target device determination module, used for selecting a target device corresponding to the number information from a plurality of devices in a plurality of spaces;
[0121] A specific control instruction generating module, used to generate a specific control instruction of the target device according to the attribute information and the control information;
[0122] The device control module is used to send the specific control instruction to the target device, so that the target device performs device control according to the specific control instruction.
[0123] The device control apparatus provided in the above-mentioned embodiment of the present disclosure and the device control method provided in the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0124] The present disclosure also provides a computer device to execute the above device control method. Figure 6 , which shows a schematic diagram of a computer device provided by some embodiments of the present disclosure. Figure 6 As shown, the computer device 6 includes: a processor 600, a memory 601, a bus 602 and a communication interface 603, wherein the processor 600, the communication interface 603 and the memory 601 are connected via the bus 602; the memory 601 stores a computer program that can be run on the processor 600, and the processor 600 executes the aforementioned Figures 1 to 4 A device control method is provided in any of the illustrated embodiments.
[0125] The memory 601 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 603 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.
[0126] The bus 602 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 601 is used to store programs. After receiving the execution instruction, the processor 600 executes the program. Figures 1 to 4 The device control method disclosed in any of the illustrated embodiments may be applied to the processor 600 or implemented by the processor 600 .
[0127] The processor 600 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 600 or the instruction in the form of software. The above processor 600 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 601, and the processor 600 reads the information in the memory 601 and completes the steps of the above method in combination with its hardware.
[0128] The computer device provided by the embodiment of the present disclosure and the device control method provided by the embodiment of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by them.
[0129] The present disclosure also provides a computer-readable storage medium corresponding to the device control method provided in the above embodiment. Figure 7 The computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the device control method provided by any of the aforementioned embodiments will be executed.
[0130] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.
[0131] The computer-readable storage medium provided by the above-mentioned embodiments of the present disclosure and the device control method provided by the embodiments of the present disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.
[0132] It should be noted that:
[0133] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present disclosure can be practiced without these specific details. In some instances, well-known structures and technologies are not shown in detail so as not to obscure the understanding of this description.
[0134] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following schematic diagram: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.
[0135] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of this disclosure and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.
[0136] The above is only a preferred specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A device control method, characterized in that: The method comprises: Receive voice data from the client; The voice data is analyzed by a data analysis model to obtain a data analysis result; the data analysis model is obtained by training and optimizing a device standard object model, and the device standard object model is used to characterize functions and attributes of the same type of devices in multiple spaces; A device control instruction corresponding to the data analysis result is sent to a device control system, so that the device control system performs device control on a corresponding device in a corresponding space according to the device control instruction.
2. The method according to claim 1, characterized in that The initial model is trained in the following way to obtain a data analysis model: Collecting the same type of device data from multiple spaces, wherein the same type of device data includes device attributes and device behaviors; Associating the collected data of the same type of devices with the corresponding voice commands to obtain a label for each voice command; the label is used to characterize the specific device control command corresponding to the voice command; The voice command is used as a model input, the label of the voice command is used as a model output, and the initial model is trained to obtain the data analysis model; wherein the goal of the model training is to map the voice command to the correct label.
3. The method according to claim 1 or 2, characterized in that: The voice data is analyzed by a data analysis model to obtain data analysis results, including: Inputting the speech data into a data analysis model, and outputting a target label corresponding to the speech data; Determine identification information, attribute information and behavior information of the device to be controlled through the target tag; The identification information, attribute information and control information are used as the data analysis result.
4. The method according to claim 1 or 2, characterized in that: Before receiving voice data from the client, the method includes: The equipment standard object model is configured according to the equipment in the plurality of spaces; wherein the configuration comprises the following steps: Define common properties that apply to all devices of the same type, as well as specific properties for specific devices; Define common behaviors that apply to all devices of the same type, as well as define specific behaviors for specific devices; Define common events that apply to all devices of the same type, as well as specific events for specific devices; Define common services that apply to all devices of the same type, as well as specific services for specific devices; Add a corresponding identifier and corresponding location information for each device.
5. The method according to claim 1 or 2, characterized in that: The method further comprises: Receiving feedback data from a device control system; the feedback data is sent to the device control system by a corresponding device located in a corresponding space when a preset trigger condition is met; According to a preset adjustment strategy, an adjustment instruction corresponding to the feedback data is issued to the device control system, and the feedback data and adjustment status data are sent to the client.
6. A device control method, characterized in that: The method comprises: Receiving a device control instruction from a data analysis model; the device control instruction includes identification information, attribute information and control information; Filtering a target device corresponding to the identification information from a plurality of devices in a plurality of spaces; Generate a specific control instruction for the target device according to the attribute information and the control information; The specific control instruction is sent to the target device, so that the target device performs device regulation according to the specific control instruction.
7. A device control device, characterized in that: The device comprises: A voice data receiving module, used for receiving voice data from a client; A data analysis module, used to perform data analysis on the voice data through a data analysis model to obtain a data analysis result; the data analysis model is obtained by training and optimizing a device standard object model, and the device standard object model is used to characterize the functions and properties of the same type of devices in multiple spaces; The device control module is used to send a device control instruction corresponding to the data analysis result to the device control system, so that the device control system controls the corresponding device in the corresponding space according to the device control instruction.
8. A device control device, characterized in that: The device comprises: A device control instruction receiving module, used to receive a device control instruction from a data analysis model; the device control instruction includes number information, attribute information and control information; A target device determination module, used for selecting a target device corresponding to the number information from a plurality of devices in a plurality of spaces; A specific control instruction generating module, used to generate a specific control instruction of the target device according to the attribute information and the control information; The device control module is used to send the specific control instruction to the target device, so that the target device performs device control according to the specific control instruction.
9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 6 by executing the computer instructions.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 6.