Equipment configuration method and device, equipment, medium and product

Abstract

The embodiment of the invention provides an equipment configuration method and device, equipment, a medium and a product, and is applied to an intelligent home system, and the method comprises the steps: obtaining first real-time user voice data in a network distribution process of intelligent home equipment; performing user identity verification according to the first real-time user voice data; and under the condition that the user identity verification is passed, obtaining preset network distribution data, and performing network distribution operation on the smart home equipment by adopting the network distribution data. Through the embodiment of the invention, the network distribution operation of the smart home equipment is performed by acquiring the first real-time user voice data and performing the user identity verification, so that the network distribution security is improved, and the user does not need to perform complex operation or memorize a complex password, and the user experience is improved. And the convenience and the satisfaction degree when the user uses the smart home equipment are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a method, device, equipment, medium and product for equipment configuration. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology and the widespread application of smart home products, more and more families and businesses are beginning to connect various smart devices to the Internet to achieve functions such as remote monitoring, automatic control and data interaction.

[0003] In the prior art, the configuration of smart devices mainly relies on manual operations by users. For example, users need to manually enter Wi-Fi passwords to configure devices, or manually divide areas and set functions for smart home devices. These cumbersome operations not only reduce the user experience, but also easily introduce human errors during the configuration process, further increasing security risks. Especially in a home environment, users often lack professional network knowledge and configuration experience, making it difficult to ensure the correctness and security of device configuration.

[0004] Secondly, the network configuration process, especially for smart home devices, mainly relies on the password verification mechanism to ensure network security. However, this method has exposed many security risks in practical applications. First of all, as authentication information, the security of the password is highly dependent on the user's setting habits. Many users often choose simple and easy-to-guess passwords for easy memory, which greatly increases the risk of password cracking. Secondly, even if a complex password is used, it is difficult to completely prevent the password from being stolen by malware or obtained through brute force cracking. Once the password is leaked, illegal users can easily obtain network access rights, and then illegally control smart home devices or steal sensitive information, which brings serious security risks to users. Summary of the invention

[0005] In view of the above problems, a method, apparatus, device, medium and product for providing a device configuration to overcome the above problems or at least partially solve the above problems are proposed, including:

[0006] A device configuration method, applied to a smart home system, comprising:

[0007] In the process of configuring the network for the smart home device, obtaining first real-time user voice data;

[0008] Performing user identity authentication according to the first real-time user voice data;

[0009] When the user identity authentication is passed, the preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on the smart home device.

[0010] Optionally, when the user identity authentication is passed, obtaining preset network configuration data includes:

[0011] When the user identity authentication is passed, the current user is subjected to liveness detection;

[0012] When the user's liveness detection passes, the preset network configuration data is obtained.

[0013] Optionally, in the process of configuring the smart home device for networking, before obtaining the first real-time user voice data, the method further includes:

[0014] Obtain sample user voice data;

[0015] Establishing a voice model according to the sample user voice data;

[0016] The performing user identity authentication according to the first real-time user voice data includes:

[0017] The voice model is used to perform user identity authentication on the first real-time user voice data.

[0018] Optionally, establishing a voice model according to the sample user voice data includes:

[0019] Extracting sample user voice features from the sample user voice data;

[0020] Establishing a speech model according to the speech features of the sample user;

[0021] The adopting the voice model to perform user identity authentication on the first real-time user voice data includes:

[0022] Extracting real-time user voice features from the first real-time user voice data;

[0023] By using the voice model, similarity calculation is performed on the real-time user voice feature and the sample user voice feature, and according to the similarity calculation result, it is determined whether the first real-time user voice data is the first real-time user voice data of the authorized user;

[0024] In a case where the first real-time user voice data is first real-time user voice data of an authorized user, it is determined that the user identity authentication is passed.

[0025] Optionally, after the network configuration data is used to perform a network configuration operation on the smart home device, the method further includes:

[0026] Acquire second real-time user voice data, and perform user identity recognition based on the second real-time user voice data to obtain target user identity information;

[0027] According to the second real-time user voice data and the target user identity information, a target area in the current environment is determined and a target smart home device in the target area is determined to control the target smart home device.

[0028] Optionally, determining a target area in the current environment and determining a target smart home device in the target area according to the second real-time user voice data and the target user identity information to control the target smart home device includes:

[0029] Performing voice recognition on the second real-time user voice data to obtain text information;

[0030] Determine a target area in the current environment according to the target user identity information;

[0031] According to the text information, a target smart home device and a target control instruction in the target area are determined, and the target smart home device is controlled according to the target control instruction.

[0032] Optionally, it also includes:

[0033] When a user uses the smart home system for the first time, the user identity information is determined based on the collected user voice data, and a user profile is established based on the user identity information.

[0034] Optionally, it also includes:

[0035] Obtain the user's historical device usage and generate user habit data;

[0036] The user habit data is stored in the user profile to provide personalized services.

[0037] A device configuration apparatus, applied to a smart home system, comprising:

[0038] A first real-time user voice data acquisition module, used to acquire first real-time user voice data during the process of configuring the network for the smart home device;

[0039] A user identity authentication module, used for performing user identity authentication according to the first real-time user voice data;

[0040] The network configuration operation module is used to obtain preset network configuration data when the user identity authentication is passed, and use the network configuration data to perform network configuration operations on the smart home device.

[0041] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method described above when executed by the processor.

[0042] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0043] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described above.

[0044] The embodiments of the present invention have the following advantages:

[0045] In an embodiment of the present invention, during the process of networking a smart home device, first real-time user voice data is obtained; user identity authentication is performed based on the first real-time user voice data; if the user identity authentication is passed, preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on the smart home device. This achieves the goal of obtaining the first real-time user voice data, performing user identity authentication, and then performing network configuration operations on the smart home device. While increasing the security of network configuration, it does not require users to perform complex operations or remember complex passwords, thereby improving the convenience and satisfaction of users when using smart home devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0047] Figure 1 is a flowchart of the steps of a method for configuring a device provided by some embodiments of the present invention;

[0048] Figure 2 is a flowchart of a method for configuring a device provided in some embodiments of the present invention;

[0049] Figure 3 is a flowchart of steps of another method for device configuration provided by some embodiments of the present invention;

[0050] Figure 4 is a flowchart of steps of another method for device configuration provided by some embodiments of the present invention;

[0051] Figure 5 It is a structural block diagram of an apparatus for device configuration provided by some embodiments of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Reference Figure 1 , showing a step flow chart of a method for device configuration provided by some embodiments of the present invention, which is applied to a smart home system.

[0054] As some examples, the smart home system may be composed of a voice recognition module, a central controller, a device management module, and a user profile module.

[0055] Among them, the voice recognition module can be responsible for receiving the user's voice commands and converting them into text information. The voice recognition module can be set in any smart home device as needed; the central controller can serve as the core of the smart home system, perform area division and device control according to text information, coordinate the work of each module, and ensure stable and reliable communication; the device management module can manage the connection and operating status of smart home devices to ensure stable operation of the equipment; the user profile module can record each user's preference settings and usage habits to provide a basis for personalized services.

[0056] In some examples, smart home devices may include, but are not limited to, smart home appliances (such as smart TVs, smart refrigerators, smart washing machines, etc.), smart security equipment (such as smart door locks, surveillance cameras, smoke alarms, etc.), smart curtains, smart speakers, etc.; among them, smart home devices may have built-in wireless communication modules when they leave the factory, and be connected to the smart home system through wireless communication technologies such as Wi-Fi and Bluetooth. Smart home devices may communicate with each other through ad hoc networking to achieve collaborative work between devices.

[0057] For example, when the temperature sensor detects that the indoor temperature is too high, the air conditioner is automatically started to cool down.

[0058] Specifically, the following steps may be included:

[0059] Step 101, in the process of configuring the network for the smart home device, obtain first real-time user voice data.

[0060] Among them, the first real-time user voice data is the user's voice network configuration command for smart home devices, such as the user saying "device network configuration" to the smart speaker.

[0061] In actual applications, users can use voice commands to tell smart home devices the Wi-Fi name and password to connect to, or instruct the device to join a specific network group to configure the smart home devices.

[0062] During the networking process of smart home devices, user voice data can be directly captured through the microphone in the built-in voice recognition module of the smart home device, or it can be captured through the microphone in the built-in voice recognition module of smart speakers, mobile phones and other devices paired with the smart home devices, and transmitted to the smart home devices or related smart home systems for processing.

[0063] In some embodiments of the present invention, before obtaining the first real-time user voice data during the process of networking the smart home device, the method further includes: obtaining sample user voice data; and establishing a voice model based on the sample user voice data.

[0064] As some examples, before configuring the network, users can register on the user platform provided by the smart home system and establish a personal user profile. Then, they can use smart home devices or the application end installed with the smart home system to collect their own voice data and use it as sample voice data to establish a personal voice model as an authorized user of the smart home system in the current environment.

[0065] For example, a personal voice model can be constructed using machine learning algorithms such as Gaussian mixture models or deep neural networks.

[0066] In some embodiments of the present invention, establishing a speech model based on the sample user speech data includes: extracting sample user speech features from the sample user speech data; and establishing a speech model based on the sample user speech features.

[0067] Among them, speech features may include but are not limited to pitch (tone), intensity (volume), length (duration), timbre (sound quality), speaking speed, and specific words or phrases in the speech, and may also include parameters such as spectral characteristics, resonance peaks, and fundamental frequency of the speech.

[0068] As some examples, methods such as short-time Fourier transform can be used to identify and extract key sound features from sample user voice data, which can distinguish different voice inputs and help the personal voice model understand the user's voice commands.

[0069] After establishing a personal voice model based on the sample user's voice features, the sample user's voice features can be stored for subsequent similarity calculation of the voice features.

[0070] In some examples, encryption technology can also be used for sample user voice features during storage and transmission to prevent attackers from obtaining and tampering with feature data for forgery. At the same time, strict authentication protocols are used during voice verification, such as digital signatures, to ensure the authenticity and integrity of the voice data source. When voice data is received for verification, its digital signature is first verified to be legal. Only legal voice data can be used for subsequent feature matching and other operations to avoid interference from illegal forged voice.

[0071] Step 102: Perform user identity authentication based on the first real-time user voice data.

[0072] After acquiring the first real-time user voice data, voice features in the first real-time user voice data may be extracted to verify whether they are similar to voice features in a pre-built personal voice model, so as to determine whether the user is an authorized user.

[0073] In some embodiments of the present invention, performing user identity authentication based on the first real-time user voice data includes: using the voice model to perform user identity authentication on the first real-time user voice data.

[0074] In some embodiments of the present invention, the use of the voice model to perform user identity authentication on the first real-time user voice data includes: extracting real-time user voice features from the first real-time user voice data; performing similarity calculation on the real-time user voice features and the sample user voice features through the voice model, and judging whether the first real-time user voice data is the first real-time user voice data of an authorized user based on the similarity calculation result; and determining that the user identity authentication is successful if the first real-time user voice data is the first real-time user voice data of an authorized user.

[0075] As some examples, methods such as short-time Fourier transform can be used to extract real-time user voice features from the first real-time user voice data, and perform similarity calculations with sample user voice features stored in the personal voice model.

[0076] In some examples, a similarity score or probability can be obtained by calculating the similarity between two sets of features (such as using cosine similarity, Euclidean distance, Mahalanobis distance, etc.), indicating the degree of match between the first real-time user voice data and the sample voice data.

[0077] After performing the similarity calculation, the similarity calculation result can be compared with a pre-set similarity threshold to determine whether the first real-time user voice data is the first real-time user voice data of the authorized user. If the similarity calculation result is higher than the similarity threshold, it indicates that the first real-time user voice data is the first real-time user voice data of the authorized user; if the similarity calculation result is lower than the similarity threshold, it indicates that the first real-time user voice data is not the first real-time user voice data of the authorized user.

[0078] As some examples, the user may be required to re-collect voice samples periodically to update the user's voice features.

[0079] For example, users are reminded to say certain specific sentences again at regular intervals (such as one month) to update the feature parameters in the personal voice model. This allows the model to adapt to natural changes in the user's voice and makes it difficult for attackers to continuously obtain effective evidence for forgery. Even if attackers obtain previous voice samples for forgery or replay, the old samples will be difficult to match successfully as the features change dynamically.

[0080] In some examples, machine learning algorithms can also be used to build an anomaly detection model, using a large amount of normal voice verification data as training samples to allow the model to learn the normal voice interaction pattern; when there is a new voice verification request, the model can analyze the feature distribution, time series, etc. of the voice data to determine whether there is an anomaly; if it is found that the voice features are frequently repeated in a short period of time (possibly caused by a replay attack) or if there is a situation that does not conform to the normal user voice feature distribution pattern, it is judged as an anomaly, which is considered to be a possible voice forgery or replay attack, and then it is judged as a user identity authentication failure.

[0081] Step 103, when the user identity verification is passed, obtain preset network configuration data, and use the network configuration data to perform network configuration operations on the smart home device.

[0082] As some examples, preset network configuration data may be network configuration data that is pre-set and stored inside a smart home device, in a user's smartphone application, or in a cloud server, etc.; wherein the network configuration data may include information required for the smart home device to connect to a home or a specific network environment, such as the SSID (service set identifier, i.e., network name) and password of a Wi-Fi network or connection information required by other network protocols.

[0083] In some examples, if the user identity authentication is passed, it means that the user has the authority to perform subsequent network configuration operations, and can obtain preset network configuration data and apply it to the smart home device so that it can connect to the specified network.

[0084] For example, when a user says "device network configuration" to the smart speaker, the smart speaker verifies the voice data to determine whether the user is an authorized user. If the verification is successful, the smart speaker searches for nearby smart home devices that are not network configured, and establishes a temporary connection with the smart home device through the Wi-Fi direct connection mode. The smart speaker sends its own network configuration data to the smart home device, so that the smart home device can perform network configuration operations according to the network configuration data.

[0085] In some embodiments of the present invention, the obtaining of preset network configuration data when the user identity authentication is passed includes: performing user liveness detection on the current user when the user identity authentication is passed; and obtaining preset network configuration data when the user liveness detection is passed.

[0086] As some examples, when the user identity authentication is passed, user liveness detection can also be performed on the first real-time user voice data of the current user (such as a detection method based on acoustic features, a detection method based on voice and environment interaction, a detection method combined with multiple modalities, etc.). When the user liveness detection is passed, the preset network configuration data is obtained, and network configuration operations are performed on the smart home devices.

[0087] In some examples, the detection method based on acoustic features may include:

[0088] 1. Rhythmic feature analysis: The speech of real people has a natural rhythmic rhythm, such as the distribution of stress in sentences, the ups and downs of intonation, and the natural adjustment of speech speed. This is judged by analyzing the rhythmic features of real-time collected speech and comparing it with the rhythmic model of normal human speech.

[0089] For example, in normal speech, the intonation of interrogative sentences will rise, while the intonation of declarative sentences will be relatively stable. If it is detected that the speech does not conform to normal rhythmic rules in terms of sentence type and intonation, it may be the reproduced speech rather than the real living voice.

[0090] 2. Voiceprint stability detection: Each person's voiceprint is relatively stable in a short period of time, but there will be subtle changes that are consistent with the physiological characteristics of voice production, such as small fluctuations in voiceprint characteristics caused by factors such as breath regulation during continuous speaking; for real-time collected speech, analyze whether its voiceprint characteristics have such reasonable fluctuations that are consistent with human voice characteristics within a short period of time. If the voiceprint is too stable and has no changes, it may be a voice that is replayed after recording, and it is determined that it is not a living voice.

[0091] In some examples, the detection method based on speech and environment interaction may include:

[0092] 1. Background noise fusion judgment: The real speech environment is usually mixed with environmental background noise, and this noise will change dynamically during the speaking process and merge with the speech. When collecting speech, the characteristics of the background noise in the speech and its fusion with the speech signal can be analyzed.

[0093] For example, if the background noise detected in the speech is completely fixed or inconsistent, or does not match the noise in the actual environment (for example, the speech collected in a noisy living room environment does not have the corresponding noisy background noise), it is possible that the speech is replayed, not the real speech of a living person, and the risk of speech replay attack is determined.

[0094] 2. Reverberation characteristic analysis: Different spatial environments will cause different reverberation effects on speech. According to the actual situation of the space where the speech is collected (such as the reverberation characteristics determined by factors such as room size and wall material), analyze whether the reverberation characteristics in the speech are consistent with the real environment. If the reverberation characteristics of the speech do not match the actual space, such as a long reverberation effect similar to that of a large auditorium in a small room, it may be a replayed speech, which can be judged as non-living sound, posing a security risk.

[0095] In some examples, multimodal detection methods can be combined with other modal information to assist in liveness detection; such as using a camera to capture facial images to determine whether there are corresponding lip movements, facial expression changes, etc. that match the voice content when speaking (such as detecting whether there is a smiling facial expression when saying "smile"); or combined with device sensor information, such as detecting whether the holding posture and movement trajectory of the mobile phone are consistent with the operating habits during normal speaking (normally holding a mobile phone to speak may have slight shaking, angle changes, etc.), through these multimodal information to comprehensively determine whether the voice is from a real living person, thereby effectively preventing voice replay attacks and enhancing the security of identity authentication.

[0096] In some embodiments of the present invention, the method further includes: when a user uses the smart home system for the first time, determining the user identity information based on the collected user voice data, and establishing a user profile based on the user identity information.

[0097] As some examples, when a user first starts or begins to use a smart home system, the smart home system may require the user to input some information or execute certain voice commands through voice to collect the user's voice data. After collecting the voice data, the smart home system may generate the user's identity information and establish a user profile.

[0098] In some embodiments of the present invention, after the network configuration data is used to perform a network configuration operation on the smart home device, the method further includes: acquiring second real-time user voice data, and performing user identity recognition based on the second real-time user voice data to obtain target user identity information; determining a target area in the current environment and determining a target smart home device in the target area based on the second real-time user voice data and the target user identity information to control the target smart home device.

[0099] As some examples, the second real-time user voice data is a voice instruction for controlling a target smart home device, such as a user saying to a smart speaker, "Turn on the air conditioner in my bedroom."

[0100] In some examples, the target area is determined based on the specific area that needs to be implemented in the voice command (such as a living room, bedroom, etc.) combined with the user identity information; the target smart home device is determined based on the specific smart home device that needs to be controlled in the voice command (such as a smart light bulb, air conditioner, TV, etc.) combined with the user identity information and the target area.

[0101] For example, after the user says "turn on the air conditioner in my bedroom" to the smart speaker, voice recognition technology can be used to analyze the voice command to identify the speaker. After identifying the user as A, by analyzing the content of user A's voice command, it is determined that user A wants to operate the air conditioner in his or her bedroom. If user A's bedroom is the second bedroom, the air conditioner in the second bedroom can be controlled.

[0102] In some examples, if the user identity information cannot be clearly identified in the text information, the target area can be determined based on the location information of the device and the default area division rules; for example, the default area can be divided according to the room function, and all smart home devices in the living room are divided into the living room area, including smart TVs, smart speakers, smart curtains, etc.; all devices in the bedrooms are divided according to different bedrooms, such as the smart lights and smart air conditioners in the master bedroom are divided into the master bedroom area, and the corresponding devices in the second bedroom are divided into the second bedroom area; smart rice cookers, smart range hoods, etc. in the kitchen are divided into the kitchen area; it can also be divided according to the type of equipment, such as all smart lighting devices are divided into lighting areas, and smart temperature control devices are divided into temperature control areas, etc. Technical personnel in this field can determine these default division rules based on factors such as ease of management and usage habits.

[0103] After determining the target area, the central controller can send the area information of the device to the device management module, and the device management module will group the devices accordingly to coordinate the management and control of smart home devices. For example, if the devices in the same area are grouped, when a certain area is controlled as a whole, such as shutting down all devices in a certain area (such as shutting down the living room area), it is only necessary to send instructions to the corresponding group to operate all related devices in the area at one time, without having to control each device separately. This improves management efficiency and makes it easier to view the operating status of equipment in each area, so as to better realize the system's stable management and control of equipment.

[0104] In some embodiments of the present invention, determining a target area in the current environment and determining a target smart home device in the target area based on the second real-time user voice data and the target user identity information to control the target smart home device includes: performing voice recognition on the second real-time user voice data to obtain text information; determining the target area in the current environment based on the target user identity information; determining the target smart home device and target control instructions in the target area based on the text information, and controlling the target smart home device according to the target control instructions.

[0105] As some examples, control instructions may be specific control commands issued in voice commands to smart home devices, such as turning on / off devices, adjusting the working status of devices (such as brightness, temperature, volume, etc.), setting scheduled tasks, etc.; target control instructions are determined based on the target user identity information combined with the target area, target smart home device, and control instructions.

[0106] For example, in a smart home scenario, there are three users A, B and C in the home. After the voice recognition module receives the voice command "turn on the air conditioner in my bedroom", it can identify the user identity of the voice command and determine that the user identity information of the voice command is "A". Then, the user identity information and the voice command are converted into text information and sent to the central controller. By identifying the text information, the central controller can determine that the user identity information is "A", since the bedroom of user A is the master bedroom, the target area is the "master bedroom", and the control command is "turn on the air conditioner". The target control command is "turn on the master bedroom air conditioner", and the central controller performs control according to the target control command.

[0107] As some examples, if there are multiple users sending out the second real-time user voice data at the same time, control can be performed based on the priority of the voice instructions in the second real-time user voice data.

[0108] For example, in a living room scenario, user A wants to watch sports programs and issues a voice command "turn on the sports channel", while user B simultaneously wants to adjust the brightness of the living room lights and issues a voice command "brighten the living room lights". In this case, multiple users issue commands at the same time. At this time, corresponding processing can be performed based on the priority of the commands to coordinate the operating status of the devices.

[0109] In some embodiments of the present invention, the method further includes: obtaining the user's historical device usage to generate user habit data; and storing the user habit data in the user profile to provide personalized services.

[0110] As some examples, a user's historical device usage may include, but is not limited to, the times when the user turns the device on and off, the frequency and pattern of usage, and the like.

[0111] After obtaining the user's historical device usage, we can analyze and extract the user's habit patterns, such as the user's daily activity patterns (such as waking up time, working hours, rest time, etc.), preferences (such as favorite devices, etc.) and device control modes (such as frequently used device modes, etc.), and store the user's habit data in the user's profile to provide personalized services.

[0112] For example, user A says "turn on the air conditioner" to the smart speaker, and after user identification is performed and the target user's identity information is obtained, based on the user habit data recorded in user A's profile, it is determined that user A likes to set the air conditioner temperature to 26°. The central controller can automatically adjust the air conditioner setting to 26°.

[0113] As some examples, users can also set up and modify their user profiles (preferences, usage habits, etc.) through voice commands or mobile phone apps.

[0114] Among them, usage habits refer to some relatively fixed operating methods, parameter settings, etc. when users use the device in daily life, and preference settings refer to the relevant settings of users' subjective preferences for the display of device functions.

[0115] For example, some users prefer warmer light color temperature, so in the relevant settings of the lighting equipment, the color temperature parameters can be adjusted to the values ​​corresponding to warmer tones; or some users are accustomed to setting the bedroom air-conditioning temperature to 26 degrees Celsius when they go to bed at night, which is a temperature usage habit setting; for example, some users are accustomed to automatically opening the smart curtains in the bedroom after getting up every morning, which is also a reflection of usage habits; based on this information, we can provide users with services that better meet their personalized needs.

[0116] In an embodiment of the present invention, during the process of networking a smart home device, first real-time user voice data is obtained; user identity authentication is performed based on the first real-time user voice data; if the user identity authentication is passed, preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on the smart home device. This achieves the goal of obtaining the first real-time user voice data, performing user identity authentication, and then performing network configuration operations on the smart home device. While increasing the security of network configuration, it does not require users to perform complex operations or remember complex passwords, thereby improving the convenience and satisfaction of users when using smart home devices.

[0117] The following is combined with Figure 2 The present invention is exemplified as follows:

[0118] Step 201, user registration.

[0119] In actual applications, when a user uses the smart home system for the first time, he / she needs to register; this process may require the user to provide some basic information, such as name, age, contact information, etc. This information will be stored in the user profile module for subsequent identification of the user and provision of personalized services.

[0120] For example, in a home environment, after different family members register, the system can distinguish the operating instructions of different users and control the equipment according to their respective preferences.

[0121] Step 202: Collect voice samples.

[0122] After the user completes registration, the system may prompt the user to provide voice samples; the user needs to follow the system's guidance and say a series of specific sentences or words. These voice samples will serve as the basic data for building a personal voice model.

[0123] For example, users can be asked to read aloud numbers, common phrases, operating instructions for smart home devices, etc., so that the system can comprehensively collect characteristic information of the user's voice, such as pitch, timbre, speaking speed, pronunciation habits, etc.

[0124] Step 203: speech preprocessing.

[0125] In practical applications, the collected voice samples can be preprocessed, including but not limited to removing noise interference in the voice signal, such as environmental noise, current noise, etc., to make the voice signal clearer. At the same time, the voice signal is framed and the continuous voice signal is divided into shorter time periods to facilitate more accurate analysis of voice features. For example, a 5-second voice can be divided into 20 millisecond frames, so that the frequency, amplitude and other characteristic changes of the voice can be analyzed in each small time period.

[0126] Step 204: feature extraction.

[0127] After speech preprocessing, representative feature parameters can be extracted from the speech data; such as Mel-frequency cepstral coefficients (MFCC), which can well simulate the perception characteristics of the human auditory system to speech; by calculating feature parameters such as MFCC, the speech signal can be converted into a set of digital feature vectors, which can effectively represent the user's speech characteristics. For example, a user's unique pronunciation method will have a specific numerical representation in the MFCC feature vector.

[0128] Step 205: construct a personal voice model and store the personal voice model.

[0129] In practical applications, the extracted speech feature parameters can be used to build a personal speech model; this model can be a classification model or a recognition model built based on a machine learning algorithm, such as a Gaussian mixture model (GMM) or a deep neural network model.

[0130] After constructing the personal voice model, it can be trained by taking the extracted feature vector as input and the corresponding user identity as output for learning and training, so that the model can accurately recognize the user's voice features; after the training is completed, the constructed personal voice model is stored in the system's database for voice matching verification during subsequent network configuration and equipment operation.

[0131] Step 206: network configuration request.

[0132] In actual applications, when users need to connect new smart home devices to the network, they can initiate a network configuration request to the system; for example, the user can trigger it by clicking a network configuration button on a mobile phone APP or speaking a specific network configuration voice command.

[0133] For example, when a user says "start configuring the smart camera", the voice recognition module receives the command, converts it into text information and passes it to the central controller, which starts the configuration request process.

[0134] Step 207: real-time voice collection.

[0135] After the network configuration request is initiated, the system can immediately perform real-time voice collection; collect the user's current voice data, which will be used for matching and verification with the previously constructed personal voice model.

[0136] For example, the user may be asked to speak again a specific sentence or word used when the voice sample was previously collected to confirm that the user identity of the current operation is consistent with the registered user.

[0137] Step 208: speech preprocessing, feature extraction, and feature matching.

[0138] The real-time collected speech is preprocessed, which may include but is not limited to noise removal, framing and other operations, and then feature parameters are extracted and feature matching is performed with the stored personal speech model; the similarity or distance measurement between the real-time speech features and the personal model is calculated, such as using methods such as Euclidean distance or cosine similarity; if the similarity exceeds a set threshold, the speech match is considered successful; otherwise, the match fails.

[0139] Step 209, determine whether the verification is successful, if yes, proceed to step 210, otherwise proceed to step 211.

[0140] Step 210: authorize network configuration, execute network configuration operations, and record logs.

[0141] Step 211, reject the request, allow retry or other verification.

[0142] After feature matching, it can be determined whether the verification is successful. If so, the network configuration is authorized, the network configuration process is executed, and the log is recorded; otherwise, the request is rejected and a retry or other verification is allowed.

[0143] In actual applications, if the verification is successful, it means that the current user identity is legal and the system authorizes the device to perform network configuration operations. The central controller can send network configuration-related parameters and instructions to the device, such as Wi-Fi account and password information, and the device connects to the home network based on this information; at the same time, the system records detailed logs of the network configuration process, including network configuration time, network configuration device information, user identity, etc., for subsequent query and troubleshooting.

[0144] If the verification fails, the system rejects the network configuration request and, depending on the settings, allows the user to retry voice verification, or provides other identity authentication methods, such as password verification, SMS verification code verification, etc., to ensure the security and reliability of smart home device network configuration.

[0145] In an embodiment of the present invention, by combining voice biometric recognition technology with the network equipment configuration process, analyzing the user's voice characteristics to establish a personal voice model, and performing real-time voice matching verification during network configuration, safe and convenient identity authentication is achieved, effectively preventing illegal network configuration operations.

[0146] The embodiments of the present invention have the following advantages:

[0147] 1. Significantly improve the security of the network equipment configuration process and effectively prevent illegal users from performing network configuration operations.

[0148] 2. Simplified the user authentication process, improved the user experience, and eliminated the need to remember complex passwords.

[0149] 3. More accurate identity authentication is achieved through voice biometric recognition.

[0150] 4. It has wide applicability and can be applied to the configuration process of various network devices.

[0151] 5. Enhanced the system's anti-attack capability and effectively defended against voice forgery and replay attacks.

[0152] Reference Figure 3 , shows a flowchart of another method for configuring a device provided in some embodiments of the present invention, which is applied to a smart home system and may specifically include the following steps:

[0153] Step 301, in the process of configuring the network for the smart home device, obtain first real-time user voice data.

[0154] Step 302: Perform user identity authentication based on the first real-time user voice data.

[0155] Step 303, when the user identity authentication is passed, obtain preset network configuration data, and use the network configuration data to perform network configuration operations on the smart home device.

[0156] Step 304: Acquire second real-time user voice data, and perform user identity recognition based on the second real-time user voice data to obtain target user identity information.

[0157] Step 305: Determine a target area in the current environment and a target smart home device in the target area according to the second real-time user voice data and the target user identity information, so as to control the target smart home device.

[0158] In an embodiment of the present invention, during the process of configuring a network for a smart home device, first real-time user voice data is obtained; user identity authentication is performed based on the first real-time user voice data; when the user identity authentication is passed, preset network configuration data is obtained, and the network configuration data is used to configure the smart home device; second real-time user voice data is obtained, and user identity recognition is performed based on the second real-time user voice data to obtain target user identity information; based on the second real-time user voice data and the target user identity information, a target area in the current environment is determined and a target smart home device in the target area is determined to control the target smart home device, thereby achieving user identity authentication and then configuring a network for a smart home device by obtaining the first real-time user voice data, thereby increasing the security of the network configuration without requiring the user to perform complex operations or remember complex passwords, thereby improving the convenience and satisfaction of the user when using the smart home device.

[0159] Reference Figure 4, shows a flowchart of the steps of another device configuration method provided by some embodiments of the present invention, which is applied to a smart home system and may specifically include the following steps:

[0160] Step 401: Acquire sample user voice data.

[0161] Step 402: Establish a speech model based on the sample user speech data.

[0162] Step 403: Acquire first real-time user voice data during network configuration of smart home devices.

[0163] Step 404: Use the voice model to perform user identity verification on the first real-time user voice data.

[0164] Step 405, when the user identity authentication is passed, the preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on the smart home device.

[0165] In an embodiment of the present invention, sample user voice data is obtained; a voice model is established based on the sample user voice data; in the process of networking smart home devices, first real-time user voice data is obtained; the voice model is used to perform user identity verification on the first real-time user voice data; when the user identity verification is passed, preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on smart home devices. This achieves user identity verification and then network configuration operations on smart home devices by obtaining the first real-time user voice data. While increasing the security of network configuration, it does not require users to perform complex operations or remember complex passwords, thereby improving the convenience and satisfaction of users when using smart home devices.

[0166] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0167] Reference Figure 5 , showing a schematic diagram of the structure of a device configuration apparatus provided by some embodiments of the present invention, which may specifically include the following modules:

[0168] The first real-time user voice data acquisition module 501 is used to acquire the first real-time user voice data during the process of configuring the network for the smart home device;

[0169] A user identity authentication module 502, configured to perform user identity authentication according to the first real-time user voice data;

[0170] The network configuration operation module 503 is used to obtain preset network configuration data when the user identity authentication is passed, and use the network configuration data to perform network configuration operation on the smart home device.

[0171] In some embodiments of the present invention, the network distribution operation module 503 includes:

[0172] The user liveness detection submodule is used to perform liveness detection on the current user when the user identity authentication is passed;

[0173] The user liveness detection result determination submodule is used to obtain preset network configuration data when the user liveness detection passes.

[0174] In some embodiments of the present invention, the device further comprises:

[0175] A sample user voice data acquisition module is used to acquire sample user voice data;

[0176] The voice model building module is used to build a voice model based on the sample user voice data.

[0177] In some embodiments of the present invention, the user identity verification module 502 includes:

[0178] The first real-time user voice data verification submodule is used to use the voice model to perform user identity authentication on the first real-time user voice data.

[0179] In some embodiments of the present invention, the speech model building module is used to:

[0180] Extracting sample user voice features from the sample user voice data;

[0181] A speech model is established according to the sample user speech features.

[0182] In some embodiments of the present invention, the first real-time user voice data verification submodule is used to:

[0183] Extracting real-time user voice features from the first real-time user voice data;

[0184] By using the voice model, similarity calculation is performed on the real-time user voice feature and the sample user voice feature, and according to the similarity calculation result, it is determined whether the first real-time user voice data is the first real-time user voice data of the authorized user;

[0185] In a case where the first real-time user voice data is first real-time user voice data of an authorized user, it is determined that the user identity authentication is passed.

[0186] In some embodiments of the present invention, the device further comprises:

[0187] A user identity recognition module, used to obtain second real-time user voice data, and perform user identity recognition based on the second real-time user voice data to obtain target user identity information;

[0188] The target smart home device determination module is used to determine the target area in the current environment and the target smart home device in the target area according to the second real-time user voice data and the target user identity information, so as to control the target smart home device.

[0189] In some embodiments of the present invention, the target smart home device determination module includes:

[0190] A text information determination submodule, configured to perform speech recognition on the second real-time user speech data to obtain text information;

[0191] A target area determination submodule, used to determine a target area in the current environment according to the target user identity information;

[0192] The device control submodule is used to determine the target smart home device and the target control instruction in the target area according to the text information, and control the target smart home device according to the target control instruction.

[0193] In some embodiments of the present invention, the device further comprises:

[0194] When a user uses the smart home system for the first time, the user profile establishment module determines the user identity information based on the collected user voice data, and establishes a user profile based on the user identity information.

[0195] In some embodiments of the present invention, the device further comprises:

[0196] A user habit data generation module, used to obtain the user's historical device usage and generate user habit data;

[0197] The user habit data storage module is used to store the user habit data in the user profile to provide personalized services.

[0198] Some embodiments of the present invention further provide an electronic device, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the above method is implemented when the computer program is executed by the processor.

[0199] Some embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, and the computer program implements the above method when executed by a processor.

[0200] Some embodiments of the present invention further provide a computer program product, including a computer program, which implements the above method when executed by a processor.

[0201] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0202] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0203] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0204] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0205] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0206] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0208] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0209] Finally, it should be noted that, 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, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the above elements.

[0210] The above is a detailed introduction to the method, device, equipment, medium and product of a device configuration provided. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in the field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for device configuration, characterized in that: Applied to smart home systems, including: In the process of configuring the network for the smart home device, obtaining first real-time user voice data; Performing user identity authentication according to the first real-time user voice data; When the user identity authentication is passed, the preset network configuration data is obtained, and the network configuration data is used to perform network configuration operations on the smart home device.

2. The method according to claim 1, characterized in that When the user identity verification is passed, obtaining the preset network configuration data includes: When the user identity authentication is passed, the current user is subjected to liveness detection; When the user's liveness detection passes, the preset network configuration data is obtained.

3. The method according to claim 1, characterized in that In the process of configuring the network for the smart home device, before obtaining the first real-time user voice data, the method further includes: Obtain sample user voice data; Establishing a voice model according to the sample user voice data; The performing user identity authentication according to the first real-time user voice data includes: The voice model is used to perform user identity authentication on the first real-time user voice data.

4. The method according to claim 3, characterized in that The step of establishing a voice model according to the sample user voice data comprises: Extracting sample user voice features from the sample user voice data; Establishing a speech model according to the speech features of the sample user; The adopting the voice model to perform user identity authentication on the first real-time user voice data includes: Extracting real-time user voice features from the first real-time user voice data; By using the voice model, similarity calculation is performed on the real-time user voice feature and the sample user voice feature, and according to the similarity calculation result, it is determined whether the first real-time user voice data is the first real-time user voice data of the authorized user; In a case where the first real-time user voice data is first real-time user voice data of an authorized user, it is determined that the user identity authentication is passed.

5. The method according to any one of claims 1 to 4, characterized in that: After the network configuration data is used to perform the network configuration operation on the smart home device, the method further includes: Acquire second real-time user voice data, and perform user identity recognition based on the second real-time user voice data to obtain target user identity information; According to the second real-time user voice data and the target user identity information, a target area in the current environment is determined and a target smart home device in the target area is determined to control the target smart home device.

6. The method according to claim 5, characterized in that The step of determining a target area in the current environment and a target smart home device in the target area according to the second real-time user voice data and the target user identity information, so as to control the target smart home device, includes: Performing voice recognition on the second real-time user voice data to obtain text information; Determine a target area in the current environment according to the target user identity information; According to the text information, a target smart home device and a target control instruction in the target area are determined, and the target smart home device is controlled according to the target control instruction.

7. The method according to claim 5, characterized in that Also includes: When a user uses the smart home system for the first time, the user identity information is determined based on the collected user voice data, and a user profile is established based on the user identity information.

8. The method according to claim 7, characterized in that Also includes: Obtain the user's historical device usage and generate user habit data; The user habit data is stored in the user profile to provide personalized services.

9. A device for equipment configuration, characterized in that: Applied to smart home systems, including: A first real-time user voice data acquisition module, used to acquire first real-time user voice data during the process of configuring the network for the smart home device; A user identity authentication module, used for performing user identity authentication according to the first real-time user voice data; The network configuration operation module is used to obtain preset network configuration data when the user identity authentication is passed, and use the network configuration data to perform network configuration operations on the smart home device.

10. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 8 when executed by the processor.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.

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