Processing method and device of smart home network, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a kind of smart home network processing method, device, electronic equipment and storage medium, it is related to smart home technical field, the method comprises: in response to voice query instruction for smart home network, the instruction type corresponding to voice query instruction is acquired, and corresponding target partition is determined from several virtual partitions according to instruction type;Each target partition corresponding network resource load is acquired;If network resource load represents that target partition appears load imbalance, then determine the target smart home device in the first partition where load imbalance occurs, and execute network resource allocation operation for target smart home device, to realize intelligent scheduling based on semantic interaction control, reduce the management difficulty of user, while reduce the probability of network congestion, improve the utilization of network resources, while it can guarantee the stable operation of smart home device.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and in particular to a processing method for a smart home network, a processing device for a smart home network, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In a smart home network, as smart home devices operate, different devices may have varying network resource dependencies. For example, some smart home devices require continuous network access, necessitating more network resources; while others only need network access under specific circumstances, exhibiting lower network resource dependence. Because of this, different smart home devices have different network resource requirements, and during the allocation of network resources among these devices, an imbalance in resource distribution can easily occur, potentially leading to network congestion and resource waste. Summary of the Invention

[0003] The present invention provides a processing method, apparatus, electronic device, and computer-readable storage medium for smart home networks, in order to solve or partially solve the problem of network congestion and resource waste caused by imbalance in network resource allocation in smart home networks.

[0004] This invention discloses a processing method for a smart home network, applied to a smart home network containing several virtual partitions. The method includes:

[0005] In response to a voice query command for a smart home network, the command type corresponding to the voice query command is obtained, and the corresponding target partition is determined from the plurality of virtual partitions according to the command type;

[0006] Obtain the network resource load corresponding to each of the target partitions;

[0007] If the network resource load indicates that the target partition has an unbalanced load, then the target smart home device in the first partition where the load imbalance occurs is identified, and a network resource allocation operation is performed for the target smart home device.

[0008] In some feasible implementations, the network resource load includes the number of smart home devices using the smart home network in the target partition. If the network resource load indicates a load imbalance in the target partition, then determining the target smart home devices in the first partition where the load imbalance occurs includes:

[0009] Calculate the difference in the number of devices between each of the target partitions based on the number of devices;

[0010] The target partition where the number of devices is greater than or equal to a first preset threshold, and / or the difference in the number of devices is greater than or equal to a second preset threshold, is designated as the first partition where load imbalance occurs.

[0011] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0012] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0013] In some feasible implementations, the network resource load includes the network bandwidth utilization rate corresponding to the target partition. If the network resource load indicates a load imbalance in the target partition, then identifying the target smart home devices in the first partition experiencing the load imbalance includes:

[0014] The target partition with a network bandwidth utilization rate greater than or equal to the third preset threshold is designated as the first partition where load imbalance occurs.

[0015] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0016] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0017] In some feasible implementations, the network resource load includes the network latency corresponding to the target partition, and if the network resource load indicates that the target partition has a load imbalance, then determining the target smart home devices in the first partition where the load imbalance occurs includes:

[0018] The target partition with network latency greater than or equal to the fourth preset threshold is designated as the first partition where load imbalance occurs.

[0019] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0020] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0021] In some feasible implementations, selecting the target smart home device from the first smart home devices according to the amount of network bandwidth occupied includes:

[0022] Smart home devices that consume network bandwidth greater than or equal to the fifth preset threshold are designated as target smart home devices.

[0023] In some feasible implementations, the network resource allocation operation for the target smart home device includes:

[0024] Unbind the target smart home device from the network configuration of the first partition;

[0025] Select the second partition from the target partitions where no load imbalance has occurred, obtain the network configuration information corresponding to the second partition, and connect the target smart home device to the second partition according to the network configuration information.

[0026] In some feasible implementations, connecting the target smart home device to the second partition according to the network configuration information includes:

[0027] In response to a network switching command for the target smart home device, if the target smart home device is currently in a data transmission state, the target smart home device is controlled to pause data transmission.

[0028] In response to the target smart home device being in an idle state, the target smart home device is connected to the second partition according to the network configuration information.

[0029] Among some feasible implementation methods are:

[0030] In response to the target smart home device accessing the second partition, the target smart home device is controlled to resume the data transmission state.

[0031] In some feasible implementations, the smart home network is configured with a partition device list for the virtual partition, the partition device list including smart home devices bound to each virtual partition, and the method further includes:

[0032] In response to the target smart home device accessing the second partition, the device list of the partition is updated.

[0033] In some feasible implementations, the instruction type includes a specified partition type and an overall query type. The step of determining the corresponding target partition from the plurality of virtual partitions based on the instruction type includes:

[0034] If the instruction type is the specified partition type, then the target partition corresponding to the voice query instruction is selected from the plurality of virtual partitions;

[0035] If the instruction type is the overall query type, then all the virtual partitions are used as the target partitions.

[0036] In some feasible implementations, the step of responding to a voice query command for a smart home network and obtaining the command type corresponding to the voice query command includes:

[0037] Receive network device configuration requests for the smart home network sent by the user terminal;

[0038] Randomly generate verification text for the network device configuration request;

[0039] The system receives target speech sent by the user terminal for the verification text, wherein the target speech is the speech collected by the user terminal in response to the user's voice input for the verification text.

[0040] Authentication is performed based on the target speech to obtain an authentication result for the target speech;

[0041] If the verification result indicates that the user's identity verification is successful, then in response to a voice query command for the smart home network, the command type corresponding to the voice query command is obtained.

[0042] In some feasible implementations, the verification result includes either verification pass information or verification fail information, and the step of performing identity verification based on the target speech to obtain a verification result for the target speech includes:

[0043] Extract the speech features corresponding to the target speech;

[0044] The speech features are matched with a preset personal speech model to calculate the similarity score corresponding to the target speech.

[0045] If the similarity score is greater than or equal to the sixth preset threshold, then verification pass information for the target speech is generated;

[0046] If the similarity score is less than the sixth preset threshold, a verification failure message is generated for the target speech.

[0047] This invention also discloses a processing device for a smart home network, applied to a smart home network containing several virtual partitions. The device includes:

[0048] The instruction processing module is used to respond to a voice query instruction for a smart home network, obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from the plurality of virtual partitions according to the instruction type;

[0049] The load acquisition module is used to acquire the network resource load corresponding to each of the target partitions;

[0050] The device processing module is configured to, if the network resource load indicates that the target partition has an unbalanced load, identify the target smart home device in the first partition where the load imbalance occurs, and perform a network resource allocation operation for the target smart home device.

[0051] In some feasible implementations, the network resource load includes the number of smart home devices in the target partition that are using the smart home network, and the device processing module is specifically used for:

[0052] Calculate the difference in the number of devices between each of the target partitions based on the number of devices;

[0053] The target partition where the number of devices is greater than or equal to a first preset threshold, and / or the difference in the number of devices is greater than or equal to a second preset threshold, is designated as the first partition where load imbalance occurs.

[0054] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0055] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0056] In some feasible implementations, the network resource load includes the network bandwidth utilization rate corresponding to the target partition, and the device processing module is specifically used for:

[0057] The target partition with a network bandwidth utilization rate greater than or equal to the third preset threshold is designated as the first partition where load imbalance occurs.

[0058] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0059] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0060] In some feasible implementations, the network resource load includes the network latency corresponding to the target partition, and the device processing module is specifically used for:

[0061] The target partition with network latency greater than or equal to the fourth preset threshold is designated as the first partition where load imbalance occurs.

[0062] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0063] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0064] In some feasible implementations, the device processing module is specifically used for:

[0065] Smart home devices that consume network bandwidth greater than or equal to the fifth preset threshold are designated as target smart home devices.

[0066] In some feasible implementations, the device processing module is specifically used for:

[0067] Unbind the target smart home device from the network configuration of the first partition;

[0068] Select the second partition from the target partitions where no load imbalance has occurred, obtain the network configuration information corresponding to the second partition, and connect the target smart home device to the second partition according to the network configuration information.

[0069] In some feasible implementations, the device processing module is specifically used for:

[0070] In response to a network switching command for the target smart home device, if the target smart home device is currently in a data transmission state, the target smart home device is controlled to pause data transmission.

[0071] In response to the target smart home device being in an idle state, the target smart home device is connected to the second partition according to the network configuration information.

[0072] Among some feasible implementation methods are:

[0073] The status recovery module is used to control the target smart home device to restore the data transmission status in response to the target smart home device accessing the second partition.

[0074] In some feasible implementations, the smart home network is configured with a partition device list for the virtual partition, the partition device list including smart home devices bound to each virtual partition, and the device further includes:

[0075] The list update module is used to update the device list of the partition in response to the target smart home device accessing the second partition.

[0076] In some feasible implementations, the instruction type includes a specified partition type and an overall query type, and the instruction processing module is specifically used for:

[0077] If the instruction type is the specified partition type, then the target partition corresponding to the voice query instruction is selected from the plurality of virtual partitions;

[0078] If the instruction type is the overall query type, then all the virtual partitions are used as the target partitions.

[0079] In some feasible implementations, the instruction processing module is specifically used for:

[0080] Receive network device configuration requests for the smart home network sent by the user terminal;

[0081] Randomly generate verification text for the network device configuration request;

[0082] The system receives target speech sent by the user terminal for the verification text, wherein the target speech is the speech collected by the user terminal in response to the user's voice input for the verification text.

[0083] Authentication is performed based on the target speech to obtain an authentication result for the target speech;

[0084] If the verification result indicates that the user's identity verification is successful, then in response to a voice query command for the smart home network, the command type corresponding to the voice query command is obtained.

[0085] In some feasible implementations, the verification result includes either verification pass information or verification fail information, and the instruction processing module is specifically used for:

[0086] Extract the speech features corresponding to the target speech;

[0087] The speech features are matched with a preset personal speech model to calculate the similarity score corresponding to the target speech.

[0088] If the similarity score is greater than or equal to the sixth preset threshold, then verification pass information for the target speech is generated;

[0089] If the similarity score is less than the sixth preset threshold, a verification failure message is generated for the target speech.

[0090] This invention also discloses an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0091] The memory is used to store computer programs;

[0092] When the processor executes a program stored in the memory, it implements the method described in the embodiments of the present invention.

[0093] This invention also discloses a computer-readable storage medium storing instructions that, when executed by one or more processors, cause the processors to perform the methods described in this invention.

[0094] The embodiments of the present invention have the following advantages:

[0095] In this embodiment of the invention, it can be applied to a smart home network, which may include several virtual partitions. During the network management of smart home devices in the smart home network, users can input voice query commands for the smart home network. The system can obtain the command type corresponding to the voice query command, determine the corresponding target partition from several virtual partitions according to the command type, and obtain the network resource load corresponding to each target partition. If the network resource load indicates that the target partition has an unbalanced load, the system determines the target smart home device in the first partition where the load imbalance occurs and performs a network resource allocation operation for the target smart home device. Thus, in the process of managing the smart home network, on the one hand, users can achieve intelligent scheduling based on semantic interactive control, reducing the management difficulty for users; on the other hand, load balancing detection is performed according to the command type of the user's input voice command, and when an unbalanced load is detected, a targeted network resource allocation operation is performed, reducing the probability of network congestion, improving the utilization rate of network resources, and ensuring the stable operation of smart home devices. Attached Figure Description

[0096] Figure 1 This is a flowchart of the steps of a smart home network processing method provided in an embodiment of the present invention;

[0097] Figure 2 This is a schematic diagram of the voice control process provided in an embodiment of the present invention;

[0098] Figure 3 This is a structural block diagram of a smart home network processing device provided in an embodiment of the present invention. Detailed Implementation

[0099] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0100] As an example, different smart home devices have different network resource requirements, and in the process of allocating network resources for smart home devices, an imbalance in network resource allocation can easily occur, leading to problems such as network congestion and resource waste.

[0101] To address this, this invention introduces voice interaction control. During network management of smart home devices within a smart home network, users can input voice query commands. The system can obtain the command type corresponding to the voice query command and determine the target partition from several virtual partitions based on the command type. It also obtains the network resource load corresponding to each target partition. If the network resource load indicates a load imbalance in the target partition, the system identifies the target smart home device in the first partition where the load imbalance occurs and performs network resource allocation operations for that target smart home device. Thus, in the process of managing the smart home network, on the one hand, user-based semantic interaction control enables intelligent scheduling, reducing the user's management difficulty; on the other hand, load balancing detection is performed based on the command type of the user's input voice command, and targeted network resource allocation operations are executed when load imbalance is detected, reducing the probability of network congestion, improving network resource utilization, and ensuring the stable operation of smart home devices.

[0102] Reference Figure 1 This diagram illustrates a flowchart of a processing method for a smart home network according to an embodiment of the present invention. The method is applied to a smart home network, which includes several virtual partitions, and specifically includes the following steps:

[0103] Step 101: In response to a voice query command for the smart home network, obtain the command type corresponding to the voice query command, and determine the corresponding target partition from the plurality of virtual partitions according to the command type;

[0104] A smart home network can be composed of network management devices, smart home devices, and user terminals. The network management device is responsible for network management-related functions and acts as a hub for data interaction between different smart home devices and between user terminals and smart home devices. Smart home devices execute smart home functions, and user terminals are devices that interact with the user, allowing the user to manage and control the smart home devices. The smart home network can be partitioned based on device function, security level, physical location, network traffic and bandwidth requirements, user roles, time sensitivity, device type, data sensitivity, automation scenarios, and network protocols, thus dividing the smart home network into several virtual partitions.

[0105] Based on device function: Divide the space into zones according to the function type of the devices. For example:

[0106] Entertainment devices: smart TVs, audio equipment, game consoles, etc.

[0107] Security equipment: cameras, door locks, alarm systems, etc.

[0108] Environmental control: smart thermostats, smart lighting, smart curtains, etc.

[0109] Health equipment: smart scales, smart blood pressure monitors, etc.

[0110] Advantages: Facilitates the management and optimization of devices with specific functions, and reduces interference between devices with different functions.

[0111] Based on security requirements: Zone the equipment according to its security level. For example:

[0112] High security level: Devices involving personal privacy or financial information, such as smart locks, cameras, payment devices, etc.

[0113] Medium security level: Smart home appliances used daily, such as smart light bulbs and smart sockets.

[0114] Low security level: Entertainment devices, such as smart speakers and smart TVs.

[0115] Advantages: Protects high-security devices through different security strategies (such as encryption and access control), reducing security risks.

[0116] Based on device location: Zones are created according to the physical location of the devices. For example:

[0117] Living room: Smart TV, smart speaker, smart lighting, etc.

[0118] Bedroom: Smart lighting, smart thermostat, smart curtains, etc.

[0119] Kitchen: Smart refrigerator, smart oven, smart socket, etc.

[0120] Outdoors: Smart cameras, smart irrigation systems, etc.

[0121] Advantages: Facilitates device management based on location, optimizes network performance, and reduces network latency across regions.

[0122] Based on user roles: Divide the user space into zones according to their permissions and needs. For example:

[0123] Administrator: Has access to all device and system settings.

[0124] Family members: can access devices they use daily, such as smart lights and smart appliances.

[0125] Visitors can only access specific entertainment devices, such as smart TVs and smart speakers.

[0126] Advantages: By controlling access permissions through user roles, it ensures that different users can only access the devices they need, thereby improving security and privacy protection.

[0127] Based on the network bandwidth requirements of the devices: The network should be partitioned according to the network bandwidth requirements of the devices. For example:

[0128] High bandwidth requirements: Video streaming devices (such as smart TVs and cameras), online gaming devices, etc.

[0129] Medium bandwidth requirements: Smart home control devices (such as smart lights and smart sockets).

[0130] Low bandwidth requirements: Simple sensor devices (such as temperature and humidity sensors).

[0131] Advantages: By allocating bandwidth reasonably, it ensures that devices with high bandwidth requirements can obtain sufficient network resources and avoids network congestion.

[0132] Based on the equipment's time-sensitivity requirements: partition the system according to the equipment's real-time performance requirements. For example:

[0133] High real-time requirements: security equipment (such as cameras and door locks) and health monitoring equipment (such as smart blood pressure monitors).

[0134] Low real-time requirements: Entertainment devices (such as smart TVs and smart speakers).

[0135] Advantages: Priority scheduling ensures that high real-time devices can respond promptly, avoiding delays.

[0136] Based on device type: Partition the system according to the type of device. For example:

[0137] IoT devices: smart light bulbs, smart sockets, sensors, etc.

[0138] Traditional equipment: Non-smart traditional home appliances, such as ordinary televisions and air conditioners.

[0139] Computing devices: smartphones, tablets, computers, etc.

[0140] Advantages: Facilitates the management and optimization of different types of devices, ensuring the security and stability of IoT devices.

[0141] Based on data sensitivity: partition the system according to the types of data processed by the device. For example:

[0142] Highly sensitive data: Devices involving personal privacy and financial information, such as smart locks, cameras, and payment devices.

[0143] Low-sensitivity data: entertainment equipment, environmental control equipment, etc.

[0144] Advantages: By employing different data protection strategies, the privacy and security of highly sensitive data can be ensured.

[0145] Based on automation scenarios: Divide the space into zones according to different automation scenarios. For example:

[0146] Security scenario: Involves the automated control of security equipment.

[0147] Entertainment scenarios: involving the automated control of entertainment equipment.

[0148] Energy-saving scenarios: involving the automated control of environmental control equipment.

[0149] Based on the network protocol used by the device: Partition the system according to the network protocol used by the device. For example:

[0150] Wi-Fi devices: smart speakers, smart TVs, etc.

[0151] Zigbee devices: smart light bulbs, sensors, etc.

[0152] Z-Wave devices: smart door locks, security equipment, etc.

[0153] Bluetooth devices: smartwatches, health monitoring devices, etc.

[0154] By properly partitioning the network, we can improve network security, performance, and manageability, while meeting the needs of different users and devices.

[0155] In this embodiment of the invention, during the network management of smart home devices in a smart home network, users can input corresponding voice query commands to query the network status of the smart home devices. Specifically, the network management device can respond to the voice query command for the smart home network, obtain the command type corresponding to the voice query command, and determine the corresponding target partition from several virtual partitions according to the command type. Based on voice interaction control, intelligent scheduling is realized, reducing the management difficulty for users.

[0156] The user-input voice query command can include a specified partition type and an overall query type. The specified partition type indicates that a network query is performed on a specified partition in the virtual partition, while the overall query type indicates that a network query is performed on all virtual partitions. Specifically, if the command type is a specified partition type, the target partition corresponding to the voice query command is selected from several virtual partitions; if the command type is an overall query type, all virtual partitions are used as target partitions.

[0157] For example, suppose a user divides the entire smart home network into virtual partitions ①, ②, ③, and ④, etc. For a specific partition type, the user can specify a voice query command to query the network status of one or more of the virtual partitions ①, ②, ③, and ④. For an overall query, the user can query the network status of all partitions, including virtual partitions ①, ②, ③, and ④. Thus, different network management can be achieved based on different voice commands, improving the management flexibility of the smart home network.

[0158] In some feasible implementations, before formally querying the network status, the network management device can first verify the user's identity. Only if the user's identity is verified will it respond to the user's voice query command. Optionally, the network management device can receive a network device configuration request for the smart home network sent by the user terminal, then randomly generate a verification text for the network device configuration request and send this verification text to the user terminal. After receiving the verification text, the user terminal can output the verification text and corresponding verification prompt information, prompting the user to input the voice corresponding to the verification text. The user's voice input is collected, and the collection result is sent to the network management device. When the network management device receives the target voice for the verification text sent by the user terminal, it can perform identity verification based on the target voice and obtain a verification result for the target voice. If the verification result indicates that the user's identity verification is successful, it will respond to the voice query command for the smart home network and obtain the command type corresponding to the voice query command.

[0159] The verification result includes either a verification pass message or a verification fail message. Specifically, the network management device can first extract the speech features corresponding to the target speech, then match the speech features with a preset personal speech model, and calculate the similarity score corresponding to the target speech. If the similarity score is greater than or equal to a preset threshold, a verification pass message is generated for the target speech; if the similarity score is less than the preset threshold, a verification fail message is generated for the target speech.

[0160] In one example, assuming the voice recognition system in a smart home network is used to verify user identity and query network load comparisons across different zones, the network management device performs voice verification through the following steps:

[0161] 1. Speech Feature Extraction

[0162] Target speech: User A said, "Check the network load comparison of each partition."

[0163] Speech Feature Extraction: The network management device extracts the following speech features from the target speech:

[0164] Voiceprint characteristics (such as fundamental frequency, formants, MFCC, etc.).

[0165] Speech rate, pitch, and clarity of pronunciation, etc.

[0166] Example of extracted speech features:

[0167] Base frequency: 220Hz

[0168] Resonance peaks: F1 = 550 Hz, F2 = 1600 Hz

[0169] MFCC (Mel frequency cepstral coefficients): [0.15, 0.36, 0.58, 0.80, ...]

[0170] Speech rate: 110 words / minute

[0171] 2. Matching personal voice models

[0172] Preset personal voice model: The network management device has stored the voice model of user A, which includes its unique voice characteristics.

[0173] Matching process: The extracted target speech features are matched with user A's speech model.

[0174] Matching example:

[0175] Baseband matching degree: 92%

[0176] Resonance peak matching degree: 88%

[0177] MFCC matching degree: 90%

[0178] Speech rate matching accuracy: 95%

[0179] 3. Calculate the similarity score

[0180] Similarity score calculation: The matching results are combined to calculate a similarity score.

[0181] Calculation formula:

[0182] Similarity score = ∑(match degree × weight) / ∑weight

[0183] Assume the weights are assigned as follows:

[0184] Fundamental frequency: 0.3

[0185] Resonance peak: 0.2

[0186] MFCC: 0.4

[0187] Speech rate: 0.1

[0188] Calculation process: Similarity score = [(92% × 0.3) + (88% × 0.2) + (90% × 0.4) + (95% × 0.1)] / (0.3 + 0.2 + 0.4 + 0.1) = 90.7%

[0189] 4. Validation result generation

[0190] Preset threshold: Assume the system's verification pass threshold is set to 85%.

[0191] Verification results:

[0192] If the similarity score is ≥85%, then a verification pass message will be generated.

[0193] If the similarity score is less than 85%, a verification failure message will be generated.

[0194] Example results:

[0195] The similarity score is 90.7%, which is greater than the preset threshold of 85%.

[0196] The verification message reads: "User A's voice verification is successful. You are now allowed to query the network load comparison for each partition."

[0197] Through the above process, when a user wants to manage the network of smart home devices, they can input the corresponding voice command. The network management device will then authenticate the user's identity based on the voice command. If the authentication is successful, the device can further respond to the voice command and provide feedback on the network status to the user.

[0198] Step 102: Obtain the network resource load corresponding to each of the target partitions;

[0199] Once the target partition is determined, the network management device can obtain the network resource load corresponding to each target partition, and the network resource load can characterize the network status corresponding to the target partition.

[0200] The network resource load can include at least the number of smart home devices using the smart home network in the target partition, the network bandwidth utilization rate of the target partition, and the network latency of the target partition. For example, if the number of smart home devices using the smart home network in the target partition is higher, the network bandwidth utilization rate is higher, and the network latency is higher, it can be determined that the target partition is in a state of unbalanced load. Conversely, it can be determined that the target partition is in a state of balanced load.

[0201] Step 103: If the network resource load indicates that the target partition has an unbalanced load, then identify the target smart home device in the first partition where the load imbalance occurs, and perform a network resource allocation operation for the target smart home device.

[0202] After obtaining the network resource load corresponding to the target partition, the network management device can perform network detection on the target partition based on the network resource load to determine whether the target partition has a load imbalance. If the network resource load indicates that the target partition has a load imbalance, the target smart home device in the first partition where the load imbalance occurs is identified, and a network resource allocation operation is performed for the target smart home device. Thus, in the process of managing the smart home network, on the one hand, users can achieve intelligent scheduling based on semantic interaction, reducing the management difficulty for users; on the other hand, load balancing detection is performed based on the type of voice command input by the user, and when a load imbalance is detected, a targeted network resource allocation operation is performed, reducing the probability of network congestion, improving the utilization rate of network resources, and ensuring the stable operation of smart home devices.

[0203] In one optional implementation, the network resource load includes the number of smart home devices using the smart home network in the target partition. The network management device can calculate the difference in the number of devices between each target partition based on the number of devices. Then, the target partition with a number of devices greater than or equal to a preset threshold, and / or with a difference in the number of devices greater than or equal to the preset threshold, is designated as the first partition where load imbalance occurs. Then, the network bandwidth occupied by each first smart home device in the first partition is obtained, and the target smart home device is selected from the first smart home devices according to the amount of network bandwidth occupied.

[0204] In another optional implementation, the network resource load includes the network bandwidth utilization rate corresponding to the target partition. The network management device can take the target partition with a network bandwidth utilization rate greater than or equal to a preset threshold as the first partition where the load imbalance occurs. Then, it obtains the network bandwidth occupied by each first smart home device in the first partition, and selects the target smart home device from the first smart home devices according to the level of network bandwidth occupied.

[0205] In another optional implementation, the network resource load includes the network latency corresponding to the target partition. The network management device can then use the target partition with a network latency greater than or equal to a preset threshold as the first partition where load imbalance occurs. Next, it obtains the network bandwidth occupied by each first smart home device in the first partition, and then selects the target smart home device from the first smart home devices according to the amount of network bandwidth occupied.

[0206] Optionally, if it is determined that there is a load imbalance in the target partition, the network management device can select smart home devices that occupy network bandwidth greater than or equal to a preset threshold as target smart home devices.

[0207] In some examples, it is assumed that the smart home network is divided into 4 zones:

[0208] Zone 1 (Living Room): Smart TV, Smart Speaker, Smart Lighting;

[0209] Partition 2 (Bedroom): Smart lighting, smart thermostat, smart curtains;

[0210] Zone 3 (Kitchen): Smart refrigerator, smart oven, smart socket;

[0211] Zone 4 (Outdoor): Smart cameras, smart irrigation system.

[0212] When a network management device determines whether a load imbalance occurs based on the number of devices:

[0213] Target partitions: partition 1, partition 2, partition 3, partition 4.

[0214] Quantity of equipment:

[0215] Zone 1: 3 devices (smart TV, smart speaker, smart lights).

[0216] Zone 2: 3 devices (smart lights, smart thermostat, smart curtains).

[0217] Zone 3: 3 devices (smart refrigerator, smart oven, smart socket).

[0218] Zone 4: 2 devices (smart camera, smart irrigation system).

[0219] Next, calculate the difference in the number of devices between each partition:

[0220] The difference in the number of devices between partition 1 and partition 4 is 3 - 2 = 1.

[0221] The difference in the number of devices between partition 2 and partition 4 is 3 - 2 = 1.

[0222] The difference in the number of devices between partition 3 and partition 4 is 3 - 2 = 1.

[0223] Then, identify the partitions with unbalanced loads:

[0224] Preset threshold: Difference in the number of devices ≥ 1.

[0225] Result: The difference in the number of devices in partitions 1, 2, 3 and 4 is greater than or equal to 1. Therefore, partitions 1, 2 and 3 are the first partitions where load imbalance occurs.

[0226] Once the first partition with the unbalanced load is identified, the network bandwidth used by the devices in that partition can be further obtained:

[0227] Section 1 (Living Room):

[0228] Smart TV: 50Mbps.

[0229] Smart speaker: 5Mbps.

[0230] Smart lighting: 1Mbps.

[0231] Partition 2 (Bedroom):

[0232] Smart lighting: 1Mbps.

[0233] Intelligent thermostat: 2Mbps.

[0234] Smart curtains: 1Mbps.

[0235] Section 3 (Kitchen):

[0236] Smart refrigerator: 20Mbps.

[0237] Smart oven: 10Mbps.

[0238] Smart socket: 5Mbps.

[0239] Based on the above data, target smart home devices can be selected according to their network bandwidth usage:

[0240] Partition 1: Smart TV (50Mbps).

[0241] Section 2: Smart thermostat (2Mbps).

[0242] Partition 3: Smart Refrigerator (20Mbps).

[0243] In other words, smart TVs, smart thermostats, and smart refrigerators need to be adjusted for network connectivity.

[0244] When network management devices determine whether a load imbalance occurs based on network bandwidth utilization:

[0245] Target partitions: partition 1, partition 2, partition 3, partition 4.

[0246] Network bandwidth utilization:

[0247] Partition 1: 80%.

[0248] Partition 2: 30%.

[0249] Partition 3: 60%.

[0250] Partition 4: 10%.

[0251] Next, identify the partitions with unbalanced loads:

[0252] Preset threshold: Network bandwidth utilization ≥ 50%.

[0253] Results: Partition 1 (80%) and Partition 3 (60%) were the first partitions to experience load imbalance.

[0254] Once the first partition with the unbalanced load is identified, the network bandwidth used by the devices in that partition can be further obtained:

[0255] Section 1 (Living Room):

[0256] Smart TV: 50Mbps.

[0257] Smart speaker: 5Mbps.

[0258] Smart lighting: 1Mbps.

[0259] Section 3 (Kitchen):

[0260] Smart refrigerator: 20Mbps.

[0261] Smart oven: 10Mbps.

[0262] Smart socket: 5Mbps.

[0263] Based on the above parameters, target smart home devices can be selected according to their network bandwidth usage:

[0264] Partition 1: Smart TV (50Mbps).

[0265] Partition 3: Smart Refrigerator (20Mbps).

[0266] In other words, smart TVs and smart refrigerators need to make network adjustments.

[0267] When a network management device determines whether a load imbalance occurs based on network latency:

[0268] Target partitions: partition 1, partition 2, partition 3, partition 4.

[0269] Network latency:

[0270] Partition 1: 100ms.

[0271] Partition 2: 50ms.

[0272] Partition 3: 80ms.

[0273] Partition 4: 20ms.

[0274] Next, the partitions with unbalanced loads can be identified:

[0275] Preset threshold: network latency ≥ 70ms.

[0276] Result: Partition 1 (100ms) is the first partition where load imbalance occurred.

[0277] Once the first partition with the unbalanced load is identified, the network bandwidth used by the devices in that partition can be further obtained:

[0278] Section 1 (Living Room):

[0279] Smart TV: 50Mbps.

[0280] Smart speaker: 5Mbps.

[0281] Smart lighting: 1Mbps.

[0282] Based on the above parameters, target smart home devices can be selected according to their network bandwidth usage:

[0283] Partition 1: Smart TV (50Mbps).

[0284] In other words, network adjustments are made to smart TVs.

[0285] Through the above example, when a user queries the network load of the smart home network via voice command, the network management device can identify whether there are corresponding unbalanced partitions in the smart home network based on different network resource loads, and optimize the network for smart home devices in the unbalanced partitions, thereby optimizing the load balance of the smart home network and improving network performance and user experience.

[0286] When it is determined that network adjustments are needed for the target smart home device, the network management device can first unbind the target smart home device from the network configuration of the first partition, then select the second partition from the target partition where no load imbalance has occurred, obtain the network configuration information corresponding to the second partition, and connect the target smart home device to the second partition according to the network configuration information.

[0287] In practical implementation, during network switching, the network management device can respond to network switching commands for the target smart home device. If the target smart home device is currently in data transmission mode, it controls the target smart home device to pause data transmission, putting the target smart home device into an idle state. Then, in response to the target smart home device being in an idle state, it connects the target smart home device to the second partition according to the network configuration information. This achieves real-time intelligent scheduling and balanced utilization of network resources by transferring the smart home device from a high-load partition to a low-load partition. This ensures both the stability of smart home device operation and the full utilization of network resources. Furthermore, by adjusting the device status of the smart home device during network switching, the stability and integrity of data transmission can be effectively guaranteed.

[0288] Furthermore, the network management device can also control the target smart home device to resume data transmission in response to the target smart home device accessing the second partition. Optionally, if the smart home network is configured with a partition device list for virtual partitions, and the partition device list includes the smart home devices bound to each virtual partition, then the network management device can also update the partition device list in response to the target smart home device accessing the second partition.

[0289] It should be noted that the embodiments of the present invention include, but are not limited to, the examples described above. It is understood that those skilled in the art can make further settings according to actual needs under the guidance of the ideas in the embodiments of the present invention, and the present invention does not limit such settings.

[0290] In this embodiment of the invention, it can be applied to a smart home network, which may include several virtual partitions. During the network management of smart home devices in the smart home network, users can input voice query commands for the smart home network. The system can obtain the command type corresponding to the voice query command, determine the corresponding target partition from several virtual partitions according to the command type, and obtain the network resource load corresponding to each target partition. If the network resource load indicates that the target partition has an unbalanced load, the system determines the target smart home device in the first partition where the load imbalance occurs and performs a network resource allocation operation for the target smart home device. Thus, in the process of managing the smart home network, on the one hand, users can achieve intelligent scheduling based on semantic interactive control, reducing the management difficulty for users; on the other hand, load balancing detection is performed according to the command type of the user's input voice command, and when an unbalanced load is detected, a targeted network resource allocation operation is performed, reducing the probability of network congestion, improving the utilization rate of network resources, and ensuring the stable operation of smart home devices.

[0291] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples are provided for illustrative purposes:

[0292] 1. User registration and voice model establishment

[0293] a) Users register their identity through mobile applications or web interfaces.

[0294] b) The system prompts the user to read the preset text content and collects multiple voice samples through the device's microphone.

[0295] c) Preprocess the collected speech samples, including noise reduction, framing, and windowing.

[0296] d) Extract speech features, such as Mel frequency cepstral coefficients and linear prediction coefficients.

[0297] e) Use Gaussian mixture models or deep neural networks to build personal speech models.

[0298] f) Securely store the speech model on a server or local device.

[0299] 2. Network configuration request and voice recording

[0300] a) Users initiate network device configuration requests via mobile applications.

[0301] b) The system randomly generates a text and requires the user to read it aloud for authentication.

[0302] c) Real-time acquisition of user voice via device microphone.

[0303] 3. Speech feature extraction and matching

[0304] a) Preprocess the real-time collected voice data, similar to the registration phase.

[0305] b) Extract speech features, ensuring that the same feature extraction method is used as in the registration phase.

[0306] c) Match the extracted features with the pre-stored personal speech model.

[0307] d) Calculate the similarity score or likelihood ratio.

[0308] 4. Identity verification and network configuration authorization

[0309] a) Compare the calculated similarity score with the preset threshold.

[0310] b) If the score exceeds the threshold, the verification passes; otherwise, the verification fails.

[0311] c) For users who pass authentication, authorize them to perform network device configuration operations.

[0312] d) In the event of authentication failure, users can be allowed to retry or be required to use other forms of authentication.

[0313] 5. Safety Enhancement Measures

[0314] a) Implement liveness detection technologies, such as analyzing natural changes in speech or requiring users to read dynamically generated text.

[0315] b) Use encryption technology to protect the voice model and data during transmission.

[0316] c) Regularly update the speech model to adapt to the natural changes in the user's voice.

[0317] 6. Distribution network operation and log recording

[0318] a) After successful verification, the system guides the user to complete the network device configuration process.

[0319] b) Record all network configuration operations and verification attempts for subsequent auditing and security analysis.

[0320] The above process, from user registration to successful network configuration, takes into account both security and user experience. By using voice processing technology and machine learning algorithms, this method can effectively verify user identity and significantly improve the security of the network device configuration process.

[0321] In one example, refer to Figure 2 The diagram illustrates the flow chart of voice control provided in this embodiment of the invention. When the user inputs the corresponding voice command, the voice recognition module can identify and analyze the command type, such as single partition load query, whole network load query, etc. Then, the load detection module can count the load corresponding to each partition, and then compare the load situation through the load balancing control module. If the load is balanced, the status quo is maintained; if the load is unbalanced, device migration is performed. After the migration is completed, the corresponding migration result is fed back to the user through the voice feedback module.

[0322] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0323] Reference Figure 3 This diagram illustrates a structural block diagram of a processing device for a smart home network provided in an embodiment of the present invention. The device is applied to a smart home network, which includes several virtual partitions and may specifically include the following modules:

[0324] The instruction processing module 301 is used to respond to a voice query instruction for a smart home network, obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from the plurality of virtual partitions according to the instruction type;

[0325] The load acquisition module 302 is used to acquire the network resource load corresponding to each of the target partitions;

[0326] The device processing module 303 is used to determine the target smart home device in the first partition where the network resource load indicates a load imbalance, and to perform a network resource allocation operation for the target smart home device if the network resource load indicates a load imbalance in the target partition.

[0327] In some feasible implementations, the network resource load includes the number of smart home devices in the target partition that are using the smart home network, and the device processing module 303 is specifically used for:

[0328] Calculate the difference in the number of devices between each of the target partitions based on the number of devices;

[0329] The target partition where the number of devices is greater than or equal to a first preset threshold, and / or the difference in the number of devices is greater than or equal to a second preset threshold, is designated as the first partition where load imbalance occurs.

[0330] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0331] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0332] In some feasible implementations, the network resource load includes the network bandwidth utilization rate corresponding to the target partition, and the device processing module 303 is specifically used for:

[0333] The target partition with a network bandwidth utilization rate greater than or equal to the third preset threshold is designated as the first partition where load imbalance occurs.

[0334] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0335] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0336] In some feasible implementations, the network resource load includes the network latency corresponding to the target partition, and the device processing module 303 is specifically used for:

[0337] The target partition with network latency greater than or equal to the fourth preset threshold is designated as the first partition where load imbalance occurs.

[0338] Obtain the network bandwidth occupied by each first smart home device in the first partition;

[0339] The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

[0340] In some feasible implementations, the device processing module 303 is specifically used for:

[0341] Smart home devices that consume network bandwidth greater than or equal to the fifth preset threshold are designated as target smart home devices.

[0342] In some feasible implementations, the device processing module 303 is specifically used for:

[0343] Unbind the target smart home device from the network configuration of the first partition;

[0344] Select the second partition from the target partitions where no load imbalance has occurred, obtain the network configuration information corresponding to the second partition, and connect the target smart home device to the second partition according to the network configuration information.

[0345] In some feasible implementations, the device processing module 303 is specifically used for:

[0346] In response to a network switching command for the target smart home device, if the target smart home device is currently in a data transmission state, the target smart home device is controlled to pause data transmission.

[0347] In response to the target smart home device being in an idle state, the target smart home device is connected to the second partition according to the network configuration information.

[0348] Among some feasible implementation methods are:

[0349] The status recovery module is used to control the target smart home device to restore the data transmission status in response to the target smart home device accessing the second partition.

[0350] In some feasible implementations, the smart home network is configured with a partition device list for the virtual partition, the partition device list including smart home devices bound to each virtual partition, and the device further includes:

[0351] The list update module is used to update the device list of the partition in response to the target smart home device accessing the second partition.

[0352] In some feasible implementations, the instruction type includes a specified partition type and an overall query type, and the instruction processing module 301 is specifically used for:

[0353] If the instruction type is the specified partition type, then the target partition corresponding to the voice query instruction is selected from the plurality of virtual partitions;

[0354] If the instruction type is the overall query type, then all the virtual partitions are used as the target partitions.

[0355] In some feasible implementations, the instruction processing module 301 is specifically used for:

[0356] Receive network device configuration requests for the smart home network sent by the user terminal;

[0357] Randomly generate verification text for the network device configuration request;

[0358] The system receives target speech sent by the user terminal for the verification text, wherein the target speech is the speech collected by the user terminal in response to the user's voice input for the verification text.

[0359] Authentication is performed based on the target speech to obtain an authentication result for the target speech;

[0360] If the verification result indicates that the user's identity verification is successful, then in response to a voice query command for the smart home network, the command type corresponding to the voice query command is obtained.

[0361] In some feasible implementations, the verification result includes either verification pass information or verification fail information, and the instruction processing module 301 is specifically used for:

[0362] Extract the speech features corresponding to the target speech;

[0363] The speech features are matched with a preset personal speech model to calculate the similarity score corresponding to the target speech.

[0364] If the similarity score is greater than or equal to the sixth preset threshold, then verification pass information for the target speech is generated;

[0365] If the similarity score is less than the sixth preset threshold, a verification failure message is generated for the target speech.

[0366] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0367] In addition, this invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described smart home network processing method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0368] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described smart home network processing method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0369] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0370] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, EEPROM, Flash, and eMMC, etc.) containing computer-usable program code.

[0371] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0372] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0373] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0374] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0375] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0376] The present invention has provided a detailed description of a smart home network processing method and a smart home network processing device. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A processing method for a smart home network, characterized in that, Applied to a smart home network, wherein the smart home network includes several virtual partitions, the method includes: In response to a voice query command for a smart home network, the command type corresponding to the voice query command is obtained, and the corresponding target partition is determined from the plurality of virtual partitions according to the command type; Obtain the network resource load corresponding to each of the target partitions; If the network resource load indicates that the target partition has an unbalanced load, then the target smart home device in the first partition where the load imbalance occurs is identified, and a network resource allocation operation is performed for the target smart home device.

2. The method according to claim 1, characterized in that, The network resource load includes the number of smart home devices using the smart home network in the target partition. If the network resource load indicates a load imbalance in the target partition, then determining the target smart home devices in the first partition experiencing the load imbalance includes: Calculate the difference in the number of devices between each of the target partitions based on the number of devices; The target partition where the number of devices is greater than or equal to a first preset threshold, and / or the difference in the number of devices is greater than or equal to a second preset threshold, is designated as the first partition where load imbalance occurs. Obtain the network bandwidth occupied by each first smart home device in the first partition; The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

3. The method according to claim 1, characterized in that, The network resource load includes the network bandwidth utilization rate corresponding to the target partition. If the network resource load indicates that the target partition has a load imbalance, then the target smart home devices in the first partition where the load imbalance occurs are identified, including: The target partition with a network bandwidth utilization rate greater than or equal to the third preset threshold is designated as the first partition where load imbalance occurs. Obtain the network bandwidth occupied by each first smart home device in the first partition; The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

4. The method according to claim 1, characterized in that, The network resource load includes the network latency corresponding to the target partition. If the network resource load indicates a load imbalance in the target partition, then identifying the target smart home devices in the first partition experiencing the load imbalance includes: The target partition with network latency greater than or equal to the fourth preset threshold is designated as the first partition where load imbalance occurs. Obtain the network bandwidth occupied by each first smart home device in the first partition; The target smart home device is selected from the first smart home devices according to the amount of network bandwidth used.

5. The method according to any one of claims 2 to 4, characterized in that, Selecting a target smart home device from the first smart home devices based on the amount of network bandwidth used includes: Smart home devices that consume network bandwidth greater than or equal to the fifth preset threshold are designated as target smart home devices.

6. The method according to claim 5, characterized in that, The operation of allocating network resources for the target smart home device includes: Unbind the target smart home device from the network configuration of the first partition; Select the second partition from the target partitions where no load imbalance has occurred, obtain the network configuration information corresponding to the second partition, and connect the target smart home device to the second partition according to the network configuration information.

7. The method according to claim 6, characterized in that, Connecting the target smart home device to the second partition according to the network configuration information includes: In response to a network switching command for the target smart home device, if the target smart home device is currently in a data transmission state, the target smart home device is controlled to pause data transmission. In response to the target smart home device being in an idle state, the target smart home device is connected to the second partition according to the network configuration information.

8. The method according to claim 7, characterized in that, Also includes: In response to the target smart home device accessing the second partition, the target smart home device is controlled to resume the data transmission state.

9. The method according to claim 8, characterized in that, The smart home network is configured with a partition device list for the virtual partition, the partition device list including smart home devices bound to each virtual partition, and the method further includes: In response to the target smart home device accessing the second partition, the device list of the partition is updated.

10. The method according to claim 1, characterized in that, The instruction type includes a specified partition type and an overall query type. Determining the corresponding target partition from the plurality of virtual partitions based on the instruction type includes: If the instruction type is the specified partition type, then select the target partition corresponding to the voice query instruction from the plurality of virtual partitions; If the instruction type is the overall query type, then all the virtual partitions are used as the target partitions.

11. The method according to claim 1 or 10, characterized in that, The step of responding to a voice query command for a smart home network and obtaining the command type corresponding to the voice query command includes: Receive network device configuration requests for the smart home network sent by the user terminal; Randomly generate verification text for the network device configuration request; The system receives target speech sent by the user terminal for the verification text, wherein the target speech is the speech collected by the user terminal in response to the user's voice input for the verification text. Authentication is performed based on the target speech to obtain an authentication result for the target speech; If the verification result indicates that the user's identity verification is successful, then in response to a voice query command for the smart home network, the command type corresponding to the voice query command is obtained.

12. The method according to claim 11, characterized in that, The verification result includes either a verification pass message or a verification fail message. The step of verifying identity based on the target speech to obtain a verification result for the target speech includes: Extract the speech features corresponding to the target speech; The speech features are matched with a preset personal speech model to calculate the similarity score corresponding to the target speech. If the similarity score is greater than or equal to the sixth preset threshold, then verification pass information for the target speech is generated; If the similarity score is less than the sixth preset threshold, a verification failure message is generated for the target speech.

13. A processing device for a smart home network, characterized in that, The device is used in a smart home network, which includes several virtual partitions, and comprises: The instruction processing module is used to respond to a voice query instruction for a smart home network, obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from the plurality of virtual partitions according to the instruction type; The load acquisition module is used to acquire the network resource load corresponding to each of the target partitions; The device processing module is configured to, if the network resource load indicates that the target partition has an unbalanced load, identify the target smart home device in the first partition where the load imbalance occurs, and perform a network resource allocation operation for the target smart home device.

14. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; The memory is used to store computer programs; When the processor executes a program stored in the memory, it implements the method as described in any one of claims 1-12.

15. A computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the processors to perform the method as described in any one of claims 1-12.

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