Smart home network processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a smart home network processing method and device, electronic equipment and a storage medium, and relates to the technical field of smart home, and the method comprises the steps: obtaining an instruction type corresponding to a voice query instruction in response to the voice query instruction for a smart home network, determining a corresponding target partition from the plurality of virtual partitions according to the instruction type; obtaining a network resource load corresponding to each target partition; and if the network resource load represents that the load of the target partition is unbalanced, determining the target smart home device in the first partition where the load is unbalanced, and executing a network resource allocation operation for the target smart home device, thereby realizing intelligent scheduling based on semantic interaction control, reducing the management difficulty of a user, and improving the user experience. And meanwhile, the probability of network congestion is reduced, the utilization rate of network resources is improved, and stable operation of the smart home equipment can be ensured.

Description

Technical Field

[0001] The present 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 Art

[0002] In a smart home network, as smart home devices run, different smart home devices may have different network resource dependencies. For example, some smart home devices need to access the network continuously, and these smart home devices require more network resources; while some smart home devices only need to access the network under specific circumstances, and these smart home devices have a lower dependence on network resources. Based on this, different smart home devices have different demands for network resources, and in the process of allocating network resources for smart home devices, it is easy to have an imbalance in network resource allocation, which may lead to problems such as network congestion and resource waste. Summary of the invention

[0003] The embodiments of the present invention provide a processing method, device, electronic device and computer-readable storage medium for a smart home network to solve or partially solve the problem that unbalanced allocation of network resources in a smart home network may lead to network congestion and waste of resources.

[0004] The embodiment of the present invention discloses a processing method for a smart home network, which is applied to a smart home network, wherein the smart home network includes a plurality of virtual partitions, and the method comprises:

[0005] In response to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type;

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

[0007] If the network resource load indicates that a load imbalance occurs in the target partition, a target smart home device in the first partition where the load imbalance occurs is determined, 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 in the target partition that are using the smart home network, and if the network resource load indicates that a load imbalance occurs in the target partition, determining the target smart home device in the first partition where the load imbalance occurs includes:

[0009] Calculate the difference in the number of devices between the target partitions according to 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 used as the first partition where load imbalance occurs;

[0011] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0012] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

[0013] In some feasible implementations, the network resource load includes a network bandwidth occupancy rate corresponding to the target partition, and if the network resource load indicates that a load imbalance occurs in the target partition, determining a target smart home device in a first partition where the load imbalance occurs includes:

[0014] The target partition whose network bandwidth occupancy rate is greater than or equal to the third preset threshold is used as the first partition where load imbalance occurs;

[0015] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0016] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0018] The target partition where the network delay is greater than or equal to the fourth preset threshold is used as the first partition where the load imbalance occurs;

[0019] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0020] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0022] The smart home devices that occupy a network bandwidth greater than or equal to a fifth preset threshold are taken as target smart home devices.

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

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

[0025] A second partition is selected from a target partition where no load imbalance occurs, and network configuration information corresponding to the second partition is obtained, and the target smart home device is connected 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 instruction for the target smart home device, if the target smart home device is currently in a data transmission state, controlling the target smart home device to suspend data transmission;

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

[0029] Some possible implementations also include:

[0030] In response to the target smart home device accessing the second partition, controlling the target smart home device 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 includes 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 partition device list is updated.

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

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

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

[0036] In some feasible implementations, in response to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction includes:

[0037] Receiving a network device configuration request for the smart home network sent by a user terminal;

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

[0039] Receiving a target voice for the verification text sent by the user terminal, wherein the target voice is a voice collected by the user terminal in response to a user performing voice input for the verification text;

[0040] Perform identity authentication according to the target voice to obtain a verification result for the target voice;

[0041] If the verification result indicates that the identity authentication of the user is passed, then in response to a voice query instruction for a smart home network, an instruction type corresponding to the voice query instruction is obtained.

[0042] In some feasible implementations, the verification result includes one of verification pass information or verification fail information, and the identity authentication according to the target voice to obtain the verification result for the target voice includes:

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

[0044] Matching the voice feature with a preset personal voice model and calculating a similarity score corresponding to the target voice;

[0045] If the similarity score is greater than or equal to a sixth preset threshold, generating verification pass information for the target voice;

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

[0047] The embodiment of the present invention further discloses a processing device for a smart home network, which is applied to a smart home network. The smart home network includes a plurality of virtual partitions. The device includes:

[0048] An instruction processing module, for responding to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type;

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

[0050] The device processing module is used to determine the target smart home device in the first partition where the load imbalance occurs if the network resource load indicates that the target partition has a load imbalance, 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 to:

[0052] Calculate the difference in the number of devices between the target partitions according to 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 used as the first partition where load imbalance occurs;

[0054] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0055] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0057] The target partition whose network bandwidth occupancy rate is greater than or equal to the third preset threshold is used as the first partition where load imbalance occurs;

[0058] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0059] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0061] The target partition where the network delay is greater than or equal to the fourth preset threshold is used as the first partition where the load imbalance occurs;

[0062] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0063] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0065] The smart home devices that occupy a network bandwidth greater than or equal to a fifth preset threshold are taken as target smart home devices.

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

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

[0068] A second partition is selected from a target partition where no load imbalance occurs, and network configuration information corresponding to the second partition is obtained, and the target smart home device is connected to the second partition according to the network configuration information.

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

[0070] In response to a network switching instruction for the target smart home device, if the target smart home device is currently in a data transmission state, controlling the target smart home device to suspend data transmission;

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

[0072] Some possible implementations also include:

[0073] A state recovery module is used to control the target smart home device to recover the data transmission state 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 includes smart home devices bound to each virtual partition, and the apparatus further includes:

[0075] A list updating module is used to update the partition device list 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 to:

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

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

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

[0080] Receiving a network device configuration request for the smart home network sent by a user terminal;

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

[0082] Receiving a target voice for the verification text sent by the user terminal, wherein the target voice is a voice collected by the user terminal in response to a user performing voice input for the verification text;

[0083] Perform identity authentication according to the target voice to obtain a verification result for the target voice;

[0084] If the verification result indicates that the identity authentication of the user is passed, then in response to a voice query instruction for a smart home network, an instruction type corresponding to the voice query instruction is obtained.

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

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

[0087] Matching the voice feature with a preset personal voice model and calculating a similarity score corresponding to the target voice;

[0088] If the similarity score is greater than or equal to a sixth preset threshold, generating verification pass information for the target voice;

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

[0090] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

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

[0092] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0093] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

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

[0095] In an embodiment of the present invention, it can be applied to a smart home network, which can include several virtual partitions. In the process of network management of smart home devices in the smart home network, the user can input a voice query instruction for the smart home network, and the system can obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from several virtual partitions according to the instruction type, and obtain the network resource load corresponding to each target partition. 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 determined, and a network resource allocation operation is performed for the target smart home device. Therefore, in the process of managing the smart home network, on the one hand, the user realizes intelligent scheduling based on semantic interactive control, which reduces the management difficulty of the user. On the other hand, load balancing detection is performed according to the instruction type of the voice instruction input by the user, and when a load imbalance is detected, a targeted network resource allocation operation is performed, which reduces the probability of network congestion and improves the utilization rate of network resources. At the same time, the stable operation of the smart home devices can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0097] Figure 2 is a flow chart of voice control provided in an embodiment of the present invention;

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

[0099] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0100] As an example, different smart home devices have different demands for network resources. In the process of allocating network resources to smart home devices, an imbalance in network resource allocation is likely to occur, which may lead to problems such as network congestion and resource waste.

[0101] In this regard, in the present invention, voice interactive control is introduced. In the process of network management of smart home devices in the smart home network, the user can input a voice query instruction for the smart home network. The system can obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from several virtual partitions according to the instruction type, and obtain the network resource load corresponding to each target partition. If the network resource load characterizes that the target partition has a load imbalance, the target smart home device in the first partition where the load imbalance occurs is determined, and the network resource allocation operation for the target smart home device is performed. Therefore, in the process of managing the smart home network, on the one hand, the user realizes intelligent scheduling based on semantic interactive control, which reduces the management difficulty of the user. On the other hand, load balancing detection is performed according to the instruction type of the voice instruction input by the user, and when a load imbalance is detected, a targeted network resource allocation operation is performed, which reduces the probability of network congestion and improves the utilization rate of network resources. At the same time, the stable operation of the smart home devices can be guaranteed.

[0102] Reference Figure 1 , shows a flowchart of a processing method for a smart home network provided in an embodiment of the present invention, which is applied to a smart home network, wherein the smart home network includes several virtual partitions, and specifically may include the following steps:

[0103] Step 101, in response to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type;

[0104] For a smart home network, it can be a network composed of network management devices, smart home devices, and user terminals. The network management device can be responsible for functions related to network management in the smart home network, and can serve as a hub for data interaction between different smart home devices and between user terminals and smart home devices; smart home devices are devices for executing smart home functions; user terminals are devices that interact with users, and users can manage and control smart home devices through user terminals. In the smart home network, partitions can be made based on device functions, security levels, physical locations, network traffic and bandwidth requirements, user roles, time sensitivity, device types, data sensitivity, automation scenarios, and network protocols, thereby dividing the smart home network into several virtual partitions.

[0105] Based on device function: partition according to the function type of the device. For example:

[0106] Entertainment equipment: smart TV, speakers, game consoles, etc.

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

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

[0109] Health equipment: smart weight scale, smart blood pressure monitor, etc.

[0110] Advantages: It is easy to manage and optimize devices with specific functions and reduce interference between devices with different functions.

[0111] Based on security requirements: partition according to the security level of the device. For example:

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

[0113] Medium security level: smart home appliances used in daily life, such as smart light bulbs, smart sockets, etc.

[0114] Low security level: entertainment devices such as smart speakers, smart TVs, etc.

[0115] Advantages: Protect high-security level devices through different security policies (such as encryption and access control) to reduce security risks.

[0116] Based on device location: Partition based on the physical location of the device. For example:

[0117] Living room: smart TV, smart speakers, smart lamps, etc.

[0118] Bedroom: smart lamps, smart thermostats, smart curtains, etc.

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

[0120] Outdoor: smart cameras, smart irrigation systems, etc.

[0121] Advantages: Facilitates device management based on location, optimizes network performance, and reduces cross-regional network latency.

[0122] Based on user roles: partition according to the permissions and needs of different users. For example:

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

[0124] Family members: can access devices used in daily life, such as smart lamps, smart appliances, etc.

[0125] Guest: Can only access specific entertainment devices, such as smart TVs, smart speakers, etc.

[0126] Advantages: Access rights are controlled through user roles, ensuring that different users can only access the devices they need, improving security and privacy protection.

[0127] Based on the device's demand for network bandwidth: partition based on the device's demand for network bandwidth. For example:

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

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

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

[0131] Advantages: Through reasonable bandwidth allocation, it ensures that devices with high bandwidth requirements can obtain sufficient network resources and avoid network congestion.

[0132] Based on the time sensitivity of the device: Partition according to the real-time requirements of the device. For example:

[0133] High real-time requirements: security equipment (such as cameras, door locks), 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 in a timely manner and avoid delays.

[0136] By device type: partition according to the device type. For example:

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

[0138] Traditional equipment: non-intelligent traditional home appliances, such as ordinary TVs, air conditioners, etc.

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

[0140] Advantages: It is easy to manage and optimize different types of devices and ensure the security and stability of IoT devices.

[0141] Based on data sensitivity: Partition based on the type of data processed by the device. For example:

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

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

[0144] Advantages: Ensure the privacy and security of highly sensitive data through different data protection strategies.

[0145] Based on automation scenarios: partition according to different automation scenarios. For example:

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

[0147] Entertainment scene: involves the automated control of entertainment equipment.

[0148] Energy-saving scenario: involves automated control of environmental control equipment.

[0149] Based on the network protocol used by the device: partition based on the network protocol used by the device. For example:

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

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

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

[0153] Bluetooth devices: smart watches, health monitoring devices, etc.

[0154] Through reasonable partitioning, the security, performance, and manageability of the network can be improved while meeting the needs of different users and devices.

[0155] In an embodiment of the present invention, during the process of network management of smart home devices in a smart home network, a user can input a corresponding voice query command to query the network status corresponding to the smart home device. 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 interactive control, intelligent scheduling is realized, reducing the management difficulty of the user.

[0156] Among them, the instruction type of the voice query instruction input by the user may include a specified partition type and an overall query type. The specified partition type represents a network query on a specified partition in a virtual partition, and the overall query type represents a network query on all virtual partitions. Specifically, if the instruction type is a specified partition type, a target partition corresponding to the voice query instruction is selected from several virtual partitions; if the instruction type is an overall query type, all virtual partitions are taken as target partitions.

[0157] For example, assuming that the user divides the entire smart home network into virtual partition ①, virtual partition ②, virtual partition ③ and virtual partition ④, then for the specified partition type, the user can specify in the voice query command to query the network status of one or more partitions among virtual partition ①, virtual partition ②, virtual partition ③ and virtual partition ④, and for the overall query type, the network status corresponding to all partitions such as virtual partition ①, virtual partition ②, virtual partition ③ and virtual partition ④ is queried. Therefore, different network management can be achieved based on different voice commands, thereby improving the management flexibility of the smart home network.

[0158] In some feasible implementations, before formally querying the network status, the network management device may first verify whether the user's identity is legitimate, and only respond to the voice query command input by the user when the user's identity is verified to be legitimate. Optionally, the network management device may receive a network device configuration request for a smart home network sent by a user terminal, and then randomly generate a verification text for the network device configuration request, and send the verification text to the user terminal. After receiving the verification text, the user terminal may output the verification text and the corresponding verification prompt information, prompt the user to input a voice corresponding to the verification text through the verification prompt information, collect the voice input by the user, and send the collection result to the network management device. When the network management device receives the target voice for the verification text sent by the user terminal, it may perform identity authentication according to the target voice, obtain a verification result for the target voice, and if the verification result indicates that the user's identity authentication is passed, then in response to the voice query command for the smart home network, obtain the instruction type corresponding to the voice query command.

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

[0160] In one example, assuming that the voice recognition system in the smart home network is used to verify the identity of the user in order to query the network load comparison of each partition, the network management device performs voice verification through the following steps:

[0161] 1. Speech feature extraction

[0162] Target voice: User A said "Query 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 features (such as fundamental frequency, formant, MFCC, etc.).

[0165] Speaking speed, pitch, clarity of pronunciation, etc.

[0166] Example of extracted speech features:

[0167] Base frequency: 220Hz

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

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

[0170] Speech speed: 110 words per minute

[0171] 2. Matching personal voice model

[0172] Preset personal voice model: The network management device has stored the voice model of user A, which contains his unique voice features.

[0173] Matching process: Match the extracted target speech features with the speech model of user A.

[0174] Matching example:

[0175] Fundamental frequency matching: 92%

[0176] Formant matching: 88%

[0177] MFCC matching: 90%

[0178] Speech speed matching: 95%

[0179] 3. Calculate similarity score

[0180] Similarity score calculation: The matching results are comprehensively calculated into a similarity score.

[0181] Calculation formula:

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

[0183] Assume the weight distribution is as follows:

[0184] Base 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. Verification result generation

[0190] Preset threshold: Assume that the verification pass threshold set by the system is 85%.

[0191] Verification results:

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

[0193] If the similarity score is <85%, a verification failure message is generated.

[0194] Example results:

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

[0196] Generate verification pass information: "User A's voice verification has passed, and you are allowed to query the network load comparison of each partition."

[0197] Through the above process, when the user wants to manage the network of smart home devices, he can enter the corresponding voice command. The network management device authenticates the voice command. If the authentication is successful, it can further respond to the voice command to feedback the corresponding network status to the user.

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

[0199] After the target partitions are determined, the network management device may obtain the network resource loads corresponding to the target partitions. The network resource loads may represent the network states corresponding to the target partitions.

[0200] The network resource load may include at least the number of smart home devices using the smart home network in the target partition, the network bandwidth occupancy rate corresponding to the target partition, and the network delay corresponding to the target partition, etc. For example, when the number of smart home devices using the smart home network in the target partition is larger, the network bandwidth occupancy rate is larger, the network delay is larger, etc., it can be determined that the target partition is in an unbalanced load state, otherwise, it can be determined that the target partition is in a balanced load state.

[0201] Step 103: If the network resource load indicates that the target partition has a load imbalance, a target smart home device in the first partition where the load imbalance occurs is determined, and a network resource allocation operation is performed 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 determined, and the network resource allocation operation for the target smart home device is performed. In the process of managing the smart home network, on the one hand, users can realize intelligent scheduling based on semantic interactive control, which reduces the difficulty of user management. On the other hand, load balancing detection is performed according to the command type of the voice command input by the user, and when a load imbalance is detected, targeted network resource allocation operations are performed, which reduces the probability of network congestion, improves the utilization rate of network resources, and ensures the stable operation of smart home devices.

[0203] In an optional implementation, the network resource load includes the number of smart home devices that are 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, and then use the target partition where the number of devices is greater than or equal to a preset threshold and / or the difference in the number of devices is greater than or equal to the preset threshold as the first partition where load imbalance occurs, and then obtain the occupied network bandwidth corresponding to each first smart home device in the first partition, and then select the target smart home device from the first smart home devices according to the level of occupied network bandwidth.

[0204] In another optional implementation, the network resource load includes the network bandwidth occupancy rate corresponding to the target partition. The network management device may take the target partition whose network bandwidth occupancy rate is greater than or equal to a preset threshold as the first partition where load imbalance occurs, then obtain the occupied network bandwidth corresponding to each first smart home device in the first partition, and then select the target smart home device from the first smart home devices according to the level of occupied network bandwidth.

[0205] In another optional implementation, the network resource load includes the network delay corresponding to the target partition. The network management device can use the target partition whose network delay is greater than or equal to a preset threshold as the first partition where load imbalance occurs, and then obtain the occupied network bandwidth corresponding to each first smart home device in the first partition, and then select the target smart home device from the first smart home devices according to the occupied network bandwidth.

[0206] Optionally, when it is determined that a load imbalance occurs in the target partition, the network management device may take a smart home device that occupies a network bandwidth greater than or equal to a preset threshold as a target smart home device.

[0207] For some examples, let's assume that the smart home network is divided into 4 partitions:

[0208] Partition 1 (living room): smart TV, smart speakers, smart lamps;

[0209] Partition 2 (bedroom): smart lamps, smart thermostats, smart curtains;

[0210] Partition 3 (kitchen): smart refrigerator, smart oven, smart socket;

[0211] Zone 4 (outdoor): smart cameras, smart irrigation systems.

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

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

[0214] Number of devices:

[0215] Partition 1: 3 devices (smart TV, smart speaker, smart lamp).

[0216] Partition 2: 3 devices (smart lamps, smart thermostats, smart curtains).

[0217] Partition 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 the partitions:

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

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

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

[0223] Then, identify the partitions that are unbalanced in load:

[0224] Preset threshold: Device quantity difference ≥ 1.

[0225] Result: The difference in the number of devices among 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] After the first partition with unbalanced load is determined, the network bandwidth occupied by the devices in the first partition can be further obtained:

[0227] Partition 1 (Living Room):

[0228] Smart TV: 50Mbps.

[0229] Smart speakers: 5 Mbps.

[0230] Smart lighting: 1Mbps.

[0231] Zone 2 (Bedroom):

[0232] Smart lighting: 1Mbps.

[0233] Smart thermostat: 2Mbps.

[0234] Smart curtains: 1Mbps.

[0235] Zone 3 (Kitchen):

[0236] Smart refrigerator: 20Mbps.

[0237] Smart oven: 10Mbps.

[0238] Smart socket: 5Mbps.

[0239] Based on the above data, we can select the target smart home devices according to the network bandwidth occupied:

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

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

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

[0243] That is, smart TVs, smart thermostats, and smart refrigerators need to be network adjusted.

[0244] When the network management device determines whether load imbalance occurs based on the network bandwidth usage:

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

[0246] Network bandwidth usage:

[0247] Partition 1: 80%.

[0248] Partition 2: 30%.

[0249] Partition 3: 60%.

[0250] Partition 4: 10%.

[0251] Next, identify the partitions that are unbalanced in load:

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

[0253] Result: Partition 1 (80%) and Partition 3 (60%) are the first partitions where load imbalance occurs.

[0254] After the first partition with unbalanced load is determined, the network bandwidth occupied by the devices in the first partition can be further obtained:

[0255] Partition 1 (Living Room):

[0256] Smart TV: 50Mbps.

[0257] Smart speakers: 5 Mbps.

[0258] Smart lighting: 1Mbps.

[0259] Zone 3 (Kitchen):

[0260] Smart refrigerator: 20Mbps.

[0261] Smart oven: 10Mbps.

[0262] Smart socket: 5Mbps.

[0263] Based on the above parameters, you can select the target smart home devices according to the network bandwidth occupied:

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

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

[0266] That is, smart TVs and smart refrigerators need to make network adjustments.

[0267] When the network management device determines whether load imbalance occurs based on network delay:

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

[0269] Network delay:

[0270] Partition 1: 100ms.

[0271] Partition 2: 50ms.

[0272] Partition 3: 80ms.

[0273] Partition 4: 20ms.

[0274] Next, you can identify the partitions that are unbalanced in load:

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

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

[0277] After the first partition with unbalanced load is determined, the network bandwidth occupied by the devices in the first partition can be further obtained:

[0278] Partition 1 (Living Room):

[0279] Smart TV: 50Mbps.

[0280] Smart speakers: 5 Mbps.

[0281] Smart lighting: 1Mbps.

[0282] Based on the above parameters, you can select the target smart home devices according to the network bandwidth occupied:

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

[0284] That is, make network adjustments to the smart TV.

[0285] Through the above example, when the user queries the network load status corresponding to the smart home network through voice query commands, the network management device can identify whether there are corresponding load-unbalanced partitions in the smart home network based on different network resource loads, and perform network optimization on the smart home devices in the load-unbalanced partitions, thereby optimizing the load balancing of the smart home network and improving network performance and user experience.

[0286] When it is determined that network adjustment of the target smart home device is required, 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 load imbalance has never occurred, and 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 a specific implementation, during the network switching process, the network management device can respond to the network switching instruction 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 suspend data transmission, so that the target smart home device is in an idle state. Then, 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, thereby realizing 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, which can not only ensure the stability of the operation of the smart home device, but also ensure the full utilization of network resources. In addition, during the network switching process, by adjusting the device status of the smart home device, the stability and integrity of data transmission can be effectively guaranteed.

[0288] Furthermore, the network management device may also control the target smart home device to resume the data transmission state in response to the target smart home device accessing the second partition. Optionally, the smart home network is configured with a partition device list for the virtual partition, and the partition device list includes smart home devices bound to each virtual partition. The network management device may 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 above examples. It is understandable that those skilled in the art can also make settings according to actual needs under the guidance of the ideas of the embodiments of the present invention, and the present invention is not limited to this.

[0290] In an embodiment of the present invention, it can be applied to a smart home network, which can include several virtual partitions. In the process of network management of smart home devices in the smart home network, the user can input a voice query instruction for the smart home network, and the system can obtain the instruction type corresponding to the voice query instruction, and determine the corresponding target partition from several virtual partitions according to the instruction type, and obtain the network resource load corresponding to each target partition. 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 determined, and a network resource allocation operation is performed for the target smart home device. Therefore, in the process of managing the smart home network, on the one hand, the user realizes intelligent scheduling based on semantic interactive control, which reduces the management difficulty of the user. On the other hand, load balancing detection is performed according to the instruction type of the voice instruction input by the user, and when a load imbalance is detected, a targeted network resource allocation operation is performed, which reduces the probability of network congestion and improves the utilization rate of network resources. At the same time, the stable operation of the smart home devices can be guaranteed.

[0291] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the following examples are used for exemplary description:

[0292] 1. User registration and voice model establishment

[0293] a) Users register their identity through a mobile application or web interface.

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

[0295] c) Preprocessing the collected speech samples, including noise reduction, frame division and windowing.

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

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

[0298] f) The speech model is securely stored in a server or local device.

[0299] 2. Network configuration request and voice collection

[0300] a) The user initiates a network device configuration request through a mobile application.

[0301] b) The system randomly generates a text and asks the user to read it for identity verification.

[0302] c) Collect user voice in real time through the device microphone.

[0303] 3. Speech feature extraction and matching

[0304] a) Preprocess the real-time collected speech, the same as the registration stage.

[0305] b) Extract speech features, making sure to use the same feature extraction method as in the registration phase.

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

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

[0308] 4. Identity verification and network configuration authorization

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

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

[0311] c) For users who have passed the verification, they are authorized to perform network device configuration operations.

[0312] d) In the event of authentication failure, the user may be allowed to retry or another form of authentication may be required.

[0313] 5. Security Enhancements

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

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

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

[0317] 6. Network configuration operation and logging

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

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

[0320] In the above process, the complete 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, referring to Figure 2 , showing a flow chart of voice control provided in an embodiment of the present invention. When the user inputs a corresponding user 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 loads corresponding to each partition, and then compare the load conditions 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, and then 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 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.

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

[0324] The instruction processing module 301 is used to respond to the voice query instruction for the 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] A 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 load imbalance occurs if the network resource load indicates that the target partition has a load imbalance, and perform a network resource allocation operation for the target smart home device.

[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 to:

[0328] Calculate the difference in the number of devices between the target partitions according to 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 used as the first partition where load imbalance occurs;

[0330] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0331] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0333] The target partition whose network bandwidth occupancy rate is greater than or equal to the third preset threshold is used as the first partition where load imbalance occurs;

[0334] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0335] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0337] The target partition where the network delay is greater than or equal to the fourth preset threshold is used as the first partition where the load imbalance occurs;

[0338] Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition;

[0339] A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

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

[0341] The smart home devices that occupy a network bandwidth greater than or equal to a fifth preset threshold are taken as target smart home devices.

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

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

[0344] A second partition is selected from a target partition where no load imbalance occurs, and network configuration information corresponding to the second partition is obtained, and the target smart home device is connected to the second partition according to the network configuration information.

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

[0346] In response to a network switching instruction for the target smart home device, if the target smart home device is currently in a data transmission state, controlling the target smart home device to suspend data transmission;

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

[0348] Some possible implementations also include:

[0349] A state recovery module is used to control the target smart home device to recover the data transmission state 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 includes smart home devices bound to each virtual partition, and the apparatus further includes:

[0351] A list updating module is used to update the partition device list 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 to:

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

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

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

[0356] Receiving a network device configuration request for the smart home network sent by a user terminal;

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

[0358] Receiving a target voice for the verification text sent by the user terminal, wherein the target voice is a voice collected by the user terminal in response to a user performing voice input for the verification text;

[0359] Perform identity authentication according to the target voice to obtain a verification result for the target voice;

[0360] If the verification result indicates that the identity authentication of the user is passed, then in response to a voice query instruction for a smart home network, an instruction type corresponding to the voice query instruction is obtained.

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

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

[0363] Matching the voice feature with a preset personal voice model and calculating a similarity score corresponding to the target voice;

[0364] If the similarity score is greater than or equal to a sixth preset threshold, generating verification pass information for the target voice;

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

[0366] 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.

[0367] In addition, an embodiment of the present invention further 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, the various processes of the above-mentioned smart home network processing method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0368] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned smart home network processing method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0369] 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.

[0370] It should be understood by those skilled in the art that the embodiments of 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, EEPROM, Flash, and eMMC, etc.) containing computer-usable program codes.

[0371] 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.

[0372] 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.

[0373] 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 A step that specifies a function in one or more boxes.

[0374] 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.

[0375] 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 "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0376] The above is a detailed introduction to a processing method for a smart home network and a processing device for a smart home network provided by the present invention. Specific examples are used herein 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 those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A processing method for a smart home network, characterized in that: Applied to a smart home network, the smart home network includes a plurality of virtual partitions, the method comprising: In response to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type; Obtaining the network resource load corresponding to each of the target partitions; If the network resource load indicates that the target partition has a load imbalance, a target smart home device in the first partition where the load imbalance occurs is determined, 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 in the target partition that are using the smart home network. If the network resource load indicates that the target partition has a load imbalance, determining the target smart home device in the first partition where the load imbalance occurs includes: Calculate the difference in the number of devices between the target partitions according to 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 used as the first partition where load imbalance occurs; Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition; A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

3. The method according to claim 1, characterized in that The network resource load includes a network bandwidth occupancy rate corresponding to the target partition, and if the network resource load indicates that the target partition has a load imbalance, determining a target smart home device in a first partition where the load imbalance occurs includes: The target partition whose network bandwidth occupancy rate is greater than or equal to the third preset threshold is used as the first partition where load imbalance occurs; Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition; A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

4. The method according to claim 1, characterized in that: The network resource load includes a network delay corresponding to the target partition, and if the network resource load indicates that the target partition has a load imbalance, determining a target smart home device in a first partition where the load imbalance occurs includes: The target partition where the network delay is greater than or equal to the fourth preset threshold is used as the first partition where the load imbalance occurs; Obtaining the occupied network bandwidth corresponding to each first smart home device in the first partition; A target smart home device is selected from the first smart home devices according to the level of occupied network bandwidth.

5. The method according to any one of claims 2 to 4, characterized in that: The selecting a target smart home device from the first smart home devices according to the level of occupied network bandwidth includes: The smart home devices that occupy a network bandwidth greater than or equal to a fifth preset threshold are taken as target smart home devices.

6. The method according to claim 5, characterized in that The performing of the network resource allocation operation for the target smart home device includes: Unbinding the target smart home device from the network configuration of the first partition; A second partition is selected from a target partition where no load imbalance occurs, and network configuration information corresponding to the second partition is obtained, and the target smart home device is connected to the second partition according to the network configuration information.

7. The method according to claim 6, characterized in that The step of connecting the target smart home device to the second partition according to the network configuration information includes: In response to a network switching instruction for the target smart home device, if the target smart home device is currently in a data transmission state, controlling the target smart home device to suspend data transmission; In response to the target smart home device being in an idle state, connecting the target smart home device 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, controlling the target smart home device 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 includes 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 partition device list 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, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type includes: If the instruction type is the specified partition type, selecting the target partition corresponding to the voice query instruction from the plurality of virtual partitions; If the instruction type is the overall query type, all the virtual partitions are taken as target partitions.

11. The method according to claim 1 or 10, characterized in that: The step of obtaining, in response to a voice query instruction for a smart home network, an instruction type corresponding to the voice query instruction includes: Receiving a network device configuration request for the smart home network sent by a user terminal; Randomly generate a verification text for the network device configuration request; Receiving a target voice for the verification text sent by the user terminal, wherein the target voice is a voice collected by the user terminal in response to a user performing voice input for the verification text; Perform identity authentication according to the target voice to obtain a verification result for the target voice; If the verification result indicates that the identity authentication of the user is passed, then in response to a voice query instruction for a smart home network, an instruction type corresponding to the voice query instruction is obtained.

12. The method according to claim 11, characterized in that The verification result includes one of verification pass information and verification fail information, and the identity authentication is performed according to the target voice to obtain the verification result for the target voice, including: Extracting speech features corresponding to the target speech; Matching the voice feature with a preset personal voice model and calculating a similarity score corresponding to the target voice; If the similarity score is greater than or equal to a sixth preset threshold, generating verification pass information for the target voice; If the similarity score is less than the sixth preset threshold, verification failure information for the target voice is generated.

13. A processing device for a smart home network, characterized in that: Applied to a smart home network, the smart home network includes several virtual partitions, and the device includes: An instruction processing module, for responding to a voice query instruction for a smart home network, obtaining an instruction type corresponding to the voice query instruction, and determining a corresponding target partition from the plurality of virtual partitions according to the instruction type; A load acquisition module, used to acquire the network resource load corresponding to each of the target partitions; The device processing module is used to determine the target smart home device in the first partition where the load imbalance occurs if the network resource load indicates that the target partition has a load imbalance, 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; The processor is used to implement the method according to any one of claims 1 to 12 when executing the program stored in the memory.

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

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