Fault recovery system for smart home networks, related methods and products

Abstract

The application provides a fault recovery system of a smart home network, a related method and a product, and the fault recovery system comprises: a voice interaction and intelligent analysis module, which is used for triggering fault detection on a smart home device to be detected in the plurality of smart home devices in response to a user voice instruction; a fault positioning module, which is used for acquiring a first performance parameter of the smart home device to be detected, comparing the first performance parameter with a first dynamic performance profile set for each smart home device to be detected, and determining a target fault smart home device based on a comparison result; a fault recovery module, which determines a first fault adjustment strategy for the target fault smart home device; and performing fault recovery on the target fault smart home device based on the first fault adjustment strategy. Through the application, efficient processing of smart home network faults can be realized.

Description

Technical Field

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

[0002] With the increasing popularity of smart home devices, more and more of them are connecting to home networks. However, network or device malfunctions can affect the normal operation of smart homes. Traditional troubleshooting and recovery require professional personnel or management through complex devices such as mobile phones and computers, which have a high barrier to entry and are difficult for users without network technical knowledge. Summary of the Invention

[0003] In view of the above problems, a fault recovery system for a smart home network, a fault recovery method for a smart home network, an electronic device, and a computer-readable storage medium are proposed to overcome or at least partially solve the above problems, comprising:

[0004] A fault recovery system for a smart home network, the smart home network including multiple smart home devices, the fault recovery system comprising:

[0005] The voice interaction and intelligent analysis module is used to respond to user voice commands and trigger fault detection for the smart home device to be detected among the multiple smart home devices;

[0006] The fault location module is used to acquire the first performance parameters of the smart home device to be tested, compare them with the first dynamic performance profile set for each smart home device to be tested, and determine the target faulty smart home device based on the comparison result; the first dynamic performance profile records the performance parameters of the smart home device to be tested during normal operation.

[0007] The fault recovery module determines a first fault adjustment strategy for the target faulty smart home device; and performs fault recovery on the target faulty smart home device based on the first fault adjustment strategy.

[0008] Optionally, the voice interaction and intelligent analysis module is used to analyze the user's voice commands to obtain voice command analysis results; when the voice command analysis results point to at least one smart home device, the at least one smart home device is used as the smart home device to be detected; when the voice command analysis results do not point to a smart home device, the multiple smart home devices are used as the smart home devices to be detected.

[0009] Optionally, the fault location module is further configured to monitor each smart home device according to a preset time interval; when a fault event is detected, it acquires the second performance parameters of the smart home device associated with the fault event and compares them with the second dynamic performance profile set for the smart home device associated with the fault event.

[0010] Optionally, the fault recovery module is configured to use a second fault adjustment strategy to recover the target faulty smart home device when the fault recovery based on the first fault adjustment strategy fails.

[0011] Optionally, the voice interaction and intelligent analysis module is also used to display the recovery progress to the user.

[0012] Optionally, the fault recovery system further includes:

[0013] The network dynamic adjustment module is used to dynamically adjust the network of the target faulty smart home device based on the severity and scope of the fault.

[0014] Optionally, the fault recovery system further includes:

[0015] The data learning and optimization module optimizes the analysis of voice commands in the voice interaction and intelligent analysis module based on the fault recovery process and results of the fault recovery module; optimizes the method by which the fault location module determines the target faulty smart home device based on the fault recovery process and results of the fault recovery module; and optimizes the logic of the fault recovery module in determining the fault adjustment strategy based on the fault recovery process and results of the fault recovery module.

[0016] Optionally, the fault recovery module is further configured to isolate the target faulty smart home device from other smart home devices; and, when the target faulty smart home device returns to normal, to cancel the isolation between the target faulty smart home device and the other smart home devices.

[0017] Optionally, the performance parameters of the smart home device under test recorded in the first dynamic performance profile during normal operation include at least one of the following:

[0018] The normal signal strength range of the smart home device to be tested, the normal response time benchmark of the smart home device to be tested, and the normal network traffic characteristics of the smart home device to be tested.

[0019] This invention also provides a fault recovery method for a smart home network, applied to the fault recovery system described above, the method comprising:

[0020] Identify the target faulty smart home device and determine a first fault adjustment strategy for the target faulty smart home device;

[0021] Based on the first fault adjustment strategy, the target faulty smart home device is restored.

[0022] This invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the above-described method for fault recovery of a smart home network.

[0023] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for fault recovery of a smart home network.

[0024] The embodiments of the present invention have the following advantages:

[0025] In this embodiment of the invention, the fault recovery system includes: a voice interaction and intelligent analysis module, used to respond to user voice commands and trigger fault detection for a smart home device to be tested among multiple smart home devices; a fault location module, used to acquire first performance parameters of the smart home device to be tested, compare them with a first dynamic performance profile set for each smart home device to be tested, and determine the target faulty smart home device based on the comparison result; the first dynamic performance profile records the performance parameters of the smart home device to be tested during normal operation; and a fault recovery module, used to determine a first fault adjustment strategy for the target faulty smart home device; and to perform fault recovery on the target faulty smart home device based on the first fault adjustment strategy. Through this embodiment of the invention, efficient handling of smart home network faults can be achieved. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the structure of a fault recovery system according to an embodiment of the present invention;

[0028] Figure 2This is a schematic diagram of another fault recovery system according to an embodiment of the present invention;

[0029] Figure 3 This is a flowchart illustrating the steps of detecting and restoring a smart home network according to an embodiment of the present invention;

[0030] Figure 4 This is a flowchart illustrating the steps of a fault recovery method for a smart home network according to an embodiment of the present invention. Detailed Implementation

[0031] 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. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0032] Traditional smart home network fault detection usually requires professional technicians or complex operating procedures, making it difficult for ordinary users to troubleshoot and locate network faults on their own.

[0033] When smart home networks malfunction, it often requires manual troubleshooting and restoration of each device, which is inefficient. Network troubleshooting generally relies on devices such as mobile phones and computers, and the process is cumbersome and not user-friendly for those unfamiliar with network technology. Existing systems lack real-time capabilities in detecting and handling network faults and cannot provide timely feedback.

[0034] To address this, embodiments of the present invention provide a fault recovery system for smart home networks. This fault recovery system may include a voice interaction and intelligent analysis module for receiving user commands, a fault location module for automatically locating faulty devices, and a fault recovery module for automatically performing fault recovery. Through the fault recovery system mentioned in these embodiments, efficient handling of smart home network faults can be achieved.

[0035] For specific details, please refer to Figure 1 , Figure 1 A schematic diagram of a fault recovery system according to an embodiment of the present invention is shown.

[0036] like Figure 1 As shown, the fault recovery system 10 may include a voice interaction and intelligent analysis module 110, which is used to trigger fault detection for the smart home device to be detected among multiple smart home devices in response to a user's voice command.

[0037] The fault location module 120 is used to acquire the first performance parameters of the smart home device to be tested, compare them with the first dynamic performance profile set for each smart home device to be tested, and determine the target faulty smart home device based on the comparison result; the first dynamic performance profile records the performance parameters of the smart home device to be tested during normal operation.

[0038] The fault recovery module 130 determines a first fault adjustment strategy for the target faulty smart home device; and performs fault recovery on the target faulty smart home device based on the first fault adjustment strategy.

[0039] In practical applications, the fault recovery system 10 can be a system for fault diagnosis and fault recovery of smart home networks; the smart home network can be composed of multiple smart home devices, which can include smart home devices that provide smart services, or auxiliary devices that provide network and connectivity for each smart home device. This embodiment of the invention does not limit this.

[0040] For example, a smart home network can consist of multiple components, including smart home devices, a smart home hub, network infrastructure, a cloud service platform, mobile applications, sensors and actuators, and a security system. These components work together to enable interconnection, remote control, and automated operation of smart home devices.

[0041] Smart home devices refer to household appliances that can be connected to a network and controlled automatically, such as smart light bulbs, smart sockets, smart door locks, smart cameras, and smart thermostats. These devices can connect to a smart home system via wireless or wired networks to enable remote control, automated operation, and data collection.

[0042] A smart home hub is a core device, typically a physical device or software platform, responsible for managing and coordinating communication and operation between various smart home devices. The smart home hub can receive signals from devices, process data, and control other devices according to preset rules or user commands.

[0043] Network infrastructure includes devices such as routers, switches, and gateways, which provide the infrastructure for network connectivity and data transmission. These devices ensure that smart home devices can connect to the internet via wireless protocols such as Wi-Fi, Zigbee, Z-Wave, and Bluetooth, or wired protocols such as Ethernet, and enable communication between devices.

[0044] The cloud service platform is a remote server system used to store and manage data from smart home devices, and to provide remote control, data analysis, and automation services. Users can remotely control smart home devices, view device status, set automation rules, and receive data reported by the devices through the cloud service platform.

[0045] Mobile applications are the primary interface for users to interact with smart home systems, and are typically installed on smartphones or tablets. Users can control smart home devices, view device status, set automation rules, receive notifications and alarms, and more through mobile applications.

[0046] Sensors are used to detect various parameters in the environment, such as temperature, humidity, light, and motion; actuators are used to perform specific operations, such as turning lights on and off or adjusting the temperature. Sensors and actuators are the core components of smart home devices, achieving automated control through data collection and execution.

[0047] The security system, including firewalls, encryption technology, and authentication, is used to protect the security and privacy of the smart home network. It ensures that smart home devices and networks are not subject to unauthorized access and attacks, protecting user data and privacy.

[0048] In practical applications, users can activate the fault recovery system 10 for fault diagnosis and recovery through voice interaction. Specifically, users can input voice commands to the fault recovery system 10 for fault detection and recovery.

[0049] Upon receiving a user's voice command, the voice interaction and intelligent analysis module 110 can respond to the user's voice command by triggering fault detection for the smart home device to be detected among multiple smart home devices. For example, the user's voice command may include an identifier of the smart home device to be detected, and based on this identifier, the voice interaction and intelligent analysis module 110 can trigger fault detection for the smart home device to be detected.

[0050] When triggering fault detection for a smart home device to be tested, the fault location module 120 can first obtain the first performance parameters of the smart home device to be tested.

[0051] For example, the first performance parameter may include the signal strength range of the smart home device to be detected, the response time reference of the smart home device to be detected, and the network traffic characteristics of the smart home device to be detected. The first performance parameter may be the current performance parameter of the smart home device to be detected, or it may be the performance parameter of the smart home device to be detected over a period of time including the current time period. This embodiment of the invention does not limit this.

[0052] In practical applications, the fault location module 120 can also store a first dynamic performance profile set for each smart home device; the first dynamic performance profile records the performance parameters of the smart home device to be tested when it is running normally.

[0053] For example, the performance parameters of the smart home device under test recorded in the first dynamic performance profile during normal operation include at least one of the following:

[0054] The normal signal strength range of the smart home device under test, the normal response time benchmark of the smart home device under test, and the normal network traffic characteristics of the smart home device under test.

[0055] After obtaining the first performance parameters and the first dynamic performance profile of the smart home device to be tested, the fault location module 120 can compare the data in the first performance parameters with the data in the first dynamic performance profile, and based on the comparison results, determine the target faulty smart home device from among multiple smart home devices.

[0056] After identifying the target smart home device, the fault recovery module 130 can first determine a first fault adjustment strategy for the target smart home device; then, based on the first fault adjustment strategy, it can automatically perform fault recovery processing on the target faulty smart home device.

[0057] In this embodiment of the invention, the fault recovery system 10 includes: a voice interaction and intelligent analysis module 110, used to respond to a user's voice command to trigger fault detection for a smart home device to be tested among multiple smart home devices; a fault location module 120, used to acquire first performance parameters of the smart home device to be tested, compare them with a first dynamic performance profile set for each smart home device to be tested, and determine the target faulty smart home device based on the comparison result; the first dynamic performance profile records the performance parameters of the smart home device to be tested during normal operation; and a fault recovery module 130, used to determine a first fault adjustment strategy for the target faulty smart home device; and to perform fault recovery on the target faulty smart home device based on the first fault adjustment strategy. Through this embodiment of the invention, efficient handling of smart home network faults can be achieved.

[0058] In one embodiment of the present invention, the voice interaction and intelligent analysis module 110 is used to analyze the user's voice commands to obtain voice command analysis results; when the voice command analysis results point to at least one smart home device, at least one smart home device is selected as the smart home device to be detected; when the voice command analysis results do not point to a smart home device, multiple smart home devices are selected as the smart home devices to be detected.

[0059] In some feasible embodiments, after receiving a user's voice command, the voice interaction and intelligent analysis module 110 can first analyze the user's voice command to obtain the voice command analysis result.

[0060] If the voice command analysis results point to at least one identified smart home device, then that at least one smart home device can be used as the smart home device to be detected.

[0061] Conversely, if the voice command analysis result is ambiguous and does not point to a specific smart home device, then multiple smart home devices can be considered as the smart home devices to be tested.

[0062] For example, users can issue commands at any time via a smart voice assistant or speaker, such as "check the smart home network" or "check the status of faulty devices." The voice interaction and intelligent analysis module 110 quickly converts the voice command into text and initiates the intelligent analysis program. This program analyzes the specific intent of the user's command. If the user's command is vague, such as "check it," the fault recovery system 10 will perform a comprehensive smart home network scan; if the user explicitly specifies a device, such as "check the network connection of the smart TV," the fault recovery system 10 will specifically test that device.

[0063] In one embodiment of the present invention, the fault location module 120 is further configured to monitor each smart home device according to a preset time interval; when a fault event is detected, it acquires the second performance parameters of the smart home device associated with the fault event and compares them with the second dynamic performance profile set for the smart home device associated with the fault event.

[0064] In some feasible embodiments, the fault location module 120 can also automatically and continuously monitor each smart home device according to a preset time interval. During the monitoring process, if a fault event is detected in the smart home network, such as a device losing network connection or a device reporting an error, the second performance parameter of the smart home device associated with the fault event can be obtained, and the second performance parameter can be compared with the second dynamic performance profile set for the smart home device associated with the fault event.

[0065] The second dynamic performance profile and the first dynamic performance profile are dynamic performance profiles for different smart home devices. The performance parameters of the smart home devices during normal operation associated with the fault events recorded in the second dynamic performance profile include at least one of the following:

[0066] The normal signal strength range of smart home devices associated with the fault event, the normal response time benchmark of smart home devices associated with the fault event, and the normal network traffic characteristics of smart home devices associated with the fault event.

[0067] Based on the comparison results, the fault location module 120 can locate the target faulty smart home device, and the fault recovery module 130 can perform fault recovery. The logic of location and fault recovery is similar to that of the aforementioned embodiments, and will not be repeated here.

[0068] In one embodiment of the present invention, the fault recovery module 130 is used to perform fault recovery on the target faulty smart home device by adopting a second fault adjustment strategy when the fault recovery of the target faulty smart home device fails based on the first fault adjustment strategy.

[0069] In some feasible embodiments, the fault recovery module 130 can formulate a progressive recovery strategy based on the type and severity of the fault. Specifically, the fault recovery module 130 can first perform fault recovery on the target faulty smart home device based on a first fault adjustment strategy.

[0070] When the fault recovery of the target faulty smart home device fails based on the first fault adjustment strategy, the fault recovery module 130 can adopt the second fault adjustment strategy to recover the target faulty smart home device.

[0071] For example, if the problem is with device connectivity, first try a soft reboot to re-establish the network connection. If the soft reboot fails, the system will attempt to reset network parameters, such as IP (Internet Protocol) address and DNS (Domain Name System) settings. If the problem persists, the system will gradually escalate recovery measures, such as reconfiguring device parameters and updating firmware.

[0072] In one embodiment of the present invention, the voice interaction and intelligent analysis module 110 is also used to display the recovery progress to the user.

[0073] In some feasible embodiments, the voice interaction and intelligent analysis module 110 can also provide real-time feedback to the user and display the recovery progress. For example, the voice interaction and intelligent analysis module 110 can display the recovery progress to the user through voice feedback, such as "Attempting a soft reboot of the device" or "Network parameters being reset," so that the user is clear about the processing status of the fault recovery system 10, thereby increasing the user's sense of participation.

[0074] In one embodiment of the present invention, such as Figure 2 As shown, the fault recovery system 10 also includes:

[0075] The network dynamic adjustment module 140 is used to dynamically adjust the network of the target faulty smart home device according to the severity and scope of the fault.

[0076] In some feasible embodiments, the fault recovery module 130 may further include a network dynamic adjustment module 140, which can dynamically adjust the network of the target faulty smart home device according to the severity and scope of the fault.

[0077] For example, if a localized device malfunction has a minor impact on the overall smart home network, the network dynamic adjustment module 140 can adjust the Wi-Fi signal strength to enhance signal coverage near the malfunctioning smart home device, or adjust channel allocation to avoid channel interference. For minor malfunctions caused by network congestion, the system can automatically optimize network traffic allocation to ensure the normal operation of critical equipment.

[0078] For example, when multiple smart home devices simultaneously transmit high-definition video, causing network congestion, the network dynamic adjustment module 140 will automatically reduce the network bandwidth usage of some non-critical devices, prioritizing the smooth operation of video transmission devices. Simultaneously, the network dynamic adjustment module 140 will monitor the adjusted network status in real time and continuously optimize the adjustment strategy based on feedback to ensure network stability and performance.

[0079] In one embodiment of the present invention, such as Figure 2 As shown, the fault recovery system 10 also includes:

[0080] The data learning and optimization module 150 optimizes the analysis of voice commands in the voice interaction and intelligent analysis module 110 based on the fault recovery process and results of the fault recovery module 130; optimizes the method by which the fault location module 120 determines the target faulty smart home device based on the fault recovery process and results of the fault recovery module 130; and optimizes the logic of the fault recovery module 130 in determining the fault adjustment strategy based on the fault recovery process and results of the fault recovery module 130.

[0081] In some feasible embodiments, the fault recovery system 10 may further include a data learning and optimization module 150; the data learning and optimization module 150 may be used to optimize the analysis of the voice commands of the voice interaction and intelligent analysis module 110 based on the fault recovery process and the fault recovery result of the fault recovery module 130.

[0082] In addition, the data learning and optimization module 150 can also optimize the way the fault location module 120 determines the target faulty smart home device based on the fault recovery process and results of the fault recovery module 130.

[0083] In addition, the data learning and optimization module 150 can also optimize the logic of the fault recovery module 130 in determining the fault adjustment strategy based on the fault recovery process and the fault recovery result of the fault recovery module 130.

[0084] In one embodiment of the present invention, the fault recovery module 130 is further configured to isolate the target faulty smart home device from other smart home devices; and to cancel the isolation between the target faulty smart home device and other smart home devices when the target faulty smart home device returns to normal.

[0085] In some feasible embodiments, in order to prevent the target faulty smart home device from affecting other smart home devices, the fault recovery module 130 can also isolate the target faulty smart home device from other smart home devices in the network.

[0086] In addition, the fault recovery module 130 can also cancel the isolation between the target faulty smart home device and other smart home devices when the target faulty smart home device returns to normal, so that the target faulty smart home device that has returned to normal can continue to operate in the smart home network.

[0087] For example, such as Figure 3 As shown, users can first issue a user voice command to activate the fault recovery system 10 to perform fault detection and fault repair.

[0088] Users can issue commands at any time via a smart voice assistant or speaker, such as "check the smart home network" or "check the status of faulty devices." The voice interaction and intelligent analysis module 110 quickly converts the user's voice command into text and initiates the intelligent analysis program. This program analyzes the specific intent of the user's voice command. If the user's command is vague, such as "check it," the fault recovery system 10 will activate the fault location module 120 to perform a comprehensive scan of the smart home network. If the user explicitly specifies a device, such as "check the network connection of the smart TV," the fault location module 120 will activate to perform targeted testing on that device.

[0089] Meanwhile, the data learning and optimization module 150 can continuously optimize its understanding and response to commands based on user habits and historical commands, thereby improving the accuracy and efficiency of the interaction.

[0090] For example: User habit analysis and optimization:

[0091] (1) Instruction frequency analysis

[0092] The data learning and optimization module 150 records the frequency of various commands issued by the user. For example, if the user frequently issues the command "turn on the bedroom light" at night, the voice interaction and intelligent analysis module 110 can prioritize predicting and preparing to respond to such commands during specific time periods at night. When the user issues some ambiguous commands during this time period, the voice interaction and intelligent analysis module 110 can intelligently guess the probability of high-frequency commands. For example, if the user says "it's a bit dark," the voice interaction and intelligent analysis module 110 judges that it is very likely that the user wants to turn on the bedroom light, and thus proactively asks "Do you want to turn on the bedroom light?" to improve the accuracy of the response.

[0093] (2) Command Scenario Association

[0094] The system analyzes the association between user commands and specific scenarios. For example, when a user frequently issues the command "turn up the volume" while watching TV, the data learning and optimization module 150 can establish a connection between this command and the TV viewing scenario. The next time the TV is detected to be on and the user issues a vague command such as "turn the volume up," the voice interaction and intelligent analysis module 110 can accurately interpret it as turning up the TV volume, rather than adjusting the volume of other devices.

[0095] (3) Habitual adaptation adjustment

[0096] As user habits change, the voice interaction and intelligent analysis module 110 dynamically adjusts its understanding and response to commands. When a user changes the way they express certain commands, the data learning and optimization module 150 can quickly learn and adapt to the new expression. For example, if a user originally said "turn on the light" but later changed it to "turn on the light," the data learning and optimization module 150 can continuously learn from the changes in historical commands and adjust the voice interaction and intelligent analysis module 110's recognition and response to the new command in a timely manner.

[0097] Another example is historical instruction analysis and optimization:

[0098] (1) Command pattern recognition

[0099] The data learning and optimization module 150 analyzes the user's historical command sequences to identify specific command patterns. For example, if the user frequently issues consecutive command sequences such as "turn on the air conditioner" and "set the temperature to 25 degrees," the data learning and optimization module 150 can treat these two commands as a single pattern. The next time the user only issues "turn on the air conditioner," the voice interaction and intelligent analysis module 110 can automatically ask, "Do you want to set the temperature to 25 degrees?" to improve operational efficiency.

[0100] (2) Learning to correct errors in instructions

[0101] When the voice interaction and intelligent analysis module 110 misunderstands a user's command and performs an incorrect operation, it records the error and the user's correct feedback. For example, if a user issues the command "open the window," but the intelligent system incorrectly opens the curtains, after the user corrects the error, the data learning and optimization module 150 records this error and strengthens its ability to differentiate between the commands "open the window" and "open the curtains." By analyzing the characteristics of erroneous commands, such as voice similarity and contextual confusion, it improves the voice recognition algorithm and command analysis logic to reduce the occurrence of similar errors.

[0102] (3) Optimization of historical instruction feedback

[0103] Based on user feedback on the results of historical command execution, subsequent responses are optimized. For example, if a user is dissatisfied with the response speed of a certain device, the fault recovery system 10 can prioritize adjusting the network connection or device parameters of that device when executing the relevant command again, in order to improve the response speed.

[0104] For example, regarding fault recovery:

[0105] (1) Optimized execution of recovery commands: Optimization of command understanding and response ensures accurate execution of user voice commands during the fault recovery phase. For example, when a user issues the command "Try to recover the smart TV", the voice interaction and intelligent analysis module 110 can accurately parse it and initiate the recovery process for the smart TV, such as soft reboot, resetting network parameters, etc. Moreover, if the voice interaction and intelligent analysis module 110 understands the user's habits in similar fault recovery scenarios (such as soft rebooting a specific device first), it can execute the recovery operation more efficiently.

[0106] (2) Improve the efficiency of interaction during the recovery process: During the fault recovery process, the voice interaction and intelligent analysis module 110 can more effectively provide feedback on the recovery status and ask for further instructions to the user based on its understanding of the user's habits. For example, if the user is accustomed to obtaining detailed information during the recovery process, the voice interaction and intelligent analysis module 110 can report in real time and in detail that "the network parameters of the smart TV are being reset". This good interaction helps to complete the fault recovery smoothly and reduces recovery interruptions or erroneous operations caused by misunderstandings.

[0107] The fault location module 120 uses multi-dimensional detection technology to perform comprehensive detection or specific equipment detection.

[0108] Signal strength analysis determines whether there is an unstable connection between the device and the network node by measuring changes in signal strength.

[0109] Equipment response time measurement records the time it takes for the equipment to respond to commands. If the response time is significantly prolonged or there is no response, there may be a malfunction.

[0110] Network traffic monitoring can monitor the network traffic of a device in real time, and abnormal fluctuations in traffic may indicate a malfunction.

[0111] The fault location module 120 can establish a dynamic performance profile for each device. The dynamic performance profile records in detail information such as the signal strength range, response time reference, and network traffic characteristics of the smart home device during normal operation.

[0112] When it is necessary to locate a faulty smart home device, the fault location module 120 can compare the device's current performance parameters with its dynamic performance profile, thereby quickly narrowing down the scope of the fault. For example, if the signal strength of a smart camera suddenly drops below the normal range, the system will prioritize checking the connectivity of the camera and its surrounding devices.

[0113] When all devices are functioning correctly, monitoring can continue. If a device malfunction is detected, the network dynamic adjustment module 140 can first adjust the network dynamically based on the severity and scope of the malfunction. If the malfunction is localized and has a minor impact on the overall network, the network dynamic adjustment module 140 can adjust the Wi-Fi signal strength to enhance signal coverage near the malfunctioning device, or adjust channel allocation to avoid channel interference. For minor malfunctions caused by network congestion, the network dynamic adjustment module 140 can automatically optimize network traffic allocation to ensure the normal operation of critical equipment.

[0114] For example, when multiple smart devices simultaneously transmit high-definition video, causing network congestion, the network dynamic adjustment module 140 automatically reduces the network bandwidth usage of some non-critical devices, prioritizing the smooth operation of video transmission devices. Simultaneously, the network dynamic adjustment module 140 monitors the adjusted network status in real time and continuously optimizes the adjustment strategy based on feedback, ensuring network stability and performance.

[0115] When a device malfunction is detected, the fault recovery module 130 can also formulate a progressive recovery strategy based on the type and severity of the fault. If it is a device connectivity issue, it will first attempt a soft reboot of the device to re-establish the network connection. If the soft reboot is ineffective, the fault recovery module 130 will attempt to reset network parameters, such as IP address and DNS settings. If the problem persists, the fault recovery module 130 will gradually escalate the recovery measures, such as reconfiguring device parameters and updating firmware.

[0116] If the system returns to normal, the device will be re-integrated into the smart home network, and the data learning and optimization module 150 will be activated to optimize the recovery, positioning, and identification processes; if the system fails to recover, the detection will continue.

[0117] During the recovery process, the voice interaction and intelligent analysis module 110 provides real-time updates on the recovery progress to the user via voice feedback. For example, it may say things like "Attempting a soft reboot of the device" or "Network parameters are being reset," allowing the user to clearly understand the system's status. If some devices recover successfully, the network dynamic adjustment module 140 will reintegrate them into normal network operation and continue processing the unrecovered devices. If a complex fault is encountered that is difficult to recover automatically, the fault recovery module 130 will provide the user with detailed fault information and suggestions, such as "The fault is quite complex; it is recommended to contact a professional technician for repair."

[0118] The data learning and optimization module 150 records the process and results of each fault handling, including fault type, location method, and recovery strategy. Machine learning algorithms are used to analyze this data, continuously optimizing fault detection and recovery strategies. Over time, the fault recovery system 10 can more accurately predict potential faults and take preventative measures in advance.

[0119] For example, if the fault recovery system 10 detects that a device is prone to failure during a specific time period or under a specific network environment, it can issue an early warning to the user and automatically adjust network parameters or device settings to reduce the probability of failure. At the same time, the fault recovery system 10 can further optimize the interactive experience based on user feedback and usage, making voice commands more in line with user habits and needs.

[0120] The following is a specific example illustrating the implementation process of this voice-based smart home network fault accurate location and progressive recovery system:

[0121] Imagine a family with various smart home devices, such as a smart TV, smart speaker, smart camera, smart light bulb, and smart robot vacuum cleaner, all connected via the family's Wi-Fi network.

[0122] One evening, while watching a smart TV, the user experienced sudden screen freezes and then completely loses signal. The user said to the smart speaker, "Check the smart home network and see why the TV has no signal."

[0123] The voice interaction and intelligent analysis module 110 quickly recognizes the user's voice commands and initiates the fault detection process. The fault location module 120 then begins operation, first performing signal strength analysis, which reveals that the signal strength in the area where the smart TV is located is weak, while the signal strength of other devices is normal. Next, device response time measurement shows that the smart TV's response time to commands is infinite, while network traffic monitoring reveals that the smart TV's network traffic suddenly drops to zero.

[0124] The fault location module 120 compared the smart TV's dynamic performance profile and found that during normal operation, the signal strength should be within a certain range, the response time should typically be within a few milliseconds, and the network traffic should also have a relatively stable value range. Based on these comparisons, the fault location module 120 determined that the smart TV had malfunctioned and initially identified it as a network connection problem.

[0125] The network dynamic adjustment module 140 immediately takes action to enhance the Wi-Fi signal strength in the area where the smart TV is located. At the same time, to avoid affecting other devices, the network dynamic adjustment module 140 optimizes the allocation of network traffic, temporarily reducing the network bandwidth usage of some non-critical devices (such as smart bulbs).

[0126] The fault recovery module 130 first attempts to soft-reboot the smart TV. The voice interaction and intelligent analysis module 110 provides voice feedback to the user: "Attempting a soft-reboot of the smart TV." After the soft-reboot, the smart TV still has no signal. The fault recovery module 130 then attempts to reset network parameters, reassigning an IP address and DNS settings to the smart TV. During this process, the voice interaction and intelligent analysis module 110 continuously updates the user with the recovery progress: "Network parameters being reset." However, the problem remains unresolved.

[0127] At this point, the fault recovery module 130 upgrades its recovery measures and begins checking if the smart TV's firmware needs an update. After detection, a new firmware version is found that can resolve some known network connectivity issues. The fault recovery module 130 automatically downloads and installs the firmware, while the voice interaction and intelligent analysis module 110 informs the user: "Updating smart TV firmware, please wait."

[0128] After the firmware update was completed, the smart TV regained normal signal. The voice interaction and intelligent analysis module 110 informed the user via voice: "The smart TV has regained connection, the fault has been resolved."

[0129] The data learning and optimization module 150 recorded the process and results of this fault handling, including the fault type being a network connectivity problem, the localization method being signal strength analysis and response time measurement combined with dynamic performance profile comparison, and the recovery strategy being a soft reboot, resetting network parameters, and updating firmware. Through the analysis of this data, the fault recovery system 10 can handle similar problems more quickly and accurately in the future. It can also predict potential faults based on the family's network usage and take corresponding preventative measures.

[0130] Specifically, as smart home devices are continuously upgraded and the network environment dynamically changes, the fault recovery system 10 continuously monitors and learns in real time. When a new device joins the smart home network, it quickly learns its normal performance characteristics and updates its dynamic performance profile and fault mode library to better cope with potential faults brought by the new device. At the same time, if new interference sources or other changes occur in the smart home network environment, the fault recovery system 10 automatically adjusts algorithm parameters such as signal strength analysis and response time measurement to adapt to the new network conditions, ensuring the accuracy and efficiency of fault location and handling.

[0131] The system actively collects user feedback on fault handling results to further optimize its learning process. If users are dissatisfied with the effectiveness of the recovery strategy, the data learning and optimization module 150 will conduct in-depth analysis of the causes and adjust the strategy selection and execution process based on the feedback. For example, if a user reports that a certain device frequently malfunctions and the existing recovery strategy is ineffective, the data learning and optimization module 150 will conduct a more in-depth performance analysis of that device, explore more suitable fault location and recovery methods, or provide users with more detailed fault diagnosis suggestions to improve the user experience.

[0132] Through in-depth analysis of a large amount of historical fault data, the fault recovery system 10 strives to achieve predictive maintenance. When it detects that a device is prone to failure during a specific time period or under a particular usage pattern, it issues an early warning to allow users to prepare. Simultaneously, it automatically takes preventative measures, such as adjusting network parameters to optimize device connection stability and optimizing device settings to reduce the probability of failure. Furthermore, the fault recovery system 10 regularly performs comprehensive testing and optimization of the smart home network, proactively identifying potential faults and ensuring the stable and reliable operation of the entire network.

[0133] For fault localization, through learning from a large number of fault cases, the fault recovery system 10 can more accurately identify common fault patterns. For example, when a network failure occurs, based on previously recorded fault patterns, if multiple devices simultaneously experience a sudden drop in signal strength and an extended response time, the fault recovery system 10 can quickly determine that the router may be the problem.

[0134] The system continuously updates and refines the dynamic performance profiles of devices. In addition to basic signal strength range, response time baselines, and network traffic characteristics, it can also record performance changes under different scenarios (such as day / night, high / low load usage periods). This allows the fault recovery system 10 to more accurately locate the faulty device when an anomaly occurs, by combining the current scenario with detailed profile data. For example, if a smart speaker experiences a decrease in signal strength under low load conditions at night, the fault recovery system 10 can quickly compare the profile data in night mode to determine if it exceeds the normal range.

[0135] The system considers the interrelationships between smart home devices. When a device malfunctions, the fault recovery system 10 analyzes not only the device's own data but also the data of related devices. For example, if a smart camera and a smart light are in the same area, and the camera malfunctions, the fault recovery system 10 will simultaneously check the light's operation, as the problem might be with a network node or power supply in that area. This correlation analysis narrows down the scope of the fault.

[0136] For fault recovery, the success rate of recovery strategies is ranked based on historical fault data. For example, for network connectivity failures, the system found that a soft reboot resolves the issue in 70% of cases, while resetting network parameters has a 40% success rate, and updating firmware has a 30% success rate (these success rates are continuously updated as data accumulates). Therefore, when encountering similar faults, the fault recovery system 10 will prioritize a soft reboot to improve the efficiency of problem-solving.

[0137] For complex faults, the fault recovery system 10 can automatically execute a set of combined recovery strategies based on past successful experiences. For example, for frequently occurring device software crashes, the fault recovery system 10 can perform both a soft reboot and a check for software updates simultaneously, instead of trying them one by one, thus saving time.

[0138] If the fault recovery system 10 fails to resolve the issue after multiple attempts, it can automatically connect to a remote technical support center (provided the user authorizes it). This sends fault-related data to professionals, enabling them to quickly understand the situation, provide more effective solutions, and reduce troubleshooting time.

[0139] The fault recovery system 10 comprehensively collects detailed information for each fault, including the time of occurrence, the devices involved, the type of fault (such as network connection interruption, device malfunction, etc.), and the fault manifestations (such as changes in signal strength and the specific duration of response time delays). It also records the localization methods used (such as the detection protocol used and how dynamic performance profiles were compared) and recovery strategies (the order and results of operations such as soft reboot, parameter reset, and firmware update). This data is organized into a structured record, like creating a detailed fault profile, facilitating subsequent analysis and learning.

[0140] In addition, machine learning algorithms (such as decision trees and neural networks) can be used to establish a correlation model between fault features and fault types. For example, data such as device signal strength, response time, and network traffic can be used as input features, and different fault types can be used as output labels. The model can be trained using a large amount of historical fault data. In this way, when new fault data is input, the model can quickly predict the fault type.

[0141] Rules are extracted from historical fault handling processes. For example, if a device has a higher probability of successfully recovering via a soft reboot when the signal strength is below a certain value and the response time exceeds a certain threshold, the system will learn this rule. As new data accumulates, these rules will be continuously updated and optimized to adapt to various complex and changing fault conditions.

[0142] If a user reports dissatisfaction with a fault handling result—for example, the restored device still has problems or the restoration process is too lengthy—the system will re-examine the fault handling data. It will analyze whether the problem stems from inaccurate localization methods or an inappropriate restoration strategy. If the issue is with the localization method, the system will re-evaluate the detection techniques and data comparison methods used for localization; if the issue is with the restoration strategy, it will adjust the priority or combination of restoration strategies to improve efficiency and accuracy in subsequent similar fault handling.

[0143] As smart home devices are continuously updated and new fault scenarios emerge, the system continuously accumulates data. By constantly adding new fault samples, the machine learning model can learn more fault patterns and effective handling strategies. Simultaneously, the data learning and processing processes are optimized; for example, by optimizing the data storage structure to improve query and analysis efficiency, and by improving the parameters of the machine learning algorithm to enhance the model's accuracy and generalization ability, thereby achieving continuous optimization of fault location and handling.

[0144] Based on the above-described fault recovery system, this embodiment of the invention also provides a fault recovery method for a smart home network, which can be referred to... Figure 4 The diagram illustrates a flowchart of a fault recovery method for a smart home network according to an embodiment of the present invention.

[0145] like Figure 4 As shown, the fault recovery method for this smart home network may include the following steps:

[0146] Step 401: Identify the target faulty smart home device and determine the first fault adjustment strategy for the target faulty smart home device.

[0147] In practical applications, users can activate the fault recovery system for fault diagnosis and recovery through voice interaction. Specifically, users can input voice commands into the fault recovery system to detect and recover from faults.

[0148] Upon receiving a user's voice command, the voice interaction and intelligent analysis module can respond by triggering fault detection for the smart home device to be detected among multiple smart home devices. For example, the user's voice command may include an identifier for the smart home device to be detected; based on this identifier, the voice interaction and intelligent analysis module can trigger fault detection for that smart home device.

[0149] When a fault detection is triggered for a smart home device to be tested, the fault location module can first obtain the first performance parameters of the smart home device to be tested.

[0150] After obtaining the first performance parameters and the first dynamic performance profile of the smart home device to be tested, the fault location module can compare the data in the first performance parameters with the data in the first dynamic performance profile, and based on the comparison results, determine the target faulty smart home device from among multiple smart home devices.

[0151] After identifying the target smart home device, the fault recovery module can first determine the initial fault handling strategy for the target smart home device.

[0152] Step 402: Based on the first fault adjustment strategy, perform fault recovery on the target faulty smart home device.

[0153] After determining the first fault adjustment strategy, the fault recovery process can be automatically performed on the target faulty smart home device based on the first fault adjustment strategy.

[0154] In this embodiment of the invention, a target faulty smart home device is identified, and a first fault adjustment strategy is determined for the target faulty smart home device; based on the first fault adjustment strategy, fault recovery is performed on the target faulty smart home device. Through this embodiment of the invention, efficient handling of smart home network faults can be achieved.

[0155] 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.

[0156] This invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the above-described method for fault recovery of a smart home network.

[0157] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for fault recovery of a smart home network.

[0158] 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.

[0159] 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.

[0160] 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, etc.) containing computer-usable program code.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 present invention.

[0165] 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.

[0166] The above provides a detailed description of a fault recovery system for a smart home network, a fault recovery method for a smart home network, an electronic device, and a computer-readable storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A fault recovery system for a smart home network, characterized in that, The smart home network includes multiple smart home devices, and the fault recovery system includes: The voice interaction and intelligent analysis module is used to respond to user voice commands and trigger fault detection for the smart home device to be detected among the plurality of smart home devices; the voice interaction and intelligent analysis module is also used to analyze the user voice commands to obtain voice command analysis results; when the voice command analysis results point to at least one smart home device, the at least one smart home device is selected as the smart home device to be detected; when the voice command analysis results do not point to a smart home device, the plurality of smart home devices are selected as the smart home device to be detected. The fault location module is used to acquire the first performance parameters of the smart home device to be tested, compare them with the first dynamic performance profile set for each smart home device to be tested, and determine the target faulty smart home device based on the comparison result; the first dynamic performance profile records the performance parameters of the smart home device to be tested during normal operation. The fault recovery module determines a first fault adjustment strategy for the target faulty smart home device; and performs fault recovery on the target faulty smart home device based on the first fault adjustment strategy. The network dynamic adjustment module is used to dynamically adjust the network for the target faulty smart home device based on the severity and scope of the fault. When the target faulty smart home device is a local device fault and its overall impact on the smart home network is less than a preset value, the module adjusts the Wi-Fi signal strength to enhance signal coverage near the target faulty smart home device or adjusts channel allocation. When the target faulty smart home device is caused by network congestion, the module automatically optimizes network traffic allocation to ensure the normal operation of critical equipment. The fault recovery system also includes: The data learning and optimization module optimizes the analysis of voice commands in the voice interaction and intelligent analysis module based on the fault recovery process and results of the fault recovery module; optimizes the method by which the fault location module determines the target faulty smart home device based on the fault recovery process and results of the fault recovery module; and optimizes the logic by which the fault recovery module determines the fault adjustment strategy based on the fault recovery process and results of the fault recovery module. The fault recovery module is further configured to isolate the target faulty smart home device from other smart home devices; and, when the target faulty smart home device returns to normal, to cancel the isolation between the target faulty smart home device and the other smart home devices.

2. The fault recovery system according to claim 1, characterized in that, The fault location module is also used to monitor each smart home device according to a preset time interval; when a fault event is detected, it obtains the second performance parameters of the smart home device associated with the fault event and compares them with the second dynamic performance profile set for the smart home device associated with the fault event.

3. The fault recovery system according to claim 1, characterized in that, The fault recovery module is used to perform fault recovery on the target faulty smart home device by adopting a second fault adjustment strategy when the fault recovery based on the first fault adjustment strategy fails.

4. The fault recovery system according to claim 3, characterized in that, The voice interaction and intelligent analysis module is also used to display the recovery progress to the user.

5. The fault recovery system according to claim 1, characterized in that, The performance parameters of the smart home device under test recorded in the first dynamic performance profile during normal operation include at least one of the following: The normal signal strength range of the smart home device to be tested, the normal response time benchmark of the smart home device to be tested, and the normal network traffic characteristics of the smart home device to be tested.

6. A method for fault recovery in a smart home network, characterized in that, Applied to the fault recovery system as described in any one of claims 1-5, the method comprises: Identify the target faulty smart home device and determine a first fault adjustment strategy for the target faulty smart home device; Based on the first fault adjustment strategy, the target faulty smart home device is restored.

7. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the fault recovery method for the smart home network as described in claim 6.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the fault recovery method for the smart home network as described in claim 6.

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