Fault recovery system for smart home network, related method and product

Abstract

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

Description

Technical Field

[0001] The present 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 Art

[0002] With the popularity of smart home devices, more and more smart home devices are connected to the home network. However, network failure or device failure may affect the normal operation of smart home. Traditional troubleshooting and recovery require professional operation or management through complex devices such as mobile phones and computers. The threshold for use is high and it is difficult for users who do not have network technology 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 the above problems or at least partially solve the above problems, including:

[0004] A fault recovery system for a smart home network, wherein the smart home network includes a plurality of smart home devices, and the fault recovery system includes:

[0005] A voice interaction and intelligent analysis module, configured to trigger fault detection for a smart home device to be detected among the multiple smart home devices in response to a user voice command;

[0006] a fault location module, configured to obtain a first performance parameter of the smart home device to be detected, compare the first performance parameter with a first dynamic performance file set for each smart home device to be detected, and determine a target faulty smart home device based on the comparison result; the first dynamic performance file records the performance parameters of the smart home device to be detected when it is operating normally;

[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 voice commands to obtain a voice command analysis result; when the voice command analysis result points 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 result does 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 also used to monitor each smart home device according to a preset time interval; when a fault event is detected, obtain a second performance parameter of the smart home device associated with the fault event, and compare it with a second dynamic performance profile set for the smart home device associated with the fault event.

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

[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 according to the severity and impact scope of the fault of the target faulty smart home device.

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

[0015] The data learning and optimization module optimizes the analysis of the voice instructions of the voice interaction and intelligent analysis module according to the fault recovery process and the results of the fault recovery of the fault recovery module; and optimizes the way in which the fault location module determines the target faulty smart home device according to the fault recovery process and the results of the fault recovery of the fault recovery module; and optimizes the logic of the fault recovery module in determining the fault adjustment strategy according to the fault recovery process and the results of the fault recovery of the fault recovery module.

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

[0017] Optionally, the performance parameters of the smart home device to be detected during normal operation recorded in the first dynamic performance file include at least one of the following:

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

[0019] The embodiment of the present invention further provides a fault recovery method for a smart home network, which is applied to the fault recovery system as described above, and the method includes:

[0020] Determine a 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, fault recovery is performed on the target faulty smart home device.

[0022] An embodiment of the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the above-mentioned smart home network fault recovery method when executed by the processor.

[0023] An 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, the fault recovery method of the smart home network as described above is implemented.

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

[0025] In an embodiment of the present invention, the fault recovery system includes: a voice interaction and intelligent analysis module, which is used to trigger fault detection for a smart home device to be detected among multiple smart home devices in response to a user voice command; a fault location module, which is used to obtain a first performance parameter of the smart home device to be detected, and compare it with a first dynamic performance file set for each smart home device to be detected, and determine the target faulty smart home device based on the comparison result; the first dynamic performance file records the performance parameters of the smart home device to be detected when it is operating normally; a fault recovery module, which determines 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 fault-recovered. Through the embodiment of the present invention, efficient processing of smart home network faults can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

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

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

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

[0030] Figure 4 The present invention is a flowchart of a method for recovering a fault in a smart home network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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 a smart home network fails, it is often necessary to manually check and restore devices one by one, which is inefficient. Network fault handling generally relies on mobile phones, computers and other devices. The operation process is cumbersome and not user-friendly for users who are not familiar with network technology. The existing system lacks real-time performance when detecting and handling network faults and cannot provide timely feedback.

[0034] To this end, an embodiment of the present invention provides a fault recovery system for a smart home network, which may include a voice interaction and intelligent analysis module for receiving user instructions, a fault location module for automatically locating faulty equipment, and a fault recovery module for automatically performing fault recovery. The fault recovery system mentioned in the embodiment of the present invention can achieve efficient processing of smart home network faults.

[0035] For details, please refer to Figure 1 , Figure 1 A structural 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 a smart home device to be detected among multiple smart home devices in response to a user voice command;

[0037] The fault location module 120 is used to obtain a first performance parameter of the smart home device to be detected, and compare it with a first dynamic performance file set for each smart home device to be detected, and determine the target faulty smart home device based on the comparison result; the first dynamic performance file records the performance parameters of the smart home device to be detected when it is operating normally;

[0038] The fault recovery module 130 determines a first fault adjustment strategy for a 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 actual applications, the fault recovery system 10 can be a system for fault diagnosis and fault recovery for a smart home network; a smart home network can be composed of multiple smart home devices, which can include both smart home devices that provide smart services and auxiliary devices for providing networks, connections, etc. for each smart home device, and the embodiments of the present invention are not limited to this.

[0040] For example, a smart home network can be composed of multiple components such as smart home devices, smart home centers, network infrastructure, cloud service platforms, mobile applications, sensors and actuators, and security systems. These components work together to achieve interconnection, remote control, and automated operation of smart home devices.

[0041] Among them, smart home devices refer to household devices that can be connected through the network and realize automatic control, such as smart light bulbs, smart sockets, smart door locks, smart cameras, smart thermostats, etc. These devices can be connected to the smart home system through wireless or wired networks to realize remote control, automatic operation and data collection.

[0042] A smart home hub is a core device, usually a physical device or software platform, responsible for managing and coordinating the 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 instructions.

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

[0044] The cloud service platform is a remote server system that stores and manages the data of smart home devices and provides 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 devices through the cloud service platform.

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

[0046] Sensors are used to detect various parameters in the environment, such as temperature, humidity, light, motion, etc. Actuators are used to perform specific operations, such as turning lights on and off, adjusting temperature, etc. Sensors and actuators are the core components of smart home devices, which realize automatic control by collecting and executing operations.

[0047] Security systems include firewalls, encryption technology, authentication, etc., which are used to protect the security and privacy of smart home networks. Security systems ensure that smart home devices and networks are protected from unauthorized access and attacks, protecting user data and privacy.

[0048] In actual applications, the user can call up the fault recovery system 10 to perform fault diagnosis and fault recovery through voice interaction. Specifically, the user can input user voice instructions for fault detection and fault recovery to the fault recovery system 10.

[0049] After receiving the user voice command, the voice interaction and intelligent analysis module 110 can trigger fault detection of a smart home device to be detected among multiple smart home devices in response to the user voice command. Exemplarily, the user voice command can include an identifier of the smart home device to be detected, and based on the 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 detected, the fault location module 120 may first obtain a first performance parameter of the smart home device to be detected.

[0051] Exemplarily, the first performance parameter may include a signal strength range of the smart home device to be detected, a response time benchmark of the smart home device to be detected, and a network traffic feature of the smart home device to be detected. The first performance parameter may be a current performance parameter of the smart home device to be detected, or may be a performance parameter of the smart home device to be detected within a period of time including the current time period, which is not limited in the embodiment of the present invention.

[0052] In actual applications, the fault location module 120 may also store a first dynamic performance file set for each smart home device; the first dynamic performance file records the performance parameters of the smart home device to be detected when it operates normally.

[0053] Exemplarily, the performance parameters of the smart home device to be detected during normal operation recorded in the first dynamic performance file include at least one of the following:

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

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

[0056] After determining the target smart home device, the fault recovery module 130 may first determine a first fault adjustment strategy for the target smart home device; then, based on the first fault adjustment strategy, the fault recovery process may be automatically performed on the target faulty smart home device.

[0057] In the embodiment of the present invention, the fault recovery system 10 includes: a voice interaction and intelligent analysis module 110, which is used to trigger fault detection for a smart home device to be detected among multiple smart home devices in response to a user voice command; a fault location module 120, which is used to obtain a first performance parameter of the smart home device to be detected, and compare it with a first dynamic performance file set for each smart home device to be detected, and determine the target faulty smart home device based on the comparison result; the first dynamic performance file records the performance parameters of the smart home device to be detected when it is operating normally; a fault recovery module 130, which determines 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 fault-recovered. Through the embodiment of the present invention, efficient processing 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 a voice command analysis result; when the voice command analysis result points to at least one smart home device, at least one smart home device is used as a smart home device to be detected; when the voice command analysis result does not point to a smart home device, multiple smart home devices are used as smart home devices to be detected.

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

[0060] If the voice command analysis result points to at least one determined smart home device, the at least one smart home device can be used as the smart home device to be detected.

[0061] On the contrary, if the voice command analysis result is ambiguous and does not point to a specific smart home device, multiple smart home devices can be used as smart home devices to be detected.

[0062] For example, users can issue commands at any time through intelligent voice assistants or speakers, such as "check the smart home network" and "check the status of faulty devices". The voice interaction and intelligent analysis module 110 quickly converts the voice commands into text and starts the intelligent analysis program. The program will analyze the specific intention 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 clearly specifies the device, such as "check the network connection of the smart TV", the fault recovery system 10 will specifically detect the device.

[0063] In one embodiment of the present invention, the fault location module 120 is further used to monitor each smart home device according to a preset time interval; when a fault event is detected, a second performance parameter of the smart home device associated with the fault event is obtained, and compared with a 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: the device is disconnected from the network, the device reports an error, etc., then 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 file and the first dynamic performance file are dynamic performance files for different smart home devices, and the performance parameters of the smart home device associated with the fault event recorded in the second dynamic performance file during normal operation include at least one of the following:

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

[0067] Based on the comparison result, the fault location module 120 can locate the target faulty smart home device, and the fault recovery module 130 performs fault recovery; the logic of positioning and fault recovery is similar to that of the aforementioned embodiment and will not be repeated here.

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

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

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

[0071] For example, if it is a device connection problem, first try to soft restart the device to reestablish the network connection. If the soft restart does not work, the system will try to reset the network parameters, such as IP (Internet Protocol) address, DNS (Domain Name System) settings, etc. If the problem persists, the system will gradually upgrade the recovery measures, such as reconfiguring device parameters, updating firmware, etc.

[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. Exemplarily, the voice interaction and intelligent analysis module 110 can display the recovery progress to the user through voice feedback, such as: "Trying to soft restart the device", "Resetting network parameters", etc., so that the user can clearly understand the processing status of the fault recovery system 10 to increase the user's sense of participation.

[0074] In one embodiment of the present invention, 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 impact scope of the fault of the target faulty smart home device.

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

[0077] For example, if it is a local device failure and has little impact on the overall smart home network, the network dynamic adjustment module 140 can adjust the Wi-Fi signal strength to enhance the signal coverage near the target faulty smart home device, or adjust the channel allocation to avoid channel interference. For some minor failures caused by network congestion, the system can automatically optimize network traffic allocation to ensure the normal operation of key devices.

[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, giving priority to ensuring the smooth operation of the video transmission device. At the same time, 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 the stability and performance of the network.

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

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

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

[0082] In addition, the data learning and optimization module 150 can also optimize the way in which the fault location module 120 determines the target faulty smart home device according to the fault recovery process and the fault recovery result of the fault recovery module 130.

[0083] In addition, the data learning and optimization module 150 may also optimize the logic of the fault recovery module 130 in determining the fault adjustment strategy according to 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 used to isolate the target faulty smart home device from other smart home devices; and, when the target faulty smart home device returns to normal, cancel the isolation of the target faulty smart home device from other smart home devices.

[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 may also isolate the target faulty smart home device from other smart home devices through a network.

[0086] In addition, the fault recovery module 130 can also cancel the isolation of the target faulty smart home device from 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, Figure 3 As shown, the user can first issue a user voice command to call up the fault recovery system 10 to perform fault detection and fault repair.

[0088] The user can issue instructions at any time through the intelligent voice assistant or speaker, such as "check the smart home network", "check the status of the faulty device", etc. The voice interaction and intelligent analysis module 110 quickly converts the user's voice instructions into text and starts the intelligent analysis program. The program will analyze the specific intention of the user's voice instructions. If the user's instructions are vague, such as "check it", the fault recovery system 10 will start the fault location module 120 to perform a comprehensive scan of the smart home network; if the user clearly specifies the device, such as "check the network connection of the smart TV", the fault location module 120 will be started to perform targeted detection on the device.

[0089] At the same time, the data learning and optimization module 150 can continuously optimize the understanding and response to instructions based on the user's usage habits and historical instructions, thereby improving the accuracy and efficiency of the interaction.

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

[0091] (1) Instruction frequency analysis

[0092] The data learning and optimization module 150 records the frequency of various instructions issued by the user. For example, if the user often issues the instruction of "turn on the bedroom light" at night, the voice interaction and intelligent analysis module 110 can prioritize and prepare to respond to such instructions during a specific time period at night. When the user issues some ambiguous instructions during this time period, the voice interaction and intelligent analysis module 110 can make intelligent guesses based on the possibility of high-frequency instructions. For example, if the user says "it's a bit dark", the voice interaction and intelligent analysis module 110 determines that it is very likely that the user wants to turn on the bedroom light at this time, and thus actively asks "Do you want to turn on the bedroom light?" to improve the accuracy of the response.

[0093] (2) Command scene association

[0094] Analyze the association between user commands and specific scenarios. For example, when a user often issues the command "turn up the volume" while watching TV, the data learning and optimization module 150 can associate this command with the TV watching scenario. The next time when the TV is detected to be turned on and the user issues some vague commands such as "turn up the volume", the voice interaction and intelligent analysis module 110 can accurately understand it as turning up the TV volume instead of adjusting the volume of other devices.

[0095] (3) Habitual Adaptive Adjustment

[0096] As the user's usage habits change, the voice interaction and intelligent analysis module 110 dynamically adjusts its understanding and response to instructions. When the user changes the way some instructions are expressed, the data learning and optimization module 150 can quickly learn and adapt to the new expression. For example, the user originally said "turn on the light", and later changed it to "turn on the light". The data learning and optimization module 150 continuously learns the changes in historical instructions and timely adjusts the recognition and response of the voice interaction and intelligent analysis module 110 to the new instructions.

[0097] For example, historical instruction analysis and optimization:

[0098] (1) Instruction pattern recognition

[0099] The data learning and optimization module 150 analyzes the user's historical command sequence and identifies specific command patterns. For example, if the user often issues a command sequence such as "turn on the air conditioner" and "set the temperature to 25 degrees", the data learning and optimization module 150 can regard these two commands as an overall 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) Error instruction correction learning

[0101] When the voice interaction and intelligent analysis module 110 misunderstands the user's instructions and performs an incorrect operation, the error and the user's correct feedback are recorded. For example, the user issues the command "open the window", but the intelligent system mistakenly opens the curtains. After the user corrects it, the data learning and optimization module 150 records the error and strengthens the learning of the distinction between the two instructions "open the window" and "open the curtains". By analyzing the characteristics of the wrong instructions, such as voice similarity, context confusion, etc., the voice recognition algorithm and instruction analysis logic are improved to reduce the occurrence of similar errors.

[0102] (3) Historical instruction feedback optimization

[0103] Optimize subsequent responses based on user feedback on historical command execution results. For example, if a user is not satisfied with the response speed of a device, the fault recovery system 10 can prioritize adjusting the network connection or device parameters of the device to improve the response speed when executing related commands next time.

[0104] For another example, for failure recovery:

[0105] (1) Optimizing the execution of recovery instructions: Optimizing the understanding and response of instructions can ensure that the user's voice instructions are accurately executed during the fault recovery phase. For example, when the user issues a command to "try to restore the smart TV", the voice interaction and intelligent analysis module 110 can accurately analyze and start the recovery process for the smart TV, such as soft restart, 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 restarting a specific device first), the recovery operation can be performed more efficiently.

[0106] (2) Improving the interactive efficiency of the recovery process: During the fault recovery process, the voice interaction and intelligent analysis module 110 can more effectively provide feedback to the user on the recovery status and ask for further instructions based on 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 smoothly complete the fault recovery and reduce 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 measures changes in signal strength between a device and a network node to determine whether the device has an unstable connection.

[0109] The device response time measurement records the device's response time to instructions. If the response time is significantly prolonged or there is no response, there may be a fault.

[0110] Network traffic monitoring can monitor the network traffic of the device in real time. Abnormal fluctuations in traffic may indicate a fault.

[0111] The fault location module 120 can create a dynamic performance file for each device, which records in detail information such as the signal strength range, response time benchmark, and network traffic characteristics when the smart home device is operating normally.

[0112] When it is necessary to locate a faulty smart home device, the fault location module 120 can compare the current performance parameters of the device with the dynamic performance profile of the device to quickly narrow 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 first check the connection status of the camera and its peripheral devices.

[0113] When all devices are not faulty, monitoring can continue; when a device fault is detected, the network dynamic adjustment module 140 can first dynamically adjust the network according to the severity and scope of the fault. If the device fault is local and has little impact on the overall network, the network dynamic adjustment module 140 can adjust the Wi-Fi signal strength, enhance the signal coverage near the faulty device, or adjust the channel allocation to avoid channel interference. For some minor faults caused by network congestion, the network dynamic adjustment module 140 can automatically optimize the network traffic distribution to ensure the normal operation of key equipment.

[0114] For example, when multiple smart devices are transmitting high-definition videos at the same time, causing network congestion, the network dynamic adjustment module 140 will automatically reduce the network bandwidth usage of some non-critical devices, giving priority to ensuring the smooth operation of the video transmission devices. At the same time, 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 the stability and performance of the network.

[0115] When a device fault 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 connection problem, first try to soft restart the device to reestablish the network connection. If the soft restart does not work, the fault recovery module 130 will try to reset the network parameters, such as IP address, DNS settings, etc. If the problem still exists, the fault recovery module 130 will gradually upgrade the recovery measures, such as reconfiguring device parameters, updating firmware, etc.

[0116] When it is restored to normal, the device is reintegrated into the smart home network, and the data learning and optimization module 150 is started to optimize the recovery process, positioning process and identification process; if it is not restored, detection continues.

[0117] During the recovery process, the voice interaction and intelligent analysis module 110 informs the user of the recovery progress in real time through voice feedback. For example, "Trying to soft restart the device" and "Resetting network parameters" allow the user to clearly understand the system processing status. If some devices are successfully restored, the network dynamic adjustment module 140 will reintegrate them into normal network operation and continue to process the unrestored devices. If a complex fault 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 relatively complex, it is recommended to contact professional technicians for maintenance."

[0118] The data learning and optimization module 150 records the process and results of each fault handling, including data such as fault type, location method, and recovery strategy. The data is analyzed using machine learning algorithms to continuously optimize fault detection and recovery strategies. Over time, the fault recovery system 10 can more accurately predict possible faults and take preventive measures in advance.

[0119] For example, if the fault recovery system 10 finds that a certain device is prone to failure in a specific time period or in 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 also further optimize the interactive experience based on user feedback and usage, so that voice commands are more in line with user habits and needs.

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

[0121] Suppose a family has multiple smart home devices such as smart TVs, smart speakers, smart cameras, smart light bulbs, and smart sweeping robots, which are connected through the home Wi-Fi network.

[0122] One night, when the user was watching smart TV, the picture suddenly froze and then lost signal completely. The user said to the smart speaker: "Check the smart home network to see why the TV has no signal."

[0123] The voice interaction and intelligent analysis module 110 quickly recognizes the user's voice command and starts the fault detection process. The fault location module 120 starts working and first performs a signal strength analysis, finding that the signal strength in the area where the smart TV is located is weak, but the signal strength of other devices is normal. Then, the device response time measurement shows that the response time of the smart TV to the command is infinite, and the network traffic monitoring finds that the network traffic of the smart TV suddenly becomes zero.

[0124] The fault location module 120 compares the dynamic performance profile of the smart TV and finds that the signal strength should be within a certain range when it is operating normally, the response time is usually within a few milliseconds, and the network traffic also has a relatively stable value range. Based on these comparisons, the fault location module 120 determines that the smart TV has a fault and preliminarily determines that it is a network connection problem.

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

[0126] The fault recovery module 130 first tries to soft-restart the smart TV. The voice interaction and intelligent analysis module 110 gives the user feedback through voice: "Trying to soft-restart the smart TV." After the soft restart, the smart TV still has no signal. The fault recovery module 130 then tries to reset the network parameters and reassign the IP address and DNS settings to the smart TV. During this process, the voice interaction and intelligent analysis module 110 keeps notifying the user of the recovery progress: "Network parameters are being reset." However, the problem is still not solved.

[0127] At this point, the fault recovery module 130 upgrades the recovery measures and starts to check whether the firmware of the smart TV needs to be updated. After detection, it is found that there is a new firmware version that can solve some known network connection problems. The fault recovery module 130 automatically downloads and installs the firmware, and the voice interaction and intelligent analysis module 110 informs the user: "The smart TV firmware is being updated, please wait."

[0128] After the firmware update is completed, the smart TV has restored normal signal. The voice interaction and intelligent analysis module 110 gives feedback to the user through voice: "The smart TV has restored connection and the fault has been eliminated."

[0129] The data learning and optimization module 150 records the process and results of this fault handling, including that the fault type is a network connection problem, the positioning method is signal strength analysis and response time measurement combined with dynamic performance profile comparison, and the recovery strategy is soft restart, resetting network parameters and updating firmware, etc. By analyzing these data, the fault recovery system 10 can handle similar problems more quickly and accurately in the future. At the same time, it can also predict possible faults in advance based on the network usage of this family and take corresponding preventive measures.

[0130] Specifically, with the continuous upgrading of smart home devices and the dynamic changes in the network environment, the fault recovery system 10 continues to monitor and learn in real time. When a new device is added to the smart home network, its normal performance characteristics are quickly learned, and the dynamic performance archive and fault mode library are updated to better cope with the fault conditions that may be caused by the new device. At the same time, if new interference sources or other changes appear 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 processing.

[0131] Actively collect user feedback on the results of fault handling to further optimize the system's learning process. If the user is not satisfied with the execution effect of the recovery strategy, the data learning and optimization module 150 will conduct an in-depth analysis of the cause and adjust the strategy selection and execution process based on the feedback. For example, if the user reports that a certain device frequently fails and the existing recovery strategy is not effective, the data learning and optimization module 150 will conduct a more in-depth performance analysis of the device, explore more appropriate fault location and recovery methods, or provide users with more detailed troubleshooting 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 a device is found to be prone to failure in a specific time period or in a specific usage mode, early warning information is issued in advance to allow users to prepare. At the same time, some preventive measures are automatically taken, such as adjusting network parameters to optimize device connection stability, optimizing device settings to reduce the probability of failure, etc. In addition, the fault recovery system 10 will regularly conduct comprehensive inspections and optimizations on the smart home network, proactively discover potential fault hazards, and ensure the stable and reliable operation of the entire network.

[0133] For fault location, by learning from a large number of fault cases, the fault recovery system 10 can more accurately identify common fault modes. For example, when a network fault occurs, based on previously recorded fault modes, if the signal strength of multiple devices drops sharply and the response time increases at the same time, the fault recovery system 10 can quickly determine that there may be a problem with the router.

[0134] Continuously update and refine the dynamic performance profile of the device. In addition to the basic signal strength range, response time benchmark, and network traffic characteristics, the performance changes of the device in different scenarios (such as day / night, high load / low load usage periods) can also be recorded. In this way, when an abnormality occurs, the fault recovery system 10 can combine the current scenario and detailed archival data to more accurately locate the faulty device. For example, the signal strength of the smart speaker decreases under low load at night. The fault recovery system 10 can quickly compare the archival data in night mode to determine whether it exceeds the normal range.

[0135] Consider the correlation between smart home devices. When a device fails, the fault recovery system 10 not only analyzes the data of the device itself, but also checks the data of the devices associated with it. For example, if a smart camera and a smart light are in the same area and the camera fails, the fault recovery system 10 will also check the operation of the light, because there may be a problem with the network node or power supply in the area, and narrow the scope of the fault through this correlation analysis.

[0136] For fault recovery, the success rate of the recovery strategy is ranked according to the historical fault data. For example, for a network connection failure, the system found that soft restarting the device can solve the problem in 70% of cases, while the success rate of resetting network parameters is 40%, and the success rate of updating firmware is 30% (these success rates will be continuously updated as data accumulates). Then, when encountering similar faults, the fault recovery system 10 will give priority to soft restarting to improve the efficiency of solving the problem.

[0137] For some complex faults, the fault recovery system 10 can automatically execute a set of combined recovery strategies based on past successful experiences. For example, for the frequent device software crash problem, the fault recovery system 10 can perform both soft restart and check for software updates at the same time, rather than trying each one individually, saving time.

[0138] If the fault recovery system 10 fails to solve the problem after multiple attempts, it can automatically connect to the remote technical support center (provided that the user authorizes it), and send the fault-related data to professionals, so that they can quickly understand the situation, provide more effective solutions, and reduce the time for fault resolution.

[0139] The fault recovery system 10 will comprehensively collect detailed information about each fault, including the time when the fault occurred, the equipment involved, the type of fault (such as network connection interruption, equipment malfunction, etc.), and the manifestation of the fault (such as the value of the signal strength change, the specific duration of the response time extension, etc.). At the same time, the positioning method adopted (such as which detection protocol is used, how to compare dynamic performance files) and the recovery strategy (the sequence and results of operations such as soft restart, resetting parameters, and updating firmware) will also be recorded. These data will be organized into a structured record, just like making a detailed fault file, which is convenient for subsequent analysis and learning.

[0140] In addition, machine learning algorithms (such as decision trees, neural networks, etc.) can also be used to establish an association 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 through a soft restart 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 continues to accumulate, these rules will be continuously updated and optimized to adapt to various complex and changing fault situations.

[0142] If a user reports that they are not satisfied with the results of a certain fault handling, such as problems still exist on the device after recovery or the recovery process is too long, the system will review the data of this fault handling and analyze whether the problem is caused by an inaccurate positioning method or an improper recovery strategy. If it is a problem with the positioning method, the system will re-evaluate the detection technology and data comparison method used for positioning; if it is a problem with the recovery strategy, the priority or combination of the recovery strategy will be adjusted to improve efficiency and accuracy in subsequent similar fault handling.

[0143] With the continuous update of smart home devices and the emergence of new fault conditions, the system continues to accumulate data. By continuously adding new fault samples, the machine learning model can learn more fault modes and effective processing strategies. At the same time, the data learning and processing process is optimized, such as optimizing the data storage structure to improve query and analysis efficiency, and improving the parameters of the machine learning algorithm to improve the accuracy and generalization ability of the model, so as to achieve continuous optimization of fault location and processing.

[0144] Based on the above fault recovery system, the embodiment of the present invention also provides a fault recovery method for a smart home network, which can be referred to as Figure 4 , shows a flowchart of the steps 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 of the smart home network may include the following steps:

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

[0147] In actual applications, the user can call up the fault recovery system to perform fault diagnosis and fault recovery through voice interaction. Specifically, the user can input user voice instructions for fault detection and fault recovery to the fault recovery system.

[0148] After receiving the user voice command, the voice interaction and intelligent analysis module can trigger fault detection of a smart home device to be detected among multiple smart home devices in response to the user voice command. Exemplarily, the user voice command can include an identifier of the smart home device to be detected, and based on the identifier, the voice interaction and intelligent analysis module can trigger fault detection for the smart home device to be detected.

[0149] When triggering fault detection for a smart home device to be detected, the fault location module may first obtain a first performance parameter of the smart home device to be detected.

[0150] After obtaining the first performance parameter and the first dynamic performance file of the smart home device to be detected, the fault location module can compare the first performance parameter with the data in the first dynamic performance file, and based on the comparison result, determine the target faulty smart home device from multiple smart home devices.

[0151] After determining the target smart home device, the fault recovery module may first determine a first fault adjustment 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 the first fault adjustment strategy is determined, the target faulty smart home device can be automatically subjected to fault recovery processing based on the first fault adjustment strategy.

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

[0155] 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.

[0156] 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 capable of running on the processor, and when the computer program is executed by the processor, the above-mentioned smart home network fault recovery method is implemented.

[0157] An 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, the fault recovery method of the smart home network as described above is implemented.

[0158] 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.

[0159] 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.

[0160] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0161] 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.

[0162] 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.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] 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.

[0165] 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.

[0166] The above provides a detailed introduction to a smart home network fault recovery system, a smart home network fault recovery method, an electronic device and a computer-readable storage medium. 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 fault recovery system for a smart home network, characterized in that: The smart home network includes a plurality of smart home devices, and the fault recovery system includes: A voice interaction and intelligent analysis module, configured to trigger fault detection for a smart home device to be detected among the multiple smart home devices in response to a user voice command; a fault location module, configured to obtain a first performance parameter of the smart home device to be detected, compare the first performance parameter with a first dynamic performance file set for each smart home device to be detected, and determine a target faulty smart home device based on the comparison result; the first dynamic performance file records the performance parameters of the smart home device to be detected when it is operating normally; 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.

2. The fault recovery system according to claim 1, characterized in that: The voice interaction and intelligent analysis module is used to analyze the user voice command to obtain a voice command analysis result; when the voice command analysis result points 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 result does not point to a smart home device, the multiple smart home devices are used as the smart home devices to be detected.

3. 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, a second performance parameter of the smart home device associated with the fault event is obtained, and compared with a second dynamic performance profile set for the smart home device associated with the fault event.

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

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

6. The fault recovery system according to claim 1, characterized in that: The fault recovery system also includes: The network dynamic adjustment module is used to dynamically adjust the network of the target faulty smart home device according to the severity and impact scope of the fault of the target faulty smart home device.

7. The fault recovery system according to claim 1, characterized in that: The fault recovery system also includes: The data learning and optimization module optimizes the analysis of the voice instructions of the voice interaction and intelligent analysis module according to the fault recovery process and the results of the fault recovery of the fault recovery module; and optimizes the way in which the fault location module determines the target faulty smart home device according to the fault recovery process and the results of the fault recovery of the fault recovery module; and optimizes the logic of the fault recovery module in determining the fault adjustment strategy according to the fault recovery process and the results of the fault recovery of the fault recovery module.

8. The fault recovery system according to claim 1, characterized in that: The fault recovery module is also used to isolate the target faulty smart home device from other smart home devices; and, when the target faulty smart home device returns to normal, cancel the isolation of the target faulty smart home device from the other smart home devices.

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

10. A fault recovery method for a smart home network, characterized in that: Applied to the fault recovery system according to any one of claims 1 to 9, the method comprises: Determine a 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, fault recovery is performed on the target faulty smart home device.

11. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the fault recovery method of the smart home network as claimed in claim 10 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the fault recovery method of the smart home network as claimed in claim 10 is implemented.

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