Remote diagnosis method and system for online intelligent equipment

Abstract

The invention provides a remote diagnosis method and system for an online intelligent device, and the method comprises the following steps: querying the basic information of a to-be-diagnosed intelligent device through a cloud diagnosis platform according to a diagnosis request after a user feedback problem is received; when the equipment is in the online mode, the cloud diagnosis platform sends a first instruction to an intelligent module of the equipment, and obtains and analyzes historical data which is stored in the intelligent module and reflects the basic operation state of the intelligent module; if the fault is not positioned, the cloud diagnosis platform sends a second instruction to the intelligent module, and obtains and analyzes a real-time operation log containing a data interaction state with the external component; and after the diagnosis is finished, the cloud diagnosis platform sends a third instruction to the intelligent module, and the log printing level of the intelligent module is recovered to a default state. According to the invention, the problems of low fault positioning accuracy, long response period and high labor cost caused by the fact that the existing scheme cannot remotely diagnose the fault of the intelligent module and needs to depend on manual on-site troubleshooting are solved.

Description

Technical Field

[0001] This invention relates to the field of smart home appliance technology, and in particular to a remote diagnostic method and system for online smart devices. Background Technology

[0002] With the deep integration of artificial intelligence and Internet of Things technologies, smart home devices have become widespread. The core control unit of these devices is usually a smart module, which is responsible for data communication and control command forwarding between the device and the cloud platform and mobile terminal. It is the key hardware for realizing device intelligence.

[0003] In existing technologies, remote control and fault notification systems for smart devices mainly include smart modules, electronic control terminals, home routers, cloud-based device management platforms, MQTT message service platforms, and mobile terminals, such as... Figure 1 As shown in the diagram. The cloud-based device management platform is used for device activation and registration during the smart device network configuration phase, and for fault reporting during smart device operation. The MQTT message service platform is used for message subscription and publishing between the mobile terminal and the smart devices that have completed network configuration binding. For smart devices that are network-bound and online, when the device's operating parameters change, the changed data from the control unit is pushed to the mobile terminal via the smart module, the cloud-based device management platform, and the MQTT message service platform during serial communication between the control unit and the smart module. For known faults generated during normal operation of the smart device (such as sensor faults or control unit faults), the above system can push information to the user's mobile terminal, which then provides the user with instructions on how to handle the fault.

[0004] However, the current system has significant diagnostic capability limitations and efficiency bottlenecks when dealing with complex faults directly related to the intelligent module itself, including but not limited to abnormal serial communication between the intelligent module and the electric control end, out-of-sync data keys for communication encryption between the intelligent module and the MQTT message service platform, or abnormal connection between the intelligent module and the home router due to unstable hardware performance of the intelligent module. The root cause is that existing remote diagnosis techniques focus on analyzing fault data of device application layers (such as sensors and actuators), and the diagnosis process often starts after user feedback problems are reported and attempts are made to remotely obtain limited device state information. Due to the comprehensive consideration of power consumption, network traffic, and user privacy, the default operation mode of intelligent devices does not continuously and actively upload detailed internal runtime logs and core state data. Therefore, when the fault root cause is in the intelligent module, the core communication and control unit, remote maintenance personnel often face an "information black box" and cannot remotely obtain decisive diagnostic information. Ultimately, they still need to rely on on-site maintenance personnel to obtain internal logs of the intelligent module by disassembling the device and connecting a serial port print line, so as to perform post-confirmation and positioning. This process not only has ambiguous fault positioning and long response cycle, but also consumes a large amount of labor cost, which seriously restricts the operation and maintenance efficiency and user experience. SUMMARY

[0005] In view of the deficiencies in the prior art, the present application provides an online intelligent device remote diagnosis method and system, which solves the problem that the existing solution cannot remotely diagnose the intelligent module itself and must rely on manual on-site troubleshooting, resulting in low fault positioning accuracy, long response cycle, and high labor cost.

[0006] According to an embodiment of the present application, an online intelligent device remote diagnosis method is provided, comprising the following steps:

[0007] After receiving user feedback problems, the cloud diagnosis platform queries the basic information of the intelligent device to be diagnosed according to the diagnosis request of the user or the maintenance personnel;

[0008] When the device is in online mode, the cloud diagnosis platform sends a first instruction to the intelligent module of the device, obtains and analyzes historical data stored in the intelligent module reflecting its basic running state, and performs first fault diagnosis;

[0009] If the first fault diagnosis does not locate the fault, the cloud diagnosis platform sends a second instruction to the intelligent module to dynamically adjust the log printing level of the intelligent module to obtain and analyze real-time running logs containing data interaction status with external components, and performs second fault diagnosis;

[0010] After the fault diagnosis ends, the cloud diagnosis platform sends a third instruction to the intelligent module to restore the log printing level of the intelligent module to a default state.

[0011] In another aspect, according to an embodiment of the present application, a remote diagnosis system for online intelligent equipment is also provided, which comprises an intelligent equipment, a cloud equipment management platform and an MQTT message service platform, the intelligent equipment comprises an intelligent module and an electric control end, the intelligent module is in communication connection with the electric control end, the cloud equipment management platform and the MQTT message service platform respectively, the cloud equipment management platform and the MQTT message service platform are in communication connection, and the system further comprises a cloud diagnosis platform, which is in communication connection with the cloud equipment management platform, is used for receiving a diagnosis request of a user or a maintenance personnel, and initiates two-level remote diagnosis instructions to the intelligent module in sequence based on the diagnosis request to diagnose a fault of the intelligent module itself; and is further used for sending a recovery instruction to the intelligent module after the diagnosis ends to restore the log printing level of the intelligent module to a default state.

[0012] Compared with the prior art, the present application has the following beneficial effects:

[0013] By the cloud diagnosis platform initiating two-level remote diagnosis instructions to the intelligent module in sequence based on a diagnosis request of a user or a maintenance personnel, the cloud diagnosis platform can sequentially and gradually deeply diagnose the fault of the intelligent module, thereby more systematically and accurately locating the root cause of the problem, greatly improving the accuracy of fault location compared with the traditional diagnosis scheme relying on manual on-site troubleshooting, saving labor costs, and meanwhile, the maintenance personnel can know and handle the fault problem through the cloud diagnosis platform, realizing rapid response and shortening the response cycle.

[0014] By the cloud diagnosis platform sending a recovery instruction to the intelligent module after the diagnosis ends to restore the log printing level of the intelligent module to a default state, the intelligent module of the intelligent equipment can immediately return to a safe and efficient default log state after completing fault troubleshooting, effectively protecting the information security of the intelligent equipment and avoiding long-term security and performance risks caused by improper log setting. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 It is a control principle diagram of a traditional intelligent equipment remote control and fault push system.

[0016] Figure 2 It is a flow step diagram of a remote diagnosis method for online intelligent equipment according to an embodiment of the present application.

[0017] Figure 3This is a flowchart illustrating a remote diagnostic method for an online intelligent device according to an embodiment of the present invention.

[0018] Figure 4 This is a flowchart illustrating the automatic early warning process of a remote diagnostic method for online intelligent devices according to an embodiment of the present invention.

[0019] Figure 5 This is a control principle diagram of a remote diagnostic system for an online intelligent device according to another embodiment of the present invention.

[0020] In the above attached diagram: 1. Smart device; 2. Cloud device management platform; 3. MQTT message service platform; 4. Cloud diagnostic platform; 5. Home router; 6. Mobile terminal; 11. Smart module; 12. Electrical control terminal. Detailed Implementation

[0021] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] like Figures 2-4 As shown, this embodiment of the invention provides a remote diagnostic method for online intelligent devices, including the following steps:

[0023] S1. After receiving user feedback, the cloud diagnostic platform 4 queries the basic information of the smart device 1 to be diagnosed based on the diagnostic request from the user or maintenance personnel.

[0024] The cloud-based diagnostic platform 4 queries the basic information of the device based on diagnostic requests from users or maintenance personnel, specifically including:

[0025] Users or maintenance personnel can log in to the cloud diagnostic platform 4 and, after logging in, input a diagnostic request to the cloud diagnostic platform 4. The diagnostic request includes the unique identification information of the smart device 1 to be diagnosed.

[0026] After receiving the unique identifier information, the cloud diagnostic platform 4 sends an interface request to the cloud device management platform 2, enabling the cloud device management platform 2 to query the basic information of the smart device 1 to be diagnosed stored in its own database.

[0027] Specifically, when the smart device 1 malfunctions, and the user reports the problem to the maintenance personnel, the maintenance personnel will first log in to the cloud diagnostic platform 4 and access its remote diagnostic page. After logging in, the maintenance personnel will input a diagnostic request into the remote diagnostic page of the cloud diagnostic platform 4. This diagnostic request includes the unique identifier information of the smart device 1 to be diagnosed (specifically, the SN code of the smart device 1, i.e., the serial number code). After receiving the unique identifier information of the smart device 1 to be diagnosed from the user, the cloud diagnostic platform 4 will send a corresponding interface request to the cloud device management platform 2 based on the unique identifier information. This will allow the cloud device management platform 2 to query the basic information of the smart device 1 to be diagnosed stored in its own database. The basic information includes the device's registration and connection status mode, registration region, and bound customer contact information (such as the customer's mobile phone number).

[0028] If the device is in online mode (the online smart device 1 has already been activated and registered on the cloud device management platform 2 during its device configuration and binding process), remote diagnostics can continue; otherwise, it indicates that the device does not support remote diagnostics. If the device is in offline mode, it is determined that the device has completed registration and binding, but it is currently not connected to the network and cannot be further remotely diagnosed. However, the user can be contacted based on the user's registration information to request the user to connect the device to the network. If the device cannot be found in either offline or online mode, it indicates that the device has not been registered and bound, and the remote diagnostic function cannot be completed.

[0029] S2. When the device is in online mode, the cloud diagnostic platform 4 sends a first instruction to the device's smart module 11 to obtain and analyze the historical data stored inside the smart module 11 that reflects its basic operating status, and performs the first fault diagnosis.

[0030] The first instruction is used to acquire and analyze the data stored in the flash of the smart module 11. The data includes the number of software restarts, the number of hardware restarts, the account information of the home router 5 connected to the device, the number of times the device connects to the MQTT message service platform 3, and the encrypted data key information of the device.

[0031] Specifically, after the device is in online mode, the cloud diagnostic platform 4 sends the first instruction to the smart module 11 of the smart device 1 to be diagnosed through the cloud device management platform 2, requesting the smart module 11 to return the data stored in its own flash. The data includes the number of software restarts (restarts of the smart module 11 application caused by software reasons), the number of hardware restarts (restarts of the smart module 11 application caused by hardware reasons), the account information of the home router 5 connected to the device (router name, password, encryption method, router network signal strength), the number of times the device connects to the MQTT message service platform 3, and the device's encrypted data key information, etc.

[0032] After receiving the first instruction, the intelligent module 11 will package and report the aforementioned data and forward it to the cloud diagnostic platform 4 through the cloud device management platform 2. Upon receiving the data, the cloud diagnostic platform 4 can analyze it for initial fault diagnosis, based on the following criteria:

[0033] (1) Determine whether the smart module 11 is a hardware problem (hard reboot) or a software problem (software) based on the number of software and hardware restarts of the smart device 1.

[0034] (2) Based on the account information of the home router 5 and the number of times the smart device 1 connects to the MQTT message service platform 3, locate the user's home router 5 configuration problem, network quality problem and stability problem of the MQTT message service platform 3.

[0035] S3. If the fault is not located in the first fault diagnosis, the cloud diagnosis platform 4 sends a second instruction to the intelligent module 11 to dynamically adjust the log printing level of the intelligent module 11 in order to obtain and analyze its real-time operation log containing the data interaction status with external components, and perform a second fault diagnosis.

[0036] The second instruction adjusts the log printing level of the intelligent module 11 to the debug level, thereby obtaining the serial communication interaction log between the intelligent module 11 and the electronic control terminal 12, as well as the network communication interaction log between the intelligent module 11 and the MQTT message service platform 3.

[0037] Specifically, if the first fault diagnosis fails to locate the fault, i.e., the fault problem of the smart device 1 is not diagnosed, the cloud diagnosis platform 4 continues to send the second instruction to the smart module 11 of the smart device 1 to be diagnosed through the cloud device management platform 2, and dynamically adjusts the log printing level of the smart module 11 (the printing information of the smart module 11 contains part of the enterprise's communication protocol. For product safety considerations, the printing level of the smart module 11 of the smart device 1 is at the lowest after leaving the factory, and the effective printing logs are also in a masked state) to the debug level, so as to further obtain more detailed timestamped operation logs of the smart module 11. The more detailed timestamped operation logs of the smart module 11 include the serial communication interaction logs between the smart module 11 and the electronic control terminal 12, and the network communication interaction logs between the smart module 11 and the MQTT message service platform 3.

[0038] After obtaining its own operation log, the intelligent module 11 will report the operation log to the cloud diagnostic platform 4 through the cloud device management platform 2. After receiving the operation log, the cloud diagnostic platform 4 can analyze the operation log for a second fault diagnosis. This allows for systematic monitoring of the entire data flow of the intelligent device 1, and can quickly locate faults such as serial port connection failures between the electronic control terminal 12 and the intelligent module 11, and data inability to be parsed by the MQTT message service platform 3 due to key synchronization issues between the intelligent module 11 and the MQTT message service platform 3.

[0039] S4. After the fault diagnosis is completed, the cloud diagnosis platform 4 sends a third instruction to the intelligent module 11 to restore the log printing level of the intelligent module 11 to the default state.

[0040] The third instruction is used to restore the log printing level of the smart module 11 to the lowest level set by factory default.

[0041] Specifically, to protect the confidentiality of relevant technical protocols, the user defaults to setting the printing level to the lowest when using the smart device 1, and printing permissions are only granted during remote diagnostics.

[0042] Furthermore, it also includes the following steps:

[0043] S5. Before receiving user feedback, the cloud diagnostic platform 4 continuously receives and parses the operating status data and abnormal event information actively reported by the smart device 1 through its internal pre-embedded fault reporting interface.

[0044] S6. When the frequency of reporting of a specific type of abnormal event within a preset time period or the number of smart devices 1 that trigger the event reaches a preset warning threshold, the warning mechanism is automatically triggered.

[0045] Specifically, the smart device 1 has several pre-embedded fault reporting interfaces. When the smart device 1 malfunctions during operation, the corresponding fault reporting interface will report the corresponding fault problem to the cloud diagnostic platform 4 through the cloud device management platform 2. This enables the cloud diagnostic platform 4 to continuously receive and parse the operating status data and abnormal event information actively reported by the fault reporting interface of the smart device 1. When the reporting frequency of a specific type of abnormal event within a preset time period or the number of smart devices 1 that trigger the event reaches a preset warning threshold, an early warning mechanism will be automatically triggered.

[0046] Furthermore, it also includes the following steps:

[0047] S7. After the early warning mechanism is triggered, the cloud-based diagnostic platform 4 will send the early warning information to the back-end administrators to proactively remind them.

[0048] Specifically, the faults of the intelligent device 1 include frequent device online / offline cycles, device restarts and reconnections, serial port data packet loss, key update failures, multiple failed device upgrades, frequent data reporting to the MQTT message service platform 3, and multiple failed HTTP calls. If the intelligent device 1 experiences any of these abnormal states during operation, it can remotely report relevant information to the corresponding cloud-based device management platform 2 via a specified interface.

[0049] (1) Frequent device online / offline (cloud-based self-monitoring) - Number of times device goes online / offline and the time of occurrence

[0050] (2) Device restart and reconnection - Device restart, number of reconnections, and time of occurrence

[0051] (3) Serial port data packet loss - number of serial port data packets lost and time of occurrence

[0052] (4) Key update failure - time of occurrence

[0053] (5) Multiple device upgrade failures - time and reason for device (WiFi / MCU) upgrade failure

[0054] (6) The device frequently reports data to the MQTT message service platform 3 — the number of times the device reports data to the MQTT platform.

[0055] (7) The device failed to call HTTP multiple times - the interface where the device failed to call HTTP and the time of occurrence.

[0056] After the early warning mechanism is triggered, the cloud-based diagnostic platform 4 pushes the early warning information (including the type of abnormal event, the time of first occurrence, the cumulative number of triggered devices, and a list of affected device identifiers (such as SN codes)) to the mobile terminal 6 pre-set by the user (specifically the backend administrator) of the cloud-based diagnostic platform 4 through the backend management system interface or message push. This proactively alerts the backend administrator. Subsequently, the backend administrator can use the fault early warning interface of the cloud-based diagnostic platform 4 to filter and query the product type, module model, software archive number, software version number, material code, and time period settings (where the time period can be quickly selected: the last three months, the last six months, the last year, or manually entered and clicked on the query button) to obtain the abnormal problems that occur in the operation of the smart device 1. This allows the backend administrator to know the fault problems of the smart device 1, so that they can know the location of the fault problems of the smart device 1 in advance before the user reports the problem, in order to further determine whether the fault is an isolated case or a batch problem, and to assess whether the software of the smart device 1 needs to be upgraded in batches to solve the fault problem.

[0057] The detailed working process of this embodiment is as follows:

[0058] Before the user reports a problem, the cloud diagnostic platform 4 continuously receives and parses the operating status data and abnormal event information actively reported by the smart device 1 through its internal pre-embedded fault reporting interface. When the frequency of reporting of a specific type of abnormal event within a preset time period or the number of smart devices 1 that trigger the event reaches a preset warning threshold, the cloud diagnostic platform 4 automatically triggers the warning mechanism. At this time, the cloud diagnostic platform 4 sends the warning information to the back-end administrator to proactively remind the back-end administrator, thereby making it easier for the back-end administrator to know about the fault problem of the smart device 1.

[0059] When maintenance personnel receive user feedback, they first log in to the cloud diagnostic platform 4 and then input a diagnostic request to the platform. This request includes the unique identifier of the smart device 1 to be diagnosed. Upon receiving this unique identifier, the cloud diagnostic platform 4 sends a corresponding interface request to the cloud device management platform 2. This allows the platform to query its stored basic information about the smart device 1, including its registration and connection status, registration region, and associated customer contact information.

[0060] After the device is in online mode, the cloud diagnostic platform 4 sends the first instruction to the smart module 11 of the smart device 1 to be diagnosed through the cloud device management platform 2, requesting the smart module 11 to return the data stored in its own flash memory. After receiving the first instruction, the smart module 11 packages and reports the data and forwards it to the cloud diagnostic platform 4 through the cloud device management platform 2, so that the cloud diagnostic platform 4 can perform the first fault diagnosis based on the data. If the first fault diagnosis fails to locate the fault, the cloud diagnostic platform 4 continues to send the second instruction to the smart module 11 of the smart device 1 to be diagnosed through the cloud device management platform 2, dynamically adjusting the log printing level of the smart module 11 to the debug level, so as to further obtain more detailed timestamped operation logs of the smart module 11. After obtaining its own operation logs, the smart module 11 will report the operation logs to the cloud diagnostic platform 4 through the cloud device management platform 2, so that the cloud diagnostic platform 4 can perform the second fault diagnosis after receiving the operation logs. Through two sequential fault diagnoses performed by the cloud-based diagnostic platform 4, the platform can remotely diagnose the faults of the intelligent module 11 in an orderly and progressively in-depth manner. This allows for a more systematic and accurate identification of the root cause of the problem. Compared with traditional diagnostic solutions that rely on manual on-site inspections, this significantly improves the accuracy of fault location and saves on labor costs.

[0061] After the fault diagnosis is completed, the cloud diagnostic platform 4 sends a recovery command to the smart module 11 to restore the log printing level of the smart module 11 to the default state (lowest level). This allows the smart module 11 of the smart device 1 to immediately return to the safe and efficient default log state after the fault is diagnosed, effectively protecting the information security of the smart device 1 and avoiding long-term security and performance risks caused by improper log settings.

[0062] like Figure 5As shown, another embodiment of the present invention also proposes a remote diagnostic system for online intelligent devices, including an intelligent device 1, a cloud device management platform 2, and an MQTT message service platform 3. The intelligent device 1 includes an intelligent module 11 and an electronic control terminal 12. The intelligent module 11 is communicatively connected to the electronic control terminal 12, the cloud device management platform 2, and the MQTT message service platform 3, respectively. The cloud device management platform 2 and the MQTT message service platform 3 are also communicatively connected. The system also includes a cloud diagnostic platform 4, which is communicatively connected to the cloud device management platform 2. The cloud diagnostic platform 4 is used to receive diagnostic requests from users or maintenance personnel, and based on the diagnostic requests, to initiate two-level remote diagnostic commands to the intelligent module 11 in sequence to diagnose the faults of the intelligent module 11 itself. The cloud diagnostic platform 4 is also used to send a recovery command to the intelligent module 11 after the diagnosis is completed to restore the log printing level of the intelligent module 11 to the default state.

[0063] Specifically, the first-level instruction in the two-level remote diagnostic command system retrieves historical data reflecting the basic operating status of the intelligent module 11 stored internally, while the second-level instruction retrieves real-time operational logs showing the data interaction status between the intelligent module 11 and external components. This enables the cloud-based diagnostic platform 4 to perform remote diagnosis of faults in the intelligent module 11 in an orderly and progressively in-depth manner, thereby locating the root cause of the problem more systematically and accurately. The historical data reflecting the basic operating status stored internally in the intelligent module 11 specifically refers to data stored in the flash memory of the intelligent module 11 (including the number of software restarts, hardware restarts, account information of the home router 5 connected to the device, the number of times the device connects to the MQTT message service platform 3, and the device's encrypted data key information, etc.). The real-time operational logs showing the data interaction status between the intelligent module 11 and external components include serial communication logs between the intelligent module 11 and the electronic control terminal 12, as well as network communication logs between the intelligent module 11 and the MQTT message service platform 3.

[0064] Furthermore, it also includes a mobile terminal 6, which is communicatively connected to the cloud diagnostic platform 4 and is used to receive user instructions and send control commands or diagnostic requests to the cloud diagnostic platform 4; the mobile terminal 6 is also communicatively connected to the MQTT message service platform 3 and is used to receive and display the device status information and fault diagnosis results of the smart device 1 sent by the cloud diagnostic platform 4 or the MQTT message service platform 3.

[0065] Specifically, the mobile terminal 6 (such as a smartphone, tablet, etc.) can view the operating status of the smart device 1 in real time, receive fault warning information, and initiate diagnostic programs or submit maintenance requests through mobile applications such as mobile APP or WeChat mini program. At the same time, for complex equipment faults, maintenance personnel can use the mobile terminal 6 to remotely access and control the smart device 1 to perform in-depth configuration checks, log analysis, and troubleshooting, thereby solving geographical limitations and allowing maintenance personnel to solve the fault problems of the smart device 1 without being physically present on site.

[0066] Furthermore, it also includes a home router 5, which is communicatively connected to the smart module 11 and is used to provide local wireless network access for the smart device 1.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A remote diagnostic method for online intelligent devices, characterized in that, Includes the following steps: After receiving user feedback, the cloud-based diagnostic platform queries the basic information of the smart device to be diagnosed based on the diagnostic request from the user or maintenance personnel. When the device is in online mode, the cloud diagnostic platform sends a first instruction to the device's smart module to obtain and analyze historical data stored inside the smart module that reflects its basic operating status, and performs the first fault diagnosis. If the first fault diagnosis fails to locate the fault, the cloud-based diagnostic platform sends a second instruction to the intelligent module to dynamically adjust the log printing level of the intelligent module in order to obtain and analyze its real-time operation log containing the data interaction status with external components, and perform a second fault diagnosis. After the fault diagnosis is completed, the cloud-based diagnostic platform sends a third instruction to the intelligent module to restore the intelligent module's log printing level to the default state.

2. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, The cloud-based diagnostic platform queries the basic information of the device based on diagnostic requests from users or maintenance personnel, specifically including: Users or maintenance personnel can log in to the cloud diagnostic platform and, after logging in, input a diagnostic request to the cloud diagnostic platform. The diagnostic request includes the unique identification information of the smart device to be diagnosed. After receiving the unique identifier information, the cloud-based diagnostic platform sends an interface request to the cloud-based device management platform, enabling the cloud-based device management platform to query the basic information of the smart device to be diagnosed stored in its own database.

3. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, The basic information of the smart device to be diagnosed includes the device's registration and connection status mode, registration region, and bound customer contact information.

4. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, The first instruction is used to acquire and analyze the data stored in the flash of the smart module. The data includes the number of software restarts, the number of hardware restarts, the home router account information connected to the device, the number of times the device connects to the MQTT message service platform, and the encrypted data key information of the device.

5. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, The second instruction adjusts the log printing level of the intelligent module to the debug level, and obtains the serial communication interaction log between the intelligent module and the electronic control terminal, as well as the network communication interaction log between the intelligent module and the MQTT message service platform.

6. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, The third instruction is used to restore the log printing level of the smart module to the lowest level that is the factory default.

7. The remote diagnostic method for an online intelligent device as described in claim 1, characterized in that, It also includes the following steps: Before receiving user feedback, the cloud-based diagnostic platform continuously receives and parses operational status data and abnormal event information proactively reported by smart devices through their internally embedded fault reporting interfaces. When the frequency of reporting a specific type of abnormal event or the number of smart devices that trigger the event within a preset time period reaches a preset warning threshold, the warning mechanism is automatically triggered.

8. A remote diagnostic system for online intelligent devices, employing the remote diagnostic method for online intelligent devices as described in any one of claims 1-7, characterized in that, It includes intelligent devices, a cloud-based device management platform, and an MQTT message service platform. The intelligent devices include intelligent modules and electronic control terminals. The intelligent modules are communicatively connected to the electronic control terminals, the cloud-based device management platform, and the MQTT message service platform, respectively. The cloud-based device management platform and the MQTT message service platform are also communicatively connected. It also includes a cloud-based diagnostic platform, which is communicatively connected to the cloud-based device management platform. The cloud-based diagnostic platform is used to receive diagnostic requests from users or maintenance personnel, and based on the diagnostic requests, to send two-level remote diagnostic commands to the intelligent module in sequence to diagnose the faults of the intelligent module itself. The cloud-based diagnostic platform is also used to send a recovery command to the intelligent module after the diagnosis is completed, so as to restore the log printing level of the intelligent module to the default state.

9. The remote diagnostic system for online intelligent devices as described in claim 8, characterized in that, It also includes a mobile terminal, which is communicatively connected to the cloud diagnostic platform and is used to receive user instructions and send control commands or diagnostic requests to the cloud diagnostic platform; the mobile terminal is also communicatively connected to the MQTT message service platform and is used to receive and display the device status information and fault diagnosis results of the smart device sent by the cloud diagnostic platform or the MQTT message service platform.

10. The remote diagnostic system for online intelligent devices as described in claim 8, characterized in that, It also includes a home router, which is communicatively connected to the smart module to provide local wireless network access for the smart device.

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