Fault processing method, system and device, electronic equipment, storage medium and product

Abstract

The invention provides a fault processing method, system and device, electronic equipment, a storage medium and a product, and the method comprises the steps: determining a fault detection result in response to a fault detection instruction, and obtaining user perception information and third-party perception information; determining a fault priority based on the fault detection result, the user perception information, the third-party perception information and a preset fault level; determining a fault early warning mode based on the fault priority; and performing fault popup window early warning and fault voice broadcast based on the fault early warning mode. According to the method disclosed by the invention, the fault priority is determined by utilizing the fault detection result, the user perception information, the third-party perception information and the preset fault level, so that dynamic determination of the fault priority can be realized, a proper maintenance sequence is provided for maintenance personnel, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of fault handling, and in particular to a fault handling method, system, device, electronic device, storage medium and product. Background Art

[0002] With the rapid development of information technology, servers can be managed and controlled through the baseboard management controller (BMC) remote intelligent platform management (IPMI) interface or Redfish interface, providing a series of configuration functions such as obtaining server asset information and user configuration, as well as fault diagnosis. IPMI is a management interface independent of the operating system and application. Even when the system crashes or is not started, the administrator can remotely access and manage the server's hardware resources through the IPMI interface. Through it, the server's hardware can be remotely accessed and managed.

[0003] However, due to the professionalism and complexity of the IPMI protocol, when a server system fails, professional operation and maintenance personnel are required to perform fault analysis based on information such as server logs to determine the priority of fault repair, which affects the efficiency of fault repair. Summary of the invention

[0004] The present disclosure provides a fault handling method, system, device, electronic device, storage medium and product, which can dynamically determine the priority level of faults according to the user's concern for different faults, so as to provide maintenance personnel with a suitable maintenance sequence and improve maintenance efficiency.

[0005] The first aspect of the present disclosure proposes a fault handling method, including: responding to a fault detection instruction, determining a fault detection result, and obtaining user perception information and third-party perception information; determining a fault priority based on the fault detection result, user perception information, third-party perception information and a preset fault level; determining a fault warning method based on the fault priority; and performing a fault pop-up warning and a fault voice broadcast based on the fault warning method.

[0006] In some embodiments of the present disclosure, obtaining user perception information includes: obtaining at least one of fault query voice information, equipment monitoring operation information, and user-defined attention information; based on the fault query voice information, using a preset voice recognition model, determining semantic features in the fault query voice information; based on the equipment monitoring operation information, determining a query thermal distribution map; and determining at least one of the semantic features, the query thermal distribution map, and the user-defined attention information as user perception information.

[0007] In some embodiments of the present disclosure, determining the fault priority based on fault detection results, user perception information, third-party perception information and preset fault levels includes: determining the user's fault concern based on the fault detection results and user perception information; determining the recommended fault concern using a clustering mining algorithm based on the fault detection results and third-party perception information; determining the fault priority based on the user's fault concern, the recommended fault concern and the preset fault level.

[0008] In some embodiments of the present disclosure, determining the user's fault concern based on fault detection results and user perception information includes: determining historical operation events in the user perception information and a first operation time corresponding to the historical operation events based on the fault detection results; determining the user's fault concern based on the first operation time, the second operation time corresponding to the fault detection result, and the operation type of the historical operation event, the operation type including: voice operation and action operation.

[0009] In some embodiments of the present disclosure, based on the fault detection results and the third-party perception information, a cluster mining algorithm is used to determine the recommended fault concern, including: based on the third-party perception information, determining an information feature vector; based on the information feature vector, using a clustering algorithm, clustering the information feature vector to obtain attention clustering data; based on the attention clustering data, using a frequent mining algorithm, determining a recommended fault concern list; based on the fault detection results, determining a recommended fault concern from the recommended fault concern list.

[0010] In some embodiments of the present disclosure, determining the fault priority based on the user's fault concern, the recommended fault concern and the preset fault level includes: determining a first weight by using a decision tree model based on historical fault handling data within a preset time, the historical fault handling data including at least: fault handling time, user case solution adoption rate and fault warning accuracy; determining the fault priority by using weighted summation based on the user's fault concern, the recommended fault concern, the preset fault level and the first weight.

[0011] The second aspect of the present disclosure proposes a fault handling system, including: a voice recognition chip and a device management control module; the device management control module is used to respond to a fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information; based on the fault detection result, user perception information, third-party perception information and preset fault level, determine the fault priority; based on the fault priority, determine the fault warning method; based on the fault warning method, perform a fault pop-up warning; a voice recognition chip is used to perform fault voice broadcast based on the fault warning method; wherein the voice recognition chip is communicatively connected to the device management control module via a serial communication protocol.

[0012] The third aspect embodiment of the present disclosure proposes a fault handling device, including: a response unit, used to respond to a fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information; a first determination unit, used to determine the fault priority based on the fault detection result, user perception information, third-party perception information and a preset fault level; a second determination unit, used to determine the fault warning method based on the fault priority; and a warning unit, used to perform fault pop-up warning and fault voice broadcast based on the fault warning method.

[0013] The fourth aspect embodiment of the present disclosure proposes an electronic device, comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor, when used to run the computer program, executes the method described in the first aspect embodiment of the present disclosure.

[0014] The fifth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the method described in the first aspect embodiment of the present disclosure.

[0015] The sixth aspect embodiment of the present disclosure proposes a computer program product, including a computer program, which implements the method described in the first aspect embodiment of the present disclosure when executed by a processor.

[0016] In summary, a fault handling method provided by the present disclosure includes: responding to a fault detection instruction, determining a fault detection result, and obtaining user perception information and third-party perception information; determining a fault priority based on the fault detection result, user perception information, third-party perception information, and a preset fault level; determining a fault warning method based on the fault priority; and performing a fault pop-up warning and a fault voice broadcast based on the fault warning method. The method disclosed herein determines the fault priority by utilizing the fault detection result, user perception information, third-party perception information, and a preset fault level, and can achieve dynamic determination of the fault priority, thereby providing maintenance personnel with a suitable maintenance sequence and improving maintenance efficiency.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0019] Figure 1 A flowchart of a fault handling method provided by an embodiment of the present disclosure;

[0020] Figure 2 A flowchart of another fault handling method provided by an embodiment of the present disclosure;

[0021] Figure 3 A flowchart of another fault handling method provided by an embodiment of the present disclosure;

[0022] Figure 4 A schematic diagram of a fault handling system provided by an embodiment of the present disclosure;

[0023] Figure 5 A schematic diagram of the structure of a fault handling device provided by an embodiment of the present disclosure;

[0024] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0026] With the rapid development of information technology, servers can be managed and controlled through the baseboard management controller (BMC) remote intelligent platform management (IPMI) interface or Redfish interface, providing a series of configuration functions such as obtaining server asset information and user configuration, as well as fault diagnosis. IPMI is a management interface independent of the operating system and application. Even when the system crashes or is not started, the administrator can remotely access and manage the server's hardware resources through the IPMI interface. Through it, the server's hardware can be remotely accessed and managed.

[0027] However, due to the professionalism and complexity of the IPMI protocol, when a server system fails, professional operation and maintenance personnel are required to perform fault analysis based on information such as server logs to determine the priority of fault repair, which affects the efficiency of fault repair.

[0028] In order to solve the technical problems existing in the related art, an embodiment of the present disclosure provides a fault handling method.

[0029] Before describing the embodiments of the present disclosure in detail, the technical terms involved in the present disclosure are explained below.

[0030] The Baseboard Management Controller (BMC) is an independent microcontroller integrated on the operating system motherboard, responsible for monitoring and managing the physical status of the operating system. BMC usually communicates with external management systems through the Intelligent Platform Management Interface (IPMI) to provide remote management and monitoring functions.

[0031] The embodiments of the present disclosure will be described in detail below.

[0032] like Figure 1 As shown, an embodiment of the present disclosure provides a fault handling method, comprising the following steps:

[0033] Step 101, in response to a fault detection instruction, determining a fault detection result, and acquiring user perception information and third-party perception information.

[0034] In some embodiments, the fault detection instruction may be a voice instruction or an operation instruction.

[0035] In some embodiments, the fault detection result may include information such as the fault location, detection event, and maintenance suggestions.

[0036] In some embodiments, the user perception information may be the user's behavior information, such as the frequency of the user's query on the server component status, the user's query location, etc., which is not limited in this disclosure.

[0037] In some embodiments, the third-party perception information may be data on the degree of concern of users in the same industry and user enterprises of the same size regarding equipment failures.

[0038] In some embodiments, the user perception information can be obtained through the user's fault query voice information, the user's equipment monitoring operation information, and the user's customized attention information.

[0039] In some embodiments, third-party perception information can be obtained by accessing a third-party enterprise's database through the cloud.

[0040] Step 102, determining the fault priority based on the fault detection result, user perception information, third-party perception information and preset fault level.

[0041] In some embodiments, one or more fault locations of the server can be determined through the fault detection results; then, based on the determined fault locations, the perception information corresponding to the fault locations is determined from the user perception information and the third-party perception information, so as to determine the fault priority using the corresponding perception information and the preset fault level.

[0042] In some embodiments, since users have different concerns about faults in different areas of the server, the user's concern about the fault location can be determined based on information such as user operation time and operation frequency in the user perception information.

[0043] In some embodiments, the third perception information can be clustered, frequently mined, and other operations performed to obtain the attention distribution of different fault locations in the third perception information, that is, the recommended fault attention.

[0044] In some embodiments, the fault priority of the fault location is determined by using the user fault concern, recommended fault concern and preset fault level obtained as described above.

[0045] Step 103: determine the fault warning method based on the fault priority.

[0046] In some embodiments, since faults of different degrees have different impacts on the server, different fault warning methods may be assigned to different fault priorities to distinguish the urgency of the fault.

[0047] In some embodiments, the fault warning method can be determined by querying a preset comparison table.

[0048] For example, when the fault priority value is (0.8, 1.0], it is determined to be a red warning, and an immediate report is given (for example, "Serious warning! GPU0 temperature reaches 95°C and must be shut down immediately! [Repeat 3 times]"), and a UI pop-up window is overlaid.

[0049] For example, when the fault priority value is (0.6, 0.8], it is determined as an orange alarm, and a report is made (for example, "Attention: CPU load continues to exceed"), and a historical solution prompt is given.

[0050] Step 104, based on the fault warning method, a fault pop-up warning and a fault voice broadcast are performed.

[0051] In some embodiments, a fault pop-up window warning is performed using a display device through a determined fault warning method, and a fault voice broadcast is performed through a voice playback device.

[0052] In summary, the fault handling method proposed in the present disclosure includes: responding to a fault detection instruction, determining a fault detection result, and obtaining user perception information and third-party perception information; determining a fault priority based on the fault detection result, user perception information, third-party perception information, and a preset fault level; determining a fault warning method based on the fault priority; and performing a fault pop-up warning and a fault voice broadcast based on the fault warning method. The method disclosed in the present disclosure determines the fault priority by utilizing the fault detection result, user perception information, third-party perception information, and a preset fault level, and can achieve dynamic determination of the fault priority, thereby providing maintenance personnel with a suitable maintenance sequence and improving maintenance efficiency.

[0053] Figure 2 A flowchart of a fault handling method proposed in the present disclosure is further shown. Figure 1 The embodiment shown further explains step 101. Figure 2 The following steps may be included.

[0054] Step 201, obtaining at least one of fault query voice information, equipment monitoring operation information and user-defined attention information.

[0055] In some embodiments, a voice identification chip (such as an NRK330 chip) can be used to obtain fault query voice information sent by a user, wherein the fault query voice information is, for example, "check CPU temperature".

[0056] In some embodiments, the device monitoring operation information may be the area and frequency at which the user views the monitoring page, where the area may be, for example, the CPU monitoring page / memory status page / storage page, and the frequency may be, for example, 3 times / minute.

[0057] In some embodiments, the user-defined attention information may be a key monitoring area or a mandatory marking area set by the user. For example, if the user sets priority monitoring of GPU0 video memory load, then GPU0 video memory load is the user-defined attention information.

[0058] Optionally, the acquired fault query voice information, equipment monitoring operation information and user-defined attention information may also be stored in a preset user behavior database to record data such as the operation type, operation timestamp, context information, etc.

[0059] Step 202: Based on the fault query voice information, a preset voice recognition model is used to determine semantic features in the fault query voice information.

[0060] In some embodiments, the preset speech recognition model is, for example, an LSTM-ASR model, but is not limited thereto and the present disclosure is not limited thereto.

[0061] In some embodiments, keywords in the fault query voice information are identified by a preset voice recognition model, and semantic features are extracted by semantic analysis of the keywords.

[0062] For example, take the fault query voice information of "query CPU temperature" as an example, the recognized keywords are "CPU" and "temperature", and then the LSTM-ASR model is used to perform semantic analysis on "CPU" and "temperature" to extract semantic features and generate event tags (for example: event type, occurrence timestamp, hardware directionality).

[0063] Step 203: determining to query a thermal distribution map based on the equipment monitoring operation information.

[0064] In some embodiments, the device can monitor operation information to determine the user's viewing frequency of different areas, thereby generating a thermal distribution map based on the different viewing frequencies, wherein the thermal distribution map is used to indicate the user's attention to different areas.

[0065] Step 204 : determining at least one of the semantic feature, the query heat distribution map, and the user-defined attention information as user-perceived information.

[0066] In some embodiments, semantic features, query thermal distribution maps, and user-defined attention information may all be determined as user-perceived information, or only part of the semantic features, query thermal distribution maps, and user-defined attention information may be determined as user-perceived information, which is not limited in the present disclosure.

[0067] In summary, the fault handling method proposed in the present disclosure includes: obtaining at least one of fault query voice information, equipment monitoring operation information, and user-defined attention information; based on the fault query voice information, using a preset voice recognition model, determining the semantic features in the fault query voice information; based on the equipment monitoring operation information, determining the query thermal distribution map; determining at least one of the semantic features, the query thermal distribution map, and the user-defined attention information as user perception information. The method disclosed in the present disclosure achieves the acquisition of user perception information through multiple channels, ensures the diversity of user perception information, and thus improves the accuracy of fault priority determination.

[0068] Figure 3 A flowchart of a fault handling method proposed in the present disclosure is further shown. Figure 1 The embodiment shown further explains step 102. Figure 3 The following steps may be included.

[0069] Step 301: Determine the user's fault concern based on the fault detection result and user perception information.

[0070] In some embodiments, the historical operation events and the first operation time corresponding to the fault type in the user perception information can be determined according to the fault type in the fault monitoring result; then, the user's fault concern can be determined based on the first operation time, the second operation time corresponding to the fault detection result, and the operation type of the historical operation event.

[0071] For example, taking the fault detection result of the CPU temperature being too high as an example, when the fault detection result is that the CPU temperature is too high, the time when the user historically inquired about the CPU temperature (i.e., the first operation time) and the type of query made by the user on the CPU temperature (such as voice inquiry or monitoring view) can be determined through user perception information.

[0072] In an optional embodiment, the user's fault concern degree may be determined by the following formula:

[0073] User concern = ∑(operation event weight × e-λ·Δt)

[0074] Among them, the weight of the operation item can be determined according to the operation type. For example, when the operation type is voice operation, the weight is set to 0.6; when the operation type is action operation, the weight is set to 0.4. Δt is the time difference between the first operation time and the second operation time, and λ is the attenuation coefficient (for example, the default is 0.1 / hour, which can be automatically adjusted to 0.2 / hour in the face of high failure periods).

[0075] For example, if the user queried "CPU temperature" 1 hour ago, then Δt = 1: e-0.1×1≈0.9048, and if the query for "CPU temperature" was 5 hours ago, then e-0.1×5≈0.6065. This setting can avoid the indiscriminate accumulation of weights of historical operations and give priority to reflecting the user's recent behavior preferences.

[0076] In some optional embodiments, for periodic operations (such as instruction operation refresh every 10 minutes), Δt may be determined by segmented cumulative calculation.

[0077] Step 302: Based on the fault detection result and the third-party perception information, a cluster mining algorithm is used to determine the recommended fault concern.

[0078] In some embodiments, an information feature vector can be determined based on third-party perception information; then, based on the information feature vector, the information feature vector can be clustered using a clustering algorithm to obtain attention clustering data; and based on the attention clustering data, a frequent mining algorithm can be used to determine a recommended fault concern list; finally, based on the fault detection result, a recommended fault concern is determined from the recommended fault concern list.

[0079] Specifically, the information feature vector can be determined from the third-party perception data by means of data compression, wherein the feature vector is, for example: F = [click rate, voice complexity, attention hardware ID vector, historical solution score, ...].

[0080] In some embodiments, attention clustering data can be obtained by dynamically dividing the attention clustering data based on a sliding time window through an optimized version of the DBSCAN algorithm, wherein examples of adjusted hyperparameter values ​​are Eps=3 (Manhattan distance), MinPts=50, but not limited to this. The present disclosure does not limit the clustering algorithm used to obtain attention clustering data.

[0081] In some embodiments, the FP-Growth mining frequent item set model can be used to identify the attention levels of different fault types in the attention clustering data, thereby determining the recommended fault attention level. It should be understood that the present disclosure does not limit the frequent mining algorithm or model used to determine the recommended fault attention level.

[0082] Step 303: Determine the fault priority based on the user's fault concern, the recommended fault concern and the preset fault level.

[0083] In some embodiments, a first weight can be determined based on historical fault processing data within a preset time using a decision tree model; and then the fault priority can be determined using weighted sum based on user fault concern, recommended fault concern, preset fault level and the first weight.

[0084] In some embodiments, the historical fault handling data includes at least: fault handling time, user case solution adoption rate and fault warning accuracy.

[0085] In some embodiments, the LightGBM model can be used to iteratively analyze historical fault handling data (e.g., fault handling duration in the past 30 days, user case solution adoption rate, and alarm accuracy) until the applicable scenario rate of the determined weight is greater than a preset threshold (e.g., the determined first weight should be greater than 80% of the scenarios), and the corresponding weight is determined as the first weight.

[0086] In some embodiments, the fault priority may be determined by the following formula:

[0087] Priority = α·U+β·G+γ·S

[0088] Among them, Priority is the fault priority, α, β and γ are the first weights, U is the user's fault concern, G is the recommended fault concern, and S is the preset fault level.

[0089] In summary, the fault handling method proposed in the present disclosure includes: determining the user's fault concern based on the fault detection result and the user's perception information; determining the recommended fault concern using a cluster mining algorithm based on the fault detection result and the third-party perception information; and determining the fault priority based on the user's fault concern, the recommended fault concern, and the preset fault level. The method disclosed in the present disclosure dynamically determines the fault priority through the user's fault concern, the recommended fault concern, and the preset fault level, thereby improving the rationality of the priority determination.

[0090] Figure 4 Further showing a system block diagram of a fault handling system proposed in the present disclosure, which can be applied to Figure 1-Figure 3 The method shown.

[0091] In some embodiments, the fault handling system 400 may include a voice recognition chip 402 and a device management control module 401 .

[0092] In some embodiments, the device management control module 401 is used to respond to fault detection instructions, determine fault detection results, and obtain user perception information and third-party perception information; determine the fault priority based on the fault detection results, user perception information, third-party perception information and preset fault levels; determine the fault warning method based on the fault priority; and issue a fault pop-up warning based on the fault warning method.

[0093] In some embodiments, the voice recognition chip 402 is used to perform fault voice broadcast based on the fault warning method.

[0094] In some embodiments, the voice recognition chip 401 is in communication with the device management control module 402 via a serial communication protocol.

[0095] In some embodiments, the device management control module 401 is, for example, a BMC.

[0096] In some examples, the voice recognition chip 401 is an ASCII chip such as an NRK330 chip.

[0097] In some embodiments, the voice recognition chip 401 may store fault warning methods to facilitate voice broadcasting of faults.

[0098] For example, after the device management control module 402 (such as BMC) performs automatic fault diagnosis and analysis, it transmits the information to the voice recognition chip 401 (such as ASCII) chip via the UART protocol, and plays the corresponding preset audio according to the fault diagnosis and analysis results.

[0099] In some embodiments, the voice recognition chip 401 can be used to obtain fault query voice information, such as receiving a "query CPU temperature" from a user.

[0100] In order to implement the fault handling method provided by the embodiment of the present disclosure, the embodiment of the present disclosure also provides a fault handling device, such as Figure 5 As shown, the fault handling device 500 includes:

[0101] The response unit 501 is used to respond to the fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information;

[0102] A first determining unit 502, configured to determine a fault priority based on a fault detection result, user perception information, third-party perception information, and a preset fault level;

[0103] A second determining unit 503 is used to determine a fault warning mode based on the fault priority;

[0104] The warning unit 504 is used to perform fault pop-up warning and fault voice broadcast based on the fault warning method.

[0105] In some embodiments, the response unit 501 is also used to: obtain at least one of fault query voice information, equipment monitoring operation information and user-defined attention information; based on the fault query voice information, determine the semantic features in the fault query voice information using a preset voice recognition model; based on the equipment monitoring operation information, determine the query thermal distribution map; determine at least one of the semantic features, query thermal distribution map and user-defined attention information as user perceived information.

[0106] In some embodiments, the first determination unit 502 is also used to: determine the user's fault concern based on the fault detection results and the user's perception information; determine the recommended fault concern using a clustering mining algorithm based on the fault detection results and the third-party perception information; determine the fault priority based on the user's fault concern, the recommended fault concern and the preset fault level.

[0107] In some embodiments, the first determination unit 502 is also used to: determine the historical operation events in the user perception information and the first operation time corresponding to the historical operation events based on the fault detection results; determine the user's fault concern based on the first operation time, the second operation time corresponding to the fault detection results, and the operation type of the historical operation events, the operation types including: voice operation and action operation.

[0108] In some embodiments, the first determination unit 502 is also used to: determine an information feature vector based on third-party perception information; cluster the information feature vector using a clustering algorithm based on the information feature vector to obtain attention clustering data; determine a recommended fault concern list using a frequent mining algorithm based on the attention clustering data; and determine a recommended fault concern from the recommended fault concern list based on the fault detection result.

[0109] In some embodiments, the first determination unit 502 is also used to: determine the first weight based on historical fault handling data within a preset time, using a decision tree model, where the historical fault handling data at least includes: fault handling time, user case adoption rate and fault warning accuracy; determine the fault priority by weighted summation based on user fault concern, recommended fault concern, preset fault level and the first weight.

[0110] In summary, the fault handling device proposed in the present disclosure includes: a response unit, which is used to respond to a fault detection instruction, determine a fault detection result, and obtain user perception information and third-party perception information; a first determination unit, which is used to determine the fault priority based on the fault detection result, user perception information, third-party perception information and a preset fault level; a second determination unit, which is used to determine the fault warning method based on the fault priority; and a warning unit, which is used to perform a fault pop-up window warning and a fault voice broadcast based on the fault warning method. The device of the present disclosure can dynamically determine the fault priority by using the fault detection result, user perception information, third-party perception information and a preset fault level to determine the fault priority, thereby providing maintenance personnel with a suitable maintenance sequence and improving maintenance efficiency.

[0111] It should be noted that: the fault handling device provided in the above embodiment only uses the division of the above program modules as an example when performing fault handling. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the fault handling device is divided into different program modules to complete all or part of the processing described above. In addition, the fault handling device provided in the above embodiment and the fault handling method embodiment provided in the embodiment of the present disclosure belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0112] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure is shown in FIG. Figure 6As shown, the electronic device 600 includes at least one processor 602; and a memory 601 communicatively connected to the at least one processor 602; wherein the memory 601 stores instructions executable by the at least one processor 602, and the instructions are executed by the at least one processor 602 to implement the steps of the fault handling method described in the embodiment of the present disclosure.

[0113] Optionally, the electronic device may specifically be a fault handling device in an embodiment of the present application, and the electronic device may implement the corresponding processes implemented by the fault handling device in each method in the embodiment of the present application, which will not be described in detail here for the sake of brevity.

[0114] It is understood that the electronic device also includes a communication interface 603. The various components in the electronic device are coupled together through a bus system 604. It is understood that the bus system 604 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 604 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Figure 6 Various buses are labeled as bus system 604 .

[0115] It can be understood that the memory 601 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAMbus random access memory (DRRAM).The memory 601 described in the embodiment of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0116] The method disclosed in the above embodiment of the present disclosure can be applied to the processor 602, or implemented by the processor 602. The processor 602 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the processor 602 or the instruction in the form of software. The above processor 602 may be a general-purpose processor, a DSP, or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 602 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 601, and the processor 602 reads the information in the memory 601 and completes the steps of the above method in combination with its hardware.

[0117] In an exemplary embodiment, the electronic device may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), FPGA, general purpose processor, controller, MCU, microprocessor, or other electronic components to execute the aforementioned method.

[0118] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the steps of the fault handling method described in the embodiment of the present disclosure when executed.

[0119] The embodiment of the present disclosure further provides a computer program product, including a computer program, which implements the steps of the fault handling method described in the embodiment of the present disclosure when executed by a processor.

[0120] Optionally, the computer-readable storage medium can be applied to the fault handling device in the embodiments of the present application, and the computer instructions enable the computer to execute the corresponding processes implemented by the fault handling device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0122] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0123] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0124] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, disks or optical disks.

[0125] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0126] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A fault handling method, characterized in that: The method comprises: In response to the fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information; Determining a fault priority based on the fault detection result, the user perception information, the third-party perception information and a preset fault level; Based on the fault priority, determine the fault warning method; Based on the fault warning method, a fault pop-up warning and a fault voice broadcast are performed.

2. The method according to claim 1, characterized in that The obtaining of user perception information comprises: Obtain at least one of fault query voice information, equipment monitoring operation information, and user-defined attention information; Based on the fault query voice information, using a preset voice recognition model, determining semantic features in the fault query voice information; Based on the equipment monitoring operation information, determining to query a thermal distribution map; At least one of the semantic feature, the query heat distribution map and the user-defined focus information is determined as the user perception information.

3. The method according to claim 1, characterized in that The determining of the fault priority based on the fault detection result, the user perception information, the third-party perception information and the preset fault level includes: Determining a user's fault concern based on the fault detection result and the user perception information; Based on the fault detection result and the third-party perception information, a cluster mining algorithm is used to determine a recommended fault concern; A fault priority is determined based on the user fault concern, the recommended fault concern and the preset fault level.

4. The method according to claim 3, characterized in that The determining of the user's fault concern based on the fault detection result and the user perception information includes: Based on the fault detection result, determining a historical operation event in the user perception information and a first operation time corresponding to the historical operation event; The user's fault concern is determined based on the first operation time, the second operation time corresponding to the fault detection result, and the operation type of the historical operation event, where the operation type includes: voice operation and action operation.

5. The method according to claim 3, characterized in that: Based on the fault detection result and the third-party perception information, using a cluster mining algorithm, determining the recommended fault concern level includes: Determining an information feature vector based on the third-party perception information; Based on the information feature vector, clustering the information feature vector using a clustering algorithm to obtain attention clustering data; Based on the attention clustering data, a recommended fault attention list is determined using a frequent mining algorithm; Based on the fault detection result, the recommended fault concern level is determined from the recommended fault concern level list.

6. The method according to claim 3, characterized in that: The determining of the fault priority based on the user fault concern, the recommended fault concern and the preset fault level includes: Based on historical fault processing data within a preset time, a first weight is determined using a decision tree model, wherein the historical fault processing data at least includes: fault processing time, user case solution adoption rate, and fault warning accuracy; Based on the user fault concern, the recommended fault concern, the preset fault level and the first weight, a fault priority is determined by weighted summation.

7. A fault handling system, characterized in that: include: Voice recognition chip and device management control module; The device management control module is used to respond to the fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information; Determining a fault priority based on the fault detection result, the user perception information, the third-party perception information and a preset fault level; Based on the fault priority, determine the fault warning method; Based on the fault warning method, a fault pop-up window warning is performed; The voice recognition chip is used to perform voice broadcast of faults based on the fault warning method; Wherein, the voice recognition chip is communicatively connected with the device management control module via a serial communication protocol.

8. A fault handling device, characterized in that: The device comprises: A response unit, configured to respond to the fault detection instruction, determine the fault detection result, and obtain user perception information and third-party perception information; A first determining unit, configured to determine a fault priority based on the fault detection result, the user perception information, the third-party perception information and a preset fault level; A second determining unit, configured to determine a fault warning mode based on the fault priority; The early warning unit is used to perform fault pop-up window warning and fault voice broadcast based on the fault early warning method.

9. An electronic device, characterized in that: include: a processor and a memory for storing a computer program capable of being executed on the processor, Wherein, when the processor is used to run the computer program, it executes the method described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

11. A computer program product, characterized in that The method comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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