User equipment maintenance method and apparatus, electronic device, and storage medium

Abstract

The application provides a user equipment maintenance method and device, electronic equipment and storage medium. The user equipment maintenance method is applied to a server, and the method comprises the following steps: receiving running information associated with a predetermined component in a user equipment and sent by the user equipment; determining a failure probability of the predetermined component by using a prediction model based on the running information; and performing a maintenance action associated with maintenance of the predetermined component based on the failure probability.

Description

Technical Field

[0001] This invention relates to the field of smart home appliances, and in particular to a user equipment maintenance method, apparatus, electronic device, and storage medium. Background Technology

[0002] While smart TVs, stereos, and other home appliances offer powerful functions, they still experience malfunctions and aging issues after long-term use. Users often only report repairs after obvious malfunctions occur, leading to prolonged downtime. Other problems include difficulty in finding compatible parts, long repair wait times, complicated on-site service appointments, and uncertain delivery times for parts. Manufacturers and after-sales service providers cannot predict aging components in advance, hindering their ability to prepare spare parts and allocate repair resources, resulting in poor customer experience and high temporary logistics and labor costs. Existing remote diagnostic or repair systems only record logs and generate work orders after a malfunction occurs, lacking an end-to-end closed loop from prediction to order placement and repair.

[0003] Therefore, how to predict the possibility of user equipment failure in advance, automatically trigger the maintenance process, and improve the user experience is an urgent problem to be solved. Summary of the Invention

[0004] This disclosure provides user equipment maintenance methods, apparatus, electronic devices, and storage media.

[0005] According to a first aspect of the present disclosure, a user equipment maintenance method is provided, applied to a server, the method comprising: Receive operational information associated with predetermined components in the user equipment, sent by the user equipment. Based on the operational information, a prediction model is used to determine the failure probability of the predetermined component; Based on the failure probability, perform maintenance actions associated with the maintenance of the predetermined component.

[0006] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0007] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0008] In some embodiments, performing maintenance actions related to the maintenance of the predetermined component based on the failure probability includes at least one of the following: The failure probability is a first failure probability level, and a first prompt message is sent to the user to remind the user of the failure risk of the predetermined component; The failure probability is the second failure probability level, and a second prompt message is sent to the user so that the user can confirm whether to purchase a spare replacement part for the predetermined component; The failure probability is the third failure probability level, and a third prompt message is sent to the user so that the user can confirm whether to purchase the replacement part of the predetermined component and / or carry out repairs. Wherein, the first fault probability level is lower than the second fault probability level, and the second fault probability level is lower than the third fault probability level.

[0009] In some embodiments, the method further includes: In response to user confirmation of purchase of the replacement part, a purchase order is created based on at least one of the following: the user equipment serial number, the predetermined part model, the predetermined part manufacturing batch, and the user equipment firmware version.

[0010] In some embodiments, the method further includes at least one of the following: In response to user confirmation of repair, an on-site repair order is created based on at least one of the following: logistics information of the replacement part, location information of the user equipment, skill information of the maintenance personnel, and available window information of the maintenance personnel. In response to the user's confirmation of repair and selection of self-service repair, a self-service repair order is triggered to send a self-service repair package to the user.

[0011] In some embodiments, the self-service repair kit includes: replacement parts, repair instructions, repair tools, and a temporary maintenance personal identification number (PIN).

[0012] In some embodiments, the method further includes: In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment is under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded; In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment being under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded, and the user's payment order is triggered.

[0013] In some embodiments, the method further includes: In response to the user's unsuccessful attempt to perform the self-service repair, the on-site repair order is triggered.

[0014] In some embodiments, sending the first prompt message, the second prompt message, and / or the third prompt message to the user includes: Send the first prompt message, the second prompt message, and / or the third prompt message to the user equipment to display them to the user; Send the first prompt message, the second prompt message, and / or the third prompt message to the user's terminal to display them to the user; Send the first notification message, the second notification message, and / or the third notification message to the user's email address.

[0015] In some embodiments, the method further includes performing at least one of the following processes on the runtime information: cleaning; summarizing; filtering; anomaly detection; and compression.

[0016] In some embodiments, the method further includes: The prediction model is trained based on the operational information and / or the records of the maintenance performed.

[0017] According to a second aspect of the present disclosure, a user equipment maintenance method is provided, applied to a user equipment, the method comprising: Obtain operational information of predetermined components in the user equipment; The operation information is sent to the server, wherein the operation information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions associated with the maintenance of the predetermined component based on the failure probability.

[0018] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0019] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0020] According to a third aspect of the present disclosure, a user equipment maintenance device is provided, disposed on a server, the device comprising: a transceiver module and a processing module, wherein... The transceiver module is used to receive operating information associated with predetermined components in the user equipment sent by the user equipment. The processing module is used to determine the failure probability of the predetermined component based on the operating information using a prediction model. The processing module is also configured to perform maintenance actions associated with the maintenance of the predetermined component based on the failure probability.

[0021] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0022] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0023] In some embodiments, the processing module is specifically used for at least one of the following: The failure probability is a first failure probability level, and a first prompt message is sent to the user to remind the user of the failure risk of the predetermined component; The failure probability is the second failure probability level, and a second prompt message is sent to the user so that the user can confirm whether to purchase a spare replacement part for the predetermined component; The failure probability is the third failure probability level, and a third prompt message is sent to the user so that the user can confirm whether to purchase the replacement part of the predetermined component and / or carry out repairs. Wherein, the first fault probability level is lower than the second fault probability level, and the second fault probability level is lower than the third fault probability level.

[0024] In some embodiments, the processing module is further configured to: In response to user confirmation of purchase of the replacement part, a purchase order is created based on at least one of the following: the user equipment serial number, the predetermined part model, the predetermined part manufacturing batch, and the user equipment firmware version.

[0025] In some embodiments, the processing module is further configured to perform at least one of the following: In response to user confirmation of repair, an on-site repair order is created based on at least one of the following: logistics information of the replacement part, location information of the user equipment, skill information of the maintenance personnel, and available window information of the maintenance personnel. In response to the user's confirmation of repair and selection of self-service repair, a self-service repair order is triggered to send a self-service repair package to the user.

[0026] In some embodiments, the self-service repair kit includes: replacement parts, repair instructions, repair tools, and a temporary maintenance personal identification number (PIN).

[0027] In some embodiments, the processing module is further configured to: In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment is under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded; In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment being under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded, and the user's payment order is triggered.

[0028] In some embodiments, the processing module is further configured to: In response to the user's unsuccessful attempt to perform the self-service repair, the on-site repair order is triggered.

[0029] In some embodiments, the processing module is specifically used for: Send the first prompt message, the second prompt message, and / or the third prompt message to the user equipment to display them to the user; Send the first prompt message, the second prompt message, and / or the third prompt message to the user's terminal to display them to the user; Send the first notification message, the second notification message, and / or the third notification message to the user's email address.

[0030] In some embodiments, the processing module is further configured to perform at least one of the following processes on the runtime information: cleaning; summarizing; filtering; anomaly detection; and compression.

[0031] In some embodiments, the processing module is further configured to: The prediction model is trained based on the operational information and / or the records of the maintenance performed.

[0032] According to a fourth aspect of the present disclosure, a user equipment maintenance apparatus is provided, characterized in that it is disposed in a user equipment, the apparatus comprising: a transceiver module and a processing module, wherein... The processing module is used to obtain the operating information of predetermined components in the user equipment; The transceiver module is used to send the operation information to the server, wherein the operation information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions related to the maintenance of the predetermined component based on the failure probability.

[0033] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0034] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0035] According to a fifth aspect of the present disclosure, a user equipment maintenance system is provided, comprising: a user equipment and a server. The server is used to execute the user equipment maintenance method described in the first aspect; The user equipment is used to perform the user equipment maintenance method described in the second aspect.

[0036] According to a sixth aspect of the present disclosure, an electronic device is provided, the electronic device comprising: One or more processors; The processor is used to invoke instructions to cause the electronic device to perform the user equipment maintenance method described in the first or second aspect.

[0037] According to a seventh aspect of the present disclosure, a storage medium is provided that stores instructions that, when executed on an electronic device, cause the electronic device to perform the user equipment maintenance method described in the first or second aspect.

[0038] This disclosure provides a user equipment maintenance method, apparatus, electronic device, and storage medium. The user equipment maintenance method, applied to a server, includes: receiving operational information sent by the user equipment associated with predetermined components within the user equipment; determining the failure probability of the predetermined components using a predictive model based on the operational information; and performing maintenance actions associated with the maintenance of the predetermined components based on the failure probability. Thus, by receiving operational information from the user equipment at the server end and determining the failure probability of predetermined components based on a predictive model, it is possible to pre-judge potential failures of the user equipment, allowing the server to proactively execute associated maintenance actions. This enables maintenance actions to be performed before the user equipment fails. It reduces the problem of prolonged equipment downtime and repair waiting time caused by maintenance after user equipment failure, thereby improving the user experience. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 1 ; Figure 2 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 2 ; Figure 3 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 3 ; Figure 4 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 4 ; Figure 5 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 5 ; Figure 6 This is a flowchart illustrating a user equipment maintenance method according to an exemplary embodiment. Figure 6 ; Figure 7 This is a schematic diagram of the structure of an electronic device according to an exemplary embodiment. Detailed Implementation

[0040] To make the technical solution and beneficial effects of the present invention more apparent and understandable, a detailed description is provided below by listing specific embodiments. The accompanying drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application pertains.

[0041] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0042] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0043] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0044] In this disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular or a plural expression.

[0045] In the embodiments of this disclosure, "multiple" can be two or more.

[0046] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0047] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "A in one case, B in another", etc., may include the following technical solutions depending on the situation: in some embodiments, A (A is executed regardless of B); in some embodiments, B (B is executed regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, both A and B are executed. The same applies when there are more branches such as A, B, C, etc.

[0048] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, selective execution from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, and C.

[0049] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, value, or content of the descriptive objects. The description of the descriptive objects should be found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the value of the descriptive object is not limited by ordinal numbers and can be one or more. For example, in "first device," the value of "device" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0050] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0051] In some embodiments, terms such as “…”, “determine…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably.

[0052] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0053] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “graph node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0054] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0055] This disclosure provides a user equipment maintenance method, applied to a server, such as... Figure 1 As shown, the method includes: Step 101: Receive operating information associated with a predetermined component in the user equipment sent by the user equipment; Step 102: Based on the operational information, use a prediction model to determine the failure probability of the predetermined component; Step 103: Based on the failure probability, perform maintenance actions associated with the maintenance of the predetermined component.

[0056] The server can be a computing entity that executes user equipment maintenance methods. The server may include at least one of the following: one or more servers; a cloud computing platform; or a system deployed in a distributed manner. The server can be responsible for receiving and processing data and executing maintenance logic.

[0057] User equipment can be equipment that requires maintenance. For example, user equipment can be a smart TV, smart speaker, smart home device, etc., which contains multiple functional components.

[0058] The designated component can be a component in the user equipment that is designated as a maintenance object. For example, the designated component may include at least one of the following: display panel, speaker, fan, power module.

[0059] Operational information can be data generated or collected by a predetermined component during operation. Operational information can be used to characterize the working state of the predetermined component at the time the operational information is acquired.

[0060] Predictive models can include machine learning models, such as time-series-based deep learning, logistic regression or linear (LR) regression for lifespan prediction, Weibull regression for lifespan prediction, and / or anomaly scoring models. Predictive models are used to analyze operational information and assess the probability of a predetermined component failing at a given point in time. For example, a predictive model might be used to predict the probability of a predetermined component failing within the next T days.

[0061] Failure probability can be the likelihood of a predetermined component failing at a specific point in time, calculated by a predictive model based on operational information. Failure probability can be expressed as a percentage or a grade. Maintenance actions can be preventative or remedial measures taken for predetermined components based on the failure probability, aiming to reduce the risk of failure or resolve existing failures.

[0062] For example, the operating information of predetermined components in a user device, such as speakers in a smart TV or smart speaker, can be collected by a monitoring module inside the device. For example, the speaker's real-time operating current and voltage can be periodically packaged and transmitted to the server via a wireless network.

[0063] The server can utilize a pre-deployed predictive model to analyze received operating information such as speaker current and voltage. Based on the parameter range of normal operating conditions in historical data, the model can calculate the probability of speaker failure if the parameter values ​​in the current operating information consistently exceed that range.

[0064] In one possible implementation, the predictive model could be a rule-based expert system that outputs a corresponding failure probability based on pre-defined logical rules, such as if the speaker's operating current exceeds a threshold N times consecutively, in which case the failure probability is considered moderate.

[0065] When the predicted speaker failure probability output by the model reaches a preset threshold, the server can trigger a maintenance action. For example, it can send a general notification to the user's registered contact information, informing the user that their smart speaker may have a potential failure risk and suggesting they have it checked. Alternatively, the server can generate a maintenance work order internally based on the failure probability. This work order only includes device identification information and a component failure risk warning, awaiting further manual evaluation. Or, the server can directly generate a repair work order for maintenance personnel based on the failure probability, requesting repair.

[0066] In this way, by receiving operational information from user equipment on the server side and determining the failure probability of predetermined components based on predictive models, it is possible to pre-judge potential failures of user equipment. The server can then proactively execute related maintenance actions, enabling repairs to be performed before user equipment fails. This reduces the problems of prolonged equipment downtime and repair waiting times caused by repairs after user equipment failure, thereby improving the user experience.

[0067] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; the characteristic parameters of the predetermined component; the operating parameters of the predetermined component.

[0068] Operation logs can include event sequences or status records automatically logged by predetermined components during operation. These logs typically contain information such as timestamps, event types, operation results, and system status, reflecting the component's historical behavior, abnormal situations, or the execution of specific operations. For example, they can record component startup and shutdown times, software or firmware update records, internal self-test results, and any warnings or error codes detected by the system. By analyzing operation logs, component lifecycle events can be tracked, abnormal patterns can be identified, and crucial historical data support can be provided for fault diagnosis.

[0069] Characteristic parameters can include quantitative indicators that reflect the physical or performance characteristics of a component, whether inherent in its design or manufacturing or gradually developed during long-term operation. These parameters are generally relatively stable, but changes over time or under usage conditions may indicate performance degradation or potential failure. For example, parameters such as brightness uniformity and color saturation of a display panel are fixed at the factory, but may drift with increasing usage time, indicating aging or damage. Similarly, deviations from standard values ​​in parameters such as the frequency response curve of a speaker or the sensitivity of a microphone may signify a decline in component performance. Monitoring these parameters helps assess the health status and performance degradation trends of components.

[0070] Operating parameters can include dynamic physical quantities or performance indicators collected in real time or periodically by predetermined components under actual working conditions. These parameters directly reflect the component's current workload, environmental conditions, and immediate performance. Examples include the component's real-time operating temperature, power consumption, voltage, current, rotational speed, and vibration frequency. Abnormal fluctuations in these parameters or exceeding preset thresholds are often direct signals that the component is about to fail or is already in a sub-optimal state. By continuously monitoring and analyzing operating parameters, abnormal component behavior can be detected in a timely manner, providing real-time input data for predictive models.

[0071] In this way, the server can obtain more comprehensive and targeted data, thereby improving the accuracy and reliability of fault probability determination.

[0072] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0073] The startup count can be the frequency at which a predetermined component transitions from a shutdown state to a normal operating state, reflecting the intensity of use and wear of the component. For example, by setting a counter in the control module of the predetermined component, or by analyzing the component's state change records in the system log, the precise startup count can be obtained.

[0074] Error codes are specific codes automatically generated and recorded by the system when a predetermined component experiences an abnormality or malfunction during operation. Error codes can correspond to predefined fault types or abnormal events. The server can quickly locate the cause of the fault by parsing these error codes.

[0075] Parameter anomalies can occur when one or more operating parameters of a component deviate from its normal operating range or preset threshold, such as sudden changes in power consumption, excessive voltage, or excessive current. Parameter anomalies are often early warning signals of impending component failure. They can be identified by real-time monitoring and comparison with preset normal parameter ranges.

[0076] The brightness curve of a display panel can include the trend of its brightness output changing over time under different usage durations or operating conditions. This curve can reflect the aging degree and performance degradation of the display panel. For example, brightness data can be periodically collected and plotted using a light sensor integrated into the display panel's driving circuit. Alternatively, the brightness of the display panel can be indirectly determined based on the driving current of the panel's driving circuit, and then the curve can be plotted accordingly.

[0077] The impedance variation of a loudspeaker can include the change in the loudspeaker's electrical impedance with frequency or usage time during operation. Impedance variations may indicate damage or aging of the loudspeaker's physical structure, such as the voice coil or diaphragm, for example, by integrating an impedance measurement module into the loudspeaker driver circuit for real-time or periodic measurements.

[0078] Fan speed can be measured by the number of revolutions per minute (RPM) of the cooling fan. Speed ​​directly affects the device's heat dissipation efficiency. Abnormal speeds (too low or too high) may indicate fan bearing wear, blockage, or control circuit malfunctions. For example, fan speed can be monitored in real-time using Hall effect sensors or photoelectric sensors.

[0079] Temperature can include the real-time temperature of a predetermined component during operation. Excessively high temperatures are usually a direct indication of component overload, poor heat dissipation, or internal malfunction. The real-time temperature of the predetermined component during operation can be sensed by setting a thermistor or similar device.

[0080] Power can be the rate at which a component consumes electrical energy during operation. Abnormal power levels (too high or too low) may indicate abnormal component load, decreased efficiency, or internal circuitry failure. Real-time power can be calculated using current and voltage sensors integrated into the power management module.

[0081] In this way, predictive models can improve the accuracy of identifying abnormal trends and failure risks in components based on refined data. This, in turn, improves the accuracy and timeliness of maintenance actions, enhancing the user experience.

[0082] In some embodiments, performing maintenance actions related to the maintenance of the predetermined component based on the failure probability includes at least one of the following: The failure probability is a first failure probability level, and a first prompt message is sent to the user to remind the user of the failure risk of the predetermined component; The failure probability is the second failure probability level, and a second prompt message is sent to the user so that the user can confirm whether to purchase a spare replacement part for the predetermined component; The failure probability is the third failure probability level, and a third prompt message is sent to the user so that the user can confirm whether to purchase the replacement part of the predetermined component and / or carry out repairs. Wherein, the first fault probability level is lower than the second fault probability level, and the second fault probability level is lower than the third fault probability level.

[0083] For example, the failure probability levels determined by the prediction model may include three levels: low, medium, and high. These three failure probability levels correspond to the first failure probability level, the second failure probability level, and the third failure probability level, respectively.

[0084] When the failure probability of a predetermined component, as determined by the predictive model, falls within the first failure probability level, it indicates a low potential failure risk for the predetermined component. At this point, the server can send a first alert message to the user. This first alert message aims to gently remind the user to pay attention to the operational status of the predetermined component.

[0085] When the failure probability of a predetermined component, as determined by the predictive model, reaches the second failure probability level, it indicates that the failure risk of the predetermined component has increased and it may fail in the near future. The server can send a second alert message to the user. This second alert message can inform the user of the increased risk level and can also ask the user whether they want to consider purchasing a spare replacement part in advance. This provides the user with the option of preventative maintenance, reducing downtime caused by sudden component failure.

[0086] When the failure probability of a predetermined component, as determined by the predictive model, reaches the third failure probability level, it indicates that the component is about to fail or has already failed, requiring urgent intervention. The server can send a third alert message to the user. This third alert message carries the highest urgency and directly provides solution options, guiding the user to choose to purchase a replacement part or arrange professional repair services. This improves the user's ability to obtain necessary maintenance support in a timely manner, minimizing losses caused by the failure.

[0087] The predictive model can classify these levels by setting different failure probability thresholds based on various factors such as historical failure data, component type, and operating environment. These thresholds can be dynamically adjusted and optimized according to actual operating conditions.

[0088] There is a clear progressive relationship between the first, second, and third failure probability levels. This tiered mechanism improves the accuracy of maintenance actions, enabling the system to provide appropriate and timely maintenance suggestions and support based on the severity of the risk, thereby improving the efficiency of user equipment maintenance and user satisfaction.

[0089] In some embodiments, the method further includes: In response to user confirmation of purchase of the replacement part, a purchase order is created based on at least one of the following: the user equipment serial number, the predetermined part model, the predetermined part manufacturing batch, and the user equipment firmware version.

[0090] When the server determines, based on its predictive model, that the failure probability of a pre-ordered component reaches either the second or third failure probability level, it will send a corresponding notification message (the second or third notification message) to the user. For example, it might ask the user if they confirm the purchase of a replacement part. The user can confirm their intention to purchase the replacement part through an interface on their device, an application on their terminal, an email reply, or other interactive methods. Once the server receives the user's confirmation, it will trigger the subsequent order creation process to purchase the replacement part.

[0091] Creating a purchase order ensures that the purchased replacement parts accurately match the user's equipment and the required components. During this process, the server uses the user's equipment serial number as a unique identifier to trace the equipment's specific configuration and determine the model and / or quantity of the required components. The component model directly indicates the type and specifications of the necessary replacement parts, reducing the likelihood of purchasing incorrect components. The component manufacturing batch helps track production information to match the current user's equipment. The user's equipment firmware version may also affect component compatibility or functionality, and this is taken into account when creating the order to improve the compatibility of the selected replacement parts. By comprehensively utilizing this information, the server can automatically generate a purchase order containing all necessary details.

[0092] When a user confirms the purchase of a replacement part for a pre-ordered component, the server can automatically create a detailed and accurate purchase order based on key information such as the user's device serial number, the model number of the pre-ordered component, the manufacturing batch of the pre-ordered component, and the firmware version of the user's device. This not only simplifies the process of purchasing replacement parts for users and reduces errors that may be caused by users manually entering information, but also improves the accuracy of the purchased replacement parts matching the user's device and its pre-ordered components.

[0093] In some embodiments, the method further includes at least one of the following: In response to user confirmation of repair, an on-site repair order is created based on at least one of the following: logistics information of the replacement part, location information of the user equipment, skill information of the maintenance personnel, and available window information of the maintenance personnel. In response to the user's confirmation of repair and selection of self-service repair, a self-service repair order is triggered to send a self-service repair package to the user.

[0094] Users can operate or provide feedback through prompts on their devices, client applications, web interfaces, or customer service channels, and the user will be able to accept the repair service.

[0095] After the user confirms the repair, the server can create an on-site repair order for maintenance personnel based on various information.

[0096] Logistics information for replacement parts can include data such as inventory, location, shipping status, and estimated delivery time related to the replacement parts needed for repair. The server can query the internal inventory system or third-party logistics platforms to improve the timely arrival of required replacement parts when arranging on-site repairs.

[0097] The location information of a user's device can include the device's geographic coordinates or a detailed address provided by the user. The server can obtain location information through the device's built-in positioning module, user purchase records, user manual input, or historical service records, in order to accurately plan the travel of maintenance personnel.

[0098] Maintenance personnel's skill information can include their professional qualifications, repair experience, and ability to repair specific equipment or components. For example, a skill matrix for maintenance personnel can be pre-set to showcase their different repair capabilities. The server can then match maintenance personnel with the appropriate skills based on the type of user equipment and the fault characteristics of the specified components, thereby improving repair quality.

[0099] The availability information for maintenance personnel can include their schedules, available time slots, and current task status. The server can intelligently schedule available and skilled maintenance personnel by comprehensively considering the workload, rest time, and user-expected repair time.

[0100] Based on the above information, the server generates a formal work order containing details of the repair task, designated maintenance personnel, estimated repair time, and required replacement parts. This order will be sent to the maintenance personnel and simultaneously sent to the user, serving as proof and guidance for on-site repair services.

[0101] When a user confirms repair and selects self-service repair, the server can immediately initiate a process to generate a self-service repair order. This selection can be made through an option on the user interface; for example, the user can choose "Professional Repair" or "Self-Service Repair." The self-service repair order instructs the system to prepare and send a self-service repair package to the user.

[0102] Self-service repair kits may include physical repair parts and / or virtual repair tools (such as tutorials).

[0103] In one possible implementation, the self-service repair kit may include replacement parts, detailed repair instructions, necessary repair tools, and possibly a temporary maintenance personal identification number (PIN) (used to allow the user to access the maintenance mode of the user's device, etc.), providing the user with the resources needed to complete the self-service repair.

[0104] After the user confirms the repair, the server can intelligently create and schedule on-site repairs by comprehensively considering the logistics information of replacement parts, the location information of the user's equipment, the skill information of the maintenance personnel, and the availability of maintenance personnel. This improves the timeliness and accuracy of the repair service in responding to user needs, thereby increasing the efficiency of the repair service and user satisfaction. Furthermore, by providing self-service repair options and triggering the delivery of self-service repair packages, this application offers users flexible repair choices, meeting the needs of some users to solve problems themselves, reducing service costs, and making user equipment maintenance methods more comprehensive and practical.

[0105] In some embodiments, the self-service repair kit includes: replacement parts, repair instructions, repair tools, and a temporary maintenance personal identification number (PIN).

[0106] Replacement parts can be components used to replace predetermined components in user equipment that have malfunctioned or experienced performance degradation.

[0107] Repair tutorials provide detailed steps and instructions to guide users in performing self-repairs. Their purpose is to lower the technical barrier to self-repairs and improve users' ability to complete component replacement or repair operations correctly and safely.

[0108] Repair tutorials can be provided in various forms, such as printed manuals, electronic documents (e.g., PDFs), links to video tutorials, or interactive guides within embedded applications.

[0109] Repair tools can be specialized or general-purpose tools needed by users for self-repair. Repair tools can include both physical tools and software tools. They provide users with the necessary auxiliary means to complete repair operations. A temporary maintenance Personal Identification Number (PIN) is a combination of numbers or characters used to provide temporary access or authorization in specific maintenance scenarios. For example, when a user's equipment needs to enter a special maintenance mode or unlock specific functions for component replacement, a PIN can serve as a one-time or time-limited authorization credential. This helps prevent unauthorized access or operation while improving the controllability and traceability of the maintenance process.

[0110] When a user chooses self-service repair, the server provides a complete self-service repair package including replacement parts, repair tutorials, repair tools, and a temporary maintenance personal identification PIN. This eliminates the need for users to find or purchase all the necessary repair resources themselves, greatly simplifying the preparation process for self-service repair, improving the user experience, and reducing overall maintenance costs.

[0111] In some embodiments, the method further includes: In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment is under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded; In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment being under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded, and the user's payment order is triggered.

[0112] The server can determine whether the user's device is under warranty. "User device is under warranty" means that the user's device is within the warranty period provided by the manufacturer or seller.

[0113] The server can obtain and verify information such as the user's device serial number and purchase date, and combine this information with a pre-set warranty policy database to determine whether the current device meets the warranty conditions.

[0114] When a user's equipment is under warranty and the user confirms the purchase of a replacement part or repair, the system can process the corresponding order according to the specific warranty policy. Order records serve as the foundation for service process management, used to track service progress, manage inventory, schedule personnel, and facilitate subsequent financial settlements.

[0115] When a user's equipment is out of warranty, the server, while recording purchase orders for replacement parts and / or on-site repair orders from maintenance personnel, can calculate the amount the user should pay based on preset billing rules and warranty terms, and trigger a payment order. This payment order can guide the user through the payment process via an integrated payment interface, such as redirecting to a payment platform, sending a payment link, or generating a payment QR code. This mechanism improves the accuracy of fee collection under specific warranty terms.

[0116] In one possible implementation, once an order is paid for, the server can execute the on-site repair order and / or the purchase order for replacement parts.

[0117] In some embodiments, the method further includes: In response to the user's unsuccessful attempt to perform the self-service repair, the on-site repair order is triggered.

[0118] After a user receives the self-service repair kit and attempts to perform self-repair, if the server detects that the repair operation has failed to effectively resolve the fault of the designated component, or if the user reports that the repair operation has failed, the server will automatically or semi-automatically initiate a process for on-site service by a professional repair technician. Specifically, the server can automatically generate an on-site repair order based on the user's device serial number, the designated component model, the fault description, and the contact information and location information provided by the user. This order can then be sent to the repair dispatch system, which will assign a suitable repair technician to the user's site based on the technician's skills, available service windows, and the logistics information of the replacement parts. This process ensures that even if the self-repair fails, the user can still receive timely and effective professional support.

[0119] The server can determine whether self-service repair is successful in several ways. For example, after completing self-service repair, the user can actively submit feedback that the repair failed through the user interface; or, the server can continuously receive the operating information sent by the user's device and determine whether the fault status of the predetermined component has been resolved based on this information. If the fault status persists or reappears, it is determined that the self-service repair was unsuccessful; or, the system can require the user to perform a specific diagnostic procedure after self-service repair and determine whether the repair was successful based on the diagnostic results.

[0120] This effectively reduces the predicament of users being unable to repair equipment due to self-service repair failures, and improves the continuity and integrity of user equipment maintenance processes.

[0121] In some embodiments, sending the first prompt message, the second prompt message, and / or the third prompt message to the user includes: Send the first prompt message, the second prompt message, and / or the third prompt message to the user equipment to display them to the user; Send the first prompt message, the second prompt message, and / or the third prompt message to the user's terminal to display them to the user; Send the first notification message, the second notification message, and / or the third notification message to the user's email address.

[0122] The server can directly send alert messages (including first, second, or third alert messages) to the user device. Upon receiving the message, the user device can immediately display the potential malfunction to the user through its display screen, indicator lights, or built-in speaker, in the form of pop-ups, notification messages, or voice prompts. This allows the user to perceive any abnormal device status immediately.

[0123] In addition, the server can send notification messages (including first, second, or third notification messages) to the user's device. The user's device typically refers to their personal mobile device, such as a smartphone, tablet, or personal computer. The server can send notification messages to the user's device via push notifications from pre-installed applications (Apps), SMS messages, or instant messaging messages. This improves the timeliness of maintenance reminders received by the user, enhances information accessibility and user response flexibility, and reduces the chance of missing important information due to users not checking their devices in a timely manner.

[0124] The server can also send notification messages (including first, second, or third notification messages) to the user's email address. The content of these messages can be richer and more traceable, providing users with more comprehensive information support.

[0125] In this way, the server can flexibly choose the most suitable notification method, and even combine multiple methods to improve the efficiency and accuracy of delivering notification information to users.

[0126] In some embodiments, the method further includes performing at least one of the following processes on the runtime information: cleaning; summarizing; filtering; anomaly detection; and compression.

[0127] Here, the cleaning, aggregation, filtering, anomaly detection, and / or compression of runtime information can be performed by a home gateway or a vendor's local edge server on the server side. Through these operations, only the necessary features of the summary or model are uploaded, saving bandwidth and protecting user privacy.

[0128] Cleaning can be the process of cleaning received operational information to eliminate or correct errors, inconsistencies, or incompleteness in the data.

[0129] Summarization involves aggregating or statistically analyzing large amounts of raw operational information according to certain rules to generate higher-level, more generalized data. For example, continuous operational parameters (such as temperature and power) over a period of time can be summarized into average, maximum, minimum, or standard deviation. Summarization helps reduce the amount of data, extract key features, thereby simplifying the input to predictive models and improving processing efficiency. Filtering can be based on preset conditions or rules to select a subset of data that meets specific requirements from operational information, or to exclude data that is privacy-sensitive, irrelevant, or of low value. Filtering helps focus on the information most relevant to fault prediction, reduces interference from irrelevant data, thereby improving the efficiency and accuracy of the prediction model and enhancing user privacy protection.

[0130] Anomaly detection identifies data points, events, or sequences in operational information that deviate from normal behavior patterns. When an operational parameter suddenly fluctuates significantly or exceeds the normal range for an extended period, the system can mark it as an anomaly. Through anomaly detection, potential precursors to faults or data acquisition problems can be detected in a timely manner, providing important early warning signals for fault prediction.

[0131] Compression can reduce the storage space or transmission bandwidth of operational information without losing critical information. This reduces the cost of data storage and transmission, and improves the efficiency of data processing.

[0132] In some embodiments, the method further includes: The prediction model is trained based on the operational information and / or the records of the maintenance performed.

[0133] Training a predictive model can be done by learning from historical data to optimize the model's parameters, enabling it to more accurately identify and predict the failures of predetermined components.

[0134] The collected operational information and / or maintenance records can be used as input, and the model's internal weights and biases can be adjusted through machine learning algorithms (e.g., supervised learning, semi-supervised learning, or unsupervised learning) to minimize prediction errors.

[0135] Training can be performed periodically offline, or incrementally online when new data is received, to improve the model's ability to always keep its predictions up-to-date.

[0136] Operational information refers to data generated by predetermined components within a user device during actual operation, such as sensor readings, system logs, and performance metrics. In model training, this operational information is used as feature input to help the model learn the behavioral patterns of components under normal and abnormal conditions.

[0137] Maintenance records are historical data about past maintenance activities for planned components, including maintenance dates, fault types, replaced parts, and diagnostics performed by maintenance personnel. These records enable models to correlate specific operational information patterns with past faults or maintenance needs, thereby improving the accuracy of fault prediction. By combining operational information and maintenance records, predictive models can gain a more comprehensive understanding of the component's health status and potential failure risks.

[0138] The predictive model can continuously learn and optimize using the latest operational information and actual maintenance records. This allows the model to adapt to changes in the equipment operating environment, component aging characteristics, and new failure modes, thereby improving the accuracy and reliability of failure probability assessment. Based on more accurate failure probabilities, the server can execute more timely and precise maintenance actions. This disclosure provides a user equipment maintenance method, applied to user equipment, such as... Figure 2 As shown, the method includes: Step 201: Obtain the operating information of predetermined components in the user equipment; Step 202: Send the running information to the server.

[0139] The operational information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions related to the maintenance of the predetermined component based on the failure probability.

[0140] Here, the specific implementation method for the user equipment to obtain and send operation information to the server is as described in any of the above embodiments, and will not be repeated here.

[0141] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; the characteristic parameters of the predetermined component; the operating parameters of the predetermined component.

[0142] Operation logs can include event sequences or status records automatically logged by predetermined components during operation. These logs typically contain information such as timestamps, event types, operation results, and system status, reflecting the component's historical behavior, abnormal situations, or the execution of specific operations. For example, they can record component startup and shutdown times, software or firmware update records, internal self-test results, and any warnings or error codes detected by the system. By analyzing operation logs, component lifecycle events can be tracked, abnormal patterns can be identified, and crucial historical data support can be provided for fault diagnosis.

[0143] Characteristic parameters can include quantitative indicators that reflect the physical or performance characteristics of a component, whether inherent in its design or manufacturing or gradually developed during long-term operation. These parameters are generally relatively stable, but changes over time or under usage conditions may indicate performance degradation or potential failure. For example, parameters such as brightness uniformity and color saturation of a display panel are fixed at the factory, but may drift with increasing usage time, indicating aging or damage. Similarly, deviations from standard values ​​in parameters such as the frequency response curve of a speaker or the sensitivity of a microphone may signify a decline in component performance. Monitoring these parameters helps assess the health status and performance degradation trends of components.

[0144] Operating parameters can include dynamic physical quantities or performance indicators collected in real time or periodically by predetermined components under actual working conditions. These parameters directly reflect the component's current workload, environmental conditions, and immediate performance. Examples include the component's real-time operating temperature, power consumption, voltage, current, rotational speed, and vibration frequency. Abnormal fluctuations in these parameters or exceeding preset thresholds are often direct signals that the component is about to fail or is already in a sub-optimal state. By continuously monitoring and analyzing operating parameters, abnormal component behavior can be detected in a timely manner, providing real-time input data for predictive models.

[0145] In this way, the server can obtain more comprehensive and targeted data, thereby improving the accuracy and reliability of fault probability determination.

[0146] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0147] The startup count can be the frequency at which a predetermined component transitions from a shutdown state to a normal operating state, reflecting the intensity of use and wear of the component. For example, by setting a counter in the control module of the predetermined component, or by analyzing the component's state change records in the system log, the precise startup count can be obtained.

[0148] Error codes are specific codes automatically generated and recorded by the system when a predetermined component experiences an abnormality or malfunction during operation. Error codes can correspond to predefined fault types or abnormal events. The server can quickly locate the cause of the fault by parsing these error codes.

[0149] Parameter anomalies can occur when one or more operating parameters of a component deviate from its normal operating range or preset threshold, such as sudden changes in power consumption, excessive voltage, or excessive current. Parameter anomalies are often early warning signals of impending component failure. They can be identified by real-time monitoring and comparison with preset normal parameter ranges.

[0150] The brightness curve of a display panel can include the trend of its brightness output changing over time under different usage durations or operating conditions. This curve can reflect the aging degree and performance degradation of the display panel. For example, brightness data can be periodically collected and plotted using a light sensor integrated into the display panel's driving circuit. Alternatively, the brightness of the display panel can be indirectly determined based on the driving current of the panel's driving circuit, and then the curve can be plotted accordingly.

[0151] The impedance variation of a loudspeaker can include the change in the loudspeaker's electrical impedance with frequency or usage time during operation. Impedance variations may indicate damage or aging of the loudspeaker's physical structure, such as the voice coil or diaphragm, for example, by integrating an impedance measurement module into the loudspeaker driver circuit for real-time or periodic measurements.

[0152] Fan speed can be measured by the number of revolutions per minute (RPM) of the cooling fan. Speed ​​directly affects the device's heat dissipation efficiency. Abnormal speeds (too low or too high) may indicate fan bearing wear, blockage, or control circuit malfunctions. For example, fan speed can be monitored in real-time using Hall effect sensors or photoelectric sensors.

[0153] Temperature can include the real-time temperature of a predetermined component during operation. Excessively high temperatures are usually a direct indication of component overload, poor heat dissipation, or internal malfunction. The real-time temperature of the predetermined component during operation can be sensed by setting a thermistor or similar device.

[0154] Power can be the rate at which a component consumes electrical energy during operation. Abnormal power levels (too high or too low) may indicate abnormal component load, decreased efficiency, or internal circuitry failure. Real-time power can be calculated using current and voltage sensors integrated into the power management module.

[0155] In this way, predictive models can improve the accuracy of identifying abnormal trends and failure risks in components based on refined data. This, in turn, improves the accuracy and timeliness of maintenance actions, enhancing the user experience.

[0156] A specific example is provided in conjunction with the above embodiments for illustration.

[0157] 1. The framework of the user equipment maintenance system is as follows: Figure 3 As shown, it includes a device-side data proxy module 31, a prediction and fault risk model 32, an order placement module 33, an edge aggregation module 34, and a logistics and spurious adjustment module 35.

[0158] Device Agent Module 31: Embedded in the TV / audio (i.e., user equipment) firmware or the attached bridge, it is responsible for collecting key operating indicators, i.e. operating information (temperature, power, fan speed, number of startups, panel brightness curve, speaker impedance change, error code, log, power consumption change, etc.) and periodically reporting them to the local gateway or the cloud (i.e., the server).

[0159] Edge aggregation module 34, also known as edge aggregation and preprocessing layer (Edge Aggregator): Cleans, summarizes, performs preliminary anomaly detection and compression on the raw logs at the home gateway or the vendor's local edge server, and only uploads the summary or features required by the model to save bandwidth and protect privacy.

[0160] The Predictive and Failure Risk Model 32, also known as the Lifetime Prediction and Failure Risk Model (Predictive Engine) or Predictive Model, uses machine learning models (such as time-series-based deep learning, lifetime prediction LR / Weibull regression, and anomaly scoring models) to estimate the remaining lifetime or short-term failure probability of components (e.g., motherboards, power modules, backlight strips, speaker units, etc.) based on historical failure samples and on-site equipment data. The model supports stratified calibration based on equipment type, manufacturing batch, and differences in usage environment. Figure 4 As shown, the prediction process of the fault risk model 32 includes: step 401, log collection; step 402, feature extraction; step 403, model inference; and step 404, risk determination.

[0161] Ordering module 33, also known as the Maintenance Orchestrator module, combines forecast results with inventory, logistics, user preferences, and warranty strategies to automatically decide whether to automatically order parts, select parts sources (factory warehouse, third-party warehouse, or nearby repair station), and create repair work orders or self-service repair package orders with execution time windows.

[0162] Parts Matcher module: Based on the device serial number, part model, manufacturing batch and firmware version, it ensures that the ordered parts are original or certified compatible parts, and generates a one-time part verification code and a traceable serial number to prevent counterfeit parts.

[0163] Logistics and peripheral adjustment module 35 includes: Logistics & Trigger: Integrates supply chain interfaces (inventory query, logistics tracking) to automatically trigger repair dispatch or send user self-service prompts (such as in-store pickup / on-site appointment) when parts arrive at the user's designated location or the nearest repair point.

[0164] Technician Scheduler: Automatically matches technicians and schedules on-site visits based on geographic location, technician skill matrix, and time window; supports user confirmation or automatic scheduling of the earliest feasible time; supports verification of technicians' arrival using accessory verification codes and accessory serial numbers.

[0165] User Interaction and Preference Layer (UserUI): Presents users with prediction results, recommended actions, estimated costs, available on-site service times, and return / exchange policies in a clear and controllable manner on TV devices / mobile apps / emails. Allows users to choose between automatic / manual ordering, expedited service, or the use of a self-service repair package. 2. Key technical details and implementation points Feature engineering and modeling: Statistical features (mean, variance, mutation frequency), event features (error code sequences, restart frequency), and environmental features (usage duration, average power) are extracted from equipment logs and combined with equipment factory data to train the model. The model output is the probability of a certain part failing within the next T days and suggested preventative actions.

[0166] Risk thresholds and action strategies: Define multi-level thresholds (low / medium / high risk); for low risk, suggest users pay attention and push notifications; for medium risk, reserve parts in advance and prompt users whether they accept pre-ordering; for high risk, automatically place an order and prepare repair resources (if authorized by the user). Thresholds can be adjusted based on warranty status, user history, and cost sensitivity.

[0167] Automatic ordering and fund control: For equipment within the warranty period, the system only issues parts requests and records warranty documents; for out-of-warranty equipment, the system automatically places orders using the linked payment method based on user authorization, and clearly displays the estimated cost and return / exchange guarantee in the order.

[0168] Parts authenticity verification: Before parts arrive or are installed on-site by technicians, the authenticity of the parts is verified by scanning the code / QR code / serial number and comparing it with the equipment records or factory warehouse signature certificates.

[0169] Self-service repair kit solution: For simple replaceable parts (such as backlight strips, speaker units, remote control modules), the system can choose to mail a "self-service repair kit" to the user, which includes video tutorials, necessary tools and a one-time security PIN; after the user replaces the parts according to the steps, the system remotely verifies and closes the work order; if the user is unable to complete the repair, on-site service will be triggered.

[0170] Scheduling optimization: Prioritize the nearest available technician, combine route optimization and priority queues to reduce waiting time; support the distribution of parts to the nearest store and allow users to pick them up after confirmation, thus saving labor costs.

[0171] Rollback and Auditing: All automatic orders and repair dispatches have a rollback window (if the user cancels before the parts arrive), and an audit log is recorded for settlement and quality tracking. 3. Security, privacy, and compliance design Logs are collected only with user authorization and device permission; sensitive data is locally anonymized and uploaded with minimal data; users can choose to disable the automatic order placement function.

[0172] Payment and credential management comply with industry security standards (PCI / TLS); accessory signing and verification use public key infrastructure (PKI) to ensure authenticity and an immutable audit chain.

[0173] 4. Example Example 1 (Predict and Automated Order Placement): For example... Figure 5 As shown, the specific process for predicting and automatically placing repair orders includes: Step 501: Prediction. The system detects temperature fluctuations in a certain type of power module accompanied by an increase in error codes, predicting a high probability of failure within 14 days.

[0174] Step 502: Place an order. If the user has authorized automatic ordering and the device is out of warranty, the system will automatically order spare parts from the nearest certified warehouse.

[0175] Step 503: Logistics tracking.

[0176] Step 504: Arrival triggers repair. Arrange for a technician to visit on the estimated arrival date.

[0177] Step 505: Technician visits. This occurs when the parts arrive and pass inspection.

[0178] Step 506: Installation Verification. A technician will visit the site to replace the faulty parts and submit an installation report.

[0179] Step 507: Work order closed. The system closes the work order and records maintenance data to feed back into model training.

[0180] Example 2 (Self-service repair package): such as Figure 6 As shown, the user self-repair process includes: Step 601: Prediction. The system detects a trend of backlight brightness decay and determines that the backlight strip is aging and easy to replace.

[0181] Step 602: Place order. The system generates a self-service repair package order and mails it to the user. Step 603: User Self-Service. The user replaces the content according to the video tutorial within the app. Step 604: Remote Verification. The user uploads a replacement video or scans a QR code to complete the verification. The system remotely checks the brightness recovery, completes the work order, and provides extended warranty options. Step 605: Complete.

[0182] This disclosure provides a user equipment maintenance device, installed on a server, comprising: a transceiver module and a processing module, wherein... The transceiver module is used to receive operating information associated with predetermined components in the user equipment sent by the user equipment. The processing module is used to determine the failure probability of the predetermined component based on the operating information using a prediction model. The processing module is also configured to perform maintenance actions associated with the maintenance of the predetermined component based on the failure probability.

[0183] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0184] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0185] In some embodiments, the processing module is specifically used for at least one of the following: The failure probability is a first failure probability level, and a first prompt message is sent to the user to remind the user of the failure risk of the predetermined component; The failure probability is the second failure probability level, and a second prompt message is sent to the user so that the user can confirm whether to purchase a spare replacement part for the predetermined component; The failure probability is the third failure probability level, and a third prompt message is sent to the user so that the user can confirm whether to purchase the replacement part of the predetermined component and / or carry out repairs. Wherein, the first fault probability level is lower than the second fault probability level, and the second fault probability level is lower than the third fault probability level.

[0186] In some embodiments, the processing module is further configured to: In response to user confirmation of purchase of the replacement part, a purchase order is created based on at least one of the following: the user equipment serial number, the predetermined part model, the predetermined part manufacturing batch, and the user equipment firmware version.

[0187] In some embodiments, the processing module is further configured to perform at least one of the following: In response to user confirmation of repair, an on-site repair order is created based on at least one of the following: logistics information of the replacement part, location information of the user equipment, skill information of the maintenance personnel, and available window information of the maintenance personnel. In response to the user's confirmation of repair and selection of self-service repair, a self-service repair order is triggered to send a self-service repair package to the user.

[0188] In some embodiments, the self-service repair kit includes: replacement parts, repair instructions, repair tools, and a temporary maintenance personal identification number (PIN).

[0189] In some embodiments, the processing module is further configured to: In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment is under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded; In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment being under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded, and the user's payment order is triggered.

[0190] In some embodiments, the processing module is further configured to: In response to the user's unsuccessful attempt to perform the self-service repair, the on-site repair order is triggered.

[0191] In some embodiments, the processing module is specifically used for: Send the first prompt message, the second prompt message, and / or the third prompt message to the user equipment to display them to the user; Send the first prompt message, the second prompt message, and / or the third prompt message to the user's terminal to display them to the user; Send the first notification message, the second notification message, and / or the third notification message to the user's email address.

[0192] In some embodiments, the processing module is further configured to perform at least one of the following processes on the runtime information: cleaning; summarizing; filtering; anomaly detection; and compression.

[0193] In some embodiments, the processing module is further configured to: The prediction model is trained based on the operational information and / or the records of the maintenance performed.

[0194] This disclosure provides a user equipment maintenance device, installed in a user equipment, comprising: a transceiver module and a processing module, wherein... The processing module is used to obtain the operating information of predetermined components in the user equipment; The transceiver module is used to send the operation information to the server, wherein the operation information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions related to the maintenance of the predetermined component based on the failure probability.

[0195] In some embodiments, the operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

[0196] In some embodiments, the operation log of the predetermined component includes at least one of the following: number of startups, error codes, and abnormal parameters of the predetermined component; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

[0197] According to a fifth aspect of the present disclosure, a user equipment maintenance system is provided, comprising: a user equipment and a server. The server is used to execute the user equipment maintenance method described in the first aspect; The user equipment is used to perform the user equipment maintenance method described in the second aspect.

[0198] Here, the specific implementation of the user equipment maintenance method by the server and / or user equipment is as described in any of the above embodiments, and will not be repeated here.

[0199] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC), and the functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD), such as a field-programmable gate array (FPGA), which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0200] In this disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a type of microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), a Deep Learning Processing Unit (DPU), etc.

[0201] Figure 7 This is a schematic diagram of the structure of the electronic device 9100 provided in this embodiment. The electronic device 9100 can be a computer terminal, a server, a chip, chip system, or processor that supports the implementation of any of the above methods, or a chip, chip system, or processor that supports the implementation of any of the above user equipment maintenance methods in the terminal. The electronic device 9100 can be used to implement the user equipment maintenance methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0202] like Figure 7 As shown, the electronic device 9100 includes one or more processors 9101. The processor 9101 can be a general-purpose processor or a special-purpose processor, etc. The processor 9101 is used to invoke instructions to cause the electronic device 9100 to execute any of the user equipment maintenance methods described above.

[0203] In some embodiments, the electronic device 9100 further includes one or more memories 9102 for storing instructions. Optionally, all or part of the memories 9102 may also be located outside the electronic device 9100.

[0204] In some embodiments, the electronic device 9100 further includes one or more transceivers 9103. When the electronic device 9100 includes one or more transceivers 9103, the steps of sending, receiving and / or acquiring in the above method are performed by the transceivers 9103, and the other steps are performed by the processor 9101.

[0205] In some embodiments, the acquisition steps in the above method can also be executed by the processor 9101, for example, acquiring information from the memory 9102.

[0206] Optionally, the electronic device 9100 further includes one or more interface circuits 9104 connected to the memory 9102. The interface circuits 9104 can be used to receive signals from the memory 9102 or other devices, and can be used to send signals to the memory 9102 or other devices. For example, the interface circuits 9104 can read instructions stored in the memory 9102 and send the instructions to the processor 9101.

[0207] The electronic device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the electronic device 9100 described in this disclosure is not limited thereto, and the structure of the electronic device 9100 may vary. Figure 7 There are limitations. Electronic devices can be standalone devices or part of a larger device.

[0208] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program commands. The aforementioned program can be stored in a storage medium, including various media capable of storing program code such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks or optical disks.

[0209] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several commands to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0210] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations included in the claims. Various modifications and changes can be made to the above embodiments without departing from the scope of this disclosure. Similarly, the various technical features of the above embodiments can be arbitrarily combined to form other embodiments of the present invention that may not be explicitly described. Therefore, the above embodiments only illustrate several implementations of the present invention and do not limit the scope of protection of this patent.

Claims

1. A user equipment maintenance method, characterized in that, Applied to the server side, the method includes: Receive operational information associated with predetermined components in the user equipment, sent by the user equipment. Based on the operational information, a prediction model is used to determine the failure probability of the predetermined component; Based on the failure probability, perform maintenance actions associated with the maintenance of the predetermined component.

2. The method according to claim 1, characterized in that, The operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

3. The method according to claim 2, characterized in that, The operation log of the predetermined component includes at least one of the following: number of startups, error code, and abnormal parameters; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

4. The method according to claim 1, characterized in that, The maintenance action performed based on the failure probability, which is related to the maintenance of the predetermined component, includes at least one of the following: The failure probability is a first failure probability level, and a first prompt message is sent to the user to remind the user of the failure risk of the predetermined component; The failure probability is the second failure probability level, and a second prompt message is sent to the user so that the user can confirm whether to purchase a spare replacement part for the predetermined component; The failure probability is the third failure probability level, and a third prompt message is sent to the user so that the user can confirm whether to purchase the replacement part of the predetermined component and / or carry out repairs. Wherein, the first fault probability level is lower than the second fault probability level, and the second fault probability level is lower than the third fault probability level.

5. The method according to claim 4, characterized in that, The method further includes: In response to user confirmation of purchase of the replacement part, a purchase order is created based on at least one of the following: the user equipment serial number, the predetermined part model, the predetermined part manufacturing batch, and the user equipment firmware version.

6. The method according to claim 4, characterized in that, The method further includes at least one of the following: In response to user confirmation of repair, an on-site repair order is created based on at least one of the following: logistics information of the replacement part, location information of the user equipment, skill information of the maintenance personnel, and available window information of the maintenance personnel. In response to the user's confirmation of repair and selection of self-service repair, a self-service repair order is triggered to send a self-service repair package to the user.

7. The method according to claim 6, characterized in that, The self-service repair kit includes: replacement parts, repair instructions, repair tools, and a temporary maintenance personal identification code PIN.

8. The method according to claim 4, characterized in that, The method further includes: In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment is under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded; In response to the user's confirmation of purchasing the replacement part of the predetermined component and / or performing repairs, and the user equipment being under warranty, the purchase order for the replacement part and / or the on-site repair order by the maintenance personnel are recorded, and the user's payment order is triggered.

9. The method according to claim 7, characterized in that, The method further includes: In response to the user's unsuccessful attempt to perform the self-service repair, the on-site repair order is triggered.

10. The method according to claim 4, characterized in that, Sending the first prompt message, the second prompt message, and / or the third prompt message to the user includes: Send the first prompt message, the second prompt message, and / or the third prompt message to the user equipment to display them to the user; Send the first prompt message, the second prompt message, and / or the third prompt message to the user's terminal to display them to the user; Send the first notification message, the second notification message, and / or the third notification message to the user's email address.

11. The method according to claim 1, characterized in that, The method further includes performing at least one of the following processes on the operational information: cleaning; summarizing; filtering; anomaly detection; and compression.

12. The method according to claim 4, characterized in that, The method further includes: The prediction model is trained based on the operational information and / or the records of the maintenance performed.

13. A method for maintaining user equipment, characterized in that, Applied to user equipment, the method includes: Obtain operational information of predetermined components in the user equipment; The operation information is sent to the server, wherein the operation information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions associated with the maintenance of the predetermined component based on the failure probability.

14. The method according to claim 13, characterized in that, The operational information is used to indicate at least one of the following: The operation log of the predetermined component; The characteristic parameters of the predetermined component; The operating parameters of the predetermined component.

15. The method according to claim 14, characterized in that, The operation log of the predetermined component includes at least one of the following: number of startups, error code, and abnormal parameters; The characteristic parameters of the predetermined component include at least one of the following: the brightness curve of the display panel, the impedance change of the speaker, and the fan speed; The operating parameters of the predetermined component include at least one of the following: temperature and power.

16. A user equipment maintenance device, characterized in that, Located on the server side, the device includes: a transceiver module and a processing module, wherein... The transceiver module is used to receive operating information associated with predetermined components in the user equipment sent by the user equipment. The processing module is used to determine the failure probability of the predetermined component based on the operating information using a prediction model. The processing module is also configured to perform maintenance actions associated with the maintenance of the predetermined component based on the failure probability.

17. A user equipment maintenance device, characterized in that, The device, located in a user equipment, includes a transceiver module and a processing module. The processing module is used to obtain the operating information of predetermined components in the user equipment; The transceiver module is used to send the operation information to the server, wherein the operation information is used by the server to determine the failure probability of the predetermined component using a prediction model, and to perform maintenance actions related to the maintenance of the predetermined component based on the failure probability.

18. A user equipment maintenance system, comprising: User equipment and server The server is used to execute the user equipment maintenance method according to any one of claims 1 to 12; The user equipment is used to perform the user equipment maintenance method according to any one of claims 13 to 15.

19. An electronic device, characterized in that, The electronic device includes: One or more processors; The processor is configured to invoke instructions to cause the electronic device to perform the user equipment maintenance method according to any one of claims 1 to 15.

20. A storage medium, characterized in that, The storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the user equipment maintenance method according to any one of claims 1 to 15.

Images