Data processing method and device, electronic equipment and storage medium

Abstract

This application discloses a data processing method, apparatus, electronic device, and storage medium, belonging to the field of communication technology. The method includes acquiring operating data of an electronic device, including data used to measure the device status and network status of the electronic device; inputting the operating data into a target power amplifier early warning model to obtain evaluation data of the power amplifier in the electronic device, the evaluation data being used to characterize the degree of abnormality of the power amplifier, the target power amplifier early warning model being trained by a big data analysis model based on power amplifier abnormal data; and outputting a prompt message when the evaluation data meets preset conditions, the prompt being used to indicate abnormal operation of the power amplifier.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and specifically relates to a data processing method, apparatus, electronic device and storage medium. Background Technology

[0002] During the production and use of electronic devices, malfunctions can occur due to factors such as the yield rate of the hardware itself, manufacturing processes, pre-shipment testing, and damage during transportation. In particular, damage to the power amplifier (PA) in electronic devices is not only affected by the aforementioned factors but also related to user habits. For example, users frequently play videos on their devices while charging, causing the devices to overheat and ultimately damaging the PA. Therefore, how to provide early warnings of PA malfunctions to ensure normal user operation of electronic devices is a pressing issue that needs to be addressed. Summary of the Invention

[0003] The purpose of this application is to provide a data processing method, apparatus, electronic device, and storage medium that can solve the problem in the prior art that the inability to provide early warning of PA anomalies leads to PA damage and affects the user's use of electronic devices.

[0004] In a first aspect, embodiments of this application provide a data processing method applied to an electronic device, which may include:

[0005] Acquire operational data from electronic devices, including data used to measure the device's status and network status;

[0006] The running data is input into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model.

[0007] If the evaluation data meets the preset conditions, a prompt message is output to indicate that the power amplifier is malfunctioning.

[0008] Secondly, embodiments of this application provide a data processing apparatus applied to an electronic device, which may include:

[0009] The acquisition module is used to acquire the operating data of the electronic device, including data for measuring the device status and network status.

[0010] The processing module is used to input the running data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model.

[0011] The output module is used to output a prompt message when the evaluation data meets the preset conditions, which is used to indicate that the power amplifier is malfunctioning.

[0012] Thirdly, embodiments of this application provide an electronic device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the data processing method as described in the first aspect.

[0013] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the data processing method as shown in the first aspect are implemented.

[0014] Fifthly, embodiments of this application provide a chip, which includes a processor and a display interface, the display interface and the processor being coupled together, the processor being used to run programs or instructions to implement the steps of the data processing method as shown in the first aspect.

[0015] In a sixth aspect, embodiments of this application provide a computer program product stored in a storage medium, which is executed by at least one processor to implement the steps of the data processing method as described in the first aspect.

[0016] In this embodiment, operational data of the electronic device is acquired, including data on the device status and network status. This operational data is then input into a target power amplifier early warning model to obtain evaluation data of the power amplifier in the electronic device. This evaluation data characterizes the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained using power amplifier abnormality data on a big data analysis model. Then, when the evaluation data meets preset conditions, a prompt message is output to indicate abnormal power amplifier operation. In this way, a monitoring and early warning model for the power amplifier of the electronic device can be generated by combining the big data analysis model and the power amplifier abnormality data. Thus, the power amplifier early warning model can predict whether the current power amplifier is in normal working condition, enabling timely early warning when the power amplifier of the electronic device is damaged or shows signs of damage, allowing for early adjustment of the power amplifier and preventing power amplifier damage from affecting the user's use of the electronic device. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a data processing method provided in an embodiment of this application;

[0018] Figure 2 A schematic diagram illustrating the relationship between device model, sample set, and power amplifier early warning model in a data processing method provided in an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0021] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0023] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0024] To address the problems in related technologies, this application provides a data processing method, which can be divided into two stages: a pre-warning model training stage and a pre-warning model warning stage. In the pre-warning model training stage, a big data analysis model can be trained based on a massive amount (e.g., 1 million records) of power amplifier (PA) anomaly data already collected, to obtain a target power amplifier pre-warning model and evaluation conditions. Then, in the pre-warning model warning stage, data used to measure the device's status and network status can be input into the target power amplifier pre-warning model to obtain evaluation data for the power amplifier in the electronic device. This evaluation data is then compared with the evaluation conditions. If the evaluation data and evaluation conditions show a high degree of similarity (e.g., 90%), it is considered that the power amplifier of the current electronic device is abnormal, and a prompt message can be output to notify relevant personnel (such as the owner of the electronic device, the maintenance personnel of the electronic device, or the test personnel of the electronic device) of the power amplifier anomaly, so that corresponding adjustment measures can be prepared in advance.

[0025] Based on this, the following is in conjunction with the appendix Figures 1 to 2 The data processing method provided in this application will be described in detail through specific embodiments and application scenarios.

[0026] First, combined Figure 1 The data processing method provided in the embodiments of this application will be described in detail.

[0027] Figure 1 This is a flowchart of a data processing method provided in an embodiment of this application.

[0028] like Figure 1 As shown, the data processing method provided in this application embodiment can be applied to electronic devices, and the method may include the following steps:

[0029] Step 110: Obtain the operating data of the electronic device, including data on the device status and network status used to measure the electronic device's condition; Step 120: Input the operating data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model; Step 130: If the evaluation data meets the preset conditions, output the prompt information to indicate that the power amplifier is operating abnormally.

[0030] In this way, by combining big data analysis models with abnormal power amplifier data, a monitoring and early warning model for the power amplifier of electronic devices can be generated. Thus, the power amplifier early warning model can predict whether the current power amplifier is in normal working condition, enabling timely warnings when the power amplifier of electronic devices is damaged or shows signs of damage, so that the power amplifier can be adjusted in advance to avoid the situation where PA damage affects the user's use of electronic devices.

[0031] The steps described above are explained in detail below.

[0032] First, regarding step 110, in one or more possible embodiments, the operating data may include, but is not limited to, at least one of the following: frequency band, transmit power (TX power), QCI (QoS Class Identifier), block error rate (BLER), reference signal receiving power (RSRP), reference signal receiving quality (RSRQ) representing Long Term Evolution (LTE), signal-to-noise ratio (SINR), frequency point, duration of user use of electronic device during charging, temperature of electronic device during charging, correlation index between duration of user use of electronic device during charging and temperature, radio interface, and radio bearer.

[0033] Here, QCI is a scaling value used to measure specific packet forwarding behavior provided to the Service Data Flow (SDF), such as packet loss rate and packet delay budget.

[0034] Next, regarding step 120, in one or more possible embodiments, before performing step 120, the data processing method provided in this application embodiment may further include:

[0035] Step 1401: Obtain a sample set of abnormal power amplifier data. The sample set includes first sample data and second sample data. The first sample data corresponds to a first quantity, and the second sample data corresponds to a second quantity. The first quantity is greater than the second quantity.

[0036] Step 1402: Train the preset big data analysis model based on the first sample data until the first preset training conditions are met, and obtain the first early warning model;

[0037] Step 1403: Input the second sample data into the first early warning model to obtain the first sample evaluation data;

[0038] Step 1404: Calculate the first evaluation accuracy of the first early warning model based on the first sample evaluation data and the second sample data;

[0039] Step 1405: If the first evaluation accuracy is greater than or equal to the preset evaluation accuracy, the first early warning model is determined as the target power amplifier early warning model.

[0040] For example, assuming the existing sample set C of abnormal PA data is large enough to allow the power amplifier warning model to reach the expected state, the sample set C is specifically divided into two parts: a first sample data C1 and a second sample data C2. The overall allocation principle is that the first quantity of the first sample data C1 is greater than the second quantity of the second sample data C2. Next, the first sample data C1 can be input into the big data analysis model until the first preset training condition is met, resulting in the first warning model P1. The second sample data is then input into the first warning model P1 for verification, resulting in the first sample evaluation data. Then, by comparing the first sample evaluation data and the second sample data, the first evaluation accuracy rate rate1 of the first warning model P1 is calculated. If the first evaluation accuracy rate rate1 reaches the expected level (greater than or equal to the preset evaluation accuracy rate), the first warning model P1 can be directly used to monitor the abnormal PA status of electronic devices, thus determining the first warning model P1 as the target power amplifier warning model.

[0041] Conversely, there may be situations where the accuracy of the first evaluation is lower than the preset accuracy. Based on this, in one example, the second sample data is divided into several equal parts, that is, the second sample data includes N sub-sample data, where N is a positive integer greater than 1. Thus, after step 1404, the data processing may also include:

[0042] The first early warning model is trained based on the i-th subsample data in N subsample data and the first sample data until the second preset training condition is met, and the second early warning model is obtained, i∈[1,N];

[0043] Input the N-1 sub-sample data from the second sample data into the second early warning model to obtain the second sample evaluation data;

[0044] Based on the second sample evaluation data and N-1 subsample data, calculate the second evaluation accuracy of the second early warning model;

[0045] If the second evaluation accuracy is greater than or equal to the preset evaluation accuracy, the second early warning model will be determined as the target power amplifier early warning model.

[0046] For example, the second sample data C2 is divided into several equal parts and numbered C21, C22, ..., C2N, i.e., C2 = {C21, C22, ..., C2N}. Next, the sub-sample data C21 in the second sample data C2 is combined with the first sample data C1 to generate a new first sample data C1 = {C1, C21}. This new first sample data C1 = {C1, C21} is input into the first early warning model P1, and the first early warning model P1 is trained until the second preset training condition is met, resulting in the second early warning model P2. The N-1 sub-sample data points (C22, ..., C2N) in the second sample data C2, excluding sub-sample data C21, are then input into the second early warning model P2 for verification, resulting in the second sample evaluation data. Then, by comparing the N-1 subsample data and the second sample data, the second evaluation accuracy rate rate2 of the second early warning model P2 is calculated. If the second evaluation accuracy rate rate2 reaches the expected level, that is, is greater than or equal to the preset evaluation accuracy rate, the second early warning model P2 can be used to monitor the PA abnormality of electronic equipment, that is, the second early warning model P2 is determined as the target power amplifier early warning model.

[0047] If the second evaluation accuracy rate rate2 does not reach the expected level, i.e., it is less than the preset evaluation accuracy rate, then a new sub-sample data, C22, from the second sample data C2 = {C22,...,C2N} will be added to C1 = {C1,C21} to obtain a new C1 = {C1,C21,C22}. The above process will be repeated until the evaluation accuracy rate rate of the power amplifier is greater than or equal to the preset evaluation accuracy rate.

[0048] In this way, the power amplifier early warning model can predict whether the current power amplifier is in normal working condition, and can provide timely warnings when the power amplifier of electronic devices is damaged or has a tendency to be damaged, so as to adjust the power amplifier in advance and avoid the situation where PA damage affects the user's use of electronic devices.

[0049] It should be noted that the big data analysis model in this embodiment is a big data training model for state classification based on decision trees. Using the big data analysis model and PA anomaly data, abnormal data can be effectively monitored and warned. The big data analysis model in this embodiment can be a random forest model, or any other big data training model such as a regression model. As long as the implementation complexity of the big data model is lower than or comparable to that of the random forest model, and its prediction accuracy and operational stability are higher than or equal to those of the random forest model, then the random forest model can be used as a replacement.

[0050] Furthermore, considering that the conditions under which power amplifiers may fail may differ between different electronic device models due to factors such as design methods, manufacturing processes, and usage, and that the types of power amplifiers in different electronic device models may also lead to differences in the conditions under which power amplifiers may fail, it is obviously unreasonable to use the same training model for all electronic device models. Therefore, to address this issue, this application provides the following steps to generate a monitoring and early warning model for the power amplifiers of electronic devices of different models.

[0051] like Figure 2 As shown, if there are electronic devices with different device models such as UE TYPE 1, UE TYPE 2, ..., UE TYPE M, then there are M sample sets, such as sample set Data1, sample set Data2, ..., sample set DataM. Each of the M sample sets includes abnormal power amplifier data from electronic devices with the same device model, where M is a positive integer greater than 1.

[0052] Therefore, the sample set Data1 can be used as the above sample set C to train the power amplifier warning model corresponding to UE TYPE 1. Similarly, each of the above M sample sets can be used as sample set C to train the power amplifier model corresponding to its device model. In this way, each sample set corresponds to a power amplifier warning model, and each power amplifier warning model is associated with a device model of a type of electronic device. Thus, the target power amplifier warning model corresponding to the target device model of the electronic device involved in step 110 can be determined in the following way.

[0053] Based on this, in one example, the sample set consists of M sample sets. Each sample set includes power amplifier anomaly data from electronic devices with the same device model. Each sample set corresponds to a power amplifier early warning model, and each power amplifier early warning model is associated with a type of electronic device model. M is a positive integer greater than 1. Based on this, before step 120, the data processing method may also include:

[0054] Obtain the target device model of the electronic device;

[0055] Based on the preset association information between the equipment model and the power amplifier early warning model, the target power amplifier early warning model corresponding to the target equipment model is selected from the preset model database. The preset model data includes M power amplifier early warning models.

[0056] Furthermore, in addition to considering that different power amplifier warning models correspond to different device models, this application embodiment also considers the impact of applications on power amplifiers. Therefore, based on the above embodiments, the data processing method provided in this application embodiment can also train a power amplifier warning model corresponding to each application based on the power amplifier anomaly data caused by each application affecting the electronic device. Thus, each sample set includes power amplifier anomaly data caused by one application, each sample set corresponds to one power amplifier warning model, and each power amplifier warning model is associated with the application identifier of the application. In this way, the target power amplifier warning model corresponding to the target application installed in the electronic device involved in step 110 can be determined in the following manner.

[0057] Based on this, in another example, the sample set includes L sample sets, each of which includes power amplifier anomaly data caused by an application. Each sample set corresponds to a power amplifier warning model, and each power amplifier warning model is associated with an application identifier of the application. L is a positive integer greater than 1. Based on this, before step 120, the data processing method may also include:

[0058] Obtain the target application identifier for each of the j target applications in the electronic device;

[0059] Based on the preset association information between the application identifier and the power amplifier early warning model, the power amplifier early warning model corresponding to each target application identifier is selected from the preset model database;

[0060] j power amplifier early warning models are identified as the target power amplifier early warning models.

[0061] Based on this, assuming the running data includes business data from each of the j applications, step 120 may specifically include:

[0062] The business data of each application in j applications is input into the power amplifier early warning model corresponding to the application identifier of each application to obtain the sub-evaluation data of the power amplifier of each application affecting electronic devices;

[0063] The sub-evaluation data of j applications are used as the evaluation data of the power amplifier in the electronic device.

[0064] It should be noted that, in practical applications, users may download different numbers of target applications to their electronic devices, so the value of j can specifically be [1, L].

[0065] Then, regarding step 130, the preset conditions in this application embodiment can be preset by the user based on experience values, or they can be obtained based on the power amplifier warning model obtained during the above training process, and the evaluation conditions corresponding to the power amplifier warning model will be obtained. The evaluation conditions are related to the power amplifier warning model.

[0066] Based on this, data on the device status and network status of electronic devices can be input into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is then compared with the evaluation conditions related to the target power amplifier early warning model. If the evaluation data and the evaluation conditions are highly similar (e.g., 90%), it is considered that the power amplifier of the current electronic device is abnormal, and a prompt message can be output to notify relevant personnel (such as the owner of the electronic device, the maintenance personnel of the electronic device, or the test personnel of the electronic device) that the power amplifier of the electronic device is abnormal, so that corresponding adjustment measures can be prepared in advance.

[0067] Furthermore, after step 130, the data processing method provided in this application embodiment may further include:

[0068] Based on the preset association information between the evaluation value and the abnormal problem, obtain the target abnormal problem of the power amplifier in the electronic device corresponding to the evaluation value of the evaluation data;

[0069] Based on the preset association information between the problem and the solution operation information, obtain the target solution operation information corresponding to the target abnormal problem;

[0070] Based on the objectives, resolve operational information and adjust electronic devices.

[0071] For example, the evaluation data corresponds to an evaluation value. Multiple evaluation ranges can be set as preset conditions, namely evaluation range 1, evaluation range 2, evaluation range 3... evaluation range F, where F is a positive integer. Each evaluation range corresponds to an abnormal problem of the power amplifier within its evaluation range (which can be one or more), such as abnormal problem 1, abnormal problem 2, abnormal problem 3... abnormal problem F. Then, if the evaluation value is within evaluation range 2, the target abnormal problem of the power amplifier in the electronic device corresponding to the evaluation value is abnormal problem 2. Since an abnormal problem can correspond to one or more solution operation information to resolve it, the target solution operation information corresponding to abnormal problem 2 is obtained based on the preset association information between the problem and the solution operation information. Then, the target solution operation information can be output to remind the user to adjust the electronic device according to the target solution operation information, or the electronic device can be automatically adjusted based on the target solution operation information.

[0072] In this way, by combining big data analysis models with abnormal power amplifier data, a monitoring and early warning model for the power amplifier of electronic devices can be generated. Thus, the power amplifier early warning model can predict whether the current power amplifier is in normal working condition, enabling timely warnings when the power amplifier of electronic devices is damaged or shows signs of damage, so that the power amplifier can be adjusted in advance to avoid the situation where PA damage affects the user's use of electronic devices.

[0073] The data processing method provided in this application can be executed by a data processing device. This application uses an example of a data processing device executing the data processing method to illustrate the data processing apparatus provided in this application.

[0074] Based on the same inventive concept, this application also provides a data processing device. (Specifically combined with...) Figure 3 Please provide a detailed explanation.

[0075] Figure 3 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application.

[0076] like Figure 3 As shown, the data processing device 30 is applied to an electronic device and may specifically include:

[0077] The acquisition module 301 is used to acquire the operating data of the electronic device, including data for measuring the device status and network status of the electronic device;

[0078] The processing module 302 is used to input the running data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model.

[0079] The output module 303 is used to output a prompt message when the evaluation data meets the preset conditions, and the prompt message is used to indicate that the power amplifier is malfunctioning.

[0080] The data processing device 30 will be described in detail below:

[0081] In one or more possible embodiments, the data processing apparatus 30 provided in this application may further include a first training module, a first calculation module, and a first determination module; wherein...

[0082] The acquisition module 301 can also be used to acquire a sample set of abnormal power amplifier data, the sample set including first sample data and second sample data;

[0083] The first training module is used to train the preset big data analysis model based on the first sample data until the first preset training conditions are met, and thus obtain the first early warning model.

[0084] The processing module 302 can also be used to input the second sample data into the first early warning model to obtain the first sample evaluation data;

[0085] The first calculation module is used to calculate the first evaluation accuracy of the first early warning model based on the first sample evaluation data and the second sample data.

[0086] The first determining module is used to determine the first early warning model as the target power amplifier early warning model when the first evaluation accuracy is greater than or equal to the preset evaluation accuracy.

[0087] In another or more possible embodiments, the data processing apparatus 30 provided in this application may further include a second training module, a second calculation module, and a second determination module; wherein,

[0088] The second training module is used to input N-1 sub-samples from the second sample data into the second early warning model to obtain the second sample evaluation data, when the second sample data includes N sub-sample data, where N is a positive integer greater than 1.

[0089] The second calculation module is used to calculate the second evaluation accuracy of the second early warning model based on the second sample evaluation data and N-1 sub-sample data.

[0090] The second determining module is used to determine the second early warning model as the target power amplifier early warning model when the second evaluation accuracy is greater than or equal to the preset evaluation accuracy.

[0091] In one or more possible embodiments, the data processing apparatus 30 provided in this application may further include a first filtering module; wherein,

[0092] The acquisition module 301 can also be used to acquire the target device model of the electronic device when the sample set is M sample sets, each sample set in the M sample sets includes abnormal power amplifier data in electronic devices with the same device model, each sample set corresponds to a power amplifier early warning model, and each power amplifier early warning model is associated with a device model of a class of electronic devices, and M is a positive integer greater than 1.

[0093] The first filtering module is used to filter the target power amplifier warning model corresponding to the target device model from the preset model database based on the preset association information between the device model and the power amplifier warning model. The preset model data includes M power amplifier warning models.

[0094] In one or more possible embodiments, the data processing apparatus 30 provided in this application may further include a second filtering module and a third determining module; wherein,

[0095] The acquisition module 301 can also be used to acquire the target application identifier of each target application in j target applications of an electronic device, where the sample set includes L sample sets, each sample set in the L sample sets includes power amplifier abnormal data caused by an application, each sample set corresponds to a power amplifier warning model, and each power amplifier warning model is associated with the application identifier of the application, and L is a positive integer greater than 1.

[0096] The second filtering module is used to filter the power amplifier early warning model corresponding to each target application identifier from the preset model database based on the preset association information between the application identifier and the power amplifier early warning model.

[0097] The third determination module is used to determine j power amplifier early warning models as the target power amplifier early warning model.

[0098] In one or more possible embodiments, the processing module 302 may be specifically used to input the business data of each application in the j applications into the power amplifier early warning model corresponding to the application identifier of each application, when the running data includes the business data of each application in the j applications, to obtain the sub-evaluation data of the power amplifier of each application affecting the electronic device.

[0099] The sub-evaluation data of j applications are used as the evaluation data of the power amplifier in the electronic device.

[0100] In one or more possible embodiments, the data processing apparatus 30 provided in this application embodiment may further include an adjustment module; wherein,

[0101] The acquisition module 301 can also be used to acquire, based on the preset association information between the assessment value and the abnormal problem, the target abnormal problem of the power amplifier in the electronic device corresponding to the assessment value of the assessment data; and to acquire the target resolution operation information corresponding to the target abnormal problem based on the preset association information between the problem and the resolution operation information.

[0102] The adjustment module is used to adjust the electronic equipment according to the target solution operation information.

[0103] The data processing device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television set (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0104] The data processing device in this application embodiment can be a device with an operating system. The operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system.

[0105] The data processing device provided in this application embodiment can achieve... Figures 1 to 2 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0106] Based on this, the data processing apparatus provided in this application acquires the operating data of the electronic device, including data for measuring the device status and network status of the electronic device. Then, the operating data is input into a target power amplifier early warning model to obtain evaluation data of the power amplifier in the electronic device. The evaluation data characterizes the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained from the power amplifier abnormality data using a big data analysis model. Then, when the evaluation data meets preset conditions, a prompt message is output to indicate abnormal operation of the power amplifier. In this way, by combining the big data analysis model and the power amplifier abnormality data, a monitoring and early warning model for the power amplifier of the electronic device can be generated. Thus, the power amplifier early warning model can predict whether the current power amplifier is in normal working condition, enabling timely early warning when the power amplifier of the electronic device is damaged or shows a tendency to be damaged, so that the power amplifier can be adjusted in advance to avoid the situation where PA damage affects the user's use of the electronic device.

[0107] Optional, such as Figure 4 As shown, this application embodiment also provides an electronic device 40, including a processor 401 and a memory 402. The memory 402 stores a program or instructions that can run on the processor 401. When the program or instructions are executed by the processor 401, they implement the various steps of the above-described data processing method embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0108] It should be noted that the electronic devices in the embodiments of this application include the aforementioned mobile electronic devices and non-mobile electronic devices.

[0109] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application.

[0110] The electronic device 500 includes, but is not limited to, components such as: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, and processor 510.

[0111] Those skilled in the art will understand that the electronic device 500 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 510 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0112] In this embodiment, the processor 510 is used to acquire operational data of the electronic device, including data for measuring the device status and network status of the electronic device. The processor 510 can also be used to input the operational data into a target power amplifier early warning model to obtain evaluation data of the power amplifier in the electronic device. The evaluation data characterizes the degree of abnormality of the power amplifier, and the target power amplifier early warning model is trained from power amplifier abnormality data using a big data analysis model. The processor 510 can also be used to output a prompt message when the evaluation data meets preset conditions, indicating that the power amplifier is operating abnormally.

[0113] In one or more possible embodiments, the processor 510 provided in this application embodiment can also be used to acquire a sample set of power amplifier abnormal data, the sample set including first sample data and second sample data;

[0114] The preset big data analysis model is trained based on the first sample data until the first preset training conditions are met, and the first early warning model is obtained.

[0115] Input the second sample data into the first early warning model to obtain the first sample evaluation data;

[0116] Based on the first sample evaluation data and the second sample data, calculate the first evaluation accuracy of the first early warning model;

[0117] If the accuracy of the first assessment is greater than or equal to the preset accuracy of the assessment, the first early warning model will be determined as the early warning model for the target power amplifier.

[0118] In another or more possible embodiments, the processor 510 provided in this application embodiment can also be used to input N-1 sub-sample data from the second sample data into the second early warning model to obtain the second sample evaluation data when the second sample data includes N sub-sample data, where N is a positive integer greater than 1.

[0119] Based on the second sample evaluation data and N-1 subsample data, calculate the second evaluation accuracy of the second early warning model;

[0120] If the second evaluation accuracy is greater than or equal to the preset evaluation accuracy, the second early warning model will be determined as the target power amplifier early warning model.

[0121] In one or more possible embodiments, the processor 510 provided in this application embodiment can also be used to obtain the target device model of the electronic device when the sample set is M sample sets, each sample set in the M sample sets includes power amplifier abnormal data in electronic devices with the same device model, each sample set corresponds to a power amplifier early warning model, each power amplifier early warning model is associated with a device model of a class of electronic devices, and M is a positive integer greater than 1.

[0122] Based on the preset association information between the equipment model and the power amplifier early warning model, the target power amplifier early warning model corresponding to the target equipment model is selected from the preset model database. The preset model data includes M power amplifier early warning models.

[0123] In one or more possible embodiments, the processor 510 provided in this application embodiment can also be used to obtain the target application identifier of each target application in j target applications of the electronic device, where the sample set includes L sample sets, each sample set in the L sample sets includes power amplifier abnormal data caused by an application, each sample set corresponds to a power amplifier warning model, each power amplifier warning model is associated with the application identifier of the application, and L is a positive integer greater than 1.

[0124] Based on the preset association information between the application identifier and the power amplifier early warning model, the power amplifier early warning model corresponding to each target application identifier is selected from the preset model database;

[0125] j power amplifier early warning models are identified as the target power amplifier early warning models.

[0126] In one or more possible embodiments, the processor 510 may be specifically used to input the business data of each application in the j applications into the power amplifier warning model corresponding to the application identifier of each application, when the running data includes the business data of each application in the j applications, to obtain the sub-evaluation data of the power amplifier of each application affecting the electronic device.

[0127] The sub-evaluation data of j applications are used as the evaluation data of the power amplifier in the electronic device.

[0128] In one or more possible embodiments, the processor 510 provided in this application embodiment can also be used to obtain the target abnormal problem of the power amplifier in the electronic device corresponding to the evaluation value of the evaluation data, based on the preset association information between the evaluation value and the abnormal problem;

[0129] Based on the preset association information between the problem and the solution operation information, obtain the target solution operation information corresponding to the target abnormal problem;

[0130] Based on the objectives, resolve operational information and adjust electronic devices.

[0131] It should be understood that the input unit 504 may include a graphics processing unit (GPU) 5041 and a microphone 5042. The GPU 5041 processes image data of still images or videos acquired by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 506 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 5071 and other input devices 5072. The touch panel 5071 is also called a touch screen. The touch panel 5071 may include a touch detection device and a touch display. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (such as volume display buttons, power buttons, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0132] The memory 509 can be used to store software programs and various data. The memory 509 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 509 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 509 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0133] Processor 510 may include one or more processing units; optionally, processor 510 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless display signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 510.

[0134] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described data processing method embodiments and achieve the same technical effects. To avoid repetition, they will not be described again here.

[0135] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0136] In addition, this application embodiment provides another chip, which includes a processor and a display interface. The display interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above data processing method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0137] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0138] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the data processing method embodiments described above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0139] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0140] Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0142] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A data processing method, characterized in that, include: Acquire operational data of the electronic device, including data used to measure the device status and network status of the electronic device; The operating data is input into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model. If the evaluation data meets the preset conditions, a prompt message will be output, which is used to indicate that the power amplifier is malfunctioning. Based on the preset association information between the evaluation value and the abnormal problem, obtain the target abnormal problem of the power amplifier in the electronic device corresponding to the evaluation value of the evaluation data; based on the preset association information between the problem and the solution operation information, obtain the target solution operation information corresponding to the target abnormal problem; adjust the electronic device according to the target solution operation information.

2. The method according to claim 1, characterized in that, Before inputting the operating data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device, the method further includes: Obtain a sample set of abnormal power amplifier data, the sample set including first sample data and second sample data; The preset big data analysis model is trained based on the first sample data until the first preset training condition is met, and the first early warning model is obtained. The second sample data is input into the first early warning model to obtain the first sample evaluation data; Based on the first sample evaluation data and the second sample data, calculate the first evaluation accuracy of the first early warning model; If the first evaluation accuracy is greater than or equal to the preset evaluation accuracy, the first early warning model is determined as the target power amplifier early warning model.

3. The method according to claim 2, characterized in that, The second sample data includes N sub-sample data, where N is a positive integer greater than 1; after calculating the evaluation accuracy of the first early warning model based on the first sample evaluation data and the second sample data, the method further includes: If the first evaluation accuracy is less than the preset evaluation accuracy, the first early warning model is trained based on the i-th sub-sample data in the N sub-sample data and the first sample data until the second preset training condition is met, and the second early warning model is obtained, i∈[1,N]. Input N-1 sub-sample data from the second sample data into the second early warning model to obtain the second sample evaluation data; Based on the second sample evaluation data and the N-1 sub-sample data, calculate the second evaluation accuracy of the second early warning model; If the second evaluation accuracy is greater than or equal to the preset evaluation accuracy, the second early warning model will be determined as the target power amplifier early warning model.

4. The method according to claim 2, characterized in that, The sample set consists of M sample sets. Each sample set in the M sample sets includes abnormal power amplifier data from electronic devices with the same device model. Each sample set corresponds to a power amplifier early warning model. Each power amplifier early warning model is associated with a device model of a class of electronic devices. M is a positive integer greater than 1. Before inputting the operating data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device, the method further includes: Obtain the target device model of the electronic device; Based on the preset association information between the device model and the power amplifier early warning model, a target power amplifier early warning model corresponding to the target device model is selected from the preset model database. The preset model data includes M power amplifier early warning models.

5. The method according to claim 2, characterized in that, The sample set includes L sample sets, each of the L sample sets includes power amplifier abnormality data caused by an application, each sample set corresponds to a power amplifier early warning model, and each power amplifier early warning model is associated with the application identifier of the application, where L is a positive integer greater than 1. Before inputting the operating data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device, the method further includes: Obtain the target application identifier for each of the j target applications in the electronic device; Based on the preset association information between the application identifier and the power amplifier early warning model, the power amplifier early warning model corresponding to each target application identifier is selected from the preset model database; j power amplifier early warning models are determined as the target power amplifier early warning model.

6. The method according to claim 5, characterized in that, The operational data includes business data for each of the j applications; the step of inputting the operational data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device includes: The business data of each application in the j applications are input into the power amplifier early warning model corresponding to the application identifier of each application to obtain the sub-evaluation data of the power amplifier of each application affecting the electronic device; The sub-evaluation data of j applications are used as the evaluation data of the power amplifier in the electronic device.

7. A data processing apparatus, characterized in that, include: An acquisition module is used to acquire operating data of an electronic device, the operating data including data for measuring the device status and network status of the electronic device; The processing module is used to input the operating data into the target power amplifier early warning model to obtain the evaluation data of the power amplifier in the electronic device. The evaluation data is used to characterize the degree of abnormality of the power amplifier. The target power amplifier early warning model is trained by the power amplifier abnormal data on the big data analysis model. The output module is used to output a prompt message when the evaluation data meets the preset conditions. The prompt message is used to indicate that the power amplifier is malfunctioning. The acquisition module is further configured to: acquire, based on preset association information between the assessment value and the abnormal problem, acquire the target abnormal problem of the power amplifier in the electronic device corresponding to the assessment value of the assessment data; and acquire the target resolution operation information corresponding to the target abnormal problem based on preset association information between the problem and the resolution operation information. An adjustment module is used to adjust the electronic device according to the target solution operation information.

8. An electronic device, characterized in that, include: A processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the data processing method as described in any one of claims 1-6.

9. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the data processing method as described in any one of claims 1-6.

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