CN112199226B - Problem determination method and related product - Google Patents
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- CN112199226B CN112199226B CN202011100058.5A CN202011100058A CN112199226B CN 112199226 B CN112199226 B CN 112199226B CN 202011100058 A CN202011100058 A CN 202011100058A CN 112199226 B CN112199226 B CN 112199226B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
- G06F11/0736—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function
- G06F11/0742—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function in a data processing system embedded in a mobile device, e.g. mobile phones, handheld devices
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
Abstract
The embodiment of the application discloses a problem determination method and a related product, wherein the method comprises the following steps: the method comprises the steps of obtaining a problem log of a target module, wherein the problem log comprises N characteristic values, calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M reason index numbers of problems of the target module, and determining a first mark of the target module based on the M Bayesian estimation, wherein the M marks comprise the first mark.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a problem determination method and a related product.
Background
At present, when a problem (bug) occurs in a certain module of terminal equipment such as a smart phone and a computer, for example, a phone call does not have sound, the computer has a black screen, and the like, the problem generally needs to be analyzed and solved manually, which consumes long time and is slow in efficiency, and therefore, how to quickly find the reason why the terminal equipment has the problem is a problem which needs to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a problem determination method and a related product, wherein the cause of the problem of a module is determined by calculating the Bayesian estimation of each characteristic value in a problem log, so that the efficiency of solving the problem of the module is improved.
In a first aspect, an embodiment of the present application provides a problem determination method, which is applied to a terminal device, and the method includes:
obtaining a problem log of a target module, wherein the problem log comprises N characteristic values, and N is a positive integer;
calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M cause index numbers of the target module with problems, and M is a positive integer;
determining a first label for the target module based on the M Bayesian estimates, the M labels including the first label.
In a second aspect, an embodiment of the present application provides a problem determination apparatus, which is applied to a terminal device, where the apparatus includes:
the problem log acquisition unit is used for acquiring a problem log of a target module, wherein the problem log comprises N characteristic values, and N is a positive integer;
the calculation unit is used for calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M cause index numbers of the target module with problems, and the M is a positive integer;
a determining unit configured to determine a first label of the target module based on the M bayesian estimates, the M labels including the first label.
In a third aspect, an embodiment of the present application provides a terminal device, where the terminal device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for performing some or all of the steps described in the method of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, where the computer program makes a computer perform some or all of the steps described in the method of the first aspect.
In a fifth aspect, the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps described in the method according to the first aspect of the present application. The computer program product may be a software installation package.
In a sixth aspect, a chip system is provided, the chip system comprising at least one processor, a memory and an interface circuit, the memory, the transceiver and the at least one processor being interconnected by wires, the at least one memory having a computer program stored therein; the computer program, when executed by the processor, implements the method of the first aspect.
By implementing the embodiment of the application, the technical scheme provided by the application obtains the problem log of the target module, the problem log comprises N characteristic values, bayesian estimation of the N characteristic values under M marks is calculated to obtain M Bayesian estimation, the M marks are M reason index numbers of the problem of the target module, and the first mark of the target module is determined based on the M Bayesian estimation.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a terminal device according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating a problem determination method according to an embodiment of the present application;
FIG. 3a is a diagram illustrating a distribution number marked in a problem log according to an embodiment of the present disclosure;
FIG. 3b is a diagram illustrating an average eigenvalue number marked in the problem log according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a difference between a first mark and each mark according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of functional units of an issue determination apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of another terminal device provided in an embodiment of the present application.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings.
The term "at least one" as used in the embodiments of the present application means one or more, and the term "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
And, unless stated to the contrary, the embodiments of the present application refer to the ordinal numbers "first", "second", etc., for distinguishing a plurality of objects, and do not limit the sequence, timing, priority, or importance of the plurality of objects. For example, the first information and the second information are different information only for distinguishing them from each other, and do not indicate a difference in the contents, priority, transmission order, importance, or the like of the two kinds of information.
The technical solution of the embodiment of the present application may be applied to a terminal device shown in fig. 1, where the terminal device 100 shown in fig. 1 may include a processor 110, a memory 120, a USB interface 130, a charging management module 140, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a recovery (recovery) module 190, and the like. As shown in fig. 1, the memory 120, the USB interface 130, the charging management module 140, the mobile communication module 150, the wireless communication module 160, the audio module 170, the sensor module 180, and the recovery module 190 are all connected to the processor.
It is to be understood that the illustrated structure of the embodiment of the present application does not constitute a specific limitation to the terminal device 100. In other embodiments of the present application, terminal device 100 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
It should be understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is only an exemplary illustration, and does not constitute a limitation on the structure of the terminal device 100. In other embodiments of the present application, the terminal device 100 may also adopt different interface connection manners or a combination of multiple interface connection manners in the above embodiments.
The terminal device 100 may be a portable terminal device that also contains other functions such as personal digital assistant and/or music player functions, such as a mobile phone, a tablet computer, a smart speaker, a bluetooth headset, a vehicle-mounted terminal, a wearable terminal device with wireless communication functions (e.g., a smart watch), and the like. Exemplary embodiments of the portable terminal device include, but are not limited to, portable terminal devices that mount an IOS system, an Android system, a Microsoft system, or other operating systems. The above-described portable terminal device may also be other portable terminal devices such as a Laptop computer (Laptop) or the like. It should also be understood that in other embodiments, the terminal may not be a portable terminal device, but may be a desktop computer.
In order to make those skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 2, fig. 2 is a flowchart illustrating a problem determination method according to an embodiment of the present application, and the problem determination method is applied to the terminal device shown in fig. 1. As shown in fig. 2, the problem determination method includes the steps of:
s210, obtaining a problem log of the target module, wherein the problem log comprises N characteristic values, and N is a positive integer.
In practical application, a problem log (log) is generated in the operation process of the terminal device, and the problem log represents the operation state of the current system and module, namely the specific working condition of the module when the current terminal device has a problem. The question log is made up of a series of printed sentences and words. At present, the extraction of characteristic values in a problem log is realized by manually distinguishing keywords, one problem log is formed by thousands of words, the manually distinguished keywords are only dozens of words, and slight differences among different sample characteristics can not be reflected due to too few characteristic values. Therefore, in the embodiment of the application, all words in the problem log are used as the characteristic values, and the automatic extraction of the characteristic values is realized without artificial discrimination.
In one possible embodiment, the method further comprises: obtaining a first sample of the target module, wherein the first sample is a plurality of problem logs corresponding to the determined problem reasons of the target module; determining the M markers based on the first sample.
Before determining the cause of the problem of the target module, all possible causes causing the problem of the terminal device need to be determined, and then the cause of the problem of the target module is determined from all possible causes by analyzing the characteristic value of the target module.
Specifically, a plurality of historical problem logs of the target module, which have solved problems in the bug processing process, are obtained, the plurality of historical problem logs are used as a first sample of the target template, and the first sample is analyzed to obtain at least one reason causing problems of the target module. And labeling the at least one reason to obtain the M labels, wherein each label corresponds to the index number of one problem reason. For example, in the bug processing process of a recovery module of a smart phone, problem logs of 46 solved bugs are collected, that is, the number of first samples is 46, wherein the 46 bugs are caused by 21 reasons, so that the samples composed of the 46 bugs are divided into 21 marks, a schematic diagram of the distribution number of the marks in the problem logs is shown in fig. 3a, the abscissa in fig. 3a is a reason index number, that is, the marks, and the ordinate is the number of the bugs.
S220, bayesian estimation of the N characteristic values under M marks is calculated to obtain M Bayesian estimation, the M marks are M cause index numbers of the target module with problems, and M is a positive integer.
The mark corresponding to the target module is determined through the characteristic value, and the probability of a certain characteristic value appearing in a sample corresponding to a certain mark can be calculated by adopting a Bayesian classification algorithm. In the embodiment of the present application, for a problem log composed of a plurality of feature values, a joint probability of all feature values in the problem log of the target module under each label may be calculated, and the label of the target module may be determined from M labels according to the joint probability.
Optionally, the calculating bayesian estimates of the N feature values under the M labels to obtain M bayesian estimates includes: calculating N joint probabilities under the ith label in the N characteristic values, wherein i is a positive integer less than or equal to M; and accumulating after taking logarithms of the N joint probabilities to obtain Bayesian estimation of the ith marker.
Specifically, the problem log may include thousands of feature values, the feature values corresponding to each of the marks are different, after the M marks are determined by the first sample, the average number of feature values of each mark in the problem log of the target module is as shown in fig. 3b, the abscissa in fig. 3b is the cause index, and the ordinate is the number of feature values. The Bayes estimation of each mark in the problem log of the target module can be obtained by calculating the joint probability of each feature value under each mark and then accumulating the logarithms of the joint probabilities of each feature value under each mark, and the mark with the highest possibility in the M marks is determined according to the Bayes estimation.
S230, determining first marks of the target module based on the M Bayesian estimations, wherein the M marks comprise the first mark.
After the Bayesian estimation corresponding to each marker is calculated according to the characteristic value in the problem log of the target module, the size of the Bayesian estimation corresponding to each marker can be compared to judge which marker the target module belongs to.
Optionally, the determining a first label of the target module based on the M bayesian estimates includes: and if the Bayesian estimation of the ith marker is larger than the second Bayesian estimation, determining the Bayesian estimation of the ith marker as the first marker of the target module.
Specifically, M Bayesian estimations corresponding to M markers are compared respectively, the maximum Bayesian estimation is selected from the M Bayesian estimations, and the marker corresponding to the maximum Bayesian estimation is determined as the first marker of the target module, so that the most probable cause of the problem of the target module can be determined according to the first marker, and the efficiency of problem analysis is improved.
In one possible embodiment, the method further comprises: calculating a difference value between a first Bayesian estimation and a second Bayesian estimation, wherein the first Bayesian estimation is a Bayesian estimation corresponding to the first mark, and the second Bayesian estimation is any Bayesian estimation except the first Bayesian estimation in the M Bayesian estimations; based on the difference, a classification category of the first label is determined.
After the first mark of the target module is determined, a difference value between the first mark and each mark of the M marks can be calculated. According to the difference between the first mark and each mark, the classification of the first mark can be better determined and the nuances among different marks can be better reflected.
For example, the problem log of the target module is "write ultra savingunmount", which includes 3 feature values, bayesian estimates of the 3 feature values under 21 labels in fig. 3a are respectively calculated, and the bayesian estimates under 21 labels are compared to obtain the maximum bayesian estimate under the label RC =6, so that the label of the target module is determined as RC =6, and the difference between the bayesian estimates under different labels of the 3 feature values and the bayesian estimate of RC =6 is shown in fig. 4. Since bayesian estimation is the logarithm of the joint probability, the new curve in fig. 4 is the difference between the logarithm of the joint probability and the label RC =6 under different labels. Where the difference between the label RC =17 and the label RC =6 is largest, such a large difference can easily distinguish the classification of the labels and determine the cause of the problem with the target module. However, the difference between most of the marks and the mark RC =6 is not large, for example, the bayesian estimation difference between the mark RC =3 and the mark RC =6 is 0.06269, and the difference cannot be identified by a manual analysis method, so that the cause of the problem of the target module is easily determined by mistake.
According to the technical scheme, the problem log of the target module is obtained, the problem log comprises N characteristic values, bayesian estimation of the N characteristic values under M marks is calculated, M Bayesian estimation is obtained, the M marks are M reason index numbers of the target module with problems, the first mark of the target module is determined based on the M Bayesian estimation, the M marks comprise the first mark, the problem of the module is not needed to be analyzed manually, the problem of the module is directly determined through calculating the Bayesian estimation of each characteristic value under each mark in the problem log, and the problem of the module is directly determined according to the Bayesian estimation under each mark, so that the problem solving efficiency of the module is improved.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that, in order to implement the above functions, the terminal device includes a hardware structure and/or a software module for performing each function. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments provided herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed in hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal device may be divided into the functional units according to the method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Referring to fig. 5, fig. 5 is a block diagram of functional units of an issue determination apparatus 500 according to an embodiment of the present application, where the issue determination apparatus 500 is applied to a terminal device, and the apparatus 500 includes: an acquisition unit 510, a calculation unit 520 and a determination unit 530, wherein,
the obtaining unit 510 is configured to obtain a problem log of a target module, where the problem log includes N feature values, and N is a positive integer;
the calculating unit 520 is configured to calculate bayesian estimates of the N feature values under M flags to obtain M bayesian estimates, where the M flags are M cause index numbers of the target module with a problem, and M is a positive integer;
the determining unit 530 is configured to determine a first label of the target module based on the M bayesian estimates, where the M labels include the first label.
Optionally, the calculating unit 520 is further configured to: calculating a difference value between a first Bayesian estimation and a second Bayesian estimation, wherein the first Bayesian estimation is a Bayesian estimation corresponding to the first mark, and the second Bayesian estimation is any Bayesian estimation except the first Bayesian estimation in the M Bayesian estimations;
the determining unit 530 is further configured to: based on the difference, a classification category of the first label is determined.
Optionally, the calculating unit 520 is specifically configured to: calculating N joint probabilities under the ith label in the N characteristic values, wherein i is a positive integer less than or equal to M; and accumulating the N combined probabilities after taking logarithms to obtain the Bayesian estimation of the ith mark.
Optionally, the determining unit 530 is specifically configured to: and if the Bayesian estimation of the ith marker is larger than the second Bayesian estimation, the Bayesian estimation of the ith marker is determined as the first marker of the target module.
Optionally, the obtaining unit 510 is further configured to: obtaining a first sample of the target module, wherein the first sample is a plurality of problem logs corresponding to the determined problem reasons of the target module;
the determining unit 530 is further configured to: determining the M markers based on the first sample.
It can be understood that the functions of each program module of the resource updating apparatus in the embodiment of the present application can be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process of the method can refer to the related description of the foregoing method embodiment, which is not described herein again.
Referring to fig. 6, fig. 6 is another terminal device provided in an embodiment of the present application, referring to fig. 6, fig. 6 is a terminal device 100 provided in an embodiment of the present application, where the terminal device 100 includes a processor 110, a memory 120, and a communication interface 630, and the processor 110, the memory 120, and the communication interface 630 are connected to each other through a bus.
For example: the processor 110 may include an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a video codec, a Digital Signal Processor (DSP), a baseband processor, and/or a neural-Network Processing Unit (NPU), etc. Wherein the different processing units may be separate components or may be integrated in one or more processors. In some embodiments, terminal device 100 may also include one or more processors 110. The controller can generate an operation control signal according to the instruction operation code and the time sequence signal to complete the control of instruction fetching and instruction execution. In other embodiments, a memory may also be provided in processor 110 for storing instructions and data. Illustratively, the memory in the processor 110 may be a cache memory. The memory may hold instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to reuse the instruction or data, it can be called directly from the memory. This avoids repeated accesses and reduces the latency of the processor 110, thereby improving the efficiency with which the terminal device 100 processes data or executes instructions.
In some embodiments, processor 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit audio source (I2S) interface, a Pulse Code Modulation (PCM) interface, a universal asynchronous receiver/transmitter (UART) interface, a Mobile Industry Processor Interface (MIPI), a general-purpose-output (GPIO) interface, a SIM card interface, and/or a USB interface. The USB interface is an interface conforming to a USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface may be used to connect a charger to charge the terminal device 100, and may also be used to transmit data between the terminal device 100 and a peripheral device. The USB interface can also be used for connecting an earphone and playing audio through the earphone.
The processor 110 in the terminal device 100 is configured to read the computer program code stored in the memory 120, and perform the following operations:
obtaining a problem log of a target module, wherein the problem log comprises N characteristic values, and N is a positive integer;
calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M cause index numbers of the target module with problems, and M is a positive integer;
determining a first label for the target module based on the M Bayesian estimates, the M labels including the first label.
All relevant contents of each scene related to the method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again.
The embodiment of the present application further provides a chip system, where the chip system includes at least one processor, a memory and an interface circuit, where the memory, the transceiver and the at least one processor are interconnected through a line, and the at least one memory stores a computer program; the method flow shown in fig. 2 is implemented when the computer program is executed by the processor.
Embodiments of the present application further provide a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the elements may be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps of the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, the memory including: flash disk, ROM, RAM, magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (7)
1. A problem determination method is applied to a terminal device, and comprises the following steps:
obtaining a problem log of a target module, wherein the problem log comprises N characteristic values, and N is a positive integer;
calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M cause index numbers of the target module with problems, and M is a positive integer;
determining a first label for the target module based on the M Bayesian estimates, the M labels including the first label; the method further comprises the following steps:
calculating a difference value between a first Bayesian estimation and a second Bayesian estimation, wherein the first Bayesian estimation is a Bayesian estimation corresponding to the first mark, and the second Bayesian estimation is any Bayesian estimation except the first Bayesian estimation in the M Bayesian estimations;
determining a classification category of the first label based on the difference; the calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation comprises:
calculating N joint probabilities under the ith label in the N characteristic values, wherein i is a positive integer less than or equal to M;
and accumulating the N combined probabilities after taking logarithms to obtain the Bayesian estimation of the ith mark.
2. The method of claim 1, wherein determining the first label for the goal module based on the M bayesian estimates comprises:
and if the Bayesian estimation of the ith marker is larger than the second Bayesian estimation, determining the Bayesian estimation of the ith marker as the first marker of the target module.
3. The method of claim 1, further comprising:
obtaining a first sample of the target module, wherein the first sample is a plurality of problem logs corresponding to the determined problem reasons of the target module;
determining the M markers based on the first sample.
4. An issue determination apparatus, applied to a terminal device, the apparatus comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a problem log of a target module, the problem log comprises N characteristic values, and N is a positive integer;
the calculation unit is used for calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation, wherein the M marks are M cause index numbers of the target module with problems, and M is a positive integer;
a determining unit configured to determine a first label of the target module based on the M bayesian estimates, the M labels including the first label;
the computing unit is further to: calculating a difference value between a first Bayesian estimation and a second Bayesian estimation, wherein the first Bayesian estimation is a Bayesian estimation corresponding to the first mark, and the second Bayesian estimation is any Bayesian estimation except the first Bayesian estimation in the M Bayesian estimations;
the determination unit is further configured to: determining a classification category of the first label based on the difference;
determining a classification category of the first label based on the difference; the calculating Bayesian estimation of the N characteristic values under M marks to obtain M Bayesian estimation comprises:
calculating N joint probabilities under the ith label in the N characteristic values, wherein i is a positive integer less than or equal to M;
and accumulating the N combined probabilities after taking logarithms to obtain the Bayesian estimation of the ith mark.
5. A terminal device, characterized in that the terminal device comprises a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for carrying out the steps in the method according to any one of claims 1-3.
6. A chip system, the chip system comprising at least one processor, memory and interface circuitry, the memory, the interface circuitry and the at least one processor being interconnected by wires, the at least one memory having stored therein a computer program; the computer program, when executed by the processor, implements the method of any of claims 1-3.
7. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the steps of the method according to any one of claims 1-3.
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