CN117560443B - Method and system for testing mobile phone motherboard - Google Patents
Method and system for testing mobile phone motherboard Download PDFInfo
- Publication number
- CN117560443B CN117560443B CN202311391428.9A CN202311391428A CN117560443B CN 117560443 B CN117560443 B CN 117560443B CN 202311391428 A CN202311391428 A CN 202311391428A CN 117560443 B CN117560443 B CN 117560443B
- Authority
- CN
- China
- Prior art keywords
- mobile phone
- fault
- test
- data
- motherboard
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012360 testing method Methods 0.000 title claims abstract description 543
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000004891 communication Methods 0.000 claims abstract description 131
- 238000001514 detection method Methods 0.000 claims abstract description 109
- 238000012937 correction Methods 0.000 claims abstract description 102
- 238000004458 analytical method Methods 0.000 claims abstract description 85
- 238000011084 recovery Methods 0.000 claims abstract description 52
- 238000012545 processing Methods 0.000 claims abstract description 48
- 238000012549 training Methods 0.000 claims abstract description 34
- 230000008439 repair process Effects 0.000 claims abstract description 31
- 238000003745 diagnosis Methods 0.000 claims abstract description 19
- 238000004364 calculation method Methods 0.000 claims description 54
- 238000005259 measurement Methods 0.000 claims description 31
- 230000008569 process Effects 0.000 claims description 31
- 238000011056 performance test Methods 0.000 claims description 29
- 230000000737 periodic effect Effects 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 13
- 238000013527 convolutional neural network Methods 0.000 claims description 8
- 230000001419 dependent effect Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 5
- 238000013101 initial test Methods 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 238000013508 migration Methods 0.000 claims description 3
- 230000005012 migration Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims 4
- 238000010998 test method Methods 0.000 claims 2
- 230000000694 effects Effects 0.000 claims 1
- 239000000306 component Substances 0.000 description 24
- 238000012544 monitoring process Methods 0.000 description 12
- 238000010801 machine learning Methods 0.000 description 11
- 230000009286 beneficial effect Effects 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 8
- 238000012423 maintenance Methods 0.000 description 7
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000002955 isolation Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 238000007637 random forest analysis Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000013526 transfer learning Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0464—Convolutional networks [CNN, ConvNet]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/24—Arrangements for testing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Signal Processing (AREA)
- Telephone Function (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention relates to the technical field of communication test, in particular to a method and a system for testing a mobile phone motherboard. The method comprises the following steps: performing fault identification analysis and fault identification training on the mobile phone motherboard data to obtain a fault identification model; performing bottom hardware test on the mobile phone motherboard by setting up a mobile phone motherboard test environment to obtain mobile phone motherboard test data; performing fault identification detection on the mobile phone main board test data by using a fault identification model to obtain main board test fault data; performing self-adaptive self-recovery processing and unrecoverable problem diagnosis analysis on the main board test fault data to obtain fault unrecoverable problem data; performing latent fault detection analysis and model correction on the fault unrecovered problem data to obtain a fault identification correction model; and carrying out fault identification correction detection and fault repair analysis on the mobile phone motherboard test data by using the fault identification correction model to generate a test fault repair report. The invention can realize the high efficiency and accuracy of the mobile phone motherboard test.
Description
Technical Field
The invention relates to the technical field of communication test, in particular to a method and a system for testing a mobile phone motherboard.
Background
The mobile phone motherboard is one of the core components of the smart phone, and comprises a processor, a storage interface, a connection interface and other key elements. Testing the validity and reliability of the mobile phone motherboard is important to ensuring the quality of the mobile phone. Conventional mobile phone motherboard testing methods typically involve a series of hardware and high-level software tests, but these methods have certain limitations in terms of efficiency, accuracy and cost.
Disclosure of Invention
Accordingly, the present invention is directed to a method for testing a motherboard of a mobile phone, so as to solve at least one of the above-mentioned problems.
In order to achieve the above purpose, a method for testing a mobile phone motherboard includes the following steps:
Step S1: acquiring mobile phone motherboard data, performing fault identification analysis on the mobile phone motherboard data, and obtaining mobile phone motherboard normal data and mobile phone motherboard fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
Step S2: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
Step S3: performing fault identification detection on the mobile phone main board test data by using a fault identification model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data to obtain fault self-recovery result data;
Step S4: performing unrecoverable problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecoverable problem data; performing potential fault detection analysis on the fault unrecovered problem data to obtain a test potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors to obtain a fault identification correction model;
Step S5: performing fault identification correction detection on the mobile phone motherboard test data by using a fault identification correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
According to the invention, firstly, through acquiring the mobile phone main board data, the mobile phone main board can be comprehensively monitored by methods of built-in sensors, monitoring the main board hardware state, collecting various index data and the like, and meanwhile, a basic data set is created, so that basic data is provided for subsequent fault identification, state information on the mobile phone main board can be monitored, and thus, the real-time monitoring and diagnosis of faults are realized. And by carrying out fault identification analysis on the obtained mobile phone main board data, whether the mobile phone main machine data has faults or not can be automatically identified, the workload of manual examination is reduced, and a marked data set required by supervised learning can be provided for a subsequent machine learning model by marking the data as faults or normal for training the subsequent model. In addition, the machine learning model is trained on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, so that the model can automatically identify the faults of the mobile phone motherboard, and the normal and fault characteristics of the mobile phone motherboard can be distinguished. The new fault mode is continuously trained, learned and adapted according to the actual situation, so that the requirement of manual intervention can be reduced, and the accuracy and the robustness of the mobile phone main board fault detection can be improved. Secondly, a mobile phone motherboard test environment is established through a mobile phone motherboard, test equipment, test software and a test communication interface, and a mobile phone motherboard test program is generated, wherein the mobile phone motherboard test environment comprises a communication interface test, a performance test and a motherboard comprehensive test. The construction of the environment and the deployment of the test program are beneficial to testing the actual running condition of the mobile phone motherboard, so that mobile phone motherboard test data are obtained, and the test data are critical to the fault detection of the mobile phone motherboard, because they represent the possible situations in actual operation. Through hardware testing, normal operation of the mobile phone motherboard can be verified, potential hardware faults can be detected, real-time monitoring of the state of the mobile phone motherboard is facilitated, faults are predicted, and therefore stability and reliability of the mobile phone motherboard are improved. Then, by using the constructed fault recognition model to perform fault recognition detection on the mobile phone motherboard test data and obtaining motherboard test fault data from the fault recognition model, an automatic fault detection mechanism can be provided, and the mobile phone motherboard fault can be captured in time, so that the potential risk of the mobile phone motherboard fault is reduced. More importantly, the fault data can be subjected to self-adaptive self-recovery processing, so that the self-recovery capability of the mobile phone main board fault is further improved, measures can be automatically taken according to the recognized fault condition, the solving process of the fault problem is accelerated, the downtime is reduced, and the maintenance efficiency of the mobile phone main board is improved. Then, by carrying out the diagnosis and analysis of the unrecoverable problem on the fault self-recovery result data, the root cause of the unrecoverable problem in the self-recovery process can be deeply analyzed, and the mobile phone main board is helped to better understand the properties of the unrecoverable problem. In addition, through carrying out potential fault detection analysis on fault unrecovered problem data, the multi-level property hidden behind the unrecovered problem can be revealed, and a direction is provided for problem repair. Analysis of the resulting test latent fault factors helps determine which factors may lead to the continued existence of a problem, providing a data driven method of problem analysis. And the model correction is carried out on the fault identification model according to the information of the potential fault factors, so that the accuracy and the reliability of the model are further improved, the false alarm rate and the false alarm rate are reduced, and the self-recovery efficiency of faults is enhanced. Finally, by utilizing the fault identification correction model to carry out fault identification correction detection on the mobile phone motherboard test data, the accuracy of fault detection can be enhanced, and the mobile phone motherboard test problem can be detected more reliably. Then, by performing fault repair analysis on the corrected test fault correction data to generate a test fault repair report, detailed information and advice can be provided through analysis, maintenance personnel can be helped to quickly solve the problem, so that the problem processing flow can be accelerated, the maintenance time and cost are reduced, the performance and reliability of the mobile phone motherboard are improved, and the stability of the whole mobile phone motherboard is improved, thereby being beneficial to reducing the problem caused by faults and reducing the operation cost.
Preferably, the present invention also provides a system for testing a mobile phone motherboard, for executing the method for testing a mobile phone motherboard as described above, the system for testing a mobile phone motherboard includes:
The mobile phone main board fault identification module is used for acquiring mobile phone main board data, carrying out fault identification analysis on the mobile phone main board data and obtaining mobile phone main board normal data and mobile phone main board fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
The mobile phone motherboard test module is used for building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
The fault recognition self-recovery module is used for carrying out fault recognition detection on the mobile phone main board test data by utilizing the fault recognition model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data so as to obtain fault self-recovery result data;
The unrecovered potential fault analysis module is used for carrying out unrecovered problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecovered problem data; detecting potential faults of the fault unrecovered problem data to obtain a tested potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors so as to obtain a fault identification correction model;
The fault repair report generation module is used for carrying out fault recognition correction detection on the mobile phone motherboard test data by utilizing the fault recognition correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
In summary, the system for testing the mobile phone motherboard is composed of a fault identification model building module, a mobile phone motherboard testing module, a fault identification self-recovery module, an unrecovered potential fault analysis module and a fault restoration report generation module, and can realize any method for testing the mobile phone motherboard.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of steps of a method for testing a mobile phone motherboard according to the present invention;
FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;
Fig. 3 is a detailed step flow chart of step S14 in fig. 2.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a method for testing a mobile phone motherboard, the method includes the following steps:
Step S1: acquiring mobile phone motherboard data, performing fault identification analysis on the mobile phone motherboard data, and obtaining mobile phone motherboard normal data and mobile phone motherboard fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
Step S2: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
Step S3: performing fault identification detection on the mobile phone main board test data by using a fault identification model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data to obtain fault self-recovery result data;
Step S4: performing unrecoverable problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecoverable problem data; performing potential fault detection analysis on the fault unrecovered problem data to obtain a test potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors to obtain a fault identification correction model;
Step S5: performing fault identification correction detection on the mobile phone motherboard test data by using a fault identification correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
In the embodiment of the present invention, please refer to fig. 1, which is a flowchart illustrating steps of a method for testing a mobile phone motherboard according to the present invention, in this example, the method for testing a mobile phone motherboard includes:
Step S1: acquiring mobile phone motherboard data, performing fault identification analysis on the mobile phone motherboard data, and obtaining mobile phone motherboard normal data and mobile phone motherboard fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
the embodiment of the invention monitors the mobile phone motherboard comprehensively by using methods such as built-in sensors, monitoring the hardware state of the motherboard, collecting various index data and the like so as to monitor the state information on the mobile phone motherboard, thereby obtaining the mobile phone motherboard data. Then, the fault problem of the mobile phone motherboard becomes quantitative by carrying out fault calculation on the mobile phone motherboard data, and the mobile phone motherboard data are respectively marked as mobile phone motherboard normal data and mobile phone motherboard fault data according to the fault degree of the judging result by comparing and judging through a preset fault threshold value. Finally, training a machine learning model by using a convolutional neural network to the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, so that the model can automatically identify the faults of the mobile phone motherboard, thereby accelerating the detection and the solution of the fault problems, and finally obtaining a fault identification model.
Step S2: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
The embodiment of the invention firstly obtains the target mobile phone motherboard and ensures that the target mobile phone motherboard is intact, prepares test equipment including a connecting wire, a test tool and the like, ensures test software suitable for testing the mobile phone motherboard, including communication interface test, performance test, motherboard comprehensive test and the like, and simultaneously establishes a test communication environment among the mobile phone motherboard, the test equipment and the test software (the mobile phone motherboard is connected with the test equipment through the test communication interface and the test equipment is connected with the test software through the test communication interface) through setting a proper test communication interface, thereby obtaining the mobile phone motherboard test environment. Then, compatibility detection is carried out on the built mobile phone motherboard testing environment so as to ensure that the mobile phone motherboard testing environment is compatible with normal operation of the mobile phone motherboard, and a Docker containerization platform is installed and configured to containerize the mobile phone motherboard testing environment, so that the mobile phone motherboard testing environment can be used on the motherboard compatibility testing environment, and a compatibility testing environment container is obtained. And then, automatically deploying the mobile phone main board by using test software in a compatible test environment container to generate a main board test program comprising a communication interface test program, a performance test program and a main board comprehensive test program, wherein the communication interface test program is used for testing interface problems on the mobile phone main board, the performance test program is used for testing performance problems on the mobile phone main board, and the main board comprehensive test program is used for comprehensively testing the interface and performance problems on the mobile phone main board, so that the mobile phone main board test program is obtained. Finally, the generated mobile phone motherboard test program is loaded into the test equipment by using the test communication interface so as to ensure that the test equipment can understand and execute the mobile phone motherboard test program, and the mobile phone motherboard test program is operated on the test equipment to carry out hardware test on the mobile phone motherboard, so that mobile phone motherboard test data is finally obtained.
Step S3: performing fault identification detection on the mobile phone main board test data by using a fault identification model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data to obtain fault self-recovery result data;
According to the embodiment of the invention, the tested mobile phone motherboard test data are input into the constructed fault recognition model for recognition detection, so that whether motherboard fault problems exist in the mobile phone motherboard test data is detected, if the fault recognition detection result shows that motherboard fault problems exist, corresponding motherboard debugging, resetting or repairing and other self-recovery processing operations are executed, so that faults are repaired, the self-recovery capacity of the mobile phone motherboard faults is further improved, and finally fault self-recovery result data are obtained.
Step S4: performing unrecoverable problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecoverable problem data; performing potential fault detection analysis on the fault unrecovered problem data to obtain a test potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors to obtain a fault identification correction model;
The embodiment of the invention establishes a model by acquiring relevant data in a fault self-recovery result, including information such as fault type, processing method and time stamp, and by using proper machine learning (support vector machine, random forest, neural network and the like) to detect whether the fault is not recovered, cleans and preprocesses the collected data, including operations such as removing abnormal values and filling missing values, and the like, predicts the processed data by using the established model and analyzes the predicted data to analyze and determine why the self-recovery processing process cannot solve the corresponding fault problem, and determines the specific property and reason of the fault problem not to recover, thereby obtaining fault unrecoverable problem data. And then, analyzing the diagnosed fault unrecovered problem data by using a potential fault analysis algorithm to find out the complexity and multi-level property hidden behind the fault problem, further exploring the root cause and potential fault factor of the fault problem, and calculating the corresponding fault factor according to the analysis result so as to obtain the tested potential fault factor. And finally, adjusting and correcting the constructed fault recognition model by using the calculated test potential fault factors so that the fault recognition model can automatically learn and adapt to a new fault mode, the detection accuracy of the fault unrecovered problem is improved, and finally, the fault recognition correction model is obtained.
Step S5: performing fault identification correction detection on the mobile phone motherboard test data by using a fault identification correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
According to the embodiment of the invention, the corrected fault recognition correction model is used for further fault recognition detection of the mobile phone motherboard test data, the mobile phone motherboard test data is input into the corrected fault recognition correction model, potential fault information and fault information with more perfect surface are detected according to the output result of the model, and the correction effectiveness is determined, so that the test fault correction data is obtained. The test fault correction data is then analyzed to determine the specific cause and solution of the fault, including repairing hardware faults, changing configurations, or performing other operations to repair the fault. Finally, according to the result of the fault repair analysis, a test fault repair report is finally generated, wherein the report comprises the description of the fault, repair measures and verification results so as to record and refer to.
According to the invention, firstly, through acquiring the mobile phone main board data, the mobile phone main board can be comprehensively monitored by methods of built-in sensors, monitoring the main board hardware state, collecting various index data and the like, and meanwhile, a basic data set is created, so that basic data is provided for subsequent fault identification, state information on the mobile phone main board can be monitored, and thus, the real-time monitoring and diagnosis of faults are realized. And by carrying out fault identification analysis on the obtained mobile phone main board data, whether the mobile phone main machine data has faults or not can be automatically identified, the workload of manual examination is reduced, and a marked data set required by supervised learning can be provided for a subsequent machine learning model by marking the data as faults or normal for training the subsequent model. In addition, the machine learning model is trained on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, so that the model can automatically identify the faults of the mobile phone motherboard, and the normal and fault characteristics of the mobile phone motherboard can be distinguished. The new fault mode is continuously trained, learned and adapted according to the actual situation, so that the requirement of manual intervention can be reduced, and the accuracy and the robustness of the mobile phone main board fault detection can be improved. Secondly, a mobile phone motherboard test environment is established through a mobile phone motherboard, test equipment, test software and a test communication interface, and a mobile phone motherboard test program is generated, wherein the mobile phone motherboard test environment comprises a communication interface test, a performance test and a motherboard comprehensive test. The construction of the environment and the deployment of the test program are beneficial to testing the actual running condition of the mobile phone motherboard, so that mobile phone motherboard test data are obtained, and the test data are critical to the fault detection of the mobile phone motherboard, because they represent the possible situations in actual operation. Through hardware testing, normal operation of the mobile phone motherboard can be verified, potential hardware faults can be detected, real-time monitoring of the state of the mobile phone motherboard is facilitated, faults are predicted, and therefore stability and reliability of the mobile phone motherboard are improved. Then, by using the constructed fault recognition model to perform fault recognition detection on the mobile phone motherboard test data and obtaining motherboard test fault data from the fault recognition model, an automatic fault detection mechanism can be provided, and the mobile phone motherboard fault can be captured in time, so that the potential risk of the mobile phone motherboard fault is reduced. More importantly, the fault data can be subjected to self-adaptive self-recovery processing, so that the self-recovery capability of the mobile phone main board fault is further improved, measures can be automatically taken according to the recognized fault condition, the solving process of the fault problem is accelerated, the downtime is reduced, and the maintenance efficiency of the mobile phone main board is improved. Then, by carrying out the diagnosis and analysis of the unrecoverable problem on the fault self-recovery result data, the root cause of the unrecoverable problem in the self-recovery process can be deeply analyzed, and the mobile phone main board is helped to better understand the properties of the unrecoverable problem. In addition, through carrying out potential fault detection analysis on fault unrecovered problem data, the multi-level property hidden behind the unrecovered problem can be revealed, and a direction is provided for problem repair. Analysis of the resulting test latent fault factors helps determine which factors may lead to the continued existence of a problem, providing a data driven method of problem analysis. And the model correction is carried out on the fault identification model according to the information of the potential fault factors, so that the accuracy and the reliability of the model are further improved, the false alarm rate and the false alarm rate are reduced, and the self-recovery efficiency of faults is enhanced. Finally, by utilizing the fault identification correction model to carry out fault identification correction detection on the mobile phone motherboard test data, the accuracy of fault detection can be enhanced, and the mobile phone motherboard test problem can be detected more reliably. Then, by performing fault repair analysis on the corrected test fault correction data to generate a test fault repair report, detailed information and advice can be provided through analysis, maintenance personnel can be helped to quickly solve the problem, so that the problem processing flow can be accelerated, the maintenance time and cost are reduced, the performance and reliability of the mobile phone motherboard are improved, and the stability of the whole mobile phone motherboard is improved, thereby being beneficial to reducing the problem caused by faults and reducing the operation cost.
Preferably, step S1 comprises the steps of:
step S11: acquiring mobile phone motherboard data;
Step S12: performing fault calculation on the mobile phone motherboard data by using a fault measurement degree calculation formula to obtain the fault degree of the mobile phone motherboard;
step S13: judging the mobile phone motherboard fault degree according to a preset mobile phone motherboard fault threshold, and marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard fault data when the mobile phone motherboard fault degree is greater than or equal to the preset mobile phone motherboard fault threshold; when the mobile phone motherboard fault degree is smaller than a preset mobile phone motherboard fault threshold value, marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard normal data;
step S14: and performing fault recognition training on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard to obtain a fault recognition model.
As an embodiment of the present invention, referring to fig. 2, a detailed step flow chart of step S1 in fig. 1 is shown, in which step S1 includes the following steps:
step S11: acquiring mobile phone motherboard data;
According to the embodiment of the invention, the mobile phone main board is comprehensively monitored by using methods such as built-in sensors, monitoring the hardware state of the main board, collecting various index data and the like, so that the state information on the mobile phone main board is monitored, and finally the mobile phone main board data is obtained.
Step S12: performing fault calculation on the mobile phone motherboard data by using a fault measurement degree calculation formula to obtain the fault degree of the mobile phone motherboard;
According to the embodiment of the invention, a proper fault measurement degree calculation formula is formed by combining the time parameter, the communication measurement parameter, the communication fault weight coefficient, the performance measurement parameter, the performance fault weight coefficient, the environment measurement parameter, the environment fault influence parameter, the periodic fault amplitude range parameter, the periodic fault amplitude weight coefficient, the fault loss measurement parameter, the fault loss measurement weight coefficient, the periodic fault amplitude adjustment parameter and the related parameter of fault calculation, so that fault problems of a mobile phone motherboard become quantized, and finally the fault degree of the mobile phone motherboard is obtained. In addition, the fault measure calculation formula can also use any fault measure algorithm in the field to replace the fault calculation process, and is not limited to the fault measure calculation formula.
Step S13: judging the mobile phone motherboard fault degree according to a preset mobile phone motherboard fault threshold, and marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard fault data when the mobile phone motherboard fault degree is greater than or equal to the preset mobile phone motherboard fault threshold; when the mobile phone motherboard fault degree is smaller than a preset mobile phone motherboard fault threshold value, marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard normal data;
According to the embodiment of the invention, the calculated mobile phone motherboard fault degree is compared and judged according to the preset mobile phone motherboard fault threshold, if the mobile phone motherboard fault degree is greater than or equal to the preset mobile phone motherboard fault threshold, the mobile phone motherboard data corresponding to the mobile phone motherboard fault degree is marked as mobile phone motherboard fault data, and if the mobile phone motherboard fault degree is smaller than the preset mobile phone motherboard fault threshold, the mobile phone motherboard data corresponding to the mobile phone motherboard fault degree is marked as mobile phone motherboard normal data.
Step S14: and performing fault recognition training on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard to obtain a fault recognition model.
According to the embodiment of the invention, the convolutional neural network is used for training the machine learning model for the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, and the model can automatically identify the fault of the mobile phone motherboard, so that the detection and the solution of the fault problem are accelerated. And then, the training process of the model is adjusted and optimized by continuously training, learning and adapting to a new fault mode according to actual conditions, so that the accuracy and the robustness of the fault detection of the mobile phone main board are improved, and finally, a fault identification model is obtained.
According to the invention, the corresponding mobile phone main board data are acquired, the mobile phone main board can be comprehensively monitored by the methods of built-in sensors, monitoring main board hardware states, collecting various index data and the like, basic data can be provided for subsequent fault detection, so that state information on the mobile phone main board can be monitored, and thus, the real-time monitoring and diagnosis of faults are realized. And secondly, by using a proper fault measurement degree calculation formula to perform fault calculation on mobile phone motherboard data, the fault problem of the mobile phone motherboard can be quantized, the fault problem on the mobile phone motherboard can be automatically identified and evaluated, the measurement index for timely monitoring and rapidly detecting the fault is provided, the requirement for manual fault analysis is reduced, and the efficiency and accuracy of subsequent fault detection are improved. Then, through comparing and judging the fault degree of the mobile phone main board by using a preset fault threshold value of the mobile phone main board, whether the mobile phone host has faults or not can be automatically identified through judging the fault degree of the mobile phone main board, the workload of manual examination is lightened, the fault or normal is marked by data, a marking data set required by supervised learning can be provided for a subsequent machine learning model, and the method is used for training the subsequent model. In addition, through judging and marking the fault degree, the real-time monitoring and timely processing of the fault are facilitated, and therefore the efficiency and quality of the mobile phone motherboard test are improved. Finally, the machine learning model is trained on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, so that the model can automatically identify the faults of the mobile phone motherboard, and the detection and the solution of the fault problem are accelerated. The new fault mode is continuously trained, learned and adapted according to the actual situation, so that the requirement of manual intervention can be reduced, and the accuracy and the robustness of the mobile phone main board fault detection can be improved.
Preferably, the failure metric degree calculation formula in step S12 is specifically:
Wherein F D is the fault degree of the mobile phone motherboard, T is the time range parameter of fault calculation, T is the integral time variable of fault calculation, X (T) is the communication measurement parameter in the mobile phone motherboard data at time T, a 1 is the communication fault weight coefficient, Y (T) is the performance measurement parameter in the mobile phone motherboard data at time T, alpha 2 is the performance fault weight coefficient, exp is an exponential function, Z (T) is the environment measurement parameter in the mobile phone motherboard data at time T, beta is the environment fault influence parameter, a is the lower limit of the periodic fault amplitude range of the mobile phone motherboard, b is the upper limit of the periodic fault amplitude range of the mobile phone motherboard, θ (T) is the periodic fault amplitude parameter of the mobile phone motherboard at time T, ρ 1 is the periodic fault amplitude weight coefficient, λ (T) is the fault loss measurement parameter of the mobile phone motherboard at time T, ρ 2 is the fault loss measurement weight coefficient, ρ 3 is the periodic fault amplitude adjustment parameter, and μ is the correction value of the fault degree of the mobile phone motherboard.
The invention constructs a fault measurement degree calculation formula for carrying out fault calculation on the mobile phone motherboard data, and the fault measurement degree calculation formula synthesizes fault measurement parameters of the mobile phone fault in different aspects, including communication, performance, environment, periodic amplitude and fault loss, so as to comprehensively evaluate the fault degree of the mobile phone motherboard. Second, the importance of each metric parameter in the failure degree calculation is adjusted by using the corresponding weight coefficient so as to make trade-off according to the specific situation. In addition, correction values are introduced to correct or correct the fault degree so as to adapt to different fault conditions and calculation requirements. The formula fully considers the fault degree F D of the mobile phone motherboard, the time range parameter T of fault calculation, the integral time variable T of fault calculation, the communication measurement parameter X (T) in the mobile phone motherboard data at the time T, the communication fault weight coefficient alpha 1, the performance measurement parameter Y (T) in the mobile phone motherboard data at the time T, the performance fault weight coefficient alpha 2, the exponential function exp, the environment measurement parameter Z (T) in the mobile phone motherboard data at the time T, the environment fault influence parameter beta, the periodic fault amplitude range lower limit alpha of the mobile phone motherboard, the periodic fault amplitude range upper limit b of the mobile phone motherboard, the periodic fault amplitude parameter theta (T) of the mobile phone motherboard at the time T, the periodic fault amplitude weight coefficient rho 1, the fault loss measurement parameter lambda (T) of the mobile phone motherboard at the time T, the fault loss measurement weight coefficient rho 2, the periodic fault amplitude adjustment parameter rho 3, the correction value mu of the mobile phone motherboard fault degree form a functional relation according to the correlation relation between the mobile phone motherboard fault degree F D and the above parameters:
the formula can realize the fault calculation process of the mobile phone motherboard data, and meanwhile, the correction value mu of the fault degree of the mobile phone motherboard can be introduced to adjust according to the error condition in the calculation process, so that the accuracy and the applicability of the fault measurement degree calculation formula are improved.
Preferably, step S14 comprises the steps of:
step S141: carrying out feature engineering extraction processing on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard to obtain normal features of the mobile phone motherboard and fault features of the mobile phone motherboard;
Step S142: analyzing the normal characteristics of the mobile phone motherboard and the fault characteristics of the mobile phone motherboard to obtain a motherboard normal characteristic mode and a motherboard fault characteristic mode;
Step S143: performing mode distinguishing identification detection on the normal characteristic mode of the main board and the fault characteristic mode of the main board to obtain fault distinguishing mode data;
Step S144: performing fault pre-training on the fault distinguishing mode data through a convolutional neural network to obtain a fault recognition pre-training model;
Step S145: and performing migration learning on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard by using the fault recognition pre-training model to obtain a fault recognition model.
As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S14 in fig. 2 is shown, in which step S14 includes the following steps:
step S141: carrying out feature engineering extraction processing on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard to obtain normal features of the mobile phone motherboard and fault features of the mobile phone motherboard;
The method comprises the steps of firstly cleaning and preprocessing normal data of a mobile phone motherboard and fault data of the mobile phone motherboard, including missing value processing, abnormal value detection and data normalization, extracting corresponding key characteristic information from the preprocessed data by using methods such as statistics, time-frequency domain analysis and the like, and finally obtaining normal characteristics of the mobile phone motherboard and fault characteristics of the mobile phone motherboard.
Step S142: analyzing the normal characteristics of the mobile phone motherboard and the fault characteristics of the mobile phone motherboard to obtain a motherboard normal characteristic mode and a motherboard fault characteristic mode;
According to the embodiment of the invention, the dimension of the normal characteristic of the mobile phone motherboard and the dimension of the fault characteristic of the mobile phone motherboard are reduced by using a dimension reduction technology such as a principal component analysis technology so as to better understand the characteristic information, and then the dimension-reduced characteristic data are subjected to statistical analysis by using a mode analysis method so as to identify the characteristic modes of the motherboard in normal and fault states, and finally the normal characteristic mode and the fault characteristic mode of the motherboard are obtained.
Step S143: performing mode distinguishing identification detection on the normal characteristic mode of the main board and the fault characteristic mode of the main board to obtain fault distinguishing mode data;
the embodiment of the invention determines the distinction and commonality between the normal characteristic mode of the main board and the fault characteristic mode of the main board by using a mode recognition algorithm so as to detect the information data for distinguishing the normal mode from the fault mode and finally obtain the fault distinguishing mode data.
Step S144: performing fault pre-training on the fault distinguishing mode data through a convolutional neural network to obtain a fault recognition pre-training model;
The embodiment of the invention uses a convolutional neural network to pretrain the fault distinguishing mode data so as to obtain initial weights and characteristic representations, and is used for learning complex characteristics and modes of the fault distinguishing mode data to capture abstract representations of the fault distinguishing mode data, and finally, a fault recognition pretraining model is obtained.
Step S145: and performing migration learning on the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard by using the fault recognition pre-training model to obtain a fault recognition model.
According to the embodiment of the invention, the weight and the characteristic representation transfer learning of the fault recognition pre-training model are applied to the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, so that the obtained model can learn the knowledge of the fault recognition pre-training model, and the model after transfer learning is subjected to fine adjustment so as to adapt to the accurate fault recognition of the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, and finally the fault recognition model is obtained.
According to the invention, firstly, through carrying out feature engineering extraction processing on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard, key feature information can be extracted from the normal data and the fault data of the mobile phone motherboard, and the extracted feature information can be quantization information related to the state, performance and sensor data of the mobile phone motherboard, so that the original data can be beneficially converted into a form which can be used for machine learning, and the characteristics of the data can be understood by a model are helped, so that fault problems are identified and fault detection is carried out. The extraction of normal features and fault features is helpful for accurately distinguishing the state of the mobile phone motherboard. And secondly, the characteristic mode analysis is carried out on the normal characteristics of the mobile phone main board and the fault characteristics of the mobile phone main board, so that the characteristic modes of the main board in normal and fault states can be recognized. By identifying the modes, different states of the mobile phone motherboard can be better known and used in subsequent identification and detection, so that a mode identification basic model aiming at the mobile phone motherboard problem can be established. Then, through carrying out mode distinguishing, identifying and detecting on the analyzed normal characteristic modes and the main board fault characteristic modes of the main board, information for distinguishing the normal modes and the fault modes is found out, and therefore the fault detection accuracy of the model on different main board states can be further improved. Since the fault and the normal state have different characteristic modes, the false alarm rate can be reduced by the mode discrimination identification. And then, the convolutional neural network is used for carrying out fault pre-training on the fault distinguishing mode data, the convolutional neural network can be used for learning complex features and modes of the fault distinguishing mode data, and the pre-training model obtained through pre-training can capture abstract representation of the fault distinguishing mode data, so that the performance and generalization capability of the model are improved, and the method is beneficial to accurately identifying the faults of the main board. Finally, through using the trained fault recognition pre-training model to transfer and learn the normal data of the mobile phone motherboard and the fault data of the mobile phone motherboard, the obtained model can learn the knowledge of the fault recognition pre-training model, thereby more effectively recognizing the motherboard fault, further improving the performance and robustness of the model, and the model can be used for an actual fault detection task and can timely take measures aiming at the detection result to solve the problem of the mobile phone motherboard.
Preferably, step S2 comprises the steps of:
step S21: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment;
The embodiment of the invention firstly obtains the target mobile phone motherboard and ensures that the target mobile phone motherboard is intact, meanwhile prepares test equipment comprising a connecting wire, a test tool and the like, then ensures test software suitable for testing the mobile phone motherboard, comprising a communication interface test, a performance test, a motherboard comprehensive test and the like, finally establishes a test communication environment among the mobile phone motherboard, the test equipment and the test software (the mobile phone motherboard is connected with the test equipment through the test communication interface and the test equipment is connected with the test software through the test communication interface) through setting a proper test communication interface, and finally obtains the mobile phone motherboard test environment.
Step S22: performing compatibility detection calculation on the mobile phone motherboard test environment by using a test environment compatibility calculation formula to obtain a test environment compatibility score;
The embodiment of the invention combines the compatibility detection contribution parameter, the compatibility detection parameter and the compatibility weight parameter of the mobile phone motherboard, the test equipment, the test software and the test communication interface in the mobile phone motherboard test environment, the time parameter of the compatibility detection calculation, the test compatibility influence parameter, the communication compatibility influence parameter, the corresponding weight parameter and the related parameter to form a proper test environment compatibility calculation formula to carry out compatibility detection calculation on the mobile phone motherboard test environment so as to quantitatively evaluate whether the mobile phone motherboard test environment is compatible with the mobile phone motherboard, provide accurate test conditions or not and finally obtain the test environment compatibility score. In addition, the test environment compatibility calculation formula can also use any environment compatibility detection algorithm in the field to replace the process of compatibility detection calculation, and is not limited to the test environment compatibility calculation formula.
Step S23: performing compatibility restoration processing on the mobile phone motherboard test environment according to the test environment compatibility score to obtain a motherboard compatibility test environment;
According to the embodiment of the invention, the factors which lead to low test environment compatibility scores are analyzed and determined through analyzing and calculating the test environment compatibility scores, wherein the factors comprise mismatching of software versions, unsuitable equipment parameters and the like. Then, repairing treatment is carried out on the identified incompatible factors, wherein the repairing treatment comprises the repairing treatment processes of upgrading a software version, replacing equipment or adjusting communication interface setting and the like, so that the compatibility of the mobile phone main board testing environment and the normal operation of the mobile phone main board is ensured, and finally, the main board compatibility testing environment is obtained.
Step S24: performing containerization treatment on the mainboard compatible test environment to obtain a compatible test environment container;
The embodiment of the invention firstly ensures that the Docker containerized platform can be used in a mainboard compatibility test environment by installing and configuring the Docker containerized platform. Then, by creating Dockerfile a file that includes the definition and configuration of the motherboard compatible test environment, and by building a Docker container using Dockerfile, the motherboard compatible test environment is packaged into a container image, ultimately yielding a compatible test environment container.
Step S25: carrying out automatic deployment processing on a mobile phone motherboard through test software in a compatible test environment container so as to generate a mobile phone motherboard test program, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program;
The embodiment of the invention automatically deploys the mobile phone motherboard by using the test software in the compatible test environment container to generate the motherboard test program comprising the communication interface test program, the performance test program and the motherboard integrated test program, wherein the communication interface test program is used for testing the interface problem on the mobile phone motherboard, the performance test program is used for testing the performance problem on the mobile phone motherboard, and the motherboard integrated test program is used for comprehensively testing the interface and performance problems on the mobile phone motherboard, and finally the mobile phone motherboard test program is obtained.
Step S26: and loading the mobile phone motherboard test program into test equipment through the test communication interface, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program through the test equipment to obtain mobile phone motherboard test data.
The embodiment of the invention loads the generated mobile phone motherboard test program into the test equipment by using the test communication interface so as to ensure that the test equipment can understand and execute the mobile phone motherboard test program, then runs the mobile phone motherboard test program on the test equipment to carry out hardware test on the mobile phone motherboard, records test data generated during the test, including communication interface test data, performance test data and motherboard comprehensive test data, and finally obtains mobile phone motherboard test data.
The invention firstly establishes a complete mobile phone motherboard test environment by integrating the mobile phone motherboard, the test equipment, the test software and the test communication interface, and the test environment can simulate the running condition of the actual mobile phone motherboard, thereby providing a reliable basis for subsequent tests. By setting up the test environment, the accuracy and reliability of the test can be ensured, and a reliable tool is provided for performance evaluation and fault diagnosis of the mobile phone motherboard. The compatibility detection calculation is carried out on the built mobile phone motherboard test environment by using a proper test environment compatibility calculation formula, so that the calculation formula can quantitatively evaluate whether the mobile phone motherboard test environment is compatible with the mobile phone motherboard or not by calculating a proper index, and whether accurate test conditions can be provided or not. The potential compatibility problem can be identified by calculating the compatibility score, so that a basic data source is provided for the subsequent processing process of repairing by taking corresponding measures. And secondly, the compatibility restoration processing is carried out on the mobile phone motherboard test environment by using the calculated test environment compatibility score, so that the mobile phone motherboard test environment is compatible with the normal operation of the mobile phone motherboard, and the reliability and accuracy of the test can be improved by restoring the compatibility problem existing in the mobile phone motherboard test environment, thereby ensuring the validity of the mobile phone motherboard test result. Then, the mainboard compatible test environment after the compatible repair treatment is subjected to containerization treatment, so that the mainboard compatible test environment can be packaged into a container, the isolation and portability of the environment can be improved, and the consistency of the test is ensured. The containerization process helps to simplify the deployment process of the test equipment, thereby improving maintainability of the test environment. And then, automatically deploying the mobile phone motherboard by using the test software in the compatible test environment container to generate a mobile phone motherboard test program including a communication interface test program, a performance test program and a motherboard integrated test program. The method has the advantages that automatic test program generation is realized, the efficiency and the accuracy of testing the mobile phone motherboard in the subsequent process are improved, and the comprehensiveness of the test is ensured. Finally, the generated mobile phone motherboard test program is loaded into test equipment by using the test communication interface, the bottom hardware test is carried out on the mobile phone motherboard by using the programs, and the test program is executed by the test equipment, so that detailed mobile phone motherboard test data including the stability and performance index of the communication interface and the overall working state of the motherboard can be obtained, and important basis can be provided for subsequent analysis and evaluation.
Preferably, the test environment compatible calculation formula in step S22 is specifically:
wherein, C is the compatibility score of the test environment, delta is the compatibility detection contribution parameter of the test environment of the mobile phone motherboard, M is the compatibility detection parameter of the mobile phone motherboard in the test environment of the mobile phone motherboard, ζ 1 is the compatibility weight parameter of the mobile phone motherboard, D is the compatibility detection parameter of the test equipment in the test environment of the mobile phone motherboard, ζ 2 is the compatibility weight parameter of the test equipment, S is the compatibility detection parameter of the test software in the test environment of the mobile phone motherboard, ζ 3 is the compatibility weight parameter of the test software, K is the compatibility detection parameter of the test communication interface in the test environment of the mobile phone motherboard, ζ 4 is the compatibility weight parameter of the test communication interface, u 1 is the initial time of the compatibility detection calculation, u 2 is the termination time of the compatibility detection calculation, u is the integral time variable of the compatibility detection calculation, R (u) is the test compatibility influence parameter of the test environment of the mobile phone motherboard at the time u, φ 1 is the weight parameter of the test compatibility influence of the test equipment, I (u) is the communication influence parameter of the test environment at the time u, ζ 2 is the compatibility influence parameter of the communication influence of the test environment of the communication influence of the communication environment, and ε is the compatibility score of the test environment.
The invention constructs a test environment compatibility calculation formula for carrying out compatibility detection calculation on the test environment of the mobile phone motherboard, integrates the compatibility of the mobile phone motherboard, test equipment, test software and test communication interfaces, influences on the compatibility by time variation, considers the variation condition of the test environment compatibility in the whole time range through integral processing, and simultaneously uses corresponding weight parameters for adjusting the importance of each compatibility parameter. To suit particular needs. The formula fully considers the test environment compatibility score C, the compatibility detection contribution parameter delta of the mobile phone motherboard test environment, the contribution condition for adjusting the overall compatibility measurement, the compatibility detection parameter M of the mobile phone motherboard in the mobile phone motherboard test environment, the compatibility of the mobile phone motherboard in the test environment, the compatibility weight parameter xi 1 of the mobile phone motherboard, the importance of the compatibility parameter of the mobile phone motherboard, the compatibility detection parameter D of the test equipment in the mobile phone motherboard test environment, the compatibility of the test equipment in the test environment, the compatibility weight parameter xi 2 of the test equipment, the method is used for adjusting the importance of the compatibility parameter of the test software in the test environment of the mobile phone motherboard, the compatibility detection parameter S of the test software in the test environment, the compatibility weight parameter xi 3 of the test software, the importance of the compatibility parameter of the test software, the compatibility detection parameter K of the test communication interface in the test environment of the mobile phone motherboard, the compatibility of the test communication interface in the test environment, the compatibility weight parameter xi 4 of the test communication interface, the importance of the compatibility parameter of the test communication interface, the initial time u 1 of the compatibility detection calculation, termination time u 2 of the compatibility detection calculation, integration time variable u of the compatibility detection calculation, test compatibility influence parameter R (u) of the mobile phone motherboard test environment at time u for measuring compatibility change of the test environment along with time, weight parameter phi 1 of test compatibility influence for adjusting importance of the test compatibility influence parameter, communication compatibility influence parameter I (u) of the mobile phone motherboard test environment at time u for measuring compatibility change of communication, weight parameter phi 2 of communication compatibility influence for adjusting importance of the communication compatibility influence parameter, the correction value epsilon of the test environment compatibility score forms a functional relation according to the correlation relation between the test environment compatibility score C and the parameters:
The formula can realize the compatibility detection calculation process of the mobile phone motherboard test environment, and meanwhile, the introduction of the correction value epsilon of the test environment compatibility score can be adjusted according to the error condition in the calculation process, so that the accuracy and the applicability of the test environment compatibility calculation formula are improved.
Preferably, step S24 comprises the steps of:
step S241: performing environment component analysis on the mainboard compatible test environment to obtain a mainboard test environment component;
The embodiment of the invention firstly determines the environment components which need to be used for the mainboard compatibility test, including an operating system, a library, a driver, an application program, a service, a configuration file and the like, creates a detailed list, lists the name, version and function of each environment component, and simultaneously verifies the configuration of the needed components including the correct version and setting thereof to ensure that the needed components meet the test requirements, and finally obtains the mainboard test environment components.
Step S242: performing environment dependency analysis on the mainboard compatible test environment to obtain a mainboard test environment dependency item;
The embodiment of the invention firstly determines all the dependency items in the mainboard test environment, including version dependency, runtime dependency, configuration dependency and the like among components, creates a dependency item list, lists the name, version and configuration requirement of each dependency item, and simultaneously verifies the availability and correct setting of the dependency items to ensure that the dependency items meet the test requirements, and finally obtains the mainboard test environment dependency items.
Step S243: performing container customization treatment on the mainboard test environment components and the mainboard test environment dependent items through a Docker containerization technology to obtain a test environment initial container;
According to the embodiment of the invention, through installing and configuring the Docker containerization platform and analyzing the environment demand characteristics between the mainboard test environment components and the mainboard test environment dependent items, a Dockerfile file is created by using the Docker containerization platform according to the analysis result, and the file comprises the definition and the installation steps of the mainboard test environment components and the dependent items. Then, a Docker container is custom built by using Dockerfile so as to be deployed and operated in different environments, and finally, a test environment initial container is obtained.
Step S244: and carrying out environment automatic configuration on the initial test environment container to obtain a compatible test environment container.
The embodiment of the invention firstly writes an automatic script which can be executed when the initial test environment container is started to configure the mainboard test environment, in the automatic script, the environment components are configured, including necessary libraries, network connection, configuration files, certificates and the like, and meanwhile, the requirements of dependent items are met through configuration processes of network configuration, hardware drive installation and the like, then, the test tool and the script are integrated so as to execute the mainboard compatibility test in the initial test environment container, simultaneously, the test environment in the initial test environment container can successfully run the mainboard compatibility test, any compatibility problem is repaired, and finally, the compatible test environment container is obtained.
The invention firstly analyzes the environment components of the mainboard compatible testing environment, and aims to identify various components in the mainboard compatible testing environment, including software, libraries, services, configuration files and the like, which is helpful for knowing the constitution of the mainboard compatible testing environment, and ensures that all necessary components are incorporated into the containerization process, thereby avoiding missing key components and improving the integrity and reliability of the testing environment. By analyzing the environment components, the constitution of the test environment can be accurately described, and a foundation is provided for subsequent containerization customization. Secondly, through carrying out environment dependency analysis on the mainboard compatible testing environment, the dependency relationship among all components in the mainboard compatible testing environment is determined, wherein the dependency relationship comprises version dependency, runtime dependency, configuration dependency and the like among the components. By analyzing the environment dependent items, it can be ensured that the containerization process does not destroy the interrelationship between the components, thereby maintaining the consistency and stability of the test environment. This step helps to eliminate potential dependency problems, ensuring that the containerized environment is able to function properly. Then, by using the Docker containerization technique, the motherboard test environment components and motherboard test environment dependencies are subjected to container customization processing, which means that the environment components and dependencies can be packaged into Docker containers for deployment and operation in different environments. The isolation and portability of the environment can be improved through containerization processing, the consistency of the environment is ensured, and the customization processing is subjected to personalized adjustment according to the characteristics of the components so as to meet the test requirements, so that the complexity and diversity of the environment can be effectively managed, and the reliability of deployment is improved. Finally, by performing an environment automation configuration of the test environment initial container, including installing, setting up, initializing, and optimizing components and dependencies in the container. The availability and consistency of the container environment can be ensured through automatic configuration, errors and workload of manual configuration are reduced, the environment is quickly deployed, a reliable basis is provided for subsequent testing, and the maintainability and expandability of the environment can be improved, so that the operation and maintenance cost is reduced.
Preferably, step S26 includes the steps of:
step S261: loading a mobile phone main board test program into test equipment through a test communication interface, and carrying out communication connection processing on the test equipment and the mobile phone main board through the test communication interface to obtain a main board communication test channel;
The embodiment of the invention loads the generated mobile phone motherboard test program into the test equipment by using the test communication interface so as to ensure that the test equipment can understand and execute the mobile phone motherboard test program, then connects the test equipment and the mobile phone motherboard by using the proper test communication interface so as to ensure that the test equipment and the mobile phone motherboard can communicate with each other, initializes the communication channel after the communication channel is established, ensures that data can be transmitted normally, and finally obtains the motherboard communication test channel.
Step S262: performing interface test on the mobile phone motherboard by using a communication interface test program through the motherboard communication test channel to obtain motherboard interface test data;
The embodiment of the invention firstly selects a proper communication interface test program, and the program can be used for testing various interfaces on a mobile phone main board, such as USB, SIM card slots, audio jacks and the like. And then, executing a selected interface test program by using the established main board communication test channel to test each interface on the mobile phone main board, recording test results including the state, performance, problems and other information of each interface, and finally obtaining main board interface test data.
Step S263: performing interface evaluation analysis on the mainboard interface test data to obtain a communication correction factor;
according to the embodiment of the invention, the interface test analysis tool or algorithm is used for analyzing the tested mainboard interface test data to determine any interface problem or interface failure, and the correction factor is estimated and calculated according to the analysis result, so that the communication condition of the subsequent process is corrected, the subsequent test process is ensured to be carried out in an accurate communication environment, and finally the communication correction factor is obtained.
Step S264: performing communication condition operation correction on the performance test program according to the communication correction factors to obtain a performance test correction program;
The embodiment of the invention firstly selects a proper performance test program, and the program can be used for testing the performance of the mobile phone main board, such as the speed of a processor, the use of a memory and the like. And then, modifying the selected performance test program according to the communication correction factor obtained by evaluation so as to correct the communication condition of the operation of the performance test program, and simultaneously verifying the modified performance test program to ensure that the performance test program can normally operate, and finally obtaining the performance test correction program.
Step S265: performing performance test on the mobile phone motherboard by using a performance test correction program through the motherboard communication test channel to obtain motherboard performance test data;
According to the embodiment of the invention, various performance problems on the mobile phone motherboard are tested by executing the modified performance test correction program through the established motherboard communication test channel, and the performance test results, including information such as processor performance, memory use, battery life and the like, are recorded, so that motherboard performance test data is finally obtained.
Step S266: performing performance evaluation analysis on the motherboard performance test data to obtain a performance correction factor;
According to the embodiment of the invention, the performance test analysis tool or algorithm is used for analyzing the tested motherboard performance test data to determine any performance problem or performance failure, and the correction factor is estimated and calculated according to the analysis result, so that the performance condition of the subsequent process is corrected, the subsequent test process is ensured to be carried out under the actual performance level, and finally the performance correction factor is obtained.
Step S267: performing performance level operation correction on the main board comprehensive test program according to the performance correction factors to obtain a main board comprehensive test correction program;
the embodiment of the invention can test the comprehensive performance of the mobile phone main board, including the aspects of interfaces and performance, by selecting a proper comprehensive test program. And then modifying the selected comprehensive test program according to the estimated performance correction factors to correct the running performance level of the comprehensive test program, and verifying the modified comprehensive test program to ensure that the comprehensive test program can normally run, so as to finally obtain the comprehensive test correction program of the main board.
Step S268: and carrying out comprehensive hardware test on the mobile phone motherboard by using the motherboard comprehensive test correction program through the motherboard communication test channel to obtain mobile phone motherboard test data.
The embodiment of the invention tests the comprehensive interface and performance problems on the mobile phone motherboard by executing the modified motherboard comprehensive test correction program by using the established motherboard communication test channel, records the comprehensive test result including the information of the interface and the performance, so as to obtain the comprehensive test data of the mobile phone motherboard, and finally obtains the test data of the mobile phone motherboard.
According to the invention, the test communication interface is used for loading the mobile phone main board test program into the test equipment, and communication connection is established through the test communication interface, so that the test equipment and the mobile phone main board can exchange data and instructions with each other, a main board communication test channel is generated in the process, a reliable communication basis can be provided for the subsequent test process, and the test equipment and the mobile phone main board can effectively interact, thereby providing necessary environment for the test. And the communication test channel of the main board established through connection uses a communication interface test program to carry out interface test on the mobile phone main board, and the data obtained by the test contains the performance and availability information of the interface. The reliability evaluation of the mobile phone motherboard interfaces can be provided through motherboard interface testing, and normal operation of the mobile phone motherboard interfaces can be ensured, so that a reliable basis is provided for subsequent testing. Meanwhile, the communication correction factors are obtained through evaluation analysis on the mainboard interface test data after the interface test is finished, the correction factors can be used for correcting the communication conditions of the subsequent process, the subsequent performance test is ensured to be carried out in an accurate communication environment, and the generation of the communication correction factors is also beneficial to improving the accuracy and the repeatability of the performance test. Secondly, the communication condition operation correction is carried out on the performance test program by using the communication correction factor, so that the performance test can be ensured to be carried out under the actual communication condition, thereby more accurately simulating the use condition of the real world and further being beneficial to improving the authenticity and comparability of the performance test. And the performance test is carried out on the mobile phone motherboard through the motherboard communication test channel by using the corrected performance test correction program, and the data obtained by the test comprise the performance of the mobile phone motherboard under the actual communication condition, which is helpful for evaluating the performance of the mobile phone motherboard so as to ensure that the mobile phone motherboard meets the expected performance standard. And then, performing performance evaluation analysis on the performance test data of the mainboard after the performance test to obtain a performance correction factor by analysis, wherein the correction factor can be used for correcting the subsequent comprehensive test so as to ensure that the comprehensive test is performed under the actual performance level, which is helpful for improving the reliability and the repeatability of the comprehensive test. And then, performance level operation correction is carried out on the integrated test program of the main board by using the performance correction factors, so that the integrated test can be ensured to be carried out under the actual performance condition, more accurate test results are provided, and the performance level operation correction is helpful for ensuring the reliability and comparability of the integrated test. And finally, carrying out comprehensive hardware test on the mobile phone motherboard by using a motherboard comprehensive test correction program through a motherboard communication test channel, so that comprehensive test data of the mobile phone motherboard can be generated, performance of the mobile phone motherboard in all aspects under the actual performance level condition is included, comprehensive evaluation of hardware performance of the mobile phone motherboard is facilitated, normal operation and performance level of the mobile phone motherboard are ensured to meet the requirements, and quality and performance of the mobile phone motherboard are ensured.
Preferably, step S4 comprises the steps of:
step S41: performing fault unrecoverable detection analysis on the fault self-recovery result data to obtain fault unrecoverable detection data;
The embodiment of the invention obtains relevant data in a fault self-recovery result, including information such as fault type, processing method, time stamp and the like, then establishes a model by using proper machine learning (support vector machine, random forest, neural network and the like) for detecting whether the fault is not recovered, cleans and preprocesses the collected data, includes operations such as removing abnormal values, filling missing values and the like, predicts the processed data by using the established model, and finally obtains fault unrecoverable detection data.
Step S42: performing problem diagnosis and analysis on the fault unrecovered detection data to obtain fault unrecovered problem data;
According to the embodiment of the invention, the detected fault unrecoverable detection data is analyzed by using a problem diagnosis algorithm, so that the corresponding fault problems cannot be solved in the self-recovery processing process, the specific nature and reason of unrecoverable fault problems are determined, and the fault unrecoverable problem data is finally obtained.
Step S43: performing potential fault detection on the fault unrecovered problem data to obtain fault unrecovered potential data;
According to the embodiment of the invention, the diagnosed fault unrecoverable problem data is analyzed by using a potential fault analysis algorithm to find out the complexity and multi-level property hidden behind the fault problem, and the root cause and potential fault factors of the fault problem are further explored at the same time, so that the fault unrecoverable potential data is finally obtained.
Step S44: evaluating, analyzing and calculating the potential data of unrecovered faults to obtain test potential fault factors;
According to the embodiment of the invention, the severity and influence of potential fault factors are evaluated by carrying out detailed evaluation analysis on the detected fault unrecovered potential data so as to analyze potential fault influence or influence probability and the like in the fault unrecovered potential data, and corresponding fault factors are calculated according to the evaluation analysis result, so that the test potential fault factors are finally obtained.
Step S45: and carrying out model correction processing on the fault identification model according to the tested potential fault factors to obtain a fault identification correction model.
According to the embodiment of the invention, the constructed fault recognition model is adjusted and corrected by using the calculated test potential fault factors, so that the fault recognition model can automatically learn and adapt to a new fault mode, the detection accuracy of the fault unrecovered problem is improved, and the fault recognition correction model is finally obtained.
According to the invention, firstly, fault self-recovery result data is subjected to fault non-recovery detection analysis, and the analysis process is helpful for identifying fault conditions which are not solved by self-recovery, so that fault non-recovery detection data is generated, faults which cannot be solved in the self-recovery process are helpful for timely finding, and basic data is provided for further fault detection processing. Secondly, by performing problem diagnosis analysis on the fault unrecovered detection data, the purpose is to know in depth why the faults cannot be solved in the self-recovery processing process, so that fault unrecovered problem data is generated, the method is beneficial to determining the specific properties and reasons of the problems, information required by problem diagnosis is provided, a direction is provided for potential fault detection, the efficiency of potential fault problem processing is improved, the duration time of unrecovered faults is reduced, and the reliability and performance of a mobile phone main board are improved. Then, through carrying out latent fault detection on the fault unrecovered problem data, the fault unrecovered problem data can be deeply analyzed to further explore the root cause and latent fault factors of the fault problem, so that more comprehensive problem analysis is provided, the complexity and multi-level property hidden behind the problem can be found, the mobile phone main board can be more comprehensively used for coping with the fault unrecovered problem, more information and clues are provided for solving the fault problem, and the accuracy of fault problem processing is improved. And then, evaluating, analyzing and calculating the potential fault unrecovered data, so that the severity and influence of potential fault factors can be evaluated, and the factors possibly causing the persistent existence of unrecovered problems can be determined. And finally, correcting the fault recognition model according to the estimated test potential fault factors, thereby being beneficial to systematically optimizing the fault recognition model, improving the detection accuracy of unrecovered problems, helping the model to automatically learn and adapt to new fault modes, improving the robustness and performance of the model, and enhancing the efficiency of processing potential fault problems and the reliability of a mobile phone main board. Comprehensively, the series of processes construct an automatic fault unrecovered problem analysis and model repair process, and the efficiency and the accuracy of fault processing and the stability of the mobile phone main board are improved through data analysis and model correction.
Preferably, the present invention also provides a system for testing a mobile phone motherboard, for executing the method for testing a mobile phone motherboard as described above, the system for testing a mobile phone motherboard includes:
The mobile phone main board fault identification module is used for acquiring mobile phone main board data, carrying out fault identification analysis on the mobile phone main board data and obtaining mobile phone main board normal data and mobile phone main board fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
The mobile phone motherboard test module is used for building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
The fault recognition self-recovery module is used for carrying out fault recognition detection on the mobile phone main board test data by utilizing the fault recognition model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data so as to obtain fault self-recovery result data;
The unrecovered potential fault analysis module is used for carrying out unrecovered problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecovered problem data; detecting potential faults of the fault unrecovered problem data to obtain a tested potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors so as to obtain a fault identification correction model;
The fault repair report generation module is used for carrying out fault recognition correction detection on the mobile phone motherboard test data by utilizing the fault recognition correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
In summary, the system for testing the mobile phone motherboard is composed of a fault identification model building module, a mobile phone motherboard testing module, a fault identification self-recovery module, an unrecovered potential fault analysis module and a fault restoration report generation module, and can realize any method for testing the mobile phone motherboard.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. The method for testing the mobile phone motherboard is characterized by comprising the following steps:
Step S1: acquiring mobile phone motherboard data, performing fault identification analysis on the mobile phone motherboard data, and obtaining mobile phone motherboard normal data and mobile phone motherboard fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model; wherein, step S1 comprises the following steps:
step S11: acquiring mobile phone motherboard data;
Step S12: performing fault calculation on the mobile phone motherboard data by using a fault measurement degree calculation formula to obtain the fault degree of the mobile phone motherboard; the calculation formula of the fault measurement degree is specifically as follows:
；
In the method, in the process of the invention, For the fault degree of the mobile phone motherboard,/>Time range parameters calculated for faults,/>Integration time variable calculated for failure,/>To be at time/>Communication measurement parameters in mobile phone motherboard data,/>For the communication failure weight coefficient,/>To be at time/>Performance measurement parameters in mobile phone motherboard data,/>Is a performance failure weight coefficient,/>As an exponential function,/>To be at time/>Environmental measurement parameters in mobile phone motherboard data,/>Is an environmental fault influencing parameter,/>Is the lower limit of the amplitude range of periodic faults of the mobile phone motherboard,/>Is the upper limit of the periodic fault amplitude range of the mobile phone motherboard,/>To be at time/>Periodic fault amplitude parameter of mobile phone motherboardIs a periodic fault amplitude weighting coefficient,/>To be at time/>Fault loss measurement parameter of mobile phone motherboardWeight coefficient is measured for fault loss,/>For periodic fault amplitude regulation parameters,/>The correction value of the fault degree of the mobile phone main board;
step S13: judging the mobile phone motherboard fault degree according to a preset mobile phone motherboard fault threshold, and marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard fault data when the mobile phone motherboard fault degree is greater than or equal to the preset mobile phone motherboard fault threshold; when the mobile phone motherboard fault degree is smaller than a preset mobile phone motherboard fault threshold value, marking mobile phone motherboard data corresponding to the mobile phone motherboard fault degree as mobile phone motherboard normal data;
Step S14: performing fault recognition training on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard to obtain a fault recognition model; wherein, step S14 includes the following steps:
step S141: carrying out feature engineering extraction processing on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard to obtain normal features of the mobile phone motherboard and fault features of the mobile phone motherboard;
Step S142: analyzing the normal characteristics of the mobile phone motherboard and the fault characteristics of the mobile phone motherboard to obtain a motherboard normal characteristic mode and a motherboard fault characteristic mode;
Step S143: performing mode distinguishing identification detection on the normal characteristic mode of the main board and the fault characteristic mode of the main board to obtain fault distinguishing mode data;
Step S144: performing fault pre-training on the fault distinguishing mode data through a convolutional neural network to obtain a fault recognition pre-training model;
Step S145: performing migration learning on normal data of the mobile phone motherboard and fault data of the mobile phone motherboard by using the fault recognition pre-training model to obtain a fault recognition model;
Step S2: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data; wherein, step S2 includes the following steps:
step S21: building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment;
Step S22: performing compatibility detection calculation on the mobile phone motherboard test environment by using a test environment compatibility calculation formula to obtain a test environment compatibility score; the test environment compatible calculation formula specifically comprises:
；
In the method, in the process of the invention, Scoring for test environmental compatibility,/>Contribution parameters for compatible detection of mobile phone motherboard test environment,/>For compatibility detection parameters of mobile phone motherboard in mobile phone motherboard test environment,/>Is a compatibility weight parameter of the mobile phone motherboard,/>For compatibility detection parameters of test equipment in mobile phone motherboard test environment,/>For testing the compatibility weight parameter of the equipment,/>For compatibility detection parameters of test software in mobile phone motherboard test environment,/>For testing the compatibility weight parameter of software,/>For testing the compatibility detection parameter of the communication interface in the mobile phone motherboard test environment,/>To test the compatibility weight parameters of the communication interface,/>Initial time calculated for compatible detection,/>Termination time calculated for compatible detection,/>For compatible detection of the calculated integration time variable,Time/>, for mobile phone motherboard test environmentTest compatibility influencing parameters at/>To test the weighting parameters for compatibility effects,/>Time/>, for mobile phone motherboard test environmentCommunication compatibility influencing parameters at/>For the weighting parameters of the communication compatibility influence,/>Correction values for test environmental compatibility scores;
Step S23: performing compatibility restoration processing on the mobile phone motherboard test environment according to the test environment compatibility score to obtain a motherboard compatibility test environment;
Step S24: performing containerization treatment on the mainboard compatible test environment to obtain a compatible test environment container;
step S25: carrying out automatic deployment processing on a mobile phone motherboard through test software in a compatible test environment container so as to generate a mobile phone motherboard test program, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program;
Step S26: loading a mobile phone motherboard test program into test equipment through a test communication interface, and performing bottom hardware test on the mobile phone motherboard by the test equipment by utilizing the mobile phone motherboard test program to obtain mobile phone motherboard test data;
Step S3: performing fault identification detection on the mobile phone main board test data by using a fault identification model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data to obtain fault self-recovery result data;
Step S4: performing unrecoverable problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecoverable problem data; performing potential fault detection analysis on the fault unrecovered problem data to obtain a test potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors to obtain a fault identification correction model;
Step S5: performing fault identification correction detection on the mobile phone motherboard test data by using a fault identification correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
2. The method of testing a motherboard of a mobile phone according to claim 1, wherein step S24 comprises the steps of:
step S241: performing environment component analysis on the mainboard compatible test environment to obtain a mainboard test environment component;
Step S242: performing environment dependency analysis on the mainboard compatible test environment to obtain a mainboard test environment dependency item;
Step S243: performing container customization treatment on the mainboard test environment components and the mainboard test environment dependent items through a Docker containerization technology to obtain a test environment initial container;
Step S244: and carrying out environment automatic configuration on the initial test environment container to obtain a compatible test environment container.
3. The method of testing a motherboard of a mobile phone according to claim 1, wherein step S26 comprises the steps of:
step S261: loading a mobile phone main board test program into test equipment through a test communication interface, and carrying out communication connection processing on the test equipment and the mobile phone main board through the test communication interface to obtain a main board communication test channel;
step S262: performing interface test on the mobile phone motherboard by using a communication interface test program through the motherboard communication test channel to obtain motherboard interface test data;
step S263: performing interface evaluation analysis on the mainboard interface test data to obtain a communication correction factor;
step S264: performing communication condition operation correction on the performance test program according to the communication correction factors to obtain a performance test correction program;
Step S265: performing performance test on the mobile phone motherboard by using a performance test correction program through the motherboard communication test channel to obtain motherboard performance test data;
step S266: performing performance evaluation analysis on the motherboard performance test data to obtain a performance correction factor;
Step S267: performing performance level operation correction on the main board comprehensive test program according to the performance correction factors to obtain a main board comprehensive test correction program;
step S268: and carrying out comprehensive hardware test on the mobile phone motherboard by using the motherboard comprehensive test correction program through the motherboard communication test channel to obtain mobile phone motherboard test data.
4. The method for testing a mobile phone motherboard according to claim 1, wherein step S4 comprises the steps of:
step S41: performing fault unrecoverable detection analysis on the fault self-recovery result data to obtain fault unrecoverable detection data;
Step S42: performing problem diagnosis and analysis on the fault unrecovered detection data to obtain fault unrecovered problem data;
Step S43: performing potential fault detection on the fault unrecovered problem data to obtain fault unrecovered potential data;
step S44: evaluating, analyzing and calculating the potential data of unrecovered faults to obtain test potential fault factors;
Step S45: and carrying out model correction processing on the fault identification model according to the tested potential fault factors to obtain a fault identification correction model.
5. A system for testing a motherboard of a mobile phone, wherein the system for testing a motherboard of a mobile phone comprises:
The mobile phone main board fault identification module is used for acquiring mobile phone main board data, carrying out fault identification analysis on the mobile phone main board data and obtaining mobile phone main board normal data and mobile phone main board fault data; performing fault recognition training on normal data of the mobile phone main board and fault data of the mobile phone main board to obtain a fault recognition model;
The mobile phone motherboard test module is used for building a test environment through a mobile phone motherboard, test equipment, test software and a test communication interface to obtain a mobile phone motherboard test environment; generating a mobile phone motherboard test program by deploying test software in a mobile phone motherboard test environment, wherein the mobile phone motherboard test program comprises a communication interface test program, a performance test program and a motherboard comprehensive test program; loading a mobile phone motherboard test program into test equipment, and performing bottom hardware test on the mobile phone motherboard by using the mobile phone motherboard test program to obtain mobile phone motherboard test data;
The fault recognition self-recovery module is used for carrying out fault recognition detection on the mobile phone main board test data by utilizing the fault recognition model to obtain main board test fault data; performing self-adaptive self-recovery processing on the main board test fault data so as to obtain fault self-recovery result data;
The unrecovered potential fault analysis module is used for carrying out unrecovered problem diagnosis and analysis on the fault self-recovery result data to obtain fault unrecovered problem data; detecting potential faults of the fault unrecovered problem data to obtain a tested potential fault factor; performing model correction processing on the fault identification model according to the test potential fault factors so as to obtain a fault identification correction model;
The fault repair report generation module is used for carrying out fault recognition correction detection on the mobile phone motherboard test data by utilizing the fault recognition correction model to obtain test fault correction data; and performing fault repair analysis on the test fault correction data to generate a test fault repair report.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311391428.9A CN117560443B (en) | 2023-10-24 | 2023-10-24 | Method and system for testing mobile phone motherboard |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311391428.9A CN117560443B (en) | 2023-10-24 | 2023-10-24 | Method and system for testing mobile phone motherboard |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117560443A CN117560443A (en) | 2024-02-13 |
| CN117560443B true CN117560443B (en) | 2024-06-11 |
Family
ID=89819405
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311391428.9A Active CN117560443B (en) | 2023-10-24 | 2023-10-24 | Method and system for testing mobile phone motherboard |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117560443B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118133133A (en) * | 2024-05-08 | 2024-06-04 | 深圳市国硕宏电子有限公司 | Intelligent industrial control main board fault prediction and prevention method and device based on deep learning |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101046762A (en) * | 2006-03-29 | 2007-10-03 | 鸿富锦精密工业（深圳）有限公司 | Master plate monitoring system and method |
| CN103167080A (en) * | 2013-02-04 | 2013-06-19 | 北京大友迅捷科技有限公司 | System and method of testing functions of main board of mobile phone |
| CN111382029A (en) * | 2020-03-05 | 2020-07-07 | 清华大学 | Mainboard abnormity diagnosis method and device based on PCA and multidimensional monitoring data |
| CN114490213A (en) * | 2021-12-31 | 2022-05-13 | 深圳市智仁科技有限公司 | Method, system, device and storage medium for monitoring and diagnosing computer mainboard faults |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110314331A1 (en) * | 2009-10-29 | 2011-12-22 | Cybernet Systems Corporation | Automated test and repair method and apparatus applicable to complex, distributed systems |
| US20230110616A1 (en) * | 2021-10-13 | 2023-04-13 | Garrett Transportation I Inc. | Fault model editor and diagnostic tool |
-
2023
- 2023-10-24 CN CN202311391428.9A patent/CN117560443B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101046762A (en) * | 2006-03-29 | 2007-10-03 | 鸿富锦精密工业（深圳）有限公司 | Master plate monitoring system and method |
| CN103167080A (en) * | 2013-02-04 | 2013-06-19 | 北京大友迅捷科技有限公司 | System and method of testing functions of main board of mobile phone |
| CN111382029A (en) * | 2020-03-05 | 2020-07-07 | 清华大学 | Mainboard abnormity diagnosis method and device based on PCA and multidimensional monitoring data |
| CN114490213A (en) * | 2021-12-31 | 2022-05-13 | 深圳市智仁科技有限公司 | Method, system, device and storage medium for monitoring and diagnosing computer mainboard faults |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117560443A (en) | 2024-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117560443B (en) | Method and system for testing mobile phone motherboard | |
| US5500941A (en) | Optimum functional test method to determine the quality of a software system embedded in a large electronic system | |
| US11520693B2 (en) | Method, apparatus, and system for blaming a test case/class for a survived mutation | |
| US7478000B2 (en) | Method and system to develop a process improvement methodology | |
| CN111177655B (en) | Data processing method and device and electronic equipment | |
| CN117421217B (en) | Automatic software function test method, system, terminal and medium | |
| CN111060512A (en) | Intelligent defect identification system | |
| CN115952081A (en) | Software testing method, device, storage medium and equipment | |
| US7184932B1 (en) | Reliability prediction for complex components | |
| CN112199295B (en) | Spectrum-based deep neural network defect positioning method and system | |
| CN112685320A (en) | Software defect repairing method and device based on multiple candidate programs | |
| CN116453437A (en) | Display screen module testing method, device, equipment and storage medium | |
| Dzakowic et al. | Advanced Techniques for the verification and validation of prognostics & health management capabilities | |
| JP7371430B2 (en) | Information processing equipment and programs | |
| CN118132451B (en) | Automatic test and error diagnosis system and method for computer operating system | |
| CN112506784B (en) | System and method for evaluating product performance with autonomous learning capability | |
| CN117472629B (en) | Multi-fault diagnosis method and system for electronic information system | |
| CN117828499B (en) | PCBA abnormal part determination method, system, storage medium and electronic equipment | |
| CN112395205B (en) | Software testing system and method | |
| Gaol et al. | Software testing model by measuring the level of accuracy fault output using neural network algorithm | |
| CN117932394A (en) | Electronic component fault management method and system | |
| CN114860581A (en) | Software reliability testing integrated platform | |
| CN116627700A (en) | Memory fault detection and processing method, device, equipment and storage medium | |
| CN114968791A (en) | Detection method and device | |
| CN118132451A (en) | Automatic test and error diagnosis system and method for computer operating system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |