CN113992778A - Equipment detection method and device, chip and module equipment - Google Patents
Equipment detection method and device, chip and module equipment Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 143
- 230000002159 abnormal effect Effects 0.000 claims abstract description 201
- 238000012360 testing method Methods 0.000 claims abstract description 201
- 238000000034 method Methods 0.000 claims abstract description 60
- 238000004891 communication Methods 0.000 claims description 38
- 230000005856 abnormality Effects 0.000 claims description 34
- 238000012545 processing Methods 0.000 claims description 26
- 238000012549 training Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 8
- 238000005192 partition Methods 0.000 claims description 6
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- 230000008569 process Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 5
- 238000013112 stability test Methods 0.000 description 4
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/24—Arrangements for testing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
Abstract
The application discloses a method, a device, a chip and module equipment for equipment detection, wherein the method comprises the following steps: acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images; and after the N tested devices are tested, storing the detection result into the test system, and/or sending the detection result to the target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test. By adopting the method described in the application, the equipment detection can be more conveniently carried out.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method, an apparatus, a chip, and a module device for device detection.
Background
The system stability test is used as an important test means for the terminal equipment with the screen and is also an important index for visual test delivery. The system stability test has such characteristics: 1. the number of tested terminal devices is large; 2. the testing time is long, and the requirement on the electric quantity supply of the terminal equipment is high; 3. the types of the abnormal conditions of the terminal equipment are more, and the abnormal conditions can include various crash abnormal conditions. Under a common condition, in the process of carrying out the system stability test, a tester is required to monitor on site, and the abnormity occurring in the terminal equipment is artificially judged and processed, so that time and labor are wasted.
Disclosure of Invention
The application provides a method, a device, a chip and module equipment for equipment detection, which are beneficial to more conveniently carrying out equipment detection.
In a first aspect, the present application provides a method for device detection, the method comprising: acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images; and after the N tested devices are tested, storing the detection result into the test system, and/or sending the detection result to the target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
Based on the method described in the first aspect, a tester can determine whether the tested device is abnormal or not and the abnormal type of the tested device which is tested abnormally without checking and processing each tested device in person, so that the convenience of device detection is improved, and time and labor are saved.
In a possible implementation manner, when the step of determining the abnormal tested device among the N tested devices and the abnormal type of the abnormal tested device is executed, the method specifically includes: dividing M first images into K second images, dividing one first image into L second images, wherein N third images exist in the L second images, each third image comprises a display screen of equipment to be tested, K is a positive integer, L is a positive integer, and L is larger than or equal to N; and determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on M x N third images in the K second images.
In one possible implementation manner, the M third images corresponding to the tested device with the abnormal test are the same.
In a possible implementation manner, when the executing step determines the abnormality type of the device under test with abnormal test based on M × N third images in the K second images, the method specifically includes: inputting M x N third images in the K second images into a training model; and determining the abnormal type of the tested equipment with abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model.
In a possible implementation manner, if the abnormal type of the tested device with abnormal test is non-crash abnormal, determining whether M third images corresponding to the tested device with abnormal test contain preset keywords; and if the M third images corresponding to the abnormal tested equipment contain preset keywords, determining that the abnormal type of the abnormal tested equipment is the first-class non-dead halt abnormality. And if the M third images corresponding to the abnormal tested equipment do not contain the preset keywords, determining that the abnormal type of the abnormal tested equipment is the second non-dead halt abnormality.
In one possible implementation, the crash-like exception includes one or more of: blank screen, memory data dump, android resume, and splash screen.
In one possible implementation, the first type of non-crash-like exception includes one or more of: the method comprises the following steps of restarting a system, dropping a user identification card SIMCardDraped, model interrupt Modemassert, wireless network interrupt WCNAssert, not supporting mobile phone type UnsuportPhone, engineering type UnsuportProject, android version UnsuportVerison, abnormal stop ExStop, small memory card read-only TReadOnly, data read-only DReadOnly, read-write failure IOError and data partition are filled with DataFull.
In one possible implementation, the second type of non-dead type exception is a fixed screen.
In a second aspect, the application provides an apparatus for device detection, which includes a communication unit and a processing unit, where the communication unit is configured to obtain M first images acquired by an image acquisition device, where the first images include display screens of N devices under test, M is a positive integer, and N is a positive integer; the processing unit is used for determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images; the processing unit is used for storing the detection result into the test system after the test of the N tested devices is finished, and/or sending the detection result to the target device through the communication unit, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
In a third aspect, the present application provides a chip, configured to: acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images; and after the N tested devices are tested, storing the detection result into the test system, and/or sending the detection result to the target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
In a fourth aspect, the present application provides a module device, which includes a communication module, a power module, a storage module, and a chip module, wherein the power module is configured to provide electrical energy to the module device; the storage module is used for storing data and instructions; the communication module is used for carrying out internal communication of the module equipment or is used for carrying out communication between the module equipment and external equipment; this chip module is used for: acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images; and after the N tested devices are tested, storing the detection result into the test system, and/or sending the detection result to the target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
In a fifth aspect, the present application proposes a detection device comprising a processor, a memory, and a transceiver; the transceiver is used for receiving data or sending data; the memory for storing a computer program; the processor is configured to invoke the computer program from the memory to perform the method of the first aspect and any possible implementation thereof.
In a sixth aspect, the present application proposes a computer-readable storage medium having stored thereon a computer program which, when run on a detection device, causes the detection device to perform the method proposed by the first aspect and any possible implementation manner thereof.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic diagram of a network architecture provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of an apparatus detection method according to an embodiment of the present application;
fig. 3 is a schematic diagram of an image capturing apparatus provided in an embodiment of the present application capturing a first image;
FIG. 4 is a schematic diagram of a first image provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a first image segmentation provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of a third image provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of a crash-like exception according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a non-crash exception provided by an embodiment of the present application;
FIG. 9 is a schematic structural diagram of an apparatus for device detection according to an embodiment of the present disclosure;
FIG. 10 is a schematic structural diagram of a detection apparatus provided in an embodiment of the present application;
fig. 11 is a schematic structural diagram of a module apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the following embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the specification of the present application and the appended claims, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the listed items.
It should be noted that the terms "first," "second," "third," and the like in the description and claims of the present application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the present application may be applied to the schematic network architecture shown in fig. 1, where the network architecture shown in fig. 1 is a network architecture of a wireless communication system, and the network architecture generally includes at least one device under test, at least one image capturing device, and at least one detecting device, and the number and form of the devices do not form a limitation to the embodiment of the present application, for example, 10 devices under test, one image capturing device, and one detecting device are included in fig. 1. The device to be tested is used for detecting the stability of the system, the image acquisition device is used for acquiring images containing the device to be tested, and the detection device is used for detecting whether the device to be tested is abnormal or not. Image acquisition equipment and check out test set link to each other, can be with the image transmission to check out test set during the collection, image acquisition equipment and check out test set can link to each other through modes such as data line, wireless network or bluetooth connection, and this application embodiment does not limit to how image acquisition equipment and check out test set connect.
The device to be tested may be a terminal device (UE), such as a mobile phone (mobile phone), a tablet computer, a smart watch, and other devices with a display screen; the image acquisition equipment can be equipment which is provided with a camera and can acquire images, such as a camera, a video camera, terminal equipment, a tablet personal computer and the like; the detection device can be a computer, a terminal device and other devices with image recognition and processing functions.
When a large number of devices under test are subjected to a system stability test, some of the devices under test may be abnormal. In order to determine the type of abnormality occurring in the device under test, in general, a tester checks and processes each device under test in person to determine the device under test having the abnormality and the type of the abnormality, but such a method is time-consuming and labor-consuming. In order to improve the testing efficiency and convenience, the application provides a device detection method.
Referring to fig. 2, fig. 2 is a schematic flowchart of an apparatus detection method according to an embodiment of the present disclosure. As shown in fig. 2, the device detection method includes the following steps 201 to 203. The method execution body shown in fig. 2 comprises a detection device. Alternatively, the method execution body shown in fig. 2 may be a chip in the inspection apparatus. Fig. 2 illustrates an example of the detection apparatus. The main execution body of the subsequent flow chart is the same, and is not described in detail later.
Wherein:
201. the detection equipment acquires M first images acquired by the image acquisition equipment.
In the embodiment of the application, the first image includes display screens of N devices under test, M is a positive integer, and N is a positive integer. Illustratively, as shown in fig. 3, fig. 3 includes 10 devices under test, an image capturing device, and a first image captured by the image capturing device, where the image capturing device is placed at a position where a display screen of the 10 devices under test can be captured, and the first image includes the display screens of the 10 devices under test.
The detection equipment periodically acquires M first images acquired by the image acquisition. Illustratively, the detection device performs the abnormality check in a cycle of 30 minutes, the image capturing device continuously captures 10 first images in units of 10 seconds and transmits the 10 first images to the detection device, that is, when the time interval from the last abnormality detection reaches 30 minutes, the image capturing device captures one first image every 10 seconds, the image capturing device captures 10 first images after 100 seconds, and transmits the 10 first images to the detection device through any one of a wired connection, a wireless network, a bluetooth connection and the like. By means of the mode of periodically acquiring the first image, the detection device can be used for confirming whether the detected device is abnormal or not more timely.
In a possible implementation manner, the first image may further include an identifier of the device under test, as shown in fig. 4, the first image includes 10 devices under test, and each device under test is attached with a corresponding identifier of the device under test, for example, the identifier of the first device under test is 1, and the identifier of the second device under test is 6. Based on the implementation mode, the detection equipment can determine the corresponding detected equipment through the identification, and the detection accuracy of the detection equipment is improved.
In one possible implementation, the device under test may be charged offline using Alternating Current (AC). Based on this implementation, be favorable to ensureing the electric quantity supply of equipment under test, avoid appearing equipment under test because the electric quantity is not enough to lead to shutting down to influence the test result.
202. The detection device determines the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with the abnormal test based on the M first images.
In a possible implementation manner, the specific implementation manner of step 202 may be: the detection equipment divides M first images into K second images, one first image into L second images, N third images exist in the L second images, the third image comprises a display screen of the equipment to be detected, K is a positive integer, L is a positive integer, and L is larger than or equal to N; and the detection equipment determines the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with the abnormal test based on M x N third images in the K second images. Based on the realization mode, the method does not need the self-confirmation of testers, can determine whether the tested equipment is abnormal or not only by detecting the image of the equipment, is more intelligent, and is favorable for improving the convenience of equipment detection.
Illustratively, the detection device acquires 5 first images through the image acquisition device, as shown in fig. 5, the first images include 9 devices under test, one first image is divided into 10 second images, and 9 third images are included in the 10 second images, that is, only the 9 third images include the display screen of the devices under test. The detection device divides 5 first images into 50 second images according to the division mode, wherein the 50 second images comprise 45 third images, and the 5 third images correspond to one tested device.
Optionally, the M third images corresponding to the tested device with the test abnormality are the same. The detection equipment sequentially compares the similarity of the M third images by using an algorithm of picture acquaintance contrast for the M third images collected by each equipment. In the process of testing the system stability, the screen of the tested device is constantly changed, if the comparison results are different, the test is normally carried out, no abnormity occurs, and if the comparison results are the same, the abnormity can be judged to occur. For example, if the detection device determines that the M third images corresponding to the first device under test are all the same through detection, it may be determined that the first device under test is an abnormal device under test, and the first device under test is any one of the N devices under test.
Optionally, the M third images corresponding to the device under test determined by the detection device may have two types:
one mode is that an identifier corresponding to the tested device is attached to the tested device, and the testing device determines M third images corresponding to the tested device according to the identifier in the third image. Illustratively, assume that the detection device acquires 10 first images and divides one first image into 10 second images. After image segmentation, the detection device acquires 100 second images, at the moment, character recognition is carried out on the second images, classification is carried out according to the recognized identification numbers, the images with the same identification are classified into the same folder, and the folder is named by the identification numbers. The detection device can determine 10 third images corresponding to the device to be tested according to the identifier of the device to be tested and the identifier of the folder. Based on the implementation mode, the detection equipment can more accurately determine the image corresponding to the detected equipment.
The other mode is that the detection device determines M third images corresponding to the device to be detected according to the position of the device to be detected in the first image. For example, as shown in fig. 6, the detection device acquires two first images, which are a first image 601 and a first image 602, respectively, the target device under test is located at a first position in the first image 601 and the first image 602, and after the detection device divides the first image 601 and the first image 602, the detection device determines, according to the first position of the target device under test, that a third image corresponding to the target device under test is a third image 603 and a third image 604, respectively. Based on the implementation mode, the detection device only needs to determine the third image corresponding to the detected device according to the position condition of the detected device in the first image and the segmentation mode of the first image, and the power consumption of the detection device is favorably reduced.
Optionally, when the executing step determines the abnormal type of the tested device with the abnormal test based on M × N third images in the K second images, the specific implementation manner of the step may be: inputting M x N third images in the K second images into a training model; and determining the abnormal type of the tested equipment with the abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model. After the detection device judges that the detected device is abnormal, the detection device can use a pre-trained training model to identify the type of the abnormality displayed on the screen and judge whether the abnormality is a crash abnormality. The system stops running when the crash anomaly occurs, the interface of the tested equipment is blocked, and image recognition can be carried out through the abnormal interface characteristics. Based on the implementation mode, the detection equipment can more accurately judge whether the equipment to be detected is abnormal in the dead halt class.
Wherein the crash-like exception comprises one or more of: blank screen, memory data dump (sysdump), Android resume (Android Recovery), and splash screen. Black screen is the screen that would normally appear black; the sysdump is usually displayed as a preset display interface on a screen; the Android Recovery common screen can be displayed as a preset display interface; a screensaver will typically present the screen in stripes, speckles or patches of different, often constant color.
Exemplarily, as shown in fig. 7, the third image 701 shows that the screen interface of the first device under test is completely black, and the detection device may identify and determine that the first device under test has a crash-like abnormality through the training model, where the specific type is a black screen; the third image 702 displays that a color block different from a normal color appears on the screen of the second device under test, and the detection device can identify and determine that the second device under test has a crash-like abnormality through the training model, wherein the specific type is a screen splash.
Further optionally, if the abnormal type of the tested device with the abnormal test is a non-crash abnormal, determining whether M third images corresponding to the tested device with the abnormal test contain preset keywords; and if M third images corresponding to the tested equipment with abnormal test contain preset keywords, determining that the abnormal type of the tested equipment with abnormal test is the first-class non-dead halt abnormality. And if the M third images corresponding to the tested equipment with abnormal test do not contain the preset keywords, determining that the abnormal type of the tested equipment with abnormal test is the second non-dead halt abnormality. Before testing, an application program is pre-installed in the tested equipment, the application program can monitor the tested equipment in the testing process, and when the first-class non-crash abnormality occurs to the tested equipment, a preset keyword corresponding to the abnormal type can be popped up on a screen; and if the detection equipment cannot detect the preset keywords from the screen of the abnormal tested equipment and the tested equipment is determined not to be in the crash anomaly, determining that the second non-crash anomaly occurs in the tested equipment. Based on the mode, the detection equipment can more accurately judge whether the detected equipment has non-crash abnormality and the corresponding abnormality type.
The non-dead halt type exceptions comprise first type non-dead halt exceptions and second type non-dead halt exceptions. The first type of non-crash-like anomaly includes one or more of: system reboot (systeserverreboot), subscriber identity card drop (simcarddreped), model interrupt (ModemAssert), wireless network interrupt (wcnarsert), handset type unsupported (UnsupportPhone), engineering type unsupported (UnsupportProject), android version unsupported (uns), exception stop (extop), small memory card read-only (treadon), data read-only (DReadOnly), read-write failure (IOError), and data partition filled (DataFull); the second type of non-dead anomaly is fixed screen.
Exemplarily, as shown in fig. 8, in the third image 801, there is no text on the screen interface of the first device under test, and meanwhile, the detection device identifies, through the training model, that it has been determined that the first device under test does not belong to the crash anomaly, and based on the above, the detection device determines that the first device under test belongs to the second crash anomaly; in the third image 802, a preset keyword "ModemAssert" appears on a screen interface of the second device under test, and the detection device determines that the second device under test has the first kind of dead halt abnormality, specifically, the type of the second device under test is ModemAssert.
In one possible implementation, steps 201 and 202 are performed periodically before N end-of-test tests are performed. The abnormity detection is carried out in a periodic mode, and the detection equipment can be used for more timely confirming whether the detected equipment has abnormal conditions.
203. And after the N tested devices are tested, the detection device stores the detection result into the test system, and/or the detection device sends the detection result to the target device.
In the embodiment of the application, after the test of the N tested devices is finished, the detection device only needs to store the detection result into the test system, and does not need to send the detection result to the target device; the detection device can also only send a detection result to the target device without storing the detection result in the test system; the detection device may both store the detection result in the test system and send the detection result to the target device. The target device refers to a device of a tester, such as a mobile phone, a notebook computer, etc. of the tester. Sending the detection result to the target device means that the detection device may send a mail to a mailbox of the tester, or may send a short message to a mobile phone of the tester, which is not limited in the embodiment of the present application. The detection result comprises the identification of the tested device with the test exception and the exception type. Based on the method, the testing condition of the tested equipment and the abnormal type of the abnormal equipment can be determined as soon as possible by testing personnel.
Based on the method described in the application, a tester can determine whether the tested equipment is abnormal or not and the abnormal type of the tested equipment with abnormal test without checking and processing each tested equipment in person, so that the convenience of equipment detection is improved, and the time and the labor are saved.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an apparatus according to an embodiment of the present disclosure. The apparatus shown in fig. 9 may be used to perform some or all of the functions of the detection device described above. The device can be a detection device, a device in the detection device or a device capable of being matched with the detection device for use. Wherein, the device can also be a chip system. The apparatus shown in fig. 9 may comprise a communication unit 901 and a processing unit 902. The processing unit 902 is configured to perform data processing. A communication unit 901 configured to communicate with other devices. The communication unit 901 is integrated with a receiving unit and a transmitting unit. The communication unit 901 may also be referred to as a transceiving unit. Alternatively, communication section 901 may be divided into a reception section and a transmission section. The processing unit 902 and the communication unit 901 are similar, and are not described in detail below. Wherein:
the communication unit 901 is configured to obtain M first images acquired by an image acquisition device, where the first images include display screens of N devices under test, M is a positive integer, and N is a positive integer; the processing unit 902 is configured to determine, based on the M first images, an abnormal device under test among the N devices under test and an abnormal type of the abnormal device under test;
the processing unit 902 is further configured to store the detection result in the test system after the N tested devices are tested, and/or send the detection result to the target device through the communication unit 901, where the detection result includes the identifier and the abnormality type of the tested device with abnormal test.
In one possible implementation manner, when the processing unit 902 is configured to determine, based on the M first images, the device under test with the test exception and the exception type of the device under test with the test exception in the N devices under test, the processing unit is further configured to: dividing M first images into K second images, dividing one first image into L second images, wherein N third images exist in the L second images, each third image comprises a display screen of equipment to be tested, K is a positive integer, L is a positive integer, and L is larger than or equal to N; and determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on M x N third images in the K second images.
In one possible implementation manner, the M third images corresponding to the tested device with the abnormal test are the same.
In a possible implementation manner, when the processing unit 902 is configured to determine the anomaly type of the device under test with the test anomaly based on M × N third images in the K second images, the processing unit 902 is further configured to: inputting M x N third images in the K second images into a training model; and determining the abnormal type of the tested equipment with abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model.
In one possible implementation, the processing unit 902 is further configured to: if the abnormal type of the tested equipment with abnormal test is non-crash abnormal, determining whether M third images corresponding to the tested equipment with abnormal test contain preset keywords or not; if M third images corresponding to the tested equipment with abnormal test contain preset keywords, determining the abnormal type of the tested equipment with abnormal test as a first-class non-dead halt abnormality; and if the M third images corresponding to the abnormal tested equipment do not contain the preset keywords, determining that the abnormal type of the abnormal tested equipment is the second non-dead halt abnormality.
In one possible implementation, the crash-like exception includes one or more of: blank screen, memory data dump, android resume, and splash screen.
In one possible implementation, the first type of non-crash-like exception includes one or more of: the method comprises the following steps of restarting a system, dropping a user identification card SIMCardDraped, model interrupt Modemassert, wireless network interrupt WCNAssert, not supporting mobile phone type UnsuportPhone, engineering type UnsuportProject, android version UnsuportVerison, abnormal stop ExStop, small memory card read-only TReadOnly, data read-only DReadOnly, read-write failure IOError and data partition are filled with DataFull.
In one possible implementation, the second type of non-dead type exception is a fixed screen.
The embodiment of the application also provides a chip, and the chip can execute the relevant steps of the detection equipment in the embodiment of the method.
The chip is used for acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; the chip is used for determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images;
the chip is further used for storing the detection result into the test system and/or sending the detection result to the target device after the test of the N tested devices is finished, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
In a possible implementation manner, when the chip is configured to determine, based on the M first images, the device under test with the test exception and the exception type of the device under test with the test exception in the N devices under test, the processing unit is further configured to: dividing M first images into K second images, dividing one first image into L second images, wherein N third images exist in the L second images, each third image comprises a display screen of equipment to be tested, K is a positive integer, L is a positive integer, and L is larger than or equal to N; and determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on M x N third images in the K second images.
In one possible implementation manner, the M third images corresponding to the tested device with the abnormal test are the same.
In a possible implementation manner, when the chip is configured to determine the anomaly type of the device under test with the test anomaly based on M × N third images in the K second images, the chip is further configured to: inputting M x N third images in the K second images into a training model; and determining the abnormal type of the tested equipment with abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model.
In one possible implementation, the chip is further configured to: if the abnormal type of the tested equipment with abnormal test is non-crash abnormal, determining whether M third images corresponding to the tested equipment with abnormal test contain preset keywords or not; if M third images corresponding to the tested equipment with abnormal test contain preset keywords, determining the abnormal type of the tested equipment with abnormal test as a first-class non-dead halt abnormality; and if the M third images corresponding to the abnormal tested equipment do not contain the preset keywords, determining that the abnormal type of the abnormal tested equipment is the second non-dead halt abnormality.
In one possible implementation, the crash-like exception includes one or more of: blank screen, memory data dump, android resume, and splash screen.
In one possible implementation, the first type of non-crash-like exception includes one or more of: the method comprises the following steps of restarting a system, dropping a user identification card SIMCardDraped, model interrupt Modemassert, wireless network interrupt WCNAssert, not supporting mobile phone type UnsuportPhone, engineering type UnsuportProject, android version UnsuportVerison, abnormal stop ExStop, small memory card read-only TReadOnly, data read-only DReadOnly, read-write failure IOError and data partition are filled with DataFull.
In one possible implementation, the second type of non-dead type exception is a fixed screen.
Fig. 10 shows a detection device 100 provided in this embodiment of the present application, for implementing the functions of the detection device in fig. 2
The detection device 100 includes at least one processor 1020, which is configured to implement the data processing function of the terminal device in the method provided by the embodiment of the present application. The detection device 100 may further include a communication interface 1010, which is used to implement the transceiving operation of the terminal device in the method provided by the embodiment of the present application. In embodiments of the present application, the communication interface may be a transceiver, circuit, bus, module, or other type of communication interface for communicating with other devices over a transmission medium. For example, communication interface 1010 is used to detect that devices in device 100 may communicate with other devices. The processor 1020 transmits and receives data using the communication interface 1010 and is configured to implement the method described above with respect to the method embodiment of fig. 2.
The detection device 100 may also include at least one memory 1030 for storing program instructions and/or data. A memory 1030 is coupled to the processor 1020. The coupling in the embodiments of the present application is an indirect coupling or a communication connection between devices, units or modules, and may be an electrical, mechanical or other form for information interaction between the devices, units or modules. Processor 1020 may operate in conjunction with memory 1030. Processor 1020 may execute program instructions stored in memory 1030. At least one of the at least one memory may be included in the processor.
When the detection apparatus 100 is powered on, the processor 1020 may read the software program in the memory 1030, interpret and execute the instructions of the software program, and process the data of the software program. When data needs to be sent wirelessly, the processor 1020 outputs a baseband signal to a radio frequency circuit (not shown) after performing baseband processing on the data to be sent, and the radio frequency circuit performs radio frequency processing on the baseband signal and sends the radio frequency signal to the outside in the form of electromagnetic waves through an antenna. When data is transmitted to the detection apparatus 100, the rf circuit receives an rf signal through the antenna, converts the rf signal into a baseband signal, and outputs the baseband signal to the processor 1020, and the processor 1020 converts the baseband signal into data and processes the data.
In another implementation, the rf circuitry and antenna may be provided independently of the processor 1020 for baseband processing, for example in a distributed scenario, the rf circuitry and antenna may be in a remote arrangement from being independent of the detection device.
The specific connection medium among the communication interface 1010, the processor 1020 and the memory 1030 is not limited in the embodiments of the present application. In the embodiment of the present application, the memory 1030, the processor 1020, and the communication interface 1010 are connected by a bus 1040 in fig. 10, the bus is represented by a thick line in fig. 10, and the connection manner between other components is merely illustrative and not limited thereto. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 10, but this is not intended to represent only one bus or type of bus.
When the detection device 100 is specifically used in a terminal device, for example, when the detection device 100 is specifically a chip or a chip system, the output or the reception of the communication interface 1010 may be a baseband signal. When the detection device 100 is a terminal device, the communication interface 1010 may output or receive a radio frequency signal. In the embodiments of the present application, the processor may be a general-purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, operations, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The operations of the methods disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in a processor.
It should be noted that the detection device may perform relevant steps of the detection device in the foregoing method embodiments, and specific reference may be made to implementation manners provided in the foregoing steps, which are not described herein again.
For each device and product applied to or integrated in the detection apparatus, each module included in the detection apparatus may be implemented by using hardware such as a circuit, different modules may be located in the same component (e.g., a chip, a circuit module, etc.) or different components in the terminal, or at least a part of the modules may be implemented by using a software program running on a processor integrated in the terminal, and the rest (if any) part of the modules may be implemented by using hardware such as a circuit.
As shown in fig. 11, fig. 11 is a schematic structural diagram of a module device according to an embodiment of the present application. The module device 110 can perform the steps related to the first network access device in the foregoing method embodiments, and the module device 110 includes: a communication module 1101, a power module 1102, a memory module 1103, and a chip module 1104.
The power module 1102 is configured to provide power for the module device; the storage module 1103 is used for storing data and instructions; the communication module 1101 is configured to perform module device internal communication, or perform module device communication with an external device.
The chip module 1104 is used for acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer; the chip module 1104 is configured to determine, based on the M first images, an abnormal device to be tested among the N devices to be tested and an abnormal type of the abnormal device to be tested;
the chip module 1104 is further configured to store the detection result in the test system and/or send the detection result to the target device after the N devices under test are tested, where the detection result includes the identifier and the abnormality type of the device under test with abnormal test.
In one possible implementation manner, when the chip module 1104 is configured to determine, based on the M first images, the device under test with the test exception and the exception type of the device under test with the test exception in the N devices under test, the processing unit is further configured to: dividing M first images into K second images, dividing one first image into L second images, wherein N third images exist in the L second images, each third image comprises a display screen of equipment to be tested, K is a positive integer, L is a positive integer, and L is larger than or equal to N; and determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on M x N third images in the K second images.
In one possible implementation manner, the M third images corresponding to the tested device with the abnormal test are the same.
In a possible implementation manner, when the chip module 1104 is configured to determine the abnormal type of the abnormal device under test based on M × N third images in the K second images, the chip module 1104 is further configured to: inputting M x N third images in the K second images into a training model; and determining the abnormal type of the tested equipment with abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model.
In one possible implementation, the chip module 1104 is further configured to: if the abnormal type of the tested equipment with abnormal test is non-crash abnormal, determining whether M third images corresponding to the tested equipment with abnormal test contain preset keywords or not; if M third images corresponding to the tested equipment with abnormal test contain preset keywords, determining the abnormal type of the tested equipment with abnormal test as a first-class non-dead halt abnormality; and if the M third images corresponding to the abnormal tested equipment do not contain the preset keywords, determining that the abnormal type of the abnormal tested equipment is the second non-dead halt abnormality.
In one possible implementation, the crash-like exception includes one or more of: blank screen, memory data dump, android resume, and splash screen.
In one possible implementation, the first type of non-crash-like exception includes one or more of: the method comprises the following steps of restarting a system, dropping a user identification card SIMCardDraped, model interrupt Modemassert, wireless network interrupt WCNAssert, not supporting mobile phone type UnsuportPhone, engineering type UnsuportProject, android version UnsuportVerison, abnormal stop ExStop, small memory card read-only TReadOnly, data read-only DReadOnly, read-write failure IOError and data partition are filled with DataFull.
In one possible implementation, the second type of non-dead type exception is a fixed screen.
Embodiments of the present application further provide a computer-readable storage medium, in which instructions are stored, and when the computer-readable storage medium is executed on a processor, the method flow of the above method embodiments is implemented.
Embodiments of the present application further provide a computer program product, where when the computer program product runs on a processor, the method flow of the above method embodiments is implemented.
It is noted that, for simplicity of explanation, the foregoing method embodiments are described as a series of acts or combination of acts, but those skilled in the art will appreciate that the present application is not limited by the order of acts, as some acts may, in accordance with the present application, occur in other orders and/or concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
The descriptions of the embodiments provided in the present application may be referred to each other, and the descriptions of the embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. For convenience and brevity of description, for example, the functions and operations performed by the devices and apparatuses provided in the embodiments of the present application may refer to the related descriptions of the method embodiments of the present application, and may also be referred to, combined with or cited among the method embodiments and the device embodiments.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (13)
1. A method for device detection, the method comprising:
acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer;
determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on the M first images;
and after the N tested devices are tested, storing the detection result into a test system, and/or sending the detection result to a target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
2. The method according to claim 1, wherein said determining the device under test with the test abnormality in the N devices under test and the abnormality type of the device under test with the test abnormality based on the M first images comprises:
dividing the M first images into K second images, dividing one first image into L second images, wherein N third images exist in the L second images, the third image comprises a display screen of equipment to be tested, K is a positive integer, K is M multiplied by L, L is a positive integer, and L is greater than or equal to N;
and determining the tested devices with abnormal test in the N tested devices and the abnormal types of the tested devices with abnormal test based on M x N third images in the K second images.
3. The method according to claim 2, wherein the M third images corresponding to the device under test with the test abnormality are the same.
4. The method according to claim 3, wherein the determining the anomaly type of the device under test with the test anomaly based on M x N third images of the K second images comprises:
inputting M x N third images in the K second images into a training model;
and determining the abnormal type of the tested equipment with the abnormal test as a dead halt type abnormal or a non-dead halt abnormal based on the output result of the training model.
5. The method of claim 4, further comprising:
if the abnormal type of the tested equipment with the abnormal test is non-crash abnormal, determining whether M third images corresponding to the tested equipment with the abnormal test contain preset keywords or not;
if M third images corresponding to the tested equipment with the abnormal test contain preset keywords, determining that the abnormal type of the tested equipment with the abnormal test is a first-class non-dead halt abnormality;
and if the M third images corresponding to the abnormal tested equipment do not contain preset keywords, determining that the abnormal type of the abnormal tested equipment is a second non-dead halt abnormality.
6. The method of claim 4, wherein the dead airplane-like exception comprises one or more of: blank screen, memory data dump, android resume, and splash screen.
7. The method of claim 5, wherein the first type of non-crash-like anomaly comprises one or more of: the method comprises the following steps of restarting a system, dropping a user identification card SIMCardDraped, model interrupt Modemassert, wireless network interrupt WCNAssert, not supporting mobile phone type UnsuportPhone, engineering type UnsuportProject, android version UnsuportVerison, abnormal stop ExStop, small memory card read-only TReadOnly, data read-only DReadOnly, read-write failure IOError and data partition are filled with DataFull.
8. The method of claim 5, wherein the second type of non-dead type exception is a fixed screen.
9. An apparatus for device detection, the apparatus comprising a communication unit and a processing unit;
the communication unit is used for acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer;
the processing unit is used for determining the tested equipment with abnormal test in the N tested equipment and the abnormal type of the tested equipment with abnormal test based on the M first images;
and the processing unit is used for storing the detection result into a test system after the test of the N tested devices is finished, and/or sending the detection result to the target device through the communication unit, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
10. A chip, wherein the chip is configured to:
acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer;
determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on the M first images;
and after the N tested devices are tested, storing the detection result into a test system, and/or sending the detection result to a target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
11. The utility model provides a module equipment, its characterized in that, module equipment includes communication module, power module, storage module and chip module, wherein:
the power supply module is used for providing electric energy for the module equipment;
the storage module is used for storing data and instructions;
the communication module is used for carrying out internal communication of module equipment or is used for carrying out communication between the module equipment and external equipment;
the chip module is used for:
acquiring M first images acquired by image acquisition equipment, wherein the first images comprise display screens of N pieces of equipment to be tested, M is a positive integer, and N is a positive integer;
determining the tested device with abnormal test in the N tested devices and the abnormal type of the tested device with abnormal test based on the M first images;
and after the N tested devices are tested, storing the detection result into a test system, and/or sending the detection result to a target device, wherein the detection result comprises the identification and the abnormal type of the tested device with abnormal test.
12. A detection device, comprising a processor, a memory, and a transceiver;
the transceiver is used for receiving data or sending data;
the memory for storing a computer program;
the processor, which is used for calling the computer program from the memory to execute the method of any claim 1-8.
13. A computer-readable storage medium, in which a computer program is stored which, when run on a detection device, causes the detection device to perform the method of any one of claims 1 to 8.
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