CN110969082B - Clock synchronous test inspection method and system - Google Patents
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Abstract
The application provides a clock synchronization test inspection method and a system, wherein the system comprises the following steps: the system comprises an image acquisition device, a testing device and a server; the image acquisition device acquires pictures of a testing environment where the testing device is located according to a preset period and sends the pictures to the server; the testing device is used for collecting performance data when performing clock synchronization performance test, intercepting performance test results in the form of pictures according to a preset period and sending the acquired pictures to the server; the server determines a classification label of the picture based on a preset AI model when receiving the synchronous picture sent by the image acquisition device and/or the testing device; and outputting alarm information when the determined classification label indicates abnormal performance test. According to the scheme, the clock synchronization test can be efficiently completed on the premise of saving labor cost.
Description
Technical Field
The invention relates to the technical field of vision processing, in particular to a clock synchronous test inspection method and system.
Background
In clock synchronization tests of laboratories or existing networks, some performance test projects need to be tested for a long time, for example, frequency stability month time keeping performance needs to be tested for at least 30 days, frequency stability day time keeping performance needs to be tested for at least 15 days, end-to-end transmission network frequency synchronization performance needs to be tested for at least 3 days, and the like, in these long-term performance tests, due to some objective factor limitations (such as test field requirements for test personnel isolation, test cross days and the like, the long-term execution cannot be performed), sudden situations such as abnormal instrument states (power failure, dead halt, restarting, lock losing, blue screen and the like) and abnormal test results (signal interruption, abnormal jump points, performance offset and the like) are difficult to find in time, the probability of occurrence of the abnormal problems is low, but the influence is large, the test data quality is low, even the test failure needs to be repeatedly retested, the test efficiency and the test experience are seriously influenced, and the manpower cost and the time cost are unnecessarily improved.
In the existing clock synchronization performance long-term test, the watch is manually carried out, so that the test environment including the instrument state, the equipment state, the on-off state of the test cable and the like is ensured to be in a normal state at any time until the synchronization long-term performance test project is completed.
Taking the person to watch requires a lot of manpower and financial resources, and most of the time is ineffective.
Disclosure of Invention
In view of this, the present application provides a method and a system for inspecting clock synchronization test, which can efficiently complete clock synchronization test on the premise of saving labor cost.
In order to solve the technical problems, the technical scheme of the application is realized as follows:
in one embodiment, a clock synchronization test patrol system is provided, the system comprising: the system comprises an image acquisition device, a testing device and a server;
the image acquisition device acquires pictures of a testing environment where the testing device is located according to a preset period and sends the pictures to the server;
the testing device is used for collecting performance data when performing clock synchronization performance test, intercepting performance test results in the form of pictures according to a preset period and sending the acquired pictures to the server;
the server determines a classification label of the picture based on a preset AI model when receiving the synchronous picture sent by the image acquisition device and/or the testing device; and outputting alarm information when the determined classification label indicates abnormal performance test.
In another embodiment, a clock synchronization test inspection method is provided, applied to a server in the inspection system of claims 1-7, the method comprising:
when receiving a picture sent by an image acquisition device and/or a testing device, determining a classification label of the picture based on a preset AI model; the image acquisition device acquires the image of the testing environment where the testing device is located according to a preset period; the pictures sent by the testing device are performance testing results which are intercepted by the testing device in the form of pictures according to a preset period;
and outputting alarm information when the determined classification label indicates abnormal performance test.
In another embodiment, an electronic device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor that when executed implements steps of a method for clock synchronization test patrol as described.
In another embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the clock synchronization test patrol method.
As can be seen from the above technical solution, in the above embodiment, the test device intercepts the test picture, and combines the image acquisition device to collect the picture for the test environment state, and respectively converts the clock synchronous test performance data and the test environment state into the picture which is easy to identify and process by the AI identification model, and effectively identifies, determines the abnormal phenomenon and outputs the alarm. According to the scheme, the clock synchronization test can be efficiently completed on the premise of saving labor cost.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic diagram of a clock synchronous test inspection system architecture in an embodiment of the present application;
fig. 2 is a schematic structural diagram of an image capturing device according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a test apparatus according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a clock synchronization test inspection flow in an embodiment of the present application;
fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
The embodiment of the application provides a clock synchronous test inspection system. Referring to fig. 1, fig. 1 is a schematic diagram of a clock synchronization test inspection system architecture in an embodiment of the present application. The system comprises: image acquisition device, testing arrangement and server.
Before performing clock synchronization test and inspection, a clock synchronization performance test environment needs to be built, so that the image acquisition device can acquire the running state of the test device and the environment; and meanwhile, the normal operation of the testing device is ensured.
The image acquisition device acquires pictures of a testing environment where the testing device is located according to a preset period and sends the pictures to the server;
in particular implementations, the image acquisition apparatus may be implemented by the architecture of fig. 2. Referring to fig. 2, fig. 2 is a schematic structural diagram of an image capturing device in an embodiment of the present application. The image acquisition device includes: the camera and the first electronic device;
the camera is used for collecting videos of the testing environment where the testing device is located and transmitting the videos to the electronic equipment;
the camera is used for collecting the video of the testing environment where the testing device is located, and the video of the whole environment is collected, so that the method also comprises the step of collecting relevant content on a screen of the testing device; that is, the camera should be deployed in a position to capture as much as possible the full view of the entire test device and its environment.
And the first electronic equipment intercepts pictures according to a preset period from the video transmitted by the camera and sends the pictures to the server.
The first electronic device may be a PC, and may have a screenshot function for video.
The preset period can be reasonably set according to actual needs, such as test duration, server capacity and the like, for example, 1 minute for 1 time or 15 minutes for one time.
The pictures can be transmitted to the server through a local area network or a wide area network, and the network transmission can be determined according to the current available network or the deployment position of the server.
And the testing device is used for collecting performance data when performing clock synchronization performance test, intercepting performance test results in the form of pictures according to a preset period and sending the acquired pictures to the server.
In a specific implementation, the test device may include: a test meter and a second electronic device; a test meter may also be included;
when the testing device only comprises the testing instrument, the testing instrument collects performance data when performing clock synchronization performance test, intercepts performance test results in the form of pictures according to a preset period, and sends the obtained pictures to the server.
Aiming at the test instrument in the scene, the test instrument needs to have a screenshot function, and the screenshot can be directly performed through embedding screenshot software, such as the test instrument of a Windows system.
When the test device includes the test meter and the second electronic device, referring to fig. 3, fig. 3 is a schematic structural diagram of the test device in the embodiment of the present application. The testing device comprises a testing instrument and second electronic equipment;
the test instrument is used for performing clock synchronization performance test;
the second electronic equipment is used for collecting performance data when the test instrument performs clock synchronization performance test; and intercepting a performance test result in a picture form according to a preset period, and sending the acquired picture to the server.
The second electronic device here may be a PC.
The test device may transmit the picture to the server via a local area network or a wide area network, and the network transmission may be determined according to the currently available network or the deployment location of the server.
The preset period can be reasonably set according to actual needs, such as test duration, server capacity and the like, for example, 1 minute for 1 time or 15 minutes for one time.
The period of capturing pictures in the testing device and the image acquisition device is the same, and the capturing time is also set to be the same.
When receiving synchronous pictures sent by an image acquisition device and/or a testing device, a server determines classification labels of the pictures based on a preset AI model; and outputting alarm information when the determined classification label indicates abnormal performance test.
Because the power failure, dead halt, blue screen of the instrument and the electronic equipment, the on-off of the test cable and the like can cause that the test device and the image acquisition device can not transmit pictures to the server at the same time, the server can recognize and classify the pictures sent by the single device when receiving the pictures;
if a plurality of pictures are received, determining and recording which two pictures are synchronous pictures according to the time stamps of the corresponding pictures.
When the server receives the pictures, the corresponding relation between the pictures and the corresponding time stamps is recorded, so that maintenance personnel can find out the corresponding time and nodes of the test operation according to the time stamps; the timestamp is the time at which the picture was taken by the electronic device.
The server may include, in particular implementations: a local server, and/or a cloud server;
when the server comprises a local server and a cloud server, the local server and the cloud server perform data sharing and primary and standby switching.
When the environment test is performed in a laboratory, the pictures are stored in the local server, and when the environment test is performed in the current network, the pictures can be transmitted to the cloud server through the network, and the cloud server and the local server are in a mutually communicated state to perform data sharing and main and standby switching.
The preset AI model stored on the server can be generated by server training or by training of other devices and transmitted to the server for storage.
The acquisition process of the preset AI model specifically comprises the following steps:
labeling the pictures, wherein the labeling can be performed manually at present or can be performed on the labeled pictures, such as directly acquiring data in a database;
dividing the marked picture into a plurality of data sets, namely a training set, a verification set, a model test set and a gray test set;
the training set is used for training network parameters of the initial AI model;
if the data size of the training set is smaller than a preset value, establishing the initial AI model by using a logistic regression or support vector machine algorithm;
and if the data size of the training set is not smaller than a preset value, establishing the initial AI model by using a deep learning algorithm. Among them, deep learning algorithms such as ResNet, googleNet, SEnet, etc.
The verification set is used for super-parameter adjustment of the initial AI model;
the model test set is used for evaluating the AI model performance after training parameters;
the gray test set is used for evaluating the consistency and stability of the AI model after training parameters;
when the performance evaluation, consistency and stability evaluation of the AI model after the training parameters reach the standards, determining the AI model after the training parameters as a preset AI model; otherwise, continuing training, verifying, testing and the like until the performance evaluation, consistency and stability evaluation of the AI model after the training parameters reach the standard, and determining the AI model after the training parameters as a preset AI model.
When labeling pictures in the embodiment of the application, labeling test data, namely labeling by using synchronous expert knowledge, comprises the following steps: free oscillation, normal locking, hold, phase transient, phase discontinuity, noise transfer;
specific labeling includes, for the case of an anomaly: the method comprises the following steps of instrument power failure, instrument crash, instrument blue screen, electronic equipment power failure, computer crash, computer blue screen, cable interruption and the like.
In specific implementation, each label can be used as a label of a type, and normal pictures can be used as a type for labeling, and the abnormal pictures are subjected to the detailed classification labeling so as to clearly present abnormal alarms.
In a specific implementation, the server outputs each picture, the timestamp of the picture, and the determined classification label, wherein the output mode can be voice, text (such as a web page mode) and the like, and the alarm output is performed on the image of the abnormal phenomenon corresponding to the classification label, and the alarm output can inform related personnel such as test personnel, maintenance personnel and the like in a mode of short message, weChat, telephone, voice and the like.
Based on the same inventive concept, the embodiment of the application also provides a clock synchronization test inspection method, which is applied to the server in the inspection system. Referring to fig. 4, fig. 4 is a schematic diagram of a clock synchronization test inspection flow in an embodiment of the present application. The method comprises the following specific steps:
The image acquisition device acquires the image of the testing environment where the testing device is located according to a preset period; the pictures sent by the testing device are performance testing results which are intercepted by the testing device in the form of pictures according to a preset period;
the preset AI model can be generated by server training or by other device training and transmitted to a server for storage.
The acquisition process of the preset AI model specifically comprises the following steps:
labeling the pictures, wherein the labeling can be performed manually at present or can be performed on the labeled pictures, such as directly acquiring data in a database;
dividing the marked picture into a plurality of data sets, namely a training set, a verification set, a model test set and a gray test set;
the training set is used for training network parameters of the initial AI model;
if the data size of the training set is smaller than a preset value, establishing the initial AI model by using a logistic regression or support vector machine algorithm;
and if the data size of the training set is not smaller than a preset value, establishing the initial AI model by using a deep learning algorithm. Among them, deep learning algorithms such as ResNet, googleNet, SEnet, etc.
The verification set is used for super-parameter adjustment of the initial AI model;
the model test set is used for evaluating the AI model performance after training parameters;
the gray test set is used for evaluating the consistency and stability of the AI model after training parameters;
when the performance evaluation, consistency and stability evaluation of the AI model after the training parameters reach the standards, determining the AI model after the training parameters as a preset AI model; otherwise, continuing training, verifying, testing and the like until the performance evaluation, consistency and stability evaluation of the AI model after the training parameters reach the standard, and determining the AI model after the training parameters as a preset AI model.
When labeling pictures in the embodiment of the application, labeling test data, namely labeling by using synchronous expert knowledge, comprises the following steps: free oscillation, normal locking, hold, phase transient, phase discontinuity, noise transfer;
specific labeling includes, for the case of an anomaly: the instrument is powered down, the instrument is dead, the instrument blue screen, the electronic equipment, the computer is dead, the computer blue screen, the cable is interrupted and the like.
In specific implementation, each label can be used as a label of a type, and normal pictures can be used as a type for labeling, and the abnormal pictures are subjected to the detailed classification labeling so as to clearly present abnormal alarms.
And step 402, outputting alarm information when the determined classification label represents abnormal performance test.
In summary, in the embodiment of the present application, on the one hand, by capturing a screen through an instrument or a matched electronic device, and capturing a video of a test environment state by combining a network camera, clock synchronization performance test data and the test environment state are respectively converted into image files which are easy to identify and process by using an AI, so that the advantage of the AI algorithm in the field of computer vision is fully utilized, and an abnormal event or state is effectively distinguished; on the other hand, the picture data are classified, mode identified and inferred through an AI model, the result is displayed explicitly, real-time early warning events and abnormal events are notified to testers or maintainers in time, and the traditional manual passive discovery and analysis test fault mode is converted into an AI active service mode, so that the test task can be completed more quickly and efficiently.
In another embodiment, there is also provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the clock synchronization test patrol method when executing the program.
In another embodiment, a computer readable storage medium having stored thereon computer instructions which when executed by a processor perform the steps of the clock synchronization test patrol method is also provided.
Fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the electronic device may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform the following method:
when receiving a picture sent by an image acquisition device and/or a testing device, determining a classification label of the picture based on a preset AI model; the image acquisition device acquires the image of the testing environment where the testing device is located according to a preset period; the pictures sent by the testing device are performance testing results which are intercepted by the testing device in the form of pictures according to a preset period;
and outputting alarm information when the determined classification label indicates abnormal performance test.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather to enable any modification, equivalent replacement, improvement or the like to be made within the spirit and principles of the invention.
Claims (9)
1. A clock synchronization test patrol system, the system comprising: the system comprises an image acquisition device, a testing device and a server;
the image acquisition device acquires pictures of a testing environment where the testing device is located according to a preset period and sends the pictures to the server;
the testing device is used for collecting performance data when performing clock synchronization performance test, intercepting performance test results in the form of pictures according to a preset period and sending the acquired pictures to the server;
the server determines a classification label of the picture based on a preset AI model when receiving the synchronous picture sent by the image acquisition device and/or the testing device; when the determined classification label indicates that the performance test is abnormal, outputting alarm information;
wherein, the liquid crystal display device comprises a liquid crystal display device,
when training the preset AI model, if the data size of the training set is smaller than a preset value, establishing an initial AI model by using a logistic regression or support vector machine algorithm;
and if the data size of the training set is not smaller than the preset value, establishing the initial AI model by using a deep learning algorithm.
2. The system of claim 1, wherein the image acquisition device comprises: the camera and the first electronic device;
the camera is used for collecting videos of the testing environment where the testing device is located and transmitting the videos to the electronic equipment;
and the first electronic equipment intercepts pictures according to a preset period from the video transmitted by the camera and sends the pictures to the server.
3. The system of claim 1, wherein the testing device comprises: a test meter and a second electronic device;
the test instrument is used for performing clock synchronization performance test;
the second electronic equipment collects performance data when the test instrument performs clock synchronization performance test; and intercepting a performance test result in a picture form according to a preset period, and sending the acquired picture to the server.
4. The system of claim 1, wherein the testing device comprises: a test meter;
and when the test instrument performs clock synchronization performance test, performance data collection is performed, a performance test result is intercepted in a picture form according to a preset period, and the acquired picture is sent to the server.
5. The system of claim 1, wherein the system further comprises a controller configured to control the controller,
the server includes: a local server, and/or a cloud server;
when the server comprises a local server and a cloud server, the local server and the cloud server perform data sharing and primary and standby switching.
6. The system of any one of claims 1-5, wherein,
the server prepares a training set, a verification set, a model test set and a gray test set which are marked on the pictures;
the training set is used for training network parameters of an initial AI model, the verification set is used for super-parameter adjustment of the initial AI model, the model test set is used for evaluating the performance of the AI model after training parameters, and the gray test set is used for evaluating the consistency and stability of the AI model after training parameters;
and when the performance evaluation, consistency and stability evaluation of the AI model after the training parameters reach the standards, determining the AI model after the training parameters as a preset AI model.
7. A method of clock synchronous test inspection, applied to a server in the inspection system of any one of claims 1-6, the method comprising:
when receiving a picture sent by an image acquisition device and/or a testing device, determining a classification label of the picture based on a preset AI model; the image acquisition device acquires the image of the testing environment where the testing device is located according to a preset period; the pictures sent by the testing device are performance testing results which are intercepted by the testing device in the form of pictures according to a preset period;
when the determined classification label indicates that the performance test is abnormal, outputting alarm information;
wherein, the liquid crystal display device comprises a liquid crystal display device,
when training the preset AI model, if the data size of the training set is smaller than a preset value, establishing an initial AI model by using a logistic regression or support vector machine algorithm;
and if the data size of the training set is not smaller than the preset value, establishing the initial AI model by using a deep learning algorithm.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of claim 7 when executing the program.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of claim 7.
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| CN109359032A (en) * | 2018-09-05 | 2019-02-19 | Oppo（重庆）智能科技有限公司 | Test data collection method, device, system and electronic equipment |
| CN109685131A (en) * | 2018-12-20 | 2019-04-26 | 斑马网络技术有限公司 | Automobile vehicle device system exception recognition methods and device |
| US20190171950A1 (en) * | 2019-02-10 | 2019-06-06 | Kumar Srivastava | Method and system for auto learning, artificial intelligence (ai) applications development, operationalization and execution |
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| CN109359032A (en) * | 2018-09-05 | 2019-02-19 | Oppo（重庆）智能科技有限公司 | Test data collection method, device, system and electronic equipment |
| CN109685131A (en) * | 2018-12-20 | 2019-04-26 | 斑马网络技术有限公司 | Automobile vehicle device system exception recognition methods and device |
| US20190171950A1 (en) * | 2019-02-10 | 2019-06-06 | Kumar Srivastava | Method and system for auto learning, artificial intelligence (ai) applications development, operationalization and execution |
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