CN110620698A - Software abnormity diagnosis method, device, equipment and system - Google Patents
Software abnormity diagnosis method, device, equipment and system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/14—Arrangements for monitoring or testing data switching networks using software, i.e. software packages
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Abstract
The application provides a software abnormity diagnosis method, a device, equipment and a system, comprising the following steps: receiving a software exception log sent by the camera equipment when the locally running software is abnormal; positioning an abnormal reason of the software running abnormity according to the software abnormal log; and outputting the abnormal reason to a client associated with the software. By using the method provided by the application, the efficiency and the accuracy of software abnormity diagnosis can be effectively improved.
Description
Technical Field
The present application relates to the field of computer communications, and in particular, to a method, an apparatus, a device, and a system for diagnosing software anomalies.
Background
At present, the application of the image pickup apparatus has become very wide, and the functions of the image pickup apparatus are also more abundant. For example, current image capturing apparatuses have more abundant functions such as feature extraction in addition to basic functions such as capturing images.
In order to implement the above functions, the image pickup apparatus is generally loaded with various kinds of software, which may include algorithm software and system software. The algorithm software has multiple functions of detecting and identifying input image data, and the system software has important functions of ensuring normal operation of software in the camera equipment, data transmission and the like. Since the functions implemented by the software of the image pickup apparatus are complex and rich, there are many defects in software design, and these defects can only be expressed in the form of software exception when the image pickup apparatus is deployed to a field test.
However, in the existing software anomaly diagnosis method, when the software of the image pickup device fails, manual diagnosis is needed, and on one hand, the efficiency of manual anomaly diagnosis is low; on the other hand, the error rate of manual abnormality diagnosis is high.
Disclosure of Invention
In view of this, the present application provides a software anomaly diagnosis method, device, apparatus, and system, which can automatically diagnose the cause of a software anomaly and push the cause of the anomaly to a user, so that software anomaly diagnosis and anomaly recovery efficiency are greatly improved.
Specifically, the method is realized through the following technical scheme:
according to a first aspect of the present application, there is provided a software anomaly diagnosis method, which is applied to a server and includes:
receiving a software exception log sent by the camera equipment when the locally running software is abnormal;
positioning an abnormal reason of the software running abnormity according to the software abnormal log;
and outputting the abnormal reason to a terminal associated with the software.
Optionally, the locating the abnormal reason of the software running abnormality according to the software abnormality log includes:
determining a program calling relation of the software according to the software exception log;
analyzing the business logic information corresponding to each program in the software recorded in the software exception log to obtain the variable value output by each program in the software;
analyzing the hardware state information recorded in the software exception log to determine the abnormal hardware;
and determining the abnormal reason of the abnormal software operation according to the program calling relation of the software, the variable value output by each program in the software and the abnormal hardware.
Optionally, the determining, according to the software exception log, a program call relationship of the software includes:
and determining the program calling relation of the software according to the stack and stack information of the software program, the register information related to the software and the executable file of the software recorded in the software exception log.
Optionally, the method further includes:
determining the abnormal position of an abnormal program in the software according to the program calling relationship of the software, the variable value output by each program in the software and the abnormal hardware;
and pushing the abnormal position to a terminal associated with the software.
Optionally, the method further includes:
searching an abnormal repairing mode corresponding to the received information recorded by the software abnormal log according to the corresponding relation between the preset information recorded by the software abnormal log and the abnormal repairing mode;
and pushing the abnormal repairing mode to a terminal associated with the software.
Optionally, the software-related terminal is determined by:
searching a user identifier corresponding to the abnormal program of the software according to the corresponding relation between a preset program and the user identifier;
and determining the terminal associated with the determined user identifier as the terminal associated with the software.
According to a second aspect of the present application, there is provided a software abnormality diagnosis apparatus, which is applied to a server and includes:
the receiving unit is used for receiving a software exception log sent by the shooting equipment when the software running locally is abnormal;
the determining unit is used for positioning the abnormal reason of the software running abnormity according to the software abnormity log;
and the output unit is used for outputting the abnormal reason to a terminal associated with the software.
Optionally, the determining unit is specifically configured to determine a program call relationship of the software according to the software exception log; analyzing the business logic information corresponding to each program in the software recorded in the software exception log to obtain the variable value output by each program in the software; analyzing the hardware state information recorded in the software exception log to determine the abnormal hardware; and determining the abnormal reason of the abnormal software operation according to the program calling relation of the software, the variable value output by each program in the software and the abnormal hardware.
Optionally, the determining unit is configured to determine a program call relationship of the software according to the file and stack information of the software program, the register information associated with the software, and the executable file of the software recorded in the software exception log.
Optionally, the determining unit is further configured to determine an exception position where an exception program in the software is located according to a program calling relationship of the software, variable values output by programs in the software, and the exception hardware;
the output unit is further used for pushing the abnormal position to a terminal associated with the software.
Optionally, the apparatus further comprises:
the searching unit is used for searching an abnormal repairing mode corresponding to the received information recorded by the software abnormal log according to the corresponding relation between the preset information recorded by the software abnormal log and the abnormal repairing mode;
and the output unit is used for pushing the abnormal repairing mode to a terminal associated with the software.
Optionally, the software-related terminal is determined by:
searching a user identifier corresponding to the abnormal program of the software according to the corresponding relation between a preset program and the user identifier;
and determining the terminal associated with the determined user identifier as the terminal associated with the software.
According to a third aspect of the application, there is provided a server comprising a processor and a machine-readable storage medium storing machine-executable instructions executable by the processor, the processor being caused by the machine-executable instructions to perform the method of any one of claims 1 to 6.
According to a fourth aspect of the present application, there is provided a machine-readable storage medium having stored thereon machine-executable instructions that, when invoked and executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.
According to a fifth aspect of the present application, there is provided a software abnormality diagnosis system, including an image pickup apparatus and a server;
the camera device is used for generating a software exception log when detecting that local software is abnormal and sending the software exception log to a server;
the server is used for receiving a software exception log sent by the camera equipment when the software running locally is abnormal; positioning an abnormal reason of the software running abnormity according to the software abnormal log; and outputting the abnormal reason to a terminal associated with the software.
According to the method and the device, the abnormal reason of the abnormal operation of the software of the camera equipment can be located based on the software abnormal log sent by the camera equipment, and the abnormal reason is pushed to the terminal of the corresponding developer, so that the efficiency and the accuracy of software diagnosis of the camera equipment are greatly improved.
Drawings
FIG. 1 is a software anomaly diagnosis system shown in an exemplary embodiment of the present application;
FIG. 2 is a schematic diagram illustrating an overall scheme for software anomaly diagnosis according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart illustrating operation of a camera terminal system in a video surveillance system according to an exemplary embodiment of the present application;
FIG. 4 is a flowchart illustrating operation of a server subsystem in a video surveillance system according to an exemplary embodiment of the present application;
FIG. 5 illustrates a software anomaly diagnosis method according to an exemplary embodiment of the present application;
FIG. 6 illustrates another software anomaly diagnosis method according to an exemplary embodiment of the present application;
FIG. 7 is a diagram illustrating a hardware architecture of a server in which a software anomaly diagnosis apparatus is located according to an exemplary embodiment of the present application;
fig. 8 illustrates a software abnormality diagnosis apparatus according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", 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 associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The application aims to provide a software abnormity diagnosis method, in the method, camera equipment can automatically generate a software abnormity log after detecting the running abnormity of the software of the camera equipment, and the software abnormity log is reported to a server side. The server side can analyze the software exception log, generate a software exception reason, an exception position where the exception program is located and an exception repair mode, and push the software exception log, the exception position, the exception reason and the exception repair mode to a terminal corresponding to the exception program.
On one hand, the method and the device can realize the automatic software abnormity repair process from the automatic discovery of software abnormity and the generation of a software abnormity log to the automatic diagnosis of the reason, the abnormal position and the abnormity repair mode, and push the result to the corresponding developer for abnormity repair, so that the software diagnosis and repair efficiency of the camera equipment is greatly improved.
On the other hand, when software abnormity diagnosis is carried out, the software abnormity diagnosis method provided by the application adopts a mode of combining algorithm analysis and abnormity information database matching to carry out software abnormity diagnosis, and compared with manual diagnosis, the accuracy of software abnormity diagnosis is greatly improved, and the efficiency of software abnormity diagnosis is also greatly improved.
In a third aspect, the software exception log includes information of multiple dimensions to describe the software exception, for example, the software exception log includes bottom layer operation information of the software program, service logic information corresponding to the software program, and hardware state information to describe the software exception, so that the description of the software exception is more accurate.
Before describing the software diagnostic method provided by the present application in detail, a software anomaly diagnostic system to which the software diagnostic method is applied will be described first.
Referring to fig. 1, fig. 1 illustrates a software anomaly diagnosis system according to an exemplary embodiment of the present application. The software abnormity diagnosis system can comprise a camera device and a server.
The image capturing apparatus may include a camera or the like. The camera equipment not only has the traditional functions of collecting images and the like, but also has more intelligent functions, such as image feature extraction and the like. This makes the software installed in the image pickup apparatus more complicated.
Generally, the software loaded on the image pickup apparatus may include at least system software and algorithm software. The system software plays a role in ensuring normal operation of the camera equipment algorithm software and the like, and data transmission and other system functions. The algorithm software is mainly used for realizing some intelligent functions, such as image feature extraction and the like through the algorithm software.
The camera device can perform algorithm analysis processing on image data captured by the camera, and then sends the image data and an algorithm processing result to the server.
For a server, the server may be a server, or a server cluster formed by a group of servers. The server can further analyze the data sent by the camera equipment and the algorithm processing result, and the like, so as to provide an analysis result for the user.
For example, the server may perform further processing such as image recognition on the image features extracted by the image capturing apparatus, and output the recognition result to the user.
Of course, the above description of the image pickup apparatus and the server functions is merely an exemplary illustration, and is not particularly limited thereto.
The working principle of the software anomaly diagnosis system provided by the application is described below with reference to fig. 2.
The software abnormity diagnosis system mainly comprises two software subsystems, namely an intelligent video monitoring camera terminal subsystem and a server terminal system. The intelligent video monitoring camera terminal subsystem is installed on the camera equipment and comprises a detection module and a transmission module. The detection module is mainly responsible for monitoring the running states of software such as an algorithm and the like, and once software abnormity is detected, a software abnormity log is generated; the transmission module is mainly responsible for sending the software exception log to the server side after the software exception log is generated. The server terminal system is installed on the server and can comprise a fault diagnosis module and a diagnosis result pushing module. The fault diagnosis module is mainly responsible for analyzing the fault log and generating a fault diagnosis result; the diagnosis result pushing module is mainly responsible for pushing the core information to corresponding developers after the fault diagnosis result is generated.
For the camera subsystem, the workflow can be seen in fig. 3, and the image capturing apparatus receives data such as input video and audio. Software on the camera device acquires input video or image information and then executes an algorithm program, wherein the program includes but is not limited to processing such as detection and identification of information including the input video, input video frames and audio, and processing of data streams. When the software execution is abnormal, the detection system monitors the abnormal information of the software and stops the running of the software. The detection module collects software exception information from the software and system information and generates a software exception log. The detection module stores all collected software exception information in a storage unit, which can be a certain designated path under the file system. And the transmission module of the camera subsystem sends the software exception log to a server.
For the server terminal system, the working process can be shown in fig. 4, and after the server receives the software exception log sent by the transmission module of the intelligent video surveillance camera subsystem, the server starts the exception information diagnosis module. And the abnormal information diagnosis module searches a record matched with the current abnormal information to be analyzed from the abnormal information database. The abnormal information diagnosis module determines the abnormal position and the abnormal cause of the software according to the software abnormal log, finally gives an abnormal repair suggestion by combining the repair mode contained in the record provided by the abnormal information database, and adds the abnormal information to the abnormal information database; the diagnostic result pushing module is combined with a module corresponding to the abnormal position in the diagnostic result, and a terminal or camera equipment of a responsible person corresponding to the module is found out through the associated information database; the diagnosis result pushing module pushes the abnormal information, the abnormal diagnosis result and other information to relevant responsible personnel through a mail, a short message and other modes, and the relevant personnel can give a quick response.
It should be noted that, in the present application, data transmission between the image capturing apparatus and the server may be implemented through a wired or wireless network. The image pickup apparatus platform of the present application includes, but is not limited to, an ARM (a low power consumption and low cost microprocessor), an MPLS (a low power consumption and low cost microprocessor), and the like, and the image pickup apparatus system includes, but is not limited to, a Linux (an operating system) system. The server side platform and system are not particularly limited.
The software abnormality diagnosis method provided by the present application is explained in detail below.
Referring to fig. 5, fig. 5 illustrates a software anomaly diagnosis method according to an exemplary embodiment of the present application. The method may include the following process.
Step 501: when detecting that the locally operated software is abnormal, the camera device generates a software abnormal log and sends the software abnormal log to the server.
The software exception log is used for describing software exceptions and can include multiple dimensions. For example, the software exception log may include information about the underlying operation of a software program, business logic information corresponding to each program of the software, and hardware status information related to the software when the software is abnormal.
The bottom-layer operation information of the software program may include file and stack information, register information, executable file, and the like of the software program.
The multidimensional software exception log is used for describing software exceptions, so that the granularity of software exception description is more detailed and more accurate.
Of course, the above description is only an exemplary description of the software exception log, and the software exception log is not specifically limited.
In the embodiment of the present application, in general, during software execution, an error code or the like indicating a software error is generated when an abnormality occurs in software, and when the image pickup apparatus detects the error code, it can be determined that the native software is abnormal, and the image pickup apparatus can stop execution of the software.
In addition, the image pickup apparatus may also collect data in the case of software abnormality, for example, the image pickup apparatus may collect stack and stack information of a software program in the case of software abnormality, register information, an executable file, service logic information corresponding to each program of the software, hardware state information, and the like, then generate a software abnormality log including these information, and send the software abnormality log to the server.
Step 502: and the server receives a software exception log sent by the camera equipment when the locally running software is abnormal.
Step 503: and the server locates the abnormal reason of the abnormal software operation according to the software abnormal log.
When the method is implemented, 1) the server side can determine the program calling relation of the software according to the software exception log;
for example, assume that the program code of the software exception appears in program 3, and the call relationship for program 3 is program 1 calling program 2, and program 2 calling program 3.
The server can determine the calling relation between the program 1 and the calling program 2, the program 2 and the calling program 3 according to the stack and stack information, the register information and the executable file of the program when the software is abnormal.
In an optional implementation manner, the server may determine the program call relationship when the software is abnormal according to the file and stack information of the program, the register information, and the program bottom layer running state information such as the executable file recorded in the software exception log.
2) The service end can analyze the business logic information corresponding to each program in the software exception log to obtain the variable value output by each program when the software is abnormal.
In general, a software program may be composed of a plurality of programs, each of which corresponds to one or more business logics, and combining the business logics of each of the programs may form a function corresponding to the software program.
For example, the software program implements a function to count the number of people in an image. Assume that the function of people counting in the image is realized by three steps. The three steps are respectively: extracting image features, recognizing human faces in the images and counting the number of the recognized human faces.
It is assumed that the software program is composed of three programs, program 1, program 2, and program 3. The business logic corresponding to the program 1 is image feature extraction, the business logic corresponding to the program 2 is face recognition, and the business logic corresponding to the program 3 is the number of the faces recognized by statistics.
And the server determines the variable value of the business logic output corresponding to each program aiming at each program according to the business logic information corresponding to each program.
Taking the program 3 as an example, after the program 3 outputs a variable value (assuming that the variable value is 1), the server determines that the number of the statistically recognized faces is 1.
3) The server side can analyze the hardware state information to determine abnormal hardware.
When the software exception is implemented, the server side can analyze the state information of the hardware related to the software to determine whether the software exception is caused by the hardware exception or not.
4) The server can determine the abnormal reason of the abnormal software operation according to the program calling relationship of the software, the variable value output by each program and the abnormal hardware obtained by the analysis.
Step 504: and the server outputs the abnormal reason to a terminal associated with the software.
The terminal may be an image pickup device, a developer terminal responsible for the software development, or a user terminal of the software abnormality diagnosis system, and the terminal is only described as an example and is not specifically limited.
For example, the server may search for a user identifier corresponding to an abnormal program of the software according to a preset correspondence between the program and the user identifier, and push the abnormal reason to a terminal associated with the user identifier.
For another example, the server may push the reason for the abnormality to the image capturing apparatus.
As can be seen from the above description, the server according to the present application can locate the abnormal reason of the abnormal operation of the software of the image capturing device based on the software abnormal log sent by the image capturing device, and push the abnormal reason to the corresponding terminal, so that the efficiency and accuracy of the software diagnosis of the image capturing device are greatly improved.
Referring to fig. 6, fig. 6 illustrates another software anomaly diagnosis method according to an exemplary embodiment of the present application, which may include the following steps.
Step 601: when detecting that the local software operates abnormally, the image pickup device generates a software abnormity log aiming at the software abnormity.
The software exception log is used for describing software exceptions and can include multiple dimensions. For example, the software exception log may include information about the underlying operation of a software program, business logic information corresponding to each program of the software, and hardware status information related to the software when the software is abnormal.
The bottom-layer operation information of the software program may include file and stack information, register information, executable file, and the like of the software program.
The multidimensional software exception log is used for describing software exceptions, so that the granularity of software exception description is more detailed and more accurate.
Of course, the above description is only an exemplary description of the software exception log, and the software exception log is not specifically limited.
In the embodiment of the present application, in general, during software execution, an error code or the like indicating a software error is generated when an abnormality occurs in software, and when the image pickup apparatus detects the error code, it can be determined that the native software is abnormal, and the image pickup apparatus can stop execution of the software.
Furthermore, the image pickup apparatus may also collect data at the time of a software abnormality, such as stack and stack information of a software program at the time of a software abnormality, register information, an executable file, business logic information corresponding to each program of software, hardware state information, and the like, and then generate a software abnormality log containing these information.
Step 602: and the camera equipment sends the generated software exception log to a server.
When the software exception log is realized, the camera shooting equipment can pack the software exception log, adds a timestamp when the exception occurs, equipment information of the camera shooting equipment, tags such as geographical position information of the camera shooting equipment and the like, and sends the tags to the server.
Step 603: and the server receives the software exception log sent by the camera equipment.
Step 604: and the server side generates an abnormal reason, an abnormal position and an abnormal repairing mode aiming at the software running abnormity according to the software abnormal log.
The exception location may be a location of an exception program in the exception software.
The above-mentioned abnormal cause may refer to a cause causing the software abnormality, for example, when a mathematical operation is performed in a software program, 0 is used as a divisor, and so on. As another example, an illegal device has accessed the device, and so on.
The exception causes may include a divide-by-zero error, an illegal address access, a hardware state error, and an error exit, among others.
In the embodiment of the present application, the server generates an exception location, an exception cause, and an exception recovery method for the software exception according to the software exception log through steps 311 to 312.
Step 611: the server side can analyze the software exception log to obtain the exception position and exception reason of the software exception in the software program.
In implementation, 1) the server may determine the program call relationship when the software is abnormal according to the stack and stack information, the register information, and the program bottom layer running state information such as the executable file of the program in the software exception log.
For example, assume that the program code of the software exception appears in program 3, and the call relationship for program 3 is program 1 calling program 2, and program 2 calling program 3.
The server side can determine the calling program 2 of the program 1 and the calling program 3 of the program 2 according to the stack and stack information, the register information and the executable file of the program when the software is abnormal, and the program calling relation of the programs.
2) The service end can analyze according to the service logic information in the software exception log to obtain the variable value output by the program when the software is abnormal.
In general, a software program may be composed of a plurality of programs, each of which corresponds to one or more business logics, and combining the business logics of each of the programs may form a function corresponding to the software program.
For example, the software program implements a function to count the number of people in an image. Assume that the function of people counting in the image is realized by three steps. The three steps are respectively: extracting image features, recognizing human faces in the images and counting the number of the recognized human faces.
It is assumed that the software program is composed of three programs, program 1, program 2, and program 3. The business logic corresponding to the program 1 is image feature extraction, the business logic corresponding to the program 2 is face recognition, and the business logic corresponding to the program 3 is the number of the faces recognized by statistics.
And the server determines the variable value of the business logic output corresponding to each program aiming at each program according to the business logic information corresponding to each program.
Taking the program 3 as an example, after the program 3 outputs a variable value (assuming that the variable value is 1), the server determines that the number of the statistically recognized faces is 1.
3) The server side can analyze the hardware state information to determine abnormal hardware.
When the software exception is implemented, the server side can analyze the state information of the hardware related to the software to determine whether the software exception is caused by the hardware exception or not.
4) The server can determine the abnormal position and abnormal reason of the software abnormality in the software program according to the calling relationship of the program in the software abnormality, the variable value output by each program and the abnormal hardware obtained by the analysis.
Step 612: and the server side searches the abnormal repairing mode corresponding to the information recorded by the software abnormal log according to the corresponding relation between the preset information recorded by the software abnormal log and the abnormal repairing mode.
In the embodiment of the application, an abnormal information database is arranged, and the abnormal information database records the corresponding relation between the information recorded by the software abnormal log and the repairing mode. For example, the correspondence relationship may be as shown in table 1.
TABLE 1
Of course, the software exception log may also include other information, which is only an exemplary illustration here, and the software exception log is not specifically limited.
In order to improve the accuracy of identifying the abnormality repairing method, the abnormality information database is configured by a large number of data samples. In other words, the abnormal information database records the corresponding relationship between information recorded by a large number of software abnormal logs and an abnormal repairing mode. This makes it possible to match an abnormality repair manner corresponding to the information of the software abnormality log record when matching the information of the software abnormality log record received from the image capturing apparatus using the abnormality information database.
It should be noted that other information, such as an abnormal reason corresponding to the information recorded in the software abnormal log, may also be recorded in the abnormal information database, and the abnormal reason is consistent with the above located abnormal reason. Here, the correspondence between the information recorded in the software exception log and the exception recovery method is exemplarily described, and the correspondence is not particularly limited.
In this embodiment of the application, the server may search, in the correspondence between the information recorded in the software exception log recorded in the exception information database and the exception recovery method, for an exception recovery method that matches the information recorded in the software exception log sent by the image capturing apparatus.
If the abnormal repairing mode matched with the information recorded in the software abnormal log sent by the camera shooting equipment is not found in the abnormal information database, the software abnormal log can be pushed to a user corresponding to the abnormal program, the user analyzes the software abnormal log to determine the abnormal repairing mode, and the determined abnormal repairing mode and the software abnormal log are uploaded to the abnormal information database so as to update the abnormal information database.
Step 605: and the server side pushes the software exception log, the exception reason, the exception position and the exception repair mode to a terminal related to the software.
In an optional implementation manner, in the embodiment of the present application, an association database is further configured, where correspondence between multiple programs and developer (user) identifiers responsible for the software programs is stored in the association database.
And the server side searches the user identification corresponding to the abnormal program in the software according to the corresponding relation between the software program and the user identification in the associated database. Then, the server can push the software exception log, the exception reason, the exception location and the exception repair mode to the terminal corresponding to the user identifier.
In another optional implementation manner, the server may further push the software exception log, the exception reason, the exception location, and the exception recovery manner to terminals such as the image capturing device. The server can also send the software abnormal log, the abnormal position and the abnormal repairing mode to the terminal corresponding to the searched user identifier in the modes of short messages, voice calls, e-mails, instant messages and the like. Here, the push method of the server is only described as an example, and is not particularly limited.
The application aims to provide a software abnormity diagnosis method, in the method, camera equipment can automatically generate a software abnormity log after detecting the running abnormity of the software of the camera equipment, and the software abnormity log is reported to a server side. The server side can analyze the software exception log, generate a software exception reason, an exception position where the exception program is located and an exception repair mode, and push the software exception log, the exception position, the exception reason and the exception repair mode to a terminal corresponding to the exception program.
On one hand, the method and the device can realize the automatic software abnormity repair process from the automatic discovery of software abnormity and the generation of a software abnormity log to the automatic diagnosis of the reason, the abnormal position and the abnormity repair mode, and push the result to the corresponding developer for abnormity repair, so that the software diagnosis and repair efficiency of the camera equipment is greatly improved.
On the other hand, when software abnormity diagnosis is carried out, the software abnormity diagnosis method provided by the application adopts a mode of combining algorithm analysis and abnormity information database matching to carry out software abnormity diagnosis, and compared with manual diagnosis, the accuracy of software abnormity diagnosis is greatly improved, and the efficiency of software abnormity diagnosis is also greatly improved.
In a third aspect, the software exception log includes information of multiple dimensions to describe the software exception, for example, the software exception log includes bottom layer operation information of the software program, service logic information corresponding to the software program, and hardware state information to describe the software exception, so that the description of the software exception is more accurate.
Referring to fig. 7, the present application further provides a hardware architecture diagram of a server where the software abnormality diagnosis apparatus is located, where the server includes: a communication interface 701, a processor 702, a machine-readable storage medium 703, and a bus 704; the communication interface 701, the processor 702, and the machine-readable storage medium 703 are in communication with one another via a bus 704. The processor 702 may perform the software anomaly diagnostic methods described above by reading and executing machine-executable instructions in the machine-readable storage medium 703 corresponding to the software anomaly diagnostic control logic.
The machine-readable storage medium 703 referred to herein may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium 703 may include at least one of the following storage media: volatile memory, non-volatile memory, other types of storage media. The volatile Memory may be a Random Access Memory (RAM), and the nonvolatile Memory may be a flash Memory, a storage drive (e.g., a hard disk drive), a solid state disk, and a storage disk (e.g., a compact disk, a DVD).
Referring to fig. 8, fig. 8 illustrates a software abnormality diagnosing apparatus according to an exemplary embodiment of the present application; the apparatus is applicable to a server, and includes the following elements.
A receiving unit 801 configured to receive a software exception log sent when software that is locally run by the image capturing apparatus is abnormal;
a determining unit 802, configured to locate an abnormal reason of the software running abnormality according to the software abnormal log;
an output unit 803, configured to output the cause of the abnormality to a terminal associated with the software.
Optionally, the determining unit 802 is specifically configured to determine a program call relationship of the software according to the software exception log; analyzing the business logic information corresponding to each program in the software recorded in the software exception log to obtain the variable value output by each program in the software; analyzing the hardware state information recorded in the software exception log to determine the abnormal hardware; and determining the abnormal reason of the abnormal software operation according to the program calling relation of the software, the variable value output by each program in the software and the abnormal hardware.
Optionally, the determining unit 802 is configured to determine a program call relationship of the software according to the file and stack information of the software program, the register information associated with the software, and the executable file of the software, which are recorded in the software exception log.
Optionally, the determining unit 802 is further configured to determine an exception position where an exception program in the software is located according to a program calling relationship of the software, variable values output by programs in the software, and the exception hardware;
the output unit 803 is further configured to push the abnormal position to a terminal associated with the software.
Optionally, the apparatus further comprises:
the searching unit 804 is configured to search for an exception recovery manner corresponding to the received information recorded by the software exception log according to a correspondence between information recorded by a preset software exception log and an exception recovery manner;
the output unit 803 is configured to push the exception recovery manner to a terminal associated with the software.
Optionally, the software-related terminal is determined by:
searching a user identifier corresponding to the abnormal program of the software according to the corresponding relation between a preset program and the user identifier;
and determining the terminal associated with the determined user identifier as the terminal associated with the software.
In addition, the embodiment of the application also provides a software abnormity diagnosis system, which is characterized by comprising camera equipment and a server;
the camera device is used for generating a software exception log when detecting that locally-operated software is abnormal, and sending the software exception log to the server;
the server is used for receiving a software exception log sent by the camera equipment when the software running locally is abnormal; positioning an abnormal reason of the software running abnormity according to the software abnormal log; and outputting the abnormal reason to a terminal associated with the software.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (15)
1. The software abnormity diagnosis method is applied to a server and comprises the following steps:
receiving a software exception log sent by the camera equipment when the locally running software is abnormal;
positioning an abnormal reason of the software running abnormity according to the software abnormal log;
and outputting the abnormal reason to a terminal associated with the software.
2. The method according to claim 1, wherein the locating the abnormal cause of the software running abnormality according to the software abnormality log comprises:
determining a program calling relation of the software according to the software exception log;
analyzing the business logic information corresponding to each program in the software recorded in the software exception log to obtain the variable value output by each program in the software;
analyzing the hardware state information recorded in the software exception log to determine the abnormal hardware;
and determining the abnormal reason of the abnormal software operation according to the program calling relation of the software, the variable value output by each program in the software and the abnormal hardware.
3. The method of claim 2, wherein determining the program call relationship of the software from the software exception log comprises:
and determining the program calling relation of the software according to the stack and stack information of the software program, the register information related to the software and the executable file of the software recorded in the software exception log.
4. The method of claim 2, further comprising:
determining the abnormal position of an abnormal program in the software according to the program calling relationship of the software, the variable value output by each program in the software and the abnormal hardware;
and pushing the abnormal position to a terminal associated with the software.
5. The method of claim 1, further comprising:
searching an abnormal repairing mode corresponding to the received information recorded by the software abnormal log according to the corresponding relation between the preset information recorded by the software abnormal log and the abnormal repairing mode;
and pushing the abnormal repairing mode to a terminal associated with the software.
6. The method according to any of claims 1-5, wherein the software-associated terminal is determined by:
searching a user identifier corresponding to the abnormal program of the software according to the corresponding relation between a preset program and the user identifier;
and determining the terminal associated with the determined user identifier as the terminal associated with the software.
7. The software abnormity diagnosis device is applied to a server side and comprises the following components:
the receiving unit is used for receiving a software exception log sent by the shooting equipment when the software running locally is abnormal;
the determining unit is used for positioning the abnormal reason of the software running abnormity according to the software abnormity log;
and the output unit is used for outputting the abnormal reason to a terminal associated with the software.
8. The apparatus according to claim 7, wherein the determining unit is specifically configured to determine a program call relationship of the software according to the software exception log; analyzing the business logic information corresponding to each program in the software recorded in the software exception log to obtain the variable value output by each program in the software; analyzing the hardware state information recorded in the software exception log to determine the abnormal hardware; and determining the abnormal reason of the abnormal software operation according to the program calling relation of the software, the variable value output by each program in the software and the abnormal hardware.
9. The apparatus of claim 8, wherein the determining unit is configured to determine the program invocation relationship of the software according to the stack and stack information of the software program, the register information associated with the software, and the executable file of the software recorded in the software exception log.
10. The apparatus according to claim 8, wherein the determining unit is further configured to determine an exception location where an exception program in the software is located according to a program call relationship of the software, a variable value output by each program in the software, and the exception hardware;
the output unit is further used for pushing the abnormal position to a terminal associated with the software.
11. The apparatus of claim 7, further comprising:
the searching unit is used for searching an abnormal repairing mode corresponding to the received information recorded by the software abnormal log according to the corresponding relation between the preset information recorded by the software abnormal log and the abnormal repairing mode;
and the output unit is used for pushing the abnormal repairing mode to a terminal associated with the software.
12. The apparatus according to any of claims 7-11, wherein the software-associated terminal is determined by:
searching a user identifier corresponding to the abnormal program of the software according to the corresponding relation between a preset program and the user identifier;
and determining the terminal associated with the determined user identifier as the terminal associated with the software.
13. A server comprising a processor and a machine-readable storage medium storing machine-executable instructions executable by the processor, the processor being caused by the machine-executable instructions to perform the method of any one of claims 1 to 6.
14. A machine-readable storage medium having stored thereon machine-executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any of claims 1 to 6.
15. The software abnormity diagnosis system is characterized by comprising a camera device and a server;
the camera device is used for generating a software exception log when detecting that locally-operated software is abnormal, and sending the software exception log to the server;
the server is used for receiving a software exception log sent by the camera equipment when the software running locally is abnormal; positioning an abnormal reason of the software running abnormity according to the software abnormal log; and outputting the abnormal reason to a terminal associated with the software.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274059A (en) * | 2020-01-21 | 2020-06-12 | 浙江大华技术股份有限公司 | Software exception handling method and device for slave equipment |
| CN112217691A (en) * | 2020-02-19 | 2021-01-12 | 杜义平 | Network diagnosis processing method and device based on cloud platform |
| CN116527870A (en) * | 2023-04-13 | 2023-08-01 | 浙江大华技术股份有限公司 | Camera control method, device, system, electronic device and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1414711A (en) * | 2002-05-23 | 2003-04-30 | 华为技术有限公司 | Fault location method of program mode |
| CN102521042A (en) * | 2011-12-16 | 2012-06-27 | 中船重工（武汉）凌久电子有限责任公司 | Quick text switching method for DSP (digital signal processor) based on Harvard structure |
| US8583779B2 (en) * | 2006-11-15 | 2013-11-12 | Cisco Technology, Inc. | Root cause analysis approach with candidate elimination using network virtualization |
| CN103701926A (en) * | 2013-12-31 | 2014-04-02 | 小米科技有限责任公司 | Method, device and system for obtaining fault reason information |
| CN104579717A (en) * | 2013-10-09 | 2015-04-29 | 中国移动通信集团江苏有限公司 | Method and device for locating fault of DCN |
| CN105093069A (en) * | 2015-09-22 | 2015-11-25 | 国网冀北电力有限公司唐山供电公司 | Power distribution network rapid operation and maintenance and first-aid repair service realization method on Android platform |
| CN105955862A (en) * | 2016-04-15 | 2016-09-21 | 乐视控股（北京）有限公司 | Abnormal problem monitoring positioning method and device |
| CN107451045A (en) * | 2016-05-31 | 2017-12-08 | 北京信威通信技术股份有限公司 | A kind of method and device of abnormal information positioning |
-
2018
- 2018-06-19 CN CN201810631972.9A patent/CN110620698A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1414711A (en) * | 2002-05-23 | 2003-04-30 | 华为技术有限公司 | Fault location method of program mode |
| US8583779B2 (en) * | 2006-11-15 | 2013-11-12 | Cisco Technology, Inc. | Root cause analysis approach with candidate elimination using network virtualization |
| CN102521042A (en) * | 2011-12-16 | 2012-06-27 | 中船重工（武汉）凌久电子有限责任公司 | Quick text switching method for DSP (digital signal processor) based on Harvard structure |
| CN104579717A (en) * | 2013-10-09 | 2015-04-29 | 中国移动通信集团江苏有限公司 | Method and device for locating fault of DCN |
| CN103701926A (en) * | 2013-12-31 | 2014-04-02 | 小米科技有限责任公司 | Method, device and system for obtaining fault reason information |
| CN105093069A (en) * | 2015-09-22 | 2015-11-25 | 国网冀北电力有限公司唐山供电公司 | Power distribution network rapid operation and maintenance and first-aid repair service realization method on Android platform |
| CN105955862A (en) * | 2016-04-15 | 2016-09-21 | 乐视控股（北京）有限公司 | Abnormal problem monitoring positioning method and device |
| CN107451045A (en) * | 2016-05-31 | 2017-12-08 | 北京信威通信技术股份有限公司 | A kind of method and device of abnormal information positioning |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274059A (en) * | 2020-01-21 | 2020-06-12 | 浙江大华技术股份有限公司 | Software exception handling method and device for slave equipment |
| CN111274059B (en) * | 2020-01-21 | 2023-10-10 | 浙江大华技术股份有限公司 | Software exception handling method and device of slave device |
| CN112217691A (en) * | 2020-02-19 | 2021-01-12 | 杜义平 | Network diagnosis processing method and device based on cloud platform |
| CN116527870A (en) * | 2023-04-13 | 2023-08-01 | 浙江大华技术股份有限公司 | Camera control method, device, system, electronic device and storage medium |
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