Equipment state detection method and device, equipment, storage medium and program product

Abstract

The invention discloses an equipment state detection method and device, equipment, a storage medium and a program product, and the equipment state detection method comprises the steps: obtaining log data of the equipment; determining an abnormal parameter interval of the log data according to the system version information of the log data; and determining the equipment state of the equipment based on the abnormal parameter interval and the discrete parameters of the log data.

Description

Technical Field

[0001] The present application relates to the field of anomaly detection technology, and in particular to a device status detection method, apparatus, device, storage medium, and program product. Background Art

[0002] In public places like large shopping malls and supermarkets, digital signage is often used for advertising, navigation, and entertainment. In digital signage applications, over-the-air (OTA) technology can be used to remotely update data and software, fixing vulnerabilities in a timely manner, reducing on-site maintenance requirements, and lowering operating costs.

[0003] To ensure the stability of over-the-air downloading technology for a large number of digital signage devices on the market, preset rules and thresholds are usually set to detect anomalies. However, the setting of preset rules and thresholds relies on experience and has poor adaptability to the system environment. Summary of the Invention

[0004] The main purpose of this application is to provide a device status detection method, apparatus, device, storage medium and program product, aiming to solve the technical problem that the existing over-the-air download technology detection solution relies on experience and has poor adaptability.

[0005] To achieve the above objectives, the present application proposes a device status detection method, which includes:

[0006] Get the device's log data;

[0007] Determine the abnormal parameter range of the log data based on the system version information of the log data;

[0008] The device status of the device is determined based on the abnormal parameter interval and the discrete parameters of the log data.

[0009] In addition, to achieve the above-mentioned purpose, the present application also proposes a device status detection device, which includes:

[0010] Log acquisition module, used to obtain log data of the device;

[0011] A log analysis module is used to determine the abnormal parameter range of the log data based on the system version information of the log data;

[0012] The status detection module is used to determine the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a device status detection device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the device status detection method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the device status detection method as described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the device status detection method as described above are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of the first embodiment of the device status detection method of the present application is provided;

[0019] Figure 2 A flow chart of the second embodiment of the device status detection method of this application is provided;

[0020] Figure 3 This is a schematic diagram of the request log data processing flow in one implementation of the device status detection method of this application;

[0021] Figure 4 A flowchart of the third embodiment of the device status detection method of this application is provided;

[0022] Figure 5 This is a flow chart of determining the device status in one implementation of the device status detection method of the present application;

[0023] Figure 6 This is a schematic diagram of the module structure of the device status detection device according to an embodiment of the present application;

[0024] Figure 7Schematic diagram of the device structure of the hardware operating environment involved in the device status detection method in the embodiment of the present application.

[0025] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0027] In order to better understand the technical solution of this application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0028] The main solution of the embodiment of the present application is: obtaining the log data of the device; determining the abnormal parameter interval of the log data according to the system version information of the log data; and determining the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

[0029] This application provides a solution that, based on the statistical principles of mean and standard deviation, detects abnormalities in over-the-air download technology functionality through inductive and statistical analysis. As the number of digital signage devices in the market increases, a large amount of sample data is available for inductive and statistical analysis, thereby continuously improving the accuracy and reliability of anomaly detection results and enhancing the sustainability of the method. Furthermore, the method of this application can perform separate inductive and statistical analysis for different versions of digital signage, ensuring that the reliability of the detection results is not affected by software version iterations, thus providing strong adaptability.

[0030] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a computer, cloud server, etc., or an electronic device, virtual device, etc. that can implement the above functions. The following uses a device status detection device (hereinafter referred to as the detection device) as an example to illustrate this embodiment and the following embodiments.

[0031] Based on this, the present invention provides a device status detection method, referring to Figure 1 , Figure 1 A flow chart illustrating the first embodiment of the device status detection method of this application.

[0032] In this embodiment, the device status detection method includes steps S10 to S30:

[0033] Step S10: Obtain log data of the device.

[0034] It should be noted that the aforementioned device can be a functional device installed on a digital signage to implement the digital signage's over-the-air (OTA) technology, such as an electronic chip or service terminal. By connecting a detection device to such a device, the detection device can obtain log data from the device, thereby enabling monitoring of the device's OTA functionality.

[0035] It is understood that, in order to facilitate monitoring, maintenance, and management, the above-mentioned devices can automatically record log data such as events, errors, warnings, message sending and receiving, system version, and version update records during operation, and generate log files based on this log data for storage. The detection device can obtain the device's log data by obtaining the log files stored in the device and parsing the log files.

[0036] In a specific implementation, the detection device can be connected to a functional device in the digital signage for implementing the over-the-air download technology, and obtain the log data of the device by obtaining the log file saved by the device.

[0037] Step S20: determining an abnormal parameter interval of the log data according to the system version information of the log data.

[0038] It is understood that by parsing the log data, we can obtain information such as request time, system version, response result, HTTP status code, etc. By analyzing this information, we can determine whether there is any anomaly in the device's over-the-air download function, that is, determine the device's operating status.

[0039] In some implementations of the embodiments of the present application, the abnormal parameter interval of the log data can be determined based on the system version information determined by the log data. Specifically, for different versions, the corresponding abnormal values may be different. In the embodiments of the present application, the system (i.e., software related to over-the-air download technology) version used by the device can be determined based on the system version information, and then the sample database corresponding to the version can be determined. Through the sample database, the abnormal parameter interval corresponding to the device can be determined.

[0040] In a specific implementation, the detection device can parse the log data to obtain the system version information of the device, and determine the abnormal parameter range of the log data according to the system version information.

[0041] Step S30: determining the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

[0042] It's important to note that a discrete parameter is a statistical parameter used in statistics to describe the degree of dispersion or dispersion in data distribution. Using discrete parameters, we can quantify the degree of dispersion in log data. In practical applications, parameters such as range, standard deviation, variance, or coefficient of dispersion can be used as discrete parameters. Based on the abnormal parameter interval and the discrete parameters of the log data, the device status can be determined.

[0043] In some implementations of the embodiments of the present application, the abnormal parameter interval may include a first parameter interval, a second parameter interval, and a third parameter interval. The first parameter interval is a parameter interval corresponding to a normal device state, the second parameter interval is a parameter interval corresponding to a possibly abnormal device state, and the third parameter interval is a parameter interval corresponding to an abnormal device state.

[0044] It can be understood that when the discrete parameter is within the first parameter range, the device is in a normal state, and the over-the-air download function of the device can be considered normal at this time; when the discrete parameter is within the second parameter range, the device is in a possibly abnormal state, and it can be considered that the over-the-air download function of the device may be abnormal, and the device needs to be monitored or maintained; when the discrete parameter is within the third parameter range, the device is in an abnormal state, and it can be considered that the over-the-air download function of the device is abnormal, and it needs to be maintained.

[0045] In a specific implementation, the monitoring device may determine the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

[0046] The present embodiment obtains device log data; determines the abnormal parameter range of the log data based on the system version information in the log data; and determines the device status of the device based on the abnormal parameter range and the discrete parameters of the log data. Because the analysis is performed by obtaining log data from the device, as the number of devices in the market gradually increases, a large amount of sample data can be provided for summary and statistics, thereby continuously improving the accuracy and reliability of detection.

[0047] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 and Figure 3 , Figure 2 This is a flow chart of the second embodiment of the device status detection method of this application. Figure 3 This is a schematic diagram of the request log data processing flow in one implementation of the device status detection method of this application.

[0048] like Figure 2 As shown, in the embodiment of the present application, the step of obtaining the log data of the device includes:

[0049] Step S11, obtaining the request log data of the device;

[0050] Step S12: performing rough data processing based on the request log data to obtain initial log information.

[0051] It should be noted that for functional devices implementing over-the-air (OTA) technology for digital signage, OTA request log data can be generated based on a preset period. Detection equipment can obtain this request log data and, based on this data, perform rough processing to determine initial log information such as request volume, request distribution, request density, request time, response results, HTTP status code, and system version.

[0052] Exemplarily, the above-mentioned device of the embodiment of the present application can create the request log data of the over-the-air download technology on a daily basis.

[0053] In some embodiments of the present application, the above-mentioned device can record all HTTP request and response data related to the over-the-air download technology on a daily basis, and obtain request log data by packaging information related to these requests and data.

[0054] In some implementation methods of the present application's embodiments, when performing rough processing, the format and structure (such as plain text format, JSON format etc.) of the request log data can be first determined, so that the required information in the log is accurately extracted. Further, based on the format of log data, log data can be parsed by modes such as regular expression, string segmentation, and then the format (such as CSV format, Excel format etc.) that is easy to analyze is obtained. By further counting and analyzing log data based on the format determined by analysis, the parameters such as the request amount, request distribution, request density, request time, response result, HTTP status code, system version of the request data can be determined.

[0055] In some implementations of the embodiments of the present application, data rough processing includes: log time extraction and system version extraction, and the initial log information includes: request time information and system version information;

[0056] The steps of roughly processing the request log data to obtain initial log information include:

[0057] Extract log time based on request log data to obtain request time information;

[0058] Extract the system version based on the request log data to obtain the system version information.

[0059] It is understood that during the use of digital signage, data requests can typically be sent and received with a management device (which can be a detection device or other device) to manage and modify the content displayed on the digital signage. The request time information can be information indicating the time corresponding to the data request for over-the-air technology, and the system version information can be information indicating the version of the system (i.e., software related to over-the-air technology) used by the device.

[0060] In some implementations of the embodiments of the present application, the location of the initial log information in the log entry can be determined by first traversing the request log data; specifically, the request log field can be obtained by performing text field identification on the request log data; the request log field can include a request time field and a system version field, the request time field can be used to record the request time information of the system, and the system version field can be used to record the system version information of the system. By finding the version field location information of the system version field and the time field location information of the request time field in the request log, and identifying and extracting the system version field and the request time field at that location, the request time information and the system version information can be obtained.

[0061] In some implementations of the embodiments of the present application, in order to determine the request time field and its corresponding time field information, as well as the system version field and its corresponding version field information, a pre-written information extraction rule can be obtained, and log time extraction and system version extraction can be performed according to the information extraction rule to obtain the request time information and system version information.

[0062] In some implementations of the embodiments of the present application, the above-mentioned information extraction rules may include log time extraction rules and system version extraction rules. For the pre-writing of log time extraction rules, the time format for saving logs can be determined first, and a time regular expression can be written based on the time format. When performing log time extraction, log time extraction can be performed based on the pre-written time regular expression. Similarly, for the pre-writing of system version extraction rules, the version format of the system version can be determined, and a version regular expression can be written based on the version format. When performing system version extraction, system version extraction can be performed based on the pre-written version regular expression.

[0063] For example, when the time format of the saved log is YYYY-MM-DD HH:MM:SS, a corresponding time regular expression can be written: (\d{4}-\d{2}-\d{2}\d{2}:\d{2}:\d{2}), thereby extracting the request time information in the request time field.

[0064] For example, when the version format of the saved log is System Version:Major.Minor.Patch, a corresponding version regular expression can be written: (\d+\.\d+\.\d+), thereby extracting the system version information in the system version field.

[0065] Step S13: Filter the requested log data based on the initial log information to obtain the log data of the device.

[0066] It should be noted that the request log data can be further processed based on the initial log data, thereby filtering the request log data and obtaining the required log data.

[0067] In some implementations of the embodiments of the present application, the step of filtering the request log data based on the initial log information to obtain the log data of the device includes:

[0068] Grouping the request log data at the same time based on the request time information to obtain a request log data group;

[0069] Extracting a text field from the request log data in the request log data group;

[0070] Calculate the similarity of the text fields of the request log data in the request log data group using a similarity algorithm to obtain the log field similarity;

[0071] Request log data whose log field similarity is greater than a preset similarity threshold is regarded as duplicate log data;

[0072] Deduplication is performed on the request log data based on the duplicate log data to obtain the log data of the device.

[0073] It should be noted that the request log data at the same time may refer to request log data within a preset time condition. In the embodiment of the present application, a day or other time period may be used as a cycle, and the preset request time condition is also within the same cycle, that is, the request log data within the same day may be divided into the same request log data group based on the request time information of the request log data.

[0074] In some implementations of the present application, since request log data can be screened in each cycle, request log data not in the current cycle can be treated as invalid data and deleted to reduce data redundancy and improve detection efficiency.

[0075] In some implementations of the embodiments of the present application, the above-mentioned preset similarity threshold can be determined according to the needs of actual applications, such as 90% or above 95%, etc., and the embodiments of the present application do not impose specific limitations on this.

[0076] It should be noted that duplicate log data can be deduplicated to obtain deduplicated device log data. Specifically, the deduplication operation can include: for a group of duplicate log data, the number of duplicate request log data in the group can be determined, and a quantity tag can be generated based on the number of request log data in the group. At the same time, the redundant request log data in the group can be deleted until only one piece remains, and the remaining request log data piece can be marked with the aforementioned quantity tag to reduce system redundancy and facilitate subsequent detection.

[0077] Reference Figure 3 In an embodiment of the present application, the request log data stored in the device can be obtained through data records; initial log information can be obtained by performing rough data processing on the request log data; whether there is duplicate log data in the request log data is determined based on the request time information; when duplicate log data exists, the duplicate log data is deleted.

[0078] The present embodiment obtains device request log data; performs rough data processing based on the request log data to obtain initial log information; and filters the request log data based on the initial log information to obtain device log data. Because the request log data is periodically processed, the stability detection capabilities of the over-the-air technology are improved, while also contributing to the continuous improvement of the accuracy and reliability of anomaly detection results.

[0079] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment and / or the second embodiment can be referred to the above introduction and will not be described in detail later. Figure 4 and Figure 5 , Figure 4 This is a flow chart of the third embodiment of the device status detection method of this application. Figure 5 This is a flow chart of determining the device status in one implementation of the device status detection method of the present application.

[0080] like Figure 4 As shown, in the embodiment of the present application, the step of determining the abnormal parameter interval of the log data according to the system version information of the log data includes:

[0081] Step S21, determining the system version used by the device based on the system version information in the log data;

[0082] Step S22: determining an abnormal parameter interval of the log data based on a sample database corresponding to the system version.

[0083] It should be noted that since different devices may use different system versions, in the embodiments of the present application, the system version information of the device can be determined by analyzing the OTA log data (request log data) of the digital signage, and thus the system version used by the device. Corresponding sample databases can be determined for different system versions.

[0084] In some implementations of the embodiments of the present application, when determining the system version, it is also possible to determine whether the device has a system upgrade plan. If so, the corresponding upgraded sample database N-0 can be imported; if not, the non-upgraded sample database N-1 is imported.

[0085] In some implementations of the embodiments of the present application, the system version of the embodiments of the present application may include version A, version B, version C, etc. The system version corresponding to the requested log data can be determined based on the system version information of the log data. For example, if the system version in use is version A, it can be determined whether there is an upgrade plan for the system version of the device. If so, the sample database corresponding to the upgraded version A is imported; if not, the sample database corresponding to the unupgraded version A is imported.

[0086] It is understandable that different sample databases may include log sample data corresponding to different versions. By performing standard deviation analysis on the log sample data, the database standard deviation corresponding to the sample database can be obtained.

[0087] In some implementations of the embodiments of the present application, for the above-mentioned database standard deviation, a deviation fluctuation range may correspond to it, and statistical calculations based on the deviation fluctuation range and the database standard deviation may be performed to obtain the abnormal parameter interval of the log data in the sample database. For example, when the deviation fluctuation range is ±P and the database standard deviation is Q, the abnormal parameter interval may be set to Q±P. That is, the step of determining the abnormal parameter interval of the log data based on the sample database corresponding to the system version includes: obtaining the log sample data in the sample database corresponding to the system version; calculating the database standard deviation of the log sample data in the sample database; and obtaining the abnormal parameter interval of the log data based on the database standard deviation and the deviation fluctuation range.

[0088] In some implementations of the embodiments of the present application, since the abnormal parameter interval may include a first parameter interval, a second parameter interval, and a third parameter interval, three corresponding deviation fluctuation ranges may be set. The deviation fluctuation ranges may be determined based on the distribution of the data request volume or in other ways, and the embodiments of the present application are not limited thereto.

[0089] It should be noted that the above-mentioned deviation fluctuation range can be obtained by evaluating the log sample data in the sample database through a machine learning model, or it can be set based on prior knowledge. The embodiments of this application do not limit its specific value.

[0090] It is understandable that for each log sample data or device log data, one or more data requests may correspond. In the embodiment of the present application, the amount of data requests involved in each log sample data (or log data) and the method for determining the amount of data requests are not limited and can be selected based on the needs of actual applications.

[0091] It should be noted that the standard deviation is a statistic that reflects the degree of dispersion of data requests corresponding to log sample data in a database, and can reflect the average deviation level of a group of data requests. Specifically, the database standard deviation can be calculated using sampling or based on the overall log sample data, which is not limited in this embodiment of the application.

[0092] In some implementations of the embodiments of the present application, the calculation method of the database standard deviation can be as follows:

[0093]

[0094] Among them, s represents the sample standard deviation, x i represents the sample data request volume corresponding to each log sample data, x represents the average sample data request volume corresponding to all log sample data, and N represents the number of log sample data.

[0095] In some implementations of the embodiments of the present application, the step of determining the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data includes:

[0096] Step S31, performing standard deviation analysis based on the log data to obtain the standard deviation of the log data;

[0097] In step S34 , the standard deviation is used as a discrete parameter of the log data, and the device status of the device is determined based on the discrete parameter.

[0098] It should be noted that the standard deviation of the above log data is also the standard deviation of the number of data requests involved in the log data, that is, the standard deviation of the target data request amount.

[0099] In some implementations of the embodiments of the present application, in order to perform standard deviation analysis on log data, the step of performing standard deviation analysis based on the log data to obtain the standard deviation of the log data includes:

[0100] determining the target data request volume included in the log data;

[0101] The average request volume is calculated based on the request volume of each target data;

[0102] The standard deviation of the log data is calculated based on the average value of the request volume.

[0103] It should be noted that the abnormal parameter ranges for the data request volume corresponding to the over-the-air download technology may vary for different system versions. The present embodiment can analyze parameters such as the data mean and data standard deviation based on parameters such as the request volume and request density of the request data in the log data. The standard deviation corresponding to the obtained log data request volume is used as the discrete parameter of the log data.

[0104] In some implementations of the embodiments of the present application, different outlier values may be determined for discrete parameters corresponding to different system versions. The specific method for determining the outlier value may be determined based on the needs of the actual application, and the embodiments of the present application are not limited thereto. Based on the outlier parameter interval in which the outlier value falls, the device status of the device may be determined. For example, when the discrete parameter falls within the first parameter interval, the device status is determined to be normal.

[0105] In some implementations of the embodiments of the present application, different system versions may correspond to different ranges of standard deviations of data request amounts, which may also serve as abnormal parameter intervals corresponding to the system version, thereby determining the device status of the device.

[0106] In some implementations of the embodiments of the present application, the step of using the standard deviation as a discrete parameter of the log data and determining the device status of the device based on the discrete parameter includes:

[0107] The standard deviation is used as a discrete parameter of the log data. If the discrete parameter is within a first parameter interval, the device state is determined to be a first state; the first state is a normal state.

[0108] If the discrete parameter is within the second parameter range, the device state is determined to be the second state; the second state is a possible abnormal state, and in the possible abnormal state, the device may have an abnormality;

[0109] If the discrete parameter is within the third interval, the device state is determined to be the third state; the third state is an abnormal state.

[0110] In some implementations of the embodiments of the present application, when the device status is in a possible abnormal state or an abnormal state, the management personnel can be notified through any one or more notification methods such as reports, emails, text messages, and phone calls to facilitate timely resolution of the problem.

[0111] The device status detection method in the embodiment of the present application is based on the statistical principles of mean and standard deviation. It detects abnormalities in the over-the-air download function through inductive and statistical methods, thereby improving the accuracy of detection. Furthermore, as the number of digital signage devices in the market gradually increases, a large amount of sample data is provided for inductive and statistical analysis, thereby continuously improving the accuracy and reliability of device status detection. Furthermore, the present application can perform separate inductive and statistical analysis for different versions of digital signage devices, ensuring that the reliability of the detection results is not affected by software version iterations, thus providing strong adaptability.

[0112] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the device status detection method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0113] This application also provides a device status detection device, please refer to Figure 6 , Figure 6 This is a schematic diagram of the module structure of the device status detection device according to an embodiment of the present application. The device status detection device includes:

[0114] Log acquisition module 10, used to obtain log data of the device;

[0115] The log analysis module 20 is used to determine the abnormal parameter interval of the log data according to the system version information of the log data;

[0116] The status detection module 30 is configured to determine the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

[0117] The device status detection device provided in this application, utilizing the device status detection method in the aforementioned embodiment, can address the technical issues of existing over-the-air (OTA) detection solutions, which rely on experience and have poor adaptability. Compared to the prior art, the device status detection device provided in this application has the same beneficial effects as the device status detection method provided in the aforementioned embodiment. Other technical features of the device status detection device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0118] The present application provides a device status detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the device status detection method in the above-mentioned embodiment 1.

[0119] Reference below Figure 7, which shows a schematic diagram of the structure of a device status detection device suitable for implementing the embodiments of the present application. The device status detection device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The device status detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0120] like Figure 7 As shown, the device status detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the device status detection device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication device 1009. The communication device 1009 can allow the device status detection device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a device status detection device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or provided instead.

[0121] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0122] The device status detection device provided in this application, utilizing the device status detection method in the aforementioned embodiment, can address the technical issues of existing over-the-air (OTA) detection solutions, which rely on experience and have poor adaptability. Compared to the prior art, the device status detection device provided in this application has the same beneficial effects as the device status detection method provided in the aforementioned embodiment. Other technical features of this device status detection device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0123] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0124] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0125] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the device status detection method in the above embodiment.

[0126] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0127] The computer-readable storage medium may be included in the device status detection device, or may exist independently without being incorporated into the device status detection device.

[0128] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the device status detection device, the device status detection device:

[0129] Get the device's log data;

[0130] Determine the abnormal parameter range of the log data based on the system version information of the log data;

[0131] The device status of the device is determined based on the abnormal parameter interval and the discrete parameters of the log data.

[0132] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0133] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0134] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0135] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned device status detection method. This computer-readable storage medium can address the technical issues of existing over-the-air (OTA) detection solutions, which rely on experience and have poor adaptability. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the device status detection method provided in the aforementioned embodiments, and are not further elaborated here.

[0136] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned device status detection method when executed by a processor.

[0137] The computer program product provided in this application can solve the technical problem that existing over-the-air (OTA) detection solutions rely on experience and have poor adaptability. Compared with the existing technology, the beneficial effects of the computer program product provided in this application are the same as those of the device status detection method provided in the above embodiment, and will not be elaborated here.

[0138] The above are only some embodiments of the present application and are not intended to limit the patent scope of the present application. All equivalent structural transformations made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A device status detection method, characterized in that: The method comprises: Get the device's log data; determining an abnormal parameter interval of the log data according to the system version information of the log data; The device status of the device is determined based on the abnormal parameter interval and the discrete parameters of the log data.

2. The device status detection method according to claim 1, wherein: The step of obtaining log data of the device includes: Get the device's request log data; Performing rough data processing based on the request log data to obtain initial log information; The request log data is filtered based on the initial log information to obtain log data of the device.

3. The device status detection method according to claim 2, wherein: The data rough processing includes: log time extraction and system version extraction, and the initial log information includes: request time information and system version information; The step of performing rough data processing based on the request log data to obtain initial log information includes: Performing text field recognition on the request log data to obtain a request log field; Find the request time field and the system version field in the request log field; Extracting the log time from the request time field based on a time regular expression to obtain request time information; The system version field is subjected to system version extraction based on a version regular expression to obtain system version information.

4. The device status detection method according to claim 3, wherein: The step of filtering the request log data based on the initial log information to obtain the log data of the device includes: Grouping the request log data at the same time based on the request time information to obtain a request log data group; Extracting a text field from the request log data in the request log data group; Performing similarity calculation on the text fields of the request log data in the request log data group using a similarity algorithm to obtain log field similarity; The request log data whose log field similarity is greater than a preset similarity threshold is regarded as duplicate log data; The request log data is deduplicated based on the duplicate log data to obtain log data of the device.

5. The device status detection method according to claim 1, wherein: The step of determining the abnormal parameter interval of the log data according to the system version information of the log data includes: Determine the system version used by the device based on the system version information of the log data; Obtain log sample data from a sample database corresponding to the system version; Calculate the database standard deviation of the log sample data in the sample database; Get the deviation fluctuation range of log data; Statistical calculation is performed based on the deviation fluctuation range and the data standard deviation to obtain the abnormal parameter interval of the log data.

6. The device status detection method according to claim 1, wherein: The step of determining the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data includes: determining a target data request volume included in the log data; Calculate the average request amount according to the request amount of each target data; Calculate the standard deviation of the log data based on the average value of the request amount; The standard deviation is used as a discrete parameter of the log data, and if the discrete parameter is within a first parameter interval, the device state is determined to be a first state; the first state is a normal state; If the discrete parameter is within the second parameter range, determining the device state as the second state; If the discrete parameter is within the third interval, the device state is determined to be the third state.

7. A device status detection device, characterized in that: The device status detection device comprises: Log acquisition module, used to obtain log data of the device; A log analysis module, configured to determine an abnormal parameter interval of the log data based on system version information of the log data; A status detection module is used to determine the device status of the device based on the abnormal parameter interval and the discrete parameters of the log data.

8. A device status detection device, characterized in that: The device includes: a memory, a processor, and a device status detection program stored in the memory and executable on the processor, wherein the device status detection program is configured to implement the steps of the device status detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a device status detection program, which, when executed by a processor, implements the steps of the device status detection method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the device status detection method according to any one of claims 1 to 6 are implemented.

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