Log management method and system, electronic device and storage medium

Abstract

Embodiments of the present application provide a log management method, system, electronic device and storage medium. In some embodiments, the log management method can include: analyzing a to-be-processed log uploaded by a log collection end, determining a feature of the to-be-processed log, and storing the to-be-processed log according to the feature of the to-be-processed log. The feature of the to-be-processed log includes an association feature, and the association feature indicates content in the stored content that has an association relationship with the to-be-processed log.

Description

Technical Field

[0001] The embodiments of this application relate to the field of computer technology, and more specifically, to a log management method, system, electronic device, and storage medium. Background Technology

[0002] Vehicle-mounted terminals (in-vehicle infotainment systems) are typically developed and implemented by the OEM (Original Equipment Manufacturer) based on its product design, either independently or by outsourcing to a developer. Due to typical R&D cycle limitations and the sheer volume of development work, potential problems, on-site application reconstruction, and in-vehicle system logs play a crucial role in this process.

[0003] Currently, the main methods for obtaining vehicle logs after mass production are manual or scheduled uploads. However, manual log acquisition requires operators to be on-site, which is extremely inconvenient. Automatic upload solutions are not yet fully developed and struggle to meet the demands of complex real-world scenarios. Summary of the Invention

[0004] The embodiments of this application provide a log management method, system, electronic device, and storage medium that can at least partially solve the above-mentioned or other problems existing in the prior art.

[0005] One embodiment of this application provides a log management method, comprising: analyzing logs to be processed uploaded by a log collection terminal, determining the characteristics of the logs to be processed, and storing the logs to be processed according to the characteristics. The characteristics of the logs to be processed include association characteristics, which indicate content in the stored data that is related to the logs to be processed.

[0006] Another embodiment of this application provides a log management system, including a log collection terminal and a log management terminal. The log collection terminal is configured to collect and upload pending logs from running programs. The log management terminal is configured to analyze the uploaded pending logs, determine the characteristics of the pending logs, including association characteristics indicating content in the stored data that is related to the pending logs; and store the pending logs according to these characteristics.

[0007] Another embodiment of this application provides an electronic device including at least one processor and a memory. The memory is communicatively connected to the at least one processor and stores instructions executable by the at least one processor. These instructions, when executed by the at least one processor, enable the at least one processor to perform the log management method mentioned in the above embodiments.

[0008] Another embodiment of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the log management method mentioned in the above embodiments.

[0009] According to some embodiments of the log management method, system, electronic device, and storage medium provided in this application, the log management terminal analyzes the logs to be processed uploaded by the log collection terminal to at least determine the association characteristics of the logs to be processed. Since the association characteristics indicate content in the already stored content that is related to the logs to be processed, the log management terminal can associate the logs to be processed with the already stored content during the storage process, which is beneficial for log management personnel to view and manage logs later, improving management efficiency. Furthermore, since the logs to be processed are uploaded by the log collection terminal, log management personnel do not need to go to the site, improving operational efficiency and service quality.

[0010] In some embodiments of this application, the stored content includes stored logs, or the stored content includes stored logs and associated information of stored logs; storing the logs to be processed according to their characteristics includes: constructing an association graph between the logs to be processed and the stored content according to the association characteristics of the logs to be processed, wherein the content corresponding to the nodes in the association graph includes at least one stored content or a log to be processed, and there is an association relationship between the nodes connected by the edges in the association graph; and saving the association graph and the logs to be processed.

[0011] In some embodiments of this application, the characteristics of the log to be processed further include classification characteristics; storing the log to be processed according to the characteristics of the log to be processed includes: constructing and saving an association graph of the log to be processed and the stored content according to the association characteristics of the log to be processed, wherein the content corresponding to the nodes in the association graph includes at least one stored content or a log to be processed, and the edges in the association graph are used to indicate that the nodes connected by the edges have an association relationship; and classifying and storing the log to be processed according to the classification characteristics of the log to be processed.

[0012] In some embodiments of this application, the method further includes: in response to a node in the association graph being triggered, determining, according to the association graph, an associated node that has an association relationship with the triggered node; and outputting the content corresponding to the triggered node and the content corresponding to the associated node.

[0013] In some embodiments of this application, the method further includes: in response to any classification feature being triggered, outputting the first content of the stored content marked with the classification feature.

[0014] In some embodiments of this application, the method further includes: in response to any classification feature being triggered, determining and outputting second content that is associated with the first content from the stored content according to the association graph.

[0015] In some embodiments of this application, the method further includes: generating a configuration instruction containing configuration data, wherein the configuration data indicates at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules; and transmitting the configuration instruction to the log collection terminal.

[0016] In some embodiments of this application, the log management terminal is further configured to: generate a configuration instruction containing configuration data, wherein the configuration data includes at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules; and transmit the configuration instruction to the log collection terminal.

[0017] In some embodiments of this application, the log collection terminal is further configured to: update the configuration according to the configuration data in the configuration instruction, and continue to collect and upload the pending logs of the running program according to the updated configuration. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. Wherein:

[0019] Figure 1 A schematic block diagram of an exemplary system architecture to which the log management method of this disclosure can be applied is shown;

[0020] Figure 2 This is a flowchart illustrating the log management method according to the first embodiment of this application;

[0021] Figure 3 This is a schematic diagram illustrating the relationships between some embodiments of this application;

[0022] Figure 4 This is a schematic diagram of the relationship between other embodiments of this application;

[0023] Figure 5 This is a flowchart illustrating the log management method according to the second embodiment of this application;

[0024] Figure 6 This is a schematic block diagram of a log management system according to the third embodiment of this application;

[0025] Figure 7 This is a schematic block diagram of a log collection terminal and a log management terminal according to some embodiments of this application;

[0026] Figure 8These are schematic block diagrams of electronic devices according to some embodiments of this application. Detailed Implementation

[0027] To better understand this application, various aspects of this application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely illustrative of exemplary embodiments of this application and are not intended to limit the scope of this application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0028] It should also be understood that expressions such as "comprising," "including," "having," "containing," and / or "comprising" are open-ended rather than closed-ended expressions in this specification, indicating the presence of the stated features, elements, and / or components, but not excluding the presence of one or more other features, elements, components, and / or combinations thereof. Furthermore, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features, not just individual elements in the list. Additionally, when describing embodiments of this application, the word "may" is used to mean "one or more embodiments of this application." And the term "exemplary" is intended to refer to examples or illustrations.

[0029] Unless otherwise specified, all terms used herein (including engineering and technical terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that, unless expressly stated herein, terms defined in common dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as having an idealized or overly formalized meaning.

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. Furthermore, unless explicitly limited or contradicted by the context, the specific steps included in the methods described in this application are not limited to the order in which they are described, but can be performed in any order or in parallel. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0031] Figure 1 A schematic block diagram of an exemplary system architecture to which the log management method of this disclosure can be applied is shown.

[0032] like Figure 1As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0033] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as video applications, live streaming applications, instant messaging tools, email clients, social media platform software, etc.

[0034] The terminal devices 101, 102, and 103 here can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, such as in-vehicle infotainment systems. When terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are made here.

[0035] Server 105 can be a server that provides various services, such as a backend server that supports terminal devices 101, 102, and 103. The backend server can analyze and store the received logs to be processed, and feed back the processing results (such as logs stored based on features) to the terminal devices.

[0036] It should be noted that the log management method provided in this embodiment can be executed by server 105 or terminal devices 101, 102, and 103.

[0037] It should be understood that, Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0038] Figure 2 This is a flowchart illustrating a log management method according to a first embodiment of this application. The log management method 200 can be executed through a log management terminal, which can be, for example, the server mentioned above or other devices with high processing power; no limitation is made here. Figure 2 As shown, the log management method 200 may include the following steps:

[0039] Step 201: Analyze the logs to be processed uploaded by the log collection terminal to determine the characteristics of the logs to be processed.

[0040] In this embodiment, the log collection terminal can be Figure 1 The example terminal device can be a vehicle-mounted terminal or other types of terminals; there are no restrictions here. The log collection terminal collects logs during its own operation and uploads these logs as pending logs to the log management terminal, allowing the log management terminal to manage logs collected by one or more log collection terminals. Upon receiving the pending log, the log management terminal analyzes it to determine its characteristics. These characteristics include association features. These association features indicate stored content that is related to the pending log, enabling the log management terminal to associate the pending log with related content during storage.

[0041] In some embodiments of this application, the log collection end and the log management end can communicate via a long-lived connection method such as MQTT (Message Queuing Telemetry Transport) to facilitate log transmission between them.

[0042] It should be understood that, without departing from the teachings of this application, the communication method between the log collection end and the log management end can also be other methods, such as short connection, and this application does not restrict this.

[0043] In some embodiments of this application, the process of analyzing the logs to be processed and determining their characteristics may include: invoking a pre-configured analysis algorithm to process the logs, and determining the characteristics of the logs based on the processing results of the analysis algorithm. The analysis algorithm may be stored in the log management terminal in the form of a model, etc., and this is not limited thereto.

[0044] As an example, the analysis algorithm could be a classification algorithm, which determines the association between the log to be processed and the stored content by classifying the log to be processed. For instance, the log management terminal could use this classification algorithm to parse the received log line by line based on information such as VIN (Vehicle Identification Number), process ID, item, date and time, keywords / phrases, detailed content, and tags in the log to be processed, thus determining the category of the log to be processed. Based on the category of the log to be processed, the log management terminal can determine the association characteristics of the log to be processed, so as to determine whether there is a relationship between the log to be processed and the stored content according to the log category.

[0045] Optionally, the process by which the log management terminal determines the association features of a log to be processed based on its classification category can be as follows: The classification category of the log to be processed can be used as the association feature. Alternatively, the process can be based on the classification category of the log to be processed and the relationships between classification categories, using the classification category of the log to be processed and other classification categories that are related to it as the association features. The embodiments of this application do not limit the process of determining association features based on classification categories.

[0046] It should be understood that, without departing from the teachings of this application, the classification dimensions of the classification algorithm can be set as needed. For example, the classification algorithm can classify the logs to be processed from multiple dimensions, such as the source of the logs (i.e., the application category that generated the logs), the log type of the logs to be processed (e.g., ordinary logs, exception logs, event logs, etc.), and the exception type of the logs to be processed. Analyzing the logs to be processed from multiple dimensions is beneficial for more comprehensive management of the collected logs. This application does not limit the classification criteria of the classification algorithm.

[0047] For example, a log to be processed is classified into an exception Y log and an application A log. An exception Y and an exception Z are related, and their related characteristics can be exception Y, exception Z and application A.

[0048] If the stored content includes stored logs, the association feature can indicate the following information: the log to be processed is associated with the stored log of exception Y; the log to be processed is associated with the stored log of exception Z; the log to be processed is associated with the stored log of application A; and the log to be processed is associated with the stored log of exception Y of application A.

[0049] If the stored content includes stored association information, this association information can be stored through tags or similar methods. This association characteristic can indicate the following: the log to be processed is associated with the tag of exception Y; the log to be processed is associated with the tag of exception Z; the log to be processed is associated with the tag of application A; and the log to be processed is associated with the tag of exception Y in application A. Based on this information, it can be further determined that the log to be processed is also associated with previously stored logs that bear the tag of exception Y, the tag of exception Z, the tag of application A, or the tag of exception Y in application A.

[0050] If the stored content includes stored logs and stored associated information, the associated feature can indicate the following information: the log to be processed is associated with the stored log of exception Y; the log to be processed is associated with the stored log of exception Z; the log to be processed is associated with the stored log of application A; the log to be processed is associated with the stored log of exception Y of application A; the log to be processed is associated with the tag of exception Y; the log to be processed is associated with the tag of exception Z; the log to be processed is associated with the tag of application A; and the log to be processed is associated with the tag of exception Y of application A.

[0051] As can be seen from the above, by determining the characteristics of the logs to be processed, the relationship between the logs to be processed and the stored content can be determined. This ensures that the subsequent storage of the logs to be processed reflects this relationship, making it easier for log administrators to understand the relationships between logs. When needed, it is possible to retrieve other logs that are related to each log, and then analyze similar logs, which helps log administrators understand the operation of the log collection terminal.

[0052] It should be understood that, without departing from the teachings of this application, the analysis algorithm used to determine the characteristics of the log to be processed can be other algorithms, as long as the algorithm can obtain the association between the log to be processed and the stored content, and there is no limitation here.

[0053] In some embodiments of this application, the log management terminal can also generate configuration instructions containing configuration data and transmit the configuration instructions to the log collection terminal. The configuration data may indicate at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules. The log upload frequency indicates the frequency at which the log collection terminal uploads local logs to the log management terminal. The log upload priority indicates the priority of various types of logs in the log collection terminal during the upload process. Logs with higher priority are uploaded first. The log storage rules indicate the rolling storage strategy of the log collection terminal for local logs, such as storing logs based on the date of log generation, log file size, etc. The log upload rules indicate the rules followed by the log collection terminal during the log upload process, such as real-time upload, etc., without limitation. Log administrators can configure any one or more of the above information from one or more dimensions on the log management terminal as needed. The log management terminal obtains configuration data based on the information configured by the log administrator and generates configuration instructions based on the configuration data. The log management terminal can actively and automatically send the configuration instructions to each log collection terminal, or it can transmit the configuration instructions to the log collection terminal after receiving a configuration update instruction from the log collection terminal. The configuration update command can be triggered automatically when the log collection terminal starts, or it can be triggered manually by the user of the log collection terminal; this application does not impose any restrictions on this. In this example, log administrators can adjust the log upload frequency and other settings of the log collection terminal through the log management terminal, thereby improving log acquisition efficiency and reducing operating costs.

[0054] It should be understood that, without departing from the teachings of this application, the log management terminal may also perform other management functions on the log collection terminal, such as software version updates, etc., which are not restricted here.

[0055] Step 202: Store the logs to be processed according to their characteristics.

[0056] In this embodiment, after obtaining the association characteristics of the logs to be processed, the log management terminal can store the logs to be processed in association based on the association characteristics, so that the log management personnel can understand the relationship between the stored logs and improve management efficiency.

[0057] In some embodiments of this application, the process of storing logs to be processed by the log management terminal based on their characteristics may include: constructing a relationship graph between the logs to be processed and the stored content based on their association characteristics; and saving the relationship graph and the logs to be processed. The content corresponding to each node in the relationship graph includes at least one stored content or log to be processed, and there are associations between the nodes connected by edges in the relationship graph. For example, the log management terminal may store the associations between analyzed logs. These associations may be stored using a graph database to support efficient and stable log storage and querying. The graph database may store the relationship graph. Each node in the relationship graph corresponds to stored content or a log to be processed, and the edges in the relationship graph represent the existence of associations.

[0058] For example, the log to be processed is Log 1, and the logs related to Log 1 are Log 2, Log 3, Log 4, Log 5, and Log 6. Specifically, there is a relationship between Log 3 and Log 5, and a relationship between Log 2 and Log 6. In this case, the relationship diagram can be, for example... Figure 3 As shown in the diagram, node A corresponding to log 1 is connected to node B corresponding to log 2, node C corresponding to log 3, node D corresponding to log 4, node E corresponding to log 5, and node F corresponding to log 6 via edges L11, L12, L13, L14, and L15 respectively. Node C corresponding to log 3 and node E corresponding to log 5 are connected via edge L16, and node B corresponding to log 2 and node F corresponding to log 6 are connected via edge L17. This relationship diagram allows log administrators to intuitively understand the relationships between logs, facilitating the identification of other logs related to the currently queried log and enabling comprehensive management.

[0059] For example, the log to be processed is log 1, and the content related to log 1 is tag 1, tag 2, and tag 3. In this case, the relationship diagram can be, for example... Figure 4 As shown in the diagram, node A corresponding to log 1 is connected to node T1 corresponding to label 1, node T2 corresponding to label 2, and node T3 corresponding to label 3 via edges L21, L22, and L23, respectively. This relationship diagram allows log administrators to intuitively understand the relationships between logs and various labels, facilitating the identification of the type of log being queried and enabling the search for other related logs, thus achieving comprehensive management.

[0060] As an example, during the process of building a graph database based on features in the log management system, a relationship graph of each log can be generated, for example... Figure 3 or Figure 4The diagram illustrates the relationships between log entries. Furthermore, this relationship diagram doesn't have to exist independently. For example, during the generation of the relationship diagram, features of historically processed logs are automatically retrieved and associated with them. Based on a graph database, relationships with the entire graph database are established during the import and generation of the log's relationship diagram. For instance, the relationship diagram shows the relationship between logs and tags; multiple relationship diagrams with the same tag are linked through that shared tag. Similarly, the relationship diagram shows the relationship between log entries; multiple relationship diagrams with the same log entry are linked through that shared log entry. All log content is interconnected through features, ultimately forming a vast relationship diagram.

[0061] It should be understood that, without departing from the teachings of this application, the construction method of the relationship diagram can be designed as needed, and this application does not impose any restrictions on it.

[0062] It should be understood that, without departing from the teachings of this application, the log management terminal can also perform associated storage of logs to be processed in other ways. For example, during the storage of logs to be processed, the storage of associated features of the logs to be processed can be added so that subsequent searches can be performed based on the associated features. Alternatively, the log management terminal can create separate folders for each type of associated feature, and during the storage of logs to be processed, the logs to be processed can be stored in the folder corresponding to the associated feature of the log. If a log to be processed has multiple associated features, then the logs to be processed can be stored in the folders corresponding to all associated features. This application does not limit the method of associated storage.

[0063] In some embodiments of this application, the log management terminal can also respond to a node in the relationship graph being triggered, and determine the associated nodes that are related to the triggered node according to the relationship graph; and output the content corresponding to the triggered node and the content corresponding to the associated nodes. For example, the log management terminal can display the relationship graph through a display screen (its own display screen or an external display screen) or other terminal devices connected to it. Log management personnel can select the associated node corresponding to the content they need to know based on the relationship graph, thereby triggering a query command. After detecting the query command, the log management terminal can determine the associated nodes that are related to the triggered node (such as nodes directly connected to the triggered node through edges, or nodes directly connected and indirectly connected through edges) according to the node triggered by the log management personnel in the relationship graph indicated by the query command. The log management terminal outputs the content corresponding to the triggered node and the content corresponding to the associated nodes for the log management personnel to view. In this example, the query process can be understood as identifying one feature from a large relationship graph, and the related content will be extracted together to form a relationship graph for the query. This allows developers to follow the graph and check any feature from the relationships, which is helpful to quickly identify defects (bugs) and solve problems.

[0064] As an example, when determining associated nodes, the log management terminal can determine the association level between the associated node and the triggered node based on the number of edges between them. Then, when displaying the content corresponding to the associated nodes, the terminal can categorize the content based on this association level. For instance, if the triggered node is node 1, and node 1 is directly connected to nodes 2 and 3 by one edge (association level 1), node 1 and node 4 are connected by node 2 (two edges, association level 2), and node 1 and node 5 are connected by nodes 2 and 4 (three edges, association level 3), then after node 1 is triggered, the log management terminal can output data indicating that the query results include three levels of display: when level 1 is triggered, the content corresponding to nodes 2 and 3 is displayed; when level 2 is triggered, the content corresponding to node 4 is displayed; and when level 3 is triggered, the content corresponding to node 5 is displayed.

[0065] It should be understood that, without departing from the teachings of this application, the content queried by the log management terminal can also be displayed in other ways, such as displaying the content corresponding to all related nodes together in a list. This application does not restrict this.

[0066] According to some embodiments of this application, the log management terminal analyzes the logs to be processed uploaded by the log collection terminal to at least determine the association characteristics of the logs to be processed. Since association characteristics indicate content in the already stored content that is related to the logs to be processed, the log management terminal can associate the logs to be processed with the already stored content during the storage process, which is beneficial for log management personnel to view and manage logs later, improving management efficiency. Furthermore, since the logs to be processed are uploaded by the log collection terminal, log management personnel do not need to go to the site, improving operational efficiency and service quality.

[0067] Figure 5 This is a flowchart illustrating a log management method according to a second embodiment of this application. This embodiment is largely the same as the first embodiment, with the main difference being that: in this embodiment, the characteristics of the logs to be processed also include classification characteristics, and the log management terminal stores the logs to be processed based on these classification characteristics. For example, the log management method 500 may include the following steps:

[0068] Step 501: Analyze the logs to be processed uploaded by the log collection terminal to determine the characteristics of the logs to be processed.

[0069] In this embodiment, the characteristics of the log to be processed include association characteristics and classification characteristics. Association characteristics indicate content in the stored data that is related to the log to be processed. The process of determining these association characteristics can be referred to the relevant description in the first embodiment, and will not be repeated here. Classification characteristics indicate the classification category of the log to be processed. For example, the log management terminal can analyze the log to be processed using a classification algorithm to determine its classification category, and then determine the classification characteristics of the log based on the classification category. The classification algorithm can employ artificial intelligence technologies such as machine learning to intelligently classify the log to be processed and determine its classification category.

[0070] Step 502: Based on the association characteristics of the logs to be processed, construct and save the association graph between the logs to be processed and the stored content.

[0071] In this embodiment, the content corresponding to each node in the association graph includes at least one stored content or a log to be processed, and the edges in the association graph are used to indicate that the nodes connected by the edges have an association relationship. The process of constructing and saving the association graph is largely the same as that of the first embodiment, and will not be described again here.

[0072] Step 503: Based on the classification characteristics of the logs to be processed, classify and store the logs to be processed.

[0073] In this embodiment, the log management terminal classifies and stores the logs to be processed according to their classification characteristics, so that logs of the same category are stored in the same or similar storage areas, which facilitates subsequent reading of these logs. In other words, in this embodiment, the log management terminal can store the original log information after compression and the analyzed log relationship information (i.e., the relationship graph) to support efficient and stable log storage and querying.

[0074] Alternatively, the log management terminal can also respond to the triggering of any category feature by outputting the first content in the stored logs marked with that category feature. For example, the log management terminal can display controls corresponding to all category features via a screen or other terminal device. Log administrators can trigger any one or more controls corresponding to any category feature as needed. Based on the controls triggered by the log administrator, the log management terminal determines the triggered category feature and outputs the first content in the stored logs marked with the triggered category feature (i.e., the stored logs) via the screen or other terminal device. In this example, log administrators can view all logs with the same category feature by category feature, improving log query efficiency.

[0075] Alternatively, the log management terminal can also respond to the triggering of any classification feature by determining and outputting second content that is related to the first content from the stored content, based on the association graph. For example, after determining the triggered classification feature, the log management terminal determines the first content marked with that classification feature. Then, based on the association graph, the log management terminal queries for the second content that is related to the first content. The log management terminal outputs the first content and the second content. The process of determining the second content based on the first content and the association graph may include, for example, determining the node corresponding to the first content in the association graph, finding the associated nodes in the association graph that are related to that node, and determining the content corresponding to the associated nodes as the second content. The process of determining the associated nodes can be referred to the relevant content of the first embodiment, and will not be repeated here. In this example, the query and retrieval function provided by the log management terminal supports retrieval based on classification features, and also supports retrieval of related logs based on classification feature retrieval, which can improve query efficiency and accuracy.

[0076] It should be understood that, without departing from the teachings of this application, the log management terminal may also provide other retrieval methods based on the relationship graph and classification features, and this application does not impose any restrictions on this.

[0077] According to some embodiments of this application, the log management terminal analyzes the logs to be processed uploaded by the log collection terminal to at least determine the association characteristics of the logs to be processed. Since association characteristics indicate content in the already stored content that is related to the logs to be processed, the log management terminal can associate the logs to be processed with the already stored content during the storage process, which is beneficial for log management personnel to view and manage logs later, improving management efficiency. Because the logs to be processed are uploaded by the log collection terminal, log management personnel do not need to go to the site, improving operational efficiency and service quality. Furthermore, the log management terminal categorizes and stores the logs, which is beneficial for subsequent querying of logs in different categories.

[0078] The steps of the various methods described above are only for clarity. In implementation, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this disclosure. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the protection scope of this disclosure.

[0079] Figure 6 This is a schematic block diagram of a log management system according to the third embodiment of this application. Figure 6 As shown, the log management system 600 may include a log collection terminal 610 and a log management terminal 620. The log collection terminal 610 is configured to collect and upload pending logs from running programs. The log management terminal 620 is configured to analyze the uploaded pending logs, determine their characteristics (including correlation characteristics indicating content in the existing storage that is related to the pending logs), and store the pending logs based on these characteristics.

[0080] In some embodiments of this application, the log collection terminal 610 may provide a log collection software development kit (SDK). Each application within the log collection terminal 610 may integrate the log collection SDK to collect logs from that application.

[0081] It should be understood that, without departing from the teachings of this application, the log collection terminal 610 may also collect logs in other ways, and this application does not impose any restrictions on this.

[0082] In some embodiments of this application, Figure 7 This is a schematic block diagram of the log collection terminal 610 and the log management terminal 620 according to some embodiments of this application. Figure 7As shown, the log collection terminal 610 is primarily responsible for collecting, storing, and uploading log information. The log collection terminal 610 may include one or more application modules 611, a log collection module 612, a configuration management module 613, and a log upload module 614. The log management terminal 620 is primarily responsible for receiving, parsing, and storing logs uploaded from the log collection terminal 610, and providing corresponding query and retrieval functions. The log management terminal 620 may include a log analysis module 621, a log query module 622, and a log storage module 623.

[0083] In some examples, the application module 611 may integrate the log collection SDK package mentioned above in order to collect the logs of the application module and transmit them to the log collection module 612.

[0084] In some examples, the log collection module 612 is responsible for collecting logs from the application module 612 and the system (not shown) and storing them locally or in memory. Optionally, the log collection module 612 can collect logs by multiple dimensions such as application and level according to the settings in the configuration management module 613, and supports rolling storage by date or file size.

[0085] In some examples, the configuration management module 613 is responsible for managing the configuration information of the log collection terminal 610. This configuration information may include, for example, log upload frequency, log upload priority, log storage rules, and log upload rules. The configuration management module 613 can receive configuration instructions from the log management terminal 620 to adjust the configuration information, and can also perform local settings to meet different needs. For example, the log management terminal 620 is also configured to generate configuration instructions containing configuration data and transmit these instructions to the log collection terminal 610. The configuration data includes at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules. The log collection terminal 610 is also configured to update the configuration according to the configuration data in the configuration instructions, and continue to collect and upload pending logs from the running program according to the updated configuration.

[0086] In some examples, the log upload module 614 is responsible for uploading the collected logs to the log management terminal 620. The log upload module 614 can upload logs in real-time or on a scheduled basis according to the settings in the configuration management module 613, and also supports immediate upload commands.

[0087] In some examples, the log storage module 623 is responsible for storing logs uploaded from the log collection terminal 610. Optionally, the log storage module 623 can store two parts of information: one part is the original log information after compression, and the other part is the log relationship information analyzed by the log analysis module 621. This log relationship information can be stored using the graph database mentioned in the above embodiments, i.e., a relationship graph, to support efficient and stable log storage and querying.

[0088] In some examples, the log analysis module 621 is responsible for parsing and classifying the uploaded logs. For instance, the log analysis module 621 may employ artificial intelligence technologies such as machine learning to intelligently classify and tag logs, thereby improving the accuracy and efficiency of log parsing. The log analysis module 621 can classify logs according to multiple dimensions, such as ordinary logs, exception logs, and event logs.

[0089] In some examples, the log query module 622 may be responsible for providing the user interface and query retrieval functionality. For instance, the log query module 622 may perform queries and retrievals based on user-input query conditions (such as triggered nodes or classification features) through logs stored in the log storage module 623, and return the corresponding query results. Optionally, the log query module 622 may provide various query retrieval methods, such as retrieval based on classification features, retrieval based on relationship graphs, etc., to improve query efficiency and accuracy.

[0090] In some embodiments of this application, the log management system 600 can be applied to the automotive manufacturing industry, the Internet industry, etc.

[0091] For example, this log management system 600 is applied in the automotive manufacturing industry. In this industry, automakers need to collect and analyze the logs of in-vehicle terminal applications and systems in order to promptly identify and resolve vehicle faults and problems. In this case, the in-vehicle terminal can serve as the log collection terminal mentioned in some embodiments of this application, and the automaker's management terminal can serve as the log management terminal mentioned in some embodiments of this application. Automakers can use this log management system 600 to collect and analyze information from the in-vehicle terminal to optimize vehicle performance and improve user satisfaction.

[0092] For example, this log management system 600 is applied in the automotive manufacturing industry. In this field, internet companies need to collect and analyze logs from in-vehicle terminal applications and systems to optimize internet services and applications. In this case, internet companies can use the log management system 600 to collect and analyze information such as vehicle location data, driving data, and user data to optimize internet services and applications.

[0093] It should be understood that, without departing from the teachings of this application, the log management system 600 can also be applied to other fields that require log collection and management, which will not be elaborated here.

[0094] It is not difficult to see that this embodiment is a system implementation method corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0095] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0096] According to some embodiments of this application, the log management system employs intelligent log collection and analysis functions. Through artificial intelligence technologies such as machine learning, it intelligently analyzes and classifies application and system logs from the log collection end. This function improves the accuracy and efficiency of log analysis, providing users with a better user experience and operational results. Furthermore, the log management system offers flexible log configuration and management functions, allowing for log configuration and management by application, level, and other dimensions. Log administrators can flexibly configure log upload frequency, log upload priority, log rolling storage strategy, and upload strategy according to actual needs to improve operational efficiency and service quality. In addition, the log management system provides intelligent log relationship graph structure storage and retrieval functions, allowing each log entry to be tagged, i.e., labeled with classification features, and log relationships stored in a relationship graph structure. Log administrators can perform retrieval queries based on tags or relationships, improving query efficiency and accuracy. Moreover, the log management system is flexibly adaptable to various log collection ends and applications. The log collection end provides a log collection SDK for integration with other applications. Log administrators can flexibly adapt to various vehicle terminal devices and applications according to actual needs to improve operational efficiency and service quality.

[0097] It should be noted that the acquisition, storage, and application of user personal information involved in the technical solution disclosed herein all comply with relevant laws and regulations and do not violate public order and good morals. It should also be noted that the information in this embodiment was obtained after being authorized by the user (i.e., with the user's consent).

[0098] Embodiments of this application also provide an electronic device, such as... Figure 8 As shown, the electronic device 800 may include: at least one processor and a memory, the memory being communicatively connected to the at least one processor and storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the log management method mentioned in the above embodiments.

[0099] One embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the log management method mentioned in the above embodiments.

[0100] Figure 8 This is a schematic block diagram of an electronic device 800 according to some embodiments of this application. For example... Figure 8 As shown, the electronic device 800 includes a processor 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a memory 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the electronic device 800. The processor 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0101] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as a touch screen; output unit 807, connected to various types of displays, speakers, etc., to output various forms of signals; memory 808, including any medium for storing computer-executable programs; and communication unit 809, such as a network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices via, for example, a local area network or other wireless communication networks.

[0102] Processor 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 801 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 801 performs the various methods and processes described above, such as the log management method mentioned in the above embodiments. For example, in some embodiments, the log management method mentioned in the above embodiments can be implemented as a computer software program tangibly contained in a computer-readable storage medium, such as memory 808. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by processor 801, one or more steps of the log management method described above can be performed. Alternatively, in other embodiments, processor 801 can be configured to perform the log management method mentioned in the above embodiments by any other suitable means (e.g., by means of firmware).

[0103] Various aspects of this application have been described herein with reference to flowchart illustrations and / or timing diagrams of methods, apparatus (systems), and computer program products according to exemplary embodiments of this application. It should be understood that each step of the flowchart illustrations and / or timing diagrams, as well as combinations of steps in the flowchart illustrations and / or timing diagrams, can be implemented by computer-readable program instructions.

[0104] These computer-readable program instructions can be provided to a processor, general-purpose computer, special-purpose computer, or other programmable data processing unit in an electronic device to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing device, they create means for implementing the functions / steps specified in one or more steps of a flowchart and / or timing diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing device, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / steps specified in one or more steps of a flowchart and / or timing diagram.

[0105] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / steps specified in one or more steps of a flowchart and / or timing diagram.

[0106] The flowcharts and timing diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each step in a flowchart or timing diagram may represent a module, segment, or part of an instruction that contains one or more executable instructions for implementing a specified logical function. In some alternative embodiments, the functions indicated in the steps may occur in a different order than those indicated in the drawings. For example, two consecutive steps may actually be performed substantially in parallel, and they may sometimes be performed in reverse order, depending on the functions involved. It should also be noted that each step in a timing diagram and / or flowchart, and combinations of steps in timing diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0107] The above description is merely an illustration of the embodiments of this application and the technical principles employed. Those skilled in the art should understand that the scope of protection involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the technical concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A log management method, comprising: The logs to be processed uploaded by the log collection terminal are analyzed to determine the characteristics of the logs to be processed. The characteristics of the logs to be processed include correlation characteristics, which indicate the content in the stored content that is related to the logs to be processed. The logs to be processed are stored according to their characteristics.

2. The method according to claim 1, wherein, The stored content includes stored logs, or the stored content includes stored logs and associated information of the stored logs; The step of storing the log to be processed according to its characteristics includes: Based on the association characteristics of the logs to be processed, an association graph is constructed between the logs to be processed and the stored content. The content corresponding to the nodes in the association graph includes at least one of the stored content or the logs to be processed, and there is an association relationship between the nodes connected by the edges in the association graph. Save the relationship graph and the log to be processed.

3. The method according to claim 1, wherein, The features of the log to be processed also include classification features; the storage of the log to be processed based on the features of the log to be processed includes: Based on the association characteristics of the log to be processed, an association graph of the log to be processed and the stored content is constructed and saved. The content corresponding to the nodes in the association graph includes at least one of the stored content or the log to be processed. The edges in the association graph are used to indicate that the nodes connected by the edges have an association relationship. The logs to be processed are classified and stored according to their classification characteristics.

4. The method according to claim 2 or 3, further comprising: In response to a node in the association graph being triggered, the associated nodes that are associated with the triggered node are determined according to the association graph; Output the content corresponding to the triggered node and the content corresponding to the associated node.

5. The method according to claim 3, further comprising: In response to any of the classification features being triggered, the first content in the stored content marked with that classification feature is output.

6. The method according to claim 5, further comprising: In response to any of the classification features being triggered, the second content that is associated with the first content is determined and output from the stored content according to the association graph.

7. The method according to claim 1, further comprising: Generate a configuration instruction containing configuration data, wherein the configuration data indicates at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules; The configuration command is transmitted to the log collection terminal.

8. A log management system, comprising: The log collection terminal is configured to collect and upload pending logs from the running program; The log management terminal is configured to analyze uploaded logs to be processed and determine the characteristics of the logs to be processed. The characteristics of the logs to be processed include correlation characteristics, which indicate the content in the stored content that is related to the logs to be processed. The logs to be processed are stored according to their characteristics.

9. The system according to claim 8, wherein, The log management terminal is also configured to: generate a configuration instruction containing configuration data, wherein the configuration data includes at least one of the following: log upload frequency, log upload priority, log storage rules, and log upload rules; and transmit the configuration instruction to the log collection terminal. The log collection terminal is also configured to: update the configuration according to the configuration data in the configuration instruction, and continue to collect and upload the pending logs of the running program according to the updated configuration.

10. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the log management method as described in any one of claims 1 to 7.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the log management method as described in any one of claims 1 to 7.

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