Information processing method, apparatus, computing platform, and computer storage medium

Abstract

Embodiments of the present application provide an information processing method, device, computing platform and computer storage medium. The information processing method comprises: in response to a task configuration file acquired by an information processing device, acquiring a data collection task, the data collection task being used to identify an information collection operation on at least one preset object; when the at least one preset object corresponding to the data collection task does not correspond to any actual collection object, generating an exception detection prompt used to detect the task configuration file; and storing the data collection task and the exception detection prompt in association by the information processing device. In the embodiments of the present application, when the data collection task does not correspond to any actual collection object, the exception detection prompt can enable a user to quickly and directly understand the exception state of the data collection task and facilitate troubleshooting operation, thereby improving the quality and efficiency of the troubleshooting operation to a certain extent.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an information processing method, apparatus, computing platform and computer storage medium. Background Technology

[0002] A data inspection system is a tool capable of performing container and microservice inspections. Users can configure the system according to their application needs to inspect or collect data from various objects in different application scenarios. When a user executes a data collection task using the system, it can obtain the task's running status and display relevant data for tasks in a normal state, allowing the user to view relevant operational information.

[0003] However, users cannot learn about the relevant information of data collection tasks in an abnormal state through the data detection system, which makes it inconvenient to carry out troubleshooting operations and reduces the efficiency of troubleshooting operations. Summary of the Invention

[0004] This application provides an information processing method, apparatus, computing platform, and computer storage medium, which can improve the quality and efficiency of troubleshooting operations to a certain extent.

[0005] This invention provides an information processing method applied to an information processing device, the method comprising:

[0006] In response to the task configuration file obtained by the information processing device, a data acquisition task is obtained, which is used to identify an information acquisition operation for at least one preset object.

[0007] When at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, an anomaly detection prompt is generated for detecting the task configuration file.

[0008] The information processing device associates and stores the data acquisition task and the anomaly detection prompt.

[0009] This invention provides an information processing apparatus, comprising:

[0010] The first acquisition module is used to acquire a data acquisition task in response to the task configuration file acquired by the information processing device. The data acquisition task is used to identify an information acquisition operation for at least one preset object.

[0011] The first generation module is used to generate an anomaly detection prompt for detecting the task configuration file when at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object.

[0012] The first processing module is used to associate and store the data acquisition task and the anomaly detection prompt.

[0013] This invention provides a computing platform, including: a memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the information processing method in the first aspect described above.

[0014] This invention provides a computer storage medium for storing a computer program, which, when executed by a computer, implements the information processing method described in the first aspect above.

[0015] This invention provides a computer program product, including: a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the information processing method in the first aspect described above.

[0016] The information processing method, apparatus, computing platform, and computer storage medium provided in this embodiment acquire a data acquisition task. When at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, an anomaly detection prompt can be generated for detecting the task configuration file. Then, the information processing apparatus associates and stores the data acquisition task and the anomaly detection prompt, allowing the user to quickly and directly understand the abnormal status of the data acquisition task through the associated storage of the data acquisition task and the anomaly detection prompt. Based on the anomaly detection prompt, the user can perform troubleshooting operations on the task configuration file corresponding to the data acquisition task. Furthermore, the situation where the data acquisition task does not correspond to any actual acquisition object is highly likely related to the task configuration file, thus effectively shortening the link length of the troubleshooting operation, thereby improving the quality and efficiency of the troubleshooting operation to a certain extent and effectively ensuring the practicality of the method. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A schematic diagram of a scenario for an information processing method provided in an exemplary embodiment of this application;

[0019] Figure 2 A flowchart illustrating an information processing method provided for an exemplary embodiment of this application;

[0020] Figure 3 A flowchart illustrating a specific embodiment of this application for detecting whether the data acquisition task corresponds to any actual acquisition object;

[0021] Figure 4 A schematic diagram illustrating the intersection between at least one first acquisition object and at least one second acquisition object, provided as an exemplary embodiment of this application;

[0022] Figure 5 This is a schematic diagram illustrating a process for associating and storing the data acquisition task and the anomaly detection prompt through the information processing device, as provided in an exemplary embodiment of this application.

[0023] Figure 6 A schematic diagram of an interface for associating and storing the task identifier, the virtual object information, and the anomaly detection information, provided as an exemplary embodiment of this application;

[0024] Figure 7 This is a schematic diagram of the data detection system provided in an exemplary application embodiment of this application;

[0025] Figure 8 A schematic diagram of the structure of an information processing apparatus provided for an exemplary embodiment of this application;

[0026] Figure 9 This is a schematic diagram of the structure of a computing platform provided for an exemplary embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] It should be noted that, in the cases involving user information in the embodiments of this application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) comply with relevant laws and standards.

[0029] Additionally, it should be noted that when user interaction operations or triggering operations are involved in the embodiments of this application, these operations include, but are not limited to, various interaction methods such as touch operations, gesture operations, voice operations, head movement operations, and eye movement operations. Touch operations include, but are not limited to, click operations, double-click operations, long-press operations, swipe operations, pinch operations, or mouse hover operations. Swipe operations include, but are not limited to, straight-line swipes and curved-line swipes.

[0030] To facilitate understanding of the implementation principles of the "information processing method, apparatus, computing platform, and computer storage medium" in this embodiment, the relevant technologies will be briefly described below:

[0031] A data inspection system can refer to a tool capable of container and microservice inspection. Users can configure the data inspection system to perform data collection tasks according to application needs, enabling the inspection or collection of data from different objects or microservices in various application scenarios. When users execute data collection tasks using the data inspection system, various operational states often occur, such as normal or abnormal (or fault) states. After obtaining the operational status of the collection task, the data inspection system can display relevant data for collection tasks in a normal state, allowing users to view relevant operational information about the collection task.

[0032] However, users cannot obtain information about data collection tasks in an abnormal state through the data detection system. For example, when a user uploads a data collection task to the data detection system, they can then perform corresponding data collection operations based on the task. During the data collection process, the target object (or target container) corresponding to the collection task may become unavailable due to various reasons.

[0033] To address the above situation, users can submit a fault ticket to the system vendor to troubleshoot. The vendor's technical staff can then perform troubleshooting through the service results page. If the fault cannot be found through the service results page, the vendor's technical staff needs to submit the fault ticket to the data detection system's development team. The development team typically needs to check the logs for troubleshooting. However, because the log update cycle in the data detection system is often quite short, such as once per day or once every two days, while the troubleshooting process is relatively long, by the time the development team receives the fault ticket, the log data may have already been updated. This means that the development team may not be able to see any errors or anomalies related to data collection configuration by simply scrolling through the logs.

[0034] At this point, to further troubleshoot, the development team needs to restart the data detection system. However, the restart process may fail due to resource scheduling issues, such as container component startup failures. This creates a system failure inherent in the troubleshooting process, significantly reducing the quality and efficiency of the troubleshooting. Even after restarting the system, the team might find no errors in the logs. However, analysis reveals that the failure is likely related to a user-uploaded configuration file—specifically, an incorrect configuration file format. Further checking the configuration file further complicates the troubleshooting process.

[0035] To address the issue of low efficiency in current troubleshooting methods, this embodiment provides an information processing method, apparatus, computing platform, and computer storage medium. For details, please refer to the appendix. Figure 1As shown, the executing entity of this information processing method can be an information processing device 200, which can be implemented as a local server, a cloud server, or a pre-set device. When the information processing device 200 is implemented as a cloud server, the information processing method can be executed in the cloud. Several computing nodes (cloud servers) can be deployed in the cloud, each with computing, storage, and other processing resources. In the cloud, multiple computing nodes can be organized to provide a certain service; of course, a single computing node can also provide one or more services. The cloud can provide this service by providing an external service interface, which users can call to use the corresponding service. Service interfaces include Software Development Kits (SDKs), Application Programming Interfaces (APIs), and other forms.

[0036] The information processing device 200 is communicatively connected to the client 100. The client 100 is used by users to trigger information processing operations by the information processing device 200. The client 100 can be any computing device with a certain information interaction capability. Specifically, the client 100 can be a mobile phone, a personal computer (PC), a tablet computer, a configuration application, etc. Furthermore, the basic structure of the client 100 may include at least one processor. The number of processors depends on the client's configuration and type. The client 100 may also include memory, which can be volatile, such as Random Access Memory (RAM), or non-volatile, such as Read-Only Memory (ROM), flash memory, etc., or both types. The memory typically stores the operating system (OS), one or more applications, and may also store program data. In addition to the processing unit and memory, the client 100 also includes some basic configurations, such as a network interface card (NIC) chip, an I / O bus, a display component, and some peripheral devices. Optionally, some peripheral devices may include, for example, a keyboard, mouse, stylus, printer, etc. Other peripheral devices are well known in the art and will not be described in detail here.

[0037] Information processing device 200 refers to a device capable of performing information processing operations in a network virtual environment, typically referring to a device that utilizes a network for information planning and processing. Physically, information processing device 200 can be any device capable of providing computing services and performing corresponding information processing operations, such as a cluster server, conventional server, cloud server, cloud host, virtual data center, etc. The main components of information processing device 200 include a processor, hard disk, memory, system bus, etc., similar to a general computer architecture.

[0038] In this embodiment described above, the client 100 establishes a network connection with the information processing device 200, which can be a wireless or wired network connection. If the client 100 can establish a communication connection with the information processing device 200, the network standard of the mobile network can be any one of 2G (Global System for Mobile Communications GSM), 2.5G (General Packet Radio Service GPRS), 3G (Wideband Code Division Multiple Access WCDMA, Time Division Synchronous Code Division Multiple Access TD-SCDMA), 4G (Long Term Evolution LTE), 4G+ (Enhanced Long Term Evolution LTE+), WiMax (Global Microwave Access Interoperability), 5G, 6G, etc.

[0039] In this embodiment, the client 100 is used by a user to obtain a task configuration file corresponding to triggering the information processing device to perform task configuration operations. The task configuration file can be generated through human-computer interaction or voice interaction. To enable the information processing device 200 to display fault or abnormal conditions corresponding to a data acquisition task when no actual acquisition object corresponds to the task, the task configuration file can be sent to the information processing device 200. This allows the information processing device 200 to obtain the data acquisition task based on the task configuration file and perform fault detection operations on the data acquisition task.

[0040] The information processing device 200 is used to obtain a task configuration file through the client 100. The task configuration file may include code fields for implementing data acquisition operations. These code fields include at least the name of the theoretical acquisition object corresponding to the data acquisition task, field information for the theoretical acquisition object, etc. After obtaining the task configuration file, the data acquisition task corresponding to the task configuration file can be obtained through parsing and understanding the configuration file. The data acquisition task identifies information acquisition operations performed on at least one preset object. The data acquisition task can then be analyzed and processed to identify whether the at least one preset object corresponding to the data acquisition task corresponds to any actual acquisition object.

[0041] When at least one preset object corresponding to a data acquisition task does not correspond to any actual acquisition object, it indicates that the data acquisition task is in an abnormal or faulty state. Since the fault type (i.e., the type where at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object) is most likely related to the task configuration file corresponding to the data acquisition task, in order to improve the efficiency of fault diagnosis to a certain extent, an anomaly detection prompt can be generated for detecting the task configuration file. Then, the information processing device 200 can associate and store the data acquisition task and the anomaly detection prompt. In this way, the displayed anomaly detection prompt can prompt the user to perform fault diagnosis operation on the task configuration file corresponding to the data acquisition task. This effectively reduces the link length of the fault diagnosis operation, thereby improving the quality and efficiency of the fault diagnosis operation to a certain extent.

[0042] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0043] Figure 2 A schematic flowchart of an information processing method provided for an exemplary embodiment of this application; see attached figures. Figure 2 This embodiment provides an information processing method. The execution subject of this method is an information processing device, meaning the information processing method can be applied to an information processing device. The information processing device can be implemented as software or a combination of software and hardware. When the information processing device is implemented as hardware, it can specifically be various computing platforms capable of performing information processing operations, including but not limited to personal computers, servers, etc. When the information processing device is implemented as software, it can be installed on the aforementioned computing platforms. Based on the above-mentioned information processing device, information processing operations can be performed, especially when a data acquisition task does not correspond to any actual acquisition object, displaying an anomaly detection prompt used to detect the task configuration file. Specifically, this information processing method may include:

[0044] Step S201: In response to the task configuration file obtained by the information processing device, a data acquisition task is obtained, which is used to identify an information acquisition operation for at least one preset object.

[0045] Step S202: When at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, generate an anomaly detection prompt for detecting the task configuration file.

[0046] Step S203: The data acquisition task and the anomaly detection prompt are associated and stored through the information processing device.

[0047] The specific implementation methods and principles of each of the above steps are explained in detail below:

[0048] Step S201: In response to the task configuration file obtained by the information processing device, a data acquisition task is obtained, which is used to identify an information acquisition operation for at least one preset object.

[0049] The information processing device can have the ability to perform container detection and / or microservice detection. Users can configure the information processing device according to application requirements to perform detection or collection operations on different objects or different microservices in various application scenarios. In some instances, the information processing device can be implemented as a cluster detection system or a node detection system.

[0050] When a user has a data acquisition requirement for an information processing device, they can upload a task configuration file to the device to implement the data acquisition operation. This task configuration file can include program code used to perform the data acquisition operation. In some instances, the task configuration file may include at least one of the following: a code segment identifying the task name, a code segment identifying the theoretical acquisition object corresponding to the data acquisition task, etc. After obtaining the task configuration file, it can be analyzed and processed to obtain the data acquisition task. The data acquisition task can refer to a task that requires information acquisition operations on at least one preset object, and it may include: the task name corresponding to the data acquisition task, the identity identifier of the preset object corresponding to the data acquisition task, the task execution time, etc. In some instances, obtaining the data acquisition task may include: obtaining the code information included in the task configuration file; parsing the code information to obtain the data acquisition task, thus ensuring the accuracy and reliability of obtaining the data acquisition task to a certain extent.

[0051] Step S202: When at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, generate an anomaly detection prompt for detecting the task configuration file.

[0052] After acquiring a data acquisition task, corresponding data acquisition operations can be performed based on that task. Since data acquisition operations may be affected by various factors and abnormal situations may occur, the data acquisition task may have different operating states during execution, such as a normal state or a fault state. When the data acquisition task is in a normal state, the information processing device can display the relevant operating data and status corresponding to the data acquisition task. When the data acquisition task is in a fault state, for example, if at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, the information processing device cannot acquire any data in this case, and therefore will not display the fault state of the data acquisition task.

[0053] Specifically, since the information processing device cannot collect relevant operational data from any of the preset objects specified in the data acquisition task, it is determined that the data acquisition task is in an abnormal or faulty state. Because such abnormal or faulty states are highly likely related to the task configuration file uploaded by the user—for example, incorrect or non-standard code segment configuration in the task configuration file—an anomaly detection prompt can be generated to check the task configuration file when the data acquisition task does not correspond to any actual acquisition object. This anomaly detection prompt not only identifies the current data acquisition task's operational status as abnormal or faulty (specifically identified by the keyword "error"), but can also be presented in any of the following forms: text prompts, voice prompts, image prompts, etc.

[0054] Regarding anomaly detection prompts, this embodiment does not limit the generation method of anomaly detection prompts. In some instances, anomaly detection prompts can be determined by a pre-trained neural network model or a large language model. In this case, generating anomaly detection prompts for detecting task configuration files may include: obtaining a pre-trained neural network model or a large language model; when the data acquisition task does not correspond to any actual acquisition object, inputting the above situation information and the task configuration file into the neural network model or the large language model to obtain the anomaly detection prompts output by the neural network model or the large language model. This ensures the accuracy and reliability of the anomaly detection prompt generation to a certain extent.

[0055] In other instances, anomaly detection prompts can be generated not only through pre-trained neural network models or large language models, but also by analyzing and identifying preset fields in the task configuration file. In this case, generating anomaly detection prompts for detecting anomalies in the task configuration file may include: determining a first preset field and / or a second preset field to limit the task collection objects based on the task configuration file, wherein the first preset field is used to identify a whitelist of objects corresponding to the data collection task, and the second preset field is used to identify a blacklist of objects corresponding to the data collection task; and generating anomaly detection prompts based on the first preset field and / or the second preset field.

[0056] Since the situation where a data acquisition task does not correspond to any actual acquisition object is highly likely related to the code fields used to limit the acquisition objects, in order to enable users to be aware of the above-mentioned anomalies in a timely manner, we can first determine the first and / or second preset fields in the task configuration file used to limit the acquisition objects. The first preset field can be implemented as a "keep field" to identify the whitelist of objects corresponding to the data acquisition task; the second preset field can be implemented as a "drop field" to identify the blacklist of objects corresponding to the data acquisition task. The first and second preset fields mentioned above can be determined through keyword analysis and matching. When the first and / or second preset fields are obtained, they can be analyzed and processed to generate anomaly detection prompts.

[0057] Specifically, the above technical solutions include the following three implementation methods:

[0058] Implementation Method 1: Generating anomaly detection prompts for detecting task configuration files may include: determining a first preset field based on the task configuration file to limit the task collection objects, wherein the first preset field is used to identify the whitelist of objects corresponding to the data collection task; generating anomaly detection prompts based on the first preset field.

[0059] For example, the first preset field in the task configuration file is the "keep field." The "keep field" is used to identify the object whitelist corresponding to the data acquisition task. If the object whitelist includes objects a1-a10, and all objects that the information processing device can detect include objects b1-b1000, then by comparison, it can be seen that the objects included in the object whitelist are not among all the objects that the information processing device can detect. In this case, it indicates that the first preset field in the task configuration file is likely misconfigured. Therefore, an anomaly detection prompt corresponding to the first preset field can be generated. This anomaly detection prompt is used to indicate that the first preset field in the task configuration file has a configuration error, and it is recommended that the user check and inspect the first preset field to improve the efficiency of troubleshooting the data acquisition task.

[0060] Implementation Method 2: Generating anomaly detection prompts for detecting task configuration files may include: determining a second preset field based on the task configuration file to limit the task collection objects, wherein the first preset field is used to identify the blacklist of objects corresponding to the data collection task; and generating anomaly detection prompts based on the second preset field.

[0061] For example, the second preset field in the task configuration file is the "drop field." The "drop field" is used to identify the object blacklist corresponding to the data acquisition task. If the object blacklist includes objects b1-b1000, and the information processing device can detect all objects including objects b1-b1000, analysis shows that the information processing device can detect 0 objects. In this case, it indicates that the second preset field in the task configuration file is likely misconfigured. Therefore, an anomaly detection prompt corresponding to the second preset field can be generated. This anomaly detection prompt is used to indicate that the second preset field in the task configuration file has a configuration error, and it is recommended that the user check and inspect the second preset field to improve the efficiency of troubleshooting the data acquisition task.

[0062] Implementation Method 3: Generating anomaly detection prompts for detecting task configuration files may include: determining a first preset field and a second preset field based on the task configuration file to limit the objects collected by the task, wherein the first preset field is used to identify the whitelist of objects corresponding to the data collection task, and the second preset field is used to identify the blacklist of objects corresponding to the data collection task; generating anomaly detection prompts based on the first preset field and the second preset field.

[0063] For example, the first preset field in the task configuration file is the "keep field," which identifies the whitelist of objects corresponding to the data acquisition task. If the whitelist includes objects a1-a10, the second preset field is the "drop field," which identifies the blacklist of objects corresponding to the data acquisition task. If the blacklist includes objects b1-b100, and the information processing device can detect objects b1-b1000, then the analysis shows that the information processing device can detect 0 objects. This indicates a high probability of a configuration error in the first and second preset fields of the task configuration file. Therefore, an anomaly detection prompt corresponding to the first and second preset fields can be generated. This prompt indicates a configuration error in the first and second preset fields of the task configuration file, and users are advised to check these fields to improve the efficiency of troubleshooting the data acquisition task.

[0064] Step S203: The data acquisition task and the anomaly detection prompt are associated and stored through the information processing device.

[0065] After receiving an anomaly detection alert, to facilitate timely viewing by users, the data acquisition task and the anomaly detection alert can be associated and stored through an information processing device. Furthermore, the data acquisition task and the anomaly detection alert can be displayed in conjunction with each other according to application requirements. This allows users to quickly and directly understand the abnormal status of the data acquisition task even when no actual acquisition object corresponds to it. Then, based on the anomaly detection alert, users can perform troubleshooting operations on the task configuration file corresponding to the data acquisition task. This effectively shortens the troubleshooting process, thereby improving the quality and efficiency of troubleshooting.

[0066] The information processing method provided in this embodiment, by acquiring a data acquisition task, can generate an anomaly detection prompt for detecting the task configuration file when at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object. Then, the information processing device associates and stores the data acquisition task and the anomaly detection prompt, allowing the user to quickly and directly understand the abnormal state of the data acquisition task through the associated storage. The user can also perform troubleshooting operations on the task configuration file corresponding to the data acquisition task based on the anomaly detection prompt. Furthermore, the situation where the data acquisition task does not correspond to any actual acquisition object is highly likely related to the task configuration file, thus effectively shortening the troubleshooting operation's link length and improving the quality and efficiency of the troubleshooting operation to a certain extent, effectively ensuring the practicality of the method.

[0067] Figure 3 This is a flowchart illustrating whether a detection data acquisition task corresponds to any actual acquisition object, provided as an exemplary embodiment of this application; based on the above embodiments, refer to the appendix... Figure 3 As shown, after acquiring the data acquisition task, the running status of the data acquisition task can be identified. Based on the identification result, it can be determined whether an anomaly detection prompt for detecting the task configuration file needs to be generated. At this time, the method in this embodiment may also include:

[0068] Step S301: Obtain at least one theoretical acquisition object corresponding to the data acquisition task.

[0069] After obtaining the data collection task, the data collection task can be analyzed and processed to obtain at least one theoretical collection object corresponding to the data collection task. The at least one theoretical collection object corresponding to the data collection task is the specified object that the user needs to collect information from or the specified object that needs to be detected.

[0070] In some instances, data acquisition tasks correspond to object whitelists, and theoretical acquisition objects can be determined through the object whitelists corresponding to the data acquisition tasks. In this case, obtaining at least one theoretical acquisition object corresponding to the data acquisition task may include: determining the object whitelist corresponding to the data acquisition task based on the task configuration file, the object whitelist including the identification information of at least one object that the data acquisition task needs to perform detection operations on; and determining at least one theoretical acquisition object corresponding to the data acquisition task based on the object whitelist.

[0071] Since the data acquisition task is determined by analyzing and processing the task configuration file, after obtaining the task configuration file, the task configuration file is analyzed and processed to determine the object whitelist corresponding to the data acquisition task. The object whitelist can include the identification information of at least one object that the data acquisition task needs to detect.

[0072] In some instances, the object whitelist can be obtained by parsing the task configuration file. In this case, determining the object whitelist corresponding to the data acquisition task based on the task configuration file may include parsing multiple code segments in the task configuration file to obtain the object whitelist corresponding to the data acquisition task. This ensures the accuracy and reliability of determining the object whitelist to a certain extent.

[0073] Alternatively, in other instances, since task configuration files often contain multiple lines of code, directly parsing all code segments in the task configuration file can easily reduce the efficiency of determining the object whitelist. Therefore, in order to improve the quality and efficiency of determining the object whitelist to a certain extent, only the preset fields in the task configuration file can be parsed. In this case, determining the object whitelist corresponding to the data acquisition task based on the task configuration file can include: determining the first preset field based on the task configuration file; and determining the object whitelist corresponding to the data acquisition task based on the first preset field.

[0074] Since the object whitelist corresponding to a data acquisition task is often related to the code fields used to limit the objects to be acquired, in order to improve the efficiency of the object whitelist, we can parse only the code fields used to limit the objects to be acquired to determine the object whitelist. Specifically, we can first extract the code from the task configuration file to obtain the first preset field included in the task configuration file. This first preset field can be implemented as a "keep field" used to identify the object whitelist corresponding to the data acquisition task. After obtaining the first preset field, we can analyze and process it to determine the object whitelist corresponding to the data acquisition task. This ensures the accuracy and reliability of the object whitelist determination to a certain extent.

[0075] After obtaining the object whitelist, it can be analyzed to determine at least one theoretical collection object corresponding to the data collection task. In some instances, the theoretical collection object can be directly determined based on the identification information of at least one object in the object whitelist. In this case, determining at least one theoretical collection object corresponding to the data collection task based on the object whitelist may include: determining at least one first collection object based on the identification information of at least one object in the object whitelist; and determining at least one first collection object as at least one theoretical collection object corresponding to the data collection task.

[0076] Since the object whitelist includes the object identification information of the data collection task, after obtaining the object whitelist, at least one first collection object can be determined based on the identification information of at least one object in the object whitelist. Specifically, the object corresponding to the identification information of at least one object can be determined as at least one first collection object. After obtaining at least one first collection object, at least one first collection object can be determined as at least one theoretical collection object corresponding to the data collection task, thus ensuring the accuracy and reliability of determining the theoretical collection object.

[0077] In other instances, since the task configuration file may contain not only an object whitelist to limit the objects to be collected, but also an object blacklist, the object whitelist and blacklist can be analyzed and processed to determine at least one theoretical object to be collected. In this case, determining at least one theoretical object to be collected based on the object whitelist may include: determining an object blacklist corresponding to the data collection task based on the task configuration file, the object blacklist including the identification information of at least one object for which the data collection task does not require detection; and determining at least one theoretical object to be collected based on the object whitelist and object blacklist.

[0078] After obtaining the task configuration file, the task configuration file is analyzed and processed to determine the object blacklist corresponding to the data acquisition task. The object blacklist may include the identification information of at least one object for which the data acquisition task does not need to perform detection operations.

[0079] In some instances, the object blacklist can be obtained by parsing the task configuration file. In this case, determining the object blacklist corresponding to the data acquisition task based on the task configuration file may involve parsing multiple code segments in the task configuration file to obtain the object blacklist corresponding to the data acquisition task. This ensures the accuracy and reliability of determining the object blacklist to a certain extent.

[0080] Alternatively, in other instances, since task configuration files often contain multiple lines of code, directly parsing all code segments in the task configuration file can easily reduce the efficiency of determining the object blacklist. Therefore, in order to improve the quality and efficiency of determining the object blacklist to a certain extent, only the preset fields in the task configuration file can be parsed. In this case, determining the object blacklist corresponding to the data acquisition task based on the task configuration file can include: determining a second preset field based on the task configuration file; and determining the object blacklist corresponding to the data acquisition task based on the second preset field.

[0081] Since the object whitelist and object blacklist corresponding to the data acquisition task are both related to the code fields used to limit the task's acquisition objects, in order to improve the efficiency of object blacklisting to a certain extent, we can parse and process only the code fields used to limit the task's acquisition objects to determine the object whitelist. Specifically, we can first perform code extraction on the task configuration file to obtain the second preset field included in the task configuration file. This second preset field can be implemented as a "drop field" used to identify the object blacklist corresponding to the data acquisition task. After obtaining the second preset field, we can analyze and process it to determine the object blacklist corresponding to the data acquisition task. This ensures the accuracy and reliability of determining the object blacklist to a certain extent.

[0082] After obtaining the object whitelist and object blacklist, the object whitelist and object blacklist can be analyzed and processed to determine at least one theoretical acquisition object corresponding to the data acquisition task. In some instances, at least one theoretical acquisition object can be determined by a pre-trained neural network model. In this case, determining at least one theoretical acquisition object corresponding to the data acquisition task based on the object whitelist and object blacklist may include: obtaining the pre-trained neural network model; inputting the object whitelist and object blacklist into the neural network model for analysis and processing to obtain at least one theoretical acquisition object corresponding to the data acquisition task output by the neural network model. This, to a certain extent, ensures the accuracy and reliability of determining at least one theoretical acquisition object.

[0083] In other instances, at least one theoretical acquisition object can be determined not only by a pre-trained neural network model, but also based on the identification information of at least one object in an object whitelist and at least one object in an object blacklist. In this case, determining at least one theoretical acquisition object corresponding to the data acquisition task based on the object whitelist and the object blacklist may include: determining at least one first acquisition object based on the identification information of at least one object in the object whitelist; determining at least one second acquisition object based on the identification information of at least one object in the object blacklist; and determining at least one theoretical acquisition object corresponding to the data acquisition task based on at least one first acquisition object and at least one second acquisition object.

[0084] Since the object whitelist identifies objects for which information monitoring or collection operations are required in the data collection task, while the object blacklist identifies objects for which information monitoring or collection operations are not required, after obtaining the object whitelist, at least one first collection object can be determined based on the identification information of at least one object in the object whitelist. Similarly, after obtaining the object blacklist, at least one second collection object can be determined based on the representation information of at least one object in the object blacklist. After obtaining at least one first collection object and at least one second collection object, they can be analyzed and processed to determine at least one theoretical collection object corresponding to the data collection task.

[0085] In some instances, determining at least one theoretical acquisition object corresponding to a data acquisition task based on at least one first acquisition object and at least one second acquisition object may include: if there is an intersection between at least one first acquisition object and at least one second acquisition object, removing the first acquisition object that matches at least one second acquisition object from the at least one first acquisition object to obtain a filtered first acquisition object, and determining the filtered first acquisition object as at least one theoretical acquisition object corresponding to the data acquisition task; or, if there is no intersection between at least one first acquisition object and at least one second acquisition object, then determining at least one first acquisition object as at least one theoretical acquisition object corresponding to the data acquisition task.

[0086] Example 1: After acquiring at least one first acquisition object and at least one second acquisition object, the first and second acquisition objects can be analyzed to identify whether there is any overlap between them. If at least one first acquisition object is an object that the data acquisition task needs to monitor, while at least one second acquisition object is an object that the data acquisition task does not need to monitor, and there is overlap between them, then... Figure 4 As shown, this indicates that there are at least one second collection object that does not need to be monitored among at least one first collection object. Therefore, the first collection object that matches at least one second collection object can be removed from the at least one first collection object to obtain the filtered first collection object. Then, the filtered first collection object can be determined as at least one theoretical collection object corresponding to the data collection task. This ensures the accuracy and reliability of determining at least one theoretical collection object to a certain extent.

[0087] Example 2: If there is no intersection between at least one first collection object and at least one second collection object, it means that there is no second collection object that does not need to be monitored among at least one first collection object. Therefore, at least one first collection object can be identified as at least one theoretical collection object corresponding to the data collection task. This ensures the accuracy and reliability of identifying at least one theoretical collection object to a certain extent.

[0088] Step S302: Determine at least one actual data collection object that the information processing device can detect.

[0089] To accurately detect whether a data acquisition task corresponds to any actual acquisition object, in addition to obtaining at least one theoretical acquisition object corresponding to the data acquisition task, it is also necessary to determine at least one actual acquisition object that the information processing device can detect. This at least one actual acquisition object can be determined through object scanning and / or object mining operations, or it can be determined through the network architecture information of the information processing device. By comparing at least one actual acquisition object with at least one theoretical acquisition object, it can be seen that at least one actual acquisition object has a larger coverage area, while at least one theoretical acquisition object corresponding to a data acquisition task has a smaller coverage area.

[0090] In some instances, at least one actual data collection object can be determined by all data collection tasks currently being executed by the information processing device. In this case, determining at least one actual data collection object that the information processing device can detect may include: obtaining all data collection tasks that the information processing device can currently execute; determining the set of data collection objects corresponding to each data collection task; and statistically analyzing the sets of data collection objects corresponding to multiple data collection tasks to obtain at least one actual data collection object that the information processing device can detect. This, to a certain extent, ensures the accuracy and reliability of determining at least one actual data collection object.

[0091] Step S303: Based on at least one theoretical acquisition object and at least one actual acquisition object, detect whether the data acquisition task corresponds to any actual acquisition object.

[0092] After obtaining at least one theoretical acquisition object and at least one actual acquisition object, these objects can be analyzed to detect whether the data acquisition task corresponds to any actual acquisition object. In some instances, the detection operation of whether the data acquisition task corresponds to any actual acquisition object can be determined by a pre-trained neural network model. In this case, detecting whether the data acquisition task corresponds to any actual acquisition object based on at least one theoretical acquisition object and at least one actual acquisition object may include: obtaining a pre-trained neural network model; inputting at least one theoretical acquisition object and at least one actual acquisition object into the neural network model for processing, and obtaining the detection result output by the neural network model. The detection result can be a first detection result indicating that the data acquisition task corresponds to at least one actual acquisition object; or, the detection result can be a second detection result indicating that the data acquisition task does not correspond to any actual acquisition object.

[0093] In other instances, the detection operation of whether a data acquisition task corresponds to any actual acquisition object can be determined not only by a pre-trained neural network model, but also by directly analyzing and processing at least one theoretical acquisition object and at least one actual acquisition object. In this case, based on at least one theoretical acquisition object and at least one actual acquisition object, detecting whether a data acquisition task corresponds to any actual acquisition object can include: if there is no intersection between at least one theoretical acquisition object and at least one actual acquisition object, then it is determined that the data acquisition task does not correspond to any actual acquisition object; if there is an intersection between at least one theoretical acquisition object and at least one actual acquisition object, then it is determined that the data acquisition task corresponds to at least one actual acquisition object.

[0094] Specifically, after acquiring at least one theoretical acquisition object and at least one actual acquisition object, these objects can be analyzed to identify whether there is any overlap between them. If there is no overlap, it means that the actual acquisition objects detectable by the information processing device do not include the theoretical acquisition object required by the data acquisition task, thus determining that the data acquisition task does not correspond to any actual acquisition object. Conversely, if there is overlap between at least one theoretical acquisition object and at least one actual acquisition object, it means that the actual acquisition objects detectable by the information processing device include the theoretical acquisition object required by the data acquisition task, thus determining that the data acquisition task corresponds to at least one actual acquisition object. This achieves a stable detection operation to determine whether the data acquisition task corresponds to any actual acquisition object.

[0095] In this embodiment, by acquiring at least one theoretical acquisition object corresponding to the data acquisition task, at least one actual acquisition object that the information processing device can detect is determined. Then, based on at least one theoretical acquisition object and at least one actual acquisition object, it is detected whether the data acquisition task corresponds to any actual acquisition object. This effectively realizes that after acquiring the data acquisition task, a stable detection operation is performed to determine whether the data acquisition task corresponds to any actual acquisition object by comparing at least one actual acquisition object and at least one theoretical acquisition object. Then, based on the detection result, it can be determined whether to generate an anomaly detection prompt for detecting the task configuration file, further improving the practicality of the information processing method.

[0096] Figure 5 This is a schematic diagram illustrating a process for associating and storing data acquisition tasks and anomaly detection prompts using an information processing device, as an exemplary embodiment of this application. Figure 6 This is a schematic diagram of an interface for associating and storing task identifiers, virtual object information, and anomaly detection information, provided as an exemplary embodiment of this application; based on any of the above embodiments, refer to the appendix. Figures 5-6 As shown, after obtaining the anomaly detection prompt, the virtual object information, task identifier, and anomaly detection information can be associated and stored. In this embodiment, associating and storing the data acquisition task and the anomaly detection prompt through the information processing device can include:

[0097] Step S501: Obtain the task identifier of the data acquisition task.

[0098] After obtaining the data acquisition task and the anomaly detection prompt, in order to help users quickly locate the data acquisition task corresponding to the anomaly detection prompt, the task identifier of the data acquisition task can be obtained first. The task identifier can be determined by analyzing and processing the data acquisition task through a preset mapping relationship.

[0099] Step S502: Determine the virtual object information corresponding to the data acquisition task.

[0100] If a data acquisition task does not correspond to any actual acquisition object, it indicates that the data acquisition task has not been executed normally and cannot perform any data acquisition operations. In this case, to facilitate the effective display of anomaly detection prompts for abnormal data acquisition tasks, the virtual object information corresponding to the data acquisition task can be identified. Specifically, the virtual object information can be virtual information generated by analyzing and processing the data acquisition task using a random algorithm. This virtual object information can include at least one of the following: virtual port, virtual status, virtual tag, etc. The virtual port can be implemented as virtual Uniform Resource Locator (URL) information composed of virtual IP address information. In some examples, refer to the appendix... Figure 6 As shown, the virtual port can be implemented as: http: / / local host:19335 / non_target_metr ics, the virtual state can be implemented as the preset "not running" state, and the virtual label is used to identify the collected virtual object values.

[0101] Step S503: The task identifier, virtual object information, and anomaly detection information are associated and stored through the information processing device.

[0102] After acquiring the task identifier, virtual object information, and anomaly detection information, the information processing device can perform associated storage operations on these three information. To ensure that users can view the anomaly detection information in a timely manner, the information processing device can highlight the task identifier and anomaly detection prompts. For example, the display color of the task identifier and anomaly detection prompts can be distinguished from the display color of other virtual object information; the display font of the task identifier and anomaly detection prompts can be distinguished from the display font of other virtual object information, and so on.

[0103] Specifically, task identifiers, virtual object information, and anomaly detection information can be associated and stored using a preset information processing template. This association and storage can include: obtaining an information processing template for displaying task identifiers, virtual object information, and anomaly detection information; adding the task identifiers, virtual object information, and anomaly detection information to preset positions in the information processing template to obtain the filled information; and then displaying the filled information through the information processing device. This achieves the associated storage operation of task identifiers, virtual object information, and anomaly detection information.

[0104] In this embodiment, the virtual object information corresponding to the data acquisition task is determined by obtaining the task identifier of the data acquisition task. Then, the task identifier, virtual object information, and anomaly detection information are associated and stored by the information processing device. This allows users to quickly and reliably view the anomaly detection information, and then facilitates users to perform troubleshooting operations on the task configuration file based on the anomaly detection information, further improving the practicality of the method.

[0105] For specific applications, please refer to the appendix. Figure 7 As shown in the illustration, this application embodiment provides a method for displaying anomaly detection information. The execution entity of this display method can be implemented as a data detection system. The data detection system may include: a detection server connected by communication, a service discovery module, and a display module. The data detection system can obtain the data acquisition task configured by the user, and then perform status detection operations on the data acquisition task before, during, or after the data acquisition operation based on the data acquisition task. It can also generate anomaly detection prompts for detecting the task configuration file when the data acquisition task does not correspond to any actual acquisition object, and can display the anomaly detection prompts to remind the user to perform troubleshooting operations on the task configuration file uploaded by the user.

[0106] Specifically, the demonstration method may include the following steps:

[0107] Step 1: Obtain the data collection task through the service discovery module.

[0108] When a user has a data collection need, they can submit a task configuration file to the service discovery module of the data detection system. The data detection system is implemented as a Prometheus cluster detection system, and the task configuration file can be a file in the "prometheus.yaml" format. After obtaining the task configuration file, the distributed platform component in the service discovery module can be used to load and configure the task configuration file, thereby obtaining the data collection task to be executed.

[0109] Step 2: Identify at least one theoretical data acquisition object corresponding to the data acquisition task.

[0110] After obtaining the data acquisition task, the distributed platform component can load and configure the task configuration file corresponding to the data acquisition task, and perform service discovery operations through the object discovery component in the service discovery module to identify at least one theoretical acquisition object corresponding to the data acquisition task. This theoretical acquisition object is used to identify the target object for which the data acquisition task needs to perform data acquisition operations. The data acquisition task can be implemented as a task to detect or collect the running data of the theoretical acquisition object. It can be understood that the data acquisition task can reflect the user's data acquisition intention to a certain extent.

[0111] For example, when the task configuration file is implemented as a file in the format "prometheus.yaml", the code segments in the task configuration file can be parsed. Specifically, the object discovery component can analyze and identify the "keep code segment" and "drop code segment" in the "prometheus.yaml" file, thereby obtaining the name identifier of at least one theoretical collection object "job" corresponding to the data collection task. By parsing all the name identifiers of the objects to be collected, at least one theoretical collection object corresponding to the data collection task can be obtained.

[0112] Step 3: Identify at least one actual data collection object that the data detection system can detect.

[0113] To accurately detect the running status of data acquisition tasks, we can first determine at least one actual acquisition object that the detection server in the data detection system can detect. The identification module in the detection server can determine this at least one actual acquisition object by parsing specific fields. For example, "data.ActiveTargets" in the specific field represents the object range obtained after the data detection system performs service discovery, and "existJobTargets" in the specific field represents the object identifiers corresponding to each actual acquisition object for a specific data acquisition task (Job) obtained after the data detection system performs service discovery. Thus, by parsing these two fields, we can determine at least one actual acquisition object that the data detection system can detect.

[0114] Step 4: The identification module compares at least one theoretical acquisition object with at least one actual acquisition object to identify whether the data acquisition task corresponds to any actual acquisition object.

[0115] After acquiring at least one theoretical data acquisition object and at least one actual data acquisition object, the identification module in the detection server can perform a difference comparison between the at least one theoretical data acquisition object and at least one actual data acquisition object, and can identify whether the data acquisition task corresponds to any actual data acquisition object based on the comparison result. Specifically, if there is no intersection between at least one theoretical data acquisition object and at least one actual data acquisition object, it is determined that the data acquisition task does not correspond to any actual data acquisition object; if there is an intersection between at least one theoretical data acquisition object and at least one actual data acquisition object, it is determined that the data acquisition task corresponds to at least one actual data acquisition object. After obtaining the difference comparison result, the difference comparison result can be stored in a preset time series database to facilitate users to view and retrieve the difference comparison result.

[0116] Step 5: If the data acquisition task does not correspond to any actual acquisition object, generate an anomaly detection prompt for checking the task configuration file.

[0117] Since the data acquisition task does not correspond to any actual acquisition object, the current abnormal state of the data acquisition task is most likely related to the configuration error of the task configuration file. In this case, in order to improve the quality and efficiency of troubleshooting operations to a certain extent, anomaly detection prompts can be generated to check the preset fields ("keep code segment" and / or "drop code segment") in the task configuration file. Based on the generated anomaly detection prompts, the user can be prompted to perform code inspection operations on the task configuration file.

[0118] Step 6: Display the data acquisition task and anomaly detection prompts in conjunction with the display module.

[0119] To ensure users can promptly view anomaly detection alerts, after generating anomaly detection alerts for detecting task configuration files, the data acquisition task and the anomaly detection alerts are associated and stored. Furthermore, the data acquisition task and anomaly detection alerts can be selectively associated and displayed. Specifically, this can be done through a display module in the data detection system (which can be a display screen, monitor, or display interface, etc.). In some examples, associating the data acquisition task and anomaly detection alerts through the display module may include: obtaining the task name identifier of the data acquisition task; randomly generating virtual object information corresponding to the data acquisition task, which may include one of the following: virtual port information, virtual status information, virtual tag information, etc., and then associating the virtual object information with the anomaly detection alerts through the display module.

[0120] The technical solution of this application embodiment can generate an anomaly detection prompt when the data acquisition task does not correspond to any actual acquisition object, prompting the user to check the task configuration file (including keep code segment and drop code segment) corresponding to the data acquisition task. This anomaly detection prompt can remind the user to locate the reason why the data acquisition task cannot find any object more quickly when the data acquisition task cannot find any object. Specifically, by associating and storing the data acquisition task and the anomaly detection prompt on the task status display interface, and further, selectively associating and displaying the data acquisition task and the anomaly detection prompt, this not only improves the interface effect of information processing on the service result interface to a certain extent, realizing a more optimized display operation of the service result page, but also prompts the user to perform troubleshooting operations on the task configuration file through the anomaly detection prompt, ensuring that the data acquisition operation meets the user's data processing intention, and allowing multiple people to simultaneously perform troubleshooting operations on the task configuration file, thus improving the quality and efficiency of the troubleshooting operation to a certain extent. Compared with the current troubleshooting operation, the efficiency of the troubleshooting can even be improved by an order of magnitude, further improving the practicality of the solution.

[0121] Furthermore, in some of the processes described in the above embodiments and accompanying drawings, multiple operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The operation numbers, such as 11, 12, etc., are merely used to distinguish different operations and do not represent any execution order. Additionally, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0122] Figure 8 A schematic diagram of the structure of an information processing apparatus provided for an exemplary embodiment of this application; see attached drawing. Figure 8 As shown, this embodiment provides an information processing device for performing the above-described... Figure 2 The information processing method shown, specifically, the information processing device may include:

[0123] The first acquisition module 11 is used to acquire a data acquisition task in response to the task configuration file acquired by the information processing device. The data acquisition task is used to identify an information acquisition operation for at least one preset object.

[0124] The first generation module 12 is used to generate an anomaly detection prompt for detecting the task configuration file when at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object.

[0125] The first processing module 13 is used to associate and store data acquisition tasks and anomaly detection prompts.

[0126] The information processing device in this embodiment may also include the aforementioned embodiments. Figures 1-7 The description of the embodiments shown is for reference only, and will not be elaborated upon here.

[0127] Figure 9 A schematic diagram of the structure of a computing platform provided in an exemplary embodiment of this application; as shown Figure 9 As shown, this embodiment provides a computing platform for performing the above-described tasks. Figure 2 The information processing method shown may, in practice, include a computing platform that includes a memory 24 and a processor 25.

[0128] Memory 24 is used to store computer programs and can be configured to store various other data to support operation on the computing platform. Examples of this data include instructions for any application or method used to operate on the computing platform, data structures, contact data, phone book data, messages, pictures, videos, etc.

[0129] The processor 25, coupled to the memory 24, is configured to execute a computer program in the memory 24 for: in response to a task configuration file obtained by the information processing device, acquiring a data acquisition task, the data acquisition task being used to identify an information acquisition operation for at least one preset object; when the at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, generating an anomaly detection prompt for detecting the task configuration file; and storing the data acquisition task and the anomaly detection prompt together.

[0130] Furthermore, such as Figure 9 As shown, the computing platform also includes other components such as a communication component 26, a display 27, a power supply component 28, and an audio component 29. Figure 9 The diagram only shows some components and does not mean that the computing platform includes only these components. Figure 9 The components shown. Additionally... Figure 9The components within the center frame are optional, not mandatory, and their specific requirements depend on the product form of the work node. In this embodiment, the work node can be a terminal device such as a desktop computer, laptop computer, smartphone, or IoT device, or a server-side device such as a conventional server, cloud server, or server array. If the work node in this embodiment is implemented as a terminal device such as a desktop computer, laptop computer, or smartphone, it may include... Figure 9 The components within the center frame; if the working node in this embodiment is implemented as a server-side device such as a conventional server, cloud server, or server array, it may not include... Figure 9 The component within the center frame.

[0131] The aforementioned memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0132] The aforementioned communication component is configured to facilitate wired or wireless communication between the device containing the communication component and other devices. The device containing the communication component can access wireless networks based on communication standards, such as 2G, 3G, 4G / LTE, 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel.

[0133] The aforementioned display includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen can be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation.

[0134] The aforementioned power supply components provide power to various components within the device in which they reside. These power supply components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to the device in which they reside.

[0135] The aforementioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, or voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0136] The aforementioned audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, or voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0137] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the steps in the above-described method embodiments. The computer-readable storage medium may be volatile, non-volatile, or a combination thereof, and may be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, CD-ROM, Digital Video Disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium.

[0138] Accordingly, this application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the processor is able to implement the steps in the above method embodiments. It should be understood that each step or combination of steps in the above method flow can be implemented by the computer program or instructions. In addition, these computer programs or instructions can be applied to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device, so that the processor of the general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device can be implemented as a means to implement the corresponding functions in the above method embodiments.

[0139] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0140] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. An information processing method, characterized in that, Applied to an information processing device, the method includes: In response to the task configuration file obtained by the information processing device, a data acquisition task is obtained, which is used to identify an information acquisition operation for at least one preset object. When at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object, an anomaly detection prompt is generated for detecting the task configuration file. The information processing device associates and stores the data acquisition task and the anomaly detection prompt.

2. The method according to claim 1, characterized in that, After acquiring the data collection task, the method further includes: Obtain at least one theoretical acquisition object corresponding to the data acquisition task; Identify at least one actual data collection object that the information processing device can detect; Based on the at least one theoretical acquisition object and the at least one actual acquisition object, detect whether the data acquisition task corresponds to any one actual acquisition object.

3. The method according to claim 2, characterized in that, Based on the at least one theoretical acquisition object and the at least one actual acquisition object, detecting whether the data acquisition task corresponds to any one actual acquisition object includes: If there is no intersection between the at least one theoretical acquisition object and the at least one actual acquisition object, then it is determined that the data acquisition task does not correspond to any actual acquisition object. If there is an intersection between the at least one theoretical acquisition object and the at least one actual acquisition object, then the data acquisition task is determined to correspond to at least one actual acquisition object.

4. The method according to claim 2, characterized in that, Obtaining at least one theoretical acquisition object corresponding to the data acquisition task includes: Based on the task configuration file, a whitelist of objects corresponding to the data acquisition task is determined. The whitelist of objects includes the identification information of at least one object for which the data acquisition task needs to perform detection operations. Based on the object whitelist, at least one theoretical collection object corresponding to the data collection task is determined.

5. The method according to claim 4, characterized in that, Based on the task configuration file, a whitelist of objects corresponding to the data acquisition task is determined, including: Based on the task configuration file, determine the first preset field; Based on the first preset field, a whitelist of objects corresponding to the data collection task is determined.

6. The method according to claim 4, characterized in that, Based on the object whitelist, at least one theoretical collection object corresponding to the data collection task is determined, including: Based on the identification information of at least one object in the object whitelist, at least one first collection object is determined; The at least one first acquisition object is determined as at least one theoretical acquisition object corresponding to the data acquisition task.

7. The method according to claim 4, characterized in that, Based on the object whitelist, at least one theoretical collection object corresponding to the data collection task is determined, including: Based on the task configuration file, a blacklist of objects corresponding to the data acquisition task is determined. The blacklist of objects includes the identification information of at least one object for which the data acquisition task does not need to perform detection operations. Based on the object whitelist and the object blacklist, at least one theoretical collection object corresponding to the data collection task is determined.

8. The method according to claim 7, characterized in that, Based on the task configuration file, a blacklist of objects corresponding to the data acquisition task is determined, including: Based on the task configuration file, determine the second preset field; Based on the second preset field, a blacklist of objects corresponding to the data collection task is determined.

9. The method according to claim 7, characterized in that, Based on the object whitelist and the object blacklist, at least one theoretical collection object corresponding to the data collection task is determined, including: Based on the identification information of at least one object in the object whitelist, at least one first collection object is determined; Based on the identification information of at least one object in the object blacklist, at least one second collection object is determined; Based on the at least one first acquisition object and the at least one second acquisition object, at least one theoretical acquisition object corresponding to the data acquisition task is determined.

10. The method according to claim 9, characterized in that, Based on the at least one first acquisition object and the at least one second acquisition object, determine at least one theoretical acquisition object corresponding to the data acquisition task, including: If there is an intersection between the at least one first acquisition object and the at least one second acquisition object, remove the first acquisition objects that match the at least one second acquisition object from the at least one first acquisition object to obtain filtered first acquisition objects, and determine the filtered first acquisition objects as at least one theoretical acquisition object corresponding to the data acquisition task; or... If there is no intersection between the at least one first acquisition object and the at least one second acquisition object, then the at least one first acquisition object is determined as at least one theoretical acquisition object corresponding to the data acquisition task.

11. The method according to any one of claims 1-10, characterized in that, Generate anomaly detection prompts for detecting the task configuration file, including: Based on the task configuration file, a first preset field and / or a second preset field are determined to limit the task collection objects, wherein the first preset field is used to identify the object whitelist corresponding to the data collection task, and the second preset field is used to identify the object blacklist corresponding to the data collection task. The anomaly detection prompt is generated based on the first preset field and / or the second preset field.

12. The method according to any one of claims 1-10, characterized in that, The information processing device associates and stores the data acquisition task and the anomaly detection prompt, including: Obtain the task identifier of the data acquisition task; Determine the virtual object information corresponding to the data acquisition task; The information processing device associates and stores the task identifier, the virtual object information, and the anomaly detection information.

13. An information processing device, characterized in that, include: The first acquisition module is used to acquire a data acquisition task in response to the task configuration file acquired by the information processing device. The data acquisition task is used to identify an information acquisition operation for at least one preset object. The first generation module is used to generate an anomaly detection prompt for detecting the task configuration file when at least one preset object corresponding to the data acquisition task does not correspond to any actual acquisition object. The first processing module is used to associate and store the data acquisition task and the anomaly detection prompt.

14. A computing platform, characterized in that, include: A memory and a processor; wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions, when executed by the processor, implement the method of any one of claims 1-12.

15. A computer storage medium, characterized in that, Used to store a computer program that, when executed by a computer, implements the method of any one of claims 1-12.

16. A computer program product, characterized in that, include: A computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the method of any one of claims 1-12.

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