Network fault identification method, device, storage medium and system

Abstract

The invention provides a network fault identification method and device, a storage medium and a system, and the method comprises the steps: receiving and responding to a detection task, and generating a detection instruction according to the detection task; obtaining index data of target detection equipment from the target detection equipment corresponding to the detection instruction, and determining abnormal index data in the index data; and generating first alarm information containing the abnormal index data and the unique identifier of the equipment corresponding to the abnormal index data so as to prompt to maintain the equipment corresponding to the abnormal index data. The problem of low operation and maintenance efficiency caused by the fact that operator network operation and maintenance mainly depends on a passive operation and maintenance mode of manual inspection and customer fault reporting in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of network monitoring and anomaly diagnosis technology, and more specifically, to a network fault identification method, a network fault identification device, a computer-readable storage medium, and a network fault identification system. Background Technology

[0002] With the continuous growth of network scale and service types, network system structures are becoming increasingly complex, and the number, models, and vendor types of network devices are constantly increasing, placing higher demands on the monitoring and maintenance of network operation status. In existing technologies, network operation and maintenance mainly relies on a passive model of manual inspection and customer fault reporting. When network or service anomalies occur, users typically need to first perceive the fault and report the problem, and then maintenance personnel manually check the device status, analyze logs, and locate the cause of the fault based on the fault report information. This operation and maintenance approach has significant limitations: on the one hand, passive operation and maintenance response is delayed, the system cannot proactively detect potential hidden dangers, and it is easy to cause the fault to escalate or services to be interrupted, affecting service quality and user experience; on the other hand, manual inspection is time-consuming and inefficient, and the inconsistent interfaces and management protocols of different vendors' devices lead to cumbersome data collection and analysis processes, making it difficult to achieve unified monitoring and rapid diagnosis; furthermore, the collaborative capabilities across network layers or across business domains are weak, and maintenance personnel often need to switch between multiple systems to compare information, resulting in long fault location cycles and a reliance on manual experience in the troubleshooting process, making it difficult to achieve intelligent and automated operation.

[0003] Therefore, existing technologies are insufficient to meet the operational and maintenance needs for real-time monitoring, proactive alarms, and rapid location in modern large-scale network environments. Summary of the Invention

[0004] The main objective of this application is to provide a network fault identification method, a network fault identification device, a computer-readable storage medium, and a network fault identification system, so as to at least solve the problem of low operation and maintenance efficiency caused by the passive operation and maintenance mode of operators' network operation and maintenance, which mainly relies on manual inspection and customer fault reporting.

[0005] To achieve the above objectives, according to one aspect of this application, a network fault identification method is provided, comprising: receiving and responding to a probe task; generating a probe instruction according to the probe task, the probe task including a unique identifier of a target probe device and a probe frequency; obtaining indicator data of the target probe device from the target probe device corresponding to the probe instruction, and determining abnormal indicator data in the indicator data, the indicator data including at least network latency and packet loss rate; generating a first alarm message containing the abnormal indicator data and a unique identifier of the device corresponding to the abnormal indicator data, to prompt maintenance of the device corresponding to the abnormal indicator data.

[0006] Optionally, obtaining the target detection device's index data from the target detection device corresponding to the detection command includes: obtaining first preliminary index data of the target detection device and determining outliers in the first preliminary index data; removing the outliers from the first preliminary index data to obtain second preliminary index data; determining the unique identifier and detection time of the target detection device corresponding to the second preliminary index data; and filling a preset data template according to the unique identifier, the detection time, and the second preliminary index data to obtain the target detection device's index data.

[0007] Optionally, before obtaining the indicator data of the target detection device from the target detection device corresponding to the detection command, the method further includes: if the key validity verification of the detection command passes, obtaining the indicator data of the target detection device corresponding to the detection command; if the key validity verification of the detection command fails, refusing to execute the detection command and generating a second alarm message to indicate the risk of unauthorized access.

[0008] Optionally, the indicator data includes verification information. After obtaining the indicator data of the target detection device from the target detection device corresponding to the detection command, the method further includes: performing an integrity verification on the indicator data according to the verification information of the indicator data; if it is determined that the indicator data is incomplete, obtaining the indicator data of the target detection device again; if it is determined that the indicator data is complete, storing the indicator data in a preset database.

[0009] Optionally, generating a detection command based on the detection task includes: determining a preset command template based on the target detection device corresponding to the detection task; and generating the detection command based on the preset command template.

[0010] Optionally, obtaining the target detection device's indicator data from the target detection device corresponding to the detection command includes: determining the detection status of the indicator data as abnormal if obtaining the indicator data fails; generating a third alarm message containing the detection status and the unique identifier of the target detection device corresponding to the detection status, to prompt the network status of the target detection device to be detected.

[0011] Optionally, determining abnormal indicator data in the indicator data includes: if the packet loss rate of the indicator data is greater than a preset packet loss rate, determining the packet loss rate as the abnormal indicator data; if the network latency of the indicator data is greater than a preset latency, determining the network latency as the abnormal indicator data.

[0012] According to another aspect of this application, a network fault identification device is provided, comprising: a receiving unit, configured to receive and respond to a detection task, and generate a detection instruction according to the detection task, the detection task including a unique identifier of a target detection device and a detection frequency; an acquisition unit, configured to acquire indicator data of the target detection device from the target detection device corresponding to the detection instruction, and determine abnormal indicator data in the indicator data, the indicator data including at least network latency and packet loss rate; and a generation unit, configured to generate first alarm information containing the abnormal indicator data and a unique identifier of the device corresponding to the abnormal indicator data, to prompt maintenance of the device corresponding to the abnormal indicator data.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, a network fault identification system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0015] By applying the technical solution of this application, proactive perception and dynamic monitoring of network device operating status are achieved through automatically generating probe commands and periodically collecting performance indicators such as network latency and packet loss rate. Upon receiving a probe task, a probe command is automatically generated and sent to the target probe device. During the probe process, the probe continuously acquires key performance indicators such as network latency, packet loss rate, reachability, and response time according to a preset probe frequency. The collected indicator data is compared with preset thresholds in real time, automatically identifying corresponding abnormal indicator data and generating a first alarm message containing the abnormal indicators for subsequent fault location and maintenance operations. This allows for the early identification of potential performance anomalies before network faults are perceived by users or cause service interruptions, realizing a shift from passive fault reporting and response to proactive intelligent monitoring. Compared with traditional passive maintenance methods that rely on manual inspection, log analysis, and customer complaint feedback, this solution can automatically execute probes and update monitoring results, avoiding the long-cycle delays of manual inspections, shortening fault detection and handling time, and improving overall maintenance efficiency. This solves the problem of low maintenance efficiency caused by the passive maintenance mode in existing technologies, which mainly relies on manual inspections and customer reports. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A schematic flowchart of a network fault identification method according to an embodiment of this application is shown;

[0018] Figure 2 A structural block diagram of a network fault identification device provided according to an embodiment of this application is shown. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] As described in the background section, in the prior art, network operation and maintenance by operators mainly relies on a passive operation and maintenance mode of manual inspection and customer fault reporting, resulting in low operation and maintenance efficiency. In order to solve the above-mentioned technical problems, the embodiments of this application provide a network fault identification method, a network fault identification device, a computer-readable storage medium, and a network fault identification system.

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] Figure 1This is a flowchart of a network fault identification method according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0025] Step S101: Receive and respond to the detection task, and generate a detection command according to the detection task, wherein the detection task includes the unique identifier of the target detection device and the detection frequency;

[0026] Specifically, the detection task is the scheduling information for network performance detection of the target detection device. The detection task includes parameters such as the unique identifier of the target detection device, detection method, detection frequency, and anomaly threshold. The detection command is an executable command generated according to the detection task, which is used to guide the target detection device or the data acquisition and control platform to perform specific network detection operations. The detection command contains specific execution protocols, port parameters, number of transmissions, timeout thresholds, and authentication information, which can be directly identified and executed by the detection component to complete the collection of performance indicators such as network latency and packet loss rate.

[0027] Step S102: Obtain the target detection device's index data from the target detection device corresponding to the above detection command, and determine the abnormal index data in the index data. The index data includes at least network latency and packet loss rate.

[0028] Specifically, after generating a probe command, it is sent to the corresponding target probe device or a probe component deployed at the network edge. Upon receiving the probe command, the probe component performs a network probe operation based on the parameters contained in the command to actively acquire the operational status data of the target probe device. This operational status data includes performance indicators such as network latency, packet loss rate, reachability, and response time of the target device during the probe process. The probe component encapsulates this indicator data into a structured format and sends it back to the probe perception management system. Upon receiving this indicator data, the probe perception management system performs real-time analysis and comparison of the collected performance indicators and determines whether any abnormal indicator data exists based on preset threshold rules or anomaly detection models. Network latency characterizes the round-trip time of network data packets, reflecting the timeliness of link transmission; packet loss rate is used to measure the proportion of data packets lost during data transmission, reflecting link stability. When network latency exceeds a preset latency threshold or packet loss rate exceeds a preset packet loss rate threshold, the system marks this data as abnormal indicator data and uses this as the basis for subsequent alarm generation and device maintenance prompts.

[0029] Step S103: Generate a first alarm message containing the above-mentioned abnormal indicator data and the unique identifier of the device corresponding to the above-mentioned abnormal indicator data, so as to prompt the device corresponding to the above-mentioned abnormal indicator data to be repaired.

[0030] Through the above embodiments, by automatically generating probe commands and periodically collecting network performance indicators such as latency and packet loss rate, proactive perception and dynamic monitoring of network device operating status are achieved. Upon receiving a probe task, a probe command is automatically generated and sent to the target probe device. During the probe process, the probe can continuously acquire key performance indicators such as network latency, packet loss rate, reachability, and response time according to a preset probe frequency. The collected indicator data is compared with preset thresholds in real time, automatically identifying corresponding abnormal indicator data and generating a first alarm message containing the abnormal indicators for subsequent fault location and maintenance operations. This allows potential performance anomalies to be identified in advance before network faults are perceived by users or cause service interruptions, realizing a shift from passive fault reporting and response to proactive intelligent monitoring. Compared with the traditional passive operation and maintenance method that relies on manual inspection, log analysis, and customer complaint feedback, this solution can automatically execute probes and update monitoring results, avoiding the long cycle delay of manual inspections, shortening fault detection and handling time, and improving overall operation and maintenance efficiency. This solves the problem of low maintenance efficiency caused by the passive maintenance mode in existing technologies, which mainly relies on manual inspections and customer reports.

[0031] In one optional embodiment, obtaining the target detection device's index data from the target detection device corresponding to the aforementioned detection command includes: obtaining first preliminary index data of the target detection device and determining outliers in the first preliminary index data; removing the outliers from the first preliminary index data to obtain second preliminary index data; determining the unique identifier and detection time of the target detection device corresponding to the second preliminary index data; and filling a preset data template based on the unique identifier, the detection time, and the second preliminary index data to obtain the target detection device's index data.

[0032] In the above embodiments, after acquiring the raw detection data from the target detection device, the collected index data is cleaned and standardized, thereby effectively improving the accuracy, reliability, and consistency of the collected data. Specifically, by identifying and removing outliers from the first preliminary index data, abnormal data points caused by network jitter, sampling errors, or abnormal device response can be eliminated, preventing such atypical values ​​from interfering with the overall analysis results.

[0033] This improves the stability and accuracy of the indicator data. After cleaning, by determining the unique identifier of the target device and the detection time corresponding to the second preliminary indicator data, a one-to-one correspondence between the collected data and specific device instances and time points can be achieved in the system, ensuring the traceability of the data source and the consistency of the time sequence. Furthermore, by filling a preset data template based on the aforementioned unique identifier, detection time, and second preliminary indicator data, the detection results reported by different probes or different devices can be unified into a structured format. This allows subsequent operations such as data integrity verification, anomaly identification, and alarm generation to be performed on a unified, accurate, and comparable data basis, thereby improving the data quality and processing efficiency of the entire network detection system.

[0034] In another alternative approach, before obtaining the indicator data of the target detection device from the target detection device corresponding to the aforementioned detection command, the method further includes: if the key validity verification of the aforementioned detection command passes, obtaining the indicator data of the target detection device corresponding to the aforementioned detection command; if the key validity verification of the aforementioned detection command fails, refusing to execute the aforementioned detection command and generating a second alarm message to indicate the risk of unauthorized access.

[0035] In the above embodiments, when the key validity verification of the probe command passes, the system confirms that the command was legally generated by the probe perception management system or authorized superior module. Only then can the indicator data of the target probe device corresponding to the command be obtained, ensuring that the probe operation is executed within a controlled and secure scope. When the key validity verification of the probe command fails, the system refuses to execute the command to avoid unexpected operations or information leakage risks to the target probe device, and generates a second alarm message to alert the operation and maintenance system or security management module of the risk of unauthorized access. This alarm message can help operation and maintenance personnel promptly detect potential command tampering, invalid credentials, or external intrusion, thereby improving the security and protection capabilities of the entire probe system in the task issuance and execution stages. It ensures that the probe process is carried out only under the condition of trusted identity and valid key, realizes access control and execution permission isolation of probe commands, effectively prevents malicious probes, accidental probe triggering, or unauthorized access, thereby ensuring the operational stability and data security of the network probe system.

[0036] In some exemplary embodiments, the aforementioned indicator data includes verification information. After obtaining the indicator data of the target detection device from the target detection device corresponding to the aforementioned detection command, the method further includes: performing an integrity verification on the indicator data based on the verification information of the indicator data; if it is determined that the indicator data is incomplete, obtaining the indicator data of the target detection device again; if it is determined that the indicator data is complete, storing the indicator data in a preset database.

[0037] In the above embodiments, the verification information carried in the indicator data may include checksums, hash values, data length identifiers, or time sequence numbers. By comparing and verifying the verification information, it is possible to determine whether there is packet loss, truncation, duplication, or tampering during data transmission or caching. When the verification result indicates that the indicator data is incomplete, a re-collection mechanism is automatically triggered to re-acquire the indicator data from the corresponding target detection device to repair data loss caused by network jitter, link interruption, or buffer overflow, thereby ensuring the continuity and temporal integrity of the collected data. When the verification result indicates that the indicator data is complete, the indicator data is stored in a preset database to ensure that the data stored meets the requirements of integrity and consistency, providing a reliable data foundation for subsequent data analysis, anomaly identification, and statistical calculations. This realizes an automatic verification and repair mechanism in the data acquisition stage, avoiding result distortion caused by data loss or transmission anomalies, thereby improving the availability of detection data and the overall stability of the system.

[0038] In other exemplary embodiments, generating a detection command based on the detection task includes: determining a preset command template based on the target detection device corresponding to the detection task; and generating the detection command based on the preset command template.

[0039] In the above embodiments, the preset instruction template includes standard format definitions for different device types and detection protocols.

[0040] This includes parameter fields, command structure, execution cycle, and security authentication fields. Through a template matching mechanism, the system can automatically select the most suitable template based on the device attributes of the detection task, avoiding errors and inconsistencies caused by manually writing commands or configuring parameters. After the template is selected, the corresponding fields in the template are filled in according to the parameter information carried in the detection task (including the unique identifier of the target device, detection frequency, and threshold requirements), thereby generating a standardized and parameter-complete detection command. The generated detection command can be directly recognized and executed by the probe, ensuring that different types of devices follow a unified format specification and communication protocol requirements when performing detection.

[0041] In some exemplary embodiments of this application, obtaining the target detection device's index data from the target detection device corresponding to the above-mentioned detection command includes: in the case of failure to obtain the index data, determining that the detection state of the index data is an abnormal state; generating third alarm information containing the detection state and the unique identifier of the target detection device corresponding to the detection state, so as to prompt the network status of the target detection device to be detected.

[0042] In the above embodiments, when attempting to obtain indicator data from the target detection device according to the detection command, if no valid response is received within a preset timeout period, or if the data format does not meet expectations, the data acquisition is determined to have failed, and the corresponding detection state is identified as an abnormal state. An abnormal state indicates that the target device failed to respond normally to the detection request within the current detection cycle, which may be caused by network link interruption, device offline, communication port closure, or probe execution abnormality. After determining that the detection state is abnormal, a third alarm message is automatically generated, recording the unique identifier of the target detection device, the detection time, and an abnormal state description in the alarm message to quickly locate the abnormal device. This alarm message is used to prompt the detection of the network status of the corresponding target detection device to confirm whether the communication link between the device and the probe sensing management system is unobstructed, thereby detecting link interruption or device offline problems at an early stage.

[0043] In some further exemplary embodiments of this application, determining abnormal indicator data in the above indicator data includes: determining the packet loss rate as abnormal indicator data when the packet loss rate of the above indicator data is greater than a preset packet loss rate; and determining the network latency as abnormal indicator data when the network latency of the above indicator data is greater than a preset latency.

[0044] In the above embodiments, when the packet loss rate exceeds a preset packet loss rate threshold, the indicator corresponding to the packet loss rate is determined to be abnormal indicator data. This abnormal indicator indicates that data packets are lost during data transmission, reflecting a decrease in link stability or fluctuations in network quality. When the network latency exceeds a preset latency threshold, the indicator corresponding to the network latency is determined to be abnormal indicator data. This abnormal indicator indicates that there is significant latency in network data transmission, reflecting a decrease in link transmission rate or performance problems such as congestion or routing anomalies. Thus, it is possible to automatically identify whether the network performance of the target detection device is in an abnormal state without relying on manual intervention, and to quickly extract key abnormal features from the original indicator data, providing accurate input basis for subsequent alarm generation, trend analysis, or anomaly location.

[0045] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the network fault identification method of this application will be described in detail below with reference to specific embodiments.

[0046] This embodiment relates to a specific network fault identification method. In this embodiment, by developing a probe sensing agent program and a supporting probe management system, the active detection, anomaly identification and fault warning of the network service operation status are realized. The implementation plan mainly includes three parts: the deployment and detection logic of the probe sensing agent program, the network device detection mechanism without probes, and the upper-layer application of the detection results. In this embodiment, the probe sensing system adopts a dual-end deployment mode of "management end - detection end". The management end is mainly responsible for the generation, scheduling and result reception of detection tasks; the detection end is responsible for executing specific detection operations and data feedback. The deployment process includes the following steps: (1) Initialization stage: Complete system registration on the management end, configure communication address, communication port and data callback interface parameters; install a lightweight probe sensing agent program on the detection end. After installation, the probe sensing agent program automatically performs network adaptation (can distinguish between intranet and public network interfaces) and completes identity authentication (connects to the permission system of operation and maintenance personnel), and sets the local resource usage limit (memory usage not exceeding 5%, processor usage not exceeding 3%) to ensure operational stability. Task issuance phase: The management end generates a probe task according to the operation and maintenance requirements. The task information includes the task number, the probe target (such as the network address or service port of the target device), the probe frequency (such as once per minute) and the indicator threshold (such as triggering an alarm when the packet loss rate is greater than 1%). After the system generates the task, it converts it into a probe instruction and sends it to the corresponding probe perception agent program through a persistent communication protocol. The probe instruction is encapsulated in a structured data format and contains task metadata and encryption keys to ensure transmission security. (3) Probe execution phase: After receiving the probe instruction, the probe perception agent program automatically identifies the network interface type it uses. When it is identified as an internal network interface, it performs internal business probes, such as core network services and dedicated line link probes; when it is identified as a public network interface, it performs external business probes, such as Internet access or cloud service connection, and adds application layer response code detection and page loading latency measurement on the basis of basic probes. The probe perception agent program adopts an on-demand startup mode, runs the probe process only when the probe task is triggered, and releases system resources immediately after the task is completed, without occupying background memory for a long time. To ensure the accuracy of the detection, the probe perception agent optimizes the length of the detection data packet, using 1400 bytes by default to avoid data fragmentation; the detection process is executed asynchronously, without blocking other business processes of the local system, thereby ensuring the stable operation of the detection server. After the probe perception agent completes the detection, it performs the following three steps: (1) Data preprocessing: the original detection data is cleaned and standardized, invalid data and outlier data are removed, the unit of indicators is unified (such as unifying the latency to milliseconds and the packet loss rate to percentages), and a structured data packet is generated, which includes the task number, detection time, target address, network indicators and business status information.(2) Data feedback: The probe sensing agent program transmits the structured data back to the probe management system through a secure communication channel according to the preset callback interface. The feedback frequency is consistent with the detection frequency. If the data cannot be uploaded due to network interruption, it will be cached locally and retransmitted in chronological order after the network is restored to ensure the continuity and integrity of the data records. (3) Result verification and storage: After receiving the feedback data, the management end first performs data integrity verification (e.g., by hash value comparison or digest verification). If the verification fails, it will be retransmitted. After the verification is passed, the detection results will be stored in the time series database and the data will be marked with the "archived" status tag for subsequent upper-layer applications to call and analyze. (II) For network devices that cannot be directly installed with probes (such as routers, switches, etc.), this embodiment designs a three-level interactive architecture of "management system - intermediate acquisition and control system - target network element" to realize remote active detection of large network devices. The management system has a built-in protocol adaptation library for multi-vendor devices, which supports command templates (such as command line interface protocol, simple network management protocol, etc.) for different brands and models of devices. The corresponding instruction template is automatically matched according to the device model of the target network element and the detection instruction is generated. The generated probe command includes the device address, execution parameters, timeout period, and security verification information. The management system sends the probe request to the data acquisition and control system via a secure communication interface. After verifying the operator's identity and key validity, the data acquisition and control system then issues the command to the corresponding target device via a secure channel (such as a secure shell protocol). Upon receiving the probe command, the target network element executes the corresponding probe command and generates raw echo data, such as returned packet loss rate, minimum and maximum latency values, and other indicator information. The data acquisition and control system receives the echo data from the network element, parses it, extracts key indicators, and transforms it into unified, standardized structured data. Subsequently, the data acquisition and control system pushes the standardized data asynchronously to the management system via a preset callback address. Data push uses a message queue mechanism to avoid synchronization blocking issues in high-concurrency scenarios and improve transmission stability. After issuing the command, the management system sets a result waiting time limit (e.g., 1 minute). If no result is received within the specified time, it automatically initiates a result query request to the data acquisition and control system. If multiple consecutive queries fail to elicit a response, the system marks the network element's detection status as "abnormal" and triggers an alarm to prompt maintenance personnel to check the device's connectivity or execution status. (III) The detection management system aggregates all detection results from probe-sensing agent programs and non-probe-sensing agent program devices, and through data integration, graph construction, and alarm linkage, achieves visualization and automation of operation and maintenance. A network operation graph is established based on graph database technology. "Network device nodes" and "service nodes" are predefined in the graph, and "device-to-device" connection relationships and "device-to-service" carrying relationships are established.Probe data is extracted from a time-series database, and a data-to-node association mapping is achieved through "device address" and "service port." Key indicators such as latency, packet loss rate, and service availability from the probe data are written into node attributes. A strategy combining real-time updates and historical version retention is adopted, meaning real-time data overwrites the latest values, while historical data is retained for a specified number of days according to the time series. When an indicator exceeds a threshold, the node or link relationship is marked as "abnormal" and displayed in a prominent color to help operations personnel quickly locate the scope of the problem and its impact on the business. Multi-level alarm rules are configured based on the probe data. For example, a level 1 alarm is generated when the packet loss rate is between 1% and 5%, and a level 2 alarm is generated when the packet loss rate exceeds 5%. Different alarm priorities can be configured for different business types to ensure that critical business processes are handled first. When an alarm is triggered, the system automatically generates an alarm record, including the alarm number, fault object, abnormal indicator, and suggested handling measures, and pushes it to operations personnel through various notification methods. Alarm information can also be synchronously integrated with the operations work order system to achieve closed-loop management of alarm dispatch, fault repair, and verification. After detecting that the indicators have returned to normal, the system automatically closes the alarm work order, forming a complete "detection-alarm-repair-verification" chain. To improve the convenience of operation and maintenance, the system provides a mobile terminal interface. Operation and maintenance personnel can directly configure detection tasks, set detection frequencies, and view detection results through the mobile application. The application interface can query real-time detection data by task number or target address, and display the changes in latency and packet loss rate in the form of trend charts. Anomalies are displayed with prominent identification; clicking on them allows viewing alarm details and related device information, enabling real-time monitoring of network operation status and improving operation and maintenance response speed and decision-making efficiency. Through the above implementation methods, this invention achieves proactive detection, automatic diagnosis, and real-time early warning of network operation status. The lightweight design and secure feedback mechanism of the probe perception agent program significantly reduce system resource consumption; the three-level detection architecture for network devices without deployed probes solves the problem that core network elements in traditional networks cannot be directly detected; the upper-layer graphing and alarm closed-loop design realizes fully automated operation and maintenance from anomaly perception, alarm generation to fault repair verification. This solution can detect potential link degradation or service anomalies in advance, reduce manual inspections and passive fault reporting, and significantly improve the real-time performance, accuracy, and operational efficiency of network fault identification.

[0047] This application also provides a specific use case for a key validity verification mechanism. Before obtaining indicator data from the target detection device, the detection command is first verified for key validity. When generating a detection command, the probe perception management system assigns a unique identity signature to each command. The signature is generated by encrypting the command content (including task identifier, target device identifier, and timestamp) based on a preset key. After receiving the detection command, the detection component first extracts the signature information from the command and calls the local security module to perform validity verification using the corresponding key. If the verification result shows that the signature of the command matches the legitimate key recorded by the system, the source of the detection command is confirmed to be trustworthy and has not been tampered with. The detection component then executes the command and collects indicator data from the target device. If the key verification fails, it indicates that the command may have been forged by an unauthorized system or malicious program. The system immediately refuses to execute the detection command to prevent unexpected operational impact on the target device or the introduction of potential security risks. At the same time, the system automatically generates a second alarm message, which includes the unique identifier of the corresponding detection command, the target device identifier, the command reception time, and the reason for the verification failure, to alert the probe perception management system or maintenance personnel of the risk of unauthorized access. By introducing a key-level security verification mechanism before the detection is executed, access control and anomaly protection at the detection task execution layer are achieved. This effectively prevents the execution of forged instructions and malicious invocation, ensuring that the detection operation is only performed within the authorized trust domain, thereby significantly improving the system's security, instruction credibility, and overall operational stability.

[0048] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0049] This application also provides a network fault identification device. It should be noted that the network fault identification device of this application can be used to execute the network fault identification method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0050] The following describes the network fault identification device provided in the embodiments of this application.

[0051] Figure 2 This is a schematic diagram of a network fault identification device according to an embodiment of this application. Figure 2 As shown, the device includes:

[0052] The receiving unit 10 is used to receive and respond to the detection task, and generate a detection command according to the detection task, wherein the detection task includes a unique identifier of the target detection device and a detection frequency;

[0053] The first acquisition unit 20 is used to acquire the indicator data of the target detection device from the target detection device corresponding to the above detection command, and to determine the abnormal indicator data in the indicator data. The indicator data includes at least network latency and packet loss rate.

[0054] The first generation unit 30 is used to generate a first alarm message containing the above-mentioned abnormal indicator data and the unique identifier of the device corresponding to the above-mentioned abnormal indicator data, so as to prompt the device corresponding to the above-mentioned abnormal indicator data to be repaired.

[0055] In one optional embodiment, the acquisition unit includes: an acquisition module, configured to acquire first preliminary indicator data of the target detection device and determine outliers in the first preliminary indicator data; a removal module, configured to remove the outliers in the first preliminary indicator data to obtain second preliminary indicator data; a first determination module, configured to determine the unique identifier and detection time of the target detection device corresponding to the second preliminary indicator data; and a filling module, configured to fill a preset data template according to the unique identifier, the detection time, and the second preliminary indicator data to obtain the indicator data of the target detection device.

[0056] In another alternative embodiment, the device further includes: a second acquisition unit, configured to acquire the indicator data of the target detection device corresponding to the detection command if the key validity verification of the detection command passes; and a second generation unit, configured to refuse to execute the detection command and generate a second alarm message to indicate the risk of unauthorized access if the key validity verification of the detection command fails.

[0057] In some exemplary embodiments, the above-described apparatus further includes: a verification unit, configured to perform integrity verification on the indicator data based on the verification information of the indicator data; a third acquisition unit, configured to acquire the indicator data of the target detection device again if it is determined that the indicator data is incomplete; and a storage unit, configured to store the indicator data in a preset database if it is determined that the indicator data is complete.

[0058] In some other exemplary embodiments, the receiving unit includes: a second determining module, configured to determine a preset instruction template based on the target detection device corresponding to the detection task; and a first generating module, configured to generate the detection instruction based on the preset instruction template.

[0059] In some exemplary embodiments of this application, the first acquisition unit further includes: a third determination module, configured to determine that the detection state of the indicator data is an abnormal state when the acquisition of the indicator data fails; and a second generation module, configured to generate third alarm information containing the detection state and the unique identifier of the target detection device corresponding to the detection state, so as to prompt the network status of the target detection device to be detected.

[0060] In some further exemplary embodiments of this application, the first acquisition unit further includes: a fourth determining module, configured to determine that the packet loss rate of the above indicator data is the above abnormal indicator data when the packet loss rate of the above indicator data is greater than a preset packet loss rate; and a fifth determining module, configured to determine that the network latency of the above indicator data is the above abnormal indicator data when the network latency of the above indicator data is greater than a preset latency.

[0061] The aforementioned network fault identification device includes a processor and a memory. The receiving unit, the acquiring unit, and the generating unit are all stored as program units in the memory, and the processor executes these program units to achieve their respective functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0062] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the problem of low maintenance efficiency caused by the current passive maintenance model where operators' network operations and maintenance mainly rely on manual inspections and customer fault reports can be addressed.

[0063] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0064] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the network fault identification method.

[0065] Specifically, network fault identification methods include:

[0066] Step S101: Receive and respond to the detection task, and generate a detection command according to the detection task, wherein the detection task includes the unique identifier of the target detection device and the detection frequency;

[0067] Specifically, the detection task is the scheduling information for network performance detection of the target detection device. The detection task includes parameters such as the unique identifier of the target detection device, detection method, detection frequency, and anomaly threshold. The detection command is an executable command generated according to the detection task, which is used to guide the target detection device or the data acquisition and control platform to perform specific network detection operations. The detection command contains specific execution protocols, port parameters, number of transmissions, timeout thresholds, and authentication information, which can be directly identified and executed by the detection component to complete the collection of performance indicators such as network latency and packet loss rate.

[0068] Step S102: Obtain the target detection device's index data from the target detection device corresponding to the above detection command, and determine the abnormal index data in the index data. The index data includes at least network latency and packet loss rate.

[0069] Specifically, after generating a probe command, it is sent to the corresponding target probe device or a probe component deployed at the network edge. Upon receiving the probe command, the probe component performs a network probe operation based on the parameters contained in the command to actively acquire the operational status data of the target probe device. This operational status data includes performance indicators such as network latency, packet loss rate, reachability, and response time of the target device during the probe process. The probe component encapsulates this indicator data into a structured format and sends it back to the probe perception management system. Upon receiving this indicator data, the probe perception management system performs real-time analysis and comparison of the collected performance indicators and determines whether any abnormal indicator data exists based on preset threshold rules or anomaly detection models. Network latency characterizes the round-trip time of network data packets, reflecting the timeliness of link transmission; packet loss rate is used to measure the proportion of data packets lost during data transmission, reflecting link stability. When network latency exceeds a preset latency threshold or packet loss rate exceeds a preset packet loss rate threshold, the system marks this data as abnormal indicator data and uses this as the basis for subsequent alarm generation and device maintenance prompts.

[0070] Step S103: Generate a first alarm message containing the above-mentioned abnormal indicator data and the unique identifier of the device corresponding to the above-mentioned abnormal indicator data, so as to prompt the device corresponding to the above-mentioned abnormal indicator data to be repaired.

[0071] In one embodiment of this application, obtaining the target detection device's index data from the target detection device corresponding to the aforementioned detection command includes: obtaining first preliminary index data of the target detection device and determining outliers in the first preliminary index data; removing the outliers from the first preliminary index data to obtain second preliminary index data; determining the unique identifier and detection time of the target detection device corresponding to the second preliminary index data; and filling a preset data template based on the unique identifier, the detection time, and the second preliminary index data to obtain the target detection device's index data.

[0072] In one embodiment of this application, before obtaining the indicator data of the target detection device from the target detection device corresponding to the above detection command, the method further includes: if the key validity verification of the above detection command passes, obtaining the indicator data of the target detection device corresponding to the above detection command; if the key validity verification of the above detection command fails, refusing to execute the above detection command and generating a second alarm message to indicate the risk of unauthorized access.

[0073] In one embodiment of this application, the aforementioned indicator data includes verification information. After obtaining the indicator data of the target detection device from the target detection device corresponding to the aforementioned detection command, the method further includes: performing an integrity verification on the indicator data based on the verification information of the indicator data; if it is determined that the indicator data is incomplete, obtaining the indicator data of the target detection device again; if it is determined that the indicator data is complete, storing the indicator data in a preset database.

[0074] In one embodiment of this application, generating a detection command according to the above-mentioned detection task includes: determining a preset command template based on the target detection device corresponding to the above-mentioned detection task; and generating the above-mentioned detection command based on the preset command template.

[0075] In one embodiment of this application, obtaining the target detection device's index data from the target detection device corresponding to the above-mentioned detection command includes: in the case of failure to obtain the index data, determining that the detection status of the index data is an abnormal state; generating a third alarm message containing the detection status and the unique identifier of the target detection device corresponding to the detection status, so as to prompt the network status of the target detection device to be detected.

[0076] In one embodiment of this application, determining abnormal indicator data in the above indicator data includes: determining the packet loss rate as abnormal indicator data when the packet loss rate of the above indicator data is greater than a preset packet loss rate; and determining the network latency as abnormal indicator data when the network latency of the above indicator data is greater than a preset latency.

[0077] This invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: Step S101, receiving and responding to a detection task, generating a detection instruction according to the detection task, wherein the detection task includes a unique identifier of the target detection device and a detection frequency.

[0078] Specifically, the detection task is the scheduling information for network performance detection of the target detection device. The detection task includes parameters such as the unique identifier of the target detection device, detection method, detection frequency, and anomaly threshold. The detection command is an executable command generated according to the detection task, which is used to guide the target detection device or the data acquisition and control platform to perform specific network detection operations. The detection command contains specific execution protocols, port parameters, number of transmissions, timeout thresholds, and authentication information, which can be directly identified and executed by the detection component to complete the collection of performance indicators such as network latency and packet loss rate.

[0079] Step S102: Obtain the target detection device's index data from the target detection device corresponding to the above detection command, and determine the abnormal index data in the index data. The index data includes at least network latency and packet loss rate.

[0080] Specifically, after generating a probe command, it is sent to the corresponding target probe device or a probe component deployed at the network edge. Upon receiving the probe command, the probe component performs a network probe operation based on the parameters contained in the command to actively acquire the operational status data of the target probe device. This operational status data includes performance indicators such as network latency, packet loss rate, reachability, and response time of the target device during the probe process. The probe component encapsulates this indicator data into a structured format and sends it back to the probe perception management system. Upon receiving this indicator data, the probe perception management system performs real-time analysis and comparison of the collected performance indicators and determines whether any abnormal indicator data exists based on preset threshold rules or anomaly detection models. Network latency characterizes the round-trip time of network data packets, reflecting the timeliness of link transmission; packet loss rate is used to measure the proportion of data packets lost during data transmission, reflecting link stability. When network latency exceeds a preset latency threshold or packet loss rate exceeds a preset packet loss rate threshold, the system marks this data as abnormal indicator data and uses this as the basis for subsequent alarm generation and device maintenance prompts.

[0081] Step S103: Generate a first alarm message containing the above-mentioned abnormal indicator data and the unique identifier of the device corresponding to the above-mentioned abnormal indicator data, so as to prompt the device corresponding to the above-mentioned abnormal indicator data to be repaired.

[0082] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0083] In one embodiment of this application, obtaining the target detection device's index data from the target detection device corresponding to the aforementioned detection command includes: obtaining first preliminary index data of the target detection device and determining outliers in the first preliminary index data; removing the outliers from the first preliminary index data to obtain second preliminary index data; determining the unique identifier and detection time of the target detection device corresponding to the second preliminary index data; and filling a preset data template based on the unique identifier, the detection time, and the second preliminary index data to obtain the target detection device's index data.

[0084] In one embodiment of this application, before obtaining the indicator data of the target detection device from the target detection device corresponding to the above detection command, the method further includes: if the key validity verification of the above detection command passes, obtaining the indicator data of the target detection device corresponding to the above detection command; if the key validity verification of the above detection command fails, refusing to execute the above detection command and generating a second alarm message to indicate the risk of unauthorized access.

[0085] In one embodiment of this application, the aforementioned indicator data includes verification information. After obtaining the indicator data of the target detection device from the target detection device corresponding to the aforementioned detection command, the method further includes: performing an integrity verification on the indicator data based on the verification information of the indicator data; if it is determined that the indicator data is incomplete, obtaining the indicator data of the target detection device again; if it is determined that the indicator data is complete, storing the indicator data in a preset database.

[0086] In one embodiment of this application, generating a detection command according to the above-mentioned detection task includes: determining a preset command template based on the target detection device corresponding to the above-mentioned detection task; and generating the above-mentioned detection command based on the preset command template.

[0087] In one embodiment of this application, obtaining the target detection device's index data from the target detection device corresponding to the above-mentioned detection command includes: in the case of failure to obtain the index data, determining that the detection status of the index data is an abnormal state; generating a third alarm message containing the detection status and the unique identifier of the target detection device corresponding to the detection status, so as to prompt the network status of the target detection device to be detected.

[0088] In one embodiment of this application, determining abnormal indicator data in the above indicator data includes: determining the packet loss rate as abnormal indicator data when the packet loss rate of the above indicator data is greater than a preset packet loss rate; and determining the network latency as abnormal indicator data when the network latency of the above indicator data is greater than a preset latency.

[0089] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0090] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0095] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0096] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change 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), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] 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.

[0099] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0100] This network fault identification method, through automatically generating probe commands and periodically collecting network performance indicators such as latency and packet loss rate, achieves proactive perception and dynamic monitoring of network device operating status. Upon receiving a probe task, it automatically generates probe commands and sends them to the target probe device. During the probe process, the probe continuously acquires key performance indicators such as network latency, packet loss rate, reachability, and response time at a preset probe frequency. It then compares the collected indicator data with preset thresholds in real time, automatically identifying corresponding abnormal indicator data and generating a first alarm message containing the abnormal indicators for subsequent fault location and maintenance operations. This allows for the early identification of potential performance anomalies before network faults are perceived by users or cause service interruptions, shifting from a passive fault reporting and response model to a proactive intelligent monitoring model. Compared to traditional passive maintenance methods relying on manual inspection, log analysis, and customer complaint feedback, this solution automatically executes probes and updates monitoring results, avoiding the long-cycle delays of manual inspections, shortening fault detection and handling time, and improving overall maintenance efficiency. This solves the problem of low maintenance efficiency caused by the passive maintenance mode in existing technologies, which mainly relies on manual inspections and customer reports.

[0101] The above description is merely a preferred embodiment of this application and is not intended to limit 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 protection scope of this application.

Claims

1. A network fault identification method characterized by, The method comprises: receiving and responding to a detection task, generating a detection instruction according to the detection task, the detection task comprising a unique identifier of a target detection device and a detection frequency; obtaining index data of the target detection device from the target detection device corresponding to the detection instruction, and determining abnormal index data in the index data, the index data at least comprising network latency and packet loss rate; generating first alarm information containing the abnormal index data and the unique identifier of the device corresponding to the abnormal index data to prompt maintenance of the device corresponding to the abnormal index data.

2. The method of claim 1, wherein, Obtaining index data of the target detection device from the target detection device corresponding to the detection instruction comprises: obtaining first preliminary index data of the target detection device and determining outliers in the first preliminary index data; removing the outliers in the first preliminary index data to obtain second preliminary index data; determining the unique identifier of the target detection device corresponding to the second preliminary index data and the detection time; filling a preset data template according to the unique identifier, the detection time and the second preliminary index data to obtain the index data of the target detection device.

3. The method of claim 1, wherein, Before obtaining the index data of the target detection device from the target detection device corresponding to the detection instruction, the method further comprises: if the key validity check of the detection instruction passes, obtaining the index data of the target detection device corresponding to the detection instruction; if the key validity check of the detection instruction does not pass, refusing to execute the detection instruction and generating second alarm information to prompt the risk of illegal access.

4. The method of claim 1, wherein, The index data comprises verification information, and after obtaining the index data of the target detection device from the target detection device corresponding to the detection instruction, the method further comprises: performing integrity check on the index data according to the verification information of the index data; if it is determined that the index data is incomplete, obtaining the index data of the target detection device again; if it is determined that the index data is complete, storing the index data to a preset database.

5. The method of claim 1, wherein, Generating a detection instruction according to the detection task comprises: determining a preset instruction template according to the target detection device corresponding to the detection task; generating the detection instruction according to the preset instruction template.

6. The method of claim 1, wherein, Obtaining index data of the target detection device from the target detection device corresponding to the detection instruction comprises: if the index data fails to be obtained, determining that the detection state of the index data is an abnormal state; generating third alarm information containing the detection state and the unique identifier of the target detection device corresponding to the detection state to prompt detection of the network state of the target detection device.

7. The method of claim 1, wherein, Determining abnormal index data in the index data comprises: if the packet loss rate of the index data is greater than a preset packet loss rate, determining that the packet loss rate is the abnormal index data; In a case where the network delay of the index data is greater than a preset delay, the network delay is determined as the abnormal index data.

8. A network failure identification apparatus characterized by comprising: Comprise: A receiving unit configured to receive a detection task, generate a detection instruction according to the detection task, the detection task comprising a unique identifier of a target detection device and a detection frequency; An obtaining unit configured to obtain index data of the target detection device from the target detection device corresponding to the detection instruction, and determine abnormal index data in the index data, the index data comprising at least network delay and packet loss rate; A generating unit configured to generate first alarm information comprising the abnormal index data and a unique identifier of a device corresponding to the abnormal index data, to prompt maintenance of the device corresponding to the abnormal index data.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program controls the device where the computer readable storage medium is located to execute the method of any one of claims 1 to 7 when the program is running.

10. A network fault identification system, characterized by, Comprise: One or more processors, memories, and one or more programs, wherein the one or more programs are stored in the memories and configured to be executed by the one or more processors, and the one or more programs comprise a program for executing the method of any one of claims 1 to 7.

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