Fault detection methods, devices, electronic equipment and readable storage media

Abstract

This disclosure provides a fault detection method, apparatus, electronic device, and readable storage medium, relating to the field of computer technology, and particularly to fault detection technology for content distribution. The specific implementation scheme is as follows: considering the correlation between bad point indicator data information of the target interface within a predetermined time period, assigning corresponding weights to the bad point indicator data based on the continuous information of the bad point indicator data, and then determining whether the target interface is a faulty interface based on the weights of each bad point indicator data. This avoids the false detection problem caused by assigning uniform weights to bad point indicator data (i.e., identifying interfaces without faults as faulty interfaces); furthermore, performing interface-level fault detection enables refined fault detection, and fault detection is performed based on indicator data generated by the target interface, resulting in good real-time performance and a short detection cycle.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to fault detection technology for content distribution. Background Technology

[0002] CDN is a content delivery network technology. During system operation, various faults may occur, and how to effectively detect these faults has become a problem. Summary of the Invention

[0003] This disclosure provides a fault detection method, apparatus, electronic device, and readable storage medium. According to a first aspect of this disclosure, a fault detection method is provided, comprising:

[0004] Determine the bad pixel index data information of the target interface within a predetermined time period, wherein the bad pixel data indicates that the output data of the target interface does not meet the index;

[0005] Based on the determined bad pixel index data, the continuous information of the bad pixel index is determined, wherein the continuous information represents the time information or the number information of consecutive occurrences of bad pixel index data.

[0006] Based on the predetermined weighting rules and the continuous information of the determined bad pixel indicators, the weight value corresponding to each bad pixel indicator data is determined.

[0007] Based on the bad point index data and the corresponding weight values ​​of the bad point index data, determine whether the target interface is a faulty interface.

[0008] According to a second aspect of this disclosure, a fault detection device is provided, comprising:

[0009] The first determining module is used to determine the bad pixel index data information of the target interface within a predetermined time period, wherein the bad pixel data indicates that the output data of the target interface does not meet the index.

[0010] The second determining module is used to determine the continuity information of the bad pixel index based on the determined bad pixel index data information, wherein the continuity information represents the time information or the number information of the consecutive occurrence of bad pixel index data.

[0011] The third determination module is used to determine the weight value corresponding to each bad point indicator data based on the predetermined weight determination rules and the continuous information of the determined bad point indicators.

[0012] The fourth determination module is used to determine whether the target interface is a faulty interface based on each bad point indicator data and the corresponding weight value of the bad point indicator data.

[0013] According to a third aspect of this disclosure, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to at least one of the aforementioned processors; wherein,

[0016] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to perform the method.

[0017] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described above.

[0018] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method.

[0019] The beneficial effects of the technical solution provided in this disclosure are:

[0020] The solution provided in this disclosure differs from existing technologies that use log files of the target interface for fault detection, which suffer from long detection cycles and a certain degree of lag. This disclosure determines bad pixel index data information of the target interface within a predetermined time period, where bad pixel data indicates that the output data of the target interface does not meet the indicators; based on the determined bad pixel index data information, it determines the continuity information of the bad pixel indicators, where the continuity information represents the time information or the number of consecutive occurrences of bad pixel index data; based on predetermined weight determination rules and the determined continuity information of the bad pixel indicators, it determines the weight value corresponding to each bad pixel index data; and based on each bad pixel index data and its corresponding weight value, it determines whether the target interface is a faulty interface. This involves considering the correlation between the defective indicator data information of the target interface within a predetermined time period, assigning corresponding weights to the defective indicator data based on the continuous information of the defective indicator data, and then determining whether the target interface is a faulty interface based on the weight of each defective indicator data. This can avoid the false detection problem caused by assigning uniform weights to defective indicator data (that is, identifying interfaces that do not have faults as faulty interfaces). In addition, performing interface-level fault detection can achieve refined fault detection, and fault detection can be performed based on the indicator data generated by the target interface. The fault detection has good real-time performance and a short detection cycle.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0023] Figure 1 This is a flowchart of the fault detection method provided in this disclosure;

[0024] Figure 2 This is a sample image illustrating the bad pixel data provided in this disclosure;

[0025] Figure 3 This is a structural schematic diagram of the fault detection device provided in this disclosure;

[0026] Figure 4 This is a block diagram of an electronic device used to implement embodiments of the present disclosure. Detailed Implementation

[0027] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0028] Example 1

[0029] Figure 1 This disclosure illustrates a fault detection method provided by an embodiment of the present disclosure, such as... Figure 1 As shown, it includes:

[0030] Step S101: Determine the bad pixel index data information of the target interface within a predetermined time period. The bad pixel data indicates that the output data of the target interface does not meet the index.

[0031] The method disclosed herein can be applied to fault detection in content distribution technology, or to fault detection in other application scenarios, as long as the corresponding data generated by the target interface can reflect whether there is an anomaly.

[0032] Taking a Content Delivery Network (CDN) as an example, its basic idea is to bypass bottlenecks and points of failure on the internet that could affect data transmission speed and stability, making content delivery faster and more stable. By placing node servers throughout the network, forming an intelligent virtual network on top of the existing internet infrastructure, the CDN system can redirect user requests to the nearest service node in real time based on comprehensive information such as network traffic, the connection and load status of each node, and the distance and response time to the user. The goal is to allow users to obtain the content they need from the nearest location, alleviating internet congestion and improving website response speed. CDN provides corresponding API interfaces, such as service operation interfaces, domain name operation interfaces, and configuration operation interfaces. These API interfaces generate corresponding indicator data, which can reflect whether the interfaces are experiencing faults.

[0033] Specifically, the bad pixel index data information of the target interface within a predetermined time period is determined by the corresponding method; bad pixel data indicates that the output data of the target interface does not meet the index; the predetermined time period can be an empirical value (such as 10 seconds, 5 seconds), which may vary depending on the specific use case.

[0034] Step S102: Determine the continuous information of the bad pixel index based on the determined bad pixel index data information. The continuous information represents the time information or the number information of consecutive occurrences of bad pixel index data.

[0035] Specifically, the continuous information of the bad pixel index can be determined based on the identified bad pixel data, such as the number of consecutive occurrences of bad pixel data and the duration of consecutive occurrences of bad pixel data.

[0036] Step S103: Based on the predetermined weight determination rules and the continuous information of the determined bad pixel indicators, determine the weight value corresponding to each bad pixel indicator data.

[0037] Specifically, certain weighting rules can be set to determine the weight value corresponding to each bad pixel indicator data based on the continuity of bad pixel information. The predetermined weighting rules are used to determine that the more consecutive bad pixel indicator data there are, or the longer the consecutive occurrence of bad pixel indicator data, the higher the weight value of each bad pixel indicator data in the continuous bad pixel indicator data. Therefore, by determining the weight corresponding to the bad pixel indicator based on the continuity of bad pixel indicator data, the correlation of bad pixel data is taken into account, avoiding the problem of assigning the same weight to bad pixel data and causing misjudgment of faults.

[0038] For example, such as Figure 2As shown, points with a y-coordinate of 0 correspond to bad point indicators (e.g., continuous type corresponds to y-coordinate of 0 and x-coordinates of 4-33; interval type corresponds to y-coordinate of 0 and x-coordinates of 2-6; discrete type corresponds to y-coordinate of 0 and x-coordinates of 3, 5, 8, and 10). If the weight of continuous bad point data is the same as that of interval and discrete bad point data, and if the number of continuous bad points is the same as the number of interval and discrete bad points, and the total weight of the bad point data exceeds the predetermined fault judgment threshold, then all of them may be considered as having a fault, while the interval or discrete type may not have experienced an interface fault. The presence of bad point data in the discrete or interval type may be normal and not an interface fault.

[0039] Step S104: Based on each bad point indicator data and the corresponding weight value of the bad point indicator data, determine whether the target interface is a faulty interface.

[0040] Specifically, based on each bad data point and its corresponding weight value, and through appropriate fault judgment conditions, it can be determined whether the target interface is a faulty interface.

[0041] The solution provided in this disclosure differs from existing technologies that use log files of the target interface for fault detection, which suffer from long detection cycles and a certain degree of lag. This disclosure determines bad pixel index data information of the target interface within a predetermined time period, where bad pixel data indicates that the output data of the target interface does not meet the indicators; based on the determined bad pixel index data information, it determines the continuity information of the bad pixel indicators, where the continuity information represents the time information or the number of consecutive occurrences of bad pixel index data; based on predetermined weight determination rules and the determined continuity information of the bad pixel indicators, it determines the weight value corresponding to each bad pixel index data; and based on each bad pixel index data and its corresponding weight value, it determines whether the target interface is a faulty interface. This involves considering the correlation between the defective indicator data information of the target interface within a predetermined time period, assigning corresponding weights to the defective indicator data based on the continuous information of the defective indicator data, and then determining whether the target interface is a faulty interface based on the weight of each defective indicator data. This can avoid the false detection problem caused by assigning uniform weights to defective indicator data (that is, identifying interfaces that do not have faults as faulty interfaces). In addition, performing interface-level fault detection can achieve refined fault detection, and fault detection can be performed based on the indicator data generated by the target interface. The fault detection has good real-time performance and a short detection cycle.

[0042] This application provides a possible implementation, wherein determining the weight value corresponding to each bad pixel index data based on a predetermined weight determination rule and the continuous information of the determined bad pixel index includes:

[0043] Based on the continuous time information or consecutive number information of the bad pixel index and the predetermined weight determination rules, determine the weight value corresponding to each continuous time or each consecutive number.

[0044] Specifically, the predetermined weighting rule can be a predetermined relational mapping table, which represents the weight value corresponding to each consecutive time period or each consecutive number of occurrences. For example, the weight corresponding to a bad pixel index data point appearing for more than 2 consecutive seconds, less than 2 consecutive seconds, less than 1 consecutive second, less than 0.5 consecutive seconds, or more consecutively, less than 10 consecutive bad pixel index data points, less than 5 consecutive bad pixel index data points, or less than 2 consecutive bad pixel index data points. In this predetermined relational mapping table, the longer the duration or the greater the number of consecutive bad pixel index data points, the higher the corresponding weight value.

[0045] Based on the determined weight values ​​corresponding to each consecutive time period or consecutive number of consecutive periods, the weight values ​​of each bad point index data corresponding to each consecutive time period or consecutive number of consecutive periods are determined.

[0046] Specifically, based on a predetermined relational mapping table, the weight values ​​corresponding to each consecutive time period or consecutive number of occurrences are determined, and the weight values ​​of each bad pixel index data corresponding to each consecutive time period or consecutive number of occurrences are also determined. For example, if the time during which bad pixel index data occurs consecutively is 2 seconds, and the weight of bad pixel index data occurring consecutively for two seconds is 4, then the weight of each bad pixel data occurring within that consecutive 2-second time period is 4.

[0047] In the embodiments of this application, the problem of determining the weight of bad pixel data is solved.

[0048] This application provides a possible implementation, wherein determining the bad pixel index data information of the target interface within a predetermined time period includes:

[0049] Obtain the target interface's indicator data within a predetermined time period;

[0050] Specifically, the metric data can be obtained directly from interface data, or it can be obtained by analyzing basic data generated based on interface data. Specifically, the metric data may include one or more of the following: strategy center stability data, execution center stability data, DNS stability data, and business local coverage data.

[0051] Based on the predetermined normal indicator data threshold corresponding to the target interface and the acquired indicator data, the bad pixel indicator data within a predetermined time period of the target interface is determined.

[0052] Specifically, based on a predetermined normal indicator data threshold corresponding to the target interface, it can be determined whether the acquired indicator data is bad data. For example, if the predetermined maximum normal indicator data threshold is 5 and the indicator data is 6, then the indicator data is bad data. Specifically, the values ​​of indicator data may differ at different times. The indicator data can be normalized, that is, normal indicators are treated as 1 and bad data as 0. Specifically, the normalized indicator data can also be displayed, for example, such as... Figure 2 As shown, users can determine whether a target interface is faulty or a bad point based on the displayed indicator data, or treat normal indicators as 0 and bad point data as 1.

[0053] The embodiments of this application solve the problem of how to determine the bad pixel index data.

[0054] This application provides a possible implementation, wherein determining whether a target interface is a faulty interface based on each bad pixel index data and the corresponding weight value of the bad pixel index data includes:

[0055] The total weight value of the bad pixel index data is determined based on each bad pixel data and the corresponding weight value of each bad pixel data.

[0056] Based on the total weight value of the determined bad point index data and the predetermined fault interface judgment threshold, it is determined whether the target interface is a fault interface.

[0057] Specifically, the total weight value of bad data within a predetermined period can be statistically determined. If the total weight value exceeds the predetermined fault interface judgment threshold, the target interface is determined to be a fault interface. If it does not exceed the predetermined fault interface threshold, the target interface is determined to be operating normally.

[0058] In this embodiment of the application, a solution is provided for determining whether a target interface is a normal interface.

[0059] This application provides a possible implementation, wherein the method further includes:

[0060] The fault level of the target interface is determined based on the total weight value of the bad pixel index data.

[0061] Specifically, the fault level of the target interface can be determined based on the total weight value of the bad pixel data, so that the corresponding fault handling personnel can handle it accordingly.

[0062] This application provides a possible implementation, wherein the method further includes:

[0063] Based on the determined fault level of the target interface, the fault type of the target interface is determined.

[0064] In the embodiments of this application, different types of faults may lead to different continuity of bad pixel data. Severe faults may result in a longer duration or a greater number of consecutive bad pixel data points. Conversely, the total weight value of the bad pixel data can also reflect the fault type, thus allowing the fault type to be determined based on the fault level or the total weight value. For example, a severe fault might correspond to a problem with the BFE entry point or a data interface acquisition issue, while a normal fault might correspond to network jitter or intermittent data interface access errors. This allows fault handlers to address faults specifically based on their type, improving fault handling efficiency.

[0065] Example 2

[0066] This disclosure provides a fault handling device, such as... Figure 3 As shown, it includes:

[0067] The first determining module 301 is used to determine the bad point index data information of the target interface within a predetermined time period, wherein the bad point data indicates that the output data of the target interface does not meet the index.

[0068] The second determining module 302 is used to determine the continuous information of the bad pixel index based on the determined bad pixel index data information, wherein the continuous information represents the time information or the number information of the consecutive occurrence of bad pixel index data.

[0069] The third determining module 303 is used to determine the weight value corresponding to each bad point index data based on the predetermined weight determination rules and the continuous information of the determined bad point index.

[0070] The fourth determination module 304 is used to determine whether the target interface is a faulty interface based on each bad point indicator data and the corresponding weight value of the bad point indicator data.

[0071] This application provides a possible implementation method in which the predetermined weight determination rule is used to determine the weight value of each bad pixel index data in the consecutive bad pixel index data as the more consecutive bad pixel index data there are or the longer the consecutive bad pixel index data lasts.

[0072] This application provides a possible implementation, wherein the third determining module includes:

[0073] The first determining unit is used to determine the weight value corresponding to each consecutive time or each consecutive number of bad points based on the continuous time information or consecutive number information of the bad point index and a predetermined weight determining rule.

[0074] The second determining unit is used to determine the weight value of each bad point index data corresponding to each consecutive time or each consecutive number of consecutive times based on the determined weight value corresponding to each consecutive time or each consecutive number of consecutive times.

[0075] This application provides a possible implementation, wherein the first determining module includes:

[0076] The acquisition unit is used to acquire indicator data for the target interface within a predetermined time period.

[0077] The third determining unit is used to determine the bad pixel index data of the target interface within a predetermined time period based on the predetermined normal index data threshold corresponding to the target interface and the acquired index data.

[0078] This application provides a possible implementation, wherein the fourth determining module includes:

[0079] The fourth determining unit is used to determine the total weight value of the bad point index data based on each bad point data and the weight value corresponding to each bad point data.

[0080] The fifth determining unit is used to determine whether the target interface is a faulty interface based on the total weight value of the determined bad point index data and the predetermined fault interface judgment threshold.

[0081] This application provides a possible implementation, wherein the apparatus further includes:

[0082] The fifth determination module is used to determine the fault level of the target interface based on the total weight value of the bad pixel index data.

[0083] This application provides a possible implementation, wherein the apparatus further includes:

[0084] The sixth determination module is used to determine the fault type of the target interface based on the determined fault level of the target interface.

[0085] The beneficial effects achieved by the embodiments of this application are the same as those of the above-described method embodiments, and will not be repeated here.

[0086] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0087] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0088] The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the methods provided in the embodiments of this disclosure.

[0089] Compared to existing technologies that detect faults through log files of the target interface, this electronic device suffers from long detection cycles and a certain degree of lag. This disclosure addresses this issue by determining defective indicator data within a predetermined time period for the target interface; these defective data represent output data from the target interface that does not meet the specified indicators; determining continuous information of the defective indicators based on the determined defective indicator data, where continuous information represents the time or number of consecutive occurrences of defective indicator data; determining the weight value corresponding to each defective indicator data based on predetermined weighting rules and the determined continuous information of the defective indicators; and determining whether the target interface is a faulty interface based on each defective indicator data and its corresponding weight value. This approach considers the correlation between defective indicator data within a predetermined time period of the target interface, assigns corresponding weights to defective indicator data based on their continuous information, and then determines whether the target interface is a faulty interface based on the weights of each defective indicator data. This avoids the false detection problem caused by assigning uniform weights to defective indicator data (i.e., identifying interfaces without faults as faulty interfaces). In addition, interface-level fault detection enables refined fault detection and fault detection based on indicator data generated by the target interface, resulting in good real-time performance and a short detection cycle.

[0090] The readable storage medium is a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods provided in the embodiments of this disclosure.

[0091] Compared to existing technologies that use log files of the target interface for fault detection, this readable storage medium suffers from long detection cycles and a certain degree of lag. This disclosure addresses this issue by determining bad pixel index data within a predetermined time period for the target interface, where bad pixel data indicates that the output data of the target interface does not meet the indicators; determining continuous information of the bad pixel index based on the determined bad pixel index data, where continuous information represents the time or number of consecutive occurrences of bad pixel index data; determining the weight value corresponding to each bad pixel index data based on predetermined weight determination rules and the determined continuous information of the bad pixel index; and determining whether the target interface is a faulty interface based on each bad pixel index data and its corresponding weight value. This approach considers the correlation between defective data points within a predetermined time period of the target interface, assigns appropriate weights to defective data points based on their continuous information, and then determines whether the target interface is faulty based on the weights of each defective data point. This avoids the false detection problem caused by assigning uniform weights to defective data points (i.e., identifying interfaces without faults as faulty interfaces). Furthermore, interface-level fault detection enables refined fault detection and provides real-time performance and a short detection cycle based on the indicator data generated by the target interface.

[0092] The computer program product includes a computer program that, when executed by a processor, implements the method as shown in the first aspect of this disclosure.

[0093] Compared to existing technologies that detect faults through log files of the target interface, this computer program product suffers from long detection cycles and a certain degree of lag. This disclosure addresses this issue by determining bad pixel index data within a predetermined time period for the target interface; these bad pixel data indicate that the output data of the target interface does not meet the specified indicators; determining continuous information of the bad pixel indicators based on the determined bad pixel index data, where continuous information represents the time or number of consecutive occurrences of bad pixel index data; determining the weight value corresponding to each bad pixel index data based on predetermined weight determination rules and the determined continuous information of the bad pixel indicators; and determining whether the target interface is a faulty interface based on each bad pixel index data and its corresponding weight value. This approach considers the correlation between defective indicator data within a predetermined time period of the target interface, assigns corresponding weights to defective indicator data based on their continuous information, and then determines whether the target interface is a faulty interface based on the weights of each defective indicator data. This avoids the false detection problem caused by assigning uniform weights to defective indicator data (i.e., identifying interfaces without faults as faulty interfaces). In addition, interface-level fault detection enables refined fault detection and fault detection based on indicator data generated by the target interface, resulting in good real-time performance and a short detection cycle.

[0094] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0095] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 402 or a computer program loaded from storage unit 408 into random access memory (RAM) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.

[0096] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method fault detection method. For example, in some embodiments, the method fault detection method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the method fault detection method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method fault detection method by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0103] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0104] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A fault detection method, comprising: Determine the bad pixel index data information of the target interface within a predetermined time period. The bad pixel index data indicates that the output data of the target interface does not meet the index. The target interface is the application programming interface (API) provided by the content delivery network. Based on the determined bad pixel index data, the continuous information of the bad pixel index is determined, wherein the continuous information represents the time information or the number information of consecutive occurrences of bad pixel index data. Based on the predetermined weighting rules and the continuous information of the determined bad pixel indicators, the weight value corresponding to each bad pixel indicator data is determined. Based on the bad point index data and the corresponding weight values ​​of the bad point index data, determine whether the target interface is a faulty interface. The predetermined weighting rule is used to determine the weight value of each bad pixel index data in the consecutive bad pixel index data when the number of consecutive bad pixel index data is greater or the duration of consecutive bad pixel index data is longer.

2. The method according to claim 1, wherein, The determination of the weight value corresponding to each bad pixel index data based on the predetermined weight determination rule and the continuous information of the determined bad pixel index includes: Based on the continuous time information or consecutive number information of the bad pixel index and the predetermined weight determination rules, determine the weight value corresponding to each continuous time or each consecutive number. Based on the determined weight values ​​corresponding to each consecutive time period or consecutive number of consecutive periods, the weight values ​​of each bad point index data corresponding to each consecutive time period or consecutive number of consecutive periods are determined.

3. The method according to claim 1, wherein, The data information on bad pixel indicators within a predetermined time period for determining the target interface includes: Obtain the target interface's indicator data within a predetermined time period; Based on the predetermined normal indicator data threshold corresponding to the target interface and the acquired indicator data, the bad pixel indicator data within a predetermined time period of the target interface is determined.

4. The method according to claim 1, wherein, The process of determining whether a target interface is a faulty interface based on each bad pixel index data and its corresponding weight value includes: The total weight value of the bad pixel index data is determined based on each bad pixel data and the corresponding weight value of each bad pixel data. Based on the total weight value of the determined bad point index data and the predetermined fault interface judgment threshold, it is determined whether the target interface is a fault interface.

5. The method according to claim 4, wherein, The method also includes: The fault level of the target interface is determined based on the total weight value of the bad pixel index data.

6. The method according to claim 5, wherein, The method also includes: Based on the determined fault level of the target interface, the fault type of the target interface is determined.

7. A fault detection device, comprising: The first determining module is used to determine the bad point index data information of the target interface within a predetermined time period. The bad point data indicates that the output data of the target interface does not meet the index. The target interface is an application programming interface (API) provided by the content delivery network. The second determining module is used to determine the continuity information of the bad pixel index based on the determined bad pixel index data information, wherein the continuity information represents the time information or the number information of the consecutive occurrence of bad pixel index data. The third determination module is used to determine the weight value corresponding to each bad point indicator data based on the predetermined weight determination rules and the continuous information of the determined bad point indicators. The fourth determination module is used to determine whether the target interface is a faulty interface based on each bad point indicator data and the corresponding weight value of the bad point indicator data. The predetermined weighting rule is used to determine the weight value of each bad pixel index data in the consecutive bad pixel index data when the number of consecutive bad pixel index data is greater or the duration of consecutive bad pixel index data is longer.

8. The apparatus according to claim 7, wherein, The third determining module includes: The first determining unit is used to determine the weight value corresponding to each consecutive time or each consecutive number of bad points based on the continuous time information or consecutive number information of the bad point index and a predetermined weight determining rule. The second determining unit is used to determine the weight value of each bad point index data corresponding to each consecutive time or each consecutive number of times based on the determined weight value corresponding to each consecutive time or each consecutive number of times.

9. The apparatus according to claim 7, wherein, The first determining module includes: The acquisition unit is used to acquire indicator data for the target interface within a predetermined time period. The third determining unit is used to determine the bad pixel index data of the target interface within a predetermined time period based on the predetermined normal index data threshold corresponding to the target interface and the acquired index data.

10. The apparatus according to claim 7, wherein, The fourth determining module includes: The fourth determining unit is used to determine the total weight value of the bad point index data based on each bad point data and the weight value corresponding to each bad point data. The fifth determining unit is used to determine whether the target interface is a faulty interface based on the total weight value of the determined bad point index data and the predetermined fault interface judgment threshold.

11. The apparatus according to claim 10, wherein, The device also includes: The fifth determination module is used to determine the fault level of the target interface based on the total weight value of the bad pixel index data.

12. The apparatus according to claim 11, wherein, The device also includes: The sixth determination module is used to determine the fault type of the target interface based on the determined fault level of the target interface.

13. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

15. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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