Data processing method and device

Abstract

The embodiment of the invention provides a data processing method. The data processing method comprises the following steps: acquiring reported data of a pre-buried point of a target scene of a target platform; determining a first success rate of the target scene based on the reported data; determining a second success rate of the target platform in a target dimension based on the first success rate, wherein the target dimension at least comprises an application dimension, a scene dimension and a service dimension; and performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate. According to the technical scheme provided by the embodiment of the invention, the target platform can be managed from various scenes and multiple dimensions, and the efficiency of platform operation management is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technology, and in particular, to a data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] A platform usually contains many physical microservices, which form businesses such as manuscript submission, manuscript processing, manuscript review, and activities. In these businesses, there are hundreds or even more functional points. For the platform to run stably, it is necessary to understand the operation of various services on the platform for better management.

[0003] However, currently the platform usually only has some understanding of the operation status of certain core services, but is not clear about the operation status of other services. As a result, it is impossible to determine which businesses and scenarios may be affected by upstream and downstream service failures, and it is also impossible to determine the operating health of various links, resulting in low efficiency of operation management.

[0004] It should be noted that the above content is not necessarily prior art, nor is it intended to limit the scope of patent protection of this application. Summary of the invention

[0005] The embodiments of the present application provide a data processing method, apparatus, computer device, computer-readable storage medium, and computer program product to solve or alleviate one or more of the technical problems raised above.

[0006] One aspect of an embodiment of the present application provides a data processing method, the method comprising: Obtain the reported data of the pre-embedded points of the target platform and target scene; Determine a first success rate of the target scenario based on the reported data; Determine a second success rate of the target platform in a target dimension based on the first success rate, where the target dimension includes at least an application dimension, a scenario dimension, and a business dimension; The target platform is operated and managed based on the first success rate and / or the second success rate.

[0007] Optionally, determining a second success rate of the target platform in a target dimension based on the first success rate includes: Determining a number of first scenarios included in the target dimension; A second success rate of the target platform in the target dimension is determined based on the first success rate of the first scenario.

[0008] Optionally, the method further comprises: Determining a plurality of second scenarios involved in the target link; A third success rate of the target link is determined based on the first success rate of the second scenario.

[0009] Optionally, the method further comprises: Flink is used to perform real-time calculation to determine the first success rate, the second success rate, or the third success rate.

[0010] Optionally, performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate includes: When the first success rate of the third scene is less than a preset threshold, the third scene is recalled, and the third scene is any scene in the target scene.

[0011] Optionally, the method further comprises: In the case where the target platform is accessed in the fourth scenario, a preset SDK is used to perform tracking in the fourth scenario, wherein the preset SDK is used to obtain the reported data, and the fourth scenario is any scenario in the target scenario; Configure a success rate threshold for the fourth scenario.

[0012] Optionally, the method further comprises: Acquire external dependency information of the target scenario, wherein the external dependency information includes middleware information and upstream and downstream service information; In response to the error information of the external dependency, determining an affected fifth scenario based on the external dependency information, wherein the fifth scenario is any one of the target scenarios; An affected dimension is determined based on the fifth scenario, where the affected dimension is at least one dimension of the target dimensions.

[0013] Another aspect of an embodiment of the present application provides a data processing device, the device comprising: An acquisition module is used to obtain the reported data of the pre-embedded points of the target scene of the target platform; A first determination module, configured to determine a first success rate of the target scenario based on the reported data; A second determination module, configured to determine a second success rate of the target platform in a target dimension based on the first success rate, wherein the target dimension includes at least an application dimension, a scenario dimension, and a business dimension; A processing module is used to perform operation and maintenance management on the target platform based on the first success rate and / or the second success rate.

[0014] Another aspect of an embodiment of the present application provides a computer device, comprising: 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described above.

[0015] Another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method described above is implemented.

[0016] Another aspect of an embodiment of the present application provides a computer program product, including a computer program, which implements the method described above when executed by a processor.

[0017] The above technical solution adopted in the embodiment of the present application may have the following advantages: By obtaining the reported data of the target scenario of the target platform in advance, determining the first success rate of the target scenario based on the reported data, determining the second success rate of the target platform in the target dimension based on the first success rate, the target dimension includes at least application dimension, scenario dimension and business dimension, and performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate, the success rate of each scenario and multiple dimensions of the target platform can be obtained, and the target platform can be managed based on the success rate, so that the target platform can be managed from various scenarios and multiple dimensions, thereby improving the efficiency of platform operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0019] Figure 1 The flowchart of the data processing method according to the first embodiment of the present application is schematically shown; Figure 2 Schematically shows Figure 2 Flow chart of sub-steps of step S104; Figure 3 The newly added process of the data processing method according to the first embodiment of the present application is schematically shown; Figure 4 Another newly added process of the data processing method according to the first embodiment of the present application is schematically shown; Figure 5 A further newly added process of the data processing method according to the first embodiment of the present application is schematically shown; Figure 6 The schematic diagram shows a principle diagram of a data processing method according to the first embodiment of the present application; Figure 7 A block diagram schematically shows a data processing device according to the second embodiment of the present application; and Figure 8 The hardware architecture diagram of the computer device according to the third embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0021] It should be noted that the descriptions involving "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0022] In the description of the present application, it should be understood that the numerical labels before the steps do not indicate the order in which the steps are executed, but are only used to facilitate the description of the present application and to distinguish each step, and therefore should not be understood as a limitation on the present application.

[0023] First, the following terms are explained: Tracking points: These are code snippets pre-set in software applications (such as websites and apps) to record relevant data.

[0024] Success rate: The ratio of successful request processing to the total number of requests.

[0025] SDK (Software Development Kit): is a set of development tools for building application software for specific software packages, software frameworks, hardware platforms, operating systems, etc.

[0026] Flink: is an open source distributed stream processing framework for stateful computing on unbounded and bounded data streams. It provides an efficient, reliable and flexible solution for real-time data processing.

[0027] Secondly, in order to facilitate those skilled in the art to understand the technical solutions provided in the embodiments of the present application, the relevant technologies are described below: In a platform, there are usually a large number of various services, which together form the platform's services. In order to ensure the stable operation of the platform, it is necessary to understand the operation of various services on the platform for better management.

[0028] However, the platform currently only has some understanding of the operation status of some core services, but knows very little about the operation status of other services. As a result, when upstream and downstream services fail, it is impossible to determine which businesses and scenarios are affected by these failures and how much impact they have. It is also impossible to determine the operating health of various links, resulting in low efficiency in the operation and management of the platform.

[0029] To this end, the embodiment of the present application provides a data processing technology solution. In this technology solution, the success rate of each scenario and multiple dimensions of the platform can be obtained, so that the target platform can be managed from each scenario and multiple dimensions, and the efficiency of platform operation management can be improved. See below for details.

[0030] The technical solutions of the present application are described below through multiple embodiments. It should be noted that these embodiments can be implemented in a variety of different forms and should not be construed as being limited to the embodiments described here.

[0031] Embodiment 1 Figure 1 The flowchart of the data processing method according to the first embodiment of the present application is schematically shown.

[0032] like Figure 1 As shown, the data processing method may include steps S100 to S106, wherein: Step S100, obtaining the reported data of the pre-embedded points of the target scene of the target platform.

[0033] Step S102: determining a first success rate of the target scenario based on the reported data.

[0034] Step S104, determining a second success rate of the target platform in a target dimension based on the first success rate, wherein the target dimension includes at least an application dimension, a scenario dimension, and a business dimension.

[0035] Step S106: performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate.

[0036] The data processing method provided in this embodiment obtains reported data of pre-embedded points in the target scenario of the target platform, determines a first success rate of the target scenario based on the reported data, determines a second success rate of the target platform in a target dimension based on the first success rate, and the target dimension includes at least an application dimension, a scenario dimension, and a business dimension. The target platform is operated and managed based on the first success rate and / or the second success rate. The success rates of various scenarios and multiple dimensions of the target platform can be obtained, and the target platform can be managed based on the success rates, so that the target platform can be managed from various scenarios and multiple dimensions, thereby improving the efficiency of platform operation management.

[0037] It should be noted that the execution subject of steps S100 to S106 and other subsequent optional embodiments may be a service platform for operation and maintenance management. Figure 1 , taking the service platform as the execution entity as an example, each step in steps S100~S106 and other optional steps are explained in detail.

[0038] Step S100 , obtain the reported data of the pre-buried points in the target scene of the target platform.

[0039] The target platform may be a platform that provides certain services (such as video) to users through the network. The target scene may be a scene in the target platform. Optionally, there may be multiple target scenes. In particular, all scenes of the target platform may be regarded as target scenes. In this case, obtaining the reported data of the pre-embedded points of the target scenes in the target platform means obtaining the reported data of the pre-embedded points of all scenes in the target platform.

[0040] When pre-tracking the target scene, the developer can pre-set some software codes in the target scene to collect data related to the success rate of the target scene, such as error data, request processing data, etc., and then report the collected data to a pre-specified path. For example, the collected data can be reported to a certain location on the service platform, and the service platform obtains the reported data from this location. In addition, the relevant settings for tracking can be made when the target scene is connected. Of course, for the target scene that has been connected to the target platform, corresponding tracking can also be performed.

[0041] Step S102 , determining a first success rate of the target scenario based on the reported data.

[0042] After obtaining the reported data, the service platform can determine the first success rate of the target scenario according to the pre-configured formula and the reported data. For example, if the reported data is the error data and request processing data of the target scenario, the error rate can be calculated based on the error data and the request processing data, and then the first success rate of the target scenario can be: first success rate = 1-error rate. Of course, the reported data can also include other data, such as response time. In the case where the reported data includes other data, how the other data affects the first success rate can be defined accordingly, so as to perform statistics of the first success rate based on other data. For example, if the reported data includes response time, the response time greater than a certain time threshold can be defined as failure, and then the success rate in terms of response time can be obtained according to a method similar to the error rate. When the reported data includes more than two types of data, a weight can be configured for each type of data, and then the first success rate is obtained by comprehensive calculation based on the weight and the reported data.

[0043] Step S104 , based on the first success rate, determining a second success rate of the target platform in a target dimension, the target dimension including at least an application dimension, a scenario dimension, and a business dimension.

[0044] When determining the second success rate of the application dimension and the business dimension, you can first determine which scenarios the application includes, and then obtain the second success rate of the application dimension or the business dimension based on the first success rate statistics of these scenarios. When determining the second success rate of the scenario dimension, you can first determine which applications or businesses a certain target scenario exists in, and then obtain the success rate of the scenario dimension based on the first success rate statistics of the target scenario in all applications and businesses. Some exemplary solutions are provided below.

[0045] In an optional embodiment, if Figure 2 As shown, step S104 may include: Step S200, determining a plurality of first scenarios included in the target dimension.

[0046] Step S202: determining a second success rate of the target platform in the target dimension based on the first success rate of the first scenario.

[0047] The service platform can maintain a correspondence table of business, application and scenario in advance. When it is necessary to determine the number of first scenarios included in the target dimension, all the first scenarios included in the target dimension are determined according to the correspondence table. In the correspondence table, a business can include multiple scenarios, and an application can include multiple scenarios. For example, if in the correspondence table, application A includes three first scenarios a, b, and c, when it is necessary to determine the second success rate of application A, the three first scenarios a, b, and c can be determined according to the correspondence table, and the second success rate of application A can be obtained according to the first success rates corresponding to the three first scenarios a, b, and c. When it is necessary to determine the second success rate of the scenario dimension, all the same scenarios can also be determined from the correspondence table, and then the second success rate of the scenario dimension can be obtained by statistics. For example, if both application A and application B include the first scenario b, the second success rate of the scenario dimension b can be obtained by comprehensive calculation according to the first success rate of the first scenario b in application A and the first success rate of the first scenario b in application B. In a specific application, an ID can be configured for each scenario, and then all the same scenarios can be determined according to the scenario ID.

[0048] When calculating the second success rate of the target dimension, the second success rate can be calculated by different calculation formulas according to the serial and parallel relationships of the several first scenarios included therein. When the first scenarios are in a serial relationship, since the success rate of any first scenario affects the final success rate, the second success rate of the target dimension can be obtained by directly multiplying the first success rates of these first scenarios; when the first scenarios are in a parallel relationship, since the success of any first scenario can make the target dimension successful, the second success rate of the target dimension can be obtained by first multiplying the failure rates of the first scenarios and then subtracting the multiplication result from 1.

[0049] In this embodiment, by determining several first scenarios included in the target dimension and determining the second success rate of the target platform in the target dimension based on the first success rate of the first scenarios, the success rate of the target dimension can be effectively determined, thereby facilitating monitoring the operating stability of the target platform from the target dimension.

[0050] In an optional embodiment, if Figure 3 As shown, the data processing method of the embodiment of the present application may also include: Step S300: determine a plurality of second scenarios involved in the target link.

[0051] Step S302: Determine a third success rate of the target link based on the first success rate of the second scenario.

[0052] Similarly, the service platform may also pre-maintain a correspondence table between a link and a corresponding second scenario. When it is necessary to determine the second scenario involved in the target link, all second scenarios involved in the target link are determined according to the pre-maintained correspondence table. After determining that the target link involves a second scenario, the third success rate of the target link is determined according to the first success rate of the second scenario and a preset calculation method (such as a calculation formula). For example, if a target link A involves three second scenarios e, f, and g, the third success rate of the target link A can be calculated based on the first success rates of the three second scenarios e, f, and g. When calculating the third success rate of the target link, since the second scenarios involved in the link are generally serial relationships, the first success rates of the second scenarios can be directly multiplied to obtain the third success rate of the target link.

[0053] In this embodiment, by determining several second scenarios involved in the target link and determining the third success rate of the target link based on the first success rate of the second scenarios, the success rate of the target link in the target platform can be effectively determined, thereby facilitating the monitoring and maintenance of the operating stability of the target link.

[0054] It is understandable that real-time computing data is required for operation and maintenance management of the platform. Therefore, when determining the first success rate, the second success rate or the third success rate, some real-time computing frameworks (such as Spark) can be used to achieve real-time computing capabilities.

[0055] In an optional embodiment, the data processing method of the embodiment of the present application may also include: using Flink real-time calculation to determine the first success rate, the second success rate or the third success rate.

[0056] Since the platform usually includes hundreds of functional points, the amount of calculation involved in the first success rate, the second success rate, and the third success rate is usually huge. Flink is optimized for stream processing and can achieve millisecond-level latency and high throughput. Therefore, using Flink to calculate the first success rate, the second success rate, and the third success rate can effectively realize the real-time calculation of these success rates and meet the real-time data needs of the operation and maintenance management of the target platform.

[0057] Step S106 , performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate.

[0058] The service platform can set thresholds for the first success rate and the second success rate respectively. When the first success rate or the second success rate is less than the set threshold, the service platform can process the first success rate or the second success rate less than the set threshold according to the preset rules. For example, when the second success rate of the target dimension is less than the set threshold, an alarm message is generated and sent to the phone or email of the operation and maintenance manager, so as to remind the operation and maintenance manager to take corresponding measures.

[0059] In an optional embodiment, step S106 may include: when the first success rate of the third scene is less than a preset threshold, recalling the third scene, where the third scene is any scene in the target scene.

[0060] Specifically, after determining the first success rate of the third scenario, the service platform can compare the first success rate of the third scenario with the corresponding preset threshold. If the first success rate of the third scenario is less than the preset threshold, the third scenario is recalled. For example, if the preset threshold of the third scenario is 90%, and its first success rate is 89%, the third scenario is recalled because the first success rate of the third scenario is less than the preset threshold. On the basis of the recall, the service platform can notify the corresponding operation and maintenance management personnel of the results of the recall, so that the operation and maintenance management personnel can analyze and attribute the reasons for the low success rate or make corresponding processing.

[0061] In this embodiment, when the first success rate of the third scene is less than a preset threshold, the third scene is recalled, and the scene with a lower success rate can be recalled, so as to perform response processing in time.

[0062] In an optional embodiment, if Figure 4 As shown, the data processing method of the embodiment of the present application may also include: Step S400, when the fourth scene is connected to the target platform, a preset SDK is used to perform tracking in the fourth scene, wherein the preset SDK is used to obtain the reported data, and the fourth scene is any scene in the target scene.

[0063] Step S402: configuring a success rate threshold for the fourth scenario.

[0064] Specifically, when the new scene (the fourth scene) is connected to the target platform, the service platform will set the preset SDK point in the fourth scene to collect the data to be reported, and configure the fourth scene according to the pre-configured success rate threshold, so that when the fourth scene falls below the corresponding success rate threshold, the fourth scene will be recalled. The success rate threshold can be configured by the operation and maintenance management personnel, and the configuration of the success rate threshold can be the same or different for different scenes.

[0065] In this embodiment, by connecting to the target platform in the fourth scenario, using the preset SDK to track points in the fourth scenario to obtain reported data, and configuring the success rate threshold of the fourth scenario, tracking points can be performed on the scenarios newly connected to the target platform, thereby improving the convenience and uniformity of tracking points and reducing the difficulty of development; at the same time, by setting the success rate threshold, automatic recall can be achieved when the success rate of the scenario is low.

[0066] In an optional embodiment, if Figure 5 As shown, the data processing method of the embodiment of the present application may also include: Step S500: acquiring external dependency information of the target scenario, wherein the external dependency information includes middleware information and upstream and downstream service information.

[0067] Step S502 : in response to the error information of the external dependency, determining an affected fifth scenario based on the external dependency information, where the fifth scenario is any scenario in the target scenario.

[0068] Step S504: determine the affected dimension based on the fifth scenario, where the affected dimension is at least one dimension of the target dimensions.

[0069] Specifically, the operation and maintenance management personnel can sort out the external dependency information of each scenario in advance, and maintain an external dependency information table of a scenario in advance based on the sorting results, so that the service platform can obtain the external dependency information corresponding to the target scenario from the external dependency information table. When receiving information such as error reporting corresponding to external dependencies, the scenario affected by the current error is determined as the fifth scenario based on the external dependency information, and the affected dimension is determined based on the target dimension involved in the fifth scenario. For example, if the upstream service of scenario A is service B, and the business dimension corresponding to scenario A is business C, then when receiving the error information of service B, scenario A can be determined as the affected fifth scenario, and the affected business dimension can be determined as business C.

[0070] In this embodiment, by obtaining external dependency information of the target scenario and responding to external dependency error information, the affected fifth scenario is determined based on the external dependency information, and the affected dimension is determined based on the fifth scenario. The scenarios and dimensions affected by the error can be determined based on the external dependency information of the middleware and upstream and downstream services, thereby facilitating corresponding operation and maintenance management and improving the efficiency and refinement of operation and maintenance management.

[0071] In order to make this application easier to understand, the following Figure 6 An exemplary application is provided.

[0072] 1. When accessing the scene, use SDK to track data and collect and report relevant data; 2. The data reported by the SDK is sent to Flink through Kafka for real-time computing. The success rates of the scenarios, businesses, and applications (APP-ID) are calculated respectively. The real-time tasks can be specifically divided into minute-level and second-level calculations. 3. The results of Flink calculations are stored accordingly; 4. Compare the calculated results with the preset threshold. If the success rate of some scenarios is lower than the preset success rate threshold, these scenarios will be recalled, and tracking records will be generated. After the line is broken, the corresponding information will be delivered to the corresponding responsible personnel. At the same time, corresponding indicators can be generated according to the corresponding calculation results for specific monitoring.

[0073] In this exemplary application, by tracking SDK points when the scene is accessed, the success rate and availability of any business scenario can be monitored; and by performing real-time calculations through Flink, real-time calculations of various dimensions can be realized, which facilitates corresponding operation and maintenance management based on the results of real-time calculations; and recalling when the success rate is less than the threshold can effectively realize automatic recall of the scene, facilitate timely processing of scenes with low success rates, and improve the operation and maintenance management efficiency of the platform.

[0074] Embodiment 2 Figure 7 The block diagram of the data processing device according to the second embodiment of the present application is schematically shown. The device can be divided into one or more program modules, one or more program modules are stored in a storage medium, and are executed by one or more processors to complete the embodiment of the present application. The program module referred to in the embodiment of the present application refers to a series of computer program instruction segments that can complete specific functions. The following description will specifically introduce the functions of each program module in this embodiment. Figure 7 As shown, the apparatus 600 may include an acquisition module 610, a first determination module 620, a second determination module 630 and a processing module 640, wherein: The acquisition module 610 is used to acquire the reported data of the pre-embedded points of the target scene of the target platform; A first determination module 620, configured to determine a first success rate of the target scenario based on the reported data; A second determination module 630, configured to determine a second success rate of the target platform in a target dimension based on the first success rate, where the target dimension at least includes an application dimension, a scenario dimension, and a business dimension; The processing module 640 is used to perform operation and maintenance management on the target platform based on the first success rate and / or the second success rate.

[0075] In an optional embodiment, the second determining module 630 is further configured to: Determining a number of first scenarios included in the target dimension; A second success rate of the target platform in the target dimension is determined based on the first success rate of the first scenario.

[0076] In an optional embodiment, the device 600 is further used for: Determining a plurality of second scenarios involved in the target link; A third success rate of the target link is determined based on the first success rate of the second scenario.

[0077] In an optional embodiment, the device 600 is further used for: Flink is used to perform real-time calculation to determine the first success rate, the second success rate, or the third success rate.

[0078] In an optional embodiment, the processing module 640 is further configured to: When the first success rate of the third scene is less than a preset threshold, the third scene is recalled, and the third scene is any scene in the target scene.

[0079] In an optional embodiment, the device 600 is further used for: In the case where the target platform is accessed in the fourth scenario, a preset SDK is used to perform tracking in the fourth scenario, wherein the preset SDK is used to obtain the reported data, and the fourth scenario is any scenario in the target scenario; Configure a success rate threshold for the fourth scenario.

[0080] In an optional embodiment, the device 600 is further used for: Acquire external dependency information of the target scenario, wherein the external dependency information includes middleware information and upstream and downstream service information; In response to the error information of the external dependency, determining an affected fifth scenario based on the external dependency information, wherein the fifth scenario is any one of the target scenarios; An affected dimension is determined based on the fifth scenario, where the affected dimension is at least one dimension of the target dimensions.

[0081] Embodiment 3 Figure 8The schematic diagram of the hardware architecture of a computer device 10000 suitable for implementing a data processing method according to the third embodiment of the present application is shown. In some embodiments, the computer device 10000 may be a terminal device such as a smart phone, a wearable device, a tablet computer, a personal computer, a vehicle-mounted terminal, a game console, a virtual device, a workbench, a digital assistant, a set-top box, a robot, etc. In other embodiments, the computer device 10000 may be a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or a server cluster composed of multiple servers), etc. Figure 8 As shown, the computer device 10000 includes but is not limited to: a memory 10010, a processor 10020, and a network interface 10030 that can communicate with each other through a system bus. Among them: The memory 10010 includes at least one type of computer-readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10010 can be an internal storage module of the computer device 10000, such as a hard disk or memory of the computer device 10000. In other embodiments, the memory 10010 can also be an external storage device of the computer device 10000, such as a plug-in hard disk equipped on the computer device 10000, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 10010 can also include both the internal storage module of the computer device 10000 and its external storage device. In this embodiment, the memory 10010 is generally used to store an operating system and various application software installed in the computer device 10000, such as program codes of data processing methods, etc. In addition, the memory 10010 can also be used to temporarily store various data that have been output or are to be output.

[0082] In some embodiments, the processor 10020 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other chips. The processor 10020 is generally used to control the overall operation of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is used to run the program code stored in the memory 10010 or process data.

[0083] The network interface 10030 may include a wireless network interface or a wired network interface, and the network interface 10030 is generally used to establish a communication link between the computer device 10000 and other computer devices. For example, the network interface 10030 is used to connect the computer device 10000 to an external terminal through a network, and to establish a data transmission channel and a communication link between the computer device 10000 and the external terminal. The network may be a wireless or wired network such as an intranet, the Internet, the Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, etc.

[0084] It should be pointed out that Figure 8 Only a computer device having components 10010 - 10030 is shown, but it should be understood that implementation of all of the components shown is not a requirement, and more or fewer components may alternatively be implemented.

[0085] In this embodiment, the data processing method stored in the memory 10010 can also be divided into one or more program modules and executed by one or more processors (such as processor 10020) to complete the embodiment of the present application.

[0086] Embodiment 4 An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the data processing method in the embodiment are implemented.

[0087] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the computer-readable storage medium can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store an operating system and various application software installed on the computer device, such as the program code of the data processing method in the embodiment, etc. In addition, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or are to be output.

[0088] Embodiment 5 An embodiment of the present application also provides a computer program product, including a computer program, which implements the method in the above embodiment when executed by a processor.

[0089] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present application can be implemented by general-purpose computer devices, they can be concentrated on a single computer device, or distributed on a network composed of multiple computer devices, optionally, they can be implemented by executable program codes of computer devices, so that they can be stored in a storage device and executed by the computer device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0090] It should be noted that the above are only preferred embodiments of the present application, and the patent protection scope of the present application is not limited thereto. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Obtain the reported data of the pre-embedded points of the target platform and target scene; Determine a first success rate of the target scenario based on the reported data; Determine a second success rate of the target platform in a target dimension based on the first success rate, where the target dimension includes at least an application dimension, a scenario dimension, and a business dimension; The target platform is operated and managed based on the first success rate and / or the second success rate.

2. The method according to claim 1, characterized in that The determining, based on the first success rate, a second success rate of the target platform in a target dimension includes: Determining a number of first scenarios included in the target dimension; A second success rate of the target platform in the target dimension is determined based on the first success rate of the first scenario.

3. The method according to claim 2, characterized in that The method further comprises: Determining a plurality of second scenarios involved in the target link; A third success rate of the target link is determined based on the first success rate of the second scenario.

4. The method according to claim 3, characterized in that The method further comprises: Flink is used to perform real-time calculation to determine the first success rate, the second success rate, or the third success rate.

5. The method according to claim 1, characterized in that The performing operation and maintenance management on the target platform based on the first success rate and / or the second success rate includes: When the first success rate of the third scene is less than a preset threshold, the third scene is recalled, and the third scene is any scene in the target scene.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: In the case where the target platform is accessed in the fourth scenario, a preset SDK is used to perform tracking in the fourth scenario, wherein the preset SDK is used to obtain the reported data, and the fourth scenario is any scenario in the target scenario; Configure a success rate threshold for the fourth scenario.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Acquire external dependency information of the target scenario, wherein the external dependency information includes middleware information and upstream and downstream service information; In response to the error information of the external dependency, determining an affected fifth scenario based on the external dependency information, wherein the fifth scenario is any one of the target scenarios; An affected dimension is determined based on the fifth scenario, where the affected dimension is at least one dimension of the target dimensions.

8. A data processing device, characterized in that: The device comprises: An acquisition module is used to obtain the reported data of the pre-embedded points of the target scene of the target platform; A first determination module, configured to determine a first success rate of the target scenario based on the reported data; A second determination module, configured to determine a second success rate of the target platform in a target dimension based on the first success rate, wherein the target dimension includes at least an application dimension, a scenario dimension, and a business dimension; A processing module is used to perform operation and maintenance management on the target platform based on the first success rate and / or the second success rate.

9. A computer device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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