Methods, devices, computer equipment, and media for handling task anomalies

Abstract

This application belongs to the field of data processing and relates to a method for determining handling measures when a task is abnormal. It is applied to a financial business system comprising multiple tasks, where each system includes multiple end-user financial applications connected to at least one node task via activity flow. The method includes: when an abnormal node task is detected in the financial business system, determining whether the abnormal node task is a critical node task from the task flow of the system; if so, determining the end-user financial applications directly or indirectly connected to the abnormal node task, and the shortest running time for each of the end-user financial applications, based on the task flow; and determining task configuration measures after the node task becomes abnormal based on the end-user financial applications and the shortest running time. This method significantly improves the efficiency of determining measures when anomalies occur in financial systems.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, apparatus, computer device, and storage medium for determining handling measures for task anomalies. Background Technology

[0002] In the information age, with the rapid development of enterprise business, business systems have become increasingly diverse and complex, and the corresponding data has become increasingly massive. Enterprises need to effectively collect and organize this data, and then conduct scientific analysis. As a result, the number of data mart processing tasks is growing exponentially. Therefore, long-term construction and maintenance of these tasks are extremely important.

[0003] During the construction and maintenance of tasks in the data pipeline, any abnormal changes in the source or midstream tasks can potentially impact the end application tables in terms of timeliness, cost, and other aspects. When this severely affects users' access to data in the end application tables, a production incident occurs. Therefore, to reduce and avoid production incidents, it is essential to monitor the tasks in the data pipeline and handle anomalies promptly.

[0004] In current task monitoring systems, when an anomaly occurs, the only way to check is to trace it layer by layer from the current anomalous task down to the downstream tasks, recording the impact of the anomalous task on the timeliness, cost, and uniqueness of each layer of tasks, until the impact on the application data report of the anomaly is located at the downstream end task. Then, remedial measures such as adding resources and starting temporary tasks are taken. In general, many end tasks are affected, and by the time remedial measures are taken, a long time has been spent, and the anomalous task may have already completed its execution, which in turn expands the scope of anomaly remediation for downstream tasks. Therefore, there is an urgent need for a method to quickly locate the impact of anomalous tasks on the end application. Summary of the Invention

[0005] Based on this, and in response to the aforementioned technical problems, this application provides a method, apparatus, computer device, and storage medium for determining handling measures for task anomalies, in order to solve the technical problem in the prior art that requires locating abnormal tasks layer by layer, which leads to an expansion of the scope of anomaly remedies for downstream tasks.

[0006] A method for determining handling measures for task anomalies, applied to a financial business system comprising multiple tasks, wherein the financial business system includes multiple terminal financial applications, and each terminal financial application is connected to at least one node task through activity flow, the method comprising:

[0007] When an abnormal node task is detected in the financial business system, determine whether the abnormal node task is a critical node task from the task chain of the financial business system.

[0008] If so, then determine the end financial application that is directly or indirectly connected to the abnormal node task according to the task chain, and the shortest running time of each end financial application;

[0009] Based on the aforementioned end-user financial application and the aforementioned shortest running time, determine the task configuration measures after a node task anomaly.

[0010] Furthermore, before detecting an abnormal node task in the financial business system, the process also includes:

[0011] The task runtime of each node task is detected by a task monitoring system; the monitoring system may include modules such as data accuracy monitoring, data integrity monitoring, data mart monitoring, and data consistency monitoring.

[0012] When the runtime of the task exceeds the preset runtime, the node task is determined to be an abnormal node task.

[0013] Furthermore, determining whether the abnormal node task is a critical node task from the task chain of the financial business system includes:

[0014] Obtain the historical execution records of the abnormal node tasks;

[0015] The task delay duration of the abnormal node task and the number of end-user financial applications connected to the abnormal node task are determined based on the historical operation records.

[0016] If the task delay duration meets the preset delay duration and the number of connected end-user financial applications meets the preset number, the abnormal node task is determined to be a critical node task.

[0017] Furthermore, the step of determining the end-user financial application directly or indirectly connected to the abnormal node task based on the task chain, and the shortest running time for each of the end-user financial applications, includes:

[0018] Obtain the activity runtime of each task node;

[0019] The critical paths of each end financial application on the task chain are calculated using a topology algorithm.

[0020] Based on the running time of each activity, the shortest running time of each critical path is calculated, and the shortest running time of the end-user financial application is determined based on the shortest running time.

[0021] Furthermore, the calculation of the critical path for each of the terminal financial applications on the task chain using a topology algorithm includes:

[0022] Obtain the earliest occurrence time and activity duration of each node's task;

[0023] The longest path length from the starting node task to the end financial application is calculated based on the earliest occurrence time and the duration of the activity.

[0024] The node path corresponding to the longest path length is taken as the critical path.

[0025] Furthermore, determining the shortest runtime for the end-user financial application based on the shortest runtime includes:

[0026] The shortest running time of the terminal financial application is the shortest running time of the critical path with the largest time value.

[0027] Furthermore, the task configuration measures after determining the node task anomaly based on the terminal financial application and the shortest running time include:

[0028] When the end-financial application is a performance processing application, and the shortest running time of the performance processing application is less than the task delay time of the abnormal node task, then processing resources are added to the end-financial application.

[0029] A device for determining handling measures for task anomalies, applied in a multi-task financial business system, the financial business system including multiple terminal financial applications, each terminal financial application being connected to at least one node task via activity flow, the device comprising:

[0030] When an abnormal node task is detected in the financial business system, determine whether the abnormal node task is a critical node task from the task chain of the financial business system.

[0031] If so, then determine the end financial application that is directly or indirectly connected to the abnormal node task according to the task chain, and the shortest running time of each end financial application;

[0032] Based on the aforementioned end-user financial application and the aforementioned shortest running time, determine the task configuration measures after a node task anomaly.

[0033] A computer device includes a memory and a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the steps of the method for determining handling measures for the aforementioned task exception are implemented when the processor executes the computer-readable instructions.

[0034] A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the method for determining handling measures for the aforementioned task exception.

[0035] The aforementioned method, apparatus, computer equipment, and storage medium for determining handling measures for task anomalies, by determining the end-user financial applications directly or indirectly connected to the abnormal node task based on the task link, and the shortest running time of each end-user financial application; and by determining the task configuration measures after a node task anomaly based on the end-user financial application and the shortest running time, solves the technical problem in the prior art of difficulty in locating abnormal tasks, which leads to an expansion of the scope of abnormal remediation for downstream tasks. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A schematic diagram illustrating the application environment for determining methods to handle task exceptions;

[0038] Figure 2 A flowchart illustrating the methods for determining handling procedures for task anomalies;

[0039] Figure 3 Here is a data link task diagram from one embodiment;

[0040] Figure 4 This is a directed graph diagram of another data link in one embodiment;

[0041] Figure 5 This is a schematic diagram of the critical path in one embodiment;

[0042] Figure 6 A schematic diagram of a device for determining measures to handle mission anomalies;

[0043] Figure 7 This is a schematic diagram of a computer device in one embodiment. Detailed Implementation

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0047] The method for determining handling measures for task anomalies provided in this embodiment of the invention can be applied to, for example... Figure 1 The application environment shown can include terminal 102, network, and server 104. The network provides a communication link between terminal 102 and server 104, and can include various connection types, such as wired, wireless communication links, or fiber optic cables.

[0048] Users can use terminal 102 to interact with server 104 via the network to receive or send messages, etc. Various communication client applications can be installed on terminal 102, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0049] Terminal 102 can be various electronic devices with a display screen and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0050] Server 104 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal 102.

[0051] It should be noted that the task exception handling measures determination method provided in the embodiments of this application is generally executed by the server / terminal, and correspondingly, the task exception handling measures determination device is generally set in the server / terminal device.

[0052] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0053] It should be understood that Figure 1 The number of terminals, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers.

[0054] The terminal 102 communicates with the server 104 via a network. The terminal 102 and the server 104 are connected via a network, which can be a wired network or a wireless network. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server 104 can be a standalone server or a cluster of multiple servers.

[0055] In one embodiment, such as Figure 2 As shown, a method for determining handling measures for task anomalies is provided, which is then applied to... Figure 1 Taking the server-side as an example, the explanation includes the following steps:

[0056] Step 202: When an abnormal node task is detected in the financial business system, determine whether the abnormal node task is a critical node task from the task chain of the financial business system.

[0057] Step 204: If yes, then determine the end financial application that is directly or indirectly connected to the abnormal node task according to the task chain, and the shortest running time of each end financial application.

[0058] Step 206: Determine the task configuration measures after a node task anomaly based on the terminal financial application and the shortest running time. The remedial measures here can be determined and implemented by professional technicians (operations personnel) who analyze the task's operating environment to deduce the cause of the task anomaly.

[0059] In one embodiment, the task runtime of each node task is detected by a task monitoring system; when the task runtime exceeds a preset runtime, the node task is determined to be an abnormal node task.

[0060] In a task monitoring system, when an anomaly occurs, the system can only query downstream tasks layer by layer, recording the impact of the anomaly on the timeliness, cost, and uniqueness of each task at each level. Only when the anomaly is detected at the downstream end-level task can the extent of its impact on application data reports be determined. Afterward, remedial measures such as adding resources, initiating temporary tasks, or suspending critical tasks are taken. Typically, many downstream tasks are affected, and by the time remedial measures are implemented, a significant amount of time has passed, and the anomaly may have already completed its execution, thus expanding the scope of anomaly remediation for downstream tasks.

[0061] Therefore, in order to solve this problem, this application considers that by analyzing and classifying the historical operation logs of each task in the data mart task chain every day, the attributes such as cost, timeliness, start time, and end time of the task can be analyzed and classified. Based on this, the critical path of each end application under different conditions can be determined, which can help the developers quickly make remedial decisions when an anomaly occurs.

[0062] like Figure 2The diagram shown is a schematic of a data link task graph. We can obtain the task link (data link task graph) of the application to be processed in this application using a lineage analysis tool. Each end application has many starting tasks, and the link graph of each application is a complete combination of these structures. In this embodiment, the link graph of an end application and one of its starting tasks (i.e., the source task) can be represented using... Figure 3 and Figure 4 The directed graph representation, in which, Figure 3 In this context, task n represents the tasks at each node, and application table A or B represents different end-user financial applications. Figure 4 In this context, V represents each task activity. The `EXPLAIN` command can be used to modify the HQL statement of a task to obtain its execution plan. The execution plan is essentially a decomposed short syntax tree. By finding the branch corresponding to the keyword `table scan`, we can extract which source tables were used in the processing logic. The tasks corresponding to these source tables are the upstream tasks of this task. By using `EXPLAIN` to perform the same operation on these upstream tasks sequentially, tracing upstream to the target level, we can obtain the upstream task chain of the final application.

[0063] Where V1 represents the source node, V7 represents the sink node, and 'a' represents the weight of the directed edge connecting two events (tasks), which indicates the execution time of the activity. A directed edge represents the execution of an activity (task). Such a directed graph is called a network with only one start node and one finish node, and can be represented by an AOE (Directed Graph). It can be used to estimate the completion time of the final application. Here, it is assumed that the execution time of the final application is zero. Figure 3 In a directed graph, only one vertex with an in-degree of 0 is called the source vertex (i.e., the starting task), indicating the beginning of the application; only one vertex with an out-degree of 0 is called the sink vertex, indicating the end of the application. Here, in-degree usually refers to the sum of the number of times a vertex in a directed graph is the endpoint of an edge in the graph, and out-degree usually refers to the sum of the number of times a vertex in a directed graph is the starting point of an edge in the graph.

[0064] Specifically, the tasks here can be processing insurance application forms, processing underwriting and policy issuance lists, processing policy attribute dimensions, processing reinsurance logic, processing claims data logic, and so on. However, they are not limited to these and can be switched according to the application scenario.

[0065] The task represented by a vertex can only begin to run after the tasks represented by the starting vertices of all directed edges leading to that vertex have been completed; and the tasks represented by the ending vertices of all directed edges emanating from that vertex can only begin to run after the tasks represented by that vertex have been completed.

[0066] Furthermore, the activity runtime of each task node is obtained; the critical path of each end financial application on the task link is calculated through a topology algorithm; the shortest runtime of each critical path is calculated based on the activity runtime, and the shortest runtime of the end financial application is determined based on the shortest runtime.

[0067] Furthermore, obtain the earliest occurrence time and activity duration of each node's task; according to the formula...

[0068] Ve(j)=Max{Ve(k)+dut(<k,j> )}

[0069] The longest path length from the starting node task to the terminal financial application is calculated, where dut(<k,j> ) indicates the activity duration of each node task;

[0070] The node path corresponding to the longest path length is taken as the critical path.

[0071] Specifically, the abnormal node task here can be a task that affects the corresponding task of the end-user financial application, such as the abnormal execution of business statistics data on the operational data platform of the end-user financial application. An application generally has many starting tasks, and the task link from each starting task to the end-user financial application is a directed graph network. Each directed graph can find a critical path, and each critical path can find a shortest runtime (shortest running time). The maximum of all shortest running times is the number of critical paths of the end-user financial application based on different starting tasks. The so-called combination is to combine these critical paths together to form the critical path for the completion of the end-user financial application.

[0072] Specifically, the completion time of the entire application is the longest path from the source node to the destination node in the directed graph, i.e., the critical path.

[0073] To determine the critical path, you must first determine the earliest and latest times.

[0074] Earliest occurrence time (for a vertex):

[0075] v is the vertex, i.e., the task; e is early, i.e., the earliest; j is the vertex vj.

[0076] ve(j) refers to the earliest occurrence time of the task (vertex);

[0077] ve(j) = the length of the longest path from the source vertex to vertex j;

[0078] ve(source point) = 0;

[0079] ve(j) = Max{ve(k) + dut(<k,j> )}, dut<k,j> Indicates the duration of the activity;

[0080] Then ve(V1) = 0

[0081] ve(V2)=ve(V1)+2=2

[0082] ve(V3)=max{ve(V2)+2,ve(V1)+3}=max{2+2,0+3}=4

[0083] ve(V4)=max{ve(V3)+1,ve(V1)+2}=max{4+1,0+2}=5

[0084] ...

[0085] Ve(V7) = 19

[0086] The value of ve(j) can be recursively derived from the source node to the sink node in topological order.

[0087] Latest occurrence time (for a vertex):

[0088] vl(k): v is the vertex, i.e., the event; l is the latest vertex; k is the vertex vk.

[0089] vl(k) is the latest occurrence time of the event (vertex);

[0090] vl(k) = the shortest path length from vertex k to the sink;

[0091] vl(sink) = ve(sink);

[0092] vl(k)=Min{vl(j)-dut<k,j>}

[0093] Therefore, vl(V7) = ve(V7) = 19

[0094] vl(V6)=vl(V7)-5=19-5=14

[0095] vl(V5)=min{vl(V7)-9,vl(V6)-3}=min{19-9,14-3}=10

[0096] vl(V4)=vl(V5)-5=10-5=5

[0097] ...

[0098] vl(V1)=min{vl(V4)-2,vl(V3)-3,vl(V2)-2}=0

[0099] The value of vl(j) can be recursively derived from the sink node in reverse topological order toward the source node.

[0100] Earliest start time and latest start time (for edges), where the edges are directed edges:

[0101] Earliest start time of the event

[0102] ee(i) = ve(j)

[0103] That is: assume the i-th arc is<j,k> If , then ee(i) is the earliest start time of the i-th activity (arc);

[0104] Latest start time of the event

[0105] el(i) = vl(k) - dut(<j,k> )

[0106] That is: el(i) is the latest start time of the i-th activity (arc).

[0107] So:

[0108] ee(a1)=ee(V1-V2)=ve(V1)=0

[0109] el(a1)=el(V1-V2)=vI(V2)-2=0

[0110] ee(a6)=ee(V2-V6)=ve(V2)=2

[0111] el(a6)=el(V2-V6)=vI(V6)-8=6

[0112] ...

[0113] Further, the historical execution records of the abnormal node task are obtained; the task delay duration of the abnormal node task and the number of end-user financial applications connected to the abnormal node task are determined based on the historical execution records; if the task delay duration meets the preset delay duration and the number of connected end-user financial applications meets the preset number, the abnormal node task is determined to be a critical node task.

[0114] Specifically, if the earliest start time and the latest start time of this activity are the same, it means that the activity cannot be delayed. If there is a difference between the two, it means that there is a margin of error and it will not affect the overall application. Key activities, or key node tasks, are (a j ) = el(a j (Activities)

[0115] First, calculate the earliest occurrence time (ve) of each event (node ​​task), then calculate the latest occurrence time from the sink to the source. Calculate the earliest and latest occurrence times of each activity, and then find the activities with the same earliest and latest occurrence times. These are the critical activities in the entire application that cannot be delayed, i.e., the critical path.

[0116] like Figure 5 As shown, the critical path of the graph is obtained.

[0117] Furthermore, based on the above algorithm and the historical execution records of the application task chain, the critical paths of all networks in the end application are calculated and combined to obtain the critical path of the end application. For recurring critical tasks, the longest execution time for each critical task is taken, and the weights of all directed edges are added together to obtain the shortest execution time in the end application.

[0118] Then, the algorithm is used to store the critical path and shortest runtime for each end application. When a task encounters an anomaly, such as a task being delayed by one hour, the critical task data mart, compiled up to the previous day, can be used to determine whether the anomaly is critical, how many end applications it affects, the severity of the delay, and what remedial measures should be taken for the affected end applications. For example, increasing resources or completing some non-essential tasks can ensure application timeliness and quickly resolve the problem.

[0119] The critical path and shortest runtime are typically stored in the HDFS distributed file system, which leverages its high fault tolerance by storing multiple copies.

[0120] The above method solves the technical problem in the prior art of expanding the scope of abnormal task remediation caused by the difficulty in locating abnormal tasks, which is due to the difficulty in locating abnormal tasks. It determines the end financial application that is directly or indirectly connected to the abnormal node task based on the task link, as well as the shortest running time of each end financial application.

[0121] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order in which these steps are executed, and they can be performed in other orders. Furthermore, Figure 2At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not have to be sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0122] In one embodiment, such as Figure 6 As shown, a device for determining handling measures for task anomalies is provided, which corresponds one-to-one with the method for determining handling measures for task anomalies in the above embodiments. This device is applied to a financial business system including multiple tasks, wherein the financial business system includes multiple terminal financial applications, and each terminal financial application is connected to at least one node task through activity flow. The device includes:

[0123] The detection module 602 is used to determine whether the abnormal node task is a critical node task from the task chain of the financial business system when an abnormal node task is detected in the financial business system.

[0124] The calculation module 604 is used to determine, if so, the end financial application that is directly or indirectly connected to the abnormal node task, and the shortest running time of each end financial application, based on the task link.

[0125] Configuration module 606 is used to determine task configuration measures after a node task anomaly based on the terminal financial application and the shortest running time.

[0126] Furthermore, the detection module 602, previously included, also includes:

[0127] The detection module is used to detect the task runtime of each node task through the task monitoring system;

[0128] The anomaly confirmation module is used to determine that the node task is an abnormal node task when the task runtime exceeds the preset runtime.

[0129] Furthermore, the detection module 602 includes:

[0130] The data acquisition submodule is used to acquire the historical execution records of the abnormal node tasks;

[0131] The data confirmation submodule is used to determine the task delay duration of the abnormal node task and the number of end-user financial applications connected to the abnormal node task based on the historical operation records.

[0132] The node confirmation submodule is used to determine the abnormal node task as a critical node task if the task delay duration meets the preset delay duration and the number of connected end financial applications meets the preset number.

[0133] Furthermore, the computing module 604 includes:

[0134] The time acquisition submodule is used to acquire the activity running time of each task node;

[0135] The path calculation submodule is used to calculate the critical path of each end financial application on the task link through a topology algorithm.

[0136] The duration confirmation submodule is used to calculate the shortest running time of each critical path based on the running time of each activity, and determine the shortest running time of the end financial application based on the shortest running time.

[0137] Furthermore, the path calculation submodule includes:

[0138] The time acquisition unit is used to acquire the earliest occurrence time and activity duration of each node's task;

[0139] The path calculation unit is used to calculate the path based on the formula Ve(j)=Max{Ve(k)+dut(j)=Max{Ve(k)+dut(j) ...+dut(j)+dut(j)+dut(j)+dut<k,j> The longest path length from the starting node task to the terminal financial application is calculated, where dut(<k,j> ) indicates the activity duration of each node task;

[0140] The path confirmation unit is used to identify the node path corresponding to the longest path length as the critical path.

[0141] Furthermore, the duration confirmation submodule includes:

[0142] The duration confirmation unit is used to take the shortest running time with the largest time value among the critical paths of the terminal financial application as the shortest running time of the terminal application.

[0143] Furthermore, the configuration module 606 includes:

[0144] The measures processing submodule is used to increase processing resources for the end-financial application when the end-financial application is a performance processing application and the shortest running time of the performance processing application is less than the task delay time of the abnormal node task.

[0145] The apparatus of this embodiment solves the technical problem in the prior art of expanding the scope of abnormal task remediation caused by the difficulty in locating abnormal tasks, which is due to the difficulty in locating abnormal tasks. It determines the end financial application that is directly or indirectly connected to the abnormal node task based on the task link, as well as the shortest running time of each end financial application. It also determines the task configuration measures after the node task becomes abnormal based on the end financial application and the shortest running time.

[0146] In one embodiment, a computer device is provided, which may be a server. The structure may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer-readable instructions, and database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the non-volatile storage media. The database stores node data. The network interface communicates with external terminals via a network connection. When the computer-readable instructions are executed by the processor, they implement a method for determining handling measures for task anomalies.

[0147] As will be understood by those skilled in the art, the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0148] In one embodiment, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When executed by a processor, these computer-readable instructions implement the steps of the task exception handling measure determination method in the above embodiments, for example... Figure 2 Steps 202 to 206 shown, or, when the processor executes computer-readable instructions, implement the functions of each module / unit of the task exception handling measure determination device in the above embodiments, for example... Figure 6The functions of modules 602 to 604 are as follows: By determining the end-user financial applications directly or indirectly connected to the abnormal node task based on the task chain, and the shortest running time of each end-user financial application; and by determining the task configuration measures after the node task becomes abnormal based on the end-user financial application and the shortest running time, this solves the technical problem in the prior art where the difficulty in locating abnormal tasks leads to an expanded scope of abnormal remediation for downstream tasks.

[0149] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware with computer-readable instructions. These computer-readable instructions can be stored in a non-volatile computer-readable storage medium. When executed, these computer-readable instructions can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0150] The blockchain referred to in this application is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0151] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications, improvements, or equivalent substitutions of some technical features without departing from the concept of this application. These modifications or substitutions do not cause the essence of the same technical solution to deviate from the spirit and scope of the technical solutions of the embodiments of this invention, and all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method of determining a processing measure for a task exception, characterized by, Applied to a multi-tasking financial business system, the financial business system including multiple terminal financial applications, each terminal financial application being connected to at least one node task via activity flow, the method includes: When an abnormal node task is detected in the financial business system, determine whether the abnormal node task is a critical node task from the task chain of the financial business system. If so, then determine the end financial application that is directly or indirectly connected to the abnormal node task according to the task chain, and the shortest running time of each end financial application; Determine the task configuration measures after a node task anomaly based on the aforementioned end-user financial application and the aforementioned shortest running time; The task configuration measures following the determination of node task anomalies based on the terminal financial application and the shortest running time include: When the end-financial application is a performance processing application, and the shortest running time of the performance processing application is less than the task delay time of the abnormal node task, then processing resources are added to the end-financial application.

2. The method according to claim 1, characterized in that, Before detecting an abnormal node task in the financial business system, the process also includes: The task runtime of each node task is detected by the task monitoring system. When the runtime of the task exceeds the preset runtime, the node task is determined to be an abnormal node task.

3. The method according to claim 1, characterized in that, Determining whether the abnormal node task is a critical node task from the task chain of the financial business system includes: Obtain the historical execution records of the abnormal node tasks; The task delay duration of the abnormal node task and the number of end-user financial applications connected to the abnormal node task are determined based on the historical operation records. If the task delay duration meets the preset delay duration and the number of connected end-user financial applications meets the preset number, the abnormal node task is determined to be a critical node task.

4. The method according to claim 1, characterized in that, The step of determining the end-user financial application that is directly or indirectly connected to the abnormal node task based on the task chain, and the shortest running time of each end-user financial application, includes: Obtain the activity runtime of each task node; The critical paths of each end financial application on the task chain are calculated using a topology algorithm. Based on the running time of each activity, the shortest running time of each critical path is calculated, and the shortest running time of the end-user financial application is determined based on the shortest running time.

5. The method according to claim 4, characterized in that, The critical path of each terminal financial application on the task chain is calculated using a topology algorithm, including: Obtain the earliest occurrence time and activity duration of each node's task; The longest path length from the starting node task to the end financial application is calculated based on the earliest occurrence time and the duration of the activity. The node path corresponding to the longest path length is taken as the critical path; This involves obtaining the earliest occurrence time and activity duration of each node's task; according to the formula Ve(j)=Max{Ve(k)+dut(<k,j> The longest path length from the starting node task to the terminal financial application is calculated, where dut(<k,j> ) represents the activity duration of each node task, ve(j) refers to the earliest occurrence time of the task, ve(j) = the longest path length from the source point to vertex j; the node path corresponding to the longest path length is taken as the critical path.

6. The method according to claim 5, characterized in that, The determination of the shortest runtime for the end-user financial application based on the shortest runtime includes: The shortest running time corresponding to the critical path with the largest time value in the end-user financial application is taken as the shortest running time of the end-user financial application. Here, the abnormal node task is the task that affects the end-user financial application. An application has many starting tasks, and the task link from each starting task to the end-user financial application is a directed graph network. Each directed graph can find a critical path, and each critical path can find a shortest runtime, i.e., the shortest running time. The maximum value of all shortest running times is the critical path of the end-user financial application. Depending on the number of different starting tasks, the so-called combination is to combine these critical paths together to form the critical path for the completion of the end-user financial application.

7. A device for determining handling measures for task anomalies, characterized in that, Applied to a multi-tasking financial business system, the financial business system including multiple terminal financial applications, each terminal financial application being connected to at least one node task via activity flow, the device includes: The detection module is used to determine whether the abnormal node task is a critical node task from the task chain of the financial business system when an abnormal node task is detected in the financial business system. The calculation module is used to determine, if so, the end financial application that is directly or indirectly connected to the abnormal node task, and the shortest running time of each end financial application, based on the task chain. The configuration module is used to determine the task configuration measures after a node task anomaly is caused based on the terminal financial application and the shortest running time. The configuration module includes: The measures processing submodule is used to increase processing resources for the end-financial application when the end-financial application is a performance processing application and the shortest running time of the performance processing application is less than the task delay time of the abnormal node task.

8. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, characterized in that, When the processor executes the computer-readable instructions, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing computer-readable instructions thereon, characterized in that, When the computer-readable instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 6.

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